bluuebunny/crossref_metadata_2025 huggingface dataset:
Vitiligo-like Lesions as a Predictor of Response to Immunotherapy in Non-Small Cell Lung Cancer: Comprehensive Review and Case Series from a University Center
Authors: Coelho João Queirós , Romão Raquel , Sousa Maria João , Azevedo Sérgio Xavier , Fidalgo Paula , Araújo António
Published: February 2024

The reference to vitiligo-like lesions (VLLs) induced by immune checkpoint inhibitors (ICIs) as a valuable predictive marker of treatment success of immunotherapy with ICIs in melanoma has been mentioned in the literature. Its role in non-small cell lung cancer (NSCLC)-treated patients remains a poorly recognized phenomenon with uncertain significance regarding its predictive value. A retrospective, observational, single-center report was performed, with descriptive analysis of clinicopathological and treatment characteristics of patients with stage IV NSCLC who developed ICI-induced VLL between January 2018 and December 2022, contextualized in a comprehensive review of the literature and reported cases regarding this phenomenon. During the first 5 years’ experience of ICI use in stage IV NSCLC treatment, three cases of ICI-induced VLLs were diagnosed. In line with the previous reports, two of the three presented cases exhibited treatment response and favorable prognosis. The recognition and understanding of the pathophysiological processes underlying ICI-induced VLLs may represent a promising opportunity to identify a predictive marker of tumor response to ICIs, with impact in treatment selection and patient management. It also may contribute to the recognition of new patterns of molecular expression that could lead to improvements in therapeutic development.
Extended Right Hepatectomy following Clearance of the Left Liver Lobe and Portal Vein Embolization for Curatively Intended Treatment of Extensive Bilobar Colorectal Liver Metastases: A Single-Center Case Series
Authors: Knitter Sebastian , Sauer Linda , Hillebrandt Karl-H. , Moosburner Simon , Fehrenbach Uli , Auer Timo A. , Raschzok Nathanael , Lurje Georg , Krenzien Felix , Pratschke Johann , Schöning Wenzel
Published: February 2024

Background: Two-staged hepatectomy (TSH) including portal vein embolization (PVE) may offer surgical treatment for extensive bilobar colorectal liver metastases (CRLM). This study aimed to investigate the feasibility and outcomes of extended right hepatectomy (ERH) within TSH including PVE for patients with extended CRLM. Methods: We retrospectively collected data of patients who underwent TSH for extended CRLM between 2015 and 2021 at our institution. Clearance of the left liver lobe (clear-up, CU) associated with PVE was followed by ERH. Results: Minimally invasive (n = 12, 46%, MIH) or open hepatectomy (n = 14, 54%, OH) was performed. Postoperative major morbidity and 90-day mortality were 54% and 0%. Three-year overall survival was 95%. Baseline characteristics, postoperative and long-term outcomes were comparable between MIH and OH. However, hospital stay was significantly shorter after MIH (8 vs. 15 days, p = 0.008). Additionally, the need for intraoperative transfusions tended to be lower in the MIH group (17% vs. 50%, p = 0.110). Conclusions: ERH following CU and PVE for extended CRLM is feasible and safe in laparoscopic and open approaches. MIH for ERH may result in shorter postoperative hospital stays. Further high-volume, multicenter studies are required to evaluate the potential superiority of MIH.
Regression of Cardiac Rhabdomyomas Producing a Severe Aortic Stenosis: Case Report and Discussion of the Literature
Authors: Fesslova Vlasta M. E. , Evangelista Martina , Piazza Luciane , Saracino Antonio , Andronache Andreea , Chiarello Carmelina , Varrica Alessandro , Giamberti Alessandro , Frigiola Alessandro
Published: February 2024

We describe a case of a voluminous rhabdomyoma (R) detected by fetal echocardiography at 32 weeks’ gestation (w.g.) obstructing the left ventricular inflow and aortic outflow tract, with a moderate aortic gradient at birth, not needing immediate surgery. At follow-up, the mass progressively regressed, leaving the aortic valve partly damaged, with a gradient that increased to a maximum of 100 mmHg at 9 years. The girl was then operated on successfully by a plasty of the aortic valve. The literature regarding R is discussed.
Association of Blood NK Cell Phenotype with the Severity of Liver Fibrosis in Patients with Chronic Viral Hepatitis C with Genotype 1 or 3
Authors: Tsukanov Vladislav Vladimirovich , Savchenko Andrei Anatolyevich , Cherepnin Mikhail Aleksandrovich , Kasparov Eduard Vilyamovich , Tikhonova Elena Petrovna , Vasyutin Alexander Viktorovich , Tonkikh Julia Leongardovna , Anisimova Anna Alexandrovna , Belenyuk Vasily Dmitrievich , Borisov Alexandr Gennadyevich
Published: February 2024

Background: NK cells phenotype and functional state in different genotypes of chronic viral hepatitis C (CVHC), depending on liver fibrosis severity, have not been sufficiently studied, which limits the possibilities for the development of pathology therapy. Methods: The CVHC diagnosis was based on the EASL recommendations (2018). Clinical examination with liver elastometry was performed in 297 patients with genotype 1 and in 231 patients with genotype 3 CVHC. The blood NK cells phenotype was determined by flow cytometry in 74 individuals with genotype 1 and in 69 individuals with genotype 3 CVHC. Results: The frequency of METAVIR liver fibrosis stages F3–F4 was 32.5% in individuals with genotype 3, and 20.5% in individuals with genotype 1 CVHC (p = 0.003). In patients with both genotype 1 and genotype 3 CVHC, a decrease in the total number of blood NK cells, CD56brightCD16+ NK cells and an increase in the proportion of CD56dimCD16+ NK cells, CD94+ and CD38 + CD73+ NK cells were registered in patients with fibrosis stage F3–F4 by METAVIR in comparison with persons with METAVIR fibrosis stage F0–F1. Conclusions: In patients with both genotype 1 and genotype 3 CVHC, an imbalance in the ratio between cytokine-producing and cytotoxic NK cells and an increase in the content of NK cells that express inhibitory molecules were determined in patients with severe liver fibrosis.
The Effect of Different Compositions and Concentrations of Etidronate-Containing Irrigants on the Antibacterial Activity of Sodium Hypochlorite against Enterococcus faecalis and Candida albicans
Authors: Novozhilova Nina , Babina Ksenia , Polyakova Maria , Sokhova Inna , Sherstneva Valeria , Zaytsev Alexandr , Makeeva Irina , Mikheikina Anna
Published: February 2024

We assessed the effect of different compositions and concentrations of two etidronate-containing irrigants on the antibacterial activity of sodium hypochlorite (SH) against Enterococcus faecalis and Candida albicans in vitro. Pure cultures of C. albicans and E. faecalis were isolated from root canal samples. The disc diffusion method was used to compare the antibacterial effect of pure SH and SH mixed with 9%, 15%, and 18% etidronate of two manufactures (dual rinse (DR); IsraDent (ID)) and EDTA. The pH and temperature of the solutions were measured immediately after mixing and within 40 min. The ANOVA revealed a significant influence of the type of irrigating solution on the C. albicans and E. faecalis inhibition zone diameters that ranged from 6.6 to 51.6 mm and from 6.4 to 12.4 mm, respectively. SH with DR 9% exhibited the highest effect against C. albicans. The antifungal activity of the other irrigants was SH = SH + DR15% = SH + DR18% = SH + ID9% > SH + EDTA > SH + ID15% > SH + ID18%. No significant differences in the anti-E. faecalis effect were revealed between the tested solutions except for the mixtures of SH and 15% and 18% ID, which exhibited no antiseptic effect. There was a strong positive correlation between antiseptic activity against both microorganisms and the pH values of the tested solutions. In conclusion, most etidronate formulations did not significantly hamper sodium hypochlorite activity against C. albicans and E. faecalis. The effect was concentration- and manufacturer-dependent.
GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations
Authors: Zhang Ruiyi , Luo Bin , Su Xin , Liu Jun
Published: February 2024

Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations.
Spatial Heterogeneity of b Values in Northeastern Tibetan Plateau and Its Interpretation
Authors: Hu Nan , Han Peng , Wang Rui , Shi Fuqiang , Chen Lichun , Li Hongyi
Published: February 2024

The northeastern margin of the Tibetan Plateau (NE Tibetan Plateau) exhibits active geological structures and has experienced multiple strong earthquakes, with M ≥ 7, throughout history. Particularly noteworthy is the 1920 M81/2 earthquake in the Haiyuan region that occurred a century ago and is documented as one of the deadliest earthquakes. Consequently, analyzing seismic risks in the northeastern margin of the Tibetan Plateau holds significant importance. The b value, a crucial parameter for seismic activity, plays a pivotal role in seismic hazard analyses. This study calculates the spatial b values in this region based on earthquake catalogs since 1970. The study area encompasses several major active faults, and due to variations in b values across different fault types, traditional grid-search methods may introduce significant errors in calculating the spatial b value within complex fault systems. To address this, we employed the hierarchical space–time point–process (HIST-PPM) method proposed by Ogata. This method avoids partitioning earthquake samples, optimizes parameters using Akaike’s Bayesian Information Criterion (ABIC) with entropy maximization, and theoretically allows for a higher spatial resolution and more accurate b value calculations. The results indicate a high spatial heterogeneity in b values within the study area. The northwestern and southeastern regions exhibit higher b values. Along the Haiyuan fault zone, the central rupture zone of the Haiyuan earthquake has relatively higher b values than other regions of this fault zone, which is possibly related to the sufficient release of stress during the main rupture of the Haiyuan earthquake. The b values vary from high in the west to low in the east along the Zhongwei fault. On the West Qinling fault zone, the epicenter of the recent Minxian–Zhangxian earthquake is associated with a low b value. In general, regions with low b values correspond well to areas with moderate–strong seismic events in the past 50 years. The spatial differences in b values may reflect variances in seismic hazards among fault zones and regions within the same fault zone.
Neuroscience-Based Information and Communication Technologies Development in Elementary School Mathematics through Games: A Case Study Evaluation
Authors: Procopio Marcos , Fernández-Cézar Raquel , Fernandes-Procopio Leandra , Yánez-Araque Benito
Published: February 2024

Universities play a pivotal role in knowledge production and in transforming teaching methodologies across various subjects. This research is a case study detailing the pedagogical implementation of a game-based methodological approach in a dual-degree program for primary and early childhood education teachers. The focus is on integrating information and communication technologies (ICT) tools for mathematics, specifically Scratch and GeoGebra, underpinned by principles of neuroscience. Based on a sample of 28 students, this study adopted game-based learning (GBL) in mathematics exercises, aimed at equipping student-teachers with strategies for educating future students (ages 7 to 11) in the 3rd to the 6th grade. Key areas of focus included plane and spatial geometry, requiring student-teachers to design game-like activities, emulating the problem-solving approaches of children. The culmination of this study involved presentations by the student-teachers, demonstrating how neuroscience principles can enhance the creation of educational games for primary school students. The findings highlight a significant divergence between the reality of teaching mathematics and the preconceived notions held by the student-teachers. This study opens avenues for integrating neuroscience into education, particularly through GBL, but also highlights the need for comprehensive understanding and application of neuroscientific principles to maximize learning outcomes.
Exploring Students’ Learning Experience and Engagement in Asynchronous Learning Using the Community of Inquiry Framework through Educational Design Research
Authors: Ang Jayden Wei Jie , Ng Yin Ni , Lee Lynette Hui-Wen , Yong Jia Ying
Published: February 2024

Students’ learning experience and their engagement in online learning environments are becoming increasingly important as blended learning grows more prevalent in tertiary education. In this study, asynchronous lectures for applied sciences courses, offered to polytechnic students of Applied Chemistry and Pharmaceutical Science at Nanyang Polytechnic, were designed using the Community of Inquiry framework. Students’ perceptions of their learning experience and cognitive, emotional, and behavioral engagement in asynchronous lectures were determined through a survey study. The results showed that students were engaged and had positive learning experiences. Through an educational design research methodology, this survey study also determined and provided design features important for designing asynchronous lectures. Further research could explore the possibility of expanding the scope of the research to other institutions with students of different cultural backgrounds, learning preferences, and learning abilities.
Storytelling as a Skeleton to Design a Learning Unit: A Model for Teaching and Learning Optics
Authors: Boscolo Alessandra , Lippiello Stefania , Pierri Anna
Published: February 2024

Instructional design is an essential part of teaching practice: both researchers and teachers are engaged in finding strategies to deal with this complex work. We propose the use of storytelling as a skeleton to structure a long-lasting teaching unit, maintaining coherence and giving meaning to its various elements. We refer to an interpretation of storytelling for educational purposes as a role-playing game, drawing inspiration from the Digital Interactive Storytelling in Mathematics framework. Designing a learning unit based on storytelling means integrating every moment of the teaching–learning process within the narrative: the teacher and students are all part of the same story, which motivates every activity carried out in the unit and contextualizes the tasks performed. Moreover, the assessment is also integrated into the narrative flow. In this study, we designed an exemplary Storytelling Learning Unit (SLU) model for the study of light, promoting modelling and argumentation skills in mathematics and physics. This unit, intended for ninth-grade students in an Italian scientific high school, was co-designed by the teacher of the class in which it will be implemented. This work particularly focuses on the design process. From reflection on the specific unit developed, general design principles for creating a SLU were hypothesized.
A Spiking LSTM Accelerator for Automatic Speech Recognition Application Based on FPGA
Authors: Yin Tingting , Dong Feihong , Chen Chao , Ouyang Chenghao , Wang Zheng , Yang Yongkui
Published: February 2024

Long Short-Term Memory (LSTM) finds extensive application in sequential learning tasks, notably in speech recognition. However, existing accelerators tailored for traditional LSTM networks grapple with high power consumption, primarily due to the intensive matrix–vector multiplication operations inherent to LSTM networks. In contrast, the spiking LSTM network has been designed to avoid these multiplication operations by replacing multiplication and nonlinear functions with addition and comparison. In this paper, we present an FPGA-based accelerator specifically designed for spiking LSTM networks. Firstly, we employ a low-cost circuit in the LSTM gate to significantly reduce power consumption and hardware cost. Secondly, we propose a serial–parallel processing architecture along with hardware implementation to reduce inference latency. Thirdly, we quantize and efficiently deploy the synapses of the spiking LSTM network. The power consumption of the accelerator implemented on Artix-7 and Zynq-7000 is only about 1.1 W and 0.84 W, respectively, when performing the inference for speech recognition with the Free Spoken Digit Dataset (FSDD). Additionally, the energy consumed per inference is remarkably efficient, with values of 87 µJ and 66 µJ, respectively. In comparison with dedicated accelerators designed for traditional LSTM networks, our spiking LSTM accelerator achieves a remarkable reduction in power consumption, amounting to orders of magnitude.
CycleGAN-Based Data Augmentation for Subgrade Disease Detection in GPR Images with YOLOv5
Authors: Yang Yang , Huang Limin , Zhang Zhihou , Zhang Jian , Zhao Guangmao
Published: February 2024

Vehicle-mounted ground-penetrating radar (GPR) technology is an effective means of detecting railway subgrade diseases. However, existing methods of GPR data interpretation largely rely on manual identification, which is not only inefficient but also highly subjective. This paper proposes a semi-supervised deep learning method to identify railway subgrade diseases. This method addresses the sample imbalance problem in the defect dataset by utilizing a data augmentation method based on a generative adversarial network model. An initial network model for disease identification is obtained by training the YOLOv5 network with a small number of existing samples. The intelligently extended samples are then labeled to achieve a balance in the disease samples. The network is trained to improve the recognition accuracy of the intelligent model using a more complete dataset. The experimental results show that the accuracy of the proposed method can reach up to 94.53%, which is 23.85% higher than that of the supervised learning model without an extended dataset. This has strong industrial application value for railway subgrade disease detection as the potential learning ability of the model can be explored to a greater extent, thereby improving the recognition accuracy of subgrade diseases.
Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools
Authors: Sun Wenhao , He Yuchen , Yan Tianfeng , Wu Zhongdong , Ma Yide
Published: February 2024

Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
Microwave Metamaterial Absorber with Radio Frequency/Direct Current Converter for Electromagnetic Harvesting System
Authors: Mizeraczyk Jerzy , Budnarowska Magdalena
Published: February 2024

This article presents the analysis of the electromagnetic (EM) properties of a novel metamaterial (MM) array in the microwave frequency range. The background for this work is the rapid development of portable devices with low individual energy consumption for the so-called “Internet of Things” (IoT) and the demand for energy harvesting from the environment on a micro scale through harvesters capable of powering billions of small receivers globally. The main goal of this work was to check the potential of the novel MM array structure for EM energy harvesting. The proposed MM array was analyzed in the CST Studio simulation environment. This resulted in the determination of the substitute average EM parameters (absorption, reflection, and transmission) of the MM array. Then, the MM array was manufactured, and the simulation results of the MM array parameters were experimentally validated in a microwave waveguide test system. Based on this conclusion, a prototype of the microwave MM absorber, together with an RF/DC converter, was designed and manufactured for harvesting EM energy from the environment. The system’s energy efficiency was evaluated, and its potential application in energy harvesting technology was appraised. Using a microwave horn antenna, the EM energy harvesting efficiency of the prototype was evaluated. It was about 50% at a microwave frequency of about 2.6 GHz. This may make the prototype attractive as an EM energy harvester or bolometric sensor.
PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots
Authors: Jiang Fei , Hu Chengyun , Liu Chongwei , Wang Rui , Zhu Jianyong , Chen Shiru , Zhang Juan
Published: February 2024

The large-scale application of industrial robots has created a demand for more intelligent and efficient health monitoring, which is more efficiently met by data-driven methods due to the surge in data and the advancement of computing technology. However, applying deep learning methods to industrial robots presents critical challenges such as data collection, application packaging, and the need for customized algorithms. To overcome these difficulties, this paper introduces a Platform of data-driven Health monitoring for IRs (PHIR) that provides a universal framework for manufacturers to utilize deep-learning-based approaches with minimal coding. Real-time data from multiple IRs and sensors is collected through a cloud-edge system and undergoes unified pre-processing to facilitate model training with a large volume of data. To enable code-free development, containerization technology is used to convert algorithms into operators, and users are provided with a process orchestration interface. Furthermore, algorithm research both for sudden fault and long-term aging failure detection is conducted and applied to the platform for industrial robot health monitoring experiments, by which the superiority of the proposed platform, in reality, is proven through positive results.
Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net
Authors: Ren Qing-Dao-Er-Ji , Wang Lele , Ma Zerui , Barintag Saheya
Published: February 2024

Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.
Toward Greener Smart Cities: A Critical Review of Classic and Machine-Learning-Based Algorithms for Smart Bin Collection
Authors: Gatti Alice , Barbierato Enrico , Pozzi Andrea
Published: February 2024

This study critically reviews the scientific literature regarding machine-learning approaches for optimizing smart bin collection in urban environments. Usually, the problem is modeled within a dynamic graph framework, where each smart bin’s changing waste level is represented as a node. Algorithms incorporating Reinforcement Learning (RL), time-series forecasting, and Genetic Algorithms (GA) alongside Graph Neural Networks (GNNs) are analyzed to enhance collection efficiency. While individual methodologies present limitations in computational demand and adaptability, their synergistic application offers a holistic solution. From a theoretical point of view, we expect that the GNN-RL model dynamically adapts to real-time data, the GNN-time series predicts future bin statuses, and the GNN-GA hybrid optimizes network configurations for accurate predictions, collectively enhancing waste management efficiency in smart cities.
Effect of Dropping Speed of Reducing Agent on the Preparation of LA/Ag Phase-Change Nanocapsules
Authors: Liu Sitong , Yuan Huanmei , Hu Dengti , Li Tonghe , Bai Hao
Published: February 2024

Lauric Acid (LA) phase-change nanocapsules prepared with silver as the shell exhibit excellent energy storage capacity and high thermal conductivity. Still, their functionality could be improved by ensuring uniform morphologies, even the size and thickness of silver particles. In this study, the LA/Ag nanocapsules were prepared under different reductant drop speeds. By adjusting the droplet speed of the reducing agent, the concentration of silver in the solution can be controlled, which affects the nucleation and growth rate of silver particles, thereby influencing the deposition of silver particles on the surface of the core material. The characterization results indicate the successful preparation of high sphericity and uniform-sized LA/Ag nanocapsules. The average diameter of capsules was 117–140 nm, the latent heat was 43.69–47.78 J/g, and the encapsulation efficiency was 80.69–82.53%. As the droplet speed increased, the thickness of the silver shell increased while the encapsulation efficiency decreased. The highest encapsulation efficiency was achieved when the reducing agent dropping speed was 0.03 mL/s.
A DCM-Based Non-Isolated Step-Down DC Transformer
Authors: Kim Minseung , Choi Donghee , Lee Soo Hyoung
Published: February 2024

DC transformers have emerged as essential devices for medium voltage DC (MVDC)-low voltage DC (LVDC) distribution systems. However, conventional step-down single-level converters have limits on the voltage level of the MVDC-LVDC distribution system. This paper proposes a non-isolated step-down (NISD) DC transformer based on discontinuous conduction mode (DCM). The proposed structure can withstand high voltage levels by sharing voltages between energy storage modules dividing voltage levels. The proposed NISD DC transformer determines operational modes based on energy storage modules and performs the voltage conversion process. The effectiveness of the proposed NISD DC transformer is verified based on a case study using a power system computer-aided design and electromagnetic transient simulation engine including DC (PSCAD/EMTDC™).
A Study on the Transient Response of Compressed Air Energy Storage in the Interaction between Gas Storage Chambers and Horseshoe-Shaped Tunnels in an Abandoned Coal Mine
Authors: Li Fuqing , Li Fufeng , Sun Rui , Zheng Jianjie , Li Xiaozhao , Shen Lan , Sun Qiang , Liu Ying , Ji Yukun , Duan Yinhang
Published: February 2024

This study focuses on the renovation and construction of compressed air energy storage chambers within abandoned coal mine roadways. The transient mechanical responses of underground gas storage chambers under a cycle are analyzed through thermal-solid coupling simulations. These simulations highlight changes in key parameters such as displacement, stress, and temperature within the chamber group during the loading and unloading processes of compressed air energy storage. It is found that within a cycle, the small circular chamber experiences the most significant deformation, with an average peak displacement of 0.24 mm, followed by the large circular chamber and horseshoe-shaped tunnels. The small circular chamber exhibits maximum tensile and compressive stresses. Therefore, special attention in engineering practice should be paid to the long-term safety and stability of small circular tunnels, and the stability of horseshoe-shaped tunnels should be also carefully considered. The findings from this study offer some insights for theoretical support and practical implementation in the planning, design, construction, and operation of high-pressure underground gas storage chambers for compressed air energy storage.
Effect of Soil Properties and Powertrain Configuration on the Energy Consumption of Wheeled Electric Agricultural Robots
Authors: Kivekäs Klaus , Lajunen Antti
Published: February 2024

Agricultural emissions can be significantly reduced with smart farming, which includes moving away from large conventional tractors to fleets of compact wheeled electric robots. This paper presents a novel simulation modeling approach for an ATV-sized wheeled electric agricultural robot pulling an implement on deformable terrain. The 2D model features a semiempirical tire–soil interaction model as well as a powertrain model. Rear-wheel drive (RWD), front-wheel drive (FWD), and all-wheel drive (AWD) versions were developed. Simulations were carried out on two different soils to examine the energy consumption and tractive performance of the powertrain options. The results showed that energy consumption varies the least with AWD. However, RWD could provide lower energy consumption than AWD with light workloads due to lower curb weight. However, with the heaviest workload, AWD had 7.5% lower energy consumption than RWD. FWD was also found to be capable of lower energy consumption than AWD on light workloads, but it was unsuited for heavy workloads due to traction limitations. Overall, the results demonstrated the importance of taking the terrain characteristics and workload into account when designing electric agricultural robots. The developed modeling approach can prove useful for designing such machines and their fleet management.
Evaluation of the Applicability of Synthetic Fuels and Their Life Cycle Analyses
Authors: Richter Sandra , Braun-Unkhoff Marina , Hasselwander Samuel , Haas Sofia
Published: February 2024

This paper summarizes the findings of a detailed assessment of synthetic, electricity-based fuels for use in aviation, shipping, and road transport. The fuels considered correspond to the most promising alternatives that were analyzed as part of the German research project BEniVer (Begleitforschung Energiewende im Verkehr—Accompanying Research for the Energy Transition in Transport) initiated by the German Federal Ministry for Economic Affairs and Climate Action (BMWK). Focusing on usage, infrastructure, and ecological analyses, several e-fuels were evaluated and compared to fossil fuels according to the specific sector. It turns out that for all sectors evaluated, the existing sustainable synthetic fuels are already compatible with current technology and regulations. In shipping and road transport, the use of advanced, sustainable fuels will allow for a more distinct reduction in emissions once technology and regulations are adopted. However, standard-compliant synthetic gasoline and diesel are considered the most promising fuels for use in road transport if the transition to electricity is not realized as quickly as planned. For the aviation sector, the number of sustainable aviation fuels (SAFs) is limited. Here, the current aim is the introduction of a 100% SAF as soon as possible to also tackle non-CO2 emissions.
Electric Vehicle Charging from Tramway Infrastructure: A New Concept and the Turin Case Study
Authors: Prussi Matteo , Cota Alfredo Felix , Laveneziana Lorenzo , Chiantera Giuseppe , Guglielmi Paolo
Published: February 2024

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin’s tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board.
Reagent Deodorization and Detoxification of Sewage Sludge with the Production of Reclamation Material
Authors: Tsybina Anna , Wünsch Christoph , Glushankova Irina
Published: February 2024

This article is devoted to the search for effective ways of neutralizing sewage sludge to obtain sludge-based reclamation material. It was necessary to solve the problems of deodorizing the sludge and fixing the mobile forms of heavy metals in its composition. The composition, effective for solving the assigned problems, was experimentally determined; it included quicklime, sodium hypochlorite and peat. In the presence of sodium hypochlorite in the sludge-based composition, oxidation processes occur with the chemical transformation of ammonia and hydrogen sulfide into non-toxic and odorless compounds. Lime combined with peat promotes the humification of sewage sludge and the binding of heavy metals. Based on the composition that showed the best results, a technology has been developed for the chemical treatment of sewage sludge in situ to produce reclamation material.
Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining
Authors: Sun Jianqiao , Zhang Wei , Guo Peng , Ding Xunan , Wang Chaohui , Wang Fei
Published: February 2024

Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.
Research on the Shale Porosity–TOC Maturity Relationship Based on an Improved Pore Space Characterization Method
Authors: Zhao Jianbin , Ke Shizhen , Xie Weibiao , Zhang Zhehao , Wei Bo , Wan Jinbin , Cheng Daojie , Li Zhenlin , Fang Chaoqiang
Published: February 2024

Shale pore structure characterization is key to shale reservoir evaluation, sweet spot selection, and economic exploitation. It remains a challenge to accurately characterize shale micro-nano pores. Common experimental characterization methods for shale pore systems are listed, and advantages and weaknesses of each method are analyzed. An improved pore structure characterization method for shale is proposed by combining Helium and NMR. The new method does not affect shale samples and has a higher accuracy. The affecting factors for shale pore evolution for shale are also discussed, showing that organic matter content and maturity are key factors in total porosity development. Furthermore, a shale porosity–TOC maturity relationship chart is developed based on the experimental data of shale samples selected from six shale reservoirs. The application of this chart in Well X in the Gulong field of Songliao Basin proves its utility in evaluating shale reservoirs.
A Review of Proton Exchange Membrane Degradation Pathways, Mechanisms, and Mitigation Strategies in a Fuel Cell
Authors: Madhav Dharmjeet , Wang Junru , Keloth Rajesh , Mus Jorben , Buysschaert Frank , Vandeginste Veerle
Published: February 2024

Proton exchange membrane fuel cells (PEMFCs) have the potential to tackle major challenges associated with fossil fuel-sourced energy consumption. Nafion, a perfluorosulfonic acid (PFSA) membrane that has high proton conductivity and good chemical stability, is a standard proton exchange membrane (PEM) used in PEMFCs. However, PEM degradation is one of the significant issues in the long-term operation of PEMFCs. Membrane degradation can lead to a decrease in the performance and the lifespan of PEMFCs. The membrane can degrade through chemical, mechanical, and thermal pathways. This paper reviews the different causes of all three routes of PFSA degradation, underlying mechanisms, their effects, and mitigation strategies. A better understanding of different degradation pathways and mechanisms is valuable in producing robust fuel cell membranes. Hence, the progress in membrane fabrication for PEMFC application is also explored and summarized.
Novel Batch and Repeated-Batch Butanol Fermentation from Sweet Sorghum Stem Juice by Co-Culture of Arthrobacter and Immobilized Clostridium in Scaled-Up Bioreactors
Authors: Thanapornsin Thanawat , Laopaiboon Lakkana , Laopaiboon Pattana
Published: February 2024

This research aims to study butanol fermentation from sweet sorghum stem juice (SSJ) by immobilized Clostridium beijerinckii TISTR 1461 cells on bamboo chopsticks using Arthrobacter sp. as an efficient bacterium for creating anaerobic conditions in scaled-up bioreactors. For batch culture in a 1-L screw-capped bottle, a butanol concentration (PB), butanol productivity (QB), and butanol yield (YB/S) were 12.09 g/L, 0.26 g/L·h and 0.28 g/g, respectively. These values were ~8 to 14% higher than those of a single culture using oxygen-free nitrogen (OFN) gas to generate anaerobic conditions. When butanol fermentation by the co-culture was scaled-up to 5-L and 30-L stirred-tank fermenters, the butanol production efficiency was not different from that using the 1-L bottles. Additionally, repeated-batch butanol fermentation in the 1-L bottles by the co-culture was successfully operated for four successive cycles with high butanol production. All results clearly indicate that Arthrobacter sp. is promising for creation of anaerobic conditions for butanol production by immobilized Clostridium in large scale bioreactors.
Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis
Authors: Santiago Rogerio Adriano da Fonseca , Barbosa Natasha Benjamim , Mergulhão Henrique Gomes , Carvalho Tassio Farias de , Santos Alex Alisson Bandeira , Medrado Ricardo Cerqueira , Filho Jose Bione de Melo , Pinheiro Oberdan Rocha , Nascimento Erick Giovani Sperandio
Published: February 2024

Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.
Digital Opportunity or a Threat? Adoption of Internet of Things (IoT) Monitoring Systems for Natural Resources in Germany
Authors: Baycheva-Merger Tanya , Selter Andy , Seijger Chris , Häublein Sabeth
Published: February 2024

In Germany, modern digital technologies like Internet of Things (IoT) have been increasingly promoted for better environmental and natural resources management through “smart” monitoring and real-time data. However, adopting such advanced technologies is complex and brings a wide array of risks and challenges, and it remains unclear whether local actors are in fact willing and sufficiently equipped to adopt them. Using the Motivations and Abilities (MOTA) framework, and quantitative data collection and analysis methods, this study explores the motivations and abilities of German local (governmental) actors by focusing on the adoption of IoT-based forest and water monitoring systems. The findings reveal an early-stage adoption of IoT environmental monitoring, with limited awareness and no plans for adoption. The lack of willingness, however, is not attributed to a lack of motivation; it is, rather, influenced by perceived insufficient financial and technical capacities and resources. This study provides novel insights for understanding the complex relationship between actors’ behavior and the adoption of advanced digital technologies in the realm of environmental and natural resources management. The results provide a robust foundation for future research, and inform policy and practice aimed at facilitating digitalized natural resources management.
An Improved Forest Smoke Detection Model Based on YOLOv8
Authors: Wang Yue , Piao Yan , Wang Haowen , Zhang Hao , Li Bing
Published: February 2024

This study centers on leveraging smoke detection for preemptive forest smoke detection. Owing to the inherent ambiguity and uncertainty in smoke characteristics, existing smoke detection algorithms suffer from reduced detection accuracy, elevated false alarm rates, and occurrences of omissions. To resolve these issues, this paper employs an efficient YOLOv8 network and integrates three novel detection modules for enhancement. These modules comprise the edge feature enhancement module, designed to identify smoke ambiguity features, alongside the multi-feature extraction module and the global feature enhancement module, targeting the detection of smoke uncertainty features. These modifications improve the accuracy of smoke area identification while notably lowering the rate of false alarms and omission phenomenon occurrences. Meanwhile, a large forest smoke dataset is created in this paper, which includes not only smoke images with normal forest backgrounds but also a considerable quantity of smoke images with complex backgrounds to enhance the algorithm’s robustness. The proposed algorithm in this paper achieves an AP of 79.1%, 79.2%, and 93.8% for the self-made dataset, XJTU-RS, and USTC-RF, respectively. These results surpass those obtained by the current state-of-the-art target detection-based and neural network-based improved smoke detection algorithms.
Forest Adaptation to Climate Change: Altitudinal Response and Wood Variation in Natural-Growth Cunninghamia lanceolata in the Context of Climate Change
Authors: Xie Gongliang , Liu Sen , Chang Ting , Zhu Ninghua
Published: February 2024

This research delves into the impact of climate change on the wood traits of Cunninghamia lanceolata across various altitudinal gradients, aiming to understand the influence of altitude and climatic factors like temperature and precipitation on key wood characteristics. Employing a comprehensive approach, samples were collected from different altitudes for detailed phenotypic analysis. Methods included Pearson correlation, principal component analysis, cluster analysis, and random forest analysis. Results revealed significant variations in wood traits such as heartwood ratio, tracheid length, and width across altitudes. Notably, wood traits in lower- and middle-elevation populations exhibited higher variability compared to higher elevations, indicating greater environmental diversity and genetic adaptability at these altitudes. Climatic factors, particularly temperature and precipitation, were found to increasingly influence wood trait variation with altitude. The research concludes that the adaptation of Cunninghamia lanceolata to climate change is significantly influenced by both altitudinal and climatic factors, highlighting their importance in forest genetic breeding and conservation strategies amidst global climate change.