Large-scale Perspective Research Articles

Linking discrete and continuous models of cell birth and migration.

Self-organization of individuals within large collectives occurs throughout biology. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the 'effective' dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of continuous models and individual-based data in settings with biologically realistic, time-varying cell numbers. Specifically, we introduce and fit scaling parameters in continuous models to account for discrepancies that can arise from low cell numbers and localized interactions. We illustrate our approach on an example motivated by zebrafish-skin pattern formation, in which we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics.

Deforestation fight in the sight of Brazilian Amazonas inhabitants

Brazilian Amazon deforestation crystallizes numerous political, economic, and social issues. Recent empirical studies explore deforestation drivers by focusing on large-scale perspective but ignore the sight of the local populations. This article aims at providing an overview of deforestation perception among local populations defined as living within or near the Amazon biome. We have conducted 197 interviews in October 2021 in the Brazilian Amazonas region. On the basis of these interviews, we provide statistical analysis to explore 5 effects capable of driving the local perception of the deforestation processes: the gender, educational, generational, geographical and indigenous effects. Our results first highlight a very high and positive educational effect for the highest education level and a high indigenous effect, both in conformity with the intuition. Second, we observe a negative gender effect, contradicting some strands in the academic literature, and a high but ambiguous geographical effect. Finally, age does not appear to be neither a driver of interest nor an engagement in the fight against deforestation. In a context where the enforcement of environmental policy has become ineffective in recent years, we argue that characterizing local perceptions of deforestation processes might help to adjust the policy campaigns against deforestation, enhance their effectivenesses, and therefore participate to the restoration of Amazonian cover.

AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis

(1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI’s role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI’s potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI’s accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI’s potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.

Spatial-temporal offshore wind speed characteristics prediction based on an improved purely 2D CNN approach in a large-scale perspective using reanalysis dataset

Accurate prediction of spatial–temporal wind speed with a large-scale perspective is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Machine-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the forecasting accuracy employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity required by the 2D regional wind farm blueprint. Furtherly, the Recurrent Neural Networks based approach they employed exhibit low efficiency in terms of model convergence on the aspect of practical engineering purposes. This study firstly proposed an improved Unet-based machine learning method using residual learning to predict the spatial–temporal wind field in the North Atlantic Ocean with great potential wind-energy in a large spatial-scale perspective. Several state-of-the-art machine learning methods were established for a better performance comparison. A public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method with multi-steps ahead predictions, the model performance was furtherly compared with the rest approaches under different criteria, consisting of Pearson correlation coefficient (PCC), Mean absolutely error (MAE), Root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE), all exhibiting considerable improvements. The overall forecasting RMSE for the proposed method dropped to 0.088 m/s and 0.087 m/s for wind component U and V correspondingly. Furtherly, comprehensive discussions were provided with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind speed prediction along timeline (the PCC equals to 0.99). The proposed data-driven method presents promising potential to accurately preserve the spatial-resolved patterns which are variant in both spatial and temporal dimensions, and show superiority in terms of computational efficiency in terms of achieving large-scale wind pattern predictions.

Cross-breeding of Tenebrio molitor strains from a large-scale perspective

Abstract To meet the increased future needs in insect Processed Animal Proteins PAPs, insect sector has to optimize the mass rearing of insects. That being the case, a means to tackle this growing demand for insects is to invest in breeding strategies aiming for the production of hybrids with improved economically and biologically valuable traits. In this framework, the objective of the present study was to evaluate the mating compatibility among four strains of the yellow mealworm, Tenebrio molitor, from different geographical areas (Greece, Belgium, Italy and USA) and to assess and compare the performance of the produced inbred and partially outbred lines. All trials were performed in crates of the size (40 × 60 cm) typically used in industrial rearing of mealworm. Adults of T. molitor were placed in the crates and were left to mate and oviposit. To assess mating compatibility, the amount of oviposited eggs (g) as well as the larval hatching rate were recorded. For the assessment of larval performance of inbred and partially outbred lines, two to three weeks old larvae were placed in crates together with Insectus as feeding substrate. The results showed that all crossed lines demonstrated compatibility, and certain combinations outperformed others as suggested by the high cumulative number of eggs and the larval hatch rates for both inbred and partially outbred lines produced. For hatched larvae, there were significant differences among the inbred and partially outbred lines. The highest hatch rates recorded for the Italian-Italian, Belgian-Italian, and Belgian-USA lines. Concerning offspring performance, all crosses followed a similar pattern in terms of final larval weight. The present study aims to draw the attention of the scientific community and insect producing companies to cross-breeding practices to start unfolding its potential for the genetical improvement of commercially farmed insects.

IMPACTS OF SLR-UPSCALED NOURISHMENT SCENARIOS ON DECADAL CROSS-SHORE DYNAMICS

Projections of high rates of sea level rise have stimulated proposals for adaptation strategies with increasingly high nourishment volumes. Nourishment strategies involving higher sand volumes can be accomplished by increasing the volume of individual nourishments or by decreasing the time interval between successive nourishments. The optimal placement of the sediment volumes in the cross-shore and alongshore to attain our coastal management goals is still under debate. From a long term, large scale perspective only the added sediment volume may be considered, regardless of the placement. A widely accepted perception is that coastal profiles respond to nourishment by rapid equilibration to an equilibrium shape including the added sand volume. However, the timescale of the redistribution of the sediment may be slower than the desired spreading rate of the added sediment, causing sediment to accumulate at some parts of the profile, while leaving other elevations sediment starved. This research aims to examine decadal-scale coastal profile response to nourishment strategies upscaled with sea level rise (SLR) whereby potential nourishment strategy impacts for beach width (fluctuations), dune growth potential and momentary coastline are mapped.

“It Would be Funny if it Wasn’t Horrifying”: A Discourse Analysis of the 2019 Conscious Eating Conference Debate on In Vitro Meat

This paper examines a high-profile debate on whether in vitro (or as referred to in the debate, cell-based) meat is good for animals. The debate is structured to present the “pro” and “con” sides to this resolve. This debate and its subsequent analysis herein illuminates tensions within the animal rights movement concerning effective tactics, and highlights main arguments for and against in vitro meat. This paper analyses both sides’ arguments, justifications given, and how both sides engage with each other. The debate is framed in terms of vegan activist tactics. Discourses concerning these tactics are drawn out in terms of how each side views their own reasoning and the other side’s. Evidence for three subsets of differences is presented: (1) a small-scale vs. large-scale perspective (2) variety of activist tactics vs. fundamentalist veganism, and (3) anger vs. naivete. Overall, two drastically differing discourses are found to be reflective of reformist versus a radical orientation towards animal rights and veganism generally. The debate over IVM has somewhat split the vegan community and this paper shows how so and along what lines, and the discourses that have emerged.

Positive fungal interactions are key drivers in Antarctic endolithic microcosms at the boundaries for life sustainability.

In the ice-free areas of Victoria Land in continental Antarctica, where the conditions reach the limits for life sustainability, highly adapted and extreme-tolerant microbial communities exploit the last habitable niches inside porous rocks (i.e. cryptoendolithic communities). These guilds host the main standing biomass and principal, if not sole, contributors to environmental/biogeochemical cycles, driving ecosystem processes and functionality in these otherwise dead lands. Although knowledge advances on their composition, ecology, genomic and metabolic features, a large-scale perspective of occurring interactions and interconnections within and between endolithic fungal assemblages is still lacking to date. Unravelling the tight relational network among functional guilds in the Antarctic cryptoendolithic communities may represent a main task. Aiming to fill this knowledge gap, we performed a correlation-network analysis based on amplicon-sequencing data of 74 endolithic microbiomes collected throughout Victoria Land. Endolithic communities' compositional pattern was largely dominated by Lichenized fungi group (83.5%), mainly represented by Lecanorales and Lecideales, followed by Saprotrophs (14.2%) and RIF+BY (2.4%) guilds led by Tremellales and Capnodiales respectively. Our findings highlighted that fungal functional guilds' relational spectrum was dominated by cooperative interactions led by lichenised and black fungi, deeply engaged in community trophic sustain and protection, respectively. On the other hand, a few negative correlations found may help in preserving niche boundaries between microbes living in such strict spatial association.

Two sides of the same coin? A comparison of research and practice orientation for teachers and doctors

Teacher training and medical degrees prepare students for evidence-based practice. This study examines how the learning opportunities of both programmes qualify students for this. Until now, a large-scale perspective on the university courses of student teachers and medical students in Germany has been lacking. The present study aims to fill this gap by comparing learning opportunities in teacher training and medical degrees. Three research questions are addressed: (1) Are the learning opportunities oriented to research and practice? (2) How are the learning opportunities designed didactically? (3) Which didactic designs are related to research orientation and practice orientation? NEPS-data from student teachers (N = 6322) and medical students (N = 1063) were used for the analyses. On this basis, implications for the design of teacher training learning opportunities are discussed.

Impacts of the development of mineral metal resources on surface water quality in the Mongolian Plateau based on meta-analysis

The mining of metal resources is one of the major sources of heavy metals in surface water, but studies on the different characteristics of the impact of the exploitation of mineral metal resources on surface water from a large-scale perspective are lacking. In the present study, we quantified the impacts of mineral metal resource development on As, Cu, Cr, Pb, Zn, Hg, Ni, Cd, Mn, and Fe in the overlying water and sediments of surface water under different scenarios (i.e., different geographic units and different mined metal types) using meta-analysis for the Mongolian Plateau of Mongolia and the Inner Mongolia Autonomous Region of China, which is rich in mineral metal resources and has high exploitation and use intensity. Finally, funnel plots and Egger’s regression analysis were used to test the publication bias of the data. The results show that the order of heavy metal content in the overlying water of the Mongolian Plateau was Fe > Mn > As > Zn > Cu > Pb > Cr > Ni > Cd > Hg, and the order of heavy metal content in the sediment was Fe > Mn > Zn > Cr > Pb > Ni > Cu > As > Cd > Hg. In addition, As, Cr, Zn, Hg, Cd, and Pb in overlying water exceeded the standard to different degrees, among which the contents of As and Pb were 1.4 and 1.3 times higher than the World Health Organization guideline, and the contents of Cr, Pb, Hg, Cd were 1.5, 1.3, 25.4, 2.6 times Chinese environmental quality standards for surface water, and the contents of As, Pb, Zn, Hg were 1.4, 1.3, 4.7, 12.7 times Mongolian water quality standards, respectively. The content of As in sediments was 2.6 times the background value of soil environmental quality in Inner Mongolia. Significant differences existed in the content of heavy metal pollutants in surface water of different countries; the content of heavy metals in the overlying water was significantly higher in Inner Mongolia Autonomous Region of China than in Mongolia. Copper and molybdenum polymetallic mines significantly increased the content of Cd, Cr, Cu, Fe, Hg, Pb, and Zn in the overlying water, while the content of As, Cr, Pb, and Zn in overlying water increased significantly due to lead–zinc mining. However, the mining of gold–silver, lead–zinc, copper–molybdenum, and other polymetallic mines all significantly affected the concentration of As in sediments. The results of the present study can provide data support for environmental protection and the restoration of surface water in metal mining areas of the Mongolian Plateau.

Symbolic Regression and Two‐Point Statistics Assisted Structure‐Property Linkage Based on Irregular‐Representative Volume Element

AbstractQuantifying the microstructure of materials is of significance in material development, especially for building the relationship between structure and property. To establish a remarkable structure‐property (SP) linkage, a novel concept referred to as irregular‐representative volume element (IRVE) based on panoramic image stitching technology (PIST) is proposed and a data‐driven scheme integrating irregular domain‐oriented two‐point statistics, principal component analysis (PCA), and symbolic regression based on genetic programming (GPSR) is constructed. Combining with advanced image processing and genetic programming technologies, this scheme improves the microstructure quantization framework. This scheme can not only be applied in different complex conditions for extracting the information of a material microstructure, but can also to embody details of microstructure from the perspective of large scale. IRVE is demonstrated to have both strong statistical representativeness and sufficient physical interpretation, which makes the scheme robust and reliable. Performing the scheme on an example of ferrite heat‐resistant steels, it shows a powerful ability in building an equational SP linkage with high precision (R = 0.91, RMSE = 13.17), the generalization ability of the linkage is also validated by an unseen steel (relative percentage error is 2.66%). The scheme has bright application prospects in predicting mechanical property and accelerating alloy design.

Identifying hair fescue in wild blueberry fields using drone images for precise application of granular herbicide

Hair fescue is a common perennial grass weed in wild blueberry fields in Nova Scotia, Canada and reduces yield if not adequately controlled. Hair fescue is typically managed with a broadcast propyzamide application which is costly for growers. Current research on the management of hair fescue in wild blueberry fields focuses on the development of a precision granular herbicide applicator with individual nozzle control. The integration of aerial images could benefit current work by providing a large-scale perspective on hair fescue location. This research suggests a new strategy to identify infield areas with hair fescue infestation, generate prescription maps, and integrate them into a widely available navigation system of heavy farming equipment to improve the processing workflow of hair fescue detection on a larger scale. This method proposes the integration of multi-resolution segmentation on multispectral (Blue, Green, Red, Red Edge, Near-Infrared) drone images to group hair fescue plants into segments with similar spectral characteristics. The best performing segmentation was classified and tested on wild blueberry fields in Nova Scotia. Results were assessed by comparing classified boundaries to reference data collected with high resolution RGB images. Findings from hair fescue and wild blueberry spectral profiles showed that hair fescue could be identified using blue, green, and red bands and showed no advantages over multispectral cameras. Results from the classification of wild blueberry and hair fescue showed high Kappa coefficient values of 0.90 and 0.94 and overall accuracy of 95 and 97% were achieved for both fields surveyed. Results also demonstrated an average potential cost saving of $CAD1246.43 ha-1 when compared to a broadcast application of granular dichlobenil. This work represents the first attempt to integrate aerial imagery and a precision granular herbicide applicator to manage important weeds in wild blueberry fields.

The contribution of organizational culture, structure, and leadership factors in the digital transformation of SMEs: a mixed-methods approach.

Contributing to the scarce literature on how companies can deal with their business model of digital transition, this work explores the digital transformation (DT) process in small and medium enterprises (SME), investigating how organizational culture, structure, and leadership influence it. While such three factors are deemed essential components to facilitate DT, how they operate and how they relate to each other are still not very well-defined issues in need of in-depth investigation. This study employed a mixed-methods approach, following an exploratory sequential design. First, a conceptual model was developed based on qualitative data collected from expert interviews and analyzed through grounded theory. This stage uncovered 25 first-order concepts about culture, structure, and leadership, further organized into 6 constructs and hypothesis paths. Then, with a sample of 192 SMEs, the structural model was measured and validated using exploratory factor analysis and PLS-SEM. As a result, our study offers robust and timely research, whose conceptual model condenses a knowledge corpus that future research can benefit from, and it provides statistical extrapolations about how and how much those factors relate to each other in SME context; moreover, given the traditional scarce resources and lack of flexibility in SMEs, it provides orientation and guidelines to managers facing DT and needing to understand the organizational factors they should be aware of, where to focus energy, and what to expect as results. From a large-scale perspective, this study carries an impactful contribution to the many countries where SMEs play a major economic and social role.

Current Knowledge, Satisfaction, and Use of E-Health Mobile Application (Seha) Among the General Population of Saudi Arabia: A Cross-Sectional Study.

BackgroundGeneral population knowledge, satisfaction, and barriers to using Seha app have not been evaluated from a large-scale perspective. Therefore, this study aimed to explore current knowledge, satisfaction, and barriers of using Seha app and identify the most common mobile health application used among the general population in Saudi Arabia.MethodsA cross-sectional online survey, consisting of 25 questions, was distributed among the general population of Saudi Arabia. Descriptive statistics were used to describe the respondents’ characteristics. Categorical variables were reported as frequencies and percentages. A chi-square (χ2) test was conducted to assess the statistical difference between respondents’ demographic characteristics and their knowledge and use of the app.ResultsOverall, 5008 respondents, both Saudi (3723: 74%) and non-Saudi (1285: 26%) as well as male 2142 (43%) and female 2866 (57%), across the Kingdom of Saudi Arabia completed the online survey. A total of 2921 (58%) had heard of the Seha app, although only 1286 (25%) had used the app. Higher percentages of users were from the western region, females and those within the age group of ≥51 years old, 388 users (29%: P<0.001), 804 (28%; P<0.001) and 67 (35%; P=0.013), respectively. Consulting a doctor was the most frequently utilized service, 576 users (58%). Respondents strongly agreed 402 (41%) that Seha was easy to use, and 538 (54%) strongly agreed that they would recommend Seha to others. The most common barrier of using Seha was a lack of knowledge about the app and its benefits, at 1556 (35%). Overall, the Tawakkalna app was the most utilized mobile health application provided by MOH used 2170 (48%).ConclusionUtilization of the Seha app is quite low due to a lack of knowledge about the app and its benefits. Thus, the MOH should promote public awareness about the app and its benefits.

Mapping the Risk for West Nile Virus Transmission, Africa.

West Nile virus (WNV) is an emergent arthropodborne virus that is transmitted from bird to bird by mosquitoes. Spillover events occur when infected mosquitoes bite mammals. We created a geopositioned database of WNV presence in Africa and considered reports of the virus in all animal components: reservoirs, vectors, and nonhuman dead-end hosts. We built various biogeographic models to determine which drivers explain the distribution of WNV throughout Africa. Wetlands of international importance for birds accounted for the detection of WNV in all animal components, whereas human-related drivers played a key role in the epizootic cases. We combined these models to obtain an integrative and large-scale perspective of the areas at risk for WNV spillover. Understanding which areas pose the highest risk would enable us to address the management of this spreading disease and to comprehend the translocation of WNV outside Africa through avian migration routes.

Dipole pattern of meridional atmospheric internal energy transport across the Arctic gate

High-latitude atmospheric meridional energy transport plays a fundamental role in the Arctic climate system. However, despite numerous studies, there are no established clear regional features of the atmospheric energy transport components from a large-scale perspective. This study aims at investigating the internal energy and its instantaneous sensible and latent heat transports in the troposphere across the Arctic gate at 70°N using the high-resolution climate reanalysis ERA5. We have done a regional analysis of the time series of heat fluxes across the zonal section and found by decomposing them into empirical orthogonal functions that they have opposing features for the Eastern and Western Hemispheres. In particular, the sensible heat transport dominates in the Western Hemisphere, whereas the latent heat transport dominates in the Eastern Hemisphere. Moreover, we detected the existence of an anti-phase dipole pattern for each of these components in the entire troposphere, which is robust because it continued during the entire studied period 1950–2019. The hemispheric net fluxes indicate that the Arctic gains internal energy mostly due to the latent heat transport.

大兴安岭南段马鹿生境适宜性分析与生态廊道构建

PDF HTML阅读 XML下载 导出引用 引用提醒 大兴安岭南段马鹿生境适宜性分析与生态廊道构建 DOI: 10.5846/stxb202109062517 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 赤峰市林业科技振兴计划项目 Habitat suitability analysis and ecological corridor designs for red deer (Cervus elaphus) in the southern Greater Khingan Mountains Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:构建生态廊道在缓解生境破碎化对生物多样性的影响、维持濒危物种的遗传多样性、维护自然生态系统结构完整与功能稳定方面具有重要作用。以内蒙古大兴安岭南段分布的马鹿（Cervus elaphus）种群为研究对象，利用MaxEnt模型对其生境适宜性进行分析，并利用最小累积阻力模型构建潜在生态扩散廊道，探讨大兴安岭南段区域隔离马鹿种群的栖息地连通方案。结果显示，马鹿栖息地呈破碎化状态，种群有明显的隔离分布趋势，现有适宜栖息地具有海拔较低（800-1200 m）、坡度较缓（<15°）、靠近水源、植被类型多为靠近山林的灌丛或草地等特点。所构建12条生态廊道具有经过河流浅水节段、远离村落等特点，便于落实栖息地生态恢复管理措施。研究从区域尺度综合分析了大兴安岭南段马鹿栖息地现状及连通性，有助于优化适宜栖息地格局，促进马鹿扩散和栖息地连通，为该物种隔离种群及其栖息地保护规划提供现实指导和基础资料。 Abstract:Effectively designed ecological corridors can eliminate the impact of habitat fragmentation on biodiversity, promote gene flow and the genetic diversity of endangered species, and maintain the structural integrity and functional stability of natural ecosystems. In this study, we used the MaxEnt model to analyze habitat suitability for red deer (Cervus elaphus) in the southern Greater Khingan Mountains, Inner Mongolia. We created a minimum cumulative resistance model to identify potentially ecological corridors for red deer. Results showed that red deer habitats in the study area were fragmented. The red deer population had an obvious trend of isolation distribution; the existing suitable habitats were characterized by low elevation (800-1200 m) and a gentle slope (<15°) close to a water source, and the suitable habitats mostly consisted of forest shrub or grassland vegetation close to woodland edges. The creation of 12 ecological corridors would encourage red deer dispersal and support habitat connectivity. Three main characteristics of the corridors that were conducive to habitat restoration management measures include-passing through shallow rivers, irregular topography, and being located far away from human residences. The comprehensive analysis of habitat suitability and ecological corridors from a large-scale perspective may help optimize red deer habitat distribution. The analysis can provide practical guidance and basic information for the conservation of the species in the southern Greater Khingan Mountains. 参考文献 相似文献 引证文献

Resolving the Formation of Cold H i Filaments in the High-velocity Cloud Complex C

Abstract The physical properties of galactic halo gas have a profound impact on the life cycle of galaxies. As gas travels through a galactic halo, it undergoes dynamical interactions, influencing its impact on star formation and the chemical evolution of the galactic disk. In the Milky Way halo, considerable effort has been made to understand the spatial distribution of neutral gas, which is mostly in the form of large complexes. However, the internal variations of their physical properties remain unclear. In this study, we investigate the thermal and dynamical state of the neutral gas in high-velocity clouds. High-resolution observations (1.′1) of the 21 cm line emission in the EN field of the DHIGLS H i survey are used to analyze the physical properties of the bright concentration CIB located at an edge of a large HVC complex, complex C. We use the Gaussian decomposition code ROHSA to model the multiphase content of CIB and perform a power spectrum analysis to analyze its multiscale structure. The physical properties of some 200 structures extracted using dendrograms are examined. Each phase exhibits different thermal and turbulent properties. We identify two distinct regions, one of which has a prominent protrusion extending from the edge of complex C that exhibits an ongoing phase transition from warm diffuse gas to cold dense gas and filaments. The scale at which the warm gas becomes unstable and undergoes thermal condensation is about 15 pc, corresponding to a cooling time of about 1.5 Myr. Our study characterizes the statistical properties of turbulence in the fluid of an HVC for the first time. We find that a transition from subsonic to transonic turbulence is associated with the thermal condensation, going from large to small scales. A large-scale perspective of complex C suggests that hydrodynamic instabilities are involved in creating the structured concentration CIB and the phase transition therein. However, the details of the dynamical and thermal processes remain unclear and will require further investigation through both observations and numerical simulations.

Effects of Water Level Fluctuations on the Growth Characteristics and Community Succession of Submerged Macrophytes: A Case Study of Yilong Lake, China

Water level fluctuation (WLF) has a significant effect on aquatic macrophytes, but few experimental studies have examined the effect of WLF on submerged community succession, especially from a large-scale perspective. In this study, field monitoring of WLF and submerged macrophytes was conducted in Yilong Lake (SE China) over two years, and the impacts of WLF on the growth characteristics and the community structure of submerged macrophytes were determined. The results show that the biomass of submerged macrophytes decreased significantly after the water level increased and submerged macrophytes could cope with the adverse environment by adjusting their growth posture, for example, increasing plant length and reducing branch number. However, different submerged plants have different regulatory abilities, which leads to a change in the community structure. Myriophyllum spicatum, Stuckenia pectinata, and Najas marina had better adaptation abilities to WLF than Najas minor and Utricularia vulgaris. Changes in water depth, dissolved oxygen, and transparency significantly contribute to the effect of WLF on submerged plant communities. Therefore, when determining the range of WLF, the above three critical factors and submerged plant species should be considered. WLF changed the spatial distribution of the aquatic plant community. When water levels rose, the density of the submerged macrophyte community in the original growth region reduced as the emergent plants migrated to shallower water, and the seed bank germination was aided by transparent water produced among emergent plants. This can be used as a pioneering measure to restore submerged plants in eutrophic lakes with low transparency. In addition, a suitable water depth created by WLF was conducive to activating the seed bank and improving the diversity of aquatic plants. Finally, a distribution map of aquatic plants in Yilong Lake is drawn.

Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review

PFAS substances, which have been under investigation in recent years, are certainly some of the most critical emerging contaminants. Their presence in drinking water, correlated with diseases, is consistently being confirmed by scientific studies in the academic and health sectors. With the aim of developing new technologies to mitigate the water contamination problem, research activity based on advanced oxidation processes for PFAS dealkylation and subsequent mineralization is active. While UV radiation could be directly employed for decontamination, there are nevertheless considerable problems regarding its use, even from a large-scale perspective. In contrast, the use of cheap, robust, and green photocatalytic materials active under near UV-visible radiation shows interesting prospects. In this paper we take stock of the health problems related to PFAS, and then provide an update on strategies based on the use of photocatalysts and the latest findings regarding reaction mechanisms. Finally, we detail some brief considerations in relation to the economic aspects of possible solutions.