bluuebunny/crossref_metadata_2025 huggingface dataset:
Long-Term Effectiveness and Safety of Proactive Therapeutic Drug Monitoring of Infliximab in Paediatric Inflammatory Bowel Disease: A Real-World Study
Authors: Clemente Bautista Susana , Segarra Cantón Óscar , Padullés-Zamora Núria , García García Sonia , Álvarez Beltrán Marina , Larrosa García María , Cabañas Poy Maria Josep , Sanz-Martínez Maria Teresa , Vázquez Ana , Gorgas Torner Maria Queralt , Miarons Marta
Published: December 2024

Background: This study evaluated the long-term effectiveness and safety of a multidisciplinary early proactive therapeutic drug monitoring (TDM) program combined with Bayesian forecasting for infliximab (IFX) dose adjustment in a real-world dataset of paediatric patients with inflammatory bowel disease (IBD). Methods: A descriptive, ambispective, single-centre study of paediatric patients with IBD who underwent IFX serum concentration measurements between September 2015 and September 2023. The patients received reactive TDM before September 2019 (n = 17) and proactive TDM thereafter (n = 21). We analysed for clinical, biological, and endoscopic remission; treatment failure; hospitalisations; emergency visits; and adverse drug reactions. The IFX doses were adjusted to maintain trough concentrations ≥ 5 µg/mL, with specific targets for proactive TDM. Results: Of the 38 patients, 21 had Crohn’s disease (CD), 16 ulcerative colitis (UC), and 1 undetermined IBD. The mean (standard deviation) IFX trough concentrations were 6.83 (5.66) µg/mL (reactive) and 12.38 (9.24) µg/mL (proactive) (p = 0.08). No statistically significant differences between groups were found in remission rates or treatment failure. The proactive group had fewer hospitalisations (14.29% vs. 23.53%; p = 0.47) and shorter median hospitalisation days (6 vs. 19; p = 0.50), although the difference was not statistically significant. The number of patients with adverse reactions (infusion related reactions and infections) was higher in the proactive group (38.10% vs. 23.53%; p = 0.34) but the difference was not significantly different. Conclusions: Proactive TDM showed no significant differences in treatment outcomes compared to reactive TDM. However, the results in both the reactive and proactive TDM groups were not worse than those reported in other studies. Further studies with larger samples are needed to optimize the treatment strategies for pediatric IBD patients.
Ecodesign Enhancement of Polymeric Resins: Reinforcing with Synthetic and Natural Fibers Using Theory of Inventive Problem Solving-Algorithm of Inventive Problem Solving for Sustainable Composite Design
Authors: Dobrotă Dan , Icociu Cristina Vasilica , Lazăr Sergiu , Racz Sever-Gabriel , Moraru Gina-Maria
Published: December 2024

This study examines the enhancement of the mechanical strength of polymer resins through reinforcement with synthetic (glass) and natural (hemp, jute) fibers, using the TRIZ-ARIZ methodology to optimize composite design for improved mechanical properties, sustainability, and economic efficiency. Mechanical testing, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) were conducted to evaluate the properties of the composite materials. Regarding tensile strength testing, the results showed the following: jute fiber achieved the best results, with a maximum tensile values of 43.75 MPa (partial reinforcement) and 43.53 MPa (complete reinforcement); glass fiber recorded maximum tensile values of 34.55 MPa (partial reinforcement) and 34.52 MPa (complete reinforcement); and hemp fiber yielded maximum tensile values of 24.98 MPa (partial reinforcement) and 24.86 MPa (complete reinforcement). The mechanical performance of partial reinforcements (in the area of maximum stress) was similar to that of complete reinforcements, enabling a reduction in material usage by up to 60%. The thermal analysis (TGA) results demonstrated that glass fiber-reinforced composites exhibit high thermal stability, with mass loss starting at 320 °C and a residual mass of 8.02%; for other composite materials, thermal degradation begins at 305 °C, with a residual mass of 3.69%; in jute fiber-reinforced composites, thermal degradation starts at 300 °C, with a residual mass of 3.71%. SEM analysis generally revealed good fiber–matrix adhesion, while defects such as voids or detached fibers contributed to reduced mechanical strength. These results demonstrate that natural fiber-reinforced composite materials, particularly those reinforced with jute, can be used in sustainable engineering applications. They also show that localized reinforcement provides high performance with minimal resource consumption.
Field-Level Classification of Winter Catch Crops Using Sentinel-2 Time Series: Model Comparison and Transferability
Authors: Vanpoucke Kato , Heremans Stien , Buls Emily , Somers Ben
Published: December 2024

Winter catch crops are promoted in the European Union under the Common Agricultural Policy to improve soil health and reduce nitrate leaching from agricultural fields. Currently, Member States often monitor farmers’ adoption through on-site inspections for a limited subset of parcels. Because of its potential for region-wide coverage, this study investigates the potential of Sentinel-2 satellite time series to classify catch crops at the field level in Flanders (Belgium). The first objective was to classify catch crops and identify the optimal model and time-series input for this task. The second objective was to apply these findings in a real-world scenario, aiming to provide reliable early-season predictions in a separate target year, testing early-season performance and temporal transferability. The following three models were compared: Random Forest (RF), Time Series Forest (TSF), and a One-Dimensional Convolutional Neural Network (1D-CNN). The results showed that, with a limited field-based training dataset, RF produced the most robust results across different time-series inputs, achieving a median F1-score of >88% on the best dataset. Additionally, the early-season performance of the models was delayed in the target year, reaching the F1-score threshold of 85% at least one month later in the season compared to the training years, with large timing differences between the models.
Application of LiDAR Sensors for Crop and Working Environment Recognition in Agriculture: A Review
Authors: Karim Md Rejaul , Reza Md Nasim , Jin Hongbin , Haque Md Asrakul , Lee Kyu-Ho , Sung Joonjea , Chung Sun-Ok
Published: December 2024

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture.
A Method for Developing a Digital Terrain Model of the Coastal Zone Based on Topobathymetric Data from Remote Sensors
Authors: Specht Mariusz , Wiśniewska Marta
Published: December 2024

This technical note aims to present a method for developing a Digital Terrain Model (DTM) of the coastal zone based on topobathymetric data from remote sensors. This research was conducted in the waterbody adjacent to the Vistula Śmiała River mouth in Gdańsk, which is characterised by dynamic changes in its seabed topography. Bathymetric and topographic measurements were conducted using an Unmanned Aerial Vehicle (UAV) and two hydrographic methods (a Single-Beam Echo Sounder (SBES) and a manual survey using a Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) receiver). The result of this research was the development of a topobathymetric chart based on data recorded by the above-mentioned sensors. It should be emphasised that bathymetric data for the shallow waterbody (less than 1 m deep) were obtained based on high-resolution photos taken by a UAV. They were processed using the “Depth Prediction” plug-in based on the Support Vector Regression (SVR) algorithm, which was implemented in the QGIS software as part of the INNOBAT project. This plug-in allowed us to generate a dense cloud of depth points for a shallow waterbody. Research has shown that the developed DTM of the coastal zone based on topobathymetric data from remote sensors is characterised by high accuracy of 0.248 m (p = 0.95) and high coverage of the seabed with measurements. Based on the research conducted, it should be concluded that the proposed method for developing a DTM of the coastal zone based on topobathymetric data from remote sensors allows the accuracy requirements provided in the International Hydrographic Organization (IHO) Special Order (depth error ≤ 0.25 m (p = 0.95)) to be met in shallow waterbodies.
FPGA Implementation for 24.576-Gbit/s Optical PAM4 Signal Transmission with MLP-Based Digital Pre-Distortion
Authors: Hu Sheng , Zheng Tianqi , Bian Chengzhen , Yang Xiongwei , Sun Xinda , Zhu Zonghui , Gou Yumeng , Meng Yuanxiao , Zhang Jie , Ge Jingtao , Li Yichen , Wang Kaihui
Published: December 2024

In this work, we implemented a short-reach real-time optical communication system using MLP for pre-distortion. Lookup table (LUT) algorithms are commonly employed for pre-distortion in intensity modulation and direct detection (IM/DD) systems. However, storage limitations typically restrict the LUT pattern length to 9, limiting its effectiveness in compensating for nonlinear effects. A multilayer perceptron (MLP) can overcome this limitation by predicting errors and generating pre-distorted signals, thus replacing the extensive storage requirements of LUTs with minimal computational resources. The MLP-based digital pre-distortion (MLP-DPD) technique enables the creation of long-pattern LUTs for improved nonlinear compensation. In this work, an MLP-DPD scheme was implemented on a field-programmable gate array (FPGA). The FPGA was used to generate a 14.7456 GBaud pre-distorted pulse amplitude modulation 4-level (PAM4) signal. This signal was then transmitted over 20 km of standard single-mode fiber (SSMF). At the receiver, the parallel constant modulus algorithm (PCMA) was applied for signal processing. The bit error rate (BER) achieved met the 2.4 × 10−2 threshold for soft-decision forward error correction (SD-FEC), enabling a net transmission bit rate of 24.576 Gbit/s. This approach demonstrates the feasibility of using MLP-DPD for effective nonlinear compensation in high-speed optical communication systems with limited resources.
Correction: Barkjohn et al. Correction and Accuracy of PurpleAir PM2.5 Measurements for Extreme Wildfire Smoke. Sensors 2022, 22, 9669
Authors: Barkjohn Karoline K. , Holder Amara L. , Frederick Samuel G. , Clements Andrea L.
Published: December 2024

In the original publication [...]
Integrating Vision and Olfaction via Multi-Modal LLM for Robotic Odor Source Localization
Authors: Hassan Sunzid , Wang Lingxiao , Mahmud Khan Raqib
Published: December 2024

Odor source localization (OSL) technology allows autonomous agents like mobile robots to localize a target odor source in an unknown environment. This is achieved by an OSL navigation algorithm that processes an agent’s sensor readings to calculate action commands to guide the robot to locate the odor source. Compared to traditional ‘olfaction-only’ OSL algorithms, our proposed OSL algorithm integrates vision and olfaction sensor modalities to localize odor sources even if olfaction sensing is disrupted by non-unidirectional airflow or vision sensing is impaired by environmental complexities. The algorithm leverages the zero-shot multi-modal reasoning capabilities of large language models (LLMs), negating the requirement of manual knowledge encoding or custom-trained supervised learning models. A key feature of the proposed algorithm is the ‘High-level Reasoning’ module, which encodes the olfaction and vision sensor data into a multi-modal prompt and instructs the LLM to employ a hierarchical reasoning process to select an appropriate high-level navigation behavior. Subsequently, the ‘Low-level Action’ module translates the selected high-level navigation behavior into low-level action commands that can be executed by the mobile robot. To validate our algorithm, we implemented it on a mobile robot in a real-world environment with non-unidirectional airflow environments and obstacles to mimic a complex, practical search environment. We compared the performance of our proposed algorithm to single-sensory-modality-based ‘olfaction-only’ and ‘vision-only’ navigation algorithms, and a supervised learning-based ‘vision and olfaction fusion’ (Fusion) navigation algorithm. The experimental results show that the proposed LLM-based algorithm outperformed the other algorithms in terms of success rates and average search times in both unidirectional and non-unidirectional airflow environments.
Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep–Wake Stage Classification
Authors: Nateghi Masoud , Rahbar Alam Mahdi , Amiri Hossein , Nasiri Samaneh , Sameni Reza
Published: December 2024

Understanding sleep stages is crucial for diagnosing sleep disorders, developing treatments, and studying sleep’s impact on overall health. With the growing availability of affordable brain monitoring devices, the volume of collected brain data has increased significantly. However, analyzing these data, particularly when using the gold standard multi-lead electroencephalogram (EEG), remains resource-intensive and time-consuming. To address this challenge, automated brain monitoring has emerged as a crucial solution for cost-effective and efficient EEG data analysis. A critical component of sleep analysis is detecting transitions between wakefulness and sleep states. These transitions offer valuable insights into sleep quality and quantity, essential for diagnosing sleep disorders, designing effective interventions, enhancing overall health and well-being, and studying sleep’s effects on cognitive function, mood, and physical performance. This study presents a novel EEG feature extraction pipeline for the accurate classification of various wake and sleep stages. We propose a noise-robust model-based Kalman filtering (KF) approach to track changes in a time-varying auto-regressive model (TVAR) applied to EEG data during different wake and sleep stages. Our approach involves extracting features, including instantaneous frequency and instantaneous power from EEG, and implementing a two-step classifier for sleep staging. The first step classifies data into wake, REM, and non-REM categories, while the second step further classifies non-REM data into N1, N2, and N3 stages. Evaluation on the extended Sleep-EDF dataset (Sleep-EDFx), with 153 EEG recordings from 78 subjects, demonstrated compelling results with classifiers including Logistic Regression, Support Vector Machines, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The best performance was achieved with the LGBM and XGBoost classifiers, yielding an overall accuracy of over 77%, a macro-averaged F1 score of 0.69, and a Cohen’s kappa of 0.68, highlighting the efficacy of the proposed method with a remarkably compact and interpretable feature set.
Real-Time Detection of IoT Anomalies and Intrusion Data in Smart Cities Using Multi-Agent System
Authors: Muntean Maria Viorela
Published: December 2024

Analyzing IoT data is an important challenge in the smart cities domain due to the complexity of network traffic generated by a large number of interconnected devices: smart cameras, light bulbs, motion sensors, voice assistants, and so on. To overcome this issue, a multi-agent system is proposed to deal with all machine learning steps, from preprocessing and labeling data to discovering the most suitable model for the analyzed dataset. This paper shows that dividing the work into different tasks, managed by specialized agents, and evaluating the discovered models by an Expert System Agent leads to better results in the learning process.
Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels
Authors: Zhang Cui , Ji Maoxin , Wu Qiong , Fan Pingyi , Fan Qiang
Published: December 2024

In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system’s average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles’ data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system’s AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system.
Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation
Authors: Liu Haibin , Tang Yanglei , Wang Huanjie
Published: December 2024

This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning. Firstly, a judgment mechanism is incorporated into the DBSCAN algorithm. This mechanism is based on the standard deviation and correlation coefficient of point cloud clusters. It improves the algorithm’s adaptive clustering capabilities. Secondly, the point cloud is partitioned into straight-line point cloud clusters, with each cluster generating adaptive grid cells. These adaptive cells extend the range of point cloud registration. This boosts the algorithm’s robustness and provides an initial value for subsequent optimization. Lastly, cell segmentation is performed, where the number of segments is determined by the lengths of the adaptively generated cells, thereby improving registration accuracy. The proposed CSNDT algorithm demonstrates superior robustness, precision, and matching efficiency compared to classical point cloud registration methods such as the Iterative Closest Point (ICP) algorithm and the NDT algorithm.
Enhanced Localization in Wireless Sensor Networks Using a Bat-Optimized Malicious Anchor Node Prediction Algorithm
Authors: Sreeja Balachandran Nair Premakumari , Sundaram Gopikrishnan , Rivera Marco , Wheeler Patrick
Published: December 2024

The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm. This approach utilizes a refined bat optimization algorithm to improve both the precision of localization and the security of WSNs. By integrating advanced optimization with density-based clustering and probabilistic analysis, BO-MAP effectively identifies and isolates malicious nodes. Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods—namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test—across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. Notably, BO-MAP achieves an impressive true positive rate of 95% and a false positive rate of 5%, with an area under the receiver operating characteristic curve of 0.98. Additionally, BO-MAP exhibits consistent reliability across different levels of attack severity and network conditions, highlighting its suitability for deployment in practical WSN environments.
Can a Light Detection and Ranging (LiDAR) and Multispectral Sensor Discriminate Canopy Structure Changes Due to Pruning in Olive Growing? A Field Experimentation
Authors: Perna Carolina , Pagliai Andrea , Sarri Daniele , Lisci Riccardo , Vieri Marco
Published: December 2024

The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees’ canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove located in Tenute di Cesa Farm, in the east of Tuscany, Italy, characterized by a 4x6 m planting layout and by developed plants. LiDAR data were used to measure canopy volumes, height, and diameter, and the generated point cloud was studied to assess the difference in density between treatments. Ten plants were selected for the study. To validate the LiDAR results, manual measurements of the canopy height and diameter dimensions of the plants were taken. The pruning weights of the monitored plants were obtained to assess the correlation with the canopy characterization data. The results obtained showed that pruning did not affect the results of the multispectral sensor, and the potential variation in canopy density and porosity did not lead to different results with this instrument. Plant volumes, height, and diameters calculated with the LiDAR sensor correlated well with the values of manual measurements, while volume differences between before and after pruning obtained good correlations with pruning weights (Pearson correlation coefficient: 0.66–0.83). The study of point cloud density in canopy thickness and height showed different shapes before and after pruning, especially in the former case. Correlations between point cloud density obtained from LiDAR and multispectral sensor results were not statistically significant. Even if more studies are necessary, the results obtained can be of interest in pruning management.
Sensor Fusion Method for Object Detection and Distance Estimation in Assisted Driving Applications
Authors: Favelli Stefano , Xie Meng , Tonoli Andrea
Published: December 2024

The fusion of multiple sensors’ data in real-time is a crucial process for autonomous and assisted driving, where high-level controllers need classification of objects in the surroundings and estimation of relative positions. This paper presents an open-source framework to estimate the distance between a vehicle equipped with sensors and different road objects on its path using the fusion of data from cameras, radars, and LiDARs. The target application is an Advanced Driving Assistance System (ADAS) that benefits from the integration of the sensors’ attributes to plan the vehicle’s speed according to real-time road occupation and distance from obstacles. Based on geometrical projection, a low-level sensor fusion approach is proposed to map 3D point clouds into 2D camera images. The fusion information is used to estimate the distance of objects detected and labeled by a Yolov7 detector. The open-source pipeline implemented in ROS consists of a sensors’ calibration method, a Yolov7 detector, 3D point cloud downsampling and clustering, and finally a 3D-to-2D transformation between the reference frames. The goal of the pipeline is to perform data association and estimate the distance of the identified road objects. The accuracy and performance are evaluated in real-world urban scenarios with commercial hardware. The pipeline running on an embedded Nvidia Jetson AGX achieves good accuracy on object identification and distance estimation, running at 5 Hz. The proposed framework introduces a flexible and resource-efficient method for data association from common automotive sensors and proves to be a promising solution for enabling effective environment perception ability for assisted driving.
Assessing the Theoretical Scope of Environmental Justice in Contemporary Literature and Developing a Pragmatic Monitoring Framework
Authors: Pandey Hari Prasad , Maraseni Tek Narayan , Apan Armando
Published: December 2024

The environmental justice (EJ) movement has evolved over five decades, encapsulating diverse theories, principles, frameworks, and practices. Despite considerable advancements in this field, the nuances of EJ in the Anthropocene era, along with its monitoring and evaluation, remain ambiguous. This paper endeavors to bridge this gap by amalgamating more than 200 review and empirical articles and theoretical literature to delve into a comprehensive exploration of the EJ discourse to date, utilizing the Planetary Justice Research Framework (PJRF). First, we build on the existing knowledge by using three dimensions of EJ from the PJRF, acknowledging historical legacies, and explaining them with practical examples. Second, we create a comprehensive framework to evaluate (in)justice in real-world applications, highlighting the contextual relationships (intra-, inter-, and transdisciplinary) and the role of spatial, temporal, and contextual factors. Finally, we explore the complex connections between living beings and non-living components, showing how (un)just actions impact the balance within and between planetary systems. Consequently, the newly devised monitoring framework highlights potential instances where questions of (in)justice may arise in practical settings, thereby guiding the formulation of measuring indicators and procedural methodologies.
Characterization of Polychlorinated Biphenyls in Transplanted Mussels (Mytilus galloprovincialis) and Surface Sediments from the Coastal Region of Nemrut Bay, Eastern Aegean Sea
Authors: Gonul Lutfi Tolga
Published: December 2024

Caged mussels enable biomonitoring investigations due to their tendency to absorb contaminants in industrial zones. This study aimed to investigate the levels of seven indicator polychlorinated biphenyls (i7PCB) congeners in the biomonitoring organism Mytillus galloprovincialis over two years (2016–2018) after transplantation from three stations in the Eastern Aegean Sea industrial zone. Additionally, i7PCBs were found in Nemrut Bay’s surface sediments. The highest PCB level was found at Site 5 located near a petroleum refinery of the Petkim Port. According to sediment quality criteria, PCB levels at Sites 1, 4, 5, and 6 may have an adverse biological impact. PCB concentrations varied among samples; congeners 28, 153, 101, and 118 were most prevalent in sediments, whereas congeners 138, 153, 101, and 118 were most prevalent in mussels. i7PCB concentrations were below the maximum residual levels permitted in fishery products. In addition, calculations of the hazard ratio and estimated daily intake show no potential negative impacts from PCB exposure. Significantly positive correlations appeared between PCB (28, 52, 101, 118) levels and the condition index of the mussels. The highest amounts of ∑i7PCB in mussels were identified in September 2016 at Site 2 and in October 2017 at Site 1. Industrialization around the Port of Nemrut had a harmful impact on Sites 1 and 2. Preventing marine pollution plays a key role in ensuring the sustainability of marine living resources and sustainable coastal management.
Sulfur Mortar Goes to Infinity: Mechanical Performance and Characterization of Sulfur Mortar Composed of Different Aggregates During Heating Cycles, Exploring Potential Sustainability, Recyclability, and Circularity
Authors: Wang Qinjian , Delplancke Marie-Paule , Snoeck Didier
Published: December 2024

Sulfur mortar hardens quickly, shows a high chemical resistance, and can be recycled, making it ideal for construction and rehabilitation in extreme environments. Despite its potential for sustainability, current research lacks sufficient characterization of sulfur mortar’s performance during recycling, particularly regarding the physical and chemical changes when iron oxide is introduced. This study investigates the replacement of conventional siliceous sand with high-iron-content sand in sulfur mortar, through a series of five break–recast cycles. The results demonstrate an 11% increase in compressive strength and a 26% increase in flexural strength after five recasting cycles. Optical microscopy and scanning electron microscopy (SEM) revealed that recasting improved the distribution of the sulfur binder, while the formation of iron sulfates filled the gaps between aggregates and the binder. X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) confirmed the presence of iron sulfates, and differential scanning calorimetry (DSC) showed that high-iron-content sulfur mortar narrowed the phase change temperature range, preventing uneven solidification within the samples. This study sheds light on the strengthening mechanisms that occur during the recycling process, enhancing the material’s durability and recyclability. This aligns with circular economy principles, contributes to resource efficiency, and supports sustainable construction practices.
Environmental Expectations, Socioeconomic Demands and Sustainability of the Zapatosa Marsh, Colombia: Perspectives from Local Stakeholders
Authors: Mejía Montoya Jhon Wilson , González Steveen Alexander , Vargas Jimmy Jolman , Rios Richard
Published: December 2024

The Zapatosa Marsh Complex (ZMC) is a strategic ecosystem in Colombia, under threat from human-driven factors such as population growth and subsistence extraction activities. Local residents experience high poverty levels and low educational attainment, exacerbating conflicts between their socio-economic needs and marsh conservation efforts. This study aimed to present a participatory approach to prioritize actions for sustainable development in Chimichagua, the municipality encompassing the largest part of the ZMC. We identified and characterized local stakeholders, engaging them in a participatory action research project to determine barriers and opportunities for economic growth and food security. The insights gathered were collaboratively used to formulate actions that balance the sustainability of the ZMC with local economic development. Stakeholders identified the ZMC as an opportunity for employment and raw materials, while pointing to poor support from territorial entities as the central barrier. A prioritized action was the construction of a concentrate feed production plant utilizing local raw materials. This work offers a methodological contribution that could be applied to address socio-economic and environmental conflicts in other strategic ecosystems.
Meso-Scale Impacts of the Urban Structure Metrics on PM2.5 in China
Authors: Hu Chaonan , Luo Nana , Cai Chao , Cui Yarui , Gao Hongtao , Yan Xing
Published: December 2024

Currently, China’s urbanization has entered a stage of high-quality development, and rapid urban growth has led to a series of environmental pollution issues, with haze pollution caused by delicate particulate matter (PM2.5) increasingly becoming a focal point for scholars. This paper focuses on Xi’an, Wuhan, Taiyuan, and Lanzhou, exploring the relationship between PM2.5 concentrations using methods such as the Pearson correlation coefficient (PCC), dominance analysis (DA), and ordinary least squares regression (OLSR). The results indicate that (1) Xi’an’s built environment is distributed radially, Wuhan is circular, Taiyuan is grid-like, and Lanzhou is strip-shaped; (2) Xi’an, Wuhan, Taiyuan, and Lanzhou exhibited different development patterns between 2014 and 2022, with Xi’an experiencing rapid urban expansion but lagging infrastructure, while Wuhan focused on improving post-urbanization quality. Taiyuan and Lanzhou advanced expansion and infrastructure construction simultaneously. (3) The regression coefficients of PM2.5 concentration concerning factors such as building density, green space density, road density, and water density in Xi’an are relatively high. In contrast, the regression coefficients of urban spatial structure factors in Wuhan, Taiyuan, and Lanzhou show consistency. This study provides a basis for reducing PM2.5 and explores the interaction and contribution relationship between urban spatial structure and PM2.5, offering a new research perspective for promoting urban sustainable development.
Driving Product Innovative Performance Through Network Connections: A Study Based on the fsQCA Approach
Authors: He Guowen , Bi Lehua , Yang Xuehui
Published: December 2024

To explore how firms’ network connections improve product innovative performance (PIP) for sustainable development, this study takes 230 firms involved in product innovation as the research object, and uses the fuzzy-set qualitative comparative analysis (fsQCA) method to explore how strong ties, bridging ties, technology exchange, technology transfer, and innovation resource interaction can stimulate product innovative performance. This study has found that firms have three configurations to achieve high product innovative performance and two configurations to achieve non-high product innovative performance. Only appropriately matched types of strong ties, bridging ties, technological exchange, technological transfer, and innovation resource interaction can promote product innovative performance. The findings enrich the theoretical understanding of the relationships among network connections, knowledge flow, innovation resource interaction, and product innovative performance.
Decision-Making in Grain Supply Chains for Sustainable Food System
Authors: Garima  , Olaru Doina , Smith Brett , Siddique Kadambot H. M.
Published: December 2024

Grain supply chains (GSCs) are crucial for global food security, economic development, and environmental sustainability. This study investigates the complexities and challenges of enhancing the resilience of GSCs. The research unravels decision-making intricacies, assesses post-harvest risks and stakeholder interactions, and identifies future research avenues. This systematic literature review (SLR), following PRISMA guidelines, and conducted with two search rounds, addressed questions of resilience and efficiency of post-harvest GSCs, key decision factors of stakeholders influencing the sustainability and operational success of GSCs, and methods that can be used to develop robust decision-making for managing risks and uncertainties in GSCs. A comprehensive database of post-2000 journal articles from the Web of Science and Scopus was analysed using bibliometric and content analysis. This research identified five key themes: (1) operational complexity requiring strategic resilience, (2) comprehensive risk management strategies, (3) critical role of transport and storage infrastructure, (4) significant impact of stakeholder decisions on GSC dynamics, and (5) diverse methodological approaches for robust analysis. These themes illustrate the multifaceted challenges and opportunities within GSCs, suggesting that integrating operations research with behavioural economics is vital for improving strategic decision-making. The results advocate for adopting advanced technologies and innovative multidisciplinary methods to improve GSC efficiency and resilience, which are essential for navigating geopolitical tensions, market fluctuations, and complex stakeholder behaviours. It identified under-researched areas such as farmer transport decisions and logistics provider selection, and integrated emerging themes (such as resilience and behavioural aspects), contributing to the body of knowledge and supporting enhanced decision-making in GSCs for a stable global food supply system.
Blockchain Adoption for a Circular Economy in the Chinese Automotive Industry: Identification of Influencing Factors Using an Integrated TOE-TAM Model
Authors: Chen Jun , Abdul-Hamid Asma-Qamaliah , Zailani Suhaiza
Published: December 2024

Although the potential of the blockchain has been extensively recognized by scholars and practitioners across multiple fields, research on its adoption in the framework of the circular economy (CE) is still scarce. In this context, this study extends the technology acceptance model (TAM) by integrating the technology–organization–environment (TOE) framework to holistically understand how technological perception factors (perceived usefulness and perceived ease of use) interact with organizational and environmental factors in influencing the intention to adopt the blockchain in the CE within the context of the Chinese automotive supply chain. Based on survey data from 305 respondents from Chinese automotive companies, the proposed hybrid TOE-TAM conceptual model was validated. The results indicate that, except for the effects of the knowledge management capability on the perceived ease of use and regulatory support on blockchain adoption intention, all of the other hypotheses are deemed significant. Moreover, by conducting an in-depth analysis of the evolution of blockchain adoption intention in the CE, this study not only deepens the understanding of how the technology is disseminated but also provides valuable insights to theory and practice within the Chinese automotive value chain.
Sustainable Consumption and Production of Edible Plants in the Context of Reaching the EU Climate Neutrality by 2050: A Literature Review
Authors: Mazur-Włodarczyk Katarzyna , Gruszecka-Kosowska Agnieszka
Published: December 2024

Background: Human population has been overexploiting natural resources for many decades. Consumerism drives a strong economy, but at the same time it degrades the environment. The solution is to turn it into sustainable development. In this study, our aim was to determine how the quest for sustainable consumption was perceived in the current scientific literature; Methods: A total of 712 papers from the Scopus, Web of Science, and PubMed databases were investigated. Descriptive statistics and VOSviewer (version 1.6.18.) and Wordcloud software were used for further analysis. The analysis involved investigating publication trends over time, engaged countries and funding agencies, co-authorships, document types, thematic categories, sustainable development goals orientation, and the most frequently occurring words. Results: Word clouds created in the WordArt program based on the titles and abstracts indicated “food” to be the most frequently occurring word. The content of the articles was analyzed based on three term clusters: (1) “food waste”, “waste”, “food security”; (2) “agriculture” and “vertical farming”; and (3) “energy”. Conclusions: Analyzed articles surpassed the European Union, indicating the global range of striving for sustainability. The co-occurrence of the following sustainable development goals (SDGs) was indicated in the study: no. 1 (“no poverty”), no. 2 (“zero hunger”), no. 3 (“good health and well-being”), no. 6 (“clean water and sanitation”), no. 7 (“affordable and clean energy”), no. 8 (“decent work and economic growth”), no. 9 (“industry, innovation and infrastructure”), no. 10 (“reduced inequalities”), no. 12 (“responsible production and consumption”), no. 13 (“climate action”), no. 14 (“life below water”), and no. 15 (“life on land”).
Construction of General Types of Fuzzy Implications Produced by Comparing Different t-Conorms: An Application Case Using Meteorological Data
Authors: Daniilidou Athina , Konguetsof Avrilia , Papadopoulos Basil
Published: December 2024

The objective of this paper is to compare a fuzzy implication produced by t-conorm probor with three other fuzzy implications constructed by t-conorms max, Einstein, and Lukasiewicz. Firstly, in methodology, six pairs of combinations of five t-conorm comparisons are performed in order to find the ranking order of five fuzzy implications. Moreover, the evaluation and calculation of the four fuzzy implications (probor, max, Einstein, and Lukasiewicz) are made using meteorological data, fuzzifying the crisp values of temperature and humidity, constructing four membership degree functions, and inserting as inputs the membership degrees of meteorological variables into the two variables of the fuzzy implications. Finally, extensive tests are made so as to find which membership degree function and which fuzzy implication receives the best and the worst results. The key findings are that the application of isosceles trapezium to the fuzzy implications of Probor and Einstein gives the best values, while fuzzy implication Lukasiewicz, although it was found to be first in the ranking order, is rejected due to unreliable results. As a result, the crucial role of these implications lies in the fact that they are non-symmetrical, i.e., there is a clear difference between the cause and the causal.
Chronic Hepatitis C Infection Treated with Direct-Acting Antiviral Agents and Occurrence/Recurrence of Hepatocellular Carcinoma: Does It Still Matter?
Authors: Smirne Carlo , Crobu Maria Grazia , Landi Irene , Vercellino Nicole , Apostolo Daria , Pinato David James , Vincenzi Federica , Minisini Rosalba , Tonello Stelvio , D’Onghia Davide , Ottobrelli Antonio , Martini Silvia , Bracco Christian , Fenoglio Luigi Maria , Campanini Mauro , Berton Alessandro Maria , Ciancio Alessia , Pirisi Mario
Published: December 2024

Hepatitis C virus (HCV) infection is a significant risk factor for liver cirrhosis and hepatocellular carcinoma (HCC). Traditionally, the primary prevention strategy for HCV-associated HCC has focused on removing infection through antiviral regimes. Currently, highly effective direct-acting antivirals (DAAs) offer extraordinary success across all patient categories, including cirrhotics. Despite these advancements, recent studies have reported that even after sustained virologic response (SVR), individuals with advanced liver disease/cirrhosis at the time of DAA treatment may still face risks of HCC occurrence or recurrence. Based on this premise, this review tries to shed light on the multiple mechanisms that establish a tumorigenic environment, first, during chronic HCV infection and then, after eventual viral eradication by DAAs. Furthermore, it reviews evidence reported by recent observational studies stating that the use of DAAs is not associated with an increased risk of HCC development but rather, with a significantly lower chance of liver cancer compared with DAA-untreated patients. In addition, it seeks to provide some practical guidance for clinicians, helping them to manage HCC surveillance of patients who have achieved SVR with DAAs.
Special Issue: “Post-COVID-19 Syndrome”
Authors: Scharf Rüdiger E.
Published: December 2024

On 30 January 2020, the World Health Organization declared COVID-19 a Public Health Emergency of International Concern (PHEIC)—the highest WHO warning level [...]
Prediction of the Dissolved Oxygen Content in Aquaculture Based on the CNN-GRU Hybrid Neural Network
Authors: Ma Ying , Fang Qiwei , Xia Shengwei , Zhou Yu
Published: December 2024

The dissolved oxygen (DO) content is one of the important water quality parameters; it is crucial for assessing water body quality and ensuring the healthy growth of aquatic organisms. To enhance the prediction accuracy of DO in aquaculture, we propose a fused neural network model integrating a convolutional neural network (CNN) and a gated recurrent unit (GRU). This model initially employs a CNN to extract primary features from water quality parameters. Subsequently, the GRU captures temporal information and long-term dependencies, while a temporal attention mechanism (TAM) is introduced to further pinpoint crucial information. By optimizing model parameters through an improved particle swarm optimization (IPSO) algorithm, we develop a comprehensive IPSO-CNN-GRU-TAM prediction model. Experiments conducted using water quality datasets collected from Eagle Mountain Lake demonstrate that our model achieves a root mean square error (RMSE) of 0.0249 and a coefficient of determination (R2) of 0.9682, outperforming other prediction models with high precision. The model exhibits stable performance across fivefold cross-validation and datasets of varying depths, showcasing robust generalization capabilities. In summary, this model allows aquaculturists to precisely regulate the DO content, ensuring fish health and growth while achieving energy conservation and carbon reduction, aligning with the practical demands of modern aquaculture.