Search Results (125)

Background and Objectives: In Italy, as in the rest of the world, government restrictions aimed at containing the spread of COVID-19 primarily imposed limitations on social relationships and personal behavior. This situation significantly affected the management of chronic illnesses, including type 2 diabetes (T2D). The objective was to evaluate the perceptions of patients with T2D regarding the quality of care received during the COVID-19 pandemic and the impact on dietary and physical activity behaviors. Materials and Methods: We conducted a retrospective cross-sectional survey. Data were collected from June to July 2023 using the convenience sampling of patients with T2D, and the Patient Assessment of Chronic Illness Care (PACIC) and Medi-Lite questionnaires were administered. Results: During the research period, out of the 130 subjects who met all enrollment criteria, 103 patients were included in this study (79.23%). The results of the administered questionnaires were heterogeneous. The average scores from the PACIC Questionnaire for each question displayed significant variability, indicating a range of experiences in the quality of care. In the Medi-Lite survey, fruit, cereals, and olive oil showed the highest adherence levels, with mean scores ranging from 2.58 (SD ± 1.18) for fruit to 1.89 (SD ± 0.34) for olive oil and 1.97 (SD ± 0.17) for cereals. Patients who reported increased food intake during the lockdown attributed it to having more time to prepare meals. Physical activity levels remained unchanged for 48 patients, decreased for 45 patients, and only 9 patients managed to exercise more during the COVID-19 restrictions. Conclusions: Healthcare systems must prioritize comprehensive care plans for T2D that address not only physical health, but also emotional and social well-being. Post-pandemic, promoting healthier lifestyles and empowering patients to manage their condition is crucial. A multidisciplinary and multidimensional approach could support the care of vulnerable individuals, such as patients with T2D, especially during crises like pandemics or other dramatic events. Full article

It is very important to enhance the risk resistance of the agricultural sector to realize the modernization transformation of the agricultural industry and strengthen the competitiveness of national agriculture. Based on the relevant spatial data of 30 provincial administrative regions in China from 2013 to 2022, this study constructs a multi-dimensional index framework to comprehensively evaluate the resilience of China’s agricultural economy by comprehensively considering the three key aspects of adaptability, management strategy, and innovation drive. This study adopts several quantitative analysis tools including the Theil index, global and local analysis of the Moran I index, and kernel density estimation (KDE), and further combines with the spatial Durbin model (SDM) to conduct an in-depth spatiotemporal analysis of the resilience of China’s agricultural economy. This study not only reveals the evolution trend of agricultural economic resilience in different times and spaces but also analyzes the differences in resilience among regions and its spread in space. Through these refined analytical tools, we aim to reveal how agricultural economic resilience changes over time, the differences in resilience levels among regions, and the geospatial interactions and diffusion. This study reveals a series of key findings: (1) The resilience of China’s agricultural economy shows a trend of steady improvement. (2) Differences within the three regions are the main factors generating differences in the development of resilience in China’s agricultural economy. (3) The resilience of the agricultural economy in different regions shows obvious spatial correlations. (4) Further analysis shows that the efficiency of agricultural production and the urbanization process have a positive direct impact on the resilience of the agricultural economy, and this impact has a significant positive spatial diffusion effect. Meanwhile, although the level of agricultural mechanization is not significant in its direct impact, it has a positive spatial impact on the enhancement of agricultural economic resilience in other regions. In addition, the restructuring of agricultural cropping has both direct negative impacts and positive spatial spillover effects on the resilience of the agricultural economy. Based on these findings, this paper suggests that agricultural policies should consider regional development differences, implement differentiated agricultural support policies, fully account for the spatial spillover effects of agricultural ecological efficiency, and strengthen the exchange and cooperation of resources between regions. This study deepens the understanding of the spatial and temporal characteristics of the resilience of China’s agricultural economy, reveals its inherent dynamic processes and spatial interactions, and provides valuable references for policymakers and practitioners to better cope with the various challenges encountered in agricultural production, and to jointly promote the sound development of China’s agricultural economy. Full article

Rapid urbanization and its associated human activities have facilitated the colonization and spread of non-native species, rendering urban ecosystems, particularly in megacities such as Beijing, highly susceptible to biological invasions. This study employed environmental DNA (eDNA) metabarcoding to evaluate the biodiversity and geographical distribution of non-native fish, as well as their interactions with native fish species, across three river basins in Beijing pertaining to the Daqing River, the North Canal, and the Ji Canal. Across all the 67 sampling sites, we identified 60 fish taxa, representing 11 orders, 23 families, and 40 genera, with an average of 33.0 taxa per site. Of these, 40 taxa were native, accounting for only 47.1% of the historically recorded native fish species. Additionally, we detected 20 non-native fish taxa, spanning 11 orders, 13 families, and 17 genera. Native fish exhibited geographical homogenization across the basins, while non-native taxa displayed varied geographical distributions. Non-metric multidimensional scaling (NMDS) and analysis of similarities (ANOSIM) revealed no significant variation in the non-native communities across the river basins. Although most of the non-native taxa were widespread, some were restricted to specific sites or basins. The North Canal exhibited significantly lower non-native biodiversity compared with the Ji Canal across all alpha diversity indices. Simple linear regression analyses indicated positive correlations between the number of taxa and species richness for both native and non-native taxa. Interestingly, species co-occurrence analyses revealed predominantly positive interactions among both native and non-native species pairs, with only two negative relationships involving one native and two non-native taxa. This study provides insights into the biodiversity and geographical distribution of non-native fish in Beijing and establishes a baseline for future biomonitoring and conservation efforts. The findings underscore the need for further investigation into the mechanisms and dynamics of biological invasions within urban environments in Beijing. Full article

Recent studies introduced the importance of using machine learning algorithms in research focused on the identification of antibiotic resistance. In this study, we highlight the importance of building solid machine learning foundations to differentiate antimicrobial resistance among microorganisms. Using advanced machine learning algorithms, we established a methodology capable of analyzing the FTIR structural profile of the samples of Streptococcus pyogenes and Streptococcus mutans (Gram-positive), as well as Escherichia coli and Klebsiella pneumoniae (Gram-negative), demonstrating cross-sectional applicability in this focus on different microorganisms. The analysis focuses on specific biomolecules—Carbohydrates, Fatty Acids, and Proteins—in FTIR spectra, providing a multidimensional database that transcends microbial variability. The results highlight the ability of the method to consistently identify resistance patterns, regardless of the Gram classification of the bacteria and the species involved, reinforcing the premise that the structural characteristics identified are universal among the microorganisms tested. By validating this approach in four distinct species, our study proves the versatility and precision of the methodology used, in addition to bringing support to the development of an innovative protocol for the rapid and safe identification of antimicrobial resistance. This advance is crucial for optimizing treatment strategies and avoiding the spread of resistance. This emphasizes the relevance of specialized machine learning bases in effectively differentiating between resistance profiles in Gram-negative and Gram-positive bacteria to be implemented in the identification of antibiotic resistance. The obtained result has a high potential to be applied to clinical procedures. Full article

This research examines the spread of disinformation on social media platforms and its impact on state resilience through a systematic literature review of 150 peer-reviewed studies published between 2014 and 2024. The analysis revealed that disinformation spreads six times faster than accurate information, with emotions and platform algorithms playing a significant role in its spread. Factors such as low digital literacy, political polarization, and declining trust in institutions increase people’s vulnerability to disinformation. Impacts on national security include threats to the integrity of democratic processes, the erosion of social cohesion, and decreased public trust. The most effective coping strategies include improving digital literacy (78 percent effective), fact-checking (65 percent), and content regulation (59 percent). However, these efforts face ethical and legal challenges, especially regarding freedom of expression. This research highlights the need for a multidimensional approach in addressing the “information pandemic”, integrating technological, educational, and policy strategies while considering ethical implications. The findings provide a foundation for further policy development and research to protect the integrity of public information spaces and state resilience in the digital age. Full article

The extensive deployment of wind farms has significantly impacted the detection capabilities of space–air bistatic radar (SABR) systems. Although space–time adaptive processing techniques are available, their performance is significantly degraded, and even unable to suppress clutter. This paper explores the geometric configuration of the SABR system and the selection of detection areas, establishing a space–time clutter model that addresses the effects of wind turbine clutter (WTC). Expressions for spatial and Doppler frequencies have been derived to deeply analyze the characteristics of clutter spreading. Building on this, the paper extends two-dimensional space–time data to three-dimensional azimuth–elevation–Doppler data. It proposes a three-dimensional space–time multi-beam (STMB) strategy incorporating the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to suppress WTC effectively. This algorithm selects WTC samples and applies OPTICS clustering to the clutter-suppressed data to achieve this effect. Simulation experiments further verify the effectiveness of the algorithm. Full article

The occurrence and spread of infectious diseases pose considerable challenges to public health. While the relationship between the built environment and the spread of infectious diseases is well-documented, there is a dearth of urban planning tools specifically designed for conducting Health Impact Assessments (HIAs) targeted at infectious diseases. To bridge this gap, this paper develops a comprehensive framework of an HIA for Urban Planning and Epidemic (HIA4UPE), formulated by considering the progression of public health incidents and the distinct transmission patterns of infectious diseases. This framework is designed to provide a comprehensive assessment by including a health risk-overlay assessment, health resource-quality assessment, health resource-equality assessment, and health outcome-impact prediction, enabling a multidimensional evaluation of the potential impacts of current environmental conditions or planning proposals on the incidence of infectious diseases. Furthermore, this paper advances the application of spatial analysis and computation, comprehensive assessment methodologies, and predictive analytics to conduct specific assessments. The theoretical framework and analytical tools presented in this paper contribute to the academic discourse and offer practical utility in urban planning and policymaking on epidemic prevention and control. Full article

After the spread of the COVID-19 pandemic, several articles have described the fragility of young adults, such as students, highlighting the severity, frequency, and nature of mental distress. However, less research has examined the resources of young people, such as their creative competence and their ability to draw on a social network. Furthermore, the analysis of these resources is not very common in international comparative studies. The main aim of this cross-national study is to investigate whether creativity factors such as creative personality and divergent thinking together with social support predict resilience in college students, controlling for gender and nationality, in a sample of college students from Italy and Spain, European Mediterranean countries particularly affected by the pandemic. The following instruments were used to measure these constructs: The Creative Personality Scale, the Runco Ideational Behavior Scale, the Multidimensional Scale of Perceived Social Support, and the Connor Davidson Resilience Scale. A total of 287 college students participated, 147 from Italy and 140 from Spain, with an average age of 22 years. The results show that there are statistically significant differences between Italian and Spanish students for all variables except resilience. The hierarchical regression shows that divergent thinking and social support are predictors of resilience for the whole sample. In light of these results, it may be important for universities to continue investing in divergent thinking and social support through workshops and activities to promote student resilience. Full article

The growing attention to the sustainable management of territories leads to a reconsideration of common properties, those institutions which concern property rights belonging to all members of a well-defined community. Spread throughout the world in a variety of forms, they can play a crucial role in addressing the challenges posed by the ecological transition promoted by the European Green Deal. In Italy, common properties represent a historical phenomenon, specifically involving rural and mountain areas. Despite the fact that national law regarding collective domains fully recognizes their economic, social, and environmental functions, there is still much to be done in terms of their recognition. As the status of knowledge is lacking, especially in some areas of southern Italy, this article represents a preliminary analysis of the current consistency of collective domains. The introductory section places the topic in the broadest context of ecological transition, tracing its regulatory evolution. Next, collective domains are framed from an economic perspective, highlighting their multidimensional values and emerging assessment issues. The subsequent sections, based on the most recent available data, critically analyze the current supply of collective domains in Italy and in the Campania region. The in-depth analysis of an inner area, characterized by socio-economic marginality, represents the starting point from which it will be possible to identify the demand and to support policy makers and local communities in the valorization of common properties. Full article

The Schlieren technique is widely adopted for visualizing supersonic jets owing to its non-invasiveness to the flow field. However, extending the classical Schlieren method for quantitative refractive index measurements is cumbersome, especially for three-dimensional supersonic flows. Background-oriented Schlieren has gained increasing popularity owing to its ease of implementation and calibration. This study utilizes multi-view-based tomographic background-oriented Schlieren (TBOS) to reconstruct axisymmetric and weakly non-axisymmetric supersonic jets, highlighting the impact of flow axisymmetry breaking on TBOS reconstructions. Several classical TBOS reconstruction algorithms, including FDK, SART, SIRT, and CGLS, are compared quantitatively regarding reconstruction quality. View spareness is identified to be the main cause of degraded reconstruction quality when the flow experiences axisymmetry breaking. The classic visual hull approach is explored to improve reconstruction quality. Together with the CGLS tomographic algorithm, we successfully reconstruct the weakly non-axisymmetric supersonic jet structures and confirm that increasing the nozzle bevel angle leads to wider jet spreads. Full article

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts—with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks—significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks. Full article

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values. Full article

Pine wilt disease is a highly contagious forest quarantine ailment that spreads rapidly. In this study, we designed a new Pine-YOLO model for pine wilt disease detection by incorporating Dynamic Snake Convolution (DSConv), the Multidimensional Collaborative Attention Mechanism (MCA), and Wise-IoU v3 (WIoUv3) into a YOLOv8 network. Firstly, we collected UAV images from Beihai Forest and Linhai Park in Weihai City to construct a dataset via a sliding window method. Then, we used this dataset to train and test Pine-YOLO. We found that DSConv adaptively focuses on fragile and curved local features and then enhances the perception of delicate tubular structures in discolored pine branches. MCA strengthens the attention to the specific features of pine trees, helps to enhance the representational capability, and improves the generalization to diseased pine tree recognition in variable natural environments. The bounding box loss function has been optimized to WIoUv3, thereby improving the overall recognition accuracy and robustness of the model. The experimental results reveal that our Pine-YOLO model achieved the following values across various evaluation metrics: [email protected] at 90.69%, [email protected]:0.95 at 49.72%, precision at 91.31%, recall at 85.72%, and F1-score at 88.43%. These outcomes underscore the high effectiveness of our model. Therefore, our newly designed Pine-YOLO perfectly addresses the disadvantages of the original YOLO network, which helps to maintain the health and stability of the ecological environment. Full article

Forest fires, as severe natural disasters, pose significant threats to ecosystems and human societies, and their spread is characterized by constant evolution over time and space. This complexity presents an immense challenge in predicting the course of forest fire spread. Traditional methods of forest fire spread prediction are constrained by their ability to process multidimensional fire-related data, particularly in the integration of spatiotemporal information. To address these limitations and enhance the accuracy of forest fire spread prediction, we proposed the AutoST-Net model. This innovative encoder–decoder architecture combines a three-dimensional Convolutional Neural Network (3DCNN) with a transformer to effectively capture the dynamic local and global spatiotemporal features of forest fire spread. The model also features a specially designed attention mechanism that works to increase predictive precision. Additionally, to effectively guide the firefighting work in the southwestern forest regions of China, we constructed a forest fire spread dataset, including forest fire status, weather conditions, terrain features, and vegetation status based on Google Earth Engine (GEE) and Himawari-8 satellite. On this dataset, compared to the CNN-LSTM combined model, AutoST-Net exhibits performance improvements of 5.06% in MIou and 6.29% in F1-score. These results demonstrate the superior performance of AutoST-Net in the task of forest fire spread prediction from remote sensing images. Full article

As a globally invasive quarantine pest, the cotton mealybug, Phenacoccus solenopsis, is spreading rapidly, posing serious threats against agricultural and forestry production and biosecurity. In recent years, the niche conservatism hypothesis has been widely debated, which is particularly evident in invasive biology research. Identifying the niche dynamics of P. solenopsis, as well as assessing its global invasion risk, is of both theoretical and practical importance. Based on 462 occurrence points and 19 bioclimatic variables, we used n-dimensional hypervolume analysis to quantify the multidimensional climatic niche of this pest in both its native and invasive ranges. We examined niche conservatism and further optimized the MaxEnt model parameters to predict the global invasion risk of P. solenopsis under both current and future climate conditions. Our findings indicated that the niche hypervolume of this pest in invasive ranges was significantly larger than that in its native ranges, with 99.45% of the niche differentiation contributed by niche expansion, with the remaining less than 1% explained by space replacement. Niche expansion was most evident in Oceania and Eurasia. The area under the receiver operating characteristic curve (0.83) and true skill statistic (0.62) indicated the model’s robust performance. The areas of suitable habitats for P. solenopsis are increasing significantly and the northward spread is obvious in future climate change scenarios. North Africa, northern China, Mediterranean regions, and northern Europe had an increased invasion risk of P. solenopsis. This study provided scientific support for the early warning and control of P. solenopsis. Full article