bluuebunny/crossref_metadata_2025 huggingface dataset:
Reappraisal of the Roles of the Sonic Hedgehog Signaling Pathway in Hepatocellular Carcinoma
Authors: Jeng Kuo-Shyang , Chang Chiung-Fang , Tsang Yuk-Ming , Sheen I-Shyan , Jeng Chi-Juei
Published: April 2024

HCC remains one of the leading causes of cancer-related death globally. The main challenges in treatments of hepatocellular carcinoma (HCC) primarily arise from high rates of postoperative recurrence and the limited efficacy in treating advanced-stage patients. Various signaling pathways involved in HCC have been reported. Among them, the Sonic hedgehog (SHH) signaling pathway is crucial. The presence of SHH ligands is identified in approximately 60% of HCC tumor tissues, including tumor nests. PTCH-1 and GLI-1 are detected in more than half of HCC tissues, while GLI-2 is found in over 84% of HCC tissues. The SHH signaling pathway (including canonical and non-canonical) is involved in different aspects of HCC, including hepatocarcinogenesis, tumor growth, tumor invasiveness, progression, and migration. The SHH signaling pathway also contributes to recurrence, metastasis, modulation of the cancer microenvironment, and sustaining cancer stem cells. It also affects the resistance of HCC cells to chemotherapy, target therapy, and radiotherapy. Reappraisal of the roles of the SHH signaling pathway in HCC may trigger some novel therapies for HCC.
The Molecular Landscape of Primary CNS Lymphomas (PCNSLs) in Children and Young Adults
Authors: Shi Zhi-Feng , Li Kay Ka-Wai , Liu Anthony Pak-Yin , Chung Nellie Yuk-Fei , Wong Sze-Ching , Chen Hong , Woo Peter Yat-Ming , Chan Danny Tat-Ming , Mao Ying , Ng Ho-Keung
Published: April 2024

Pediatric brain tumors are often noted to be different from their adult counterparts in terms of molecular features. Primary CNS lymphomas (PCNSLs) are mostly found in elderly adults and are uncommon in children and teenagers. There has only been scanty information about the molecular features of PCNSLs at a young age. We examined PCNSLs in 34 young patients aged between 7 and 39 years for gene rearrangements of BCl2, BCL6, CCND1, IRF4, IGH, IGL, IGK, and MYC, homozygous deletions (HD) of CDKN2A, and HLA by FISH. Sequencing was performed using WES, panel target sequencing, or Sanger sequencing due to the small amount of available tissues. The median OS was 97.5 months and longer than that for older patients with PCNSLs. Overall, only 14 instances of gene rearrangement were found (5%), and patients with any gene rearrangement were significantly older (p = 0.029). CDKN2A HD was associated with a shorter OS (p < 0.001). Only 10/31 (32%) showed MYD88 mutations, which were not prognostically significant, and only three of them were L265P mutations. CARD11 mutations were found in 8/24 (33%) cases only. Immunophenotypically, the cases were predominantly GCB, in contrast to older adults (61%). In summary, we showed that molecular findings identified in the PCNSLs of the older patients were only sparingly present in pediatric and young adult patients.
A DFT Study of CO Hydrogenation on Graphene Oxide: Effects of Adding Mn on Fischer–Tropsch Synthesis
Authors: Bakhtiari Hanieh , Sarabadani Tafreshi Saeedeh , Torkashvand Mostafa , Abdouss Majid , de Leeuw Nora H.
Published: April 2024

The hydrogenation of carbon monoxide (CO) offers a promising avenue for reducing air pollution and promoting a cleaner environment. Moreover, by using suitable catalysts, CO can be transformed into valuable hydrocarbons. In this study, we elucidate the mechanistic aspects of the catalytic conversion of CO to hydrocarbons on the surface of manganese-doped graphene oxide (Mn-doped GO), where the GO surface includes one OH group next to one Mn adatom. To gain insight into this process, we have employed calculations based on the density functional theory (DFT) to explore both the thermodynamic properties and reaction energy barriers. The Mn adatoms were found to significantly activate the catalyst surface by providing stronger adsorption geometries. Our study concentrated on two mechanisms for CO hydrogenation, resulting in either CH4 production via the reaction sequence CO → HCO → CH2O → CH2OH → CH2 → CH3 → CH4 or CH3OH formation through the CO → HCO → CH2O → CH2OH → CH3OH pathway. The results reveal that both products are likely to be formed on the Mn-doped GO surface on both thermodynamic grounds and considering the reaction energy barriers. Furthermore, the activation energies associated with each stage of the synthesis show that the conversion reactions of CH2 + OH → CH3 + O and CH2O + OH → CH2OH + O with energy barriers of 0.36 and 3.86 eV are the fastest and slowest reactions, respectively. The results also indicate that the reactions: CH2OH + OH → CH2 + O + H2O and CH2OH + OH → CH3OH + O are the most exothermic and endothermic reactions with reaction energies of −0.18 and 1.21 eV, respectively, in the catalytic pathways.
Enhancing Reliability in Rural Networks Using a Software-Defined Wide Area Network
Authors: Borgianni Luca , Adami Davide , Giordano Stefano , Pagano Michele
Published: April 2024

Due to limited infrastructure and remote locations, rural areas often need help providing reliable and high-quality network connectivity. We propose an innovative approach that leverages Software-Defined Wide Area Network (SD-WAN) architecture to enhance reliability in such challenging rural scenarios. Our study focuses on cases in which network resources are limited to network solutions such as Long-Term Evolution (LTE) and a Low-Earth-Orbit satellite connection. The SD-WAN implementation compares three tunnel selection algorithms that leverage real-time network performance monitoring: Deterministic, Random, and Deep Q-learning. The results offer valuable insights into the practical implementation of SD-WAN for rural connectivity scenarios, showing its potential to bridge the digital divide in underserved areas.
Soil Moisture and Litter Coverage Drive the Altitude Gradient Pattern of Soil Arthropods in a Low-Elevation Mountain
Authors: Qin Haiming , Shang Jingwen , Qi Qin , Cao Bo , Kong Yong , Li Yujian , Chen Junfeng , Yi Xianfeng
Published: April 2024

This study sought to investigate the vertical distribution pattern of the soil faunal community in a low-altitude mountain area. On 8 July 2022, a low hill was selected as the study area, and soil arthropods were collected through traps. The leaf litter, vegetation type, and distribution quantity of each sampling site were investigated while the soil faunae were collected. In addition, the soil’s physical and chemical parameters were measured. The results of a one-way ANOVA showed that there were significant differences (p < 0.05) in the soil properties, leaf litter, and plant quantities at different altitudes within the research area. A total of 1086 soil arthropods, belonging to five classes and ten orders, were collected during the study period. The dominant species of soil arthropods at different altitudes were significantly different. The dominant species in low-altitude areas were Armadillidium sp. and Aethus nigritus. However, Eupolyphaga sinensis and Philodromidae were the dominant species in high-altitude areas. The results of a non-metric multidimensional scaling (NMDS) analysis showed that the soil faunae at different altitudes were clustered into two communities: a high-altitude community and a low-altitude community. With the increase in altitude, the species richness of the soil arthropods gradually decreased, and their abundance showed a decreasing trend. A redundancy analysis (RDA) of the soil arthropods and environmental factors showed that soil moisture (p < 0.01), pH (p < 0.01) and defoliation (p < 0.05) had significant effects on the distribution of the soil fauna. The results of a Pearson correlation analysis indicated that different environmental factors had interactive effects on the distribution of the soil arthropods. The quantity and species richness of the soil arthropods in different sample lines were tested using a variance analysis. The results showed that there were significantly smaller quantities of soil arthropods in the sampling line closer to the trekking ladder. This indicates that human tourism, namely mountaineering activities, had a direct impact on the soil fauna. This study can provide a reference for and data support in the development of biodiversity conservation measures for forest parks in low mountain areas.
Diversity of Cryptofaunal Nematode Assemblages along the Jardines de La Reina Coral Reef, Southern Cuba
Authors: Marzo-Pérez Diana , Pérez-García Jose Andrés , Apprill Amy , Armenteros Maickel
Published: April 2024

Cryptofaunal nematodes, those living on the hard substrate of the coral reefs, are largely unknown in terms of diversity and assemblage structure. We described nematode assemblages at seven sites spanning about 200 km along Jardines de La Reina, a well-preserved reef system in the Greater Antilles. We identified about 3000 nematodes, mostly of the families Desmodoridae and Chromadoridae; the most abundant species were Croconema cinctum, Desmodora communis, and Euchromadora vulgaris. The regional richness was moderate with 70 observed species (0.95 confidence interval: 65–75 species) and 75 extrapolated species (0.95 CI: 68–83 species). This richness was lower than in other reef biotopes, maybe reflecting evolutionary constraints due to interactions with stony corals. The local expected richness at 100 individuals was similar among sites, with a median of 26 species (0.95 CI: 20–34 species), and likely caused by diversification rate and evolutionary time acting at the same pace on populations. The taxonomic β-diversity was high and without differences among sites (median: 0.85; 0.95 CI: 0.33–1), probably due to significant substrate heterogeneity at 10-cm scale. The prevalence of replacement over richness difference suggests that local processes (e.g., environmental filtering and competition) contribute more to β-diversity than niche availability, which would be largely similar across the reef terraces. Contrary to our expectations, no gradient of assemblage structure occurred, nor significant effect of benthic cover on nematodes. However, nematode functional structure showed a conservative set of biological traits reflecting adaptations to hydrodynamic regime: armed oral cavity/intermediate colonizing capability/ornamented cuticles/conical tail. Our results provide insights about the taxonomic and functional diversity of nematodes and highlight the vast knowledge gaps about the processes ruling the meiofauna community structure in coral reefs.
Canonical vs. Grand Canonical Ensemble for Bosonic Gases under Harmonic Confinement
Authors: Crisanti Andrea , Salasnich Luca , Sarracino Alessandro , Zannetti Marco
Published: April 2024

We analyze the general relation between canonical and grand canonical ensembles in the thermodynamic limit. We begin our discussion by deriving, with an alternative approach, some standard results first obtained by Kac and coworkers in the late 1970s. Then, motivated by the Bose–Einstein condensation (BEC) of trapped gases with a fixed number of atoms, which is well described by the canonical ensemble and by the recent groundbreaking experimental realization of BEC with photons in a dye-filled optical microcavity under genuine grand canonical conditions, we apply our formalism to a system of non-interacting Bose particles confined in a two-dimensional harmonic trap. We discuss in detail the mathematical origin of the inequivalence of ensembles observed in the condensed phase, giving place to the so-called grand canonical catastrophe of density fluctuations. We also provide explicit analytical expressions for the internal energy and specific heat and compare them with available experimental data. For these quantities, we show the equivalence of ensembles in the thermodynamic limit.
Intrinsic Information-Theoretic Models
Authors: Bernal-Casas D. , Oller J. M.
Published: April 2024

With this follow-up paper, we continue developing a mathematical framework based on information geometry for representing physical objects. The long-term goal is to lay down informational foundations for physics, especially quantum physics. We assume that we can now model information sources as univariate normal probability distributions N (μ, σ0), as before, but with a constant σ0 not necessarily equal to 1. Then, we also relaxed the independence condition when modeling m sources of information. Now, we model m sources with a multivariate normal probability distribution Nm(μ,Σ0) with a constant variance–covariance matrix Σ0 not necessarily diagonal, i.e., with covariance values different to 0, which leads to the concept of modes rather than sources. Invoking Schrödinger’s equation, we can still break the information into m quantum harmonic oscillators, one for each mode, and with energy levels independent of the values of σ0, altogether leading to the concept of “intrinsic”. Similarly, as in our previous work with the estimator’s variance, we found that the expectation of the quadratic Mahalanobis distance to the sample mean equals the energy levels of the quantum harmonic oscillator, being the minimum quadratic Mahalanobis distance at the minimum energy level of the oscillator and reaching the “intrinsic” Cramér–Rao lower bound at the lowest energy level. Also, we demonstrate that the global probability density function of the collective mode of a set of m quantum harmonic oscillators at the lowest energy level still equals the posterior probability distribution calculated using Bayes’ theorem from the sources of information for all data values, taking as a prior the Riemannian volume of the informative metric. While these new assumptions certainly add complexity to the mathematical framework, the results proven are invariant under transformations, leading to the concept of “intrinsic” information-theoretic models, which are essential for developing physics.
Assessing the Impact of COVID-19 on Capital Structure Dynamics: Evidence from GCC Economies
Authors: Ahmed Amanj Mohamed , Nugraha Deni Pandu , Hágen István
Published: April 2024

This study seeks to investigate the potential effects of the recent pandemic (COVID-19) on capital structure dynamics. The Gulf Cooperation Council (GCC) is a fascinating topic for this study because of its distinct economic characteristics. The analysis draws upon a cross-country dataset covering 208 non-financial listed firms across five GCC countries, with data spanning the years 2010 to 2022. Capital structure is a dependent variable and is measured by total debt to equity, equity multiplier, and short-term debt ratios, while the COVID-19 pandemic, firm size growth, return on assets, tangibility, and growth were applied as independent variables. Using the generalized least squares (GLS) method, findings demonstrated that COVID-19 has a significant and positive influence on debt-to-equity and equity multiplier ratios but a negative one on short-term debt ratio. Thus, non-financial firms increased their debt financing and transferred debt from short-term to long-term funding. In addition, firm-specific factors, such as firm size, tangibility, and macroeconomic factors, such as GDP growth, positively and significantly impact capital financing. Conversely, profitability has a negative relationship with financial leverage. There is a lack of empirical research on how COVID-19 affects the financial structure of non-financial listed companies in GCC nations. Consequently, by filling the previously specified gaps, this study provides proof to support the idea of using debt financing to raise capital for economic recovery. GCC policymakers need to give priority to ensuring that firms have convenient access to inexpensive finance in light of the financial consequences caused by COVID-19. This will guarantee that companies have the resources necessary to bounce back and support economic growth.
Polymorphic Clustering and Approximate Masking Framework for Fine-Grained Insect Image Classification
Authors: Huo Hua , Mei Aokun , Xu Ningya
Published: April 2024

Insect diversity monitoring is crucial for biological pest control in agriculture and forestry. Modern monitoring of insect species relies heavily on fine-grained image classification models. Fine-grained image classification faces challenges such as small inter-class differences and large intra-class variances, which are even more pronounced in insect scenes where insect species often exhibit significant morphological differences across multiple life stages. To address these challenges, we introduce segmentation and clustering operations into the image classification task and design a novel network model training framework for fine-grained classification of insect images using multi-modality clustering and approximate mask methods, named PCAM-Frame. In the first stage of the framework, we adopt the Polymorphic Clustering Module, and segmentation and clustering operations are employed to distinguish various morphologies of insects at different life stages, allowing the model to differentiate between samples at different life stages during training. The second stage consists of a feature extraction network, called Basenet, which can be any mainstream network that performs well in fine-grained image classification tasks, aiming to provide pre-classification confidence for the next stage. In the third stage, we apply the Approximate Masking Module to mask the common attention regions of the most likely classes and continuously adjust the convergence direction of the model during training using a Deviation Loss function. We apply PCAM-Frame with multiple classification networks as the Basenet in the second stage and conduct extensive experiments on the Insecta dataset of iNaturalist 2017 and IP102 dataset, achieving improvements of 2.2% and 1.4%, respectively. Generalization experiments on other fine-grained image classification datasets such as CUB200-2011 and Stanford Dogs also demonstrate positive effects. These experiments validate the pertinence and effectiveness of our framework PCAM-Frame in fine-grained image classification tasks under complex conditions, particularly in insect scenes.
Wideband Millimeter-Wave Perforated Hemispherical Dielectric Resonator Antenna
Authors: Albakosh Waled , Asfour Rawad , Khalil Yas , Khamas Salam K.
Published: April 2024

This paper presents a novel wideband circularly polarized millimeter-wave (mmWave) hemispherical dielectric resonator antenna (HDRA). Two distinct configurations of alumina dielectric resonator antennas (DRAs) are investigated, each featuring a different coating: the first configuration incorporates a polyimide layer, while the second involves a perforated alumina. Both configurations demonstrate promising characteristics, including impedance and axial ratio (AR) bandwidths of 58% and 17.7%, respectively, alongside a maximum gain of 10 dBic at 28 GHz. Leveraging additive manufacturing technology, the HDRA with the perforated coating layer is fabricated, simplifying assembly and eliminating potential air gaps between layers, thereby enhancing the overall performance. This innovative approach yields a circularly polarized (CP) HDRA suitable for Beyond 5G (B5G) communication systems. Agreement between measurements and simulations validates the efficacy of the proposed design, affirming its potential in practical applications.
Multi-Objective Optimization in 3D Floorplanning
Authors: Jiang Zhongjie , Li Zhiqiang , Yao Zhenjie
Published: April 2024

Three-dimensional integrated circuits can significantly mitigate the challenges posed by shrinking feature sizes and enable heterogeneous integration. This paper focuses on the 3D floorplanning problem. We formulate it as a multi-objective optimization issue and employ multi-objective simulated annealing to simultaneously optimize area, wirelength and number of vias. During the optimization process, neighboring solutions are explored in the design space through inter-layer or intra-layer perturbations, and decision criteria for the exploration process are formulated based on the dominance relationship of solutions. Test results on the GSRC benchmark demonstrate that our approach delivers superior performance in optimizing area and wirelength. Compared to 2D floorplanning, our method reduces the area by approximately 49% and the wirelength by 21%. Compared to other similar 3D floorplanning methods, we raise the success rate in satisfying the fixed-outline constraint to 100% and improve the wirelength by 3%. The multi-objective simulated annealing method proposed in this paper can effectively address the 3D floorplanning problem.
NRPerson: A Non-Registered Multi-Modal Benchmark for Tiny Person Detection and Localization
Authors: Yang Yi , Han Xumeng , Wang Kuiran , Yu Xuehui , Yu Wenwen , Wang Zipeng , Li Guorong , Han Zhenjun , Jiao Jianbin
Published: April 2024

In recent years, the detection and localization of tiny persons have garnered significant attention due to their critical applications in various surveillance and security scenarios. Traditional multi-modal methods predominantly rely on well-registered image pairs, necessitating the use of sophisticated sensors and extensive manual effort for registration, which restricts their practical utility in dynamic, real-world environments. Addressing this gap, this paper introduces a novel non-registered multi-modal benchmark named NRPerson, specifically designed to advance the field of tiny person detection and localization by accommodating the complexities of real-world scenarios. The NRPerson dataset comprises 8548 RGB-IR image pairs, meticulously collected and filtered from 22 video sequences, enriched with 889,207 high-quality annotations that have been manually verified for accuracy. Utilizing NRPerson, we evaluate several leading detection and localization models across both mono-modal and non-registered multi-modal frameworks. Furthermore, we develop a comprehensive set of natural multi-modal baselines for the innovative non-registered track, aiming to enhance the detection and localization of unregistered multi-modal data using a cohesive and generalized approach. This benchmark is poised to facilitate significant strides in the practical deployment of detection and localization technologies by mitigating the reliance on stringent registration requirements.
SiC Fin-Channel MOSFET for Enhanced Gate Shielding Effect
Authors: Sang Ling , Jin Rui , Cui Jiawei , Niu Xiping , Li Zheyang , Yang Junjie , Nuo Muqin , Zhang Meng , Wang Maojun , Wei Jin
Published: April 2024

A SiC fin-channel MOSFET structure (Fin-MOS) is proposed for an enhanced gate shielding effect. The gates are placed on each side of the narrow fin-channel region, while grounded p-shield regions below the gates provide a strong shielding effect. The device is investigated using Sentaurus TCAD. For a narrow fin-channel region, there is difficulty in forming an Ohmic contact to the p-base; a floating p-base might potentially store negative charges upon high drain voltage, and, thus, causes threshold voltage instabilities. The simulation reveals that, for a fin-width of 0.2 μm, the p-shield regions provide a stringent shielding effect against high drain voltage, and the dynamic threshold voltage shift (∆Vth) is negligible. Compared to conventional trench MOSFET (Trench-MOS) and asymmetric trench MOSFET (Asym-MOS), the proposed Fin-MOS boasts the lowest OFF-state oxide field and reverse transfer capacitance (Crss), while maintaining a similar low ON-resistance.
Modulation of Diamond PN Junction Diode with Double-Layered n-Type Diamond by Using TCAD Simulation
Authors: Mu Caoyuan , Li Genzhuang , Lv Xianyi , Wang Qiliang , Li Hongdong , Li Liuan , Zou Guangtian
Published: April 2024

This study proposed a novel double-layer junction termination structure for vertical diamond-based PN junction diodes (PND). The effects of the geometry and doping concentration of the junction termination structure on the PNDs’ electrical properties are investigated using Silvaco TCAD software (Version 5.0.10.R). It demonstrates that the electric performances of PND with a single n-type diamond layer are sensitive to the doping concentration and electrode location of the n-type diamond. To further suppress the electric field crowding and obtain a better balance between breakdown voltage and on-resistance, a double-layer junction termination structure is introduced and evaluated, yielding significantly improved electronic performances. Those results provide some useful thoughts for the design of vertical diamond PND devices.
The Genesis of AI by AI Integrated Circuit: Where AI Creates AI
Authors: Baungarten-Leon Emilio Isaac , Ortega-Cisneros Susana , Abdelmoneum Mohamed , Vidana Morales Ruth Yadira , Pinedo-Diaz German
Published: April 2024

The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs.
Ship Network Traffic Engineering Based on Reinforcement Learning
Authors: Yang Xinduoji , Liu Minghui , Wang Xinxin , Hu Bingyu , Liu Meng , Wang Xiaomin
Published: April 2024

This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network’s quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%.
Reinventing Web Security: An Enhanced Cycle-Consistent Generative Adversarial Network Approach to Intrusion Detection
Authors: Fang Menghao , Wang Yixiang , Yang Liangbin , Wu Haorui , Yin Zilin , Liu Xiang , Xie Zexian , Kong Zixiao
Published: April 2024

Web3.0, as the link between the physical and digital domains, faces increasing security threats due to its inherent complexity and openness. Traditional intrusion detection systems (IDSs) encounter formidable challenges in grappling with the multidimensional and nonlinear traffic data characteristic of the Web3.0 environment. Such challenges include insufficient samples of attack data, inadequate feature extraction, and resultant inaccuracies in model classification. Moreover, the scarcity of certain traffic data available for analysis by IDSs impedes the system’s capacity to document instances of malicious behavior. In response to these exigencies, this paper presents a novel approach to Web3.0 intrusion detection, predicated on the utilization of cycle-consistent generative adversarial networks (CycleGANs). Leveraging the data transformation capabilities of its generator, this method facilitates bidirectional conversion between normal Web3.0 behavioral data and potentially intrusive behavioral data. This transformative process not only augments the diversity and volume of recorded intrusive behaviors but also clandestinely simulates various attack scenarios. Furthermore, through fostering mutual competition and learning between the discriminator and generator, the approach enhances the ability to discern the defining characteristics of potential intrusive behaviors, thereby bolstering the accuracy of intrusion detection. To substantiate the efficacy of the CycleGAN-based intrusion detection method, simulation experiments were conducted utilizing public datasets, including KDD CUP 1999 (KDD), CIC-DDOS2019, CIC-IDS2018, and SR-BH 2020. The experimental findings evince the method’s remarkable accuracies across the four datasets, attaining rates of 99.81%, 97.79%, 89.25%, and 95.15%, respectively, while concurrently maintaining low false-positive rates. This research contributes novel insights and methodologies toward the advancement of Web3.0 intrusion detection through the application of CycleGAN technology, which is poised to play a pivotal role in fortifying the security landscape of Web3.0.
A Low-Voltage Self-Starting Boost Converter Using MPPT with Pulse Multiplication for Energy Harvesting
Authors: Wang Ning , Zhang Xiaofei , Xu Shuxi , Liu Yuan , Zhang Lei , Zhao Zhonghui , Hu Zhiyang , Shan Hengsheng
Published: April 2024

A single-inductor, low-voltage, three-step self-starting boost converter is proposed for photovoltaic (PV) energy harvesting. In order to enhance energy transfer efficiency, a variable-step Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) scheme has been devised based on a novel pulse multiplication technique. Upon overcoming the speed and accuracy limitations, the maximum power point (MPP) of the PV model is accurately tracked. In the boost converter, the average inductor current is utilized to implement closed-loop control of the MPPT loop, enhancing the stability of the tracking process and enabling efficient energy transmission. Finally, the boost converter is implemented using a 0.18 μm CMOS process, which is capable of self-starting and maintaining stable operations at input voltages ranging from 90 mV to 300 mV, achieving a peak efficiency of 93%.
A Review of Carbon Emissions from Electrical Machine Materials
Authors: Zhang Xuebei , Gerada David , Xu Zeyuan , Zhang Fengyu , Gerada Chris
Published: April 2024

As the world embarks on a global mission to tackle climate change, reducing carbon represents a key challenge given the escalating global warming. The U.K. is among many other nations that are determined to decarbonise all sectors and strive to achieve a net zero carbon target by 2050. While much attention has been paid to improving performance and reducing carbon emissions in electrical machines, the current research landscape focuses mainly on the thermal and electromagnetic facets. Surprisingly, carbon emissions from the production stage, especially those related to raw material consumption, remain a largely unexplored area. This paper wishes to shed light on a neglected dimension by providing a comprehensive review of carbon emissions in the manufacture of electrical machines, thus contributing significantly to the wider discourse on carbon emission reduction by comparing the carbon emission values associated with various materials commonly used for the main components of these machines. A further case study is included to assess and explore the impact of material alterations on a synchronous machine, from a carbon emission perspective. A reliable material guide will provide engineers at the design stage with the critical insight needed to make informed material selection decisions, highlighting the critical role of carbon emission values beyond conventional thermal and electromagnetic considerations, achieving sustainable and environmentally conscious electrical machine design.
Explainable Artificial Intelligence Approach for Diagnosing Faults in an Induction Furnace
Authors: Moosavi Sajad , Razavi-Far Roozbeh , Palade Vasile , Saif Mehrdad
Published: April 2024

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model’s prediction.
Numerical Simulation of Fracturing Fluid Storage in Shale Reservoirs Based on Experimental Measurements of Stress Sensitivity of Hydraulic Fracture Network Conductivity
Authors: Wang Tianhao , Zhou Fujian
Published: April 2024

Hydraulic fracturing is used in shale reservoir production, with low flowback rates and a large amount of fracturing fluid retained inside the reservoir. In this study, a stress sensitivity analysis experiment on the fracture inflow capacity was implemented to investigate the relationship between the hydraulic fracture (HF) and natural fracture (NF) inflow capacities and effective stress. A three-dimensional shale reservoir model was also constructed to couple the experimentally obtained laws with the numerical model to investigate the effects of the connection and closure of the fracture network on the retention of the fracturing fluid. The results show that the stress sensitivity of natural fractures is two orders of magnitude higher than that of hydraulic fractures. The seepage-absorption effect of capillary forces is not the whole reason for the large amount of fracturing fluid retention. The closure of the fracture network formed by natural and hydraulic fractures during the production process led to the storage of a large amount of fracturing fluid, and this process maintained the stability of the water production rate during the steady water production period.
Assessing the Costs of Commercialising Tidal Energy in the UK
Authors: Noble Donald R. , Grattan Kristofer , Jeffrey Henry
Published: April 2024

There is a need for increased renewable energy to meet net-zero targets and decarbonise the economy. Harnessing the predictable power of the tides with tidal stream turbines can contribute to this. Tidal energy is a nascent technology with higher costs at present. However, cost reductions have been observed with an increased deployment in other renewable energy technologies that have received financial support, and it is postulated that similar will happen with tidal energy. The first tidal stream projects have been awarded market support in the UK through the Contracts for Difference (CfD) scheme, with almost 100 MW expected to be commissioned by 2028. This work uses learning rates to investigate how much investment in ongoing market support might be needed to achieve cost reductions through subsidised deployment alongside research and innovation. Using a range of informed ‘what if?’ scenarios, it shows sensitivity to key inputs. The results show that the support needed is most sensitive to the learning rate, reducing it from 15% to 12.5% or 10% doubles or more than quadruples the investment required, respectively. The support is also highly dependent on the starting cost from which learning occurs, taken as the CfD Strike Price in 2025. Varying this between 156 and 220 GBP/MWh results in total investment of GBP 6.7 and 22.3 bn, respectively. Most importantly, a balance is needed between subsidising deployment to drive down costs through learning and funding innovation to maintain a high learning rate.
Systematic Development of Application-Oriented Operating Strategies for the Example of an Industrial Heating Supply System
Authors: Theisinger Lukas , Frank Michael , Weigold Matthias
Published: April 2024

The ongoing challenge to ensure a sustainable and affordable energy supply forces industrial companies to transform their energy system. This transformation usually leads to an increase in topological complexity, which in turn results in increased operational complexity. Existing approaches from the field of supervisory and optimal control are capable of mastering complex operational problems. However, due to the complex and non-transparent implementation, there is still no industrial penetration, which hinders the necessary transformation of energy systems. This work aims at establishing trust in these control approaches and presents a procedure model for the systematic development of application-oriented operating strategies for industrial energy heating systems. It combines research approaches from the fields of sequencing control and approximate MPC to extract rule-based operating strategies, which are inherently easy to understand and implementable. By splitting the procedure model into five phases, expert knowledge can be integrated in a targeted manner. The procedure model is validated by the exemplary application to an industrial heating supply system. As part of an optimization study, the operating strategy developed is compared with both an MPC strategy and a baseline strategy. While the conventional MPC approach represents the upper limit of optimality, the operating strategy developed is able to achieve comparable results. Compared to the baseline strategy, a relative reduction in operating costs of 5.4% to 37.0% is achieved.
Improved Switchable Heat Pipe Based on Adsorption: Against-Gravity Operation and Enhanced Dynamics
Authors: Boda Simon , Winkler Markus , Schießl Robert , Teicht Christian , Schwarz Daniel , Schipper Jan , Bartholomé Kilian , Schäfer-Welsen Olaf , Pappert Sandra
Published: April 2024

Controlling heat transfer through components with adjustable thermal resistance can be of great benefit in a wide range of applications such as the thermal management of spacecraft or electric vehicles. A novel concept for both thermal switching and thermal regulation is the use of a water-loaded adsorbent within a reservoir that a regular heat pipe is expanded with. By reversibly desorbing or adsorbing water, states of low and high thermal resistance can be achieved. This concept has been studied so far only in thermosiphons that rely on gravity support. To expand potential application fields, we successfully investigated the utilization of heat pipes with a capillary structure, achieving against-gravity operation. Adsorption-based heat pipe demonstrators were experimentally examined regarding their characteristic properties. Thermal resistances during the on and off state of 0.25 KW−1 and 6.5 KW−1, respectively, were measured, yielding switching ratios of up to 26. Furthermore, the role of the adsorbent reservoir heat exchanger was examined and found to have a significant potential to yield an improvement with regards to dynamic performance. With an improved demonstrator design, the dynamic performance was enhanced as the hysteresis behavior was reduced and a minimum switching time of 5 min was recorded.
Experimental Analysis of the Light Wavelength’s Impact on the Performance of a Silicon Solar Cell
Authors: Pinho Correia Valério Bernardo Catarina Pinho Correia Valério , Marques Lameirinhas Ricardo A. Marques , N. Torres João Paulo N. , Baptista António , Marques Martins Maria João Marques
Published: April 2024

The main aim of this article is to analyse the Si solar cell’s behaviour when it is exposed to light of different colours. An experimental work, with 100 experimental tests, was carried out, using an RGB LED. In order to obtain better fitting of the characteristic curves’ results, we used a novel discrete model, d1MxP. The obtained results showed that all experimental points of the tests were inside the two triangles that connected the three theoretical primary colours and the three experimental primary colours in the chromaticity diagram. With this diagram, the colour purity could be determined, which presented values between 20% and 60%. The primary colours of the three different LEDs of the light source presented a dominant wavelength that corresponded to the peak wavelengths of the light source spectrum, which showed high purity. However, the obtained results show that mixing colours may not lead to an increase in the cell’s output power. Additionally, an increase in the cell’s temperature was observed, due to the surplus absorbed energy, which was converted into heat, being one of the causes of the cell’s efficiency reduction.
The Formation–Structure–Functionality Relationship of Catalyst Layers in Proton Exchange Membrane Fuel Cells
Authors: Yang Donglei , Kakati Nitul , Sarker Mrittunjoy , Mojica Felipe , Chuang Po-Ya Abel
Published: April 2024

Understanding the relationship between the formation, structure, and functionality of catalyst layers is crucial for designing catalyst layers with specific high-current-density operations. In this study, we investigated the impact of the ionomer-to-carbon (I/C) ratio and solid content on transport properties. We conducted fuel cell performance and diagnostic measurements to demonstrate the combined effects of the I/C ratio and solid content on the mass transport, particularly oxygen transport. To elucidate the roles of the I/C ratio and solid content in catalyst layer formation, we utilized dynamic light scattering and rheological measurements. By analyzing the local and global structure of ionomer-Pt/C assemblages in the catalyst inks, we observed that the I/C ratio and solid content influence the competition between homo-aggregation and hetero-aggregation, the strengths of inter- and intra-cluster bonds, and the rigidity and connectivity of the particulate structure. Additionally, high-shear-application simulations tend to reduce the connectivity of the particulate network and induce cluster densification, unless the global structure is mechanically stable and resilient. Based on this understanding, we established the formation–structure–functionality relationship for catalyst layers, thereby providing fundamental insights for designing catalyst layers tailored to specific functionalities.
Geometrical Optimization of Segmented Thermoelectric Generators (TEGs) Based on Neural Network and Multi-Objective Genetic Algorithm
Authors: Sun Wei , Wen Pengfei , Zhu Sijie , Zhai Pengcheng
Published: April 2024

In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs (Wc), the ratios of cold-segmented length to the total lengths of the n- and p-legs (Sn,c and Sp,c), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs (Kn and Kp). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power (P) and minimizing the semiconductor volume (V). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal Sn,c, Sp,c, Kn, and Kp remained almost unchanged when V increased from 52.8 to 216.2 mm3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs.
Assessment of CH4 Emissions in a Compressed Natural Gas-Adapted Engine in the Context of Changes in the Equivalence Ratio
Authors: Jaworski Artur , Kuszewski Hubert , Balawender Krzysztof , Woś Paweł , Lew Krzysztof , Jaremcio Mirosław
Published: April 2024

The results of diagnostic tests under steady-state speed conditions of an unloaded engine do not fully reflect the emissivity of vehicles adapted to run on natural gas. Therefore, it is reasonable to pay attention to the emissions performance of these vehicles under dynamic conditions. In this regard, the tests were carried out on a chassis dynamometer with the engine fueled by gasoline and natural gas. Due to the area of operation of natural gas vehicles being usually limited to urban areas, the urban phases of the NEDC (New European Driving Cycle) and WLTC (Worldwide harmonized Light-duty vehicles Test Cycle) were adapted. While CO2 emissions are lower when fueled by natural gas, CH4 emissions can be high, which is related to momentary changes in the composition of the combustible mixture. Although CH4 emissions are higher when the engine runs on natural gas, the CO2eq value is, depending on the driving cycle, about 15–25% lower than when running on petrol. Additionally, studies have shown that in engines adapted to run on CNG (compressed natural gas), it is advisable to consider the use of catalytic converters optimized to run on natural gas, as is the case with vehicles which are factory–adapted to run on CNG.