bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-Supervised Learning Method for the Augmentation of an Incomplete Image-Based Inventory of Earthquake-Induced Soil Liquefaction Surface Effects
Authors: Asadi Adel , Baise Laurie Gaskins , Sanon Christina , Koch Magaly , Chatterjee Snehamoy , Moaveni Babak
Published: October 2023

Soil liquefaction often occurs as a secondary hazard during earthquakes and can lead to significant structural and infrastructure damage. Liquefaction is most often documented through field reconnaissance and recorded as point locations. Complete liquefaction inventories across the impacted area are rare but valuable for developing empirical liquefaction prediction models. Remote sensing analysis can be used to rapidly produce the full spatial extent of liquefaction ejecta after an event to inform and supplement field investigations. Visually labeling liquefaction ejecta from remotely sensed imagery is time-consuming and prone to human error and inconsistency. This study uses a partially labeled liquefaction inventory created from visual annotations by experts and proposes a pixel-based approach to detecting unlabeled liquefaction using advanced machine learning and image processing techniques, and to generating an augmented inventory of liquefaction ejecta with high spatial completeness. The proposed methodology is applied to aerial imagery taken from the 2011 Christchurch earthquake and considers the available partial liquefaction labels as high-certainty liquefaction features. This study consists of two specific comparative analyses. (1) To tackle the limited availability of labeled data and their spatial incompleteness, a semi-supervised self-training classification via Linear Discriminant Analysis is presented, and the performance of the semi-supervised learning approach is compared with supervised learning classification. (2) A post-event aerial image with RGB (red-green-blue) channels is used to extract color transformation bands, statistical indices, texture components, and dimensionality reduction outputs, and performances of the classification model with different combinations of selected features from these four groups are compared. Building footprints are also used as the only non-imagery geospatial information to improve classification accuracy by masking out building roofs from the classification process. To prepare the multi-class labeled data, regions of interest (ROIs) were drawn to collect samples of seven land cover and land use classes. The labeled samples of liquefaction were also clustered into two groups (dark and light) using the Fuzzy C-Means clustering algorithm to split the liquefaction pixels into two classes. A comparison of the generated maps with fully and manually labeled liquefaction data showed that the proposed semi-supervised method performs best when selected high-ranked features of the two groups of statistical indices (gradient weight and sum of the band squares) and dimensionality reduction outputs (first and second principal components) are used. It also outperforms supervised learning and can better augment the liquefaction labels across the image in terms of spatial completeness.
A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric
Authors: Ding Jiaqi , Zhao Xiaofeng , Yang Pinglv , Fu Yapeng
Published: October 2023

The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation.
Magnetoimpedance Effect in Cobalt-Based Amorphous Ribbons with an Inhomogeneous Magnetic Structure
Authors: Bukreev Dmitry A. , Derevyanko Michael S. , Semirov Alexander V.
Published: October 2023

The results of a computer simulation and experimental study of the magnetoimpedance effect (MI) in amorphous Co68.5Fe4.0Si15.0B12.5 and Co68.6Fe3.9Mo3.0Si12.0B12.5 ribbons in the ac frequency range from 0.01 to 100 MHz are presented. It was found that the maximum MI value exceeds 200%, which may be of interest in the development of magnetic field sensors. It is also shown that practically significant characteristics of the MI response strongly depend on the ac frequency, which is due to the inhomogeneous distribution of magnetic properties over the ribbon cross section. This distribution was studied using magnetoimpedance tomography based on the analysis of the experimental dependences of the reduced impedance on the ac frequency.
A Fast and Robust Lane Detection via Online Re-Parameterization and Hybrid Attention
Authors: Xie Tao , Yin Mingfeng , Zhu Xinyu , Sun Jin , Meng Cheng , Bei Shaoyi
Published: October 2023

Lane detection is a vital component of intelligent driving systems, offering indispensable functionality to keep the vehicle within its designated lane, thereby reducing the risk of lane departure. However, the complexity of the traffic environment, coupled with the rapid movement of vehicles, creates many challenges for detection tasks. Current lane detection methods suffer from issues such as low feature extraction capability, poor real-time detection, and inadequate robustness. Addressing these issues, this paper proposes a lane detection algorithm that combines an online re-parameterization ResNet with a hybrid attention mechanism. Firstly, we replaced standard convolution with online re-parameterization convolution, simplifying the convolutional operations during the inference phase and subsequently reducing the detection time. In an effort to enhance the performance of the model, a hybrid attention module is incorporated to enhance the ability to focus on elongated targets. Finally, a row anchor lane detection method is introduced to analyze the existence and location of lane lines row by row in the image and output the predicted lane positions. The experimental outcomes illustrate that the model achieves F1 scores of 96.84% and 75.60% on the publicly available TuSimple and CULane lane datasets, respectively. Moreover, the inference speed reaches a notable 304 frames per second (FPS). The overall performance outperforms other detection models and fulfills the requirements of real-time responsiveness and robustness for lane detection tasks.
Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning
Authors: De Cock Cedric , Tanghe Emmeric , Joseph Wout , Plets David
Published: October 2023

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body–tag–anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.
Study of Interference Detection of Rail Transit Wireless Communication System Based on Fourth-Order Cyclic Cumulant
Authors: Wang Peng , Yao Junliang , Pu Yong , Zhang Shuyuan , Wen Lu
Published: October 2023

The wireless communication system is used to provide dispatching, control, communication and other services for rail transit operations. In practice, interference from other wireless communication systems will affect the normal operation of trains, so it is an urgent problem to study the interference detection algorithms for rail transit applications. In this paper, the fourth-order cyclic cumulant (FOCC) of signals with different modulation modes is analyzed for the narrow-band wireless communications system of rail transit. Based on the analysis results, an adjacent-frequency interference detection algorithm is proposed according to the FOCC of the received signal within the predetermined cyclic frequency range. To detect interference with the same carrier frequency, a same-frequency interference detection algorithm using the relationship between the FOCC and the received power is proposed. The performance of the proposed detection algorithms in terms of correct rate and computational complexity is analyzed and compared with the traditional second-order statistical methods. Simulation results show that when an interference signal coexists with the expected signal, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the signal-to-noise ratio (SNR) is higher than 2 dB and −4 dB, respectively. Under the practical rail transit wireless channel with multipath propagation and the Doppler effect, the correct rates of the adjacent-frequency and the same-frequency interference detection algorithms are greater than 90% when the SNR is higher than 3 dB and 7 dB, respectively. Compared with the existing second-order statistical methods, the proposed method can detect both the adjacent-frequency and the same-frequency interference when the interference signals coexist with the expected signal. Although the computational complexity of the proposed method is increased, it is acceptable in the application of rail transit wireless communication interference detection.
The Performance Characterization and Optimization of Fiber-Optic Acoustic Pressure Sensors Based on the MOEMS Sensitized Structure
Authors: Zhou Ruling , Guan Chenggang , Lv Hui , Li Shasha , Lv Puchu , Qin Haixin , Chu Wenxiu , Zhou Yikai , Zhang Yihao , Li Xiaoqiang
Published: October 2023

In order to investigate the factors affecting the acoustic performance of the extrinsic Fabry–Perot interferometer (EFPI) fiber-optic acoustic pressure sensor and to effectively improve its detection capability, this paper enhances the sensor’s detection sensitivity by adding more sensitized rings to its acoustic pressure-sensitive film. Furthermore, a novel real-time coupled acoustic test method is proposed to simultaneously monitor the changes in the spectral and acoustic metrics of the sensor to characterize its overall performance. Finally, an EFPI-type fiber-optic acoustic pressure sensor was developed based on the Micro-Optical Electro-Mechanical System (MOEMS). The acoustic tests indicate that the optimized fiber-optic acoustic pressure sensor has a sensitivity as high as 2253.2 mV/Pa, and the acoustic overload point (AOP) and signal-to-noise ratios (SNRs) can reach 108.85 dB SPL and 79.22 dB, respectively. These results show that the sensor produced through performance characterization experiments and subsequent optimization has a very high acoustic performance index, which provides a scientific theoretical basis for improving the overall performance of the sensor and will have broad application prospects in the field of acoustic detection.
A New Lunar Dome Detection Method Based on Improved YOLOv7
Authors: Tian Yunxiang , Tian Xiaolin
Published: October 2023

Volcanism is an important geological evolutionary process on the Moon. The study of lunar volcanic features is of great significance and value to understanding the geological evolution of the Moon better. Lunar domes are one of the essential volcanic features of the Moon. However, the existing lunar dome detection methods are still traditional manual or semiautomatic identification approaches that require extensive prior knowledge and have a complex identification process. Therefore, this paper proposes an automatic detection method based on improved YOLOv7 for lunar dome detection. First, a new lunar dome dataset was created by digital elevation model (DEM) data, and the effective squeeze and excitation (ESE) attention mechanism module was added to the backbone and neck sections to reduce information loss in the feature map and enhance network expressiveness. Then, a new SPPCSPC-RFE module was proposed by adding the receptive field enhancement (RFE) module into the neck section, which can adapt to dome feature maps of different shapes and sizes. Finally, the bounding box regression loss function complete IOU (CIOU) was replaced by wise IOU (WIOU). The WIOU loss function improved the model’s performance for the dome detection effect. Furthermore, this study combined several data enhancement strategies to improve the robustness of the network. To evaluate the performance of the proposed model, we conducted several experiments using the dome dataset developed in this study. The experimental results indicate that the improved method outperforms related methods with a mean average precision (mAP@0.5) value of 88.7%, precision (P) value of 85.6%, and recall (R) value of 86.4%. This study provides an effective solution for lunar dome detection.
A Smartphone-Enabled Continuous Flow Digital Droplet LAMP Platform for High Throughput and Inexpensive Quantitative Detection of Nucleic Acid Targets
Authors: Ditchendorf Elijah , Ahmed Isteaque , Sepate Joseph , Priye Aashish
Published: October 2023

Molecular tests for infectious diseases and genetic anomalies, which account for significant global morbidity and mortality, are central to nucleic acid analysis. In this study, we present a digital droplet LAMP (ddLAMP) platform that offers a cost-effective and portable solution for such assays. Our approach integrates disposable 3D-printed droplet generator chips with a consumer smartphone equipped with a custom image analysis application for conducting ddLAMP assays, thereby eliminating the necessity for expensive and complicated photolithographic techniques, optical microscopes, or flow cytometers. Our 3D printing technique for microfluidic chips facilitates rapid chip fabrication in under 2 h, without the complications of photolithography or chip bonding. The platform’s heating mechanism incorporates low-powered miniature heating blocks with dual resistive cartridges, ensuring rapid and accurate temperature modulation in a compact form. Instrumentation is further simplified by integrating miniaturized magnification and fluorescence optics with a smartphone camera. The fluorescence quantification benefits from our previously established RGB to CIE-xyY transformation, enhancing signal dynamic range. Performance assessment of our ddLAMP system revealed a limit of detection at 10 copies/μL, spanning a dynamic range up to 104 copies/μL. Notably, experimentally determined values of the fraction of positive droplets for varying DNA concentrations aligned with the anticipated exponential trend per Poisson statistics. Our holistic ddLAMP platform, inclusive of chip production, heating, and smartphone-based droplet evaluation, provides a refined method compatible with standard laboratory environments, alleviating the challenges of traditional photolithographic methods and intricate droplet microfluidics expertise.
The Frequency-Variable Rotor-Blade-Based Two-Degree-of-Freedom Actuation Principle for Linear and Rotary Motion
Authors: Li Xiaotao , Wang Shengjiang , Peng Xiangyou , Xu Guan , Dong Jingshi , Tian Fengjun , Zhang Qiuyu
Published: October 2023

Piezoelectric accurate actuation plays an important role in industrial applications. The intrinsic frequency of previous actuators is invariable. However, variable frequency can approach the range near the low-intrinsic-frequency and realize a high actuation capability. The frequency-variable linear and rotary motion (FVLRM) principle is proposed for rotor-blade-based two-degree-of-freedom driving. Inertial force is generated by frequency-variable piezoelectric oscillators (FVPO), the base frequency and vibration modes of which are adjustable by the changeable mass and position of the mass block. The variable-frequency principle of FVPO and the FVLRM are recognized and verified by the simulations and experiments, respectively. The experiments show that the FVLRM prototype moves the fastest when the mass block is placed at the farthest position and the prototype is at the second-order intrinsic frequencies of 42 Hz and 43 Hz, achieving a linear motion of 3.52 mm/s and a rotary motion of 286.9 mrad/s. The actuator adopts a lower operating frequency of less than 60 Hz and has the function of adjusting the natural frequency. It can achieve linear and rotational motion with a larger working stroke with 140 mm linear movement and 360° rotation.
Active Reduction of Apparent Cable Capacitance
Authors: Melin Alexander , Roberts Michael , Kisner Roger
Published: October 2023

This paper presents a method for reducing apparent cable capacitance seen by sensors. The proposed method is designed for sensing in extreme environments including ultra-high temperatures, but can be applied in benign environments as well. By reducing the cable capacitance, high speed signals can be transmitted over longer distances, allowing the application of advanced control systems that require high bandwidth data for stable operation. A triaxial cable with an associated guard circuit is developed that actively reduces cable capacitance while also rejecting extraneous electric and magnetic interference on the signal. The active capacitance reduction method developed is tested experimentally and shown to reduce apparent cable capacitance to two percent of the static value.
A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm
Authors: Liu Shuai , Yang Yuqi , Qin Hui , Liu Guanjun , Qu Yuhua , Deng Shan , Gao Yuan , Li Jiangqiao , Guo Jun
Published: October 2023

An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.
Hybrid Path Planning for Unmanned Surface Vehicles in Inland Rivers Based on Collision Avoidance Regulations
Authors: Gao Pengcheng , Xu Pengfei , Cheng Hongxia , Zhou Xiaoguo , Zhu Daqi
Published: October 2023

In recent years, with the continuous advancement of the construction of the Yangtze River’s intelligent waterway system, unmanned surface vehicles have been increasingly used in the river’s inland waterways. This article proposes a hybrid path planning method that combines an improved A* algorithm with an improved model predictive control algorithm for the autonomous navigation of the “Jinghai-I” unmanned surface vehicle in inland rivers. To ensure global optimization, the heuristic function was refined in the A* algorithm. Additionally, constraints such as channel boundaries and courses were added to the cost function of A* and the planned path was smoothed to meet the collision avoidance regulations for inland rivers. The model predictive control algorithm incorporated a new path-deviation cost while imposing a cost constraint on the yaw angle, significantly minimizing the path-tracking error. Furthermore, the improved model predictive control algorithm took into account the requirements of rules in the cost function and adopted different collision avoidance parameters for different encounter scenarios, improving the rationality of local path planning. Finally, the proposed algorithm’s effectiveness was verified through simulation experiments that closely approximated real-world navigation conditions.
Ultraviolet Photodetector Based on a Beta-Gallium Oxide/Nickel Oxide/Beta-Gallium Oxide Heterojunction Structure
Authors: Nakagomi Shinji
Published: October 2023

In this paper, an n–p–n structure based on a β-Ga2O3/NiO/β-Ga2O3 junction was fabricated. The device based on the β-Ga2O3/NiO/β-Ga2O3 structure, as an ultraviolet (UV) photodetector, was compared with a p–n diode based on a NiO/β-Ga2O3 structure, where it showed rectification and 10 times greater responsivity and amplified the photocurrent. The reverse current increased in proportion to the 1.5 power of UV light intensity. The photocurrent amplification was related to the accumulation of holes in the NiO layer given by the heterobarrier for holes from the NiO layer to the β-Ga2O3 layer. Moreover, the device could respond to an optical pulse of less than a few microseconds.
Research on Estimating Rice Canopy Height and LAI Based on LiDAR Data
Authors: Jing Linlong , Wei Xinhua , Song Qi , Wang Fei
Published: October 2023

Rice canopy height and density are directly usable crop phenotypic traits for the direct estimation of crop biomass. Therefore, it is crucial to rapidly and accurately estimate these phenotypic parameters. To achieve the non-destructive detection and estimation of these essential parameters in rice, a platform based on LiDAR (Light Detection and Ranging) point cloud data for rice phenotypic parameter detection was established. Data collection of rice canopy layers was performed across multiple plots. The LiDAR-detected canopy-top point clouds were selected using a method based on the highest percentile, and a surface model of the canopy was calculated. The canopy height estimation was the difference between the ground elevation and the percentile value. To determine the optimal percentile that would define the rice canopy top, testing was conducted incrementally at percentile values from 0.8 to 1, with increments of 0.005. The optimal percentile value was found to be 0.975. The root mean square error (RMSE) between the LiDAR-detected and manually measured canopy heights for each case was calculated. The prediction model based on canopy height (R2 = 0.941, RMSE = 0.019) exhibited a strong correlation with the actual canopy height. Linear regression analysis was conducted between the gap fractions of different plots, and the average rice canopy Leaf Area Index (LAI) was manually detected. Prediction models of canopy LAIs based on ground return counts (R2 = 0.24, RMSE = 0.1) and ground return intensity (R2 = 0.28, RMSE = 0.09) showed strong correlations but had lower correlations with rice canopy LAIs. Regression analysis was performed between LiDAR-detected canopy heights and manually measured rice canopy LAIs. The results thereof indicated that the prediction model based on canopy height (R2 = 0.77, RMSE = 0.03) was more accurate.
High-Resolution L-Band TomoSAR Imaging on Forest Canopies with UAV Swarm to Detect Dielectric Constant Anomaly
Authors: Chuang Hsu-Yueh , Kiang Jean-Fu
Published: October 2023

A rigorous TomoSAR imaging procedure is proposed to acquire high-resolution L-band images of a forest in a local area of interest. A focusing function is derived to relate the backscattered signals to the reflectivity function of the forest canopies without resorting to calibration. A forest voxel model is compiled to simulate different tree species, with the dielectric constant modeled with the Maxwell-Garnett mixing formula. Five different inverse methods are applied on two forest scenarios under three signal-to-noise ratios in the simulations to validate the efficacy of the proposed procedure. The dielectric-constant profile of trees can be used to monitor the moisture content of the forest. The use of a swarm of unmanned aerial vehicles (UAVs) is feasible to carry out TomoSAR imaging over a specific area to pinpoint potential spots of wildfire hazards.
Nitric Oxide Modulates Salt Stress Tolerance in Lettuce
Authors: Marques Isabelly Cristina da Silva , Silva Dayane Mércia Ribeiro , Bispo Geane Lourenço , Oliveira Francisco de Assis de , Ono Elizabeth Orika , Rodrigues João Domingos
Published: October 2023

Crops are constantly threatened by salinity. Nitric oxide (NO) is an attenuating agent for salt stress; however, the specific roles of NO in gas exchange and lettuce production are not well established. The objective of this study was to evaluate the application of different concentrations of sodium nitroprusside (SNP) as an agent to mitigate salt stress in lettuce plants. Lettuce seedlings in pots were subjected to irrigation without and with saline water (0.2 and 3.5 dS m−1) and applications of different concentrations (0, 50, 100, 150, and 200 µM) of SNP, a NO donor. Saline stress negatively affected lettuce development with a reduction of 29.5% in leaf area, 6.3% in relative water content in the leaf, 17.2% in stem diameter, and 10.7% in dry matter mass in the control, but the application of SNP mitigated the deleterious effects of salt stress. Concentrations between 100 and 150 µM of SNP improved the photosynthetic metabolism of lettuce under salinity, with an increase of 46.7% in CO2 assimilation and 42.3% in fresh matter mass. Pearson’s correlation showed that fresh matter correlated positively with CO2 assimilation. Therefore, SNP can be used to mitigate salt stress in lettuce.
Distribution Channel Selection Using FUCOM-ADAM: A Novel Approach
Authors: Andrejić Milan , Pajić Vukašin , Kilibarda Milorad
Published: October 2023

The selection of the appropriate distribution channel is crucial for the success of any business dealing with physical goods. When dealing with this selection, it is crucial to have an effective decision support system (DSS) that can assist with such decisions. While various DSS approaches exist in the literature, not all are suitable for real-world applications. This research aims to address this gap by developing practical DSS tools that can aid decision-makers in making optimal decisions even in situations of uncertainty. The paper explores six different distribution channels (retailer’s warehouse, wholesaler’s warehouse, manufacturer’s warehouse, cross-dock, 3PL services, and direct delivery) in order to select the optimal one based on nine established criteria (inventory costs, distribution costs, delivery speed, service level, market coverage, product availability, order consolidation capability, reverse logistics, and order tracking) by using the FUCOM (Full Consistency Method) and ADAM (Axial-Distance-Based Aggregated Measurement) methods. After applying the FUCOM method, C1 (inventory costs) had the highest value when observing criteria weights, whereas C9 (order tracking) had the lowest. The results of the ADAM method showed that A5 (3PL services) was the best-ranked alternative, whereas A4 (cross-dock) was ranked as the worst. Based on the results, a model validation, and sensitivity analysis was conducted to determine whether the final ranking of the alternatives will change. This research provides decision makers with the necessary tools for better decision making, leading to improved distribution operations and increased profitability for the business.
Predictions of the Key Operating Parameters in Waste Incineration Using Big Data and a Multiverse Optimizer Deep Learning Model
Authors: Zhao Zheng , Zhou Ziyu , Lu Ye , Li Zhuoge , Wei Qiang , Xu Hongbin
Published: October 2023

In order to accurately predict the key operating parameters of waste incinerators, this paper proposes a prediction method based on big data and a Multi-Verse Optimizer deep learning model, thus providing a powerful reference for controlling the optimization of the incinerator combustion process. The key operating parameters that were predicted, according to the control objectives, were determined to be the steam flow, gas oxygen, and flue temperature. Firstly, a large amount of measurement data were collected, and 27 relevant control system parameters with a high correlation with the predicted variables were obtained via a mechanism analysis. The input variables of the prediction model were further determined using the improved WesselN symbolic transfer entropy algorithm. The delay time between the variables was found using a gray correlation coefficient, the prediction time was determined to be 6 min according to the delay time distribution of the flame feature, and the time delay compensation was applied to each parameter. Finally, the support vector machine was optimized using a Multi-Verse Optimization algorithm to complete the prediction of the key operating parameters. Experiments showed that the root mean square error of the proposed model for the three output variables—the steam flow, gas oxygen, and flue temperature—were 0.3035, 0.2477, and 1.6773, respectively, which provides a high accuracy compared to other models.
Transforming Trash into Treasure Troves: SMEs Co-Create Industrial Ecology Ecosystems with Government
Authors: Herbst Judith M.
Published: October 2023

Industrial ecology addresses newer business models that improve flows of energy, water, and materials, mimicking interconnections found in natural systems. Businesses can function interdependently to extend the life cycle of resources by setting up systems to repurpose waste or transfer a byproduct of manufacturing as an input for creating another product. Although the extant literature focuses on the role of businesses in closed-loop processes, governments can catalyse sustainable entrepreneurship to transition to a circular economy. There is a limited understanding of how public–private partnerships can facilitate this shift in small and medium enterprises. Multiple case studies were conducted to examine industrial ecology projects that were spearheaded by a state grant scheme in Australia. The long-term progress in establishing initiatives across commercial and industrial projects was monitored. The findings show government incentives to start up projects facilitate conditions to develop technology and other capabilities for responsible production and consumption. This study extends the theory of innovation ecosystems into practice. The model demonstrates that sustainable value for business and society can be realized through financial support and collaboration that enables entrepreneurship and drives circularity across cities and regions.
Appraisal of Provision Structures of Nursing Homes for Old Persons—Illustrated by Cross-Sectional Data for East Tyrol
Authors: Fischer Tatjana , Moder Karl , Jobst Markus
Published: October 2023

(1) Background: In Austria, stationary long-term care facilities for old persons in need of care are increasingly at the centre of the discussion on sustainable long-term care for old persons. So far, there is a lack of research addressing problems of fit from a spatial planning perspective. This case study on East Tyrol aims to appraise provision structures of stationary long-term care for old persons focusing on nursing homes (LTC) with regard to any intra-regional disparities. (2) Methods: Catchments and Bradshaw’s taxonomy of social needs serve as the conceptual framework. Real-world data on residents and applicants up to a certain cut-off date in 2022 for four nursing homes are statistically analysed at different spatial reference levels. The GIS mapping of catchments focuses on the intra-regional level. The findings are discussed transdisciplinarily. (3) Results: Intra-regional disparities with regard to the level of provision are evident. There are: 1) LTC-specific differences related to catchments with regard to LTC residents and applicants and travel efforts for visitors; and 2) valley-related differences with regard to nursing home choice. Normatively determined catchments broadly match the actual catchments. (4) Conclusions: This study could serve as a starting point for discussing methodological limitations of waiting lists as a parameter for unmet demand. Shortcomings of provision in relation to (future) demand as well as the significance of longitudinal studies for assessing the stability of catchments and area-wide coverage become apparent.
Climate Change, Technology Shocks and the US Equity Real Estate Investment Trusts (REITs)
Authors: Salisu Afees A. , Hammed Yinka S. , Ouattara Ibrahim Ngananga
Published: October 2023

Given the renewed interest in Real Estate Investment Trusts (REITs), we are keenly focused on exploring the possible connection between climate change and return volatility of US equity REITs, as well as the role of technology innovation for environmental sustainability in the nexus. While climate change might pose some threat to the REIT business, it is necessary to know the direction in which technological innovation can mitigate this impact. As a way to validate our evidence, we offer some additional analyses with alternative measures of technology shocks and the replacement of technology shocks with global economic expansion, as improvement in global economic activity could offer more investment options for investors to diversify their investment portfolio away from climate-prone assets. For completeness, the analyses are replicated for US mortgage REITs. Overall, we show that climate change heightens the return volatility of US equity REITs and that the former contains some predictive content for the latter. When the role of technology is examined, our results show that technology shock indeed reverses the cheering impact of temperature anomaly on the return volatility of US equity REITs. We show that these results are robust to alternative measures of economic shock and that the results equally hold for mortgage REITs. We further document some important implications of our findings for investors and policymakers alike.
A Study of Sustainable Product Design Evaluation Based on the Analytic Hierarchy Process and Deep Residual Networks
Authors: Lin Huan , Deng Xiaolei , Yu Jianping , Jiang Xiaoliang , Zhang Dongsong
Published: October 2023

Traditional product design evaluation processes are resource-intensive and time-consuming, resulting in unsustainably higher costs and longer lead times. Therefore, sustainable product design evaluation has become an increasingly crucial aspect of product design, focusing on creating a high-efficiency, high-reliability, and low-carbon-emission approach. In this study, we proposed an integrated approach that combines manual design evaluation based on the analytic hierarchy process (AHP) with an automatic design evaluation based on a ResNet-50 network in order to develop a sustainable design evaluation method. First, the evaluation level and indicators for the shape design of a tail-light were defined using the AHP. We followed this by establishing a determination matrix and weight coefficients for the design indicators to create a manual design evaluation model. Second, tail-light shape image datasets were manually annotated based on the evaluation indicators, and design datasets were constructed. The ResNet-50 algorithm was introduced to train the datasets, and the automatic evaluation model for product design was constructed through training and tuning. Finally, we validated the feasibility and effectiveness of the product design evaluation method, which was based on AHP and ResNet-50, by comparing the results obtained using both manual design and automatic design evaluations. The results showed that the proposed sustainable product design evaluation model provides an efficient and reliable method for evaluating product design, improves the decision-making process, and empowers the design and development process. The model enhances resource efficiency and economic sustainability.
Perceptions of Urban Community Resilience: Beyond Disaster Recovery in the Face of Climate Change
Authors: Fernando Felix N. , Maloney Meg , Tappel Lauren
Published: October 2023

Resilience of human systems has increasingly become a popular topic of research. The aim of this article is to present a juxtaposition of public officials’ and residents’ perceptions of community resilience along the three-class typology of resilience (basic, adaptive, and transformative) using Dayton, OH as a case study. A two-pronged data collection approach was designed to recruit public officials and residents. This approach was structured using the Community Capitals Framework. A multi-chain referral sampling process (and subsequent snowball sampling) was initiated subsequently. The data were gathered through semi-structured interviews with 75 participants. The interviews were analyzed using a three-tiered deductive structural coding approach. The findings highlight the similarities and differences in resilience perceptions between public officials and community members along the three-class typology of resilience that could inform creative policy initiatives. The factors that might undergird residents’ and public officials’ perceptions of resilience are discussed. Based on these perceptions, the importance of social capital, communication infrastructure, and addressing chronic stressors are discussed as important strategies to build community resilience, in addition to focusing on essential community infrastructure systems (such as roads, energy, water, sewer, and gas systems).
Assessing the Impact of Spatiotemporal Evolution of Urbanization on Carbon Storage in the Mega-Urban Agglomeration Area: Case Study of Yangtze River Delta Urban Agglomeration, China
Authors: Li Hongye , Hu Yutian , Li Hao , Ren Jinjie , Shao Rujie , Liu Zhicheng
Published: October 2023

A comprehensive understanding of the relationship between urbanization evolution and carbon storage is crucial for regional low-carbon development and the mitigation of global warming. In this study, we took a typical mega-urban agglomeration (Yangtze River Delta region) in China from 2000 to 2020 as an example, introduced an improved urbanization index to evaluate its urbanization level, and analyzed the impact of urbanization on carbon storage. The results show that in the past 20 years, the urbanization level of the Yangtze River Delta has increased by 3.25 times, during which, carbon storage has always shown a downward trend and decreased by 6.56 × 107 t. Furthermore, there was a gradually increasing negative correlation between urbanization and carbon storage. Lastly, the spatial loss of carbon storage is as follows: urban–rural fringes > rural area > developed urban area. From the perspective of stage characteristics, urban development periods that focus on outward expansion suffer faster losses. The results point out that we should focus on urban–rural fringes and control the urbanization development model in order to achieve carbon storage protection in rapidly urbanizing areas. This study provides a unique perspective on how to coordinate the relationship between regional urbanization and carbon storage services and achieve sustainability, especially for mega-urban agglomeration regions.
Puebla City Water Supply from the Perspective of Urban Water Metabolism
Authors: Pérez-González David , Delgado-Ramos Gian Carlo , Cedillo Ramírez Lilia , Loreto López Rosalva , Ramos Cassellis María Elena , Tamariz Flores José Víctor Rosendo , Peña Moreno Ricardo Darío
Published: October 2023

The city of Puebla is a mid-sized Mexican city facing multiple water-related challenges, from overexploitation of water sources and extreme pollution of rivers to water conflicts and contestation processes due to the privatization of water supply. Due to the complexity of urban water systems and their relevance for urban life, a holistic and integrative perspective is therefore needed to inform policymakers addressing such challenges. In this paper, Urban Water Metabolism (UWM) has been used to offer a comprehensive understanding of current water insecurity in the City of Puebla and its metropolitan area. Water inflows and outflows have been estimated using the Material Flow Analysis (MFA) method with data either obtained from official sources or simulated with the Monte Carlo method. Our findings show that the UWM configuration in the City of Puebla and its metropolitan area is effective for generating profits for service providers and water-related businesses, yet ineffective for guaranteeing citizens’ Human Right to Water and Sanitation (HRWS), a right recognized in the Constitution of Mexico. We conclude that to advance towards an inclusive and sustainable long-term provision of water, economic goals must follow socio-ecological goals, not the other way around. We consider UWM accounting useful for informing policy and decision-making processes seeking to build a new water governance based on both the best available knowledge and inclusive and vibrant social participation.
Analysis of Economic Benefits of Using Deep Geological Storage Technology to Treat High-Salinity Brine in Coal Mines
Authors: Du Song , Zhang Chao , Liu Jianguo , Zhang Chunhui , Fan Yinglin , Che Qiaohui , Song Sitong , Tao Sheng
Published: October 2023

Some provinces in China require zero discharge of coal mine wastewater, with a focus on the disposal of high-salt water, because evaporation ponds have been completely banned. Deep geological storage (DGS) technology is a novel geological environment solution that uses rock pores and microfissures within deep strata for safely storing liquid or gas to avoid its environmental impact on the biosphere. The author and his research team were the first to put forward the research idea of using DGS technology to dispose of high-salinity brine in coal mines in China and performed related research. Taking a coal mine in the south of Ordos Basin as an example, this study designed a conventional, mine-specific, zero-discharge water treatment process route based on a evaporation–crystallization process. This strategy was tailored to the unique water inflow conditions of the mine. Furthermore, the technical and economic efficiencies were assessed for the generation and treatment scenarios of a four-stage highly concentrated brine solution. In addition, the comparative analysis of the economic prospects of using DGS technology to treat high-salinity brine revealed that combining DGS with post-conventional treatment in secondary reverse osmosis, whose flow quality is 481 m3/h and TDS is 24,532.66 mg/L, can maximize the economic benefits. This integration heightened water resource utilization while maintaining a cost-effective, comprehensive water treatment approach. These results provide a valuable reference value for the future zero-discharge treatment of coal mine water.
Evaluating the Technical, Economic, and Environmental Performance of Solar Water Heating System for Residential Applications–Comparison of Two Different Working Fluids (Water and Glycol)
Authors: Agyekum Ephraim Bonah , Khan Tahir , Giri Nimay Chandra
Published: October 2023

The use of solar water heaters (SWH) in both residential and commercial facilities is one of the possible ways to reduce electricity bills and the release of greenhouse gases (GHG). This study assessed the technical, economic, and environmental performance of a SWH system at six different locations in China (i.e., Lhasa, Lauchang, Wuhan, Kashi, Yumen, and Harbin). A comparison between two different working fluids (i.e., water and glycol) were modeled in the System Advisor Model in all six cities. A sensitivity analysis was conducted on some key technical and economic parameters to assess the impact of such parameters on the performance of SWH systems in the country. According to the results, Lhasa recorded the highest capacity factor of 11% and 10.70% using water and glycol as the working fluid, respectively. Lhasa was identified as the best location among the studied locations due to its high solar irradiation. The optimization study indicates that the optimum azimuth for China is 190°. It was also found that a 25% reduction in the outlet set temperature of the water can reduce the capacity factor from 11% to about 9.2%. Using the SWH as simulated in this study can reduce carbon dioxide emissions from 1252.87–2014.85 kg per year to 138.20–330.23 kg per year; the extent of reduction depends on the location of the SWHS, and the solar energy available at the area. Net electricity bill savings of $156–296 could be obtained if SWH systems were installed and used at the studied locations.
Preserving Raw Oysters with High Hydrostatic Pressure and Irradiation Technology
Authors: Tian Haijuan , Liu Changjiao
Published: October 2023

Refrigerated raw oysters, including Pacific oysters (Magallana gigas), eastern oysters (Crassostrea virginica), and European flat oysters (Ostrea edulis), are popular seafood products. Pathogenic contamination and spoilage during storage and transport limit their shelf life. High hydrostatic pressure (HHP) and irradiation effectively reduce pathogens and spoilage microorganisms in raw oysters while preserving their taste and texture. This review article presents a comprehensive analysis of the use of HHP and irradiation as sanitation methods for raw oysters, incorporating findings from geographical distribution, mathematical modeling, and radiation quality’s impact on sterilization efficacy. The results demonstrate that untreated eastern oysters can maintain a total bacterial count below the recommended limit of 107 CFU/g for only 2–3 weeks at 5 °C, and are at risk of harboring pathogens such as Vibrio spp. and norovirus. HHP treatment at 600 MPa and irradiation treatment at 2 kGy can effectively reduce the pathogen load in raw oysters. However, supplementary measures such as additional cleaning or lower temperatures are required to prolong the shelf life of treated raw oysters to 2–3 weeks. Taken together, the application of HHP and irradiation to raw oyster sanitation represents a promising approach for enhancing the safety and quality of this beloved seafood delicacy.