bluuebunny/crossref_metadata_2025 huggingface dataset:
Structure and Sequence Aligned Code Summarization with Prefix and Suffix Balanced Strategy
Authors: Zeng Jianhui , Qu Zhiheng , Cai Bo
Published: March 2023

Source code summarization focuses on generating qualified natural language descriptions of a code snippet (e.g., functionality, usage and version). In an actual development environment, descriptions of the code are missing or not consistent with the code due to human factors, which makes it difficult for developers to comprehend and conduct subsequent maintenance. Some existing methods generate summaries from the sequence information of code without considering the structural information. Recently, researchers have adopted the Graph Neural Networks (GNNs) to capture the structural information with modified Abstract Syntax Trees (ASTs) to comprehensively represent a source code, but the alignment method of the two information encoder is hard to decide. In this paper, we propose a source code summarization model named SSCS, a unified transformer-based encoder–decoder architecture, for capturing structural and sequence information. SSCS is designed upon a structure-induced transformer with three main novel improvements. SSCS captures the structural information in a multi-scale aspect with an adapted fusion strategy and adopts a hierarchical encoding strategy to capture the textual information from the perspective of the document. Moreover, SSCS utilizes a bidirectional decoder which generates a summary from opposite direction to balance the generation performance between prefix and suffix. We conduct experiments on two public Java and Python datasets to evaluate our method and the result show that SSCS outperforms the state-of-art code summarization methods.
Information Entropy Measures for Evaluation of Reliability of Deep Neural Network Results
Authors: Gireesh Elakkat D. , Gurupur Varadaraj P.
Published: March 2023

Deep neural networks (DNN) try to analyze given data, to come up with decisions regarding the inputs. The decision-making process of the DNN model is not entirely transparent. The confidence of the model predictions on new data fed into the network can vary. We address the question of certainty of decision making and adequacy of information capturing by DNN models during this process of decision-making. We introduce a measure called certainty index, which is based on the outputs in the most penultimate layer of DNN. In this approach, we employed iEEG (intracranial electroencephalogram) data to train and test DNN. When arriving at model predictions, the contribution of the entire information content of the input may be important. We explored the relationship between the certainty of DNN predictions and information content of the signal by estimating the sample entropy and using a heatmap of the signal. While it can be assumed that the entire sample must be utilized for arriving at the most appropriate decisions, an evaluation of DNNs from this standpoint has not been reported. We demonstrate that the robustness of the relationship between certainty index with the sample entropy, demonstrated through sample entropy-heatmap correlation, is higher than that with the original signal, indicating that the DNN focuses on information rich regions of the signal to arrive at decisions. Therefore, it can be concluded that the certainty of a decision is related to the DNN’s ability to capture the information in the original signal. Our results indicate that, within its limitations, the certainty index can be used as useful tool in estimating the confidence of predictions. The certainty index appears to be related to how effectively DNN heatmaps captured the information content in the signal.
Functional Thinking: A Study with 10th-Grade Students
Authors: Martins Raquel , Viseu Floriano , Rocha Helena
Published: March 2023

This study aims to understand the functional thinking of 10th-grade students while studying functions. Specifically, we intend to answer the following research questions: what are the functional thinking processes used by 10th-grade students when studying functions? What difficulties do students present while learning functions? In view of the nature of this research objective, we adopted a qualitative and interpretative approach. In order to answer these questions, data were collected from the written records produced by the students while solving the proposed tasks, from records of the oral interactions during discussions and from a questionnaire. The results show that functional thinking processes were implicit in the resolution of the tasks proposed to the students. The students expressed an understanding of how the variables were related, presenting evidence of their functional thinking while working on the new concepts represented by the functions addressed in the proposed tasks. Some students expressed difficulties in interpreting the different types of representations associated with the functions, in retaining the necessary information from a graphical representation that would help them to draw conclusions and establish correspondences, in explaining functional relationships, and in interpreting the information provided by algebraic expressions. These difficulties can reduce the recognition of the relationships between variables and their behavior in the different representations, becoming an obstacle to learning for some students.
Teachers’ Appreciation of Benefits and Shortcomings of Online and Blended Higher STEM Education
Authors: Skliarova Iouliia , Meireles Inês , Tchemisova Tatiana , Cação Isabel , Martins Natália
Published: March 2023

This paper is devoted to identifying online teaching strategies appropriate for blended and face-to-face higher STEM (Science, Technology, Engineering, and Mathematics) education. The study is inspired by the experience gained during the COVID-19 pandemic lockdown, which forced many higher education institutions worldwide to shift abruptly to distance education and try many new tools, teaching methods, and strategies. Some of these tools and strategies were abandoned as soon as the lockdown had been lifted and the institutions returned to their regular functioning, but some of them are bound to stay. Certainly, it would be beneficial to include the most valuable of the gained skills and competences in traditional on-campus and blended courses. The study is based on an online questionnaire, addressed to the STEM faculty of the University of Aveiro, Portugal (which is an example of an institution that used to provide face-to-face instruction), whose analysis permits to derive a number of important recommendations. The results are compared with our previous work, where the students’ perspectives were analyzed, and similarities and discrepancies in appreciation of the involved parties are highlighted. This work extends the body of knowledge about the impact of the COVID-19 pandemic on STEM education by examining the challenges and opportunities faced by teachers. The recommendations derived contribute to improving the learning outcomes of online STEM education in many similar institutions.
A Keyword Co-Occurrence Analysis of Research on Service Learning: Conceptual Foci and Emerging Research Trends
Authors: Narong David Kongpiwatana , Hallinger Phillip
Published: March 2023

Although a significant body of research has accumulated on service learning over the past seven decades, to date, no reviews have analyzed the entire multi-disciplinary literature. Thus, scholars lack systematic documentation of the scope of this literature, its thematic structure, and topical foci. This bibliometric review aimed to document these features of the service learning knowledge base. The review analyzed 5615 Scopus-indexed documents on service learning published between 1950 and 2022 through citation, co-citation, and keyword co-occurrence analyses. The analyses uncovered thematic and empirical foci associated with the theory, implementation, and effects of service learning. The review identified engineering education and problem-based learning as related topics of significant interest. In recent years, educators have increasingly experimented with the integration of other active teaching and learning methods into service learning programs. This review suggests that future research examine service learning from the perspectives of public policy, stakeholder engagement, and educating for sustainability. The field would also benefit from additional theorizing on the features that distinguish service learning from other active learning pedagogies.
Unlocking Emotional Aspects of Kindergarten Teachers’ Professional Identity through Photovoice
Authors: Pan Chloe Simiao , Leung Suzannie K. Y. , Wan Sally Wai-yan
Published: March 2023

The previous literature has strongly emphasized the professional aspects of teacher identity in terms of knowledge and pedagogical matters. However, teachers’ emotions have also been academically discussed in recent decades. The voices of kindergarten teachers are critical for reflecting on professional identities within the community of kindergarten teachers. Regrettably, in Hong Kong, kindergarten teachers have become an oppressed professional community due to the marketization of kindergartens in this neoliberal city. Therefore, this arts-based participatory study aimed to investigate teachers’ identities by discovering kindergarten teachers’ emotional characteristics in Hong Kong. Teachers’ voice has been collected through photo narratives. Twelve in-service kindergarten teachers participated in this study; all of them worked in local kindergartens in Hong Kong. The teachers were invited individually to take a photo of an object in their daily lives and share their own stories. Altogether 1080 min of interview data were recorded. Through a series of oral narratives by members of this marginalized professional community, this study unlocked the emotions and voices of kindergarten teachers in Hong Kong. Through a photovoice approach, the findings revealed how the teachers’ personal aspects were a neglected but important part of their teacher identity.
A Study of the Usefulness of Physical Models and Digital Models for Teaching Science to Prospective Primary School Teachers
Authors: Robles-Moral F. Javier , Fernández-Díaz Manuel , Ayuso-Fernández Gabriel Enrique
Published: March 2023

This study focuses on the impact of the use of teaching resources on future teachers in different formats, physical and digital. We worked with a single task dealing with nutrition in humans with two groups of students, but one group worked with a version of the task that used physical resources, and the other group used digital resources as tools. Analyzing the work carried out and the answers given by the future teachers, it has been possible to observe the advantage of using digital resources over physical ones, although it did not generate significant differences between the two groups of participating students. This study shows how convenient it is to increase the use of digital models because of their lower cost, greater availability and ease of use. In short, they argue that the teaching of scientific knowledge should be complemented by the use of resources and models that facilitate learning, regardless of the format of the resource used.
Detection of Illegal Transactions of Cryptocurrency Based on Mutual Information
Authors: Zhao Kewei , Dong Guixin , Bian Dong
Published: March 2023

In recent times, there has been a swift advancement in the field of cryptocurrency. The advent of cryptocurrency has provided us with convenience and prosperity, but has also given rise to certain illicit and unlawful activities. Unlike classical currency, cryptocurrency conceals the activities of criminals and exposes their behavioral patterns, allowing us to determine whether present cryptocurrency transactions are legitimate by analyzing their behavioral patterns. There are two issues to consider when determining whether cryptocurrency transactions are legitimate. One is that most cryptocurrency transactions comply with laws and regulations, but only a small portion of them are used for illegal activities, which is related to the sample imbalance problem. The other issue concerns the excessive volume of data, and there are some unknown illegal transactions, so the data set contains an abundance of unlabeled data. As a result, it is critical to accurately distinguish between which transactions among the plethora of cryptocurrency transactions are legitimate and which are illegal. This presents quite a difficult challenge. Consequently, this paper combines mutual information and self-supervised learning to create a self-supervised model on the basis of mutual information that is used to improve the massive amount of untagged data that exist in the data set. Simultaneously, by merging the conventional cross-entropy loss function with mutual information, a novel loss function is created. It is employed to address the issue of sample imbalance in data sets. The F1-Score results obtained from our experimentation demonstrate that the novel loss function in the GCN method improves the performance of cryptocurrency illegal behavior detection by four points compared with the traditional loss function of cross-entropy; use of the self-supervised network that relies on mutual information improves the performance by three points compared with the original GCN method; using both together improves the performance by six points.
An Improved Median Filter Based on YOLOv5 Applied to Electrochemiluminescence Image Denoising
Authors: Yang Jun , Chen Junyang , Li Jun , Dai Shijie , He Yihui
Published: March 2023

In many experiments, the electrochemiluminescence images captured by smartphones often have a lot of noise, which makes it difficult for researchers to accurately analyze the light spot information from the captured images. Therefore, it is very important to remove the noise in the image. In this paper, a Center-Adaptive Median Filter (CAMF) based on YOLOv5 is proposed. Unlike other traditional filtering algorithms, CAMF can adjust its size in real-time according to the current pixel position, the center and the boundary frame of each light spot, and the distance between them. This gives CAMF both a strong noise reduction ability and light spot detail protection ability. In our experiment, the evaluation scores of CAMF for the three indicators Peak Signal-to-Noise Ratio (PSNR), Image Enhancement Factor (IEF), and Structural Similarity (SSIM) were 40.47 dB, 613.28 and 0.939, respectively. The results show that CAMF is superior to other filtering algorithms in noise reduction and light spot protection.
The Influence of Emerging Technologies on Distance Education
Authors: Garlinska Magdalena , Osial Magdalena , Proniewska Klaudia , Pregowska Agnieszka
Published: March 2023

Recently, during the COVID-19 pandemic, distance education became mainstream. Many students were not prepared for this situation—they lacked equipment or were not even connected to the Internet. Schools and government institutions had to react quickly to allow students to learn remotely. They had to provide students with equipment (e.g., computers, tablets, and goggles) but also provide them with access to the Internet and other necessary tools. On the other hand, teachers were trying to adopt new technologies in the teaching process to enable more interactivity, mitigate feelings of isolation and disconnection, and enhance student engagement. New technologies, including Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), Extended Reality (XR, so-called Metaverse), Big Data, Blockchain, and Free Space Optics (FSO) changed learning, teaching, and assessing. Despite that, some tools were implemented fast, and the COVID-19 pandemic was the trigger for this process; most of these technologies will be used further, even in classroom teaching in both schools and universities. This paper presents a concise review of the emerging technologies applied in distance education. The main emphasis was placed on their influence on the efficiency of the learning process and their psychological impact on users. It turned out that both students and teachers were satisfied with remote learning, while in the case of undergraduate children and high-school students, parents very often expressed their dissatisfaction. The limitation of the availability of remote learning is related to access to stable Internet and computer equipment, which turned out to be a rarity. In the current social context, the obtained results provided valuable insights into factors affecting the acceptance and emerging technologies applied in distance education. Finally, this paper suggests a research direction for the development of effective remote learning techniques.
GaN and SiC Device Characterization by a Dedicated Embedded Measurement System
Authors: Vella Alberto , Galioto Giuseppe , Vitale Gianpaolo , Lullo Giuseppe , Giaconia Giuseppe Costantino
Published: March 2023

This work proposes a comparison among GaN and SiC device main parameters measured with a dedicated and low-cost embedded system, employing an STM32 microcontroller designed to the purpose. The system has the advantage to avoid the use of expensive laboratory measurement equipment to test the devices, allowing to obtain their behavior in operating conditions. The following KPIs (Key Performance Indicators) are measured and critically compared: threshold voltage, on-resistance and input capacitance. All the measurements are carried out in a short time interval and on a wide range of switching frequencies, ranging from 10 kHz to 1 MHz. This investigation is focused on the deviation of the figures of merit when the switching frequency changes, since it is crucial for wide-bandgap devices. The devised, low-cost, microcontroller unit allows high flexibility and system portability, while the employed equivalent-time sampling technique overcomes some issues related to the need of high sampling frequency. It allows good performances with common microcontroller embedded AD converters. To validate the proposed system, the obtained results have been compared with the time-domain waveforms acquired with a traditional laboratory oscilloscope and a study of the system’s measurement errors has been carried out. Results show that GaN devices achieve a higher efficiency with respect to SiC devices in the considered range of switching frequencies. The on-resistance exhibited by GaN devices shows, as expected, an increase with frequency, which happens to switching losses, too. On the other hand, GaN devices are more sensitive to parasitic effects and the high dV/dt, due to the reduced switching times, can excite unwanted ringing phenomena.
Performance Analysis of Learning-Based Disturbance Observer for Pulsed Superconducting Cavity Field Control
Authors: Toor Waqas Ahmed , Ashraf Muhammad
Published: March 2023

The use of Disturbance Observer-based (DOB) control is widespread in stabilizing the electromagnetic field in superconducting radio frequency cavities to facilitate beam acceleration in particle accelerators. Repetitive disturbances such as beam loading and Lorentz force cavity detuning are compensated by DOB control, and their suppression is enhanced through the incorporation of a learning scheme into the conventional disturbance observer. This paper evaluated the performance of a learning-based disturbance observer for compensating beam loading and cavity detuning in pulsed superconducting radio frequency cavities and proposes modifications for better field stability. A superconducting cavity baseband model for π-mode was simulated in Matlab/Simulink with a trapezoidal beam pulse as the input disturbance and different cavity detuning values to analyze the controllers’ performance. The simulations were conducted for multiple observer filter bandwidths to evaluate the performance of the learning-based disturbance observer under plant model uncertainties and different detuning values. The results demonstrate that the learning-based disturbance observer yields faster convergence to the reference input and lower tracking errors during the flat top of pulse voltage in comparison to conventional disturbance observer control.
A Novel QoS Guaranteed Joint Resource Allocation Framework for 5G NR with Supplementary Uplink Transmission
Authors: Sun Yanzan , Huang Yanyu , Yu Tao , Chen Xiaojing , Zhang Shunqing
Published: March 2023

In 5G scenarios, the dynamic resource allocation of network slicing is crucial for quality-of-service (QoS) guaranteed under fluctuating traffic demands in rapidly changing communication environments. In this paper, we propose a novel QoS guaranteed joint resource allocation framework for NR with supplementary uplink (SUL) called QGJRA-SUL, where three parameters of SUL admission, TDD pattern, and band slicing scheme are jointly optimized. The framework is driven by a well-designed deep reinforcement learning agent. By combining the activation functions tanh and softmax, the agent can jointly optimize three parameters at the same time. Under the original problem of QoS satisfaction rate maximization, we introduce the load unbalance degree of slices into the reward function as a penalty term. The simulation results show that the framework can guarantee the QoS satisfaction rate well and balance the load of slices. QGJRA-SUL can accommodate 15% more user equipments (UEs) with the same QoS satisfaction rate than that of a traditional single-band solution without SUL, and achieve a 73% increase in the performance of load balancing than that without a load balancing mechanism near the full load.
A Hybrid Improved-Whale-Optimization–Simulated-Annealing Algorithm for Trajectory Planning of Quadruped Robots
Authors: Xu Ruoyu , Zhao Chunhui , Li Jiaxing , Hu Jinwen , Hou Xiaolei
Published: March 2023

Traditional trajectory-planning methods are unable to achieve time optimization, resulting in slow response times to unexpected situations. To address this issue and improve the smoothness of joint trajectories and the movement time of quadruped robots, we propose a trajectory-planning method based on time optimization. This approach improves the whale optimization algorithm with simulated annealing (IWOA-SA) together with adaptive weights to prevent the whale optimization algorithm (WOA) from falling into local optima and to balance its exploration and exploitation abilities. We also use Markov chains of stochastic process theory to analyze the global convergence of the proposed algorithm. The results show that our optimization algorithm has stronger optimization ability and stability when compared to six representative algorithms using six different test function suites in multiple dimensions. Additionally, the proposed optimization algorithm consistently constrains the angular velocity of each joint within the range of kinematic constraints and reduces joint running time by approximately 6.25%, which indicates the effectiveness of this algorithm.
Optimal Placement and Sizing of D-STATCOMs in Electrical Distribution Networks Using a Stochastic Mixed-Integer Convex Model
Authors: Gil-González Walter
Published: March 2023

This paper addresses the problem regarding the optimal placement and sizing of distribution static synchronous compensators (D-STATCOMs) in electrical distribution networks via a stochastic mixed-integer convex (SMIC) model in the complex domain. The proposed model employs a convexification technique based on the relaxation of hyperbolic constraints, transforming the nonlinear mixed-integer programming model into a convex one. The stochastic nature of renewable energy and demand is taken into account in multiple scenarios with three different levels of generation and demand. The proposed SMIC model adds the power transfer losses of the D-STATOMs in order to size them adequately. Two objectives are contemplated in the model with the aim of minimizing the annual installation and operating costs, which makes it multi-objective. Three simulation cases demonstrate the effectiveness of the stochastic convex model compared to three solvers in the General Algebraic Modeling System. The results show that the proposed model achieves a global optimum, reducing the annual operating costs by 29.25, 60.89, and 52.54% for the modified IEEE 33-, 69-, and 85-bus test systems, respectively.
Compact-Transmission-Line Decoupling and Matching Network of Three-Element Array for Wireless Applications
Authors: Zhang Chi , Jiao Yong-Chang
Published: March 2023

In this paper, a compact-transmission-line (TL) decoupling and matching network (DMN) for three-element circular arrays is presented. As a result of the miniaturization of wireless terminals, the antenna spacing is too close, leading to large couplings and deteriorating system performance. The DMN consists of an impedance transformation section and a star-shaped neutralization section which eliminates couplings between antennas while occupying a smaller area. The impedance transformation section converts the odd and even mode conductances of the antenna to the impedance of the feeding line, and the neutralization section eliminates the odd and even mode susceptances to complete the decoupling and matching of the antenna. The star-shaped circuit utilizes the area surrounded by the antennas in a more efficient manner than traditional triangle circuits. This facilitates the folding of the TL and the miniaturization of the circuit. A design formula is given for each module of the circuit after it has been analyzed analytically. A decoupling example is simulated and manufactured with the diameter of the area occupied by the DMN less than a quarter wavelength. At the working frequency, the port isolation is increased from 7.6 dB to 33.5 dB. The ECC between ports is reduced from 0.11 to 0.011, which validates the method proposed.
Health Indicator Similarity Analysis-Based Adaptive Degradation Trend Detection for Bearing Time-to-Failure Prediction
Authors: Chen Zhipeng , Zhu Haiping , Fan Liangzhi , Lu Zhiqiang
Published: March 2023

Time-to-failure (TTF) prediction of bearings is vital to the prognostic and health management of rotating machines. Owing to the shifty degradation trends (DTs) of bearings, it is still difficult to obtain accurate TTF prognostic results. To solve this problem, this paper proposes an online, continuously updated TTF prognostic method based on health indicator (HI) similarity analysis and DT detection. First, multiple degradation features are extracted and fused to construct principal component HI by using dynamic principal component analysis. Next, exponential degradation models are fitted using the HI values for future state prediction. By regarding several HI values as a tested segment, the DT is detected by analyzing the similarity of the tested segment and the fitted curve. Finally, TTF is predicted by extrapolating the DT to hit the estimated failure threshold. Two case studies based on public bearing datasets demonstrate the superiority of the proposed approach over state-of-the-art methods.
Evolution of Socially-Aware Robot Navigation
Authors: Guillén-Ruiz Silvia , Bandera Juan Pedro , Hidalgo-Paniagua Alejandro , Bandera Antonio
Published: March 2023

In recent years, commercial and research interest in service robots working in everyday environments has grown. These devices are expected to move autonomously in crowded environments, maximizing not only movement efficiency and safety parameters, but also social acceptability. Extending traditional path planning modules with socially aware criteria, while maintaining fast algorithms capable of reacting to human behavior without causing discomfort, can be a complex challenge. Solving this challenge has involved the development of proactive systems that take into account cooperation (and not only interaction) with the people around them, the determined incorporation of approaches based on Deep Learning, or the recent fusion with skills coming from the field of human–robot interaction (speech, touch). This review analyzes approaches to socially aware navigation and classifies them according to the strategies followed by the robot to manage interaction (or cooperation) with humans.
Generation of a Typical Meteorological Year for Global Solar Radiation in Taiwan
Authors: Hsieh Tsung-En , Fraincas Bianca , Chang Keh-Chin
Published: March 2023

Solar energy technology is now a mature and environmentally friendly solution. Long-term and credible solar radiation data are required for energy assessments of solar applications. Due to the lack of a typical year and accurate, long-term global solar radiation data for Taiwan, data for the typical meteorological year (TMY) of global solar radiation from 30 weather stations across Taiwan of the Central Weather Bureau were gathered for this study. The database for solar radiation contains data for the 15 years between 2004 and 2018, except for one (Chigu) weather station which provides data for the 12 years between 2004 and 2015, which possesses credible data quality and meets the requirements of the TMY method. The minimum and maximum TMY global radiation observed from the 30 weather stations are 3421.8 and 5479.9 MJ/m2 at the Zhuzihu (Station 2) and Tainan (Station 16) weather stations, respectively. The effects of topography, geography, and latitude on the global radiation distribution in Taiwan are discussed. A trend of increasing annual global radiation from the northeast to the southwest in the Taiwanese mainland, which is attributed to the combined effects of topography and latitude, is observed. This credible, long-term database for global solar radiation is a prerequisite reference for solar information for use in determining the performance of solar energy applications in Taiwan.
Fabrication and Performance Analysis of the Aero-Leaf Savonius Wind Turbine Tree
Authors: Rathore Mukesh Kumar , Agrawal Meena , Baredar Prashant , Shukla Anoop Kumar , Dwivedi Gaurav , Verma Puneet
Published: March 2023

Large wind turbines of the horizontal axis are commonly used to gather wind energy; however, their performance is found to be constrained in conditions of erratic and low-speed wind flow. In contrast, low wind conditions—which are typically present in dense urban areas—are found to favour vertical axis wind turbines (VAWT). These turbines have a simple design, are inexpensive and quiet, and are discovered to be better in low wind situations. In this research, we have chosen wind tree applications to absorb the most available wind energy. The new Aeroleaf Savonius Wind Turbine was developed numerically and a computational fluid dynamics simulation was performed on this new type of Savonius tree to predict its performance. The results indicated that the system could accept wind from any direction and could start rotating as soon as the site had a cut in wind speed of 3.3 m/s. The rotor speed increased by 10.4% from 5.5 to 6.3 m/s wind speed at 0.45 tip speed ratio. The tip speed ratio is 0.52 at the site’s high wind speed, and under these circumstances, the maximum Cp is 12.9%. The turbine was able to produce superior performance coefficients, according to the results.
Statistical Optimization of Chemo Sonic Liquefaction in Macroalgae for Biohydrogen Generation—An Energy-Effective Approach
Authors: Shankaran Shabarish , Karuppiah Tamilarasan , Jeyakumar Rajesh Banu , Vincent Godvin Sharmila
Published: March 2023

In this study, a combined pretreatment method of sonication and alkali (KOH) liquefaction (SAL) was used to increase the production of biohydrogen from macroalgae (Chaetomorpha antennina) in an energy-efficient manner. Sonication liquefaction (SL) was accomplished by varying the sonic intensities from 10% to 70% and the pretreatment time from 5 to 60 min. The ideal liquefaction conditions in SL were determined to be 50% for sonic intensity, and 30 min of pretreatment time which produces liquefied organics (LO) release of 2650 mg/L. By adjusting the pH of the alkali (KOH) from 8 to 12, SAL was carried out under SL optimal conditions. With a liquefaction efficiency of 24.61% and LO release of 3200 mg/L, pH 11 was the best for effective macroalgal liquefaction in SAL. SAL (4500 kJ/kg TS) consumed less ultrasonic specific energy (USE) than SL (9000 kJ/kg TS). More VFA was produced in SAL (2160 mg/L) than SL (1070 mg/L). Compared to SL (120 mL H2/g COD/0.005 moles of H2/g COD), SAL produced the most biohydrogen of 141 mL H2/g COD/0.006 moles of H2/g COD. The combined pretreatment (SAL) increases the LO release, which ultimately results in an additional 15% increment in biohydrogen production compared to the SL, along with 44.4% of energy savings. Overall, SAL was determined to be energy efficient in biohydrogen production.
Energy Disaggregation of Type I and II Loads by Means of Birch Clustering and Watchdog Timers
Authors: Kligman Amitay , Yaniv Arbel , Beck Yuval
Published: March 2023

A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset.
Study on the Performance of Photovoltaic/Thermal Collector–Heat Pump–Absorption Chiller Tri-Generation Supply System
Authors: Yue Han , Xu Zipeng , Chu Shangling , Cheng Chao , Zhang Heng , Chen Haiping , Ai Dengxin
Published: March 2023

The solar energy supply system has played an increasingly substantial role in realizing nearly zero-carbon buildings. In order to overcome the impact of solar randomness on the energy supply of a distributed solar system, this paper proposes a solar tri-generation supply system which integrates a photovoltaic/thermal collector (PV/T), a heat pump (HP), and an absorption chiller (AC). The PV/T-HP integration system is adopted to provide stable heating for a building and AC. The system model is established in TRNSYS software, and its performance is evaluated based on energy, exergy, and economic aspects. The results demonstrate that the system effectively meets the load demand, with an energy efficiency of 32.98% and an exergy efficiency of 17.62%. The payback period (PP) is 7.77 years. Compared with the systems proposed in the other literature, the performance of the proposed system has a certain extent of advantage. Furthermore, the equipment and system exergy performance decline with an increase in the intensity of solar radiation. Increasing the PV/T area effectively improves the system’s profitability within the actual roof area limitation of the building. Moreover, increasing the capacity of the low-temperature heat pump after 68 kW improves the system efficiency and reduces the payback period. In summary, this paper proposes an efficient distributed solar energy system that is suitable for urban building energy supply.
Operational Parameter Analysis and Performance Optimization of Zinc–Bromine Redox Flow Battery
Authors: Zhang Ye-Qi , Wang Guang-Xu , Liu Ru-Yi , Wang Tian-Hu
Published: March 2023

Zinc–bromine redox flow battery (ZBFB) is one of the most promising candidates for large-scale energy storage due to its high energy density, low cost, and long cycle life. However, numerical simulation studies on ZBFB are limited. The effects of operational parameters on battery performance and battery design strategy remain unclear. Herein, a 2D transient model of ZBFB is developed to reveal the effects of electrolyte flow rate, electrode thickness, and electrode porosity on battery performance. The results show that higher positive electrolyte flow rates can improve battery performance; however, increasing electrode thickness or porosity causes a larger overpotential, thus deteriorating battery performance. On the basis of these findings, a genetic algorithm was performed to optimize the batter performance considering all the operational parameters. It is found that the battery energy efficiency can reach 79.42% at a current density of 20 mA cm−2. This work is helpful to understand the energy storage characteristics and high-performance design of ZBFB operating at various conditions.
Framework of Virtual Plantation Forest Modeling and Data Analysis for Digital Twin
Authors: Li Wanlu , Yang Meng , Xi Benye , Huang Qingqing
Published: March 2023

Plantation forests, cultivated through artificial seeding and planting methods, are of great significance to human society. However, most experimental sites for these forests are located in remote areas. Therefore, in-depth studies on remote forest management and off-site experiments can better meet the experimental and management needs of researchers. Based on an experimental plantation forest of Triploid Populus Tomentosa, this paper proposes a digital twin architecture for a virtual poplar plantation forest system. The framework includes the modeling of virtual plantation and data analysis. Regarding this system architecture, this paper theoretically analyzes the three main entities of the physical world, digital world, and researchers contained in it, as well as their interaction mechanisms. For virtual plantation modeling, a tree modeling method based on LiDAR point cloud data was adopted. The transitional particle flow method was proposed to combine with AdTree method for tree construction, followed by integration with other models and optimization. For plantation data analysis, a database based on forest monitoring data was established. Tree growth equations were derived by fitting the tree diameter at breast height data, which were then used to predict and simulate trends in diameter-related data that are difficult to measure. The experimental result shows that a preliminary digital twin-oriented poplar plantation system can be constructed based on the proposed framework. The system consists of 2160 trees and simulations of 10 types of monitored or predicted data, which provides a new practical basis for the application of digital twin technology in the forestry field. The optimized tree model consumes over 67% less memory, while the R2 of the tree growth equation with more than 100 data items could reach more than 87%, which greatly improves the performance and accuracy of the system. Thus, utilizing forestry information networking and digitization to support plantation forest experimentation and management contributes to advancing the digital transformation of forestry and the realization of a smart management model for forests.
Effects of Microbial Communities on Elevational Gradient Adaptation Strategies of Pinus yunnanensis Franch. and Pinus densata Mast. in a Mixed Zone
Authors: Mu Dejin , Tang Junrong , Cai Nianhui , Chen Shi , He Yingnian , Deng Zijun , Yang Yi , Yang Dan , Xu Yulan , Chen Lin
Published: March 2023

Pinus densata Mast. is considered a homoploid hybrid species that originated from the putative parent species Pinus tabuliformis Carr. and Pinus yunnanensis Franch., but the mechanism of the adaptive differentiation of P. densata and its parents in native habitats has not been reported. Therefore, the overlapping distribution areas between P. densata and P. yunnanensis in the heart of the Hengduan Mountains were chosen. The adaptive differentiation mechanism of the homoploid hybrids and their parents with respect to the elevational gradient was studied based on the morphological features and the different strategies of recruiting endophytic microbial communities from the rhizosphere soil. The results showed that (1) the height and diameter at breast height were the greatest at 2600 m and 2900 m, and from 2700 m to 2900 m, three-needle pines (P. yunnanensis-like type) transitioned into two-needle pines (P. densata-like type). (2) The recruitment of rhizosphere microbial communities was driven by the C, N, P and pH values which showed significant elevation features. (3) There was a significant difference in the recruitment strategies of endophytes between the P. yunnanensis-like type and P. densata-like type. Pinus densata mainly reduced the recruitment of Mucoromycota (fungi) and increased the recruitment of Proteobacteria (bacteria), which may be related to environmental adaptability, quorum sensing and the metabolism of auxiliary factors and vitamins at high elevations. (4) The root endophytic microbiome was enriched in the rare groups from the rhizosphere soil microbial pool. The results of this study provide new insights and new ideas for environmental adaptability and differentiation in homoploid hybrid speciation.
Molecular and Technological Characterization of Saccharomyces cerevisiae from Sourdough
Authors: Aydın Furkan , Günen Tacettin Utku , Kahve Halil İbrahim , Güler Emrah , Özer Göksel , Aktepe Yeşim , Çakır İbrahim
Published: March 2023

DNA markers help detect the intraspecific genetic diversity of yeast strains. Eight ISSR (Inter Simple Sequence Repeats) primers were used to assess the intraspecific diversity of Saccharomyces cerevisiae (n = 96) from different populations (n = 3), evaluate the technological characteristics, and investigate trait-loci associations. The primers amplified 154 reproducible and scorable bands, of which 79.87% were polymorphic. The UPGMA (unweighted pair group method with arithmetic mean) dendrogram clustered 96 isolates into two main clusters, supported by STRUCTURE HARVESTER results (ΔK = 2). Analysis of molecular variance (AMOVA) indicated significant genetic differences between (15%) and within the populations (85%) (p < 0.001). Twenty-nine genetically distinct strains were selected for the technological characterization. Principal component analysis (PCA) revealed that five strains with high fermentation capacity, leavening activity, high growth index at 37 °C, and harsh growth conditions were technologically relevant. Trait-loci association analyses indicated that the highest correlation (r = 0.60) was recorded for the fermentation capacity on the 8th and 113th loci, amplified by ISSR-1 and ISSR-6 primers, respectively (p < 0.05). The strains yielding high performances and the associated loci amplified by ISSR markers possess a high potential to generate locus-specific primers to target the strains with high fermentation capacity.
A DNN Architecture Generation Method for DDoS Detection via Genetic Alogrithm
Authors: Zhao Jiaqi , Xu Ming , Chen Yunzhi , Xu Guoliang
Published: March 2023

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer’s experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model’s generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability.
Regulation of Energy Metabolism and Anti-Inflammatory Activities of Mastiha Fractions from Pistacia lentiscus L. var. chia
Authors: Kalousi Foteini D. , Pollastro Federica , Karra Aikaterini G. , Tsialtas Ioannis , Georgantopoulos Achilleas , Salamone Stefano , Psarra Anna-Maria G.
Published: March 2023

Pistacia lentiscus L. var. chia resin (Chios Mastiha), the first natural chewing gum, is widely used in Mediterranean cuisine and has been used in traditional medicine from ancient times. Regarding its chemical composition, Chios Mastiha is known to be rich in triterpenes. Triterpenes have a similar structure to glucocorticoids (GCs), the steroid hormones that exert strong anti-inflammatory activities and play crucial roles in the regulation of cellular metabolism. To simplify the characterization of the bioactive compounds of Mastiha resin, three different polarity fractions were isolated and were further analyzed regarding their main chemical composition and an assessment of their biological activities. The biological assessment focused on the evaluation of the potential anti-proliferative, anti-inflammatory, and apoptotic activities as well as the possible interference of the three different polarity Mastiha fractions with the glucocorticoid receptor signaling, with the aim of characterizing the biochemical mechanisms of the actions of the Mastiha fraction. Applying MTT cell viability assay, luciferase/β-galactosidase assay, and Western blot analysis showed that Chios Mastiha apolar, medium-polar, and polar fractions reduced the HEK293 cell viability in a dose-dependent manner, possibly by mitochondrial-mediated induction of apoptosis. Medium-polar and polar Mastiha fractions also suppressed the GR and NF-κΒ transcriptional activation and the p65 protein levels. These activities were accompanied by the modulation of protein levels of regulatory molecules playing a crucial role in cellular energy homeostasis, such as GR, phosphoenolpyruvate carboxykinase (PEPCK), and/or peroxisome proliferator-activated receptor alpha (PPARα), and by the induction of phosphorylation and the activation of the AMP-activated protein kinase (AMPK). The medium-polar fraction was found to be enriched in triterpenes, such as lupeol, 24Z-masticadienonic acid methyl ester, and 24Z-isomasticadienonic acid methyl ester, and it was the most active one, so we propose that triterpenes in medium-polar fraction are possibly the bioactive compounds responsible for Mastiha’s regulatory actions on energy metabolism and anti-inflammatory activities via interference with GR, NF-κΒ, and AMPK signaling. This highlights its potential applications in many fields of pharmaceutical, cosmetic, and nutraceutical interest.