bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification, Synthesis, and In Vitro Activities of Antimicrobial Peptide from African Catfish against the Extended-Spectrum Beta-Lactamase (ESBL)-Producing Escherichia coli
Authors: Okella Hedmon , Odongo Steven , Vertommen Didier , Okello Emmanuel
Published: June 2024

The global surge in multi-drug resistant bacteria, including extended-spectrum β-lactamase (ESBL)-producing Escherichia coli has led to a growing need for new antibacterial compounds. Despite being promising, the potential of fish-derived antimicrobial peptides (AMPs) in combating ESBL-producing E. coli is largely unexplored. In this study, native African catfish antimicrobial peptides (NACAPs) were extracted from the skin mucus of farmed African catfish, Clarias gariepinus, using a combination of 10% acetic acid solvent hydrolysis, 5 kDa ultrafiltration, and C18 hydrophobic interaction chromatography. Peptides were then sequenced using Orbitrap Fusion Lumos Tribrid Mass Spectrometry. The identified peptides were screened for potential antibacterial activity using Random Forest and AdaBoost machine learning algorithms. The most promising peptide was chemically synthesized and evaluated in vitro for safety on rabbit red blood cells and activity against ESBL-producing E. coli (ATCC 35218) utilizing spot-on-lawn and broth dilution methods. Eight peptides ranging from 13 to 22 amino acids with molecular weights between 968.42 and 2434.11 Da were identified. Peptide NACAP-II was non-hemolytic to rabbit erythrocytes (p > 0.05) with a zone of inhibition (ZOI) of 22.7 ± 0.9 mm and a minimum inhibitory concentration (MIC) of 91.3 ± 1.2 μg/mL. The peptide is thus a candidate antibacterial compound with enormous potential applications in the pharmaceutical industry. However, further studies are still required to establish an upscale production strategy and optimize its activity and safety in vivo.
Advances in Polyvinyl Alcohol-Based Membranes for Fuel Cells: A Comprehensive Review on Types, Synthesis, Modifications, and Performance Optimization
Authors: Madhuranthakam Chandra Mouli R. , Abudaqqa Weam S. K. , Fowler Michael
Published: June 2024

Fuel cell technology is at the forefront of sustainable energy solutions, and polyvinyl alcohol (PVA) membranes play an important role in improving performance. This article thoroughly investigates the various varieties of PVA membranes, their production processes, and the numerous modification tactics used to solve inherent problems. Various methods were investigated, including chemical changes, composite blending, and the introduction of nanocomposites. The factors impacting PVA membranes, such as proton conductivity, thermal stability, and selectivity, were investigated to provide comprehensive knowledge. By combining various research threads, this review aims to completely investigate the current state of PVA membranes in fuel cell applications, providing significant insights for both academic researchers and industry practitioners interested in efficient and sustainable energy conversion technologies. The transition from traditional materials such as Nafion to PVA membranes has been prompted by limitations associated with the former, such as complex synthesis procedures, reduced ionic conductivity at elevated temperatures, and prohibitively high costs, which have hampered their widespread adoption. As a result, modern research efforts are increasingly focused on the creation of alternative membranes that can compete with conventional technical efficacy and economic viability in the context of fuel cell technologies.
Zigzag Barbed Polydioxanone Thread Implantation and Evaluation Using Polydimethylsiloxane Model to Simulate Thread Migration in Tissue
Authors: Hsieh Chia-Hsien , Liu Yi-Xin , Chen Pei-Yu , Fang Hsu-Wei
Published: June 2024

Facial lifting with polydioxanone barbed threads has been widely used in aesthetic treatment for years. However, gravity resists the thread and continuously pulls the face downward. This study aims to determine methods to lift the skin more efficiently with longer longevity. The quality of the thread is important and is defined by the pulling and pullout strengths. Moreover, the method of using threads is also important. We compared five thread-implantation techniques and six angles for the V-shaped implantation methods using a polydimethylsiloxane model to simulate thread migration in tissues. The results of the simulated thread-lift techniques can provide valuable information for physicians, enabling a more precise design of facelift surgery techniques.
Ileal Digestible and Metabolizable Energy of Corn, Wheat, and Barley in Growing Japanese Quail
Authors: Khanipour Sousan , Ghazaghi Mahmoud , Abdollahi Mohammad Reza , Mehri Mehran
Published: June 2024

This study aimed to determine the ileal digestible energy (IDE), apparent metabolizable energy (AME), and nitrogen-corrected AME (AMEn) of three typical cereals for quail chicks with two age periods (day 15–21 and 22–28). The experimental diets comprised a corn–soybean meal reference diet (RD), and three test diets (TD) that were fed to quail chicks in a completely randomized design with five replicates per diet and 15 birds each. The TD comprised corn, wheat, and barley that partly replaced the RD at 300 g/kg (70% reference diet + 30% test ingredient). Age did not influence the IDE, AME, and AMEn values, while the effect of ingredient type was highly significant on the energy estimates (p < 0.001). The IDE values of corn, wheat, and barley were estimated as 2924, 3440, and 3184 kcal/kg, respectively. The AME values of corn, wheat, and barley were 3519, 2979, and 2710 kcal/kg, respectively. The estimated AMEn values of corn, wheat, and barley were 3255, 2676, and 2281 kcal/kg, respectively. These findings are crucial for optimizing diet formulations to support quail growth and performance effectively, as they provide valuable insights into the energy content of different cereals for quail production. Notably, the high IDE and AME values of wheat suggest its potential as a valuable energy source for quail diets. Understanding these values can aid in formulating diets that meet the energy requirements of quail chicks, leading to improved growth rates, feed efficiency, and overall productivity in quail production systems.
Proficient Calibration Methodologies for Fixed Photogrammetric Monitoring Systems
Authors: Guccione Davide Ettore , Turvey Eric , Roncella Riccardo , Thoeni Klaus , Giacomini Anna
Published: June 2024

This work focuses on investigating the accuracy of 3D reconstructions from fixed stereo-photogrammetric monitoring systems through different camera calibration procedures. New reliable and effective calibration methodologies that require minimal effort and resources are presented. A full-format camera equipped with fixed 50 and 85 mm focal length optics is considered, but the methodologies are general and can be applied to other systems. Four different calibration strategies are considered: (i) full-field calibration (FF); (ii) multi-image on-the-job calibration (MI); (iii) point cloud-based calibration (PC); and (iv) self (on-the-job) calibration (SC). To evaluate the calibration strategies and assess their actual performance and practicality, two test sites are used. The full-field calibration, while very reliable, demands significant effort if it needs to be repeated. The multi-image strategy emerges as a favourable compromise, offering good results with minimal effort for its realisation. The point cloud-based method stands out as the optimal choice, balancing ease of implementation with quality results; however, it requires a reference 3D point cloud model. On-the-job calibration with monitoring images is the simplest but least reliable option, prone to uncertainty and potential inaccuracies, and should hence be avoided. Ultimately, prioritising result reliability over absolute accuracy is paramount in continuous monitoring systems.
Water Dynamics and Morphometric Parameters of Lake Sevan (Armenia) in the Summer–Autumn Period According to Satellite Data
Authors: Ginzburg Anna I. , Kostianoy Andrey G. , Sheremet Nickolay A. , Lavrova Olga Yu.
Published: June 2024

Here, we explore the dynamics of the waters of eutrophicated Lake Sevan in the modern period, using MSI Sentinel-2 satellite images of different months in different years (2017–2022) and SAR Sentinel-1 images of similar dates. The main objective of the study is to investigate the spatiotemporal variability of the horizontal circulation of this lake and to establish whether the scheme of cyclonic water circulation in the deep-water part of Large Sevan, given in a number of publications, which does not imply water exchange between its littoral and deep-water zones, corresponds to the real picture of currents in the surface layer of the lake in the summer–autumn period (period of pronounced water stratification and intense phytoplankton bloom). The analysis performed convincingly showed that there is no constant cyclonic gyre on the scale of the deep-water part of Large Sevan (≈20 km) during the period under consideration. In most cases, non-stationary eddy dynamics are observed in Large Sevan, including mesoscale and submesoscale eddies, eddy dipoles (mushroom-shaped flows), and their packings. Often the entire deep-water part of Large Sevan is occupied by a two-cell (dipole) or even three-cell (cyclonic eddy with two anticyclones of similar size) water circulation. The time scale of the observed variability is several days. Such variable water circulation in different months (i.e., with different density stratification of water) of different years in a basin with a fairly homogeneous bottom and a slight indentation of the shoreline raises the assumption that the main reason for the non-stationary dynamics in Large Sevan is the variability of the wind effect on its surface layer. The cyclonic gyre in Small Sevan (8–9 km) is a permanent element of the circulation and maintains its position north of the strait between Small and Large Sevan. This gyre and attached anticyclonic eddies in the southern part of its periphery, as well as cyclonic submesoscale eddies in the northern part of Large Sevan, close to the strait, affect the water exchange between Small and Large Sevan in both directions. An additional objective of the study is a validation of the morphometric parameters of Lake Sevan (level, surface area, and water volume), contained in the near-real time HYDROWEB database, LEGOS, France (June 1995–January 2024), based on their comparison with the corresponding values of these parameters from gauging stations in Armenia. It is shown that, with a qualitative correspondence of the nature of lake level changes according to altimetric and instrumental measurements, its values in the HYDROWEB database exceed the data of gauging stations in most cases by 1–1.5 m in 1995–2012 and 0.5–0.6 m in 2013–2022, while the corresponding surface area and volume values according to HYDROWEB data turn out to be underestimated.
IceGCN: An Interactive Sea Ice Classification Pipeline for SAR Imagery Based on Graph Convolutional Network
Authors: Jiang Mingzhe , Chen Xinwei , Xu Linlin , Clausi David A.
Published: June 2024

Monitoring sea ice in the Arctic region is crucial for polar maritime activities. The Canadian Ice Service (CIS) wants to augment its manual interpretation with machine learning-based approaches due to the increasing data volume received from newly launched synthetic aperture radar (SAR) satellites. However, fully supervised machine learning models require large training datasets, which are usually limited in the sea ice classification field. To address this issue, we propose a semi-supervised interactive system to classify sea ice in dual-pol RADARSAT-2 imagery using limited training samples. First, the SAR image is oversegmented into homogeneous regions. Then, a graph is constructed based on the segmentation results, and the feature set of each node is characterized by a convolutional neural network. Finally, a graph convolutional network (GCN) is employed to classify the whole graph using limited labeled nodes automatically. The proposed method is evaluated on a published dataset. Compared with referenced algorithms, this new method outperforms in both qualitative and quantitative aspects.
Multi-Branch Attention Fusion Network for Cloud and Cloud Shadow Segmentation
Authors: Gu Hongde , Gu Guowei , Liu Yi , Lin Haifeng , Xu Yao
Published: June 2024

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.
Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements
Authors: Cheng Zhijian , Yang Lan , Yuan Qunyao , Long Yinren , Ren Hongru
Published: June 2024

Cyber-security research on networked multi-sensor systems is crucial due to the vulnerability to various types of cyberattacks. For the development of effective defense measures, attention is required to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker’s perspective. This paper aims to tackle the problem of distributed consensus estimation for networked multi-sensor systems subject to hybrid attacks and missing measurements. To account for both random denial of service (DoS) attacks and false data injection (FDI) attacks, a hybrid attack model on the estimator-to-estimator communication channel is presented. The characteristics of missing measurements are defined by random variables that satisfy the Bernoulli distribution. Then a modified consensus-based distributed estimator, integrated with the characteristics of hybrid attacks and missing measurements, is presented. For reducing the computational complexity of the optimal distributed estimation method, a scalable suboptimal distributed consensus estimator is designed. Sufficient conditions are further provided for guaranteeing the stability of the proposed suboptimal distributed estimator. Finally, a simulation experiment on aircraft tracking is executed to validate the effectiveness and feasibility of the proposed algorithm.
FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR)
Authors: Liang Tianyu , Zeng Guigen
Published: June 2024

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the mAP (mean Average Precision), with mAP and mAP50 reaching 66.7% and 84.2%, respectively.
Structural Design and Parameter Optimization of Magnetic Gradient Tensor Measurement System
Authors: Liu Gaigai , Zhang Yingzi , Liu Wenyi
Published: June 2024

Magnetic anomaly detection (MAD) technology based on the magnetic gradient tensor (MGT) has broad application prospects in fields such as unexploded ordnance detection and mineral exploration. The difference approximation method currently employed in the MGT measurement system introduces measurement errors. Designing reasonable geometric structures and configuring optimal structural parameters can effectively reduce measurement errors. Based on research into differential MGT measurement, this paper proposes three simplified planar MGT measurement structures and provides the differential measurement matrix. The factors that affect the design of the baseline distance of the MGT measurement system are also theoretically analyzed. Then, using the magnetic dipole model, the error analysis of the MGT measurement structures is carried out. The results demonstrate that the planar cross-shaped structure is optimal, with the smallest measurement error, only 3.15 × 10−10 T/m. Furthermore, employing the control variable method, the impact of sensor resolution constraints, noise level, target magnetic moment, and detection distance on the design of the optimal baseline distance of the MGT measurement system is simulated and verified. The results indicate that the smaller the target magnetic moment, the farther the detection distance, the lower the magnetometer resolution, the greater the noise, and the greater the baseline distance required. These conclusions provide reference and guidance for the construction of the MGT measurement system based on triaxial magnetometers.
Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation
Authors: Crystal Afnan Ahmed , Valero Maria , Nino Valentina , Ingram Katherine H.
Published: June 2024

Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.
Effect of Biomass Water Dynamics in Cosmic-Ray Neutron Sensor Observations: A Long-Term Analysis of Maize–Soybean Rotation in Nebraska
Authors: Morris Tanessa C. , Franz Trenton E. , Becker Sophia M. , Suyker Andrew E.
Published: June 2024

Precise soil water content (SWC) measurement is crucial for effective water resource management. This study utilizes the Cosmic-Ray Neutron Sensor (CRNS) for area-averaged SWC measurements, emphasizing the need to consider all hydrogen sources, including time-variable plant biomass and water content. Near Mead, Nebraska, three field sites (CSP1, CSP2, and CSP3) growing a maize–soybean rotation were monitored for 5 (CSP1 and CSP2) and 13 (CSP3) years. Data collection included destructive biomass water equivalent (BWE) biweekly sampling, epithermal neutron counts, atmospheric meteorological variables, and point-scale SWC from a sparse time domain reflectometry (TDR) network (four locations and five depths). In 2023, dense gravimetric SWC surveys were collected eight (CSP1 and CSP2) and nine (CSP3) times over the growing season (April to October). The N0 parameter exhibited a linear relationship with BWE, suggesting that a straightforward vegetation correction factor may be suitable (fb). Results from the 2023 gravimetric surveys and long-term TDR data indicated a neutron count rate reduction of about 1% for every 1 kg m−2 (or mm of water) increase in BWE. This reduction factor aligns with existing shorter-term row crop studies but nearly doubles the value previously reported for forests. This long-term study contributes insights into the vegetation correction factor for CRNS, helping resolve a long-standing issue within the CRNS community.
Object Detection in Multispectral Remote Sensing Images Based on Cross-Modal Cross-Attention
Authors: Zhao Pujie , Ye Xia , Du Ziang
Published: June 2024

In complex environments a single visible image is not good enough to perceive the environment, this paper proposes a novel dual-stream real-time detector designed for target detection in extreme environments such as nighttime and fog, which is able to efficiently utilise both visible and infrared images to achieve Fast All-Weatherenvironment sensing (FAWDet). Firstly, in order to allow the network to process information from different modalities simultaneously, this paper expands the state-of-the-art end-to-end detector YOLOv8, the backbone is expanded in parallel as a dual stream. Then, for purpose of avoid information loss in the process of network deepening, a cross-modal feature enhancement module is designed in this study, which enhances each modal feature by cross-modal attention mechanisms, thus effectively avoiding information loss and improving the detection capability of small targets. In addition, for the significant differences between modal features, this paper proposes a three-stage fusion strategy to optimise the feature integration through the fusion of spatial, channel and overall dimensions. It is worth mentioning that the cross-modal feature fusion module adopts an end-to-end training approach. Extensive experiments on two datasets validate that the proposed method achieves state-of-the-art performance in detecting small targets. The cross-modal real-time detector in this study not only demonstrates excellent stability and robust detection performance, but also provides a new solution for target detection techniques in extreme environments.
Contemporary Applications of Geostatistics to Soil Studies
Authors: Zawadzki Jarosław
Published: June 2024

This Special Issue presents carefully selected examples of Contemporary Applications of Geostatistics to Soil Studies [...]
Assessing the Spatiotemporal Dynamics of Environmental Sustainability in China
Authors: Uwasu Michinori , Hara Keishiro , Kuroda Masashi , Han Ji
Published: June 2024

Environmental sustainability is crucial to the Sustainable Development Goals (SDGs). We can gain an overview of a region’s or country’s dynamics by quantifying and analyzing its patterns over time and space. It can then be used to develop more sustainable policies. This study created a database of indices covering China’s environment, economy, and resource domains. Using an improved Environmental Sustainability Index (ESI) approach, we assessed sustainability and its spatiotemporal dynamics in 31 Chinese provinces between 2000 and 2017. Our findings suggest that provinces with high levels of socioeconomic development tend to have higher environmental scores, especially after experiencing severe environmental issues. Some underdeveloped provinces achieved high scores due to their relatively low environmental pressure and high resource efficiency. However, most underdeveloped provinces had high environmental pressure and low resource efficiency, leading to low environmental sustainability. We also discovered that sustainability scores improved between 2000 and 2017, mainly due to socioeconomic advancements. Yet, some environmental issues, such as air pollution, worsened during the latter part. Therefore, we confirmed an inverted U-curve relationship between environmental, resource, and socioeconomic components. Clustering analysis based on provincial economic and demographic characteristics also revealed different chronological patterns of environmental scores across the clusters. Additionally, we detected the transfer of pollution from developed to less-developed regions during the early 2000s.
Has Digital Village Construction Narrowed the Urban–Rural Income Gap: Evidence from Chinese Counties
Authors: Liu Ying , Zhang Haoyi , Ning Manxiu , Wang Linping
Published: June 2024

Can the backward endowments of rural areas support digital village construction to attain the expected results? If the answer is yes, what are the mechanisms involved? Answering these questions is related to common prosperity. Counties are China’s frontline commanders, and the urban–rural income gap is a suitable entry point for observing the urban–rural digital divide; however, there is limited research assessing the effectiveness of digital village construction from the perspective of the urban–rural income gap at the county level. In addition, counties lifted out of poverty, as counties with worse initial endowments and as counties that should be most concerned with common wealth, are more typical for examining the effectiveness of digital rural construction; however, there are few studies on counties lifted out of poverty. Based on cross-sectional data from 865 counties in China in 2020, this study empirically analyzes the impact of digital village construction on the urban–rural income gap using an OLS model. This study also conducts mechanism tests and poverty benefit tests in addition to heterogeneity and robustness tests. The findings indicate that the development of digital villages lowers the income difference between urban and rural areas, and that the shift in the industrial structure is a key driver of this effect. Different construction dimensions have varying degrees of impact, with digital infrastructure and the digitalization of the economy having the most significant impact, the digitalization of countryside governance coming second, and the digitalization of countryside life having the most negligible impact. The impact is more pronounced in the central-eastern region of China and counties that have just been lifted out of poverty than in the western region and counties that have never been in poverty. The government will benefit greatly from this study’s understanding of the main themes, areas, and scope of digital rural construction, which will help to expand and further integrate the outcomes of reducing poverty and fostering shared prosperity.
How Environmental Regulation Affects Pollution Reduction and Carbon Reduction Synergies—An Empirical Analysis Based on Chinese Provincial Data
Authors: Shi Wei , Wang Weijuan , Tang Wenwen , Qiao Fuwei , Zhang Guowei , Pei Runzhu , Zhang Luyao
Published: June 2024

Faced with the dual challenges of environmental pollution and climate change, it is of great significance to study the impact of relevant environmental regulations on the synergistic effect of pollution reduction and carbon emission reduction and their influence mechanisms. Based on a theoretical analysis using the panel data of 30 provinces in China, a spatial econometric model and an intermediary effect model are used to investigate the impact of environmental regulations on the synergistic effect of pollution reduction and carbon reduction and the transmission mechanisms potentially responsible for these effects. The empirical results show the following: (1) The three kinds of environmental regulation effectively facilitate the synergistic effect of pollution reduction and carbon reduction, taking the following order when ranked according to the intensity of their effects: command-type environmental regulation (ER1) > market-based environmental regulation (ER2) > voluntary environmental regulation (ER3). (2) Environmental regulation effectively promotes the synergistic effect of pollution and carbon reduction through the three transmission mechanisms of stimulating technological innovation (TI), industrial structure upgrading (ISU), and restricting foreign direct investment (FDI), which take the following effectiveness order: TI > FDI > ISU. Based on the results of the study, policy suggestions to facilitate pollution reduction and carbon synergies are proposed to help China’s green and low-carbon development.
Economic Complexity and Good Governance in Sub-Saharan Africa: A Cross Country Analysis
Authors: Maxwele Chuma , Anakpo Godfred , Mishi Syden
Published: June 2024

The underlying problems of Sub-Saharan African development are a result of a crisis in governance. Over the years, issues of governance and economic development have been the focus of debate in academia. However, the existing literature has not adequately explored the association between good governance and economic complexity. The purpose of this study is therefore to investigate the short-run and long-run relationships between economic complexity and good governance in 27 Sub-Saharan African countries for the period 1996–2019 using the PMG-ARDL model. The findings reveal that economic complexity, foreign aid, and the Gini coefficient have a positive and statistically significant long-run impact on good governance in Sub-Saharan Africa. Thus, the null hypothesis that economic complexity has a significantly positive impact on good governance is accepted by these findings. The short-run dynamics results reveal that economic complexity and foreign aid have a negative and insignificant impact on good governance, while foreign direct investment, the Gini coefficient, and unemployment have a positive and insignificant impact on good governance in Sub-Saharan Africa. Based on the findings, it is recommended that policymakers in the region place more emphasis on structural transformations to transform their productive structures, which will ultimately lead to higher economic growth in Sub-Saharan Africa. Policies geared towards the diversification of exports (i.e., economic complexity) in the region for economic growth and development are also recommended.
Using BIM and LCA to Calculate the Life Cycle Carbon Emissions of Inpatient Building: A Case Study in China
Authors: Zhao Li , Guo Cheng , Chen Leduan , Qiu Liping , Wu Weiwei , Wang Qingqin
Published: June 2024

Hospital buildings provide healthcare services at the costs of significant amounts of energy consumption and carbon emissions, further exacerbating the environmental load. Because of the limited research on the life cycle carbon emissions of Chinese hospitals, this study conducted a detailed carbon-accounting and comparative study. Firstly, BIM and LCA were used to quantify the carbon emissions of the inpatient building in each stage of the life cycle. Secondly, the differences in carbon emissions by stage were compared on the basis of 20 cases of public buildings. The results show that the whole-life carbon emissions of the inpatient building was 10,459.94 kgCO2/m2. The proportion of operational carbon emissions was 94.68%, with HVAC (52.57%), equipment (27.85%), and lighting (10.11%) being the main sources. Embodied carbon emissions accounted for 4.54%, and HRB400 steel and C30 concrete were the main sources of carbon emissions. Hospitals are second only to emporiums in terms of operational carbon intensity, being 1.71 and 1.41 times that of schools and office buildings, with inpatient buildings being 3 and 1.7 times that of medical complexes and outpatient buildings, respectively. The future sustainable development of hospital buildings should promote efficient building performance and good environmental quality, both in terms of energy efficiency and carbon reduction.
Use of Directed Lactic Fermentation to Obtain Plant-Based, Upcycled Beverage from Milk Thistle Endosperm
Authors: Teleszko Mirosława , Haraf Gabriela , Zając Adam , Garncarek Zbigniew , Górska Katarzyna , Krzos Grzegorz , Hałaburda Agnieszka , Kotecki Paweł
Published: June 2024

This study aimed to evaluate the possibility of using milk thistle endosperm (MTE) extract with a protein content of >2 g/100 mL to obtain a fermented product, an alternative to milk beverages. Directed lactic fermentation with Lacticaseibacillus rhamnosus was carried out. The course of the process was monitored. The changes in pH, the saturation of the medium with dissolved oxygen, and temperature were measured. The values of the main nutritional parameters, silymarin content, amino acid profile/content, and the PDCAAS value (Protein Digestibility-Corrected Amino Acid Score) were determined in the extract before and after fermentation. The lactic acid fermentation of the MTE extract took place in four phases, with the activity of L. rhamnosus being the most intense between 4 and 8 h into the process. As a result of fermentation, there were changes in the total amino acid content in the tested medium, suggesting the catabolism of aspartic acid, arginine, proline, and isoleucine via LAB took place. Particularly significant was the increase in the content of lysine and valine (4.95 and 4.68 g/100 g of total AA, respectively), which are the limiting amino acids in MTE. Although MTE contained approximately 1% silymarin, no presence of this flavonolignan complex was found in the extracts.
“Mapping Out” Sustainable Social Farming Paths in Italian Municipalities
Authors: Fanelli Rosa Maria
Published: June 2024

Social farming in Italy has not developed homogeneously. In view of this, this article adopts a multivariate analysis approach to analyse the heterogeneity and the similarities in the development paths of social farming in Italian municipalities. The article takes into account the information from a representative sample of 410 interviews. The results suggest that the offer of social farming activities is highly correlated with the distinct nature of the enterprises and with the interest of local actors, who in many cases finance these activities. Regarding the characteristics of social farms, the results of principal component analysis show that the consolidated experience of offering social services and the continuation of activities are the most important organisational elements. Concerning the territorial distribution, the findings of a hierarchical cluster analysis show that Italian municipalities have distinct experiences according to the level of maturity of the social farms in each cluster, with differences in location, the agricultural system, the organisational culture and the social services provided. Assessing enterprise characteristics and recent development paths for social services in Italy can have far-reaching implications for policy. The latter should provide information and training to farmers and users to increase understanding of the social value of social farming and foster a collaborative and sustainable approach to social farming practice.
Determinants of Students’ Satisfaction with AI Tools in Education: A PLS-SEM-ANN Approach
Authors: Almufarreh Ahmad
Published: June 2024

The emergence of Artificial Intelligence (AI) technology has significantly disrupted the educational landscape. The latest development in AI, generative AI that can generate new and tailored to specific content, has significantly impacted education. Given the value of AI technology in general and generative AI specific to users in education, such as students, the adaptability of these technologies has significantly increased. However, continuing and productive usage of AI tools depends upon students’ satisfaction with these tools. Drawing from the existing research, the present research has developed factors that affect students’ general satisfaction with AI tools. The research collected the data using a survey questionnaire from a Saudi Arabian university. The two-stage method of partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) have been employed. The two-stage method is applied in a way that PLS-SEM is used for testing the hypothesis and significance of the factor’s influence on satisfaction, and ANN is used to determine the relevant importance of the factor. The PLS-SEM results have shown that factors such as content quality, emotional wellbeing and perceived utility determine student satisfaction with AI tools. The ANN results show that emotional wellbeing is the most critical factor in satisfaction, followed equally by content quality and perceived utility.
Reusable Smart Lids for Improving Food Safety at Household Level with Programmable UV-C Technology
Authors: Gürsu Hakan
Published: June 2024

The worldwide food industry faces the multiple challenges of providing food security while also reducing environmental and health consequences. This requires transitioning to chemical-free techniques of preserving food with a long shelf life that emphasize human health. Even though millions of people are experiencing hunger, the substantial amount of food that is being wasted is impeding the advancement towards UN Sustainable Development Goal 12, which aims to reduce food waste by 50% by the year 2030. On the other hand, conventional food preservation techniques still frequently depend on chemical additives, which might give rise to persistent health issues and potentially undermine nutritional quality. This emphasizes the necessity for inventive, non-chemical remedies that prioritize both prolonged storage duration and the safety of food. Consumer storage conditions, which are the ultimate phase of the food chain, still generate substantial waste because of the proliferation of mold and bacteria on fruits and vegetables, which presents health hazards. Enhancing storage conditions and extending shelf life is important. Low-frequency ultraviolet (UV-C) light technology provides a non-thermal and highly efficient method for fighting foodborne microorganisms such as mold. This method renders pathogens inactive while maintaining product quality, providing a cost-efficient and easily available alternative. This study proposes the development of a programmable “Smart-Lid” SLID storage system that utilizes upcycled home base glass jars with UV-C light-emitting lids to prevent mold growth on various open food items, including milk- and sugar-based food, sauces, and possibly dry meals. The research seeks to assess the efficacy and potential influence of the SLID solution with UV-C light’s potential with programmable applications in this preserving environment at the home level.
Novel Algorithm for Comparing Phylogenetic Trees with Different but Overlapping Taxa
Authors: Koshkarov Aleksandr , Tahiri Nadia
Published: June 2024

Comparing phylogenetic trees is a prominent problem widely used in applications such as clustering and building the Tree of Life. While there are many well-developed distance measures for phylogenetic trees defined on the same set of taxa, the situation is contrasting for trees defined on different but mutually overlapping sets of taxa. This paper presents a new polynomial-time algorithm for completing phylogenetic trees and computing the distance between trees defined on different but overlapping sets of taxa. This novel approach considers both the branch lengths and the topology of the phylogenetic trees being compared. We demonstrate that the distance measure applied to completed trees is a metric and provide several properties of the new method, including its symmetrical nature in tree completion.
Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor
Authors: Yu Binghao , Hu Yiming , Zeng Dequan
Published: June 2024

In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).
Tubulovesicula lindbergi (Layman, 1930) (Digenea: Hemiuridae) in the Southwestern Atlantic Ocean: A Morphological and Phylogenetic Study Based on Specimens Found in Nebris microps (Actinopterygii: Sciaenidae) off the Brazilian Coast
Authors: Pantoja Camila , Paschoal Fabiano , Nunes Jorge Luiz Silva , Pinto Hudson Alves
Published: June 2024

This study presents the first record of T. lindbergi from the southwestern Atlantic Ocean, based on specimens collected from the smalleye croaker Nebris microps Cuvier (Sciaenidae), off the coast of Maranhão Island, State of Maranhão, Brazil. Our approach included a morphological analysis complemented by DNA sequencing (28S, ITS2 rDNA, and cox1 mtDNA). Our phylogenetic analysis revealed the affinity of T. lindbergi to its congener T. laticaudi Parukhin, 1969, a digenean parasite commonly found in hydrophiine snakes inhabiting the Pacific Ocean. The interspecific divergence between T. lindbergi and T. laticaudi measures 3.80% for 28S, 7.49–7.64% for ITS2, and 16.29–16.70% for cox1. Our findings expand the documented geographic range of T. lindbergi into the southwestern Atlantic Ocean, report a novel host record, and increase the number of hemiurids known from Brazil to 30 species. Additionally, this study represents the initial documentation of a marine digenean fish within the North Brazil Shelf.
Rhein Inhibits Cell Development and Aflatoxin Biosynthesis via Energy Supply Disruption and ROS Accumulation in Aspergillus flavus
Authors: Wang Xiaoyan , Sahibzada Kashif Iqbal , Du Ruibo , Lei Yang , Wei Shan , Li Na , Hu Yuansen , Lv Yangyong
Published: June 2024

Aspergillus flavus and its carcinogenic secondary metabolites, aflatoxins, not only cause serious losses in the agricultural economy, but also endanger human health. Rhein, a compound extracted from the Chinese herbal medicine Rheum palmatum L. (Dahuang), exhibits good anti-inflammatory, anti-tumor, and anti-oxidative effects. However, its effect and underlying mechanisms against Aspergillus flavus have not yet been fully illustrated. In this study, we characterized the inhibition effect of rhein on A. flavus mycelial growth, sporulation, and aflatoxin B1 (AFB1) biosynthesis and the potential mechanism using RNA-seq analysis. The results indicate that A. flavus mycelial growth and AFB1 biosynthesis were significantly inhibited by 50 μM rhein, with a 43.83% reduction in colony diameter and 87.2% reduction in AFB1 production. The RNA-seq findings demonstrated that the differentially expressed genes primarily participated in processes such as spore formation and development, the maintenance of cell wall and membrane integrity, management of oxidative stress, the regulation of the citric acid cycle, and the biosynthesis of aflatoxin. Biochemical verification experiments further confirmed that 50 μM rhein effectively disrupted cell wall and membrane integrity and caused mitochondrial dysfunction through disrupting energy metabolism pathways, leading to decreased ATP synthesis and ROS accumulation, resulting in impaired aflatoxin biosynthesis. In addition, a pathogenicity test showed that 50 μM rhein inhibited A. flavus spore growth in peanut and maize seeds by 34.1% and 90.4%, while AFB1 biosynthesis was inhibited by 60.52% and 99.43%, respectively. In conclusion, this research expands the knowledge regarding the antifungal activity of rhein and provides a new strategy to mitigate A. flavus contamination.
Role of Botulinum Toxin in Treatment of Secondary Dystonia: A Case Series and Overview of Literature
Authors: Mohanty Diksha , Riordan Heather R. M. , Hedera Peter
Published: June 2024

Introduction: Dystonia can present in primary and secondary forms, depending on co-occurring symptoms and syndromic associations. In contrast to primary dystonia, secondary forms of dystonia are often associated with lesions in the putamen or globus pallidus. Such disorders are commonly neurodegenerative or neurometabolic conditions which produce varied neurologic as well as systemic manifestations other than dystonia. Chemo-denervation with botulinum toxin has been successfully used for focal or segmental dystonia. However, studies evaluating the effect of BoNT therapy on patients with secondary dystonia are sparse, given the heterogeneity in etiology and presentation. Methods: We present a series of patients with secondary dystonia who were managed with botulinum toxin therapy. Patients included in this series had a confirmed neurometabolic cause of dystonia. Results: A total of 14 patients, with ages ranging from 17 to 36 years, with disorders including Wilson’s disease, pantothenate kinase-associated neurodegeneration (PKAN), Niemann–Pick disease type C (NPC), glutaric aciduria type 1, Sanfilippo syndrome (Mucopolysaccharidosis Type IIIb), and GM2 gangliosidosis (Sandhoff disease) are presented. Most patients experienced a mild to moderate improvement in treated dystonia with benefits ranging from 6 to 12 weeks, with the median length of the benefits lasting approximately eight weeks, without any significant adverse effects. Conclusion: Although the secondary causes of dystonia are complex and diverse, our presented data and the available reports of the use of botulinum toxin support the conclusion that chemo-denervation plays an important role in symptom alleviation.