bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacological Activities of Schiff Bases and Their Derivatives with Low and High Molecular Phosphonates
Authors: Tsacheva Ivelina , Todorova Zornica , Momekova Denitsa , Momekov Georgi , Koseva Neli
Published: June 2023

This review paper is focused on the design of anthracene and furan-containing Schiff bases and their advanced properties as ligands in complex transition metal ions The paper also provides a brief overview on a variety of biological applications, namely, potent candidates with antibacterial and antifungal activity, antioxidant and chemosensing properties. These advantageous properties are enhanced upon metal complexing. The subject of the review has been extended with a brief discussion on reactivity of Schiff bases with hydrogen phosphonates and the preparation of low and high molecular phosphonates, as well as their application as pharmacological agents. This work will be of interest for scientists seeking new challenges in discovering advanced pharmacological active molecules gaining inspiration from the versatile families of imines and aminophosphonates.
Antidepressants and the Risk of Fall-Related Injury in Older Adults with Incident Depression in the United States: A Comparative Safety Analysis
Authors: Tabah Ashley , Gold Laura S. , Marcum Zachary A. , Hansen Ryan N.
Published: June 2023

Though it is well documented that antidepressants are associated with an increased risk of falls in older adults at the drug class level, the comparative risk between individual antidepressants for fall injury in older adults with depression is unknown. Currently, clinicians are making decisions at the drug class level without consideration of the potential that there could be safer choices within classes. We compared the risk of fall injury among initiators of bupropion, duloxetine, fluoxetine, paroxetine, and venlafaxine to those of (es)citalopram and, separately, sertraline. We performed a retrospective cohort study using the MarketScan® Medicare Supplemental claims from 2007 to 2019. Individuals had incident depression (washout in previous continually enrolled year) with a first antidepressant claim up to three months after depression diagnosis. Individuals were followed for the first three months of antidepressant use until the first occurrence of fall injury, change/discontinuation of antidepressant, discontinued insurance coverage, or end of study. Propensity score inverse probability of treatment-weighted Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals for each antidepressant comparison and fall injury. We identified 114,505 individuals (mean age 76.6 years, 68% female, 97% without prior fall). A higher risk of fall injury was associated with initiating bupropion (HR 1.20 to 1.61), duloxetine (HR 1.27 to 1.36), paroxetine (HR 1.14 to 1.22), and venlafaxine (HR 1.22 to 1.34) when compared to (es)citalopram or sertraline. New use of duloxetine, bupropion, paroxetine, and venlafaxine was associated with a higher risk of fall injury compared to (es)citalopram and sertraline.
Physicochemical Properties of 3D-Printed Polylactic Acid/Hydroxyapatite Scaffolds
Authors: Pérez-Davila Sara , Garrido-Gulías Natalia , González-Rodríguez Laura , López-Álvarez Miriam , Serra Julia , López-Periago José Eugenio , González Pío
Published: June 2023

The reconstruction or regeneration of damaged bone tissue is one of the challenges of orthopedic surgery and tissue engineering. Among all strategies investigated, additive manufacturing by fused deposition modeling (3D-FDM printing) opens the possibility to obtain patient-specific scaffolds with controlled architectures. The present work evaluates in depth 3D direct printing, avoiding the need for a pre-fabricated filament, to obtain bone-related scaffolds from direct mixtures of polylactic acid (PLA) and hydroxyapatite (HA). For it, a systematic physicochemical characterization (SEM-EDS, FT-Raman, XRD, micro-CT and nanoindentation) was performed, using different PLA/HA ratios and percentages of infill. Results prove the versatility of this methodology with an efficient HA incorporation in the 3D-printed scaffolds up to 13 wt.% of the total mass and a uniform distribution of the HA particles in the scaffold at the macro level, both longitudinal and cross sections. Moreover, an exponential distribution of the HA particles from the surface toward the interior of the biocomposite cord (micro level), within the first 80 µm (10% of the entire cord diameter), is also confirmed, providing the scaffold with surface roughness and higher bioavailability. In relation to the pores, they can range in size from 250 to 850 µm and can represent a percentage, in relation to the total volume of the scaffold, from 24% up to 76%. The mechanical properties indicate an increase in Young’s modulus with the HA content of up to ~50%, compared to the scaffolds without HA. Finally, the in vitro evaluation confirms MG63 cell proliferation on the 3D-printed PLA/HA scaffolds after up to 21 days of incubation.
Influence of Reduction with NaBH4 and HCl in Obtaining Amino Derivatives of Cashew Gum and Cytotoxic Profile
Authors: Brito Francisco das C. M. , Lopes Wilton C. , Ribeiro Fábio O. S. , Rodrigues Raiza Raianne Luz , França Rodrigues Klinger Antonio da , Santos Machado Fabrício dos , Araújo Ana Jérsia , Marinho Filho José Delano Barreto , Oliveira Antônia Carla J. , Filho Edson C. S. , Ribeiro Irisvan S. , Paula Regina C. M. de , Hallwass Fernando , Viana Vicente Gálber F. , Silva Durcilene A.
Published: June 2023

Tree-exuded gums are natural polymers that represent an abundant raw material in the food and pharmaceutical industries. The cashew gum can be obtained by exudation of trees of the genus Anacardium, a native species of the Brazilian northeast; its polymer consists of monosaccharide units propitious to the action of chemical reactions that tend to improve their intrinsic characteristics among them, as the degree of hydro-solubility. The objective of this work was to modify the exudate gum of Anacardium occidentale (cashew gum (CG)) through an amine reaction. The modification was confirmed by Nuclear Magnetic Resonance (1H NMR), infrared spectroscopy (FTIR), gel permeation chromatography (GPC), zeta potential, and thermogravimetric analysis (TG). In addition, the chemical modification altered the molar mass and surface charge of the CG, and the amino group binding to the CG polymers was confirmed by FTIR spectra. In addition, cytotoxicity tests were performed where cell viability was estimated by an MTT assay on RAW 264.7 macrophages. Through these tests, it was found that the amine caused an increase in the thermal stability of the amino compounds and did not present cytotoxic potential at concentrations below 50.0 mg/L.
Anthraquinone-Polyaniline-Integrated Textile Platforms for In Situ Electrochemical Production of Hydrogen Peroxide for Microbial Deactivation
Authors: Mugo Samuel M. , Lu Weihao , Robertson Scott
Published: June 2023

Hydrogen peroxide (H2O2) is a versatile and effective disinfectant against common pathogenic bacteria such as Escherichia coli (E. coli). Electrochemical H2O2 generation has been studied in the past, but a lack of studies exists on miniaturized electrochemical platforms for the on-demand synthesis of H2O2 for antibacterial applications. In this article, a chemically modified cotton textile platform capable of in situ H2O2 production is demonstrated for E. coli deactivation. The cotton textile was modified by layer-by-layer coating with conductive carbon nanotubes/cellulose nanocrystals (CNT/CNC) and a polymer of polyaniline (PANI) decorated with anthraquinone (AQ), designated as the AQ@PANI@CNT/CNC@textile antibacterial patch. The AQ@PANI@CNT/CNC@textile antibacterial textile patch H2O2 production capabilities were evaluated using both electrochemical and colorimetric methods. The AQ@PANI@CNT/CNC@textile antibacterial patch electrochemically produced H2O2 concentrations up to 209 ± 25 µM over a 40 min period and displayed a log reduction of 3.32 for E. coli over a period of 2 h. The AQ@PANI@CNT/CNC@textile antibacterial patch offers promise for use as a self-disinfecting pathogen control platform.
Integrating TPM and Industry 4.0 to Increase the Availability of Industrial Assets: A Case Study on a Conveyor Belt
Authors: Mendes David , Gaspar Pedro D. , Charrua-Santos Fernando , Navas Helena
Published: June 2023

As the global market becomes increasingly competitive and demanding, companies face the challenge of responding swiftly and efficiently to customer needs. To maintain a competitive advantage, organisations must optimise the usage of their assets. This study focuses on the critical role of maintenance management and presents a novel, cost-effective, and easily applicable model that integrates Industry 4.0 (I4.0) and Total Productive Maintenance (TPM) principles to enhance production processes. The proposed model incorporates a real-time monitoring system equipped with sensors, a gateway, and Internet of Things (IoT) services. These components enable data acquisition, transmission, storage, and visualisation through both mobile and fixed devices. The model’s effectiveness was validated through its implementation on a conveyor belt in a feed mill. The availability of the conveyor belt was around 89.5% before TPM implementation. After the implementation of TPM, it was possible to observe that the availability of the treadmill increased to 92.7% in a workweek of 105 h. On the other hand, it was possible to observe a considerable improvement in terms of the average time of each intervention, with a reduction of around 53%. Moreover, the model also facilitated improvements in the maintenance plan for the conveyor belt. Operators were trained to effortlessly comply with the new autonomous maintenance plan, enhancing operational efficiency. The joint application of I4.0 and TPM demonstrated significant advancements in maintenance management. The flexibility of the developed model allows for easy adaptation to monitor other condition parameters, enabling real-time decision-making for both the maintenance and production departments. This integrated approach holds the potential to drive process optimisation, ultimately contributing to improved productivity, cost-effectiveness, and competitiveness.
Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm
Authors: Liu Liqun , Li Yang
Published: June 2023

With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquila optimization algorithm (IAO) is used to optimize the parameters of the LSTM neural network to establish a model for predicting the actual photovoltaic power. The experimental results show that the proposed IAO-LSTM photovoltaic power prediction model has less error, and its overall quality and performance are significantly improved compared with the previously proposed AO-CNN model.
Experimental Study on Wide-Graded Soil Transport in Unsteady Flow
Authors: Zhao Tianlong , Ma Tingsen , Fu Changjing , Zhang Chuan
Published: June 2023

A special study on the interaction mechanism between flow and soil is of great significance for revealing the macro breaching mechanism of barrier dams. To study the scouring characteristics of wide-graded sediment under different flow conditions, flume scour tests were conducted regarding the grading curve of dam material and the discharge process of the Tangjiashan barrier dam. The results show that: (1) The scouring process of narrow-graded or uniform sediments is the formation and movement of the sand wave, while the scouring mode of wide-graded sediment is mainly the formation, expansion, and movement of the scouring pit. (2) Under the condition of weak unsteady flow, the surrounding and shielding effect of coarse particles on fine particles is obvious, and the erosion resistance of the material is strong. However, under the condition of strong unsteady flow, the erosion resistance is weak. (3) The erosion of wide-graded sediment is mainly caused by slope angle collapse at the initial stage, and mainly reflected by traceable erosion at the later stage. Therefore, in the initial stage of erosion, the downstream erosion intensity is high, and the bed surface can easily form a slope inclined downstream. (4) The scouring intensity under the condition of unsteady flow is greater than that under the condition of steady flow. The sediment transport formula based on the condition of steady flow cannot be used to calculate the dam break process directly.
Sustainable Waste Management for Clean and Safe Environments in the Recreation and Tourism Sector: A Case Study of Lithuania, Turkey and Morocco
Authors: Perkumienė Dalia , Atalay Ahmet , Safaa Larbi , Grigienė Jurgita
Published: June 2023

Due to intensive tourism and increasing demand for recreational activities, people produce more waste. In this context, the aim of this research is to determine the opinions of experts working in tourism and recreation areas in Lithuania, Turkey and Morocco with respect to waste management problems. Interviews were held in Turkey, Lithuania and Morocco in order to determine the attitudes of experts about recreational activities with respect to a clean environment and sustainability. A semistructured interview form was used for interviews with forest operators and other experts. The sample group consisted of 24 experts from Turkey, Lithuania and Morocco. The prominent results of the interviews conducted in the three countries regarding waste and sustainable waste management can be listed as follows: intensive waste production caused by participants, inadequate legal regulation, insufficient recycling, damage to the environment and natural structures, inadequate control and inspections, lack of education and awareness activities, an insufficient number of personnel and insufficient infrastructure. Sustainable waste management strategies, such as reducing waste generation, improving waste collection and disposal methods, and promoting recycling and composting, can help to mitigate these negative impacts.
Enhancing Spatial Variability Representation of Radar Nowcasting with Generative Adversarial Networks
Authors: Gong Aofan , Li Ruidong , Pan Baoxiang , Chen Haonan , Ni Guangheng , Chen Mingxuan
Published: June 2023

Weather radar plays an important role in accurate weather monitoring and modern weather forecasting, as it can provide timely and refined weather forecasts for the public and for decision makers. Deep learning has been applied in radar nowcasting tasks and has exhibited a better performance than traditional radar echo extrapolation methods. However, current deep learning-based radar nowcasting models are found to suffer from a spatial “blurry” effect that can be attributed to a deficiency in spatial variability representation. This study proposes a Spatial Variability Representation Enhancement (SVRE) loss function and an effective nowcasting model, named the Attentional Generative Adversarial Network (AGAN), to alleviate this blurry effect by enhancing the spatial variability representation of radar nowcasting. An ablation experiment and a comparison experiment were implemented to assess the effect of the generative adversarial (GA) training strategy and the SVRE loss, as well as to compare the performance of the AGAN and SVRE loss function with the current advanced radar nowcasting models. The performances of the models were validated on the whole test set and inspected in two storm cases. The results showed that both the GA strategy and SVRE loss function could alleviate the blurry effect by enhancing the spatial variability representation, which helps the AGAN to achieve better nowcasting performance than the other competitor models. Our study provides a feasible solution for high-precision radar nowcasting applications.
Thaw-Season InSAR Surface Displacements and Frost Susceptibility Mapping to Support Community-Scale Planning in Ilulissat, West Greenland
Authors: Scheer Johanna , Caduff Rafael , How Penelope , Marcer Marco , Strozzi Tazio , Bartsch Annett , Ingeman-Nielsen Thomas
Published: June 2023

In permafrost regions, ground surface deformations induced by freezing and thawing threaten the integrity of the built environment. Mapping the frost susceptibility of the ground at a high spatial resolution is of practical importance for the construction and planning sectors. We processed Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data from thawing seasons 2015 to 2019, acquired over the area of Ilulissat, West Greenland. We used a least-squares inversion scheme to retrieve the average seasonal displacement (S) and long-term deformation rate (R). We secondly investigated two different methods to extrapolate active layer thickness (ALT) measurements, based on their statistical relationship with remotely sensed surface characteristics. A generalized linear model (GLM) was first implemented, but the model was not able to fit the data and represent the ALT spatial variability over the entire study domain. ALT were alternatively averaged per vegetation class, using a land cover map derived by supervised classification of Sentinel-2 images. We finally estimated the active layer ice content and used it as a proxy to map the frost susceptibility of the ground at the community scale. Fine-grained sedimentary basins in Ilulissat were typically frost susceptible and subject to average seasonal downward displacements of 3 to 8 cm. Areas following a subsiding trend of up to 2.6 cm/yr were likely affected by permafrost degradation and melting of ground ice below the permafrost table. Our approach enabled us to identify frost-susceptible areas subject to severe seasonal deformations, to long-term subsidence induced by degrading permafrost, or to both. Used in combination with traditional site investigations, InSAR maps provide valuable information for risk management and community planning in the Arctic.
The Uncertainty of SNO Cross-Calibration for Satellite Infrared Channels
Authors: Gu Zhong , Chen Lin , Dai Huixing , Tian Lin , Hu Xiuqing , Zhang Peng
Published: June 2023

The on-orbit radiometric calibration is a fundamental task in quantitative remote sensing applications. A widely used calibration method is the cross-calibration based on Simultaneous Nadir Observation (SNO), which involves using high-precision reference instruments to calibrate lower-precision onboard instruments. However, despite efforts to match the observation time, spatial location, field geometry, and instrument spectra, errors can still be introduced during the matching processes and linear regression analysis. This paper focuses on the error generated by sample matching and the error fitting method generated by the sample fitting method. An error propagation analysis is performed to develop a generic model for assessing the uncertainty of the SNO cross-calibration method itself in meteorological satellite infrared channels. The model is validated using the payload parameters of the Hyperspectral Infrared Atmospheric Sounder (HIRAS) and the Medium Resolution Spectral Imager (MERSI) instruments aboard the FengYun-3D (FY-3D). Simulation experiments are performed considering typical bright temperatures, different background fields, and varying matching threshold conditions. The results demonstrate the effectiveness of the proposed model in capturing the error propagation chain in the SNO cross-calibration process. The model provides valuable insight into error analysis in the SNO cross-calibration method and can assist in determining the optimal sample matching threshold for achieving radiometric calibration accuracy.
Dynamic Changes of Terrestrial Water Cycle Components over Central Asia in the Last Two Decades from 2003 to 2020
Authors: Odinaev Mirshakar , Hu Zengyun , Chen Xi , Mao Min , Zhang Zhuo , Zhang Hao , Wang Meijun
Published: June 2023

The terrestrial water cycle is important for the arid regions of central Asia (CA). In this study, the spatiotemporal variations in the three climate variables [temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET)] and terrestrial water cycle components [soil moisture (SM), snow water equivalent (SWE), runoff, terrestrial water storage (TWS), and groundwater storage (GWS)] of CA are comprehensively analyzed based on multiple datasets from 2003 to 2020. The major results are as follows: (1) Significant decreasing trends were observed for the TWS anomaly (TWSA) and GWS anomaly (GWSA) during 2003–2020, indicating serious water resource depletion. The annual linear trend values of TWSA and GWSA are −0.31 and −0.27 mm/a, respectively. The depletion centers are distributed over most areas of western and southern Kazakhstan (KAZ) and nearly all areas of Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK). (2) TMP and PET have the largest significant negative impacts on SM and SWE. The PRE has a positive impact on terrestrial water variations. (3) During 1999–2019, water withdrawal did not significantly increase, whereas TWS showed a significant decreasing trend. Our results provide a comprehensive analysis of the basic TWS variation that plays a significant role in the water resource management of CA.
Regional Atmospheric CO2 Response to Ecosystem CO2 Budgets in China
Authors: Li Haixiao , Lian Yi , Renyang Qianqian , Liu Le , Qu Zihan , Lee Lien-Chieh
Published: June 2023

The distribution of atmospheric CO2 is not homogenous, primarily due to variations in the CO2 budgets of regional terrestrial ecosystems. To formulate a comprehensive strategy to combat the increasing global CO2 levels and associated warming, it is crucial to consider both the distribution of atmospheric CO2 and the CO2 budgets of ecosystems. This study focused on analyzing the relationship between regional atmospheric CO2 and CO2 budgets in China from 2010 to 2017. Initially, a robust estimation model of net ecosystem CO2 exchange was developed to calculate CO2 budgets using collected emission data. Subsequently, Pearson correlation, redundancy analysis, and geographically weighted regression techniques were employed to examine the link between atmospheric CO2 levels, CO2 budgets, and other meteorological factors at various spatial and temporal scales. The findings from the redundancy analysis and geographically weighted regression indicated that the atmospheric CO2 content of each province could not be solely determined by the regional CO2 budgets. However, a significant and positive correlation between atmospheric CO2 levels and CO2 budgets was observed in non-coastal provinces for a period of six months (R2 ranging from 0.46 to 0.83). Consequently, it is essential to promote a balance between CO2 emissions and the CO2 uptake capacity of regional ecosystems. This balance would minimize positive CO2 budgets and effectively mitigate the increase in atmospheric CO2 levels.
A Cost-Effective Method for Automatically Measuring Mechanical Parts Using Monocular Machine Vision
Authors: Nogueira Vinicius V. E. , Barca Luiz F. , Pimenta Tales C.
Published: June 2023

Automatic measurements via image processing can accelerate measurements and provide comprehensive evaluations of mechanical parts. This paper presents a comprehensive approach to automating evaluations of planar dimensions in mechanical parts, providing significant advancements in terms of cost-effectiveness, accuracy, and repeatability. The methodology employed in this study utilizes a configuration comprising commonly available products in the industrial computer vision market, therefore enabling precise determinations of external contour specifications for mechanical components. Furthermore, it presents a functional prototype for making planar measurements by incorporating an improved subpixel edge-detection method, thus ensuring precise image-based measurements. The article highlights key concepts, describes the measurement procedures, and provides comparisons and traceability tests as a proof of concept for the system. The results show that this vision system did achieve suitable precision, with a mean error of 0.008 mm and a standard deviation of 0.0063 mm, when measuring gauge blocks of varying lengths at different heights. Moreover, when evaluating a circular sample, the system resulted in a maximum deviation of 0.013 mm, compared to an alternative calibrated measurement machine. In conclusion, the prototype validates the methods for planar dimension evaluations, highlighting the potential for enhancing manual measurements, while also maintaining accessibility. The presented system expands the possibilities of machine vision in manufacturing, especially in cases where the cost or agility of current systems is limited.
A Simultaneous Localization and Mapping System Using the Iterative Error State Kalman Filter Judgment Algorithm for Global Navigation Satellite System
Authors: You Bo , Zhong Guangjin , Chen Chen , Li Jiayu , Ma Ersi
Published: June 2023

Outdoor autonomous mobile robots heavily rely on GPS data for localization. However, GPS data can be erroneous and signals can be interrupted in highly urbanized areas or areas with incomplete satellite coverage, leading to localization deviations. In this paper, we propose a SLAM (Simultaneous Localization and Mapping) system that combines the IESKF (Iterated Extended Kalman Filter) and a factor graph to address these issues. We perform IESKF filtering on LiDAR and inertial measurement unit (IMU) data at the front-end to achieve a more accurate estimation of local pose and incorporate the resulting laser inertial odometry into the back-end factor graph. Furthermore, we introduce a GPS signal filtering method based on GPS state and confidence to ensure that abnormal GPS data is not used in the back-end processing. In the back-end factor graph, we incorporate loop closure factors, IMU preintegration factors, and processed GPS factors. We conducted comparative experiments using the publicly available KITTI dataset and our own experimental platform to compare the proposed SLAM system with two commonly used SLAM systems: the filter-based SLAM system (FAST-LIO) and the graph optimization-based SLAM system (LIO-SAM). The experimental results demonstrate that the proposed SLAM system outperforms the other systems in terms of localization accuracy, especially in cases of GPS signal interruption.
State-of-the-Art on Brain-Computer Interface Technology
Authors: Peksa Janis , Mamchur Dmytro
Published: June 2023

This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.
Real-Time Segmentation of Unstructured Environments by Combining Domain Generalization and Attention Mechanisms
Authors: Lin Nuanchen , Zhao Wenfeng , Liang Shenghao , Zhong Minyue
Published: June 2023

This paper presents a focused investigation into real-time segmentation in unstructured environments, a crucial aspect for enabling autonomous navigation in off-road robots. To address this challenge, an improved variant of the DDRNet23-slim model is proposed, which includes a lightweight network architecture and reclassifies ten different categories, including drivable roads, trees, high vegetation, obstacles, and buildings, based on the RUGD dataset. The model’s design includes the integration of the semantic-aware normalization and semantic-aware whitening (SAN–SAW) module into the main network to improve generalization ability beyond the visible domain. The model’s segmentation accuracy is improved through the fusion of channel attention and spatial attention mechanisms in the low-resolution branch to enhance its ability to capture fine details in complex scenes. Additionally, to tackle the issue of category imbalance in unstructured scene datasets, a rare class sampling strategy (RCS) is employed to mitigate the negative impact of low segmentation accuracy for rare classes on the overall performance of the model. Experimental results demonstrate that the improved model achieves a significant 14% increase mIoU in the invisible domain, indicating its strong generalization ability. With a parameter count of only 5.79M, the model achieves mAcc of 85.21% and mIoU of 77.75%. The model has been successfully deployed on a a Jetson Xavier NX ROS robot and tested in both real and simulated orchard environments. Speed optimization using TensorRT increased the segmentation speed to 30.17 FPS. The proposed model strikes a desirable balance between inference speed and accuracy and has good domain migration ability, making it applicable in various domains such as forestry rescue and intelligent agricultural orchard harvesting.
Understanding the Role of M13 Bacteriophage Thin Films on a Metallic Nanostructure through a Standard and Dynamic Model
Authors: Nguyen Thanh Mien , Choi Cheol Woong , Lee Ji-Eun , Heo Damun , Lee Ye-Won , Gu Sun-Hwa , Choi Eun Jeong , Lee Jong-Min , Devaraj Vasanthan , Oh Jin-Woo
Published: June 2023

The dynamic and surface manipulation of the M13 bacteriophage via the meeting application demands the creation of a pathway to design efficient applications with high selectivity and responsivity rates. Here, we report the role of the M13 bacteriophage thin film layer that is deposited on an optical nanostructure involving gold nanoparticles/SiO2/Si, as well as its influence on optical and geometrical properties. The thickness of the M13 bacteriophage layer was controlled by varying either the concentration or humidity exposure levels, and optical studies were conducted. We designed a standard and dynamic model based upon three-dimensional finite-difference time–domain (3D FDTD) simulations that distinguished the respective necessity of each model under variable conditions. As seen in the experiments, the origin of respective peak wavelength positions was addressed in detail with the help of simulations. The importance of the dynamic model was noted when humidity-based experiments were conducted. Upon introducing varied humidity levels, the dynamic model predicted changes in plasmonic properties as a function of changes in NP positioning, gap size, and effective index (this approach agreed with the experiments and simulated results). We believe that this work will provide fundamental insight into understanding and interpreting the geometrical and optical properties of the nanostructures that involve the M13 bacteriophage. By combining such significant plasmonic properties with the numerous benefits of M13 bacteriophage (like low-cost fabrication, multi-wavelength optical characteristics devised from a single structure, reproducibility, reversible characteristics, and surface modification to suit application requirements), it is possible to develop highly efficient integrated plasmonic biomaterial-based sensor nanostructures.
Crop Disease Identification by Fusing Multiscale Convolution and Vision Transformer
Authors: Zhu Dingju , Tan Jianbin , Wu Chao , Yung KaiLeung , Ip Andrew W. H.
Published: June 2023

With the development of smart agriculture, deep learning is playing an increasingly important role in crop disease recognition. The existing crop disease recognition models are mainly based on convolutional neural networks (CNN). Although traditional CNN models have excellent performance in modeling local relationships, it is difficult to extract global features. This study combines the advantages of CNN in extracting local disease information and vision transformer in obtaining global receptive fields to design a hybrid model called MSCVT. The model incorporates the multiscale self-attention module, which combines multiscale convolution and self-attention mechanisms and enables the fusion of local and global features at both the shallow and deep levels of the model. In addition, the model uses the inverted residual block to replace normal convolution to maintain a low number of parameters. To verify the validity and adaptability of MSCVT in the crop disease dataset, experiments were conducted in the PlantVillage dataset and the Apple Leaf Pathology dataset, and obtained results with recognition accuracies of 99.86% and 97.50%, respectively. In comparison with other CNN models, the proposed model achieved advanced performance in both cases. The experimental results show that MSCVT can obtain high recognition accuracy in crop disease recognition and shows excellent adaptability in multidisease recognition and small-scale disease recognition.
Flash-Calcined Sediments for Zinc Adsorption
Authors: Chu Duc Chinh , Amar Mouhamadou , Benzerzour Mahfoud , Kleib Joelle , Abriak Nor-Edine
Published: June 2023

Heavy metal contamination of the environment could pose health risks to humans. Until recently, several geochemical routes were considered to address the issue of metallic leachates from sources such as e-waste deposal sites or mining areas. Following the adsorption pathway, this study focused on investigating the ability of calcination products as a low-cost option for the remediation of zinc contaminated effluents. Sediments dredged in northern France were calcined by flash calcination method, before testing the calcination products (FCS750 hereafter) as adsorbent for zinc removal. The calcination process performed at 750 °C resulted in a denser material with a higher specific surface area and lower organic matter content, suited for aqueous remediation. Kinetic and equilibrium assessment underlined a rapid adsorption better described with the Pseudo Second Order model than the Pseudo First Order or Bangham equations. Adsorption models (Langmuir, Freundlich and Temkin) implemented to describe the interaction under two pH conditions (pH = 2; pH = 7) resulted in a maximum adsorption capacity (qmax) of 89.61 mg g−1 under the most favourable configuration. The pH conditions had poor influence on the adsorbing capacity value, which suggested a good buffering property of the calcined sediment and a potential applicability on effluents with different chemistries. A qmax comparison based on 42 studies indicated that FCS750 was more beneficial than most raw organic adsorbents but remained less effective than grafted and composite materials. Nevertheless, the low number of steps necessary for FCS750 processing, the easy access of its precursor (harboring wastes) and its low energy cost suggested good economic competitiveness and pleaded in favor of field experimentations.
Modeling Systems’ Disruption and Social Acceptance—A Proof-of-Concept Leveraging Reinforcement Learning
Authors: Walzberg Julien , Eberle Annika
Published: June 2023

As the need for a just and equitable energy transition accelerates, disruptive clean energy technologies are becoming more visible to the public. Clean energy technologies, such as solar photovoltaics and wind power, can substantially contribute to a more sustainable world and have been around for decades. However, the fast pace at which they are projected to be deployed in the United States (US) and the world poses numerous technical and nontechnical challenges, such as in terms of their integration into the electricity grid, public opposition and competition for land use. For instance, as more land-based wind turbines are built across the US, contention risks may become more acute. This article presents a methodology based on reinforcement learning (RL) that minimizes contention risks and maximizes renewable energy production during siting decisions. As a proof-of-concept, the methodology is tested on a case study of wind turbine siting in Illinois during the 2022–2035 period. Results show that using RL halves potential delays due to contention compared to a random decision process. This approach could be further developed to study the acceptance of offshore wind projects or other clean energy technologies.
An Ecological Reading of Crop–Livestock Interactions—Gers, Southwestern France, 1950 to the Present
Authors: Pédèches Rémi , Aubron Claire , Philippon Olivier , Bainville Sébastien
Published: June 2023

Mixed crop–livestock farming is usually considered to be beneficial for the environment, but the comprehensive characterisation of functional interactions between crops and livestock, and thus the assessment of their ecological relevance, remain problematic. In this article, we design a systemic reading grid focusing on the agricultural practices of crop–livestock interactions, which we organised in four groups according to the agronomic functions they fulfil and the ecological processes involved: (i) animals are used as a source of mechanical energy; (ii) rangelands and permanent grasslands, serving as a source of biomass to manage fertility, are spatially interwoven into the cultivated fields; (iii) on those cultivated fields, non-fodder crops are rotated/associated with fodder crops; (iv) the livestock consume locally produced fodder, grain and straw, and their excreta are spread on cultivated plots. Based on 86 interviews with retired and active farmers, we applied this grid to study the dynamics of crop–livestock integration in a small French agricultural region since 1950. We show that even though the number of mixed crop–livestock farms remains quite high, there has been a massive impoverishment of crop–livestock interactions within these farms. We discuss this trend and the contributions made by the reading grid.
Review on the Application of Organic Fibers as Substitutes for Asbestos in Thin Fiber Cement Sheets from a Sri Lankan Perspective
Authors: Deegoda Isuru , Buddika Samith , Yapa Hiran , Navaratnam Satheeskumar , Zhang Guomin
Published: June 2023

In the past, asbestos siding, roofing, and ceiling sheets became more popular than conventional materials due to their high strength-to-weight ratio, durability, and low cost. However, it was later identified that most types of asbestos are carcinogenic and are responsible for major lethal diseases. In terms of developing a substitute for asbestos, different alternative organic fibers have been investigated as sustainable solutions. Therefore, this article reviewed the usability of organic fibers from wood, banana, bamboo, and coconut coir, which are locally available and abundant in the region, as a substitute for asbestos in the fiber cement industry in Sri Lanka. The quantitative physicomechanical properties of organic fiber and fiber cement composites were evaluated, and the effect of treatment methods on durability was discussed. The previous literature revealed that organic fiber cement thin sheets have comparable mechanical properties in comparison to composites reinforced with asbestos. Furthermore, this study found that these organic-fiber-based composites are energy-efficient and have improved fire resistance. However, the long-term performance is questionable. Hence, further studies on fiber treatment methods and composite durability are needed. Additionally, a comprehensive cost analysis of organic fiber cement composite is recommended to introduce organic fibers into commercial products.
Influence of Climate Change on Carbon Emissions during Grain Production and Its Mechanism
Authors: Liu Muziyun , Liu Hui
Published: June 2023

Abnormal climatic changes and related disasters are increasing in prevalence, with many negative impacts on ecosystems and agricultural production. The area of land in China is vast, including diverse terrain and climate types, and a substantial area is used to grow food crops. Therefore, climate change is having a huge impact on China’s grain production. Currently, the relationship between climate change and carbon emissions during grain production and the underlying mechanism have not been fully clarified. Therefore, this study used an ordinary least squares regression (OLS) model and the system generalized method of moments (SYS-GMM) to examine the influence of climatic change and carbon emissions during grain production, and we constructed mediation effect models to explore the mechanism of influence between them by utilizing panel data in China from 2000 to 2020. In addition, we also examined the adjustment effect of green technology progress and farmland scale. The study found that China’s carbon emissions during grain production increased from 2000 to 2015 and then presented a decreasing trend after 2015. We found that the annual average temperature has a prominent positive effect on carbon emissions during grain production, while the annual average rainfall has a negative effect. Among them, temperature changes mainly lead to the increase in carbon emissions during grain production through the increase in “fertilizer use” and “multiple cropping index”, but the mechanism of rainfall changes’ impact on carbon emissions during grain production is still unclear. In addition, green technology progress and farmland scale play adjustment roles in the impact of climate change on carbon emissions during grain production, and they could significantly suppress carbon emissions. On the basis of the conclusions in this paper, we propose that strengthening climate change adaptation is an important prerequisite for reducing carbon emissions during grain production. Furthermore, China should continue to reduce fertilizer use, facilitate the application of agriculture green technology, and expand the scale of farmland to achieve agricultural carbon emission reduction.
A Novel Odd Beta Prime-Logistic Distribution: Desirable Mathematical Properties and Applications to Engineering and Environmental Data
Authors: Suleiman Ahmad Abubakar , Daud Hanita , Singh Narinderjit Singh Sawaran , Othman Mahmod , Ishaq Aliyu Ismail , Sokkalingam Rajalingam
Published: June 2023

In parametric statistical modeling, it is important to construct new extensions of existing probability distributions (PDs) that can make modeling data more flexible and help stakeholders make better decisions. In the present study, a new family of probability distributions (FPDs) called the odd beta prime generalized (OBP-G) FPDs is proposed to improve the traditional PDs. A new PD called the odd beta prime-logistic (OBP-logistic) distribution has been developed based on the developed OBP-G FPDs. Some desirable mathematical properties of the proposed OBP-logistic distribution, including the moments, moment-generating function, information-generating function, quantile function, stress–strength, order statistics, and entropies, are studied and derived. The proposed OBP-logistic distribution’s parameters are determined by adopting the maximum likelihood estimation (MLE) method. The applicability of the new PD was demonstrated by employing three data sets and these were compared by the known extended logistic distributions, such as the gamma generalized logistic distribution, new modified exponential logistic distribution, gamma-logistic distribution, exponential modified Weibull logistic distribution, exponentiated Weibull logistic distribution, and transmuted Weibull logistic distribution. The findings reveal that the studied distribution provides better results than the competing PDs. The empirical results showed that the new OBP-logistic distribution performs better than the other PDs based on several statistical metrics. We hoped that the newly constructed OBP-logistic distribution would be an alternative to other well-known extended logistic distributions for the statistical modeling of symmetric and skewed data sets.
EREMI: An Innovative Interdisciplinary Approach for Higher Education in Resource Efficient Manufacturing Environments
Authors: Sikora Axel , Titova-Kosturkova Tanya , Janevska Gordana , Kostov Mitko , Halunga Simona , Suciu George , Georgiev Georgi
Published: June 2023

This paper presents an overview of EREMI, a two-year project funded under ERASMUS+ KA203, and its results. The project team’s main objective was to develop and validate an advanced interdisciplinary higher education curriculum, which includes lifelong learning components. The curriculum focuses on enhancing resource efficiency in the manufacturing industry and optimising poorly or non-digitised industrial physical infrastructure systems. The paper also discusses the results of the project, highlighting the successful achievement of its goals. EREMI effectively supports the transition to Industry 5.0 by preparing a common European pool of future experts. Through comprehensive research and collaboration, the project team has designed a curriculum that equips students with the necessary skills and knowledge to thrive in the evolving manufacturing landscape. Furthermore, the paper explores the significance of EREMI’s contributions to the field, emphasising the importance of resource efficiency and system optimisation in industrial settings. By addressing the challenges posed by under-digitised infrastructure, the project aims to drive sustainable and innovative practices in manufacturing. All five project partner organisations have been actively engaged in offering relevant educational content and framework for decentralised sustainable economic development in regional and national contexts through capacity building at a local level. A crucial element of the added value is the new channel for obtaining feedback from students. The survey results, which are outlined in the paper, offer valuable insights gathered from students, contributing to the continuous improvement of the project.
Does Ethical Behaviour Affect Sustainable Development? Evidence from Developed and Developing Countries
Authors: Wang Hui , Chen Haiming , Tawiah Vincent
Published: June 2023

This paper examines the relationship between ethical behavior and green growth for a large sample of 109 countries, comprised of developed and developing countries. We applied panel corrected the standard error (PCSE) and system generalized moment of method (S-GMM) to achieve the set-aside objectives. We use the recent data from Organisation for Economic Co-operation and Development (OCED). Our results show that high ethical behavior is associated with an increase in green growth, suggesting that the ethical standard plays a significant role in achieving sustainable development. We also find that the relationship between ethical behavior and green growth is more pronounced in developed countries than in developing countries. This is attributed to the ethical standard laid down in most of the developed countries. The results are unaffected by alternative variable measurements and econometric estimations. Our findings highlight the need for policymakers to consider non-economic and technological factors such as ethics to achieve growth that is both environmentally and economically sustainable.