bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest
Authors: Zhang Zhaosheng , Dong Shiji , Li Da , Liu Peng , Wang Zhenpo
Published: January 2024

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.
Enhancing Grid-Forming Converters Control in Hybrid AC/DC Microgrids Using Bidirectional Virtual Inertia Support
Authors: Bakeer Abualkasim , Chub Andrii , Abid Abderahmane , Zaid Sherif A. , Alghamdi Thamer A. H. , Salama Hossam S.
Published: January 2024

This paper presents a new grid-forming strategy for hybrid AC/DC microgrids using bidirectional virtual inertia support designed to address weak grid conditions. The stability of hybrid AC/DC microgrids heavily relies on the AC mains frequency and the DC-link voltage, and deviations in these factors can lead to undesirable outcomes such as load curtailments and power system congestions and blackouts. This paper introduces a unique approach that leverages bidirectional virtual inertia support to enhance the stability and reliability of hybrid AC/DC microgrids under weak grid conditions. The proposed strategy employs virtual inertia as a buffer to mitigate rapid changes in DC-link voltage and AC frequency, thereby enhancing system stability margins. This strategy significantly contributes to a more stable and reliable grid operation by reducing voltage and frequency fluctuations. A standard hybrid AC/DC microgrid configuration is used to implement the bidirectional virtual inertia support, where a bidirectional interlinking converter control is adjusted to deliver inertia support to both the AC and DC subgrids. This converter utilizes the DC grid voltage and AC grid frequency as inputs, effectively managing active power balance and implementing auxiliary functions. Extensive simulations are conducted under weak grid conditions and standalone mode to validate the effectiveness of the proposed strategy. The simulation results demonstrate a remarkable improvement in frequency nadir, rate-of-change-of-frequency (RoCoF), and DC bus voltage deviation in the hybrid AC/DC microgrids. The bidirectional virtual inertia support substantially reduces voltage and frequency fluctuations, enhancing the microgrid stability and resilience. There is an improvement of over 45% and 25% in the frequency deviation and voltage deviation, respectively, achieved through implementing the proposed control strategy.
Effect of the Curvature Radius on Single-Droplet Dynamic Characteristics within a Concave-Wall Jet
Authors: Gong Bin , Jian Aibo , Zhang Jing , Yang Guang , Liu Yifeng
Published: January 2024

The centrifugal force field in a hydrocyclone was affected by the concave-wall curvature radius R0, and the mechanism underlying droplet deformation was closely related to the mass transfer efficiency. Numerical simulation and experimental data were collected to reveal the deformation characteristics and mechanism of a single droplet crossing concave-wall jet. Normalized interfacial energy γ and stretching performance were provided to investigate the droplet deformation process. The results showed that the droplet was stretched along the streamwise direction and shrank along the spanwise direction in the concave-wall jet. The droplet interfacial energy and deformation were the largest when the droplet crossed the jet boundary at t = 0.20 s. The maximum γ value increased with the increase in R0 by 57.3% to 71.4%, and the distance between the droplet and concave wall increased with R0. The Q-criterion was exported to show the increase in the vortex strength with the decrease in R0 at the jet boundary. The pressure distribution inside the droplet showed that the pressure decreased as R0 increased, while the pressure difference increased along the streamwise and wall-normal directions. This study suggested that the droplet breakup was more difficult for a smaller R0, which was beneficial for liquid–liquid heterogeneous separation.
A Sequential Hybrid Optimization Algorithm (SHOA) to Solve the Hybrid Flow Shop Scheduling Problems to Minimize Carbon Footprint
Authors: Geetha M. , Chandra Guru Sekar R. , Marichelvam M. K. , Tosun Ömür
Published: January 2024

In today’s world, a situational awareness of sustainability is becoming increasingly important. Leaving a better world for future generations is becoming the main interest of many studies. It also puts pressure on managers to change production methods in most industries. Reducing carbon emissions in industry today is crucial to saving our planet. Theoretical research and practical industry requirements diverge, even though numerous researchers have tackled various strategies to handle carbon emission problems. Therefore, this work considers the carbon emission problem of the furniture manufacturing industry in Hosur, Tamilnadu, India. The case study company has a manufacturing system that resembles a hybrid flow shop (HFS) environment. As the HFS scheduling problems are NP-hard in nature, exact solution techniques could not be used to solve the problems. Hence, a sequential hybrid optimization algorithm (SHOA) has been developed in this paper to minimize the carbon footprint. In the SHOA, the pigeon-inspired optimization algorithm (PIOA) is hybridized sequentially with the firefly algorithm (FA). A computational experimental design is proposed to analyze the efficiency of the introduced strategy, and the solutions indicate that the developed approach could reduce the carbon footprint by up to 9.82%. The results motivate us to implement the proposed algorithm in the manufacturing industry to reduce the carbon footprint.
A Method for Predicting Ground Pressure in Meihuajing Coal Mine Based on Improved BP Neural Network by Immune Algorithm-Particle Swarm Optimization
Authors: Lai Xingping , Tu Yuhang , Yan Baoxu , Wu Longquan , Liu Xiaoming
Published: January 2024

Based on the background of dynamic mining pressure monitoring and pressure prediction research on the No. 232205 working face of the Meihuajing coal mine, this study systematically investigates the predictive model of mining pressure manifestation on the working face of the Meihuajing coal mine by integrating methods such as engineering investigation, theoretical analysis, and mathematical modeling. A mining pressure manifestation prediction method based on IA-PSO-BP is proposed. The IA-PSO optimization algorithm is applied to optimize the hyperparameters of the BP neural network, and the working face mining pressure prediction model based on IA-PSO-BP is established. The mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) are selected as evaluation indicators to compare the prediction performance of the BP model, PSO-BP model, and IA-PSO-BP model. The experimental results of the model show that the convergence speed of the IA-PSO-BP model is about eight times faster than that of the BP model and two times faster than that of the PSO-BP model. Compared with the BP and PSO-BP models, the IA-PSO-BP model has the smallest MAE and MSE and the largest R2 on the three different data sets of the test set, indicating significantly improved prediction accuracy. The predicted results conform to the periodic variation pattern of mining pressure data and are consistent with the actual situation in the coal mine.
Model and Parameter Study of Limestone Decomposition Reaction
Authors: Zhu Rongjia , Fu Liangyu , Liu Qian , E Jiaqiang , Zhou Haoyu
Published: January 2024

To address the problem that there are many limestone particle decomposition reaction models and it is difficult to accurately select the appropriate one, this paper established two typical one-dimensional unsteady numerical calculation models for single-particle limestone decomposition, coupling the convective heat transfer, thermal conduction, and CO2 mass transfer processes. Two numerical calculation models were solved through the Matlab R2021a platform, and the internal temperature, CO2 concentration distribution, and decomposition reaction rate of the limestone particles during the period from the beginning of temperature rise to the end of decomposition were obtained. Compared with the experimental data, Model 1 has a better agreement with a relative error of less than 10%. The simulation results have shown that the average decomposition reaction rate is 20% higher than the average mass transfer rate. As the particle size increases from 20 mm to 80 mm, the time required for temperature rise in the initial stage changes from 6.6 min to 42.7 min. It is noted that the CO2 volume fraction increment increases by 0.42% with a particle size of 80 mm and a heating temperature of 1050 °C, indicating that the influence of the distribution of CO2 on the limestone decomposition may not be ignored.
The Effect of Braid Angle on Hydraulic Hose Geometry
Authors: Dýrr Filip , Bureček Adam , Hružík Lumír , Polášek Tomáš , Ledvoň Marian , Dvořák Lukáš
Published: January 2024

Hydraulic hoses are part of most hydraulic systems, from industrial hydraulics with open loop hydraulic systems to mobile hydraulics with closed loop hydraulic systems. The design parameters of hydraulic hoses may influence the duty cycle dynamics of these systems. One of the factors that influence the behavior of a hydraulic hose under pressure loading is the steel braid angle with respect to the hydraulic hose axis. This work aims to determine the effect of the hydraulic hose braid angle on the change in its geometry. The next objective is to determine the forces that occur at the hose ends under pressure loading. The stresses occur when fluid pressure is applied to the inner wall of the hydraulic hose. Consequently, these stresses are transferred to the hose ends through the steel braid or spiral. The phenomenon of the neutral braid angle provides a balance between the stresses generated inside the hydraulic hose. Therefore, hydraulic hose manufacturers try to produce hydraulic hoses with a neutral braid angle, because the lifetime of the hydraulic hose is also related to this. As part of this research work, an experimental device was constructed in order to measuring the properties of hydraulic hoses. When the hose was loaded with fluid pressure, the change in hose geometry was measured and the angle of the hose braid was measured simultaneously. Upon the measurements, the effect of the braid angle on the hose behavior under pressure loading was determined.
Unrecorded Tundra Fires in Canada, 1986–2022
Authors: Hethcoat Matthew , Jain Piyush , Parisien Marc-André , Skakun Rob , Rogic Luka , Whitman Ellen
Published: January 2024

Climate-driven changes in fire regimes are expected across the pan-Arctic region. Trends in arctic fires are thought to be generally increasing; however, fire mapping across the region is far from comprehensive or systematic. We developed a new detection workflow and built a dataset of unrecorded tundra fires in Canada using Landsat data. We built a reference dataset of spectral indices from previously mapped fires in northern Canada to train a Random Forest model for detecting new fires between 1986 and 2022. In addition, we used time series information for each pixel to reduce false positives and narrow the large search space down to a finite set of regions that had experienced changes. We found 209 previously undetected fires in the Arctic and sub-Arctic regions, increasing the mapped burned area by approximately 30%. The median fire size was small, with roughly 3/4 of the fires being <100 ha in size. The majority of newly detected fires (69%) did not have satellite-derived hotspots associated with them. The dataset presented here is commission error-free and can be viewed as a reference dataset for future analyses. Moreover, future improvements and updates will leverage these data to improve the detection workflow outlined here, particularly for small and low-severity fires. These data can facilitate broader analyses that examine trends and environmental drivers of fire across the Arctic region. Such analyses could begin to untangle the mechanisms driving heterogeneous fire responses to climate observed across regions of the Circumpolar North.
Use of Remote Sensing and Biogeochemical Modeling to Simulate the Impact of Climatic and Anthropogenic Factors on Forest Carbon Fluxes
Authors: Chiesi Marta , Fibbi Luca , Vanucci Silvana , Maselli Fabio
Published: January 2024

The current communication presents the application of a consolidated model combination strategy to analyze the medium-term carbon fluxes in two Mediterranean pine wood ecosystems. This strategy is based on the use of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC, the outputs of which are combined taking into account the actual development phase of each ecosystem. The two pine ecosystems examined correspond to an old-growth forest and to a secondary succession after clearcuts, which differently respond to the same climatic condition during a ten-year period (2013–2022). Increasing dryness, in fact, exerts a fundamental role in controlling the gross primary and net ecosystem production of the mature stand, while the effect of forest regeneration is prevalent for the uprising of the same variables in the other stand. In particular, the simulated net carbon exchange fluctuates around 200 g C m−2 year−1 in the first stand and rises to over 600 g C m−2 year−1 in the second stand; correspondingly, the accumulation of new biomass is nearly undetectable in the former case while becomes notable in the latter. The study, therefore, supports the potential of the applied strategy for predicting the forest carbon balances consequent on diversified natural and human-induced factors.
Long-Range 3D Reconstruction Based on Flexible Configuration Stereo Vision Using Multiple Aerial Robots
Authors: Sumetheeprasit Borwonpob , Rosales Martinez Ricardo , Paul Hannibal , Shimonomura Kazuhiro
Published: January 2024

Aerial robots, or unmanned aerial vehicles (UAVs), are widely used in 3D reconstruction tasks employing a wide range of sensors. In this work, we explore the use of wide baseline and non-parallel stereo vision for fast and movement-efficient long-range 3D reconstruction with multiple aerial robots. Each viewpoint of the stereo vision system is distributed on separate aerial robots, facilitating the adjustment of various parameters, including baseline length, configuration axis, and inward yaw tilt angle. Additionally, multiple aerial robots with different sets of parameters can be used simultaneously, including the use of multiple baselines, which allows for 3D monitoring at various depth ranges simultaneously, and the combined use of horizontal and vertical stereo, which improves the quality and completeness of depth estimation. Depth estimation at a distance of up to 400 m with less than 10% error using only 10 m of active flight distance is demonstrated in the simulation. Additionally, estimation of a distance of up to 100 m with flight distance of up to 10 m on the vertical axis and horizontal axis is demonstrated in an outdoor mapping experiment using the developed prototype UAVs.
A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1
Authors: Li Hui , Jing Linhai , Dou Changyong , Ding Haifeng
Published: January 2024

The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection.
Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
Authors: Salmi Younes , Bogucka Hanna
Published: January 2024

Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k′-nearest neighbors algorithms. Moreover, we examine the poisoning attacks on such ML algorithms, namely noise-like jamming and targeted data feature falsification, and their impact on the effectiveness of 2C task allocation. Then, we also present two anomaly detection methods using noise training and the silhouette score test to detect the poisoned samples and mitigate their impact. Our simulation results show that these attacks have a fatal effect on classification in feature areas where the decision boundary is unclear. They also demonstrate the effectiveness of our countermeasures against the considered attacks.
Priority-Aware Actuation Update Scheme in Heterogeneous Industrial Networks
Authors: Kyung Yeunwoong , Sung Jihoon , Ko Haneul , Song Taewon , Kim Youngjun
Published: January 2024

In heterogeneous wireless networked control systems (WNCSs), the age of information (AoI) of the actuation update and actuation update cost are important performance metrics. To reduce the monetary cost, the control system can wait for the availability of a WiFi network for the actuator and then conduct the update using a WiFi network in an opportunistic manner, but this leads to an increased AoI of the actuation update. In addition, since there are different AoI requirements according to the control priorities (i.e., robustness of AoI of the actuation update), these need to be considered when delivering the actuation update. To jointly consider the monetary cost and AoI with priority, this paper proposes a priority-aware actuation update scheme (PAUS) where the control system decides whether to deliver or delay the actuation update to the actuator. For the optimal decision, we formulate a Markov decision process model and derive the optimal policy based on Q-learning, which aims to maximize the average reward that implies the balance between the monetary cost and AoI with priority. Simulation results demonstrate that the PAUS outperforms the comparison schemes in terms of the average reward under various settings.
Data-Driven Contact-Based Thermosensation for Enhanced Tactile Recognition
Authors: Ma Tiancheng , Zhang Min
Published: January 2024

Thermal feedback plays an important role in tactile perception, greatly influencing fields such as autonomous robot systems and virtual reality. The further development of intelligent systems demands enhanced thermosensation, such as the measurement of thermal properties of objects to aid in more accurate system perception. However, this continues to present certain challenges in contact-based scenarios. For this reason, this study innovates by using the concept of semi-infinite equivalence to design a thermosensation system. A discrete transient heat transfer model was established. Subsequently, a data-driven method was introduced, integrating the developed model with a back propagation (BP) neural network containing dual hidden layers, to facilitate accurate calculation for contact materials. The network was trained using the thermophysical data of 67 types of materials generated by the heat transfer model. An experimental setup, employing flexible thin-film devices, was constructed to measure three solid materials under various heating conditions. Results indicated that measurement errors stayed within 10% for thermal conductivity and 20% for thermal diffusion. This approach not only enables quick, quantitative calculation and identification of contact materials but also simplifies the measurement process by eliminating the need for initial temperature adjustments, and minimizing errors due to model complexity.
Surface-Enhanced Raman Spectroscopy Using a Silver Nanostar Substrate for Neonicotinoid Pesticides Detection
Authors: Abu Bakar Norhayati , Fronzi Marco , Shapter Joseph George
Published: January 2024

Surface-enhanced Raman spectroscopy (SERS) has been introduced to detect pesticides at low concentrations and in complex matrices to help developing countries monitor pesticides to keep their concentrations at safe levels in food and the environment. SERS is a surface-sensitive technique that enhances the Raman signal of molecules absorbed on metal nanostructure surfaces and provides vibrational information for sample identification and quantitation. In this work, we report the use of silver nanostars (AgNs) as SERS-active elements to detect four neonicotinoid pesticides (thiacloprid, imidacloprid, thiamethoxam and nitenpyram). The SERS substrates were prepared with multiple depositions of the nanostars using a self-assembly approach to give a dense coverage of the AgNs on a glass surface, which ultimately increased the availability of the spikes needed for SERS activity. The SERS substrates developed in this work show very high sensitivity and excellent reproducibility. Our research opens an avenue for the development of portable, field-based pesticide sensors, which will be critical for the effective monitoring of these important but potentially dangerous chemicals.
BLE-Based Indoor Localization: Analysis of Some Solutions for Performance Improvement
Authors: Milano Filippo , da Rocha Helbert , Laracca Marco , Ferrigno Luigi , Espírito Santo António , Salvado José , Paciello Vincenzo
Published: January 2024

This paper addresses indoor localization using an anchor-based system based on Bluetooth Low Energy (BLE) 5.0 technology, adopting the Received Signal Strength Indicator (RSSI) for the distance estimation. Different solutions have been proposed in the scientific literature to improve the performance of this localization technology, but a detailed performance comparison of these solutions is still missing. The aim of this work is to make an experimental analysis combining different solutions for the performance improvement of BLE-based indoor localization, identifying the most effective one. The considered solutions involve different RSSI signals’ conditioning, the use of anchor–tag distance estimation techniques, as well as approaches for estimating the unknown tag position. An experimental campaign was executed in a complex indoor environment, characterized by the continuous presence in the movement of working staff and numerous obstacles. The exploitation of multichannel transmission using RSSI signal aggregation techniques showed the greater performance improvement of the localization system, reducing the positioning error (from 1.5 m to about 1 m). The other examined solutions have shown a lesser impact in the performance improvement with a decrease or an increase in the positioning errors, depending on the considered combination of the adopted solutions.
Temperature and Twist Sensor Based on the Sagnac Interferometer with Long-Period Grating in Polarization-Maintaining Fiber
Authors: Zhang Qiufang , Zheng Yiwen , Zhu Yixin , Tang Qianhao , Yu Yongqin , Wang Lihu
Published: January 2024

We utilized a CO2 laser to carve long-period fiber gratings (LPFGs) on polarization-maintaining fibers (PMFs) along the fast and slow axes. Based on the spectra of LPFGs written along two different directions, we found that when LPFG was written along the fast axis, the spectrum had lower insertion loss and fewer side lobes. We investigated the temperature and twist characteristics of the embedded structure of the LPFG and Sagnac loop and ultimately obtained a temperature sensitivity of −0.295 nm/°C and a twist sensitivity of 0.87 nm/(rad/m) for the LPFG. Compared to the single LPFG, the embedded structure of the LPFG and Sagnac loop demonstrates a significant improvement in temperature and twist sensitivities. Additionally, it also possesses the capability to discern the direction of the twist. The embedded structure displays numerous advantages, including easy fabrication, low cost, good robustness, a wide range, and high sensitivity. These features make it highly suitable for applications in structural health monitoring and other related fields.
Phase Noise Analysis of Time Transfer over White Rabbit-Network Based Optical Fibre Links
Authors: Neelam  , Kandeepan Sithamparanathan , Panja Subhasis
Published: January 2024

White Rabbit (WR) is an optical fibre-based time-frequency synchronization technology typically used in timekeeping laboratories for distributing time-frequency signals from a reference clock to distant locations. The accuracy of the received signals at the user end can be affected by random noise processes present in the WR network due to the internal electronic components of WR devices. In this paper, we investigate the presence of random noise processes in the WR network. We then study their statistical properties and model the distribution based on experimentally recorded measurements. According to our study, the probability density function (PDF) follows a Gaussian mixture model (GMM) with varying distribution parameters, and the correlation analysis indicates a strong correlation of the phase noise process over the temporal samples. Furthermore, the developed phase noise models have also been verified by comparing them against additional experimental data. Finally, we present the methodology to generate the phase noise process using computer simulations with the PDF and correlation models developed in this work to help algorithm developers and equipment manufacturers make use of our results.
Effect of Fly Ash on the Mass Transfer Performance of CO2 Removal Using MEA and DEA Solutions in a Packed Tower
Authors: Xie Wenxia , Tu Chunmin , Zhang Jun , Xu Chengwei
Published: January 2024

The accumulation of uncollected fly ash from flue gas in post-combustion CO2 capture processes is a significant concern in current coal-fired power plants due to its potential impact on the performance of CO2 absorbent and absorption towers. In order to determine the effect of fly ash on the mass transfer performance of CO2 absorption into monoethanolamine (MEA) and diethanolamine (DEA) aqueous solutions, experimental studies were carried out using a small-sized packed tower equipped with θ-ring random packing. These studies were conducted under various operating parameters, including solution temperature, liquid/gas ratio (L/G), packing height, and fly ash concentration. The results show that the effect of fly ash on the outlet CO2 concentration was primarily observed during the initial stages of the experimental process. Moreover, the presence of fly ash leads to a reduction in the volumetric overall mass transfer coefficient (KGav) when using MEA and DEA solution, and increasing the fly ash concentration further exacerbates this negative impact. However, the effect of fly ash on the reduction in KGav is not significantly related to its chemical composition but rather depends on the operational parameters. With increasing solution temperature, liquid/gas ratio (L/G), and packing height, the KGav values for different solutions exhibit an upward trend. The negative effect of fly ash on KGav remains relatively stable for MEA as solution temperature and packing height increase. Compared to MEA, fly ash has a greater negative effect on DEA solution under the same experimental conditions. The analysis reveals that the detrimental effect of fly ash on KGav primarily stems from its ability to alter the distribution state of the absorption liquid within the packed tower.
Expanding Possibilities for Inclusive Research: Learning from People with Profound Intellectual and Multiple Disabilities and Decolonising Research
Authors: Grace Joanna , Nind Melanie , de Haas Catherine , Hope Joanna
Published: January 2024

This paper pursues the argument that finding a way for people with profound intellectual and multiple disabilities to belong in inclusive research requires starting from a deep knowledge of the people in question. This paper illustrates this idea in action showing what can be possible from building research around ‘being with’ people with profound intellectual disabilities, creating intersubjective knowledge together. It follows the journey of fostering a relational research space that a young person with profound intellectual and multiple disabilities can occupy, their assenting to share the space and ensuing exploring together in embodied ways. Small stories show where this particular open road of inclusive research can lead. The analysis interacts with provocations from decolonising research that demand respect for wider ways of knowing, doing research and being human. The paper invites reflection on the ways in which research needs to be deconstructed to be inclusive for all.
Focus Group Discussions on Food Waste: An Empirical Application Providing Insights into Rural and Urban Households in Greece
Authors: Aitsidou Vasiliki , Michailidou Evangelia , Loizou Efstratios , Tsantopoulos Georgios , Michailidis Anastasios
Published: January 2024

This paper demonstrates the utility of the focus group discussions (FGDs) methodology in the scientific exploration of food waste. The main objective is to show how FGDs can be designed and implemented by collecting data on household food waste (HFW). The paper provides an empirical application of FGDs to members of urban and rural households in Greece through 10 steps. It is qualitative research that was implemented as a supplement in the framework of a large-scale study on HFW, providing an in-depth interpretation of the statistical results that were arrived at. The research shows that FGDs are an effective data collection methodology that reveals insights into HFW through interactions and complex behaviors. Further, the methodology used gives the opportunity to bring information to the fore. The role of women in relation to food-related responsibilities in the Greek household and the impact of rural experiences on HFW composition constitute two topics under exploration. A detailed understanding of HFW examined through the FGDs methodology enriches the global bibliography, mainly for the case of Greece. In addition, useful information is provided to local and governmental bodies, enabling them to collaborate with academics and experts in food waste management. There is a willingness among household members to raise their awareness of HFW reduction and prevention.
The Wharf Street Smart Park Story: A Guide to Navigating Multi-Stakeholder Innovation in Smart Cities
Authors: Tiwari Reena , Babb Courtney , Tye Marian , Hussein Fatmaelzahraa
Published: January 2024

The challenges of multistakeholder innovation in smart city projects can be navigated through collaboration and a clear understanding of each organisation’s values, technology, user groups, and potential policy changes. Increasing technology use in urban development projects brings numerous challenges, such as technology redundancy, varying stakeholder expectations, and the required policy changes. This paper aims to discuss the story of the development of Wharf Street smart park in Western Australia and highlights strategies for overcoming the challenges of multistakeholder innovation in smart city projects. It utilises a democratic collaboration tool to map out each organisation’s vision, values, and responsibilities at the initial stage of the project to create an open innovation ecosystem where knowledge can be shared. High collaboration levels using this tool have helped establish common goals, adaptive practices and overcome governance and technical challenges. However, the tool needs further development, as it was found to be insufficient in addressing long-term management issues, risk mapping, and user group identification for big data.
Exploiting Deep Eutectic Solvent-like Mixtures for Fractionation Biomass, and the Mechanism Removal of Lignin: A Review
Authors: Jančíková Veronika , Jablonský Michal
Published: January 2024

Green solvents, which include deep eutectic solvent-like mixtures (DES-like mixtures), are categorized as ecological and economical solvents for the pretreatment and fractionation of different types of biomasses. DES-like mixtures represent a group of the most promising green solvents for lignocellulosic pretreatment and are currently used effectively in the biomass pretreatment process. The present work describes the latest applications of DES-like mixtures in biomass delignification processes and, at the same time, summarizes the mechanism of action and influence of DES-like mixture systems on the removal of lignin from different types of biomasses. The results of this review indicate that the physicochemical properties (acidity, hydrogen bond capacity, polarity, viscosity, and water content) of DES-like mixtures have a significant effect on the biomass fractionation process. In addition to the nature of components forming DES-like mixtures, the reaction conditions (temperature, time) influence the efficiency of delignification. Active protons obtained from the hydrogen bond donor facilitate proton-catalyzed bond cleavage during fractionation, where the most significant step is the destruction of the ether and ester bonds between polysaccharides and lignin. DES-like mixtures can depolymerize lignin with subsequent breakdown of the β−O−4 bonds.
A Study of Environmental Education Requirements in Urban Theme Parks from the Perspective of Adolescents
Authors: Kang Peiran , Huang Kai , Zhao Yang
Published: January 2024

Starting from the perspective of adolescents, this study selects the Maritime Silk Road Art Park in Quanzhou, Fujian Province, as the focal point for investigating environmental education requirements for urban theme parks. It comprehensively reviews existing literature and research findings, establishes evaluation indices for environmental education requirements, and systematically analyzes collected data through questionnaires and interviews. The study employs the Kano model to initiate a survey focusing on requirement types and importance ranking at the Maritime Silk Road Art Park. It aims to identify improvement factors and key factors, subsequently conducting a detailed analysis, summary, and explanation of the environmental education requirements for the youth. The results indicate that 11 out of the 25 requirement factors, categorized into five groups, significantly impact youth satisfaction. Based on the sensitivity ranking of improvement factors, these include the following: environmental education game, landscape facility, leisure and recreation facility, plant landscape planning, “five senses experience” activity, trail route design, guided signage facility, public sanitation facility, facility maintenance management, park functional zoning, and consultancy services platform. Through an in-depth analysis of the five prevalent factors influencing environmental education requirements in urban theme parks for adolescents, this study establishes a scientific evaluation system centered on the construction of urban theme parks. It integrates with the development and construction of the parks, proposing innovative and constructive suggestions based on a summary of the analysis results. The aim is to provide references and insights for similar requirements in other theme parks.
Farmland Transfer Mode and Livelihood Capital Endowment Impacts on Income Inequality: Rural Survey Data of Hubei Province, China
Authors: Yu Han , Chen Kun , Zhu Qingying , Guo Baishu
Published: January 2024

Studying the impact of different farmland transfer modes on farmers’ income inequality can help understand the reasons for the income gap between farmers. Based on this, we use field survey data and OLS regression, quantile idea and Shapley decomposition to analyse the impact of farmland transfer on farmers’ income inequality. The results show that the influence of farmers’ spontaneous mode on the increase in farmers’ income is better than that of the market-led mode and government-led mode. The market-led and government-led transfer modes also significantly enhance the positive impact of livelihood capital on farmers’ income. The higher the level of natural, material, labour and social capital of farmers, the more favourable the effect on income, while the financial capital is more beneficial to high-income farmers. As for the restraining effect of the transfer modes on the income gap, the farmers’ spontaneous mode is the best, the government-led mode is the second, and the market-led mode is the worst. Further research shows that the transfer mode greatly impacts the income inequality of middle-income and low-income farmers. There are significant differences in the effects of livelihood capital on farmers’ income inequality in different transfer modes. Therefore, on the premise of following farmers’ willingness, the government should play a leading role in further standardizing the farmland transfer market and improving the household income of low-income and middle-income farmers.
Pro-Social Solutions in Residential Environments Created as a Result of Participatory Design
Authors: Kołacz Katarzyna , Podlasek Anna
Published: January 2024

The pro-social dimension of contemporary housing is one of the main postulates of sustainability. The work aims to draw attention to the residential environment created as a result of participatory design and to examine how and to what extent it supports the creation of social contacts and the identification of inhabitants with their place of residence. The study included three housing projects prepared by the communities: B.R.O.T Aspern, Seestern Aspern, and LiSA. They are part of one urban block located in Aspern, part of the 22nd district of Vienna—Donaudstadt. The case studies were analysed using the same ten evaluation criteria. They were established based on previously developed theories of sociologists, psychologists and architects on the features of architecture and spaces supporting the formation of neighbourly contacts and the identification of users with their place of residence. The research showed that although the same evaluation criteria were used, each design group found an individual way to meet them in their project such as innovative design elements, unique community spaces, or user-driven features. This is proof that not only the place where architecture is created, has its conditions, and the planners and architects creating the project give it an individual, pro-social character, but also the users who create it. However, it is important to create an appropriate organizational, legal architectural and urban framework for the participation process to be successful.
Analysis of Spatial—Temporal Variation in Ecosystem Service Value in Shandong Province over the Last Two Decades
Authors: Li Ting , Shi Donghui , Jiang Shuguang , Li Yu , Yu Huilu
Published: January 2024

During the rapid economic development process in Shandong Province, land use changes have led to the degradation of ecosystem service functions. Based on land cover data for the years 2000, 2010, and 2020, this study analyzes the spatiotemporal variation in land use and the corresponding ecosystem service value in Shandong Province. The research results reveal the following: (1) From 2000 to 2020, land use change is characterized by an increase in construction areas and water and a decrease in unused land, forestland, grassland, and cultivated land areas. Cultivated land was converted into construction land and grassland, and construction land was partially transformed into cultivated land in Shandong Province. The changes in land use types were more pronounced in the first decade compared to the later decade. (2) During the study period, the highest ESV among the primary service functions was observed in the regulating services, with hydrological regulation providing the highest ESV among the eleven secondary service categories. The spatial distribution of ESVs in Shandong Province is uneven. The high-value areas are concentrated in the Southern Four Lakes in Shandong Province and around Laizhou Bay. There was low ESV around mountainous areas in central Shandong Province. (3) Within the three time periods, cultivated land, grassland, and unused land provided a higher negative contribution rate, while water provided a higher positive contribution rate. Water had the highest positive contribution rate to ESV, while grassland and unused land had a higher negative contribution rate. Therefore, during the process of land development and utilization, it is important to pay attention to the impact of land use changes on the ecosystem, optimize the land use structure, restore ecologically fragile areas, and promote the sustainable development of the ecosystem and the economy.
Contribution of Road Vehicle Tyre Wear to Microplastics and Ambient Air Pollution
Authors: Giechaskiel Barouch , Grigoratos Theodoros , Mathissen Marcel , Quik Joris , Tromp Peter , Gustafsson Mats , Franco Vicente , Dilara Panagiota
Published: January 2024

Tyre particles are generated by shear forces between the tread and the road or by volatilisation. Tyre abrasion (wear) contributes from one-third to half of microplastics unintentionally released into the environment. The major part ends up in the soil, a considerable amount is released into the aquatic environment, and a small percentage becomes airborne. Nevertheless, tyre abrasion contributes to 5–30% of road transport particulate matter (PM) emissions. This corresponds to approximately 5% of total ambient PM emissions. The particle mass size distribution peak at around 20 to 100 μm, with a second peak in the 2–10 μm range. A nucleation mode has been reported in some studies. The absolute abrasion levels depend on the tyre, vehicle, and road characteristics, but also on environmental conditions and driving style. Most tyre particle emission factors in the literature are based on data prior to the year 2000. We aggregated recent studies and found a mean abrasion of 110 mg/km per vehicle or 68 mg/km/t for passenger cars (based on approximately 300 measurements). Based on a limited number of studies, the PM10 emissions were 1.4–2.2 mg/km per tyre. On the other hand, the particle number emissions were in the order of 1010 #/km per tyre. The ratio of PM10 to total abrasion was found to be 2.5% on average. Finally, the ratio of PM2.5 to PM10 was calculated to be around 40%. Various mitigation measures for tyre particle pollution could be envisaged; the most direct is the limitation of the tyre abrasion rate, as proposed by the European Commission for the Euro 7 regulation. Other regulatory initiatives are also discussed.