bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Compressing MBA Courses on Student Satisfaction: Empirical Results
Authors: Swart William , Kowalczyk Christine M.
Published: April 2024

A Master of Business Administration (MBA) degree is the most popular graduate degree available. It is valuable to those who work in a wide range of business management areas or to prospective entrepreneurs. Many universities have developed compressed 8-week semesters to better meet student needs. Critics question instructor course delivery and student learning. We examine the impact of compressed semesters on MBA student satisfaction and learning in both face-to-face and online courses. Five instructors were assigned courses to be taught in a 16-week semester, followed by the same courses in an 8-week semester. At least one section of the courses was taught in face-to-face and/or online formats. Student satisfaction was measured via transactional distances as well as by their willingness to recommend the course to a friend. A total of 602 responses were obtained, of which 402 were usable. Stepwise multiple regression was used to measure satisfaction. Logistic regression was used to determine which factors influenced students to recommend the course to a friend. The results indicated that there was no reason to believe that there was a significant difference in student satisfaction, nor in their willingness to recommend the course. We also did not find a difference in learning outcomes.
A Deterministic Chaos-Model-Based Gaussian Noise Generator
Authors: Haliuk Serhii , Vovchuk Dmytro , Spinazzola Elisabetta , Secco Jacopo , Bobrovs Vjaceslavs , Corinto Fernando
Published: April 2024

The abilities of quantitative description of noise are restricted due to its origin, and only statistical and spectral analysis methods can be applied, while an exact time evolution cannot be defined or predicted. This emphasizes the challenges faced in many applications, including communication systems, where noise can play, on the one hand, a vital role in impacting the signal-to-noise ratio, but possesses, on the other hand, unique properties such as an infinite entropy (infinite information capacity), an exponentially decaying correlation function, and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window, putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criterion on which the main idea of this study is based on is the central limit theorem, which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. This study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.), whose output signals are analyzed according to criteria that encompass the probability density functions of the chaotic signal itself, its envelope, and its phase and statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. Thus, a statistical-characteristic-based algorithm, which includes a series of tests, was developed for a Gaussian-like signal assessment. Following the algorithm, the analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates a Gaussian distribution. This allows for the generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.
A Two-Stage Sub-Threshold Voltage Reference Generator Using Body Bias Curvature Compensation for Improved Temperature Coefficient
Authors: Azimi Mohammad , Habibi Mehdi , Crovetti Paolo
Published: April 2024

Leakage diodes cause deviations in the thermal drift of ultra-low-power two-transistor (2T) reference circuits, resulting in either convex or concave output voltages against temperature, depending on the reference transistor types (n-type/p-type). This paper investigates the combined application of the convexity and concavity properties exhibited by the output voltage of complementary 2T references, one n-type and one p-type. By exploiting the body bias effect, this approach mitigates variations in the output reference voltage caused by temperature fluctuations. Software optimization is also used to obtain the required aspect ratios after formulating the required criteria for drain-induced barrier lowering (DIBL) elimination in the first stage. The performance of the proposed reference is evaluated by post-layout Monte Carlo simulations. In the range of 0 °C to 100 °C, the output reference voltage has an average temperature coefficient (TC) of 26.7 ppm/°C without any temperature trim. The output reference voltage is 195.5 mV with a standard deviation of 13.6 mV. The line sensitivity (LS) is 17.1 ppm/V in the supply voltage range of 0.5 V to 2.1 V at 25 °C. At 25 °C and 0.5 V, the power consumption is 28.8 pW, increasing to a maximum of 1.3 nW at 100 °C and 2.1 V.
Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems
Authors: Jeffrey Nicholas , Tan Qing , Villar José R.
Published: April 2024

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability.
Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
Authors: Jung Jaehwan
Published: April 2024

A new decoding method for low-density parity-check (LDPC) codes is presented to lower the energy consumption of LDPC decoders for NAND flash-based storage systems. Since the channel condition of NAND flash memory is reliable for most of its lifetime, it is inefficient to apply the maximum-effort decoding with the full parity-check matrix (H-matrix) from the beginning of the lifespan. As the energy consumption and the decoding latency are proportional to the size of the H-matrix used in decoding, the proposed algorithm starts the decoding with a partial H-matrix selected by considering the channel condition. In addition, the proposed partial decoding provides various error-correcting capabilities by adjusting the partial H-matrix. Based on the proposed partial decoding algorithm, a prototype decoder is implemented in a 65 nm CMOS process to decode a 4 KB LDPC code. The proposed decoder reduces energy consumption by 93% compared to the conventional LDPC decoding architecture at maximum.
A Review of Document Binarization: Main Techniques, New Challenges, and Trends
Authors: Yang Zhengxian , Zuo Shikai , Zhou Yanxi , He Jinlong , Shi Jianwen
Published: April 2024

Document image binarization is a challenging task, especially when it comes to text segmentation in degraded document images. The binarization, as a pre-processing step of Optical Character Recognition (OCR), is one of the most fundamental and commonly used segmentation methods. It separates the foreground text from the background of the document image to facilitate subsequent image processing. In view of the different degradation degrees of document images, researchers have proposed a variety of solutions. In this paper, we have summarized some challenges and difficulties in the field of document image binarization. Approximately 60 methods documenting image binarization techniques are mentioned, including traditional algorithms and deep learning-based algorithms. Here, we evaluated the performance of 25 image binarization techniques on the H-DIBCO2016 dataset to provide some help for future research.
Enhanced Inclusion through Advanced Immersion in Cultural Heritage: A Holistic Framework in Virtual Museology
Authors: Anastasovitis Eleftherios , Georgiou Georgia , Matinopoulou Eleni , Nikolopoulos Spiros , Kompatsiaris Ioannis , Roumeliotis Manos
Published: April 2024

In recent years, the digitization of cultural heritage has been favored by significant advancements in specific technologies, such as photogrammetry and three-dimensional scanning. The digital representations of artifacts, paintings, books, and collections, as well as buildings or archaeological sites, has led to the transfer of cultural organizations to the digital space. On the other hand, the rapid development of immersive technologies and the Internet of Things is expected to decisively shape virtual cultural heritage in the coming years. However, this digital transition should expand its impact on most of the population. This article aims to cover the lack of structured methodology in the design and development of inclusive virtual spaces in cultural heritage. This research introduces a holistic framework that is mainly based on the disciplines of virtual museology. The proposed methodology takes into account the advancements in extended reality and the creative industry of computer games. The multisensory approach would lead to advanced immersive experiences, while the multilayered approach of cultural heritage content would enhance accessibility in inclusive virtual spaces. Moreover, this holistic framework could provide evidence from the virtual worlds that could be applied to real cultural heritage organizations.
Performance Test, Index System Establishment, and Comprehensive Evaluation of Earthquake Rescue Robots
Authors: Li Liming , Zhao Zeang
Published: April 2024

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for a comprehensive evaluation method that encompasses establishing an evaluation index system, testing performance indexes, and conducting performance evaluation. Firstly, four main criterion and twenty-three sub-criterion indexes are established by conducting a comprehensive review of existing assessment measures for rescue robots across diverse domains. These indexes are validated through test modules developed by the National Earthquake Response Support Service to obtain corresponding values for each criterion. Moreover, a method for establishing the index system is proposed based on the fuzzy clustering analysis and grey correlation analysis methods. This method effectively addresses issues related to excessive subjectivity, redundancy, and ambiguous stratification of indexes. Subsequently, the DEMATEL is employed to scrutinize the interrelationships and causal connections among each index within the established index system, leading to the identification of input and output indexes based on the analysis outcomes. Finally, as an empirical example, three earthquake rescue robots are comprehensively evaluated and ranked using the super efficiency DEA model. Alongside analyzing results regarding input redundancy and output deficiency, targeted improvement suggestions are provided for each earthquake rescue robot. Additionally, comparison analysis with the entropy weight method and VIKOR method verifies the effectiveness of our proposed method.
Renal Pathological Image Classification Based on Contrastive and Transfer Learning
Authors: Liu Xinkai , Zhu Xin , Tian Xingjian , Iwasaki Tsuyoshi , Sato Atsuya , Kazama Junichiro James
Published: April 2024

Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning has been widely applied to computer-aided diagnosis, with an increasing number of models being used for the analysis of renal pathological images. The diversity of renal pathological images and the imbalance between data acquisition and annotation have placed a significant burden on pathologists trying to perform reliable and timely analysis. Transfer learning based on contrastive pretraining is emerging as a viable solution to this dilemma. By incorporating unlabeled positive pretraining images and a small number of labeled target images, a transfer learning model is proposed for high-accuracy renal pathological image classification tasks. The pretraining dataset used in this study includes 5000 mouse kidney pathological images from the Open TG-GATEs pathological image dataset (produced by the Toxicogenomics Informatics Project of the National Institutes of Biomedical Innovation, Health, and Nutrition in Japan). The transfer training dataset comprises 313 human immunoglobulin A (IgA) chronic nephritis images collected at Fukushima Medical University Hospital. The self-supervised contrastive learning algorithm “Bootstrap Your Own Latent” was adopted for pretraining a residual-network (ResNet)-50 backbone network to extract glomerulus feature expressions from the mouse kidney pathological images. The self-supervised pretrained weights were then used for transfer training on the labeled images of human IgA chronic nephritis pathology, culminating in a binary classification model for supervised learning. In four cross-validation experiments, the proposed model achieved an average classification accuracy of 92.2%, surpassing the 86.8% accuracy of the original RenNet-50 model. In conclusion, this approach successfully applied transfer learning through mouse renal pathological images to achieve high classification performance with human IgA renal pathological images.
Generative Design of the Architecture Platform in Multiprocessor System Design
Authors: Müller Luise , Schumacher Nico , Steffen Lukas , Haubelt Christian
Published: April 2024

When designing a system at the Electronic System Level (ESL), designers are confronted with a very large number of design decisions, each affecting the characteristics of the resulting system. Simultaneously, the demands for the system’s performance, reliability, and energy consumption have increased drastically. Design Space Exploration (DSE) aims to facilitate this complex task by automating the system synthesis and traversing the design space autonomously. Previous studies on DSE have mainly considered fixed architectures with a fixed set of hardware components only. In the paper at hand, we overcome this limitation to allow for a higher degree of freedom in the design of a multiprocessor system. Instead of a fixed architecture as input, we are using a resource library containing resource types whose instances can then be arbitrarily placed and connected. More specifically, we enable the exploration of the types, the number, and the positions of required processing-type instances in a grid-based topology template in addition to deciding on the remaining system synthesis tasks, namely, resource allocation, task binding, routing, and scheduling. We provide an extensible framework, based on Answer Set Programming (ASP) modulo Theories (ASPmT), for generating system architectures fulfilling predefined constraints. Our studies show that this higher degree of freedom, originating from fewer restrictions regarding the architecture, leads to an increased complexity of the problem. In extensive experiments, we show scalability trends for a set of parameters, demonstrating the capabilities and limits of our approach.
Incorporating Entity Type-Aware and Word–Word Relation-Aware Attention in Generative Named Entity Recognition
Authors: Mo Ying , Li Zhoujun
Published: April 2024

Named entity recognition (NER) is a critical subtask in natural language processing. It is particularly valuable to gain a deeper understanding of entity boundaries and entity types when addressing the NER problem. Most previous sequential labeling models are task-specific, while recent years have witnessed the rise of generative models due to the advantage of tackling NER tasks in the encoder–decoder framework. Despite achieving promising performance, our pilot studies demonstrate that existing generative models are ineffective at detecting entity boundaries and estimating entity types. In this paper, a multiple attention framework is proposed which introduces the attention of entity-type embedding and word–word relation into the named entity recognition task. To improve the accuracy of entity-type mapping, we adopt an external knowledge base to calculate the prior entity-type distributions and then incorporate the information input to the model via the encoder’s self-attention. To enhance the contextual information, we take the entity types as part of the input. Our method obtains the other attention from the hidden states of entity types and utilizes it in self- and cross-attention mechanisms in the decoder. We transform the entity boundary information in the sequence into word–word relations and extract the corresponding embedding into the cross-attention mechanism. Through word–word relation information, the method can learn and understand more entity boundary information, thereby improving its entity recognition accuracy. We performed experiments on extensive NER benchmarks, including four flat and two long entity benchmarks. Our approach significantly improves or performs similarly to the best generative NER models. The experimental results demonstrate that our method can substantially enhance the capabilities of generative NER models.
Effective Degradation of Metronidazole through Electrochemical Activation of Peroxymonosulfate: Mechanistic Insights and Implications
Authors: Liao Haicen , Fang Jingkai , Wang Jiahao , Long Xianhu , Zhang Igor Ying , Huang Rongfu
Published: April 2024

The investigation into the degradation of metronidazole (MNZ), a frequently employed antibiotic, through the electrochemical activation of peroxymonosulfate (PMS) utilizing either boron-doped diamond (BDD) or dimensional stable anode (DSA) as the anode, was conducted in a systematic manner. The enhancement of MNZ removal was observed with increasing current density, PMS dosage, and initial pH. Response surface methodology (RSM), based on a Box–Benken design, was utilized to evaluate the efficiency of MNZ elimination concerning current density (ranging from 11.1 to 33.3 mA/cm2), initial pH (ranging from 3 to 9), PMS dosage (ranging from 1 to 5 mmol·L−1), and reaction time (ranging from 25 to 45 min). The optimal operational conditions for MNZ removal were determined as follows: a current density of 13.3 mA/cm2, a pH of 3.7, a PMS dosage of 2.4 mmol·L−1, and a reaction time of 40 min. Electron paramagnetic resonance (EPR), quenching experiments, and chemical probe experiments confirmed the involvement of •OH, SO4•− and 1O2 radicals as the primary reactive species in MNZ degradation. The presence of HCO3− and H2PO4− hindered MNZ removal, whereas the presence of Cl− accelerated it. The degradation pathways of MNZ were elucidated by identifying intermediates and assessing their toxicity. Additionally, the removal efficiencies of other organic pollutants, such as sulfamethoxazole (SMX), carbamazepine (CBZ), and nitrobenzene (NB), were compared. This study contributes to a comprehensive understanding of MNZ degradation efficiency, mechanisms, and pathways through electrochemical activation of PMS employing BDD or DSA anodes, thereby offering valuable insights for the selection of wastewater treatment systems.
Possibilities of Utilising Biomass Collected from Road Verges to Produce Biogas and Biodiesel
Authors: Czubaszek Robert , Wysocka-Czubaszek Agnieszka , Sienkiewicz Aneta , Piotrowska-Niczyporuk Alicja , Wassen Martin J. , Bajguz Andrzej
Published: April 2024

Grass collected as part of roadside maintenance is conventionally subjected to composting, which has the disadvantage of generating significant CO2 emissions. Thus, it is crucial to find an alternative method for the utilisation of grass waste. The aim of this study was to determine the specific biogas yield (SBY) from the anaerobic mono-digestion of grass from road verges and to assess the content of Fatty Acid Methyl Esters (FAMEs) in grass in relation to the time of cutting and the preservation method of the studied material. The biochemical biogas potential (BBP) test and the FAMEs content were performed on fresh and ensiled grass collected in spring, summer, and autumn. The highest biogas production was obtained from fresh grass cut in spring (715.05 ± 26.43 NL kgVS−1), while the minimum SBY was observed for fresh grass cut in summer (540.19 ± 24.32 NL kgVS−1). The methane (CH4) content in the biogas ranged between 55.0 ± 2.0% and 60.0 ± 1.0%. The contents of ammonia (NH3) and hydrogen sulphide (H2S) in biogas remained below the threshold values for these inhibitors. The highest level of total FAMEs was determined in fresh grass cut in autumn (98.08 ± 19.25 mg gDM−1), while the lowest level was detected in fresh grass cut in spring (56.37 ± 7.03 mg gDM−1). C16:0 and C18:0, which are ideal for biofuel production, were present in the largest amount (66.87 ± 15.56 mg gDM−1) in fresh grass cut in autumn. The ensiling process significantly impacted the content of total FAMEs in spring grass, leading to a reduction in total saturated fatty acids (SFAs) and an increase in total unsaturated fatty acids (USFAs). We conclude that grass biomass collected during the maintenance of road verges is a valuable feedstock for the production of both liquid and gaseous biofuels; however, generating energy from biogas appears to be more efficient than producing biodiesel.
LNG Logistics Model to Meet Demand for Bunker Fuel
Authors: Orysiak Ewelina , Zielski Hubert , Gawle Mateusz
Published: April 2024

The main objective of this manuscript is to build a model for the distribution of LNG as a marine fuel in the southern Baltic Sea based on a genetic algorithm in terms of cost. In order to achieve this, it was necessary to develop, in detail, research sub-objectives like analysis of the intensity of ship traffic in the indicated area and analysis of LNG demand in maritime transport. In the first part of this study, the authors use data from the IALA IWRAP Mk2 and the Statistical Office in Szczecin to analyse the marine traffic density (by type of vessel) in the southern part of the Baltic Sea. LNG used as marine fuel reduces toxic emissions into the atmosphere. The authors specify the LNG fleet size and locations of LNG storage facilities in a way to ensure that the defined LNG bunker vessels can supply fuel to LNG-powered vessels within the shortest possible time period. The database contains a set of traits necessary to determine the optimal demand for LNG. The traits were developed based on an existing LNG fleet and appropriately selected infrastructure, and they represent existing LNG-powered vessels as well as LNG bunker vessels and their specifications. Based on the created LNG distribution model, were performed in Matlab R2019a software. An LNG distribution model was developed, which uses a genetic algorithm to solve the task. The demand for LNG for the sea area under analysis was determined based on data on the capacity of LNG-powered vessels (by type of vessel) and their distance from the specified port.
Topology Identification of Active Low-Voltage Distribution Network Based on Regression Analysis and Knowledge Reasoning
Authors: Liao Zhiwei , Liu Ye , Wang Bowen , Tao Wenjuan
Published: April 2024

Due to the access of distributed energy and a new flexible load, the electrical characteristics of a new distribution network are significantly different from those of a traditional distribution network, which poses a new challenge to the original topology identification methods. To address this challenge, a hierarchical topology identification method based on regression analysis and knowledge reasoning is proposed for an active low-voltage distribution network (ALVDN). Firstly, according to the new electrical characteristics of bidirectional power flow and voltage jump caused by the ALVDN, active power is selected as the electric volume for hierarchical topology identification. Secondly, considering the abnormal fluctuation of active power caused by bidirectional power flow characteristics of distributed energy users, a user attribution model based on the Elastic-Net regression algorithm is proposed. Subsequently, based on the user identification results, the logic knowledge reflecting the hierarchical topology of the ALVDN is extracted by the AMIE algorithm, and the “transformer-phase-line-user” hierarchical topology of the ALVDN is deduced by a knowledge reasoning model. Finally, the effectiveness of the proposed method is verified by an IEEE example.
Determination of the Kinetic Parameters of Thermal Degradation and Hydrodemetallization of a Mixture of the Heavy Fraction of Low-Temperature Coal Tar and Coal Shale
Authors: Baikenov Murzabek , Izbastenova Dariya , Sarsenbekova Akmaral , Balpanova Nazerke , Tusipkhan Almas , Khalikova Zukhra , Rakhimzhanova Nazym , Kochegina Elena , Tulebaeva Balzhan , Taurbaeva Gulzhan
Published: April 2024

The laws of thermal degradation of the mixture of the heavy fraction of low-temperature coal tar and coal shale were investigated using dynamic thermogravimetry. The kinetic characteristics of the process were determined using various methods, including the Ozawa–Flynn-Wall, Friedman, non-parametric kinetics and Šesták–Berggren methods. It is shown that coal shale initiated changes in the kinetic parameters and decomposition rate of the heavy fraction of coal tar. It was found that a 13% content of coal shale in the mixture led to the maximum rate of weight loss of the heavy fraction of coal tar. A hydrodemetallization kinetic model of the mixture of the heavy fraction of low-temperature coal tar and coal shale is proposed. The kinetic parameters of the hydrodemetallization process were determined; in addition, the rate constants at various temperatures were estimated. The study shows that the distribution of trace elements in the hydrogenate from the initial mixture and in the hydrogenate from the solid residue was characterized by relatively low values of reaction rate constants. The maximum microelement distribution rate was achieved in the hydrogenate solid residue. Energy indicators of activation processes indicated that hydrodemetallization at low temperatures is advantageous from an energy point of view.
Overlap Time Compensation and Characteristic Analysis for Current Source Photovoltaic Grid-Connected Inverter
Authors: Xu Cheng , Liu Ping , Miao Yiru
Published: April 2024

In the current source photovoltaic grid-connected system, to prevent the DC-link inductor from incurring an opening circuit fault, it is necessary to include the overlap time in the switching signals. However, current error and serious harmonic distortion in the inverter-side and grid-side currents are generated, which will cause additional losses and reduce the power quality of the grid, so it is important to compensate for the current error caused by the overlap time. In this paper, the relationship between the nonlinear current errors caused by the overlap time and the AC-side voltage is analyzed. Then, the mathematical expression of the low-order harmonics with losses caused by the overlap time is derived. On this basis, a current error compensation method with a discrete filter of AC-side voltage is proposed. Finally, a simulation and experiment are carried out to verify the correctness and effectiveness of the theoretical analysis and compensation scheme presented in this paper. With an overlap time of 3 μs, the THD of the grid-side current decreases from 5.93% to 1.59% after compensation.
Optimization Operation Strategy for Shared Energy Storage and Regional Integrated Energy Systems Based on Multi-Level Game
Authors: Yang Yulong , Chen Tao , Yan Han , Wang Jiaqi , Yan Zhongwen , Liu Weiyang
Published: April 2024

Regional Integrated Energy Systems (RIESs) and Shared Energy Storage Systems (SESSs) have significant advantages in improving energy utilization efficiency. However, establishing a coordinated optimization strategy between RIESs and SESSs is an urgent problem to be solved. This paper constructs an operational framework for RIESs considering the participation of SESSs. It analyzes the game relationships between various entities based on the dual role of energy storage stations as both energy consumers and suppliers, and it establishes optimization models for each stakeholder. Finally, the improved Differential Evolution Algorithm (JADE) combined with the Gurobi solver is employed on the MATLAB 2021a platform to solve the cases, verifying that the proposed strategy can enhance the investment willingness of energy storage developers, balance the interests among the Integrated Energy Operator (IEO), Energy Storage Operator (ESO) and the user, and improve the overall economic efficiency of RIESs.
Experimental Study of the Performance of Turbo-Charged Gasoline Direct-Injection Engine Based on Different Pre-Chamber Structures
Authors: Zhao Xiaowei , Sun Yuedong , Zhang Zhendong , Yin Congbo
Published: April 2024

In this paper, in order to improve the fuel economy of the actual application of the engine under multi-operating conditions, an experimental study is carried out on a turbo-charged direct-injection engine based on different pre-chamber structures. The engine used for the study is a four-cylinder turbo-charged direct-injection gasoline engine with different structures of pre-chamber spark plugs. The operating conditions in this study include load characteristics at 2000 r/min and characteristic loads at different speeds, including 3000 r/min, 3200 r/min, and 3600 r/min. With stable BMEP or fully open throttle and pedal, the experiment was conducted by the spark angle scanning method to collect data of engine power, economy, and emission under each condition. It was found that the pre-chamber structure has a direct effect on engine performance, with a clear load demarcation line for its effect. Under the WOT condition, the power of pre-chamber ignition is 1.6% higher than that of conventional spark plugs; at the low load of 2 bar, the economy of pre-chamber ignition is degraded by 6%; at the medium load of 8 bar, the economy of the two is comparable; at the large load of 16 bar, the fuel economy proves advantageous. Compared with conventional spark plugs, the pre-chamber spark angle can be advanced by 2~3 °CA, and the pre-chamber ignition with separate ground electrodes is highly reliable. The emission levels of the pre-chamber spark plugs and conventional spark plugs are comparable at all loads.
Investigation on Traffic Carbon Emission Factor Based on Sensitivity and Uncertainty Analysis
Authors: Chen Jianan , Yu Hao , Xu Haocheng , Lv Qiang , Zhu Zongqiang , Chen Hao , Zhao Feiyang , Yu Wenbin
Published: April 2024

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO2 emission and multivariate vehicle driving data, in which the vehicle specific power (VSP) is identified as the most significant factor on the CO2 emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP, the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO2 emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint.
Transmission Expansion Planning Considering Storage, Flexible AC Transmission System, Losses, and Contingencies to Integrate Wind Power
Authors: Huanca Dany H. , Falcão Djalma M. , Bento Murilo E. C.
Published: April 2024

To meet future load projection with the integration of renewable sources, the transmission system must be planned optimally. Thus, this paper introduces a comparative analysis and comprehensive methodology for transmission expansion planning (TEP), incorporating the combined effects of wind power, losses, N-1 contingency, a FACTS, and storage in a flexible environment. Specifically, the optimal placement of the FACTS, known as series capacitive compensation (SCC) devices, is used. The intraday constraints associated with wind power and energy storage are represented by the methodology of typical days jointly with the load scenarios light, heavy, and medium. The TEP problem is formulated as a mixed-integer nonlinear programming (MINLP) problem through a DC model and is solved using a specialized genetic algorithm. This algorithm is also used to determine the optimal placement of SCC devices and storage systems in expansion planning. The proposed methodology is then used to perform a comparison of the effect of the different technologies on the robustness and cost of the final solution of the TEP problem. Three test systems were used to perform the comparative analyses, namely the Garver system, the IEEE-24 system, and a real-world Colombian power system of 93 buses. The results indicate that energy storage and SCC devices lead to a decrease in transmission requirements and overall investment, enabling the effective integration of wind farms.
Study on Sedimentary Environment and Organic Matter Enrichment Model of Carboniferous–Permian Marine–Continental Transitional Shale in Northern Margin of North China Basin
Authors: Zhang Hanyu , Wang Yang , Chen Haoran , Zhu Yanming , Yang Jinghui , Zhang Yunsheng , Dou Kailong , Wang Zhixuan
Published: April 2024

The shales of the Taiyuan Formation and Shanxi Formation in the North China Basin have good prospects for shale gas exploration and development. In this study, Well KP1 at the northern margin of the North China Basin was used as the research object for rock mineral, organic geochemical, and elemental geochemical analyses. The results show that brittle minerals in the shales of the Taiyuan Formation and Shanxi Formation are relatively rare (<40%) and that the clay mineral content is high (>50%). The average TOC content is 3.68%. The organic matter is mainly mixed and sapropelic. The source rocks of the Taiyuan Formation and Shanxi Formation are mainly felsic, and the tectonic background lies in the continental island arc area. The primary variables that influenced the enrichment of organic materials during the sedimentary stage of the Taiyuan Formation were paleosalinity and paleoproductivity. Paleosalinity acted as the primary regulator of organic matter enrichment during the sedimentary stage of the Shanxi Formation.
Enhancing Electric Vehicle Charger Performance with Synchronous Boost and Model Predictive Control for Vehicle-to-Grid Integration
Authors: Hakam Youness , Gaga Ahmed , Tabaa Mohamed , El hadadi Benachir
Published: April 2024

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration.
Integration of Piezoelectric Energy Harvesting Systems into Building Envelopes for Structural Health Monitoring with Fiber Optic Sensing Technology
Authors: Pracucci Alessandro , Vandi Laura , Belletti Francesco , Melo Amanda Ramos Aragão , Vlachos Marios , Amditis Angelos , Calcagni Maria Teresa , Esteves David Seixas
Published: April 2024

This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHSs) into building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective of leveraging structural health monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into a ventilated façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHSs is defined to provide a complete system for façade monitoring. This integrated system includes the piezoelectric generator (PEG), supercapacitor (SC), Power Conditioner Circuit (PCC), Fiber Optic Sensing (FOS) interrogator, and the IoT gateway transmitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector but raise issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHSs for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of piezoelectric energy harvesting technology in building envelope monitoring.
Key Technologies and Application of Electric Scroll Compressors: A Review
Authors: Zhang Yubo , Peng Bin , Zhang Pengcheng , Sun Jian , Liao Zhixiang
Published: April 2024

The electric scroll compressor is driven by a built-in electric motor that rotates the scroll disk. It is known for its simple structure, adjustability, and high efficiency, making it highly promising for various applications. This paper reviews the current application and research status of electric scroll compressors. It covers topics such as the optimal design of scroll compressor profiles, scroll disk leakage sealing, and computer simulation optimization design methods. Additionally, the progress and development trends of vapor-injection scroll compressors (SCVIs) are discussed. This paper also presents the latest research progress on the application of the new refrigerant CO2 in electric scroll compressors, along with its latest applications that align with sustainable development requirements. Finally, this paper concludes with recommendations for the application of electric scroll compressors and suggests future directions for research.
Insights into Canopy Escape Ratio from Canopy Structures: Correlations Uncovered through Sentinel-2 and Field Observation
Authors: Lee Junghee , Im Jungho , Lim Joongbin , Kim Kyungmin
Published: April 2024

This study explores the quantitative relationship between canopy structure and the canopy escape ratio (fesc), measured as the ratio of near-infrared reflectance of vegetation (NIRv) to the fraction of absorbed photosynthetically active radiation (fAPAR). We analyzed the correlation between fesc and key indicators of canopy structure—specifically, leaf area index (LAI) and clumping index (CI)—utilizing both Sentinel-2 satellite data and in situ observations. Our analysis revealed a moderate correlation between fesc and LAI, evidenced by an R2 value of 0.37 for satellite-derived LAI, which contrasts with the lower correlation (R2 of 0.15) observed with field-measured LAI. Conversely, the relationship between fesc and CI proved to be significantly weaker (R2 < 0.1), indicating minimal interaction between foliage distribution and light escape at the canopy level. This disparity in correlation strength was further evidenced in time series analysis, which showed little phenological variation in fesc compared to LAI. Our findings elucidate the complexities of estimating fesc based on the NIRv to fAPAR ratio and underscore the need for advanced methodologies in future research to enhance the accuracy of canopy escape models.
Mechanical Performance Degradation of Decaying Straight Mortise and Tenon Joints: Tusi Manor, Yunnan–Tibet Region
Authors: Su Yanwei , Nong Zhengquan , Liu Heng , Mao Shihua , Jiang Puyan , Qiang Mingli , Wu Zhangkang
Published: April 2024

The straight mortise and tenon joints (SMTJs) of Tusi Manor, situated in the Yunnan–Tibet region of China, tend to undergo decay. This study aims to investigate the mechanism performance degradation of SMTJs due to decay. Five full-scale SMTJs were constructed and incubated with wood-rot fungi for 0, 6, 12, 18, and 24 weeks. Cyclic loading tests were conducted to assess the damage mechanisms and extent of mechanical property degradation at these different stages of decay, supported by ABAQUS finite element simulation software. The results revealed a progressive increase in damage with prolonged decay time. Comparison between decayed and undecayed specimens showed a maximum load-bearing performance degradation rate of 5.17%, 11.83%, 17.34%, and 23.54% after 6, 12, 18, and 24 weeks of fungal incubation, respectively. The cumulative energy efficiency degradation rates were 8.38%, 9.51%, 23.13%, and 33.31%, respectively. SMTJs mechanical performance degradation is correlated with wood mechanical property degradation as a function of the S-family. Finite element simulations further indicated a reduction in tenon strength from the outer parts towards the inner parts of the structure.
A Study on the Characteristics and Influencing Factors of Soundscape Perception in Landscape Spaces of Urban Greenways
Authors: Zhang Yijing , Huang Yangxin , Zheng Meijing , Zhang Hui , Zhang Qiqi , He Tianyou , Ye Jing
Published: April 2024

The acoustic landscape directly affects people’s perceptual experience, which is crucial to enhancing urban greenways’ visibility experience. This study analyzed data from three typical urban greenways in Fuzhou City. By classifying and combining the landscape space into two levels of “enclosure–coverage”, the space was categorized into six types: covered, uncovered, open, semi-open, semi-enclosed, and enclosure space. We explored the characteristics of soundscape perception and the factors affecting the evaluation of soundscape perception in different landscape spaces. The results showed that differences in sound sources between day and night led to differences in the soundscape of different spatial types. The sound pressure level decreased with an increasing degree of enclosure and coverage. The evaluation scores of sound source perception and soundscape perception were significantly different in different degrees of spatial enclosure and coverage, where the open space and covered space were the most sensitive to sound, and the open space played a positive role in enhancing the harmony of the artificial sound. Pleasantness and richness were the main factors influencing overall perception, contributing 50% and 17%, respectively. Visitors’ age, residence type, and visit frequency were the main factors affecting the evaluation of soundscape perception. The conclusions provide a reference and data for improving urban greenways’ soundscape quality.
Subgrade Strength Recovery of Fine-Grained-Soil-Containing Roads in Western Oregon Forest
Authors: Boston Kevin , Patterson Justin , Inman Aaron
Published: April 2024

Forest roads are composed of surface and subsurface layers. Determining the seasonal strength and associated moisture changes in these roads is required to understand their capacity. This study looks at changes in the subgrade layer. Laboratory and field studies were conducted to determine the changes in subgrade conditions by measuring moisture content and subgrade strength for fine-grained, non-plastic soils in Western Oregon. One field and one laboratory experiment were conducted. Drying in the field experiment occurred during the summer months in the absence of rainfall, while the laboratory study allowed drying for 48 h under controlled suction. In both cases, there was a slight reduction in the subgrade’s moisture content and no significant improvement in subgrade strength. These findings are supported by the soil physics theory that shows that limited water content reduction is expected for these fine-grained soils, as the numerous tiny soil pores can hold water at high capillary tensions. Adding rock layers adds an insulation effect for the subgrade, further reducing evaporation. Consequently, the moisture content remains high, and there will be little change in subgrade strength during the measurement periods.