bluuebunny/crossref_metadata_2025 huggingface dataset:
Photocatalytic Degradation of Diclofenac by Nitrogen-Doped Carbon Quantum Dot-Graphitic Carbon Nitride (CNQD)
Authors: Awang Huzaikha , Peppel Tim , Strunk Jennifer
Published: April 2023

In this study nitrogen-doped carbon quantum dots/graphitic carbon nitride nanosheet (CNQD) composites with different contents of nitrogen-doped carbon quantum dots (NCQDs; 2, 4, 6, and 8 wt%) were synthesized. The morphological, physicochemical, and photoelectrochemical properties were investigated using complementary methods such as scanning electron microscopy (SEM), powder X-ray diffraction (pXRD), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), UV/Vis spectroscopy in diffuse reflectance (DRS), photoluminescence (PL), nitrogen physisorption (BET), photocurrent response, and electrochemical impedance spectroscopy (EIS). The photocatalytic activity of the synthesized materials was assessed during diclofenac (DCF) degradation in an aqueous solution under visible light irradiation. As a result, improved photocatalytic efficiency in DCF degradation was observed for all the CNQD composites compared with bulk graphitic carbon nitride (bCN) and nanosheet g-C3N4 (CNS). The fastest DCF degradation was observed for the 6 wt% NCQD on the surface of CNS (CNQD-6), which removed 62% of DCF in 3 h, with an associated k value of 5.41 × 10−3 min−1. The performance test results confirmed the contribution of NCQDs to enhancing photocatalytic activity, leading to an improvement factor of 1.24 over bCN. The morphology of the CNS and the synergistic interaction between NCQDs and CNS were essential elements for enhancing photocatalytic activity. The photoelectrochemical data and photoluminescence analyses showed the efficient migration of photoexcited electrons from NCQDs to the CNS. The reduced charge recombination rates in CNQD photocatalysts might be due to the synergistic interaction between NCQDs and CNS and the unique up-conversion photoluminescence properties of NCQDs. Further investigations revealed that the photogenerated superoxide radicals (•O2−) predominated in the degradation of DCF, and this photocatalyst had good reusability and toxicity reduction abilities. This work provides insight into the effects of NCQDs on the CNS surface to enhance its potential to remove emerging organic pollutants from water and wastewater.
Feasibility Assessment on Remanufacturing of Ni–Mo/γ–Al2O3 Catalyst for Residue Hydrodesulfurization
Authors: Ahn Seon-Yong , Na Woo-Jin , Kim Kyoung-Jin , Kim Beom-Jun , Park Hea-Kyung , Roh Hyun-Seog
Published: April 2023

Residue hydrodesulfurization (RHDS) is a critical process in the petroleum refining industry for removing sulfur compounds from heavy residual oils. However, catalysts used in RHDS can easily be deactivated by numerous factors, leading to reduced process efficiency and economic benefits. The remanufacturing of spent catalysts can be a useful strategy for extending the lifespan of catalysts, reducing waste, and improving process sustainability. This paper proposes an effective catalyst remanufacturing process for commercial RHDS catalysts. In detail, sequential unit processes including oil washing (OW), complete incineration (CI), and acid leaching (AL) were conducted to remanufacture the spent RHDS catalysts. We also highlight some of the key challenges in remanufacturing catalysts, such as the key factors involved in catalyst deactivation. Finally, we provide future perspectives on the development of an effective catalyst remanufacturing process for RHDS, with the goal of improving the efficiency, sustainability, and competitiveness of the petroleum refining industry.
Distribution of Copper, Iron, and Zinc in the Retina, Hippocampus, and Cortex of the Transgenic APP/PS1 Mouse Model of Alzheimer’s Disease
Authors: Hosseinpour Mashkani Seyed Mostafa , Bishop David P. , Raoufi-Rad Newsha , Adlard Paul A. , Shimoni Olga , Golzan S. Mojtaba
Published: April 2023

A mis-metabolism of transition metals (i.e., copper, iron, and zinc) in the brain has been recognised as a precursor event for aggregation of Amyloid-β plaques, a pathological hallmark of Alzheimer’s disease (AD). However, imaging cerebral transition metals in vivo can be extremely challenging. As the retina is a known accessible extension of the central nervous system, we examined whether changes in the hippocampus and cortex metal load are also mirrored in the retina. Laser ablation inductively coupled plasma-mass spectrometry (LA-ICP-MS) was used to visualise and quantify the anatomical distribution and load of Cu, Fe, and Zn in the hippocampus, cortex, and retina of 9-month-old Amyloid Precursor Protein/Presenilin 1 (APP/PS1, n = 10) and Wild Type (WT, n = 10) mice. Our results show a similar metal load trend between the retina and the brain, with the WT mice displaying significantly higher concentrations of Cu, Fe, and Zn in the hippocampus (p < 0.05, p < 0.0001, p < 0.01), cortex (p < 0.05, p = 0.18, p < 0.0001) and the retina (p < 0.001, p = 0.01, p < 0.01) compared with the APP/PS1 mice. Our findings demonstrate that dysfunction of the cerebral transition metals in AD is also extended to the retina. This could lay the groundwork for future studies on the assessment of transition metal load in the retina in the context of early AD.
A Novel Hydrogel-Based 3D In Vitro Tumor Panel of 30 PDX Models Incorporates Tumor, Stromal and Immune Cell Compartments of the TME for the Screening of Oncology and Immuno-Therapies
Authors: Xue Bin , Schüler Julia , Harrod Christopher M. , Lashuk Kanstantsin , Bomya Zoji , Hribar Kolin C.
Published: April 2023

Human-relevant systems that mimic the 3D tumor microenvironment (TME), particularly the complex mechanisms of immuno-modulation in the tumor stroma, in a reproducible and scalable format are of high interest for the drug discovery industry. Here, we describe a novel 3D in vitro tumor panel comprising 30 distinct PDX models covering a range of histotypes and molecular subtypes and cocultured with fibroblasts and PBMCs in planar (flat) extracellular matrix hydrogels to reflect the three compartments of the TME—tumor, stroma, and immune cells. The panel was constructed in a 96-well plate format and assayed tumor size, tumor killing, and T-cell infiltration using high-content image analysis after 4 days of treatment. We screened the panel first against the chemotherapy drug Cisplatin to demonstrate feasibility and robustness, and subsequently assayed immuno-oncology agents Solitomab (CD3/EpCAM bispecific T-cell engager) and the immune checkpoint inhibitors (ICIs) Atezolizumab (anti-PDL1), Nivolumab (anti-PD1) and Ipilimumab (anti-CTLA4). Solitomab displayed a strong response across many PDX models in terms of tumor reduction and killing, allowing for its subsequent use as a positive control for ICIs. Interestingly, Atezolizumab and Nivolumab demonstrated a mild response compared to Ipilimumab in a subset of models from the panel. We later determined that PBMC spatial proximity in the assay setup was important for the PD1 inhibitor, hypothesizing that both duration and concentration of antigen exposure may be critical. The described 30-model panel represents a significant advancement toward screening in vitro models of the tumor microenvironment that include tumor, fibroblast, and immune cell populations in an extracellular matrix hydrogel, with robust and standardized high content image analysis in a planar hydrogel. The platform is aimed at rapidly screening various combinations and novel agents and forming a critical conduit to the clinic, thus accelerating drug discovery for the next generation of therapeutics.
Pediatric Interventional Neuroradiology: Opportunities and Challenges
Authors: Jerele Cene , Lovrič Dimitrij , Kuhelj Dimitrij
Published: April 2023

Pediatric interventional neuroradiology (PINR) is a relatively new field of diagnostic and therapeutic care in the pediatric population that has seen considerable advances in recent decades. However, it is still lagging behind adult interventional neuroradiology due to a variety of reasons, including the lack of evidence validating pediatric-specific procedures, the relative absence of pediatric-specific equipment, and the challenges in establishing and maintaining PINR competencies in a relatively small number of cases. Despite these challenges, the number and variety of PINR procedures are expanding for a variety of indications, including unique pediatric conditions, and are associated with reduced morbidity and psychological stigma. Continued technological advances, such as improved catheter and microwire designs and novel embolic agents, are also contributing to the growth of the field. This review aims to increase awareness of PINR and provide an overview of the current evidence base for minimally invasive neurological interventions in children. Important considerations, such as sedation, contrast agent use, and radiation protection, will also be discussed, taking into account the distinct characteristics of the pediatric population. The review highlights the usefulness and benefits of PINR and emphasizes the need for ongoing research and development to further advance this field.
Investigation of Resonant Signal Timing Plans through Comprehensive Evaluation of Various Optimization Approaches
Authors: Dobrota Nemanja , Stevanovic Aleksandar , Yang Yifei , Alshayeb Suhaib
Published: April 2023

Transportation agencies periodically conduct signal retiming (i.e., optimization) to ensure efficient signal operations. Previous studies introduced the notion of the “resonant cycle length” (RCL), which is based on the premise that a good progression of traffic on the corridor mainline for various volume fluctuations can be achieved with an appropriate value of cycle lengths, where all other signal timing parameters (splits, offsets and phase sequences) remain unaltered. Several follow-up studies brought many inconsistencies in the previously introduced concept. For instance, authors would investigate the existence of the RCL by evaluating the performance of signal timing plans for not only coordinated movements (side streets and coordinated movements together), but would optimize all signal timing parameters (not only cycle lengths) while investigating. This study sheds light on the RCL concept and highlights the importance of all signal timing parameters for signal performance. In addition, we introduce a concept called the resonant signal timing plan (RSTP) as a refinement for the RCL, which represents a combination of signal timing parameters that (unaltered) retain an acceptable performance for a variety of traffic conditions. Results show that different sets of signal timing parameters cause plans to be resonant depending on the evaluation type.
Reduced Mechanism for Combustion of Ammonia and Natural Gas Mixtures
Authors: Khade Aniket R. , Damodara Vijaya D. , Chen Daniel H.
Published: April 2023

A fuel mixture of ammonia and natural gas as a low-carbon alternative for future power generation and transportation is an attractive option. In this work, a 50-species reduced mechanism, NH3NG, suitable for computational fluid dynamics simulations (CFD), is developed for ammonia–natural gas cofiring while addressing important emission issues, such as the formation of nitrogen oxides (NOx), soot, carbon monoxide, and unburnt methane/ammonia. The adoption of reduced mechanisms is imperative not only for saving computer storage and running time but also for numerical convergence for practical applications. The NH3NG reduced mechanism can predict soot emission because it includes soot precursor species. Further, it can handle heavier components in natural gas, such as ethane and propane. The absolute error is 5% for predicting NOx and CO emissions compared to the full Modified Konnov mechanism. Validation with key performance parameters (ignition delay, laminar flame speed, adiabatic temperature, and NOx and CO emissions) indicates that the predictions of the reduced mechanism NH3NG are in good agreement with published experimental data. The average prediction error of 13% for ignition delay is within typical experimental data uncertainties of 10–20%. The predicted adiabatic temperatures are within 1 °C. For laminar flame speed, the R2 between prediction and data is 0.985. NH3NG over-predicts NOx and CO emissions, similar to all other literature methods, but the NOx predictions are closer to the experimental data.
Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions
Authors: Wienrich Carolin , Carolus Astrid , Markus André , Augustin Yannik , Pfister Jan , Hotho Andreas
Published: April 2023

Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.
Phylogeny, Distribution, and Biology of Pygmy Whitefish (Prosopium coulterii) in the Beringia Region (Chukotka)
Authors: Markevich Grigorii N. , Solovyev Mikhail M. , Vlasenko Pavel G. , Izotova Gelena V. , Kashinskaya Elena N. , Bochkarev Nikolai A. , Politov Dmitry V. , Melnik Nikolaii O. , Esin Evgeny V.
Published: April 2023

The pygmy whitefish Prosopium coulterii (C. H. Eigenmann & R. S. Eigenmann, 1892) is a freshwater fish with a highly disjunct distribution ranging from the middle part of North America to Chukotka. There is still no consensus regarding its phylogeny and dispersal history due to limited information from the Chukotkan part of the range. We investigated 22 lakes over Chukotka and found a much broader distribution than it was previously thought. Pygmy whitefish was found to be a common species in the lakes that belong to rivers draining into the Arctic. Cytochrome B, cytochrome oxidase subunit 1, and ATP synthase F0 subunit 6 mitochondrial sites were analyzed from 25 samples to reconstruct the phylogenetic history of pygmy whitefish. Two haplogroups belonging to the east and west Chukotkan ranges were identified; both groups are closely related to Alaskan pigmy whitefish and distant from the Cascadia-Mackenzie (Peace) populations. Combining the distribution patterns, phylogenetic network topology, and the contemporary knowledge on the glaciation history of the region, we suggest a possible colonization pathway over Beringia region and beyond it. The basic biological characteristics (fork length, number of gill rakers, and pyloric caeca, age structure, and feeding) are also presented to characterize the populations over the investigated range.
Safety and Diagnostic Accuracy of the Transnasal Approach for Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA)
Authors: Piro Roberto , Fontana Matteo , Casalini Eleonora , Rossi Laura , Simeone Maria Serena , Ghinassi Federica , Ruggiero Patrizia , Pollorsi Chiara , Taddei Sofia , Beghe’ Bianca , Facciolongo Nicola Cosimo
Published: April 2023

Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a safe and accurate diagnostic procedure used for investigating mediastinal pathologies. It is usually performed using an oral approach. The nasal route has been proposed but not extensively investigated. With the aim to report the use of linear EBUS through the nasal route and compare its accuracy and safety with the oral one, we conducted a retrospective analysis of the subjects who underwent an EBUS-TBNA procedure at our center. From January 2020 to December 2021, 464 subjects underwent an EBUS-TBNA, and in 417 patients, EBUS was performed through the nose or mouth. Nasal insertion of the EBUS bronchoscope was performed in 58.5% of the patients. No difference between the two insertion routes was observed in terms of location or number of stations sampled per subject. Procedure complications were mild and similar between the two groups (10.2% for the nasal group vs. 9.8% for the oral group). Minor epistaxis occurred in five subjects in the nasal group. Comparing the two groups, the rates of adequate specimens were similar (95.1% vs. 94.8%), as were the proportions of diagnostic specimens (84% vs. 82%). In conclusion, the nasal route for EBUS-TBNA is a valid alternative to the oral one.
Hybrid Multi-Dimensional Attention U-Net for Hyperspectral Snapshot Compressive Imaging Reconstruction
Authors: Zheng Siming , Zhu Mingyu , Chen Mingliang
Published: April 2023

In order to capture the spatial-spectral (x,y,λ) information of the scene, various techniques have been proposed. Different from the widely used scanning-based methods, spectral snapshot compressive imaging (SCI) utilizes the idea of compressive sensing to compressively capture the 3D spatial-spectral data-cube in a single-shot 2D measurement and thus it is efficient, enjoying the advantages of high-speed and low bandwidth. However, the reconstruction process, i.e., to retrieve the 3D cube from the 2D measurement, is an ill-posed problem and it is challenging to reconstruct high quality images. Previous works usually use 2D convolutions and preliminary attention to address this challenge. However, these networks and attention do not exactly extract spectral features. On the other hand, 3D convolutions can extract more features in a 3D cube, but increase computational cost significantly. To balance this trade-off, in this paper, we propose a hybrid multi-dimensional attention U-Net (HMDAU-Net) to reconstruct hyperspectral images from the 2D measurement in an end-to-end manner. HMDAU-Net integrates 3D and 2D convolutions in an encoder–decoder structure to fully utilize the abundant spectral information of hyperspectral images with a trade-off between performance and computational cost. Furthermore, attention gates are employed to highlight salient features and suppress the noise carried by the skip connections. Our proposed HMDAU-Net achieves superior performance over previous state-of-the-art reconstruction algorithms.
Impact of Lockdown on Column and Surface Aerosol Content over Ahmedabad and a Comparison with the Indo-Gangetic Plain
Authors: Vaghmaria Nisha , ME James , Gautam Alok Sagar , Gautam Sneha
Published: April 2023

Changes in vertical column concentration, size distribution, and surface concentration of aerosol associated with the lockdown imposed by the COVID-19 pandemic in 2020 over the Ahmedabad region in Gujarat State, India, were analyzed. The results are compared with changes over selected Indo-Gangetic Plain (IGP) regions. On 25 March 2020, the prime minister of India declared a complete lockdown throughout the country and later lifted restrictions in a phased manner. Aerosol optical depth (AOD) over the Ahmedabad region on 29 March dropped to as low as 0.11, and in the first two weeks of lockdown, the weekly average AOD was only 0.18. On almost all days of the lockdown period, AOD over the Ahmedabad region was lower than the decadal mean. It was found that the Ahmedabad region responded differently to lockdown conditions compared to the IGP regions. During the first lockdown phase, AOD decreased by about 29% compared to the pre-lockdown period over the Ahmedabad region. However, the average reduction over the IGP was much more, about 50%. The average Angstrom exponent (AE) of 0.96 during the pre-lockdown period over the Ahmedabad region increased phase-wise to 1.36 during the L3 lockdown phase, indicating dominance of fine-mode particles during the lockdown period. It suggests a reduction in anthropogenically produced coarse-mode particles, typically dust produced by vehicular movement, construction, and industrial activities. However, on the other hand, over the IGP region, the high dominance of fine-mode particles during the pre-lockdown period had changed to a high dominance of coarse-mode particles, especially over the Delhi region. This indicates a reduction in anthropogenically produced fine-mode particles, which are mainly generated by fossil and biofuels/biomass combustion, over the IGP region by lockdown conditions. Within a few days of lockdown, PM2.5 was reduced by 64% and 76% over the Ahmedabad and Delhi regions, respectively. The lockdown imposed by the pandemic provided an excellent opportunity to ascertain background aerosol conditions in the atmosphere.
FM-STDNet: High-Speed Detector for Fast-Moving Small Targets Based on Deep First-Order Network Architecture
Authors: Hu Xinyu , Kong Defeng , Liu Xiyang , Zhang Junwei , Zhang Daode
Published: April 2023

Identifying objects of interest from digital vision signals is a core task of intelligent systems. However, fast and accurate identification of small moving targets in real-time has become a bottleneck in the field of target detection. In this paper, the problem of real-time detection of the fast-moving printed circuit board (PCB) tiny targets is investigated. This task is very challenging because PCB defects are usually small compared to the whole PCB board, and due to the pursuit of production efficiency, the actual production PCB moving speed is usually very fast, which puts higher requirements on the real-time of intelligent systems. To this end, a new model of FM-STDNet (Fast Moving Small Target Detection Network) is proposed based on the well-known deep learning detector YOLO (You Only Look Once) series model. First, based on the SPPNet (Spatial Pyramid Pooling Networks) network, a new SPPFCSP (Spatial Pyramid Pooling Fast Cross Stage Partial Network) spatial pyramid pooling module is designed to adapt to the extraction of different scale size features of different size input images, which helps retain the high semantic information of smaller features; then, the anchor-free mode is introduced to directly classify the regression prediction information and do the structural reparameterization construction to design a new high-speed prediction head RepHead to further improve the operation speed of the detector. The experimental results show that the proposed detector achieves 99.87% detection accuracy at the fastest speed compared to state-of-the-art depth detectors such as YOLOv3, Faster R-CNN, and TDD-Net in the fast-moving PCB surface defect detection task. The new model of FM-STDNet provides an effective reference for the fast-moving small target detection task.
Apple-Picking Robot Picking Path Planning Algorithm Based on Improved PSO
Authors: Gao Ruilong , Zhou Qiaojun , Cao Songxiao , Jiang Qing
Published: April 2023

To solve the problem that the robot often collides with the obstacles such as branches around the fruit during picking due to its inability to adapt to the fruit growing environment, this paper proposes an apple-picking robot picking path planning algorithm based on the improved PSO. The main contents of the algorithm are: firstly, the fruit and its surrounding branches are extracted from the 3D point cloud data, and the picking direction of the fruit is calculated; then the point cloud on the surface of the fruit and branches is used to establish the spatial model of obstacles; finally, an improved particle swarm optimization (PSO) algorithm is proposed to plan the obstacle avoidance trajectory of the end-effector in space, which can dynamically adjust the velocity weights according to the trend of the particle fitness value and the position of the particle swarm center of mass. The experimental results show that the improved PSO has faster convergence speed than the standard PSO, and the path planning method proposed in this paper improves the fruit-picking success rate to 85.93% and reduces the picking cycle to 12 s. This algorithm can effectively reduce the collision between the manipulator and branches during apple picking and improve the picking success rate and picking efficiency.
A Cybersecurity Knowledge Graph Completion Method for Penetration Testing
Authors: Wang Peng , Liu Jingju , Zhong Xiaofeng , Zhou Shicheng
Published: April 2023

Penetration testing is an effective method of making computers secure. When conducting penetration testing, it is necessary to fully understand the various elements in the cyberspace. Prediction of future cyberspace state through perception and understanding of cyberspace can assist defenders in decision-making and action execution. Accurate cyberspace detection information is the key to ensuring successful penetration testing. However, cyberspace situation awareness still faces the following challenges. Due to the limited detection capability, the information obtained from cyberspace detection intelligence is incomplete. There are some errors in the cyberspace detection intelligence, which may mislead the penetration testing workers. The knowledge graph can store and manage the cybersecurity data. In order to ensure the integrity and accuracy of cyberspace information, we design a knowledge graph completion model called CSNT to complete cybersecurity data. CSNT uses the BiLSTM to capture the interaction information between entities and relationships. It models the relationship between entities by combining the neural network and tensor decomposition. The Pearson Mix Net is designed to control the generation of joint vectors. We also design a novel self-distillation strategy to reduce catastrophic forgetting during model training. After learning the relationship pattern between entities in the cyberspace detection intelligence, the model can be used to mine the knowledge not found in the cybersecurity detection intelligence and correct the erroneous records. Experiments show that our method has certain advantages for the knowledge graph completion.
Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
Authors: Wu Binbin , Zhang Bangning , Ma Wenfeng , Xie Chen , Guo Daoxing , Jiang Hao
Published: April 2023

Unmanned-aerial-vehicle (UAV)-aided data collection for Internet of Things applications has attracted increasing attention. This paper investigates motion planning for UAV collecting low-power ground sensor node (SN) data in a dynamic jamming environment. We targeted minimizing the flight energy consumption via optimization of the UAV trajectory while considering the indispensable constraints which cover the collection data demodulation threshold, obstacle avoidance, data collection volume, and motion principle. Firstly, we formulate the UAV-aided data collection problem as an energy consumption minimization problem. To solve this nonconvex optimization problem, we rewrite the original problem by introducing relaxation variables and constructing equivalence constraints to obtain a new relaxation convex problem, which can be solved iteratively using the successive convex approximation (SCA) method. However, SCA is susceptible to initial values, especially in dynamic environments where fixed initial values may lead to a wide range of results, making it difficult to obtain a truly optimal solution to the optimization problem. To solve the initial value problem in dynamic environments, we further propose a communication-flight-corridor(CFC)-based initial path generation method to improve the reliability and convergence speed of the SCA method by constructing reliable communication regions and resilient secure paths in real time. Finally, simulation results validate the performance of the proposed algorithm compared to the benchmark algorithms under different parameter configurations.
Air Pollution Monitoring via Wireless Sensor Networks: The Investigation and Correction of the Aging Behavior of Electrochemical Gaseous Pollutant Sensors
Authors: Christakis Ioannis , Tsakiridis Odysseas , Kandris Dionisis , Stavrakas Ilias
Published: April 2023

The continuously growing human activity in large and densely populated cities pollutes air and consequently puts public health in danger. This is why air quality monitoring is necessary in all urban environments. However, the creation of dense air monitoring networks is extremely costly because it requires the usage of a great number of air monitoring stations that are quite expensive. Instead, the usage of wireless sensor networks (WSNs) that incorporate low-cost electrochemical gas sensors provides an excellent alternative. Actually, sensors of this kind that are recommended for low-cost air quality monitoring applications may provide relatively precise measurements. However, the reliability of such sensors during their operational life is questionable. The research work presented in this article not only experimentally examined the correlation that exists between the validity of the measurements obtained from low-cost gas sensors and their aging, but also proposes novel corrective formulae for gas sensors of two different types (i.e., NO2, O3), which are aimed at alleviating the impact of aging on the accuracy of measurements. The following steps were conducted in order to both study and lessen the aging of electrochemical sensors: (i) a sensor network was developed to measure air quality at a place near official instruments that perform corresponding measurements; (ii) the collected data were compared to the corresponding recordings of the official instruments; (iii) calibration and compensation were performed using the electrochemical sensor vendor instructions; (iv) the divergence between the datasets was studied for various periods of time and the impact of aging was studied; (v) the compensation process was re-evaluated and new compensation coefficients were produced for all periods; (vi) the new compensation coefficients were used to shape formulae that automatically calculate the new coefficients with respect to the sensors’ aging; and (vii) the performance of the overall procedure was evaluated through the comparison of the final outcomes with real data.
A Hybrid Image Segmentation Method for Accurate Measurement of Urban Environments
Authors: Kim Hyungjoon , Lee Jae , Lee Suan
Published: April 2023

In the field of urban environment analysis research, image segmentation technology that groups important objects in the urban landscape image in pixel units has been the subject of increased attention. However, since a dataset consisting of a huge amount of image and label pairs is required to utilize this technology, in most cases, a model trained with a dataset having similar characteristics is used for analysis, and as a result, the quality of segmentation is poor. To overcome this limitation, we propose a hybrid model to leverage the strengths of each model in predicting specific classes. In particular, we first introduce a pre-processing operation to reduce the differences between the collected urban dataset and public dataset. Subsequently, we train several segmentation models with a pre-processed dataset then, based on the weight rule, the segmentation results are fused to create one segmentation map. To evaluate our proposal, we collected Google Street View images that do not have any labels and trained a model using the cityscapes dataset which contains foregrounds similar to the collected images. We quantitatively assessed its performance using the cityscapes dataset with ground truths and qualitatively evaluated the results of GSV data segmentation through user studies. Our approach outperformed existing methods and demonstrated the potential for accurate and efficient urban environment analysis using computer vision technology.
Research on Fuzzy Active Disturbance Rejection Control of LCL Grid-Connected Inverter Based on Passivity-Based Control in Weak Grid
Authors: Yan Hao , Cai Huafeng
Published: April 2023

In response to the problems of impedance variation and rich background harmonics in the weak grid environment of traditional LCL grid-connected inverters, which cause distortion of the grid-connected current and reduce the stability of the entire grid-connected system, this paper proposes a passive fuzzy-active disturbance rejection control strategy. Firstly, passivity-based control (PBC) is introduced into the grid-connected inverter system to ensure the global stability of the entire system. Secondly, in order to solve the problem of difficult selection of inverter-side current, active disturbance rejection control (ADRC) is used to directly track the grid-connected current, avoiding the tracking error of AC signal and suppressing the interference of background harmonics of grid voltage. The combination of fuzzy control and ADRC simplifies the system parameter tuning process, and the FUZZY-ADRC grid-connected current outer loop and PBC inductance current inner loop are combined to form a multi-loop control system. Finally, simulation comparison verifies that the proposed control strategy can effectively suppress the harmonics of grid-connected current, improve the quality of grid-connected current, and enhance the stability of the entire grid-connected system in a weak grid environment.
Analysis of an Urban Grid with High Photovoltaic and e-Mobility Penetration
Authors: Maurer Florian , Rieke Christian , Schemm Ralf , Stollenwerk Dominik
Published: April 2023

This study analyses the expected utilization of an urban distribution grid under high penetration of photovoltaic and e-mobility with charging infrastructure on a residential level. The grid utilization and the corresponding power flow are evaluated, while varying the control strategies and photovoltaic installed capacity in different scenarios. Four scenarios are used to analyze the impact of e-mobility. The individual mobility demand is modelled based on the largest German studies on mobility “Mobilität in Deutschland”, which is carried out every 5 years. To estimate the ramp-up of photovoltaic generation, a potential analysis of the roof surfaces in the supply area is carried out via an evaluation of an open solar potential study. The photovoltaic feed-in time series is derived individually for each installed system in a resolution of 15 min. The residential consumption is estimated using historical smart meter data, which are collected in London between 2012 and 2014. For a realistic charging demand, each residential household decides daily on the state of charge if their vehicle requires to be charged. The resulting charging time series depends on the underlying behavior scenario. Market prices and mobility demand are therefore used as scenario input parameters for a utility function based on the current state of charge to model individual behavior. The aggregated electricity demand is the starting point of the power flow calculation. The evaluation is carried out for an urban region with approximately 3100 residents. The analysis shows that increased penetration of photovoltaics combined with a flexible and adaptive charging strategy can maximize PV usage and reduce the need for congestion-related intervention by the grid operator by reducing the amount of kWh charged from the grid by 30% which reduces the average price of a charged kWh by 35% to 14 ct/kWh from 21.8 ct/kWh without PV optimization. The resulting grid congestions are managed by implementing an intelligent price or control signal. The analysis took place using data from a real German grid with 10 subgrids. The entire software can be adapted for the analysis of different distribution grids and is publicly available as an open-source software library on GitHub.
Investigation of the Electrical Properties of Mineral Oils with and without Carbon Nanotube Concentration under Different Magnetic Fields Applied in Transformer Applications
Authors: Felix Stephanie Azlyn Anak , Md Din Muhamad Faiz , Ishak Asnor Mazuan , Wang Jianli , Idris Nurul Hayati , Zamri Wan Fathul Hakim Wan
Published: April 2023

The increased voltage loading of transformers has led to research on improving transformers’ lifespans to meet demand. Insulation oil acts as cooling medium that can significantly affect the performance of a transformer. This paper discusses an experimental study on the influences of the doping of carbon nanotube (CNT) particles and magnetic fields on the electrical properties of mineral oil (MO). An analysis of electrical properties was conducted using AC breakdown tests, Tan Delta tests, Raman measurements, and simultaneous thermal analysis. Proper preparation was considered before starting the analysis of the electrical properties. The AC breakdown voltages before and after modification were measured. The experiment results indicated that the AC breakdown of mineral oil with a suitable amount of carbon nanotube particles (0.005 g/L) and a suitable magnetic field (0.45 T) gives the highest breakdown voltage. It was found that the proper treatment of nanofluid also greatly influences breakdown voltage. Additionally, Raman measurements analyzed the physical changes in the samples. From the results obtained, the addition of carbon nanotubes and the magnetic field of mineral oil leads to an improved performance of the transformer.
Process of Transformation to Net Zero Steelmaking: Decarbonisation Scenarios Based on the Analysis of the Polish Steel Industry
Authors: Gajdzik Bożena , Wolniak Radosław , Grebski Wies
Published: April 2023

The European steel industry is experiencing new challenges related to the market situation and climate policy. Experience from the period of pandemic restrictions and the effects of Russia’s armed invasion of Ukraine has given many countries a basis for including steel along with raw materials (coke, iron ore, electricity) in economic security products (CRMA). Steel is needed for economic infrastructure and construction development as well as a material for other industries (without steel, factories will not produce cars, machinery, ships, washing machines, etc.). In 2022, steelmakers faced a deepening energy crisis and economic slowdown. The market situation prompted steelmakers to impose restrictions on production volumes (worldwide production fell by 4% compared to the previous year). Despite the difficult economic situation of the steel industry (production in EU countries fell by 11% in 2022 compared to the previous year), the EU is strengthening its industrial decarbonisation policy (“Fit for 55”). The decarbonisation of steel production is set to accelerate by 2050. To sharply reduce carbon emissions, steel mills need new steelmaking technologies. The largest global, steelmakers are already investing in new technologies that will use green hydrogen (produced from renewable energy sources). Reducing iron ore with hydrogen plasma will drastically reduce CO2 emissions (steel production using hydrogen could emit up to 95% less CO2 than the current BF + BOF blast furnace + basic oxygen furnace integrated method). Investments in new technologies must be tailored to the steel industry. A net zero strategy (deep decarbonisation goal) may have different scenarios in different EU countries. The purpose of this paper was to introduce the conditions for investing in low-carbon steelmaking technologies in the Polish steel market and to develop (based on expert opinion) scenarios for the decarbonisation of the Polish steel industry.
High-Quality Syngas Production by Chemical Looping Gasification of Bituminite Based on NiFe2O4 Oxygen Carrier
Authors: Yang Ming , Song Da , Li Yang , Cao Jinzeng , Wei Guoqiang , He Fang
Published: April 2023

Chemical looping gasification (CLG) is an effective coal utilization technology. In this work, the CLG of bituminite was investigated through fixed-bed batch experiments using NiFe2O4 oxygen carrier (OC) to achieve high-quality syngas. The changes in the phase of the oxygen carrier during the chemical looping reaction and the reaction mechanism were explored. The results show that elevated temperature and adding a fraction of steam facilitate the gasification reaction. Adding an appropriate amount of ZrO2 into the NiFe2O4 and modification with alkali metal can enhance the performance of the oxygen carrier. A carbon conversion of 95% and a syngas (CO and H2) selectivity of 86% were obtained under the optimized reaction conditions of 950 °C, an oxygen-carrier-to-bituminite (O/B) ratio of 7:3, a NiFe2O4/ZrO2 ratio of 7:3, and a steam rate of 0.08 mL/min. Modification of the NiFe2O4 by doping alkali metal can significantly facilitate the CLG process. Alkali lignin ash has a more pronounced modifying effect on oxygen carriers than K2CO3. The NiFe2O4 OC underwent a gradual reduction in Ni2+ → Ni and Fe3+ → Fe8/3+ → Fe2+ → Fe processes during the gasification reaction phase. In addition, 20 redox cycles were conducted to demonstrate the oxygen carriers’ good cyclic reaction performance in the CLG process. After 20 redox cycles, the carbon conversion rate was maintained at about 90%, and the syngas selectivity was stably kept at over 80%. This work laid the theoretical foundation for the clean and efficient use of bituminite.
Wear and Leakage Behaviors of Brush Seal Considering Eccentricity and Radial Deformation
Authors: Xu Yiren , Ma Ting , Kong Lingcheng , Zhao Juying , Li Yongjian
Published: April 2023

Brush seals can improve engine performance by reducing leakage. Wear models of brush seals provide methods to predict wear and leakage. However, rotor eccentricity, radial deformation, and hysteresis effect of bristles are not considered systematically in the existing models, which may lead to large errors in some cases. To investigate the influence of rotor-stator eccentricity and radial deformation on the wear process and leakage performance of brush seal, a brush seal test was planned and executed, in which the air leakage rates were measured at different testing times and operating conditions, the eccentricity and radial deformation was measured using eddy current sensors. The test results showed that the eccentricity and radial deformation significantly affected the wear behavior and leakage performance. In the theoretical research, the abrasive wear equation is adopted to describe the material loss of bristles, and a simplified description is used to express the rotor-stator eccentric motions. To describe the effect of hysteresis on wear behavior, the deformation of the bristle pack is divided into rebounding deformation and locked deformation, and the two deformation modes are modeled separately. Then a brush seal wear model considering rotor-stator eccentricity and radial deformation is obtained, in which the hysteresis effect is especially represented. The wear model was verified quantitatively based on the brush seal test data, and the results show that there is an error of 20% with the calculated wear loss when rotor eccentricity, radial deformation, and hysteresis effect are comprehensively considered. In contrast, ignoring the hysteresis effect may increase the error by several times. This study provides a quantitative and practical method for predicting the wear and leakage of brush seals.
Energy System Low-Carbon Transition under Dual-Carbon Goals: The Case of Guangxi, China Using the EnergyPLAN Tool
Authors: Li Yao , Yang Liulin , Luo Tianlu
Published: April 2023

Guangxi is a typical developing region on the southern coast of China. The current issues encountered in the region’s development are that fossil energy accounts for about 80% of the energy structure, fossil fuels are heavily dependent on imports, and the self-sufficiency rate of resources is only 32%. These challenges have created a disparity between the current regional development state and the country’s dual carbon target. Under the premise of comprehensively considering the multi-sectors of electricity, industry, transportation, and heating, this paper presents a study on the energy system transition towards low-carbon development for Guangxi in four steps. Firstly, to demonstrate EnergyPLAN’s capability in energy modeling, a reference scenario for Guangxi is created using official yearbook data from 2020. Then, a short-term scenario is formulated to analyze the development of Guangxi’s energy system during the 14th Five-Year Plan. Furthermore, two mid-term scenarios are established, revealing that Guangxi is anticipated to reach its carbon emission peak between 2025 and 2030. Finally, three long-term scenarios are proposed for Guangxi’s energy system for 2050. These scenarios encompass the expansion of photovoltaics, nuclear, and wind power in the electricity system and emission reduction policies in the industrial, transportation, and heating sectors. As a result, compared with the 2020REF scenario, Guangxi can achieve a carbon emission reduction exceeding 57% and the share of non-fossil energy consumption can reach about 70% in the 2050 scenarios, despite a substantial increase in energy consumption, which makes it possible to achieve carbon neutrality in 2060 and to establish an energy system with less than 20% of fossil energy consumption.
Frequency Support Studies of a Diesel–Wind Generation System Using Snake Optimizer-Oriented PID with UC and RFB
Authors: Rameshar Vikash , Sharma Gulshan , Bokoro Pitshou N. , Çelik Emre
Published: April 2023

The present paper discusses the modeling and analysis of a diesel–wind generating system capable enough to cater to the electrical power requirements of a small consumer group or society. Due to high variations of the load demand or due to changes in the wind speed, the frequency of the diesel–wind system will be highly disturbed, and hence to regulate the frequency and power deviations of the wind turbine system, an effective controller design is a necessary requirement, and therefore this paper proposes a novel controller design based on PID scheme. The parameters of this controller is effectively optimized through a new snake optimizer (SO) in an offline manner to minimize frequency and power deviations of an isolated diesel–wind system. The performance of SO-PID for the diesel–wind system is evaluated by considering the integral of time multiplied absolute error (ITAE), integral absolute error (IAE), and integral of time multiplied square error (ITSE). The results were calculated for a step change in load, step change in wind speed, load change at different instants of time with diverse magnitude, and for random load patterns, and they were compared with some of the recently published results under similar working conditions. In addition, the effect of an ultracapacitor (UC) and redox flow battery (RFB) on SO-PID was investigated for the considered system, and the application results demonstrated the advantages of our proposal over other studied designs.
Reference Electrodes
Authors: Roscher Jessica , Holze Rudolf
Published: April 2023

A reference electrode is a half-cell (an electrode) with a stable, well-defined and highly reproducible electrode potential. A vast number of electrodes have been developed for different applications. They are briefly presented. For the common types, the advantages and drawbacks are discussed. Practical hints for daily use are provided.