bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Variability in Sea Surface Temperature and Chlorophyll a Concentration in the Gulf of California
Authors: López Martínez Juana , Farach Espinoza Edgardo Basilio , Herrera Cervantes Hugo , García Morales Ricardo
Published: August 2023

The Gulf of California (GC) is the only interior sea in the Eastern Pacific Ocean and is the most important fishing area in the northwestern region of the Mexican Pacific. This study focuses on the oceanographic variability of the GC, including its southern portion, which is an area with a high flow of energy and exchange of properties with the Pacific Ocean (PO), in order to determine its role in physical–biological cycles and climate change. The purpose of this work is to analyze the sea surface temperature (SST) and chlorophyll a concentration (Chl-a) during the period from 1998–2022 as indicators of long-term physical and biological processes, oceanographic variability, and primary production in the GC. In total, 513 subareas in the GC were analyzed, and a cluster analysis was applied to identify similar areas in terms of SST and Chl-a via the K-means method and using the silhouette coefficient (>0.5) as a metric to validate the clusters obtained. The trends of the time series of both variables were analyzed, and a fast Fourier analysis was performed to evaluate cycles in the series. A descriptive analysis of the SST and Chl-a series showed that the SST decreased from south to north. Six bioregions were identified using a combined of both SST and Chl-a data. The spectral analysis of the SST showed that the main frequencies in the six bioregions were annual and interannual (3–7 years), and the frequencies of their variations were associated with basin-level weather events, such as El Niño and La Niña. The SST in the GC showed a heating trend at an annual rate of ~0.036 °C (~0.73 °C in 20 years) and a decrease in Chl-a at an annual rate of ~0.012 mg/m3 (~0.25 mg/m3 in 20 years), with potential consequences for communities and ecosystems. Additionally, cycles of 10–13 and 15–20 years were identified, and the 10–13-year cycle explained almost 40–50% of the signal power in some regions. Moreover, mesoscale features (eddies and filaments) were identified along the GC, and they were mainly associated with the clusters of the SST. All these spatial and temporal variabilities induce conditions that generate different habitats and could explain the high biodiversity of the GC. If the warming trend of the SST and the decreasing trend of the Chl-a continue in the long term, concerns could be raised, as they can have important effects on the dynamics of this important marine ecosystem, including habitat loss for numerous native species, declines in the catches of the main fishery resources, and, consequently, support for the arrival of harmful invasive species.
Deep Learning for Earthquake Disaster Assessment: Objects, Data, Models, Stages, Challenges, and Opportunities
Authors: Jia Jing , Ye Wenjie
Published: August 2023

Earthquake Disaster Assessment (EDA) plays a critical role in earthquake disaster prevention, evacuation, and rescue efforts. Deep learning (DL), which boasts advantages in image processing, signal recognition, and object detection, has facilitated scientific research in EDA. This paper analyses 204 articles through a systematic literature review to investigate the status quo, development, and challenges of DL for EDA. The paper first examines the distribution characteristics and trends of the two categories of EDA assessment objects, including earthquakes and secondary disasters as disaster objects, buildings, infrastructure, and areas as physical objects. Next, this study analyses the application distribution, advantages, and disadvantages of the three types of data (remote sensing data, seismic data, and social media data) mainly involved in these studies. Furthermore, the review identifies the characteristics and application of six commonly used DL models in EDA, including convolutional neural network (CNN), multi-layer perceptron (MLP), recurrent neural network (RNN), generative adversarial network (GAN), transfer learning (TL), and hybrid models. The paper also systematically details the application of DL for EDA at different times (i.e., pre-earthquake stage, during-earthquake stage, post-earthquake stage, and multi-stage). We find that the most extensive research in this field involves using CNNs for image classification to detect and assess building damage resulting from earthquakes. Finally, the paper discusses challenges related to training data and DL models, and identifies opportunities in new data sources, multimodal DL, and new concepts. This review provides valuable references for scholars and practitioners in related fields.
Spatial and Temporal Variation in Vegetation Cover and Its Response to Topography in the Selinco Region of the Qinghai-Tibet Plateau
Authors: Huang Hongxin , Xi Guilin , Ji Fangkun , Liu Yiyang , Wang Haoran , Xie Yaowen
Published: August 2023

In recent years, the vegetation cover in the Selinco region of the Qinghai-Tibet Plateau has undergone significant changes due to the influence of global warming and intensified human activity. Consequently, comprehending the distribution and change patterns of vegetation in this area has become a crucial scientific concern. To address this concern, the present study employed MODIS-NDVI and elevation data, integrating methodologies such as trend analysis, Hurst exponent analysis, and sequential cluster analysis to explore vegetation cover changes over the past 21 years and predict future trends, while examining their correlation with topographic factors. The study findings indicate a fluctuating upward trend in vegetation cover, with a notable decrease in 2015. Spatially, the overall fractional vegetation cover (FVC) in the study area showed a basic stability with a percentage of 78%. The analysis of future trends in vegetation cover revealed that the majority of areas (68.26%) exhibited an uncertain trend, followed by stable regions at 15.78%. The proportion of areas showing an increase and decrease in vegetation cover accounted for only 9.63% and 5.61%, respectively. Elevation and slope significantly influence vegetation cover, with a trend of decreasing vegetation cover as elevation increases, followed by an increase, and then another decrease. Likewise, as the slope increases, initially, there is a rise in vegetation cover, followed by a subsequent decline. Notably, significant abrupt changes in vegetation cover are observed within the 4800 m elevation band and the 4° slope band in the Selinco region. Moreover, aspect has no significant effect on vegetation cover. These findings offer comprehensive insights into the spatial and temporal variations of vegetation cover in the Selinco region and their association with topographic factors, thus serving as a crucial reference for future research.
Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine
Authors: Yu Xinyu , Xi Mengzhu , Wu Liyang , Zheng Hui
Published: August 2023

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was >0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale.
Unmanned Ariel Vehicle (UAV) Path Planning for Area Segmentation in Intelligent Landmine Detection Systems
Authors: Barnawi Ahmed , Kumar Krishan , Kumar Neeraj , Thakur Nisha , Alzahrani Bander , Almansour Amal
Published: August 2023

Landmine contamination is a significant problem that has devastating consequences worldwide. Unmanned aerial vehicles (UAVs) can play an important role in solving this problem. The technology has the potential to expedite, simplify, and improve the safety and efficacy of the landmine detection process prior to physical intervention. Although the process of detecting landmines in contaminated environments is systematic, it is proven to be rather costly and overwhelming, especially if prior information about the location of the lethal objects is unknown. Therefore, automation of the process to orchestrate the search for landmines has become necessary to utilize the full potential of system components, particularly the UAV, which is the enabling technology used to airborne the sensors required in the discovery stage. UAVs have a limited amount of power at their disposal. Due to the complexity of target locations, the coverage route for UAV-based surveys must be meticulously designed to optimize resource usage and accomplish complete coverage. This study presents a framework for autonomous UAV-based landmine detection to determine the coverage route for scanning the target area. It is performed by extracting the area of interest using segmentation based on deep learning and then constructing the coverage route plan for the aerial survey. Multiple coverage path patterns are used to identify the ideal UAV route. The effectiveness of the suggested framework is evaluated using several target areas of differing sizes and complexities.
Microwave Electrometry with Multi-Photon Coherence in Rydberg Atoms
Authors: Yin Zheng , Li Qianzhu , Song Xiaoyun , Jia Zhengmao , Parniak Michal , Lu Xiao , Peng Yandong
Published: August 2023

A scheme for the measurement of a microwave (MW) electric field is proposed via multi-photon coherence in Rydberg atoms. It is based on the three-photon electromagnetically induced absorption (TPEIA) spectrum. In this process, the multi-photon produces a narrow absorption peak, which has a larger magnitude than the electromagnetically induced transparency (EIT) peak under the same conditions. The TPEIA peak is sensitive to MW fields, and can be used to measure MW electric field strength. We found that the magnitude of TPEIA peaks shows a linear relationship with the MW field strength. The simulation results show that the minimum detectable strength of the MW fields is about 1/10 of that based on an common EIT effect, and the probe sensitivity could be improved by about four times. Furthermore, the MW sensing based on three-photon coherence seems to be robust against the changes in the control field and shows a broad tunability, and the scheme may be useful for designing novel MW sensing devices.
A Glass-Fiber-Optic Turbidity Sensor for Real-Time In Situ Water Quality Monitoring
Authors: Vu Chi Thanh , Zahrani Amir Ahmadi , Duan Lingze , Wu Tingting
Published: August 2023

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level of drinking water distribution systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve the manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large-scale distributed fiber-optic turbidity monitoring networks.
Research Method of Discontinuous-Gait Image Recognition Based on Human Skeleton Keypoint Extraction
Authors: Han Kun , Li Xinyu
Published: August 2023

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks’ bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust.
Integrated UWB/MIMU Sensor System for Position Estimation towards an Accurate Analysis of Human Movement: A Technical Review
Authors: Yogesh Vinish , Buurke Jaap H. , Veltink Peter H. , Baten Chris T. M.
Published: August 2023

Integrated Ultra-wideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensor systems have been gaining popularity for pedestrian tracking and indoor localization applications, mainly due to their complementary error characteristics that can be exploited to achieve higher accuracies via a data fusion approach. These integrated sensor systems have the potential for improving the ambulatory 3D analysis of human movement (estimating 3D kinematics of body segments and joints) over systems using only on-body MIMUs. For this, high accuracy is required in the estimation of the relative positions of all on-body integrated UWB/MIMU sensor modules. So far, these integrated UWB/MIMU sensors have not been reported to have been applied for full-body ambulatory 3D analysis of human movement. Also, no review articles have been found that have analyzed and summarized the methods integrating UWB and MIMU sensors for on-body applications. Therefore, a comprehensive analysis of this technology is essential to identify its potential for application in 3D analysis of human movement. This article thus aims to provide such a comprehensive analysis through a structured technical review of the methods integrating UWB and MIMU sensors for accurate position estimation in the context of the application for 3D analysis of human movement. The methods used for integration are all summarized along with the accuracies that are reported in the reviewed articles. In addition, the gaps that are required to be addressed for making this system applicable for the 3D analysis of human movement are discussed.
A Pre-Training Framework Based on Multi-Order Acoustic Simulation for Replay Voice Spoofing Detection
Authors: Go Changhwan , Park Nam In , Jeon Oc-Yeub , Chun Chanjun
Published: August 2023

Voice spoofing attempts to break into a specific automatic speaker verification (ASV) system by forging the user’s voice and can be used through methods such as text-to-speech (TTS), voice conversion (VC), and replay attacks. Recently, deep learning-based voice spoofing countermeasures have been developed. However, the problem with replay is that it is difficult to construct a large number of datasets because it requires a physical recording process. To overcome these problems, this study proposes a pre-training framework based on multi-order acoustic simulation for replay voice spoofing detection. Multi-order acoustic simulation utilizes existing clean signal and room impulse response (RIR) datasets to generate audios, which simulate the various acoustic configurations of the original and replayed audios. The acoustic configuration refers to factors such as the microphone type, reverberation, time delay, and noise that may occur between a speaker and microphone during the recording process. We assume that a deep learning model trained on an audio that simulates the various acoustic configurations of the original and replayed audios can classify the acoustic configurations of the original and replay audios well. To validate this, we performed pre-training to classify the audio generated by the multi-order acoustic simulation into three classes: clean signal, audio simulating the acoustic configuration of the original audio, and audio simulating the acoustic configuration of the replay audio. We also set the weights of the pre-training model to the initial weights of the replay voice spoofing detection model using the existing replay voice spoofing dataset and then performed fine-tuning. To validate the effectiveness of the proposed method, we evaluated the performance of the conventional method without pre-training and proposed method using an objective metric, i.e., the accuracy and F1-score. As a result, the conventional method achieved an accuracy of 92.94%, F1-score of 86.92% and the proposed method achieved an accuracy of 98.16%, F1-score of 95.08%.
Research on Speech Synthesis Based on Mixture Alignment Mechanism
Authors: Deng Yan , Wu Ning , Qiu Chengjun , Chen Yan , Gao Xueshan
Published: August 2023

In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment mechanism. Mixture-TTS aims to optimize the alignment information between text sequences and mel-spectrogram. Mixture-TTS uses a linguistic encoder based on soft phoneme-level alignment and hard word-level alignment approaches, which explicitly extract word-level semantic information, and introduce pitch and energy predictors to optimally predict the rhythmic information of the audio. Specifically, Mixture-TTS introduces a post-net based on a five-layer 1D convolution network to optimize the reconfiguration capability of the mel-spectrogram. We connect the output of the decoder to the post-net through the residual network. The mel-spectrogram is converted into the final audio by the HiFi-GAN vocoder. We evaluate the performance of the Mixture-TTS on the AISHELL3 and LJSpeech datasets. Experimental results show that Mixture-TTS is somewhat better in alignment information between the text sequences and mel-spectrogram, and is able to achieve high-quality audio. The ablation studies demonstrate that the structure of Mixture-TTS is effective.
Towards Feasible Solutions for Load Monitoring in Quebec Residences
Authors: Hosseini Sayed Saeed , Delcroix Benoit , Henao Nilson , Agbossou Kodjo , Kelouwani Sousso
Published: August 2023

For many years, energy monitoring at the most disaggregate level has been mainly sought through the idea of Non-Intrusive Load Monitoring (NILM). Developing a practical application of this concept in the residential sector can be impeded by the technical characteristics of case studies. Accordingly, several databases, mainly from Europe and the US, have been publicly released to enable basic research to address NILM issues raised by their challenging features. Nevertheless, the resultant enhancements are limited to the properties of these datasets. Such a restriction has caused NILM studies to overlook residential scenarios related to geographically-specific regions and existent practices to face unexplored situations. This paper presents applied research on NILM in Quebec residences to reveal its barriers to feasible implementations. It commences with a concise discussion about a successful NILM idea to highlight its essential requirements. Afterward, it provides a comparative statistical analysis to represent the specificity of the case study by exploiting real data. Subsequently, this study proposes a combinatory approach to load identification that utilizes the promise of sub-meter smart technologies and integrates the intrusive aspect of load monitoring with the non-intrusive one to alleviate NILM difficulties in Quebec residences. A load disaggregation technique is suggested to manifest these complications based on supervised and unsupervised machine learning designs. The former is aimed at extracting overall heating demand from the aggregate one while the latter is designed for disaggregating the residual load. The results demonstrate that geographically-dependent cases create electricity consumption scenarios that can deteriorate the performance of existing NILM methods. From a realistic standpoint, this research elaborates on critical remarks to realize viable NILM systems, particularly in Quebec houses.
The Role of Ultrasound in Cancer and Cancer-Related Pain—A Bibliometric Analysis and Future Perspectives
Authors: Sridharan Badrinathan , Sharma Alok Kumar , Lim Hae Gyun
Published: August 2023

Ultrasound has a deep penetrating ability with minimal or no tissue injury, while cancer-mediated complications during diagnosis, therapy, and surgery have become a serious challenge for clinicians and lead to the severity of the primary condition (cancer). The current study highlights the importance of ultrasound imaging and focused ultrasound therapy during cancer diagnosis, pain reduction, guidance for surgical resection of cancer, and the effectiveness of chemotherapy. We performed the bibliometric analysis on research domains involving ultrasound, cancer management, pain, and other challenges (chemotherapy, surgical guidance, and postoperative care), to observe the trend by which the research field has grown over the years and propose a possible future trend. The data was obtained from the Web of Science, processed, and exported as plain text files for analysis in the Bibliometrix R web interface using the Biblioshiny package. A total of 3248 documents were identified from 1100 journal sources. A total of 390 articles were published in 2022, with almost a 100% growth rate from previous years. Based on the various network analysis, we conclude that the outcome of the constant research in this domain will result in better patient care during the management of various diseases, including cancer and other co-morbidities.
Advancements in Buoy Wave Data Processing through the Application of the Sage–Husa Adaptive Kalman Filtering Algorithm
Authors: Jiang Sha , Chen Yonghua , Liu Qingkui
Published: August 2023

In this paper, we propose a combined filtering method rooted in the application of the Sage–Husa Adaptive Kalman filtering, designed specifically to process wave sensor data. This methodology aims to boost the measurement precision and real-time performance of wave parameters. (1) This study delineates the basic principles of the Kalman filter. (2) We discuss in detail the methodology for analyzing wave parameters from the collected wave acceleration data, and deeply study the key issues that may arise during this process. (3) To evaluate the efficacy of the Kalman filter, we have designed a simulation comparison encompassing various filtering algorithms. The results show that the Sage–Husa Adaptive Kalman Composite filter demonstrates superior performance in processing wave sensor data. (4) Additionally, in Chapter 5, we designed a turntable experiment capable of simulating the sinusoidal motion of waves and carried out a detailed errors analysis associated with the Kalman filter, to facilitate a deep understanding of potential problems that may be encountered in practical application, and their solutions. (5) Finally, the results reveal that the Sage–Husa Adaptive Kalman Composite filter improved the accuracy of effective wave height by 48.72% and the precision of effective wave period by 23.33% compared to traditional bandpass filter results.
The Sector Analysis as a Coastal Management Tool for Sustainable Tourism Development on the Mediterranean Coast of Morocco
Authors: Er-Ramy Noureddine , Nachite Driss , Anfuso Giorgio , Azaaouaj Soria
Published: August 2023

Beaches are ecologically valuable ecosystems and sites that attract many tourists from all over the world, therefore, knowledge of their environmental conditions to establish sound management strategies is of extreme relevance. This study aims to assess and classify 50 beaches through an innovative coastal management approach called “Sector Analysis”, which integrates Litter grading, the coastal scenic quality and beach typology in order to classify sites into one of three sectors: Green (high value sites), Red (low value sites) and Yellow (sites with contradictory values). Litter Grade makes it possible to classify a site according to the quantity of litter using four grades from “A” (low) to “D” (high amount). The Coastal Scenic Evaluation System (CSES) allows to classify sites into five classes, ranging from extremely attractive natural sites (Class I) to unattractive degraded and urbanized sites (Class V). This innovative methodology for sustainable coastal area management can be easily applied to any beach in the world. The results on Litter Grade and CSES considered in this paper were obtained from previous studies and used to obtain the Sector Analysis that showed only 8 sites (16%) are in the Green sector, 18 sites (36%) in the Red sector and 24 sites (48%) in the Yellow sector. The significant percentage of sites in the Red sector (one third of the sites studied) clearly indicates the degradation that the Moroccan Mediterranean coast has undergone due to considerable anthropogenic activities and the lack of adequate coastal management programs. In this study, various management interventions were proposed to conserve and improve the aesthetic quality of beaches and reduce the impact and presence of litter in the coastal areas.
Impact of Illuminated Road Signs on Driver’s Perception
Authors: Choi Woochul , Sung Hongki , Chong Kyusoo
Published: August 2023

This study determined citizens’ perceptions and impact factors of illuminated road signs installed to ensure their visibility at night when the risk of traffic accidents is high. An ordered logit model was used to measure illuminated road signs’ impact on drivers’ perception based on the data from the citizens’ survey conducted by the Road Sign Center. According to the results, the internal (optical fiber) type variable had the highest impact, followed by the frequent fog variable and the complex road line variable. This study found that most citizens positively recognized road signs, preferred internal (optical fiber) types, and desired illuminated road signs that considered climate, environment, and road structure types. In Seoul, the importance and improvement of illuminated road signs at points where road structures are complex, such as city streets, were high. Additionally, the illuminated road sign recognition and road type variable were significant in Gyeonggi-do, which reflected the high number of citizens that commute to Seoul from Gyeonggi-do. Concerning local cities and counties and intercity roads highly affected by the climate, the impact was high at points with frequent fog. Fog affects the visibility distance, generates condensation on signs, and significantly degrades visibility. Therefore, an illuminated road sign installation method must be presented based on spatial analysis for regions vulnerable to climate, environment, and road location. Additionally, the road intersection point variable was significant in local cities and counties, which reflects the relatively lagged road infrastructure. Local cities and counties are financially poor and have numerous aged drivers; hence, central government support that considers these aspects is crucial.
An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management
Authors: Zhou Lingyun , Zhang Dezhi , Li Shuangyan , Luo Xiangyu
Published: August 2023

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.
Controls on Surface and Downcore Sedimentary Organic Matter in a Constructed Oyster Reef
Authors: Southwell Melissa , Veenstra Jessica , Blanco Christopher , Bowen SavannahLynne , Chaya Taryn , Cooper-Kolb Tyler , Marcum Pamela , McCaul Patricia
Published: August 2023

Oysters provide a suite of important ecosystem services, and recent research shows that oyster restoration rapidly enhances sedimentary organic carbon deposition. In 2012, an oyster reef enhancement project began in the GTM National Estuarine Research Reserve in Northeast FL, USA. We analyzed the spatial and downcore variability in sedimentary organic matter (OM) and particle sizes in the intertidal zone between the reefs and the marsh, along with oyster reef characteristics, to better understand physical and/or biological influences on sediment. Our data indicate that OM in the top 20 cm of sediment cores was negatively correlated with reef age. Similar decreases in particles <63 μm suggest remobilization of sediment, likely driven by the degradation of the reef structure over its approximately 9-year lifetime. Likewise, a survey of surface sediments showed that adjacent reef structural metrics were the best predictor of sediment OM and particle size. These results highlight the importance of reef structure as a control on sedimentary organic carbon deposition and stability in areas where physical energy is relatively high. This result is discussed in the context of implications for carbon budgets and biogeochemical ecosystem services of oysters as a part of living shorelines.
Assessment of Safety Levels in the Agricultural Sector for Supporting Social Sustainability: A Quantitative Analysis from a National Point of View
Authors: De Leo Federica , Elia Valerio , Gnoni Maria Grazia , Tornese Fabiana , De Merich Diego , Guglielmi Armando , Pellicci Mauro
Published: August 2023

The scientific debate about sustainability in the agricultural sector is growing worldwide, especially thanks to the increasing awareness of customers towards the impact of their consumption behaviors. While a great deal of attention is given to the economic and environmental dimensions of sustainability, social sustainability assessment often focuses on the quality of life of farmers and the local community; the dimension regarding occupational health and safety (OHS) is not so analyzed even if the agricultural sector could be evaluated as one of the most hazardous ones all over the world. From this point of view, workers are considered high-risk groups mainly due to the presence of hazardous equipment and chemicals. The aim of this work is to propose a cross-analysis developed on public databases reporting data about injuries in the Italian agricultural sector—provided by the Italian National Institute for the Insurance of Work-Related Injuries (INAIL)—in order to point out the main sources and causes that led to these injuries. The injury analysis will allow companies as well as institutions to define more effective prevention strategies to increase the social sustainability levels of this sector. Even if results are limited to the Italian sector, they could outline some directions for improving social sustainability levels as well as research gaps and possible future research directions in order to prevent injuries in the agricultural sector.
Research on the Impact of New Parts Price Increase on the Stability of Closed-Loop Supply Chain
Authors: Duan Wei , Cao Hengli , Xu Desheng
Published: August 2023

In the closed-loop supply chain system of component remanufacturing, new parts suppliers are likely to be affected by certain factors that lead to sudden increases in supply prices, and this sudden increase in new parts prices may trigger the chain collapse of the closed-loop supply chain system and impact the stability of the closed-loop supply chain. Therefore, this paper combines closed-loop supply chain theory, evolutionary game theory, and system dynamics theory, which are internalized to construct a complex system model. Furthermore, the impact of different price increases of new parts on the stability of the closed-loop supply chain is analyzed through scenario simulation. The results show that a 25% increase in the price of new parts will delay the time for remanufacturers and retailers to reach a stable state of product flow, reduce the profitability of remanufacturers and retailers, and inhibit the willingness of remanufacturers to carry out high-green production in the early stage. A 50% increase in the price of new parts can break the closed-loop supply chain from the remanufacturer, severely undermining the stability of the closed-loop supply. According to the simulation results, this paper presents a timely government dynamic reward and punishment optimization scheme for remanufacturers to improve the tolerance level of the closed-loop supply chain for the price increase of new parts and to provide a reference for enhancing the stability of the closed-loop supply chain and optimizing the government supervision strategy.
Research on the Optimization Path of Green Development in Shaanxi Province Based on the OECD’s Perspective
Authors: Liu Tian , Zhou Boyang
Published: August 2023

The concept of green development proposed by the OECD has effectively promoted the coordinated development of economic growth, resource conservation, and environmental protection. In addition, it has provided effective ways and means of improving the quality of global social and economic development. Therefore, this study constructed the green development evaluation index system of Shaanxi Province based on the OECD green development concept, used the AHP and the entropy weight method for weighting, and measured the green development level of ten urban areas in Shaanxi Province from 2016 to 2021. Based on the score of the green development level, the LSE model, ESDA model, and kernel density model were introduced to decompose the optimization path of green development in Shaanxi Province from the three dimensions of contributing factors, spatial distribution, and temporal evolution. The specific research conclusions are: first, from 2016 to 2021, the green development score of Shaanxi Province was between 0.92 and 1.79, and the level of green development showed an overall upward trend, but the two-level differentiation trend between regions was serious. Second, the green development levels of each region in Shaanxi Province were three-factor type I, type II, four-factor type, and five-factor type. The influencing factors of each region showed different characteristics. Third, the level of green development among various regions in Shaanxi Province did not show spatial aggregation, and the development level was significantly different. The regional characteristics show that Xi’an is obviously superior to other regions and has not played a leading role. Fourth, over time, the situation of two-level differentiation in the ten city-level administrative districts of Shaanxi Province has eased, but the development situation is still very severe. The above research conclusions provide a reference and scientific basis for Shaanxi Province to formulate economic development policies and solve the problem associated with coordinated, sustainable development of the regional society-economy-environment.
Six-Sigma Reference Model for Industry 4.0 Implementations in Textile SMEs
Authors: Caballero-Morales Santiago-Omar , Cuautle-Gutiérrez Luis , Cordero-Guridi José-de-Jesús , Alvarez-Tamayo Ricardo-Iván
Published: August 2023

The textile and apparel industry is a major contributor to Latin American economies. However, in these economies the industry is characterized by limited technological infrastructure, which has led to inefficient performance and the significant generation of water, material, and product waste through its supply chain (SC). Currently, Industry 4.0 has led to important benefits in manufacturing industries, but its application in the textile field has been limited to few case studies. The present work expands on this aspect with a critical review of Industry 4.0 concepts and principles, and our main contribution consists of an implementation guideline model. The model was based on Six Sigma and the Rothwell and Zegveld model for continuous improvement and innovation projects in manufacturing and business enterprises. In each step, key aspects such as the specific skills of the interdisciplinary team, assessment tools, and implementation/prototyping tools are highlighted. A discussion of a case study is presented to support the applicability of the model for Industry 4.0 pilot projects.
Unpacking the Principal Strategies in Leveraging Weighted Student Funding
Authors: Ho Chun Sing Maxwell
Published: August 2023

Weighted student funding (WSF) systems have been implemented in various countries to give schools more autonomy over how to allocate their funding. School principals use funding to maintain school operations and foster innovation for achieving educational goals. However, despite the importance of this process, scholarly research has largely overlooked how principals make decisions about allocating their financial resources. Accordingly, this study seeks to provide practical insights into the strategies used by one school by highlighting their staff’s perceptions about using their WSF to maintain school operations and spur innovation. Using a case study approach, we investigated a principal who effectively used a school’s WSF to transform a failing school into an innovative one. The findings revealed that the principal strategically implemented financial management mechanisms in a way that inspired teachers to consider more profoundly how a school’s WSF can help achieve educational goals. The principal fostered consensus on the school’s direction, encouraged innovation through hands-on experiential learning and strategic planning, and facilitated funding for innovative teachers by guiding proposal development. In the final section of this article, insights into the shifting cultural and practical landscape of financial resource utilization within schools are discussed.
A User-Friendly and Sustainable Toilet Based on Vermicomposting
Authors: Alonso-Marroquin Fernando , Qadir Ghulam , Nazha Jad , Pino Vanessa , Brambilla Arianna
Published: August 2023

Environmental awareness has sparked increasing interest in changing the way humans interact with their environment. This awareness includes the change in paradigm of considering human manure (humanure) not as a waste but as a valuable bioproduct instead. In this regard, composting is an age-old technique for nutrient recovery that has gained renewed interest, as it may be a sanitary and financially viable solution to closing the loop of human–nature interactions. This work investigates environmental solutions for toilet systems that are user-friendly and sustainable based on systems that filter nutrients via vermicomposting. The methodology is based on (1) reviewing several surveys across different continents to select the most appropriate interface of a targeted society, and (2) investigating the microbial dynamics of vermicomposting. The microbial activity was compared with the activity of the aerobic composting systems by measuring soil temperature, soil composition, decomposition rate, stabilization factor, and biological diversity. The microbial decomposition process in vermicomposting was faster due to the presence of earthworms, but the increase in temperature and volatile ammonia led to the earthworms burrowing into the soil. Overall, the flush toilet is still the most socially accepted toilet interface, and the connection of vermicomposting to this toilet interface poses challenges in managing high ammonia content and maintaining healthy conditions for the earthworm population.
Model of Value Transfer in Crowdfunding and Sustainable Development of Small and Medium-Sized Enterprises in Poland—Based on Survey Research
Authors: Kukurba Maria , Salwin Mariusz , Waszkiewicz Aneta Ewa
Published: August 2023

This paper presents the results of a survey of small and medium-sized enterprises (SMEs) in Poland that have benefited from crowdfunding (CF). Based on these results, a new business model was developed. On this basis, the CF equity, reward and donation models were analyzed, and the impact of CF on the way of creating value in the company in the context of sustainable development was determined. The survey results show that the use of CF promotes the sustainable development of SMEs in Poland and significantly impacts their business model. In practical terms, this research has contributed to a better understanding of value creation by these companies. The results of our analysis are useful for consulting companies and CF platforms that help SMEs organize campaigns. In theoretical terms, the study conducted and the methodology used allow the presentation of a new definition of CF and a sustainable business model for a company using CF as well as contribute to the value management theory in SMEs.
Deep Analysis of the Homogenization Phenomenon of the Ancient Water Towns in Jiangnan: A Dual Perspective on Landscape Patterns and Tourism Destination Images
Authors: Chen Xue , Yin Yue , Jiang Mu , Lin Hong
Published: August 2023

Attractiveness plays a key role in the marketing activities that promote the sustainable development of tourist destinations. Many researchers have acknowledged that regional tourist destinations face challenges in attracting tourists due to homogeneity. Achieving sustainability of the ecological landscapes of tourist destinations, creating a unique image that differentiates them from other destinations, and fostering cooperative relationships among regional tourist destinations remain significant challenges. To investigate the reasons behind this and potential solutions to the homogeneity of tourist attractions in regional tourist destinations, our research team employed methods including landscape pattern analysis, tourist destination image analysis, principal component analysis (PCA), clustering algorithm (K-means), text analysis, word frequency analysis, sentiment analysis, and semantic network analysis. Through an in-depth examination of the homogenization phenomenon in the ancient towns of the southern Yangtze River, our research confirms that the spatial form of these ancient towns has become increasingly simple and scattered over time, with a concentration of the same land use types. Half of the sampled ancient towns had exhibited homogeneity in landscape patterns by 2020, with pronounced homogeneity observed in cultivated land, water networks, and hand-made land. Excessive commercialization has further contributed to the loss of local characteristics, resulting in homogeneity issues in the image of tourist destinations in the Jiangnan water towns and ancient towns. This article discusses the characteristics of homogeneity in the ancient towns of the Jiangnan water towns and their practical implications, offering valuable insights and experiences to tourism planners, designers, researchers, and other stakeholders engaged in the study of sustainable regional tourism destinations.
Improved Artificial Rabbits Optimization with Ensemble Learning-Based Traffic Flow Monitoring on Intelligent Transportation System
Authors: Ragab Mahmoud , Abdushkour Hesham A. , Maghrabi Louai , Alsalman Dheyaaldin , Fayoumi Ayman G. , AL-Ghamdi Abdullah AL-Malaise
Published: August 2023

Traffic flow monitoring plays a crucial role in Intelligent Transportation Systems (ITS) by dealing with real-time data on traffic situations and allowing effectual traffic management and optimization. A typical approach used for traffic flow monitoring frequently depends on collection and analysis of the data through a manual process that is not only resource-intensive, but also a time-consuming process. Recently, Artificial Intelligence (AI) approaches like ensemble learning demonstrate promising outcomes in numerous ITS applications. With this stimulus, the current study proposes an Improved Artificial Rabbits Optimization with Ensemble Learning-based Traffic Flow Monitoring System (IAROEL-TFMS) for ITS. The primary intention of the proposed IAROEL-TFMS technique is to employ the feature subset selection process with optimal ensemble learning so as to predict the traffic flow. In order to accomplish this, the IAROEL-TFMS technique initially designs the IARO-based feature selection approach to elect a set of features. In addition, the traffic flow is predicted using the ensemble model that comprises a Gated Recurrent Unit (GRU), Long Short-term Memory (LSTM), and Bidirectional Gated Recurrent Unit (BiGRU). Finally, the Grasshopper Optimization Algorithm (GOA) is applied for the adjustment of the optimum hyperparameters of all three DL models. In order to highlight the improved prediction results of the proposed IAROEL-TFMS algorithm, an extensive range of simulations was conducted. The simulation outcomes imply the supremacy of the IAROEL-TFMS methodology over other existing approaches with a minimum RMSE of 16.4539.