bluuebunny/crossref_metadata_2025 huggingface dataset:
Habitat Fragmentation Enhances the Difference between Natural and Artificial Reefs in an Urban Marine Coastal Tract
Authors: Mancini Ilaria , Azzola Annalisa , Bianchi Carlo Nike , Capello Marco , Cutroneo Laura , Morri Carla , Oprandi Alice , Montefalcone Monica
Published: May 2024

Coastal urbanization and the consequent proliferation of artificial structures greatly impact rocky reef communities, productive and diverse marine environments that play a crucial role in the functioning of broader coastal ecosystems. This study, conducted along a 7 km stretch of coastline at increasing distance from the port of Genoa (Ligurian Sea), investigated whether the alternating presence of artificial and natural reefs leads to discernible differences in the biota inhabiting these two reef types. The study area is one of the most anthropized areas of the Mediterranean Sea, exhibiting nearly 60% coastal artificialization, which severely impacts coastal ecosystems, favouring the replacement of sensitive species with more tolerant species. Ten reefs (5 natural and 5 artificial) were surveyed by scuba diving at about a 6-m depth, employing quadrats of 50 cm × 50 cm to estimate visually the percent cover of conspicuous sessile organisms. The artificial reefs hosted a similar number of species (18) to their natural counterparts (19) but exhibited a distinct community composition: the former were especially characterized by Jania rubens and filamentous algae, with the latter characterized by Peyssonnelia squamaria and Mesophyllum lichenoides. This difference, however, became negligible where coastal habitat fragmentation (here measured with a purposely devised Fragmentation Index) was minimal. Reducing fragmentation may therefore represent a management strategy to minimize the potential impact of artificial structures on marine biodiversity.
Invasive Plant Species Demonstrate Enhanced Resource Acquisition Traits Relative to Native Non-Dominant Species but not Compared with Native Dominant Species
Authors: Chen Yingcan , Xie Yijie , Wei Caihong , Liu Si , Liang Xiaoyue , Zhang Jiaen , Li Ronghua
Published: May 2024

Invasive plant species are often characterized by superior resource acquisition capabilities compared with native species, contributing to their success in new environments. However, the dominance of these species varies, and not all invasive species become dominant, nor are all native species uniformly vulnerable to competitive exclusion. In this study, we analyzed 19 functional traits across 144 herbaceous plant species in Guangzhou, China. The studied species included 31 invasive dominant species (IDS), 19 invasive non-dominant species (INS), 63 native dominant species (NDS), and 31 native non-dominant species (NNS). Our findings reveal no significant differences in functional traits between IDS and INS, indicating a broad trait similarity within invasive categories. Pronounced similarities between invasive species and NDS suggest an ecological equivalency that facilitates successful integration and competition in new habitats. Notable differences in several key traits—height, leaf thickness, leaf water content, stoichiometry, photosynthetic rate, water use efficiency, and nitrogen use efficiency—indicate a competitive superiority in resource acquisition and utilization for invasive species over NNS. These distinctions are vital for understanding the mechanisms driving the success of invasive species and are crucial for developing strategies to manage their impact on native ecosystems.
Retrospective Analysis to Optimize the Detection of MET Exon 14 Skipping Mutations in Non-Small Cell Lung Cancer
Authors: Lu Jang-Jih , Tsai Shu-Hui , Lin Lee-Chung , Chiueh Tzong-Shi
Published: May 2024

Our study optimized METex14 skipping mutation detection by analyzing 223 Oncomine™ Focus Assay-positive cases using Pan Lung Cancer PCR Panel and reverse transcription (RT)-PCR. Among the 11 METex14 skipping mutation-positive cases (average read counts: 1390), 2 with Oncomine™ Focus Assay read counts of 2540 and 10,177 were positive on all platforms. Those with Oncomine™ Focus Assay read counts ranging from 179 to 612 tested negative elsewhere. Specimens with low ratios (average ratio: 0.12% for nine cases) may yield false-positive results. Our results suggested that monitoring read counts and ratios and validating the results with RT-PCR are crucial to prevent false positives.
Skeletal Anterior Open Bite Attenuates the Chewing-Related Increase in Brain Blood Flow
Authors: Kariya Chihiro , Kanzaki Hiroyuki , Kumazawa Masao , Sahara Saaya , Yoshida Kana , Inagawa Yuri , Kobayashi Fumitaka , Tomonari Hiroshi
Published: May 2024

The masticatory function of patients with skeletal anterior open bite (OPEN) is reported to be impaired compared with that of patients with normal occlusion (NORM). In this study, we compared brain blood flow (BBF) in patients with OPEN and NORM and investigated the factors related to BBF during mastication in patients with OPEN. The study included 17 individuals with NORM and 33 patients with OPEN. The following data were collected: number of occlusal contacts, jaw movement variables during mastication, and BBF measured with functional near-infrared spectroscopy during chewing. The number of occlusal contacts, maximum closing and opening speeds, closing angle, and vertical amplitude were smaller in the OPEN than in the NORM group. Interestingly, BBF increased less in the OPEN group. Correlation analysis revealed that several parameters, including number of occlusal contacts and closing angle, were correlated with changes in BBF during mastication. These results suggest that not only occlusion but also jaw movement variables and factors related to masticatory muscles contribute to the chewing-related increase in BBF. In conclusion, BBF increases less during mastication in patients with OPEN than in those with NORM. In addition, the higher increase in BBF is correlated with jaw movement. Together, we discovered that OPEN exhibits significant adverse effects not only on masticatory function but also on brain function.
Role of Supercoiling and Topoisomerases in DNA Knotting
Authors: Cebrián Jorge , Martínez-Robles María-Luisa , Martínez Victor , Hernández Pablo , Krimer Dora B. , Schvartzman Jorge B. , Fernández-Nestosa María-José
Published: May 2024

DNA knots are deleterious for living cells if not removed. Several theoretical and simulation approaches address the question of how topoisomerases select the intermolecular passages that preferentially lead to unknotting rather than to the knotting of randomly fluctuating DNA molecules, but the formation of knots in vivo remains poorly understood. DNA knots form in vivo in non-replicating and replicating molecules, and supercoiling as well as intertwining are thought to play a crucial role in both the formation and resolution of DNA knots by topoisomerase IV. To confirm this idea, we used two-dimensional agarose gel electrophoresis run with different concentrations of chloroquine to demonstrate that non-replicating pBR322 plasmids grown in a topoisomerase I-defective E. coli strain (RS2λ) were more negatively supercoiled than in a wild-type strain (W3110) and, concurrently, showed significantly fewer knots. In this way, using wild-type and E. coli mutant strains, we confirmed that one of the biological functions of DNA supercoiling is to reduce the formation of DNA knots.
Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery
Authors: Šupčík Adam , Milics Gabor , Matečný Igor
Published: May 2024

With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a substantial impact on the final product. This study’s main goals were to gather canopy morphology data using a sophisticated 3D model and assess how well different morphology characteristics predicted yield results. An unmanned aerial vehicle (UAV) with an RGB camera was used in the vineyards of Topoľčianky, Slovakia, to obtain precise orthophotos of individual vine rows. Following the creation of an extensive three-dimensional (3D) model of the assigned region, a thorough examination was carried out to determine many canopy characteristics, including thickness, side section dimensions, volume, and surface area. According to the study, the best combination for predicting grape production was the side section and thickness. Using more than one morphological parameter is advised for a more precise yield estimate as opposed to depending on only one.
Joint Drone Access and LEO Satellite Backhaul for a Space–Air–Ground Integrated Network: A Multi-Agent Deep Reinforcement Learning-Based Approach
Authors: Huang Xuan , Xia Xu , Wang Zhibo , Peng Mugen
Published: May 2024

The space–air–ground integrated network can provide services to ground users in remote areas by utilizing high-altitude platform (HAP) drones to support stable user access and using low earth orbit (LEO) satellites to provide large-scale traffic backhaul. However, the rapid movement of LEO satellites requires dynamic maintenance of the matching relationship between LEO satellites and HAP drones. Additionally, different traffic types generated at HAP drones hold varying levels of values. Therefore, a tripartite matching problem among LEO satellites, HAP drones, and traffic types jointly considering multi-dimensional characteristics such as remaining visible time, channel condition, handover latency, and traffic storage capacity is formulated as mixed integer nonlinear programming to maximize the average transmitted traffic value. The traffic generation state for HAP drones is modeled as a mixture of stochasticity and determinism, which aligns with real-world scenarios, posing challenges for traditional optimization solvers. Thus, the original problem is decoupled into two independent sub-problems: traffic–drone matching and LEO–drone matching, which are addressed by mathematical simplification and multi-agent deep reinforcement learning with centralized training and decentralized execution, respectively. Simulation results verify the effectiveness and superiority of the proposed tripartite matching approach.
Interference of Particles with Fermionic Internal Degrees of Freedom
Authors: Dajka Jerzy
Published: May 2024

The interference of fermionic particles, specifically molecules comprising a small number of fermions, in a Mach–Zehnder interferometer is being investigated under the influence of both classical and non-classical external controls. The aim is to identify control strategies that can elucidate the relationship between the interference pattern and the characteristics of internal fermion–fermion interactions.
An Adaptive Sampling Algorithm with Dynamic Iterative Probability Adjustment Incorporating Positional Information
Authors: Liu Yanbing , Chen Liping , Chen Yu , Ding Jianwan
Published: May 2024

Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers’ equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm’s efficacy.
Linear Codes Constructed from Two Weakly Regular Plateaued Functions with Index (p − 1)/2
Authors: Yang Shudi , Zhang Tonghui , Yao Zheng-an
Published: May 2024

Linear codes are the most important family of codes in cryptography and coding theory. Some codes only have a few weights and are widely used in many areas, such as authentication codes, secret sharing schemes and strongly regular graphs. By setting p≡1(mod4), we constructed an infinite family of linear codes using two distinct weakly regular unbalanced (and balanced) plateaued functions with index (p−1)/2. Their weight distributions were completely determined by applying exponential sums and Walsh transform. As a result, most of our constructed codes have a few nonzero weights and are minimal.
Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India
Authors: Choudhury Upasana , Kanga Shruti , Singh Suraj Kumar , Kumar Anand , Meraj Gowhar , Kumar Pankaj , Singh Saurabh
Published: May 2024

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making.
Person and Family Centredness—The Need for Clarity of Focus
Authors: McCormack Brendan
Published: May 2024

Congratulations to the editorial team of the European Burn Journal for having the vision to host a Special Issue on the theme of “Person-Centred and Family-Centred Care Following Burn Injuries” [...]
Modeling Tax Revenue Determinants: The Case of Visegrad Group Countries
Authors: Đurović Todorović Jadranka , Đorđević Marina , Mirović Vera , Kalaš Branimir , Pavlović Nataša
Published: May 2024

This article provides panel data estimations of the tax revenue determinants in VG (Visegrad Group) countries (the Czech Republic, Hungary, Poland, and Slovakia) for the period 1994–2023. The aim of this research was to determine how the macroeconomic determinants affect the tax revenues in the selected countries. Within the static models, the Hausman test showed that the FE (fixed effects) model is appropriate and reflects the significant effects of the gross domestic product, population, inflation, unemployment, import, government revenue, government expenditure, and EU enlargement on the tax revenue. The PMG (Pooled Mean Group) model is an adequate model among the dynamic models and manifests the significant effect of the lagged value of the tax revenue. In the short term, growth of the gross domestic product and population by 1% causes higher changes in the tax revenue of 0.14% and 2.93%. Likewise, growth of the inflation rate by 1% decreases the tax revenue by 0.037%, which is higher than in the long term. Further, the results show that EU enlargement is significant for tax revenue in the short term, as well as in the long term. In the long term, unemployment has a greater significant effect on tax revenue, where 1% growth decreases the tax revenue by 0.15%. In contrast, government revenue is significant for tax revenue only in the long term, where 1% growth increases the tax revenue by 0.77%.
Between Symbols and Particles: Investigating the Complexity of Learning Chemical Equations
Authors: Hamerská Lucie , Matěcha Tadeáš , Tóthová Martina , Rusek Martin
Published: May 2024

This study explores the pedagogical challenges in teaching chemical equations, focusing on the ability to balance chemical equations and understanding related basic chemical concepts among chemistry teacher students. Based on Johnstone’s representation theory, we examined the efficacy of digital tools designed to enhance equation-balancing skills. The key findings of this study seem to be the disconnection between students’ procedural proficiency in balancing equations and their conceptual understanding of underlying chemical reactions. Surprising was the relatively low ability to balance chemical equations when compared to the expected level of upper-secondary school students. An analysis of the results showed that students could be distinguished into four different groups of solvers based on their total score and solving time: efficient, persistent, impulsive, and inefficient solvers. Utilizing an eye-tracking study, the findings reveal a predominant reliance on symbolic representations, with additional sub-microscopic representations provided by digital applets failing to significantly improve equation-balancing capabilities. This reliance potentially hinders students’ ability to conceptualize reactions at the particulate level, impacting overall chemistry comprehension. This study underscores the need for a pedagogical shift toward integrating multiple representations into chemical education to bridge the gap between procedural skills and deep conceptual understanding. By highlighting the limitations of current teaching approaches and the use of digital tools, this research contributes to improving chemical education strategies, fostering a more integrated and nuanced understanding of chemical principles among students.
Differences in Academic Persistence Intentions among STEM Undergraduates in South Korea: Analysis of Related and Influencing Factors
Authors: Hwang Soonhee
Published: May 2024

In recent years, a decline in employment rates has been observed among science, technology, engineering, and mathematics (STEM) graduates in South Korea, influenced by diverse factors, including economic, social, and policy-related factors. Understanding the reasons behind this decline in STEM employment requires an exploration of academic persistence intentions in STEM and their interconnected relationships with relevant factors. This study aimed to comprehensively examine differences in academic persistence intentions, environmental factors, achievement-related factors, and career motivation among 2393 STEM undergraduates in Korea. Furthermore, this study investigated the factors affecting academic persistence intentions. Data were analyzed using different statistical methods, including factorial multivariate analysis of variance, Pearson’s correlation analysis, and multiple regression models conducted by adding variables of interest. The findings revealed significant differences in academic persistence intentions, environmental factors, achievement-related factors, and career motivation as perceived by STEM undergraduates, based on individual background and university characteristics. Moreover, 53.9% of academic persistence intentions were explained by 10 variables (gender, career direction after graduation, career field to enter after graduation, start period of career path preparation [career-related factors], contextual supports and career barriers [environmental factors], engineering self-efficacy, outcome expectations, major interest [achievement-related factors], and career motivation). Specifically, career motivation contributed the most to the academic persistence intentions of STEM undergraduates, followed by engineering self-efficacy and career barriers. The findings underscore the necessity for customized educational and policy support, considering individual background and university characteristics, to effectively address the challenges faced by STEM graduates in the changing employment landscape.
Current-Mode Active Filter Using EX-CCCII
Authors: Kumngern Montree , Khateb Fabian , Kulej Tomasz , Tooprakai Siraphop
Published: May 2024

This paper presents a novel multiple-input and multiple-output current-mode universal analog filter with electronic tuning capability. The proposed circuit uses a single second-generation current-controlled current conveyor with extra-X terminals (EX-CCCII) and two grounded capacitors. The filter can offer five standard filtering functions, namely low-pass, high-pass, band-pass, band-stop, all-pass responses, in the same circuit without changing the internal configuration of the filter by selecting appropriate input and output signals. To obtain the five standard filtering functions, inverted input signal and input matching conditions are absent. The natural frequency of all filter responses can be electronically controlled. The proposed circuit was simulated by SPICE using 0.18 μm CMOS process from Taiwan Semiconductor Manufacturing Company (TSMC). The results of experiments using the integrated circuit operational amplifier AD844 confirm the functionality of the new filter.
Diffusion-Based Radio Signal Augmentation for Automatic Modulation Classification
Authors: Xu Yichen , Huang Liang , Zhang Linghong , Qian Liping , Yang Xiaoniu
Published: May 2024

Deep learning has become a powerful tool for automatically classifying modulations in received radio signals, a task traditionally reliant on manual expertise. However, the effectiveness of deep learning models hinges on the availability of substantial data. Limited training data often results in overfitting, which significantly impacts classification accuracy. Traditional signal augmentation methods like rotation and flipping have been employed to mitigate this issue, but their effectiveness in enriching datasets is somewhat limited. This paper introduces the Diffusion-based Radio Signal Augmentation algorithm (DiRSA), a novel signal augmentation method that significantly enhances dataset scale without compromising signal integrity. Utilizing prompt words for precise signal generation, DiRSA allows for flexible modulation control and significantly expands the training dataset beyond the original scale. Extensive evaluations demonstrate that DiRSA outperforms traditional signal augmentation techniques such as rotation and flipping. Specifically, when applied with the LSTM model in small dataset scenarios, DiRSA enhances modulation classification performance at SNRs above 0 dB by 6%.
Multi-Modal Sentiment Analysis Based on Image and Text Fusion Based on Cross-Attention Mechanism
Authors: Li Hongchan , Lu Yantong , Zhu Haodong
Published: May 2024

Research on uni-modal sentiment analysis has achieved great success, but emotions in real life are mostly multi-modal; there are not only texts but also images, audio, video, and other forms. The various modes play a role in mutual promotion. If the connection between various modalities can be mined, the accuracy of sentiment analysis will be further improved. To this end, this paper introduces a cross-attention-based multi-modal fusion model for images and text, namely, MCAM. First, we use the ALBert pre-training model to extract text features for text; then, we use BiLSTM to extract text context features; then, we use DenseNet121 to extract image features for images; and then, we use CBAM to extract specific areas related to emotion in images. Finally, we utilize multi-modal cross-attention to fuse the extracted features from the text and image, and we classify the output to determine the emotional polarity. In the experimental comparative analysis of MVSA and TumEmo public datasets, the model in this article is better than the baseline model, with accuracy and F1 scores reaching 86.5% and 75.3% and 85.5% and 76.7%, respectively. In addition, we also conducted ablation experiments, which confirmed that sentiment analysis with multi-modal fusion is better than single-modal sentiment analysis.
On the Secrecy Sum-Rate of Internet of Things Networks: Scheduling and Power Control
Authors: Bang Inkyu , Chae Seong Ho , Jung Bang Chul
Published: May 2024

Physical-layer security (PLS) has attracted much attention in wireless communications and has been considered one of the main candidates for enhancing wireless security in future 6G networks. Recent studies in the PLS area have focused on investigating and analyzing the characteristics of secure transmissions in multiuser networks (e.g., the massive number of Internet of Things (IoT) devices in 6G networks). Due to the difficulty of obtaining the exact secrecy capacity region in wireless multiuser networks, several alternative methods are used to characterize the secrecy performance of multiuser networks. For example, we can analyze the secrecy sum-rate scaling in terms of the number of users based on multiuser diversity (MUD). In this paper, we propose an opportunistic user scheduling scheme that achieves optimal MUD gain, combined with a power control mechanism for reducing information leakage to multiple eavesdroppers in wireless networks. The proposed scheme considers multiuser transmissions in one scheduling time slot by adopting orthogonal random beamforming at the receiver to exploit the full degrees-of-freedom gain with an assumption that each user (or IoT device) is equipped with a single antenna, and base station and eavesdroppers have multiple antennas. The main contribution of this paper is to derive the analytic result of the achievable secrecy sum-rate scaling in a high signal-to-noise ratio (SNR) regime. We evaluate the performance of the proposed scheduling scheme with a power control mechanism through simulations with both internal and external eavesdropping scenarios. We further discuss the extensibility of our analysis to various applications such as satellite communications and IoT networks.
An Enhanced Single-Stage Neural Network for Object Detection in Transmission Line Inspection
Authors: Cai Changyu , Nie Jianglong , Tong Jie , Chen Zhao , Xu Xiangnan , He Zhouqiang
Published: May 2024

To address the issue of human object detection in transmission line inspection, an enhanced single-stage neural network is proposed, which is based on the improvement of the YOLOv7-tiny model. Firstly, a lighter GSConv module is utilized to optimize the original ELAN module, reducing the parameters in the network. In order to make the network less sensitive to the targets with an unconventional pose, a module based on CSPNeXt and GSConv is designed and integrated with the ELAN module to extract deep features from the targets. Moreover, a WIoU (Wise Intersection over Union) loss function is utilized to enhance the ability of the YOLOv7-tiny model to detect objects with an unconventional pose and the interference of the background. Finally, the experimental results on human targets in transmission line inspection demonstrate that the proposed network improves detection confidence and reduces missed detection. Compared to the YOLOv7-tiny model, the proposed method promotes the performance of accuracy while reducing the amount of parameters.
Intelligent Regulation of Temperature and Humidity in Vegetable Greenhouses Based on Single Neuron PID Algorithm
Authors: Huang Song , Xiang Huiyu , Leng Chongjie , Dai Tongyang , He Guanghui
Published: May 2024

In order to meet the demands of autonomy and control optimization in solar greenhouse control systems, this paper developed an intelligent temperature and humidity control system for greenhouses based on the Single Neuron Proportional Integral Derivative (SNPID) algorithm. The system is centered around the Huada HC32F460 Micro-Controller Unit (MCU) and the RT-Thread operating system, integrated with the SNPID control algorithm. Through comprehensive simulation, model construction, and comparative experiments, this system was thoroughly evaluated in comparison with traditional PID control systems (cPID) that rely on overseas software and hardwsbuare. Simulation results show that our new system significantly outperforms traditional PID (Proportional Integral Derivative) systems in terms of temperature control stability and accuracy. Experimental data further confirm that, while ensuring cost-effectiveness, the new system achieves a remarkable 50.2% improvement in temperature and humidity control precision compared to traditional systems. The temperature Root Mean Square Error (RMSE) in the experimental greenhouse is 0.734 compared to 1.594 in the comparison greenhouse, indicating better stable temperature control capability. The vents in the experimental greenhouse have a maximum opening of 67 cm and a minimum of 5 cm, showing a quick response property to high temperatures. In contrast, the control greenhouse has a maximum vent opening of 55 cm, remaining unchanged during the test period, which reflects its slower response to temperature fluctuations. These results demonstrate the significant advantages of the designed solar greenhouse temperature and humidity control system in terms of autonomy and control optimization, providing an efficient and economical solution for solar greenhouse environmental management. This system shows significant practical application perspective in promoting intelligent agriculture and sustainable agricultural production, highlighting its broad impact and potential significance.
A Multi-Modal IoT Framework for Healthy Nutritional Choices in Everyday Childhood Life
Authors: Bardis Georgios , Koumpouros Yiannis , Sideris Nikolaos , Troussas Christos
Published: May 2024

Policies, standards and recommendations for healthy childhood nutritional choices are well-defined and widely available, yet a significant percentage of children as well as parents and caregivers, fail to become aware and follow them despite the intense technological penetration and information abundance in everyday life. The aim of this work was to establish an IoT-integrated framework parlaying the current technological platforms’ capabilities to streamline the aforementioned policies, standards, and recommendations in a transparent, highly adoptable, and attractive scheme for children while being minimally demanding for responsible adults through a set of readily available innovative services and smart devices. The rationale was to obtain information concerning nutritional choices and habits with minimum intervention through smart devices, minimizing user deviation from everyday routines in order to consolidate, visualize, and exploit this information in an engaging and motivating gaming environment, maximizing visual impact while maintaining a minimal computational footprint.
Enhancing Biogas Production Amidst Microplastic Contamination in Wastewater Treatment Systems: A Strategic Review
Authors: Otieno Job Oliver , Cydzik-Kwiatkowska Agnieszka , Jachimowicz Piotr
Published: May 2024

This review highlights the significant interaction between microplastic (MP) pollution and its impact on wastewater treatment systems, focusing on optimizing biogas production. We explore various sources of MPs, including tire-derived MPs, and their introduction into wastewater environments. This review delves into the mechanical and physicochemical challenges MPs pose in treatment processes, emphasizing the need for comprehensive mitigation strategies. The biological effects of MPs on microbial consortia essential for biogas production are analyzed, particularly how these pollutants interfere with each stage of anaerobic digestion—hydrolysis, acidogenesis, acetogenesis, and methanogenesis—and, consequently, biogas generation. We examine MPs’ quantitative and qualitative impacts on biogas output and production rates, uncovering how MPs disrupt microbial activity in these stages. This review also discusses novel mitigation strategies combining different sludge pretreatment methods with MPs. Our goal is to enhance the sustainability of wastewater management by promoting efficient biogas production and environmental protection in the presence of persistent MP contamination.
Failure Characterization of Discrete SiC MOSFETs under Forward Power Cycling Test
Authors: Huang Tianqi , Singh Bhanu Pratap , Liu Yongqian , Norrga Staffan
Published: May 2024

Silicon carbide (SiC)-based metal–oxide–semiconductor field-effect transistors (MOSFETs) hold promising application prospects in future high-capacity high-power converters due to their excellent electrothermal characteristics. However, as nascent power electronic devices, their long-term operational reliability lacks sufficient field data. The power cycling test is an important experimental method to assess packaging-related reliability. In order to obtain data closest to actual working conditions, forward power cycling is utilized to carry out SiC MOSFET degradation experiments. Due to the wide bandgap characteristics of SiC MOSFETs, the short-term drift of the threshold voltage is much more serious than that of silicon (Si)-based devices. Therefore, an offline threshold voltage measurement circuit is implemented during power cycling tests to minimize errors arising from this short-term drift. Different characterizations are performed based on power cycling tests, focused on measuring the on-state resistance, thermal impedance, and threshold voltage of the devices. The findings reveal that the primary failure mode under forward power cycling tests, with a maximum junction temperature of 130 ∘C, is bond-wire degradation. Conversely, the solder layer and gate oxide exhibit minimal degradation tendencies under these conditions.
A Comprehensive Review of Physical Models and Performance Evaluations for Pavement Photovoltaic Modules
Authors: Mao Mingxuan , Ni Xiaoyu
Published: May 2024

Pavement photovoltaic (PV) is an innovative energy-harvesting technology that seamlessly integrates into road surfaces, merging established PV power generation methods with conventional roadway infrastructure. This fusion optimally utilizes the extensive spatial assets inherent in road networks. This paper offers an exhaustive examination of the literature concerning the physical models and performance evaluation of photovoltaic pavements. This study delineates the essential three-tier structure of pavement modules and juxtaposes the advantages and drawbacks of design models across these strata, thereby facilitating the development of more suitable solutions for varying application scenarios. The importance of accommodating fluctuations in shadows and countering the heat island effect (HIE) is emphasized. Nevertheless, the technology remains in its nascent research phase, characterized by challenges associated with limited long-term durability and efficacy. Building upon these findings, this study addresses the challenges confronting pavement PV from three perspectives and outlines future prospects and recommendations for its progression.
Harvesting Electric Energy Using Thermoelectric Generators in a Residential Heating Application
Authors: Chukwurah Ugochukwu , McTaggart-Cowan Gordon
Published: May 2024

Biomass combustors provide space heating by converting chemical energy in woody biomass into low-temperature thermal energy. Thermoelectric generators (TEGs) can generate electricity from the heat flux without significantly reducing heating performance. However, most current TEGs are small (40 mm × 40 mm), requiring many TEG elements to generate useful power from a biomass combustion-based space heater. This work compares the electrical generation potential of an array of small TEGs with a larger (80 mm × 120 mm) TEG in a vertical configuration representative of a residential heating appliance. An experimental facility was developed for various representative cold-side ducts and controllable hot-side temperature and cooling airflows, and the Taguchi method was used to evaluate the impacts of temperature, airspeed, and ducting configurations. The results indicate that temperature and airspeed significantly influence TEG power, while ducting configurations have an insignificant influence. The large TEG achieved more consistent temperatures but produced lower power than an array of smaller TEGs with the same total area. The study emphasizes optimizing TEG design and operating conditions to enhance electricity generation efficiency in space heating combustors.
Viscosity Reduction Behavior of Carbon Nanotube Viscosity Reducers with Different Molecular Structures at the Oil–Water Interface: Experimental Study and Molecular Dynamics Simulation
Authors: Hua Zhao , Zhang Jian , Zhu Yuejun , Huang Bo , Chen Qingyuan , Pu Wanfen
Published: May 2024

Effectively enhancing oil recovery can be achieved by reducing the viscosity of crude oil. Therefore, this paper investigated the viscosity reduction behavior of carbon nanotube viscosity reducers with different molecular structures at the oil–water interface, aiming to guide the synthesis of efficient viscosity reducers based on molecular structure. This study selected carbon nanotubes with different functional groups (NH2-CNT, OH-CNT, and COOH-CNT) for research, and carbon nanotubes with varying carbon chain lengths were synthesized. These were then combined with Tween 80 to form a nanofluid. Scanning electron microscopy analysis revealed an increased dispersibility of carbon nanotubes after introducing carbon chains. Contact angle experiments demonstrated that -COOH exhibited the best hydrophilic effect. The experiments of zeta potential, conductivity, viscosity reduction, and interfacial tension showed that, under the same carbon chain length, the conductivity and viscosity reduction rate sequence for different functional groups was -NH2 < -OH < -COOH. The dispersing and stabilizing ability and interfacial tension reduction sequence for different functional groups was -COOH < -OH < -NH2. With increasing carbon chain length, conductivity and interfacial tension decreased, and the viscosity reduction rate and the dispersing and stabilizing ability increased. Molecular dynamics simulations revealed that, under the same carbon chain length, the diffusion coefficient sequence for different functional groups was -NH2 < -OH < -COOH. The diffusion coefficient gradually decreased as the carbon chain length increased, resulting in better adsorption at the oil–water interface. This study holds significant importance in guiding viscosity reduction in heavy oil to enhance oil recovery.
Simulation of the Measured Reactivity Distributions in the Subcritical MYRRHA Reactor
Authors: Janczyszyn Jerzy , Domańska Grażyna , Oettingen Mikołaj
Published: May 2024

The designed MYRRHA reactor, in its subcritical version, will be equipped with a set of detectors monitoring its condition by measuring the current value of negative reactivity, which is a crucial parameter for its safe operation. In subcritical systems, accurate and precise measurement of negative reactivity is disturbed by the so-called spatial effect, i.e., the response of detectors depends on their placement in the reactor core. This paper focuses on the Monte Carlo simulations of reactivity measurements using the area method for natU, 238U, 241Am, 239Pu, and 232Th detectors. The simulations were performed in six positions with increasing distance from the center of the core and at three axial levels. The obtained results allow for selecting optimum locations for detectors and detector nuclides in terms of the accuracy of reactivity measurement and illustrate the dependence of the reactivity on the distance. Additionally, the possibility of using 103Rh in self-powered neutron detectors was investigated. The influence of spatial effect in calculations using the area method was directly indicated in the MYRRHA reactor core for chosen isotopes and in-core positions. The results closest to true values were obtained for the second fuel assembly for 239Pu, and the third fuel assembly for natU, 238U, 232Th, and 241Am; thus, these nuclides and positions should be preferred when selecting detectors for MYRRHA.
Multi-Energy Load Collaborative Optimization of the Active Building Energy Management Strategy
Authors: Wang Min , Gao Hang , Pan Dongqian , Sheng Xiangyu , Xu Chunxing , Wang Qiming
Published: May 2024

Under the dual-carbon target, the popularization and application of building integrated photovoltaic (BIPV) and ground source heat pump systems have made active buildings a research hotspot in the field of architecture and energy. Aiming at this issue, based on the building energy consumption model of active buildings, an active building energy management system (EMS) control strategy based on multi-energy load collaborative optimization is proposed. Firstly, based on the thermal dynamic characteristics and building performance parameters of active buildings, the overall refined energy consumption model of active buildings is constructed. Secondly, based on the construction of BIPV, the ice storage air conditioning system, the ground source heat pump system, and the integrated demand response (IDR) model, a tiered carbon transaction cost model is introduced, and an energy management strategy that leverages the synergistic application of renewable and active technologies is proposed. This strategy aims to meet the comprehensive needs of active buildings in terms of economic benefits, comfort, and environmental protection. Finally, the strategy’s effectiveness is demonstrated through a practical example.
Analyzing Current Trends in Career Choices and Employer Branding from the Perspective of Millennials within the Indonesian Energy Sector
Authors: Hakam Dzikri Firmansyah , Haryadi Fajar Nurrohman , Indrawan Harry , Hanri Muhammad , Hakam Lazuardi Imani , Kurniawan Ova , Purnomoadi Andreas Putro
Published: May 2024

This study aims to investigate the factors that influence millennials’ perceptions and preferences in regard to career choices within the state-owned energy sector in Indonesia. The research objective is to understand how to remain competitive in the current disruptive job market by examining a company’s recruitment and retention strategies, and analyzing data collected through econometric surveys. Factors significantly affecting the willingness to work at PLN include its past and present reputation, product societal impact, CSR efforts, and the individual’s gender, age, and job-seeking status, with positive views on PLN and its CSR activities encouraging the inclination to work there. Income expectations are influenced by similar aspects—PLN’s reputation, its product’s societal role, and CSR initiatives—alongside gender and education level, particularly for those with undergraduate or Master’s degrees. Notably, favorable perceptions of PLN and higher educational attainment are linked to increased salary expectations. The results from the survey indicate that a significant proportion of respondents, over 80%, expressed a desire to work at one of Indonesia’s state-owned energy companies (PLN), with a desired monthly salary of IDR 7,466,905. Furthermore, when compared to other state-owned energy companies in Indonesia, PLN holds a strong position, ranking second among this type of companies. This study provides valuable insights for energy companies in Indonesia, by understanding the career preferences of millennials and aligning their employer branding strategies accordingly, in order to remain competitive in the current job market.
Techno-Feasibility Assessment of a Floating Breakwater Concept for Supporting Marine Renewables in Deep Waters
Authors: Borg Andrew , Cutajar Charise , Sant Tonio , Farrugia Robert N. , Buhagiar Daniel
Published: May 2024

The previous research has proven that one of the fundamental requirements for ensuring increased profitability and economic competitiveness in offshore-based projects is co-locating different technologies within the same marine space. This paper presents a number of techno-feasibility analyses for floating offshore technologies for the Maltese Islands, located in the central Mediterranean Sea. The first part compares the feasibility between offshore floating solar photovoltaics with onshore-based systems, taking into consideration Malta’s average land rental price per square metre. The second part considers the use of a novel floating breakwater design that integrates energy storage and creates a sheltered water area for a multi-use marine park, thus introducing different revenue streams. The latter includes renting the sheltered marine space out to operators of floating solar farms, aquaculture cages and vessel berthing facilities, as well as the provision of energy storage services. It is found that the combined income from the multiple revenue streams from the multi-use marine park is still insufficient to justify the investment and that financial support from governments is essential to render the floating breakwaters viable.
Integrated Vehicle-Following Control for Four-Wheel Independent Drive Based on Regenerative Braking System Control Mechanism for Battery Electric Vehicle Conversion Driven by PMSM 30 kW
Authors: Techalimsakul Pataphiphat , Keyoonwong Wiwat
Published: May 2024

This study proposed the hybrid energy storage paradigm (HESP) equipped with front-wheel permanent magnet synchronous motors (PMSMs) for battery electric vehicles (BEVs). In this case, all four wheels are driven by a single motor using mechanical coupling to distribute the motor’s power to each wheel evenly. The HESP is a combination of several supercapacitors (SCs) and an NMC-lithium battery equipped with an advanced artificial neural network (ANN) that will enhance the regenerative braking system (RBS) efficiency of energy storage during braking. The three-phase inverter switching algorithm ensures efficient regenerative braking and fine adjustment of the brake force distribution. Under the RBS, the HESP with the ANN first transfers braking energy to the SC and, when the safety standard is reached, the SC transfers it to the battery. The RBS control maintains an even distribution of braking force at all distances to ensure stability during braking. The results show that a traditional BEV can drive 245.46 km (35 cycles), while an EV with an RBS-only battery can drive 282.56 km (40 cycles). An EV with HESP-RBS can drive 338.78 km (48 cycles), which is an increase of 93.32 km (13 cycles). The HESP-RBS increased the regenerative efficiency by 38.01% when compared to a traditional BEV.