bluuebunny/crossref_metadata_2025 huggingface dataset:
On-Screen Visual Feedback Effect on Static Balance Assessment with Perturbations
Authors: Valenzuela Ruben , Corral Javier , Diez Mikel , Campa Francisco J. , Herrero Saioa , Macho Erik , Pinto Charles
Published: March 2024

In this study, the novel mobile dynamometric platform, OREKA, was utilized to perform an extensive analysis of the centre of pressure behaviour during different tilt motion exercises. This platform is based on a parallel manipulator mechanism and can perform rotations around both horizontal axes and a vertical translation. A group of participants took part in an experimental campaign involving the completion of a set of exercises. The aim was to evaluate the platform’s potential practical application and investigate the impact of visual on-screen feedback on centre of pressure motion through multiple balance indicators. The use of the OREKA platform enables the study of the impact on a user’s balance control behaviour under different rotational perturbations, depending on the availability of real-time visual feedback on a screen. Furthermore, it presented data identifying postural control variations among clinically healthy individuals. These findings are fundamental to comprehending the dynamics of body balance. Further investigation is needed to explore these initial findings and fully unlock the potential of the OREKA platform for balance assessment methodologies.
Optical Sensor System for 3D Jones Matrix Reconstruction of Optical Anisotropy Maps of Self-Assembled Polycrystalline Soft Matter Films
Authors: Wójcik Waldemar , Hu Zhengbing , Ushenko Yuriy , Smolarz Andrzej , Soltys Iryna , Dubolazov Oleksander , Ushenko Oleksander , Litvinenko Olexandra , Mikirin Ivan , Gordey Ivan , Pavlyukovich Oleksandr , Pavlov Sergii , Pavlyukovich Natalia , Amirgaliyeva Saltanat , Kalizhanova Aliya , Aitkulov Zhalau
Published: March 2024

Our work uses a polarization matrix formalism to analyze and algorithmically represent optical anisotropy by open dehydration of blood plasma films. Analytical relations for Jones matrix reconstruction of optical birefringence maps of protein crystal networks of dehydrated biofluid films are found. A technique for 3D step-by-step measurement of the distributions of the elements of the Jones matrix or Jones matrix images (JMI) of the optically birefringent structure of blood plasma films (BPF) has been created. Correlation between JMI maps and corresponding birefringence images of dehydrated BPF and saliva films (SF) obtained from donors and prostate cancer patients was determined. Within the framework of statistical analysis of layer-by-layer optical birefringence maps, the parameters most sensitive to pathological changes in the structure of dehydrated films were found to be the central statistical moments of the 1st to 4th orders. We physically substantiated and experimentally determined the sensitivity of the method of 3D polarization scanning technique of BPF and SF preparations in the diagnosis of endometriosis of uterine tissue.
Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data
Authors: Guo Wei , Xu Li , Wang Tian , Zhao Danyang , Tang Xujing
Published: March 2024

Conventional point prediction methods encounter challenges in accurately capturing the inherent uncertainty associated with photovoltaic power due to its stochastic and volatile nature. To address this challenge, we developed a robust prediction model called QRKDDN (quantile regression and kernel density estimation deep learning network) by leveraging historical meteorological data in conjunction with photovoltaic power data. Our aim is to enhance the accuracy of deterministic predictions, interval predictions, and probabilistic predictions by incorporating quantile regression (QR) and kernel density estimation (KDE) techniques. The proposed method utilizes the Pearson correlation coefficient for selecting relevant meteorological factors, employs a Gaussian Mixture Model (GMM) for clustering similar days, and constructs a deep learning prediction model based on a convolutional neural network (CNN) combined with a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The experimental results obtained using the dataset from the Australian DKASC Research Centre unequivocally demonstrate the exceptional performance of QRKDDN in deterministic, interval, and probabilistic predictions for photovoltaic (PV) power generation. The effectiveness of QRKDDN was further validated through ablation experiments and comparisons with classical machine learning models.
Effect of Prenatal Yoga versus Moderate-Intensity Walking on Cardiorespiratory Adaptation to Acute Psychological Stress: Insights from Non-Invasive Beat-to-Beat Monitoring
Authors: Lučovnik Miha , Lackner Helmut K. , Žebeljan Ivan , Moertl Manfred G. , Dinevski Izidora Vesenjak , Mahlmann Adrian , Dinevski Dejan
Published: March 2024

We recently reported enhanced parasympathetic activation at rest throughout pregnancy associated with regular yoga practice. The present study presents a secondary analysis of data collected within a prospective cohort study of 33 pregnant women practicing yoga once weekly throughout pregnancy and 36 controls not involved in formal pregnancy exercise programs. The objective was to assess the impact of prenatal yoga on the autonomic nervous system stress response. Healthy pregnant women with singleton pregnancies were recruited in the first trimester. There was no significant difference in the maternal body mass index (BMI) between the yoga group and the controls (24.06 ± 3.55 vs. 23.74 ± 3.43 kg/m2, p = 0.693). Women practicing yoga were older (28.6 ± 3.9 vs. 31.3 ± 3.5 years, p = 0.005) and more often nulliparous (26 (79%) vs. 18 (50%), p = 0.001). We studied heart rate variability (HRV) parameters in the time domain (SDNN, standard deviation of regular R-R intervals, and RMSSD, square root of mean squared differences of successive R-R intervals) and frequency domain (ln(LF/HF), natural logarithm of low-frequency to high-frequency power), as well as synchronization indices of heart rate, blood pressure and respiration during and immediately following acute psychological stress of a standardized mental challenge test. Measurements were performed once per trimester before and after yoga or a 30 min moderate-intensity walk. Statistical comparison was performed using three-way analyses of variance (p < 0.05 significant). Time domain HRV parameters during and following mental challenge in the yoga group were significantly higher compared to the controls regardless of the trimester (F = 7.22, p = 0.009 for SDNN and F = 9.57, p = 0.003 for RMSSD, respectively). We observed no significant differences in the yoga group vs. the controls in terms of ln(LF/HF) and synchronization indices. Regular prenatal yoga practice was associated with a significantly reduced sympathetic response to mental challenge and quicker recovery after acute psychological stress. These effects persisted throughout pregnancy with regular practice.
Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed
Authors: Chen Zhipeng , Li Haojie , Yu Hang , Zhao Yuan , Zhang Chuanhao , Zhang He
Published: March 2024

Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV’s flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a ‘multi-level time window + threshold’ approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.
Protecting FPGA-Based Cryptohardware Implementations from Fault Attacks Using ADCs
Authors: Potestad-Ordóñez Francisco Eugenio , Casado-Galán Alejandro , Tena-Sánchez Erica
Published: March 2024

The majority of data exchanged between connected devices are confidential and must be protected against unauthorized access. To ensure data protection, so-called cryptographic algorithms are used. These algorithms have proven to be mathematically secure against brute force due to the key length, but their physical implementations are vulnerable against physical attacks. The physical implementation of these algorithms can result in the disclosure of information that can be used to access confidential data. Some of the most powerful hardware attacks presented in the literature are called fault injection attacks. These attacks involve introducing a malfunction into the normal operation of the device and then analyzing the data obtained by comparing them with the expected behavior. Some of the most common methods for injecting faults are the variation of the supply voltage and temperature or the injection of electromagnetic pulses. In this paper, a hardware design methodology using analog-to-digital converters (ADCs) is presented to detect attacks on cryptocircuits and prevent information leakage during fault injection attacks. To assess the effectiveness of the proposed design approach, FPGA-based ADC modules were designed that detect changes in temperature and supply voltage. Two setups were implemented to test the scheme against voltage and temperature variations and injections of electromagnetic pulses. The results obtained demonstrate that, in 100% of the cases, when the correct operating voltage and temperature range were established, the detectors could activate an alarm signal when the cryptographic module was attacked, thus avoiding confidential information leakage and protecting data from being exploited.
A Fairness-Enhanced Federated Learning Scheduling Mechanism for UAV-Assisted Emergency Communication
Authors: Zhu Chun , Shi Ying , Zhao Haitao , Chen Keqi , Zhang Tianyu , Bao Chongyu
Published: March 2024

As the frequency of natural disasters increases, the study of emergency communication becomes increasingly important. The use of federated learning (FL) in this scenario can facilitate communication collaboration between devices while protecting privacy, greatly improving system performance. Considering the complex geographic environment, the flexible mobility and large communication radius of unmanned aerial vehicles (UAVs) make them ideal auxiliary devices for wireless communication. Using the UAV as a mobile base station can better provide stable communication signals. However, the number of ground-based IoT terminals is large and closely distributed, so if all of them transmit data to the UAV, the UAV will not be able to take on all of the computation and communication tasks because of its limited energy. In addition, there is competition for spectrum resources among many terrestrial devices, and all devices transmitting data will bring about an extreme shortage of resources, which will lead to the degradation of model performance. This will bring indelible damage to the rescue of the disaster area and greatly threaten the life safety of the vulnerable and injured. Therefore, we use user scheduling to select some terrestrial devices to participate in the FL process. In order to avoid the resource waste generated by the terrestrial device resource prediction, we use the multi-armed bandit (MAB) algorithm for equipment evaluation. Considering the fairness issue of selection, we try to replace the single criterion with multiple criteria, using model freshness and energy consumption weighting as reward functions. The state of the art of our approach is demonstrated by simulations on the datasets.
Hybrid Multi-Infeed Receiver Line Longitudinal Protection Scheme Based on Voltage Waveform Comprehensive Distance Similarity
Authors: Gao Shuping , Li Xiaofang , Song Guobing , Zheng Han , Duan Yunqing
Published: March 2024

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating a line commutated converter-voltage source converter (LCC-VSC) and a line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data were simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced.
Dynamic Failure Experimental Study of a Gravity Dam Model on a Shaking Table and Analysis of Its Structural Dynamic Characteristics
Authors: Qiu Jianchun , He Wenqin , Zheng Dongjian , Xu Yanxin , Guo Shaolong , Ma Tianxiao , Xu Pengcheng , Liu Yongtao
Published: March 2024

Investigating the dynamic response patterns and failure modes of concrete gravity dams subjected to strong earthquakes is a pivotal area of research for addressing seismic safety concerns associated with gravity dam structures. Dynamic shaking table testing has proven to be a robust methodology for exploring the dynamic characteristics and failure modes of gravity dams. This paper details the dynamic test conducted on a gravity dam model on a shaking table. The emulation concrete material, featuring high density, low dynamic elastic modulus, and appropriate strength, was meticulously designed and fabricated. Integrating the shaking table conditions with the model material, a comprehensive gravity dam shaking table model test was devised to capture the dynamic response of the model under various dynamic loads. Multiple operational conditions were carefully selected for in-depth analysis. Leveraging the dynamic strain responses, the progression of damage in the gravity dam model under these diverse conditions was thoroughly examined. Subsequently, the recorded acceleration responses were utilized for identifying dynamic characteristic parameters, including the acceleration amplification factor in the time domain, acceleration response spectrum characteristics in the frequency domain, and modal parameters reflecting the inherent characteristics of the structure. To gain a comprehensive understanding, a comparative analysis was performed by aligning the observed damage development with the identified dynamic characteristic parameters, and the sensitivity of these identified parameters to different levels of damage was discussed. The findings of this study not only offer valuable insights for conducting and scrutinizing shaking table experiments on gravity dams but also serve as crucial supporting material for identifying structural dynamic characteristic parameters and validating damage diagnosis methods for gravity dam structures.
Optimal Sensor Placement for Vibration-Based Damage Localization Using the Transmittance Function
Authors: Zacharakis Ilias , Giagopoulos Dimitrios
Published: March 2024

A methodology for optimal sensor placement is presented in the current work. This methodology incorporates a damage detection framework with simulated damage scenarios and can efficiently provide the optimal combination of sensor locations for vibration-based damage localization purposes. A classic approach in vibration-based methods is to decide the sensor locations based, either directly or indirectly, on the modal information of the structure. While these methodologies perform very well, they are designed to predict the optimal locations of single sensors. The presented methodology relies on the Transmittance Function. This metric requires only output information from the testing procedure and is calculated between two acceleration signals from the structure. As such, the outcome of the presented method is a list of optimal combinations of sensor locations. This is achieved by incorporating a damage detection framework that has been developed and tested in the past. On top of this framework, a new layer is added that evaluates the sensitivity and effectiveness of all possible sensor location combinations with simulated damage scenarios. The effectiveness of each sensor combination is evaluated by calling the damage detection framework and feeding as inputs only a specific combination of acceleration signals each time. The final output is a list of sensor combinations sorted by their sensitivity.
Minimalist Deployment of Neural Network Equalizers in a Bandwidth-Limited Optical Wireless Communication System with Knowledge Distillation
Authors: Zhu Yiming , Wei Yuan , Chen Chaoxu , Chi Nan , Shi Jianyang
Published: March 2024

An equalizer based on a recurrent neural network (RNN), especially with a bidirectional gated recurrent unit (biGRU) structure, is a good choice to deal with nonlinear damage and inter-symbol interference (ISI) in optical communication systems because of its excellent performance in processing time series information. However, its recursive structure prevents the parallelization of the computation, resulting in a low equalization rate. In order to improve the speed without compromising the equalization performance, we propose a minimalist 1D convolutional neural network (CNN) equalizer, which is reconverted from a biGRU with knowledge distillation (KD). In this work, we applied KD to regression problems and explain how KD helps students learn from teachers in solving regression problems. In addition, we compared the biGRU, 1D-CNN after KD and 1D-CNN without KD in terms of Q-factor and equalization velocity. The experimental data showed that the Q-factor of the 1D-CNN increased by 1 dB after KD learning from the biGRU, and KD increased the RoP sensitivity of the 1D-CNN by 0.89 dB with the HD-FEC threshold of 1 × 10−3. At the same time, compared with the biGRU, the proposed 1D-CNN equalizer reduced the computational time consumption by 97% and the number of trainable parameters by 99.3%, with only a 0.5 dB Q-factor penalty. The results demonstrate that the proposed minimalist 1D-CNN equalizer holds significant promise for future practical deployments in optical wireless communication systems.
Deep Neural Network-Based Phase-Modulated Continuous-Wave LiDAR
Authors: Zhang Hao , Wang Yubing , Zhang Mingshi , Song Yue , Qiu Cheng , Lei Yuxin , Jia Peng , Liang Lei , Zhang Jianwei , Qin Li , Ning Yongqiang , Wang Lijun
Published: March 2024

LiDAR has high accuracy and resolution and is widely used in various fields. In particular, phase-modulated continuous-wave (PhMCW) LiDAR has merits such as low power, high precision, and no need for laser frequency modulation. However, with decreasing signal-to-noise ratio (SNR), the noise on the signal waveform becomes so severe that the current methods to extract the time-of-flight are no longer feasible. In this paper, a novel method that uses deep neural networks to measure the pulse width is proposed. The effects of distance resolution and SNR on the performance are explored. Recognition accuracy reaches 81.4% at a 0.1 m distance resolution and the SNR is as low as 2. We simulate a scene that contains a vehicle, a tree, a house, and a background located up to 6 m away. The reconstructed point cloud has good fidelity, the object contours are clear, and the features are restored. More precisely, the three distances are 4.73 cm, 6.00 cm, and 7.19 cm, respectively, showing that the performance of the proposed method is excellent. To the best of our knowledge, this is the first work that employs a neural network to directly process LiDAR signals and to extract their time-of-flight.
Data Augmentation Techniques for Accurate Action Classification in Stroke Patients with Hemiparesis
Authors: Oh Youngmin
Published: March 2024

Stroke survivors with hemiparesis require extensive home-based rehabilitation. Deep learning-based classifiers can detect actions and provide feedback based on patient data; however, this is difficult owing to data sparsity and heterogeneity. In this study, we investigate data augmentation and model training strategies to address this problem. Three transformations are tested with varying data volumes to analyze the changes in the classification performance of individual data. Moreover, the impact of transfer learning relative to a pre-trained one-dimensional convolutional neural network (Conv1D) and training with an advanced InceptionTime model are estimated with data augmentation. In Conv1D, the joint training data of non-disabled (ND) participants and double rotationally augmented data of stroke patients is observed to outperform the baseline in terms of F1-score (60.9% vs. 47.3%). Transfer learning pre-trained with ND data exhibits 60.3% accuracy, whereas joint training with InceptionTime exhibits 67.2% accuracy under the same conditions. Our results indicate that rotational augmentation is more effective for individual data with initially lower performance and subset data with smaller numbers of participants than other techniques, suggesting that joint training on rotationally augmented ND and stroke data enhances classification performance, particularly in cases with sparse data and lower initial performance.
Evaluation of Pipe Thickness by Magnetic Hammer Test with a Tunnel Magnetoresistive Sensor
Authors: Ito Jun , Igarashi Yudai , Odagiri Ryota , Suzuki Shigetaka , Wagatsuma Hiroshi , Sugiyama Kazuhiro , Oogane Mikihiko
Published: March 2024

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect weak magnetic fields generated by the natural vibration of a pipe with a high signal-to-noise ratio. In this study, several steel pipes with different wall thicknesses were measured using a TMR sensor to demonstrate the superiority of MHT. The results of the measurement show that wall thickness can be evaluated with the accuracy of several tens of microns from the change in the natural vibration frequency of the specimen pipe. The pipes were also inspected underwater using a waterproofed TMR sensor, which demonstrated an accuracy of less than 100 μm. The validity of these results was by simulating the shielding of magnetic fields and vibration of the pipes with the finite element method (FEM) analysis. The proposed noncontact, fast, and accurate method for thickness testing of long-distance pipes will contribute to unmanned, manpower-saving nondestructive testing (NDT) in the future.
An Underwater Source Localization Method Using Bearing Measurements
Authors: Li Peijuan , Liu Yiting , Yan Tingwu , Yang Shutao , Li Rui
Published: March 2024

Angle-of-arrival (AOA) measurements are often used in underwater acoustical localization. Different from the traditional AOA model based on azimuth and elevation measurements, the AOA model studied in this paper uses bearing measurements. It is also often used in the Ultra-Short Baseline system (USBL). However, traditional acoustical localization needs additional range information. If the range information is unavailable, the closed-form solution is difficult to obtain only with bearing measurements. Thus, a localization closed-form solution using only bearing measurements is explored in this article. A pseudo-linear measurement model between the source position and the bearing measurements is derived, and considering the nonlinear relationship of the parameters, a weighted least-squares optimization equation based on multiple constraints is established. Different from the traditional two-step least-squares method, the semidefinite programming (SDP) method is designed to obtain the initial solution, and then a bias compensation method is proposed to further minimize localization errors based on the SDP result. Numerical simulations show that the performance of the proposed method can achieve Cramer–Rao lower bound (CRLB) accuracy. The field test also proves that the proposed method can locate the source position without range measurements and obtain the highest positioning accuracy.
Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation
Authors: Shi Yeping , Shi Yunbo , Niu Haodong , Liu Jinzhou , Sun Pengjiao
Published: March 2024

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.
Broadband Eddy Current Measurement of the Sheet Resistance of GaN Semiconductors
Authors: Belkacem Ghania , Loete Florent , Phulpin Tanguy
Published: March 2024

Although the classical four-point probe method usually provides adequate results, it is in many cases inappropriate for the measurement of thin sheet resistance, especially in the case of a buried conductive layer or if the surface contacts are oxidized/degraded. The surface concentration of dislocation defects in GaN samples is known to challenge this kind of measurement. For the GaN sample presented in this study, it even totally impaired the ability of this method to even provide results without a prior deposition of gold metallic contact pads. In this paper, we demonstrate the benefits of using a new broadband multifrequency noncontact eddy current method to accurately measure the sheet resistance of a complicated-to-measure epitaxy-grown GaN-doped sample. The benefits of the eddy current method compared to the traditional four-point method are demonstrated. The multilayer-doped GaN sample is perfectly evaluated, which will allow further development applications in this field. The point spread function of the probe used for this noncontact method was also evaluated using a 3D finite element model using CST-Studio Suite simulation software 2020 and experimental measurements.
Microstructure-Based CZE Model for Crack Initiation and Growth in CGI: Effects of Graphite-Particle Morphology and Spacing
Authors: Luo Xingling , Baxevanakis Konstantinos P. , Silberschmidt Vadim V.
Published: March 2024

Compacted graphite iron (CGI) is an engineering material with the potential to fill the application gap between flake- and spheroidal-graphite irons thanks to its unique microstructure and competitive price. Despite its wide use and considerable past research, its complex microstructure often leads researchers to focus on models based on representative volume elements with multiple particles, frequently overlooking the impact of individual particle shapes and interactions between the neighbouring particles on crack initiation and propagation. This study focuses on the effects of graphite morphology and spacing between inclusions on the mechanical and fracture behaviours of CGI at the microscale. In this work, 2D cohesive-zone-element-based models with different graphite morphologies and spacings were developed to investigate the mechanical behaviour as well as crack initiation and propagation. ImageJ and scanning electron microscopy were used to characterise and analyse the microstructure of CGI. In simulations, both graphite particles and metallic matrix were assumed isotropic and ductile. Cohesive zone elements (CZEs) were employed in the whole domain studied. It was found that graphite morphology had a negligible effect on interface debonding but nodular inclusions can notably enhance the stiffness of the material and effectively impede the propagation of cracks within the matrix. Besides, a small distance between graphite particles accelerates the crack growth. These results can be used to design and manufacture better metal-matrix composites.
Inequality Evolution of Economic Gains and Environmental Losses in Chinese Interprovincial Trade during 2007–2017
Authors: Qian Yuan , Zheng Huan , Cao Xin , Li Ting , Zhao Lin , Wang Sulian
Published: March 2024

A reduction in SO2 emissions is important for sustainable development. However, China uses territorial emissions to determine its SO2 emission mitigation targets, ignoring the emissions that are incorporated into interregional trade. In addition to the transfer of pollution, value added can also be exchanged with trade, resulting in environmental inequality among regions. In this study, we estimate the embodied SO2 emissions (ESE) under production-, consumption-, and income-based accounting principles and quantify the embodied value added (EVA) within the interprovincial trade during 2007–2017 using the multi-regional input–output (MRIO) model. The inequalities between the ESE and EVA are further investigated using the Gini coefficients method and the regional environmental index method. The results indicate that ~34.7–43.4% of SO2 emissions and ~24.6–30.8% of value added were triggered by interprovincial trade. Furthermore, developed provinces mainly outsourced their emissions to less developed provinces, particularly to those nearby. Concerning the value added, it was mainly outsourced from less developed provinces to developed provinces during 2007–2010, with no clear patterns observed during 2012–2017. The study’s findings indicate that the high inequality of SO2 emissions and value added also occurred between developed and less developed provinces. Particularly, the Gini coefficients of value inflow–SO2 outflow (VISO) were larger than those of value outflow–SO2 inflow (VOSI), which indicated that, besides the direct emissions, consumption-based emissions should be considered when allocating the environmental responsibility among provinces. These findings are valuable for shaping pathways towards achieving regional economic coordination and sustainable development.
Proposal for Applying Sustainable Drainage Systems (SuDSs) as a Strategic Business Unit at a Military Development Located in Southern Europe (Córdoba, Spain): “Project BLET”
Authors: Lanceta Gutiérrez Antonio , Perales-Momparler Sara , Rico Cortés Miguel
Published: March 2024

The Spanish Army is planning to create a new Logistics Base in Córdoba, in a development known as “Project BLET” (85 agricultural hectares, developed into an industrial zone). The Sustainability Concept proposed here is framed within the context of the strategic management process as a Strategic Business Unit. Aligned with 2030 AGENDA, the above mentioned process focuses on efficient water management and is providing a drainage strategy based on the use of Sustainable Drainage Systems (SuDSs), as well as a water management plan that aims to consider rainfall as a key non-potable water resource. The purpose is to prepare a roadmap, based on the Project, and a methodology that guarantees the viability of the SuDS and its non-potable rainwater use. The final result could be a sustainable military logistics hub in Southern Europe, which would stand out for being a pioneer in the treatment of rainwater and have sustainability features that are intended to be certified and assessed by different institutions.
Wind Flow and Its Interaction with a Mobile Solar PV System Mounted on a Trailer
Authors: Eslami Majd Alireza , Adebayo David S. , Tchuenbou-Magaia Fideline , Willetts James , Nwosu Dave , Matthews Zackery , Ekere Nduka Nnamdi
Published: March 2024

Efficient implementation of clean energy technologies is paramount, with mobile solar PV systems on trailers (MSPTs) emerging as pivotal solutions, particularly in regions with limited power grid access. This endeavour is vital for meeting escalating electricity demands and aligning with the UN Sustainable Development Goal (SDG), aimed at ensuring dependable and sustainable energy provision in developing countries. This study investigates the aerodynamic behaviour of a designed MSPT using numerical simulation and experimental methods, thereby offering optimization potential for MSPT design and enhancing overall performance and reliability. Specifically, the study focuses on the effects of wind velocity and tilt angles on the drag and lift forces, as well as drag and lift coefficients on the panel used in the MSPT system. The overall wind force on the entire MSPT, including nine large solar PV panels, is scrutinised, considering combined wind flow and system geometry effects. The numerical investigations were conducted using ANSYS-Fluent software (version 2022/R2) and experimental testing was performed within the C15-10 Wind Tunnel, utilizing scaled-down models to validate the accuracy of the simulation. The findings from the numerical investigations showed an increased turbulence caused by gaps between panels, resulting in almost 62% higher suction flow velocity and 22% higher suction pressure compared to a single panel. Drag and lift forces on the entire MSPT were approximately 6.7 and 7.8 times greater than those on a single panel with the same 30-degree tilt angle, respectively. The findings revealed that scaling forces on a single panel is insufficient for accurately predicting the aerodynamic forces on the entire MSPT. The insights and the knowledge from this study pave the way for further improvements in mobile solar PV technology.
A Study on Immersion and Intention to Pay in AR Broadcasting: Validating and Expanding the Hedonic Motivation System Adoption Mode
Authors: Tu Jui-Che , Jia Xi-Hui
Published: March 2024

With the rapid growth of online entertainment live streaming, how to continuously innovate and achieve long-term sustainability has become a major challenge for the industry. Augmented reality (AR) technology offers users immersive interactive experiences and potentially addresses this challenge. The aim of this study is to explore how AR technology influences key components of user online experience—immersion and intention to pay—using survey data. Building upon the Hedonic Motivation System Adoption Model (HMSAM), this research incorporates aesthetic variables to theoretically expand the model in order to gain a deeper understanding of the mechanisms influencing user behavior. A questionnaire survey was conducted to collect 450 valid samples. Detailed analysis was conducted using structural equation modeling. The findings confirm that aesthetic design significantly impacts users’ judgments of content value and perceived ease of use, generating positive effects at the perceptual level. Additionally, AR applications enhance the quality of user experience, thereby stimulating intrinsic motivations such as curiosity and joy. Further analysis indicates that users’ curiosity and perceived behavioral control directly influence the level of immersion and intention to pay. Overall, the research results offer important insights into industry applications. This study successfully expands the HMSAM theoretically by incorporating aesthetic variables to enhance the explanatory power of user judgment mechanisms. The analytical framework proposed aids in understanding the potential mechanisms of new technologies on customer experience and commercial value creation. The research findings provide guidelines for technological design and marketing strategies of streaming platforms.
Sustainability-Driven Supplier Selection: Insights from Supplier Life Value and Z-Numbers
Authors: Tohidi Mehran , Homayoun Saeid , RezaHoseini Ali , Ehsani Razieh , Bagherpour Morteza
Published: March 2024

In recent years, the strategic selection of the most suitable supplier within the supply chain has garnered increasing attention. Incorporating vital criteria like sustainable development further complicates this decision-making process. Companies and manufacturing facilities recognize the pivotal role of suppliers in their overall success and aim for mutually advantageous partnerships. Establishing long-term relationships with suppliers can yield benefits for both parties. However, supplier selection is intricate, often transpiring within an environment of limited information. Consequently, evaluating and selecting organizational suppliers necessitate methodologies yielding more dependable and pragmatic results due to the uncertainties inherent in expert judgments. This study introduces Supplier Life Cycle Value (SLV) criteria for extended partnerships with suppliers and sustainability metrics for selecting “industrial equipment suppliers”. The Hierarchical Best-Worst Method (HBWM) is then applied to determine Sustainable Supplier Life Value (SSLV) criteria weights. Subsequently, employing the PROMETHEE-GAIA approach, suppliers are systematically ranked and comprehensively analyzed. To account for the inherent uncertainty in expert judgments, this study incorporates fuzzy numbers enriched with probability and reliability parameters (Z-Numbers) by introducing novel verbal spectra for supplier evaluation. This facilitates more effective decision making in supplier management. The findings underscore the significance of considering the supplier’s longevity beyond economic metrics, emphasizing the importance of sustained supplier participation. Moreover, the varying outcomes across definite and fuzzy scenarios, accounting for reliability (Z-Numbers), underscore the impact of data uncertainty on decision making. Given that fuzzy numbers incorporating reliability (Z-Numbers) encompass the confidence probability within the unclear number, they offer a more robust and realistic representation of real-world scenarios.
Monitoring of Ventilation, Portable Air Cleaner Operation, and Particulate Matter in California Classrooms: A Pilot Study
Authors: Chen Wenhao , Wang Zhong-Min , Peerless Kyle , Ullman Elon , Mendell Mark J. , Putney David , Wagner Jeff , Kumagai Kazukiyo
Published: March 2024

Interest in improving ventilation and indoor air quality (IAQ) in California schools has grown since the COVID-19 pandemic. This paper presents a field protocol for simultaneous monitoring of usage patterns of in-room portable air cleaners (PACs), indoor and outdoor concentrations and composition of particulate matter (PM), and CO2 as an indicator of outdoor air ventilation rates (VRs). This protocol was implemented for a 7-week pilot study in four occupied California classrooms in 2022. Monitoring results showed that VRs and indoor PM were generally well maintained in the classrooms studied. One classroom had much higher overall VRs, as well as higher average indoor PM2.5 concentrations compared to similar classrooms, suggesting a possible strong impact of window/door opening behavior on both VRs and indoor PM. The actual use patterns of PACs in these classrooms varied significantly. No clear correlations were observed between PAC use patterns and indoor PM2.5 concentrations in this pilot study, possibly due to low outdoor PM2.5 concentrations and already efficient central filtration (i.e., MERV 13 filters in central ventilation systems). Information gathered through such field monitoring can help schools to understand the actual classroom ventilation and IAQ conditions and best allocate resources to classrooms that need further IAQ improvements.
Sustainable Development of Mining Regions in the Arctic Zone of the Russian Federation
Authors: Kirsanova Natalia , Nevskaya Marina , Raikhlin Semyon
Published: March 2024

The Arctic’s specific conditions require increased attention to natural and human capital. Therefore, implementing the principles of the sustainable development concept, balancing economic, social, and environmental goals is of paramount importance. Mining is at the heart of the strategy for the socio-economic development of Russia’s Arctic territories. This study’s objective is topical: to justify measures lifting the restrictions on sustainable development of AZRF (the Arctic Zone of the Russian Federation) mining regions. The authors propose a method to identify AZRF regions where mining determines the socio-economic development level (Komi Republic, Nenets Autonomous District, Yamalo-Nenets Autonomous District, Yakutia, and Chukotka Autonomous District). The multi-factor regression analysis conducted confirms the hypothesis that living standards and achievement of social standards in the AZRF mining regions depend on the income and fiscal capacity levels, which makes the state more responsible for the region’s development. The authors prove the expediency of reconsidering the proportions of rental income redistributed between the budgets of the AZRF mining regions and the federal budget in favor of the former, until migration and natural population growth become positive and reach the target indicators of strategies of socio-economic development. The study’s results can be used to justify the state policy while elaborating strategies for AZRF development.
Navigating the Eco-Design Paradox: Criteria and Methods for Sustainable Eco-Innovation Assessment in Early Development Stages
Authors: Peigné Sarah , Ben Rejeb Helmi , Monnier Elise , Zwolinski Peggy
Published: March 2024

This paper examines the growing importance of eco-innovation in aligning sustainability with technological development. It explores the ‘eco-design paradox’, which emphasizes the tension between the need for flexible design and the availability of data required for assessing environmental impacts during early innovation stages. This paradox presents a challenge: the lack of detailed data can have an impact on the ability to make sustainable optimal design decisions as designs are still fluid. The study investigates the essential sustainability aspects to be evaluated in the initial phases of innovation and explores the involvement of decision-makers and entrepreneurs in the sustainability assessment process. The paper uses a robust action research methodology to examine various eco-innovation diagnostic tools in collaboration with two French Institutes of Technology (FITs). A comparative analysis is conducted to assess these tools’ efficacity in evaluating several dimensions of sustainability, including environmental, social, and economic aspects. Our investigation identifies key criteria that are crucial for early-stage sustainability assessments, such as innovation description, systemic vision, functionality, and stakeholder involvement. The findings will guide policy makers, researchers, and practitioners in the field of eco-innovation by providing a comprehensive framework for evaluating and promoting sustainable innovations in their early stages. The insights from our findings show how important expert knowledge is in the eco-innovation diagnostic process. They also demonstrate the need for a more integrated approach to eco-innovation.
Three-Dimensional Point Cloud Denoising for Tunnel Data by Combining Intensity and Geometry Information
Authors: Bao Yan , Wen Yucheng , Tang Chao , Sun Zhe , Meng Xiaolin , Zhang Dongliang , Wang Li
Published: March 2024

At present, three-dimensional laser scanners are used to scan subway shield tunnels and generate point cloud data as the basis for extracting a variety of information about tunnel defects. However, there are obstacles in the tunnel such as pipelines, tracks, and signaling systems that cause noise in the point cloud. Usually, the data of the tunnel point cloud are huge, and the efficiency of artificial denoising is low. Faced with this problem, based on the respective characteristics of the geometric shape and reflection intensity of the tunnel point cloud and their correlation, this paper proposes a tunnel point cloud denoising method. The method includes the following three parts: reflection intensity threshold denoising, joint shape and reflection intensity denoising, and shape denoising. Through the experiment on the single-ring segment point cloud of a shield tunnel, the method proposed in this paper takes 2 min to remove 99.77% of the noise in the point cloud. Compared with manual denoising, the method proposed in this paper takes two fifteenths of the time to achieve the same denoising effect. The method proposed in this paper meets the requirements of a tunnel point cloud data survey. Thus, it provides support for the efficient, accurate, and automatic daily maintenance and surveys of tunnels.
Influence of the Steel Slag Particle Size on the Mechanical Properties and Microstructure of Concrete
Authors: Li Maohui , Lu Youjun , Liu Yajuan , Chu Jingjun , Zhang Tongsheng , Wang Wei
Published: March 2024

The present paper probes into the influence of the steel slag particle size on the mechanical properties and microstructure of concrete, with steel slag serving as the primary raw material. Steel slag with different particle sizes was selected as the partial substitute material for concrete by mechanical grinding. The influence of steel slag on the compressive strength, bending strength, and microstructure of concrete was determined by laser particle size analyzer, specific surface area analyzer, strength experiment, X-ray diffraction (XRD), and scanning electron microscope (SEM). The results show that mechanical grinding has significant effects on the particle size distribution and specific surface area of the steel slag. The optimal grinding time is 20 min and the specific surface area is 0.65 m2/g. D10, D50 and D90 are 0.91 μm, 16.57 μm and 46.40 μm, respectively. The steel slag with a fine particle size can better fill the pores in concrete and improve the compactness, thus enhancing the mechanical properties of concrete. The change in the steel slag particle size does not change the type of hydration products, but the smaller the particle size of steel slag, the better the gelling activity, the larger the hydration products, the denser the structure, and the better the mechanical properties. Therefore, the present study provides an important theoretical basis and practical guidance for the application of steel slag as an additive in the concrete industry.