Environmental Variables Research Articles

Understanding cooperative contact tracing: Factors explored through privacy management theories

This study investigates the factors influencing the comfort level of the general public when disclosing personal information for the coronavirus disease 2019 contact tracing. This is a secondary analysis of the American Trends Panel, a national probability-based online panel of American adults, with data collected by the Pew Research Center between July 13 and 19, 2020. Grounded in privacy management theories, ordered logistic regression analyses examined three types of information disclosure: places visited, names of contacts, and location data from cell phones. Key independent variables measured trust in the stakeholders' ability to protect data and perceived risks to health and finances. The findings suggest that higher levels of trust in entities' data security capabilities can predict individuals’ comfort levels when disclosing personal data. Additionally, the participants were more comfortable with noncommercial data use, especially when it was used by researchers and state and local officials. However, financial threats showed variations in sharing certain types of data. Individuals were less likely to feel at ease sharing contact tracing data as concerns about personal finances increased. Similarly, when individuals perceived threats to the U.S. economy, they were less likely to feel comfortable sharing their location data from cell phones, which might have been perceived as intrusive. Public health outreach efforts should account for individual differences and the nature of the information requested in commercial and noncommercial contexts. Future studies can enhance the explanatory capacity of data disclosure models by incorporating additional relevant contextual and environmental variables.

Effects of management practices on Northern Bobwhite Colinus virginianus density in privately owned working forests across the Southeastern United States

Abstract Obtaining rigorous baseline density estimates of species of conservation interest is key when assisting landowners to achieve management goals on private lands. Northern bobwhite (Colinus virginianus) populations are declining throughout their range and despite being the focus of numerous private land conservation initiatives, baseline density estimates in privately owned pine forests are lacking. We sought to address this knowledge gap across the Southeastern United States by sampling 105 privately owned pine stands throughout 2018 to 2020 using observer point count and autonomous recording unit (ARU) sampling data. Using Bayesian hierarchical models, we investigated the influence of stand management (brush management or applied fire) on bobwhite density, as well as four landscape‐scale environmental variables. These included percentage cover of forest, herbaceous, agricultural or burnt land area across six different spatial scales ranging from 500‐m to 10‐km around each pine stand. Baseline density on sites with no management was estimated to be 2.24 coveys per 100 ha (1.00–5.03, 95% BCI), with little impact of applying brush management, but a trend for a positive effect of fire management (0.19, −0.01 to 0.38 95% BCI). This impact of fire was seen at both the stand‐scale, correlated with an increase in acreage of applied prescribed burn management, and across the greater landscape area, correlated with cover of burnt area within a 2‐km buffer around each site. There were also strong positive influences of herbaceous vegetation and a strong negative influence of forest cover on bobwhite density. Practical implication: our sampling efforts fill an important information gap regarding densities throughout private lands in the Southeastern United States. Our study also highlights the necessity of landscape scale planning for Northern Bobwhite conservation initiatives because the efficacy of conservation practices (i.e. prescribed fire and brush management) could be altered by the landscape surrounding the treated forest stand.

Using comparative extinction risk analysis to prioritize the IUCN Red List reassessments of amphibians

AbstractAssessing the extinction risk of species based on the International Union for Conservation of Nature (IUCN) Red List (RL) is key to guiding conservation policies and reducing biodiversity loss. This process is resource demanding, however, and requires continuous updating, which becomes increasingly difficult as new species are added to the RL. Automatic methods, such as comparative analyses used to predict species RL category, can be an efficient alternative to keep assessments up to date. Using amphibians as a study group, we predicted which species are more likely to change their RL category and thus should be prioritized for reassessment. We used species biological traits, environmental variables, and proxies of climate and land‐use change as predictors of RL category. We produced an ensemble prediction of IUCN RL category for each species by combining 4 different model algorithms: cumulative link models, phylogenetic generalized least squares, random forests, and neural networks. By comparing RL categories with the ensemble prediction and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessment based on the mismatch between predicted and observed values. The most important predicting variables across models were species’ range size and spatial configuration of the range, biological traits, climate change, and land‐use change. We compared our proposed prioritization index and the predicted RL changes with independent IUCN RL reassessments and found high performance of both the prioritization and the predicted directionality of changes in RL categories. Ensemble modeling of RL category is a promising tool for prioritizing species for reassessment while accounting for models’ uncertainty. This approach is broadly applicable to all taxa on the IUCN RL and to regional and national assessments and may improve allocation of the limited human and economic resources available to maintain an up‐to‐date IUCN RL.

METABOLIC SYNDROME: LITERATURE ANALYSIS

MetS is a significant public health concern due to its association with severe health outcomes. Early identification and comprehensive management of MetS components are crucial in reducing the global burden of this syndrome. Further research is needed to refine diagnostic criteria, understand the underlying mechanisms, and develop more effective prevention and treatment strategies. The metabolic syndrome (MetS) is a group of illnesses that together better predict a person's risk of cardiovascular disease than any one of the illnesses alone. Human MetS risk is influenced by a combination of environmental and genetic variables. As MetS is a complex disorder with a startlingly high incidence rate these days, developing suitable experimental animal models that closely resemble the illness state in people is essential to resolving the challenges associated with assessing the pathophysiology of MetS in humans. The objective of this study is to provide an overview of the pathophysiology of dietary, genetic, and pharmaceutical models of metabolic syndrome. We will also go over the applicability, advantages, and disadvantages of various animal models. Despite the establishment of many animal models of MetS, more research on.

Factors affecting microhabitat use in two agamid species from south‐eastern Australia

AbstractDespite being a central aspect of a species' ecology and having important conservation implications, the use of fine‐scale habitat features (microhabitats), and how they vary over time, are poorly known for most species. In this study, we leveraged 522 relocations of radio‐tracked mallee tree dragons (Amphibolurus norrisi) and painted dragons (Ctenophorus pictus) to investigate the effect of environmental and individual variables on microhabitat use. Mallee tree dragons were more likely to use trees on hot days and in the middle of the day (the hottest time of day), whereas shrub use was more likely on cool days and in the mornings. Painted dragons were more likely to use shrubs and grass trees on warmer days and during the middle of the day, whereas burrows were used during cooler days and in the mornings and afternoons. We expect these patterns represent animals moving from refuge resources, occupied during unsuitable thermal conditions, to microhabitats offering basking, foraging and social opportunities during periods of thermal optimum. Additionally, we found evidence of an ontogenetic shift, with larger mallee tree dragons favouring tree use and higher perches, suggesting a transition from shrub to tree use with age. Our findings underscore the need for diverse microhabitats to accommodate the thermoregulatory and ontogenetic requirements of lizards. These insights can inform conservation and habitat restoration efforts, ensuring the provision of essential microhabitats to support species persistence.

Influence of Environmental Social Governance (ESG), Profitability and Capital Structure on Firm Value

The faster information spreads, the more important it is for companies to publicize the good and minimize problems that can harm the company's reputation and value. Environmental pollution cases and issues are the reasons why positive information is very attractive to investors. Therefore, this study aims to test (1) Does Environmental Social Governance affect firm value, (2) Does Profitability affect firm value, (3) Does Capital structure affect Firm Value and (4) Does Environmental Social Governance, profitability and Capital structure jointly affect firm value in Indonesia. In this study, the population used was industrial sector companies totaling 56 companies and those used as samples in this study were 49 companies that were consecutively listed on the Indonesia Stock Exchange for 3 years in 2020-2022. The sample of this study was determined by random sampling method. The type of research and data used is quantitative with secondary data. The analysis method used is multiple linear regression analysis and uses SPSS 26 software. The results showed that (1) Environmental Social Governance variables affect firm value, (2) Profitability variables affect firm value, (3) Capital structure affects firm value and (4) Environmental Social Governance, profitability and Capital structure together affect firm value.

A Conceptual Framework to Assess Post‐Wildfire Water Quality: State of the Science and Knowledge Gaps

AbstractWildfire substantially alters aquatic ecosystems by inducing moderate to catastrophic physical and chemical changes. However, the relations of environmental and watershed variables that drive those effects are complex. We present a Driver‐Factor‐Stressor‐Effect (DFSE) conceptual framework to assess the current state of the science related to post‐wildfire water‐quality. We reviewed 64 peer‐reviewed papers using the DFSE framework to identify drivers, factors, stressors, and effects associated with each study. A total of five drivers were identified and ranked according to their frequency of occurrence in the literature: atmospheric processes > fire characteristics > ecologic processes and characteristics > land surface characteristics > soil characteristics. Commonly reported stressors include increased nutrients, runoff, and sediment transport. Furthermore, although several different factors have been used at least once to explain water‐quality effects, relatively few factors outside of precipitation and fire characteristics are frequently studied. We identified several gaps indicating the need for long‐term monitoring, multi‐factor studies, consideration of organic contaminants, consideration of groundwater, and inclusion of soil characteristics. This assessment expands on other reviews and meta‐analyses by exploring causal linkages between influential variables and overall effects in post‐wildfire watersheds. Information gathered from our assessment and the framework itself can be used to inform future monitoring plans and as a guide for modeling efforts focused on better understanding specific processes or to mitigate potential risks of post‐wildfire water quality.

Outbreaks of Ulva prolifera green tides reduce the network complexity and stability of cooccurring planktonic microbial communities

Ulva prolifera green tides are becoming a worldwide environmental problem, especially in the Yellow Sea, China. However, the effects of the occurrence of Ulva prolifera green tides on the community organization and stability of surrounding microbiomes have still not been determined. Here, the prokaryotic microbial community network stability and assembly characteristics were systematically analyzed and compared between the green tide and non-green tide periods. Ulva prolifera blooms weaken the community complexity and robustness of surrounding microbiomes, increasing fragmentation and decreasing diversity. Bacteria and archaea exhibited distinct community distributions and assembly patterns under the influence of green tides, and bacterial communities were more sensitive to outbreaks of green tides. The bacterial communities exhibited a greater niche breadth and a lower phylogenetic distance during the occurrence of U. prolifera green tides compared to those during the non-green tide period while archaeal communities remained unchanged, suggesting that the bacterial communities underwent stronger homogeneous selection and more sensitive to green tide blooms than the archaeal communities. Piecewise structural equation model analysis revealed that the different responses of major prokaryotic microbial groups, such as Cyanobacteria, to environmental variables during green tides, were influenced by the variations in pH and nitrate during green tides and correlated with the salinity gradient during the non-green tide period. This study elucidates the response of the adaptability, associations, and stability of surrounding microbiomes to outbreaks of U. prolifera green tides.

It's a Trap! Part I: Exploring the Applications of Rotating-Arm Impaction Samplers in Plant Pathology.

Although improved knowledge on the movement of airborne plant pathogens is likely to benefit plant health management, generating this knowledge is often far more complicated than anticipated. This complexity is driven by the dynamic nature of environmental variables, diversity among pathosystems that are targeted, and the unique needs of each research group. When using a rotating-arm impaction sampler, particle collection is dependent on the pathogen, environment, research objectives, and limitations (monetary, environmental, or labor). Consequently, no design will result in 100% collection efficiency. Fortunately, it is likely that multiple approaches can succeed despite these constraints. Choices made during design and implementation of samplers can influence the results, and recognizing this influence is crucial for researchers. This article is for beginners in the art and science of using rotating-arm impaction samplers; it provides a foundation for designing a project, from planning the experiment to processing samples. We present a relatively nontechnical discussion of the factors influencing pathogen dispersal and how placement of the rotating-arm air samplers alters propagule capture. We include a discussion of applications of rotating-arm air samplers to demonstrate their versatility and potential in plant pathology research as well as their limitations.

Synthesis and characterization of novel La/Rb bimetal-organic frameworks for efficient adsorption of 2,4-Dichlorophenoxyacetic acid from aqueous solutions: Adsorption isotherm, kinetics, optimization via Box-Behnken design, and DFT calculations

This work investigated the adsorption properties of lanthanum/rubidium bimetal-organic frameworks (La/Rb-MOF) with the goal of removing the hazardous herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D) from aqueous solutions. SEM, TEM, PXRD, XPS, FT-IR, and BET experiments were employed to extensively analyze the substance’s properties. A significant surface area of 1560.91 m2/g was observed in the data, suggesting a prospective for a high adsorption capability. A positive charge beyond the point of zero charge (5.14) and a negative charge after it were shown to have an impact on the adsorption behavior, according to the surface charge analysis. Through batch experiments, the study investigated how pH affected adsorption equilibrium and offered insights into the environmental variables influencing the adsorption procedure. At a pH of 4, the maximal 2,4-D adsorption onto La/Rb-MOF was found to reach 452.6 mg.g−1. The structure, reactivity and 2,4-D electrical properties were determined using density functional theory (DFT). DFT indicates that there is respectable agreement between the location of the electrophilic and nucleophilic violence sites and the molecular orbitals (HOMO and LUMO) and MEP data. The kinetics of the reaction were elucidated using pseudo-second-order kinetic models. The Langmuir isotherm model, which consistently showed a chemisorption procedure, demonstrated to be an accurate forecaster of the adsorption mechanism. Interestingly, the predicted thermodynamic characteristics of our investigation showed that the entire adsorption–desorption procedure was endothermic and spontaneous in nature (ΔH°), (ΔS°), and (ΔG°) while using the adsorbent La/Rb-MOF. La/Rb-MOF and 2,4-dichlorophenylacetic acid (2,4-D) were found to interact by a mechanism that took into account elements such as electrostatic contact, π-π interaction, and pore filling. Additional investigation evaluated the adsorbent’s capacity for regeneration while evaluating the removal efficacy and its ability to function continuously for more than eight cycles. The Box-Behnken Design was active in order to optimize the results of adsorption. This analysis highlights how La/Rb-MOF can be used to handle real-world wastewater treatment difficulties in a sustainable and practical manner.

Landscape genomics reveals genetic signals of environmental adaptation of African wild eggplants.

Crop wild relatives (CWR) provide a valuable resource for improving crops. They possess desirable traits that confer resilience to various environmental stresses. To fully utilize crop wild relatives in breeding and conservation programs, it is important to understand the genetic basis of their adaptation. Landscape genomics associates environments with genomic variation and allows for examining the genetic basis of adaptation. Our study examined the differences in allele frequency of 15,416 single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing approach among 153 accessions of 15 wild eggplant relatives and two cultivated species from Africa, the principal hotspot of these wild relatives. We also explored the correlation between these variations and the bioclimatic and soil conditions at their collection sites, providing a comprehensive understanding of the genetic signals of environmental adaptation in African wild eggplant. Redundancy analysis (RDA) results showed that the environmental variation explained 6% while the geographical distances among the collection sites explained 15% of the genomic variation in the eggplant wild relative populations when controlling for population structure. Our findings indicate that even though environmental factors are not the main driver of selection in eggplant wild relatives, it is influential in shaping the genomic variation over time. The selected environmental variables and candidate SNPs effectively revealed grouping patterns according to the environmental characteristics of sampling sites. Using four genotype-environment association methods, we detected 396 candidate SNPs (2.5% of the initial SNPs) associated with eight environmental factors. Some of these SNPs signal genes involved in pathways that help adapt to environmental stresses such as drought, heat, cold, salinity, pests, and diseases. These candidate SNPs will be useful for marker-assisted improvement and characterizing the germplasm of this crop for developing climate-resilient eggplant varieties. The study provides a model for applying landscape genomics to other crops' wild relatives.

Evidence that spatial scale and environment factors explain grassland community assembly in woodland-grassland ecotones.

How communities of living organisms assemble has long been a central question in ecology. The impact of habitat filtering and limiting similarity on plant community structures is well known, as both processes are influenced by individual responses to environmental fluctuations. Yet, the precise identifications and quantifications of the potential abiotic and biotic factors that shape community structures at a fine scale remains a challenge. Here, we applied null model approaches to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5 × 5 m, with three nested subplots measuring 1 × 1 m, from the 2021 field survey, to examine the alpha diversity as well as beta diversity of plots and subplots. Linear mixed-effects models were employed to determine the impact of environmental variables on assembly rules. Our results demonstrate that habitat filtering is the dominant assembly rules at both the plot and subplot levels, although limiting similarity assumes stronger at the subplot level. Plot-level limiting similarity exhibited a positive association with fine-scale partitioning, suggesting that trait divergence originated from a combination of limiting similarity and spatial partitioning. Our findings also reveal that the community assembly varies more strongly with the mean annual temperature gradient than the mean annual precipitation. This investigation provides a pertinent illustration of non-random assembly rules from spatial scale and environmental factors in plant communities in the loess hilly region. It underscores the critical influence of spatial and environmental constraints in understanding the assembly of plant communities.

Mountain Peatlands and Drought: Carbon Cycling in the Pyrenees Amidst Global Climate Change

AbstractThis study provides a multi‐year (2017–2022) Net Ecosystem Carbon Balance (NECB) of a Pyrenean mountainous peatland through the integration of field data, satellite imagery, and statistical modeling. Fluvial organic carbon export was measured at 30 min frequency, while gaseous (CO2 and CH4) exchanges were measured monthly using closed chambers. These measurements were combined with Sentinel‐2 derived chlorophyll index and in situ high frequency (1 hr) measurements of key environmental variables such as air temperature, photosynthetically active radiation, and water table level, to develop hourly gaseous carbon flux models (R2 = 0.69 for GPP, R2 = 0.84 for ER, R2 = 0.59 for CH4). Over the 2017–2022 period, modeled average GPP (610 ± 39 gC.m−2.year−1) and ER (641 ± 59 gC.m−2.year−1) showed that the peatland acted as a weak source of CO2 to the atmosphere, releasing 31 ± 73 gC.m−2.year−1. Considering fluvial carbon export and CH4 exchanges, the loss of carbon from the peatland increased to 55 ± 73 gC.m−2.year−1. Dissolved organic carbon constituted 8%–106% of the NECB. The estimated long‐term organic accumulation rate indicated a steady carbon accumulation rate of 16.4 gC.m−2.year−1, contrasting with the contemporary NECB, suggesting a recent shift in ecosystem functioning from a carbon sink to a source. The study underscores the role of water availability and air temperature through a drought index (DI), in shaping the NECB. The DI correlated significantly with annual carbon gaseous fluxes, except for 2022, marked by an intense drought. During this year the peatland became a large source of carbon (189 gC.m−2.year−1) to the atmosphere.

Predicting the potential distribution change of the endangered Francois' langur (Trachypithecus francoisi) across its entire range in China under climate change.

The Francois' langur (Trachypithecus francoisi) is a rare primate species indicated as endangered and distributed in karst areas in northern Vietnam and southwestern China. However, research limited to specific nature reserves or sites has hampered holistic conservation management. A comprehensive map of the potential distribution for the Francois' langur is essential to advance conservation efforts and ensure coordinated management across regions. Here, we used 82 occurrence records of Francois' langur surveyed in Guangxi, Guizhou, and Chongqing from 2017 to 2020, along with 12 environmental variables, to build the potential habitat model under current and future climate (2030, 2050, 2070, and 2090s) using maximum entropy models (MaxEnt). Our results indicated that (1) precipitation- and temperature-associated bioclimatic variables contributed the most to the distribution of Francois' langur. Vegetation, water sources, and anthropogenic variables also affected its distribution; (2) a total of 144,207.44 km2 of potential suitable habitat across the entire range in China was estimated by the current model. Moderate- and high-suitability habitats accounted for only 23.76% (34,265.96 km2) of the predicted suitable habitat and were mainly distributed in southwest Guangxi, east of Chongqing, and the border between Guizhou and Chongqing; (3) the suitable habitats of Francois' langur will contract considerably under future climate change, and the habitat centroid will move in the southeast direction with a shifting distance of approximately 2.84 km/year from current to 2100. The habitat prediction of Francois' langur and the main drivers proposed in this study could provide essential insights for the future conservation of this endangered species. The existing distribution areas should be monitored and protected, but conservation beyond existing habitats should also be a focus of effort, especially in future expansion areas. This would ensure effective and timely protection under climate change and anthropogenic pressures.

Pengaruh Lingkungan Sekolah, Perhatian Orang Tua dan Kebiasaan Belajar Siswa SMA Negeri 1 Ketapang

In the current era of globalization, students are faced with demands to consider factors influencing their academic achievements. This research aims to uncover the role of an effective school environment, parental attention, and study habits in enhancing students' educational quality. Quantitative methods were employed in this study. This approach was chosen to provide a more measurable understanding of the roles of the school environment, parental involvement, and study habits on students' academic outcomes. Involving 207 students as research samples, sampling was conducted randomly. Data collection was carried out through distributing questionnaires covering school environment variables, parental involvement, and study habits. Data analysis was conducted through validity testing, reliability testing, classical assumption testing, and multiple linear regression. The use of multiple linear regression as a data analysis tool enabled researchers to understand the extent to which certain variables, such as the school environment, parental involvement, and study habits, contribute to variations in students' academic outcomes. Findings indicate that the school environment plays a significant role in determining students' academic achievements. Emotional involvement and parental support also contribute to students' academic performance. Furthermore, consistent and effective study habits have proven to be significant factors in developing students' academic achievements. With a supportive school environment for learning and positive contributions from parents, as well as effective study habits, it is expected that students' academic outcomes will improve.

The optimized Maxent model reveals the pattern of distribution and changes in the suitable cultivation areas for Reaumuria songarica being driven by climate change.

Reaumuria songarica, a drought-resistant shrub, is widely distributed and plays a crucial role in the northern deserts of China. It is a key species for desert rehabilitation and afforestation efforts. Using the Maxent model to predict suitable planting areas for R. songarica is an important strategy for combating desertification. With 184 occurrence points of R. songarica and 13 environmental variables, the optimized Maxent model has identified the main limiting factors for its distribution. Distribution patterns and variation trends of R. songarica were projected for current and future climates (2030s, 2050s, 2070s, and 2090s) and different scenarios (ssp_126, ssp_370, and ssp_585). Results show that setting parameters to RM (regulation multiplier) = 4 and FC (feature combination) = LQHPT yields a model with good accuracy and high reliability. Currently, R. songarica is primarily suitable for desert control in eight provinces and autonomous regions, including Inner Mongolia, Xinjiang, Qinghai, and Ningxia. The total suitable planting area is 148.80 × 104 km2, representing 15.45% of China's land area. Precipitation (Precipitation of the wettest month, Precipitation of the warmest quarter, and Annual precipitation) and Ultraviolet-B seasonality are the primary environmental factors limiting the growth and distribution of R. songarica. Mean temperature of the warmest quarter is the primary factor driving changes in the distribution of suitable areas for R. songarica under future climate scenarios. In future climate scenarios, the suitable planting area of R. songarica will shrink, and the distribution center will shift towards higher latitude, potentially indicate further desertification. The area of highly suitable habitat has increased, while moderately and less suitable habitat areas have decreased. Increased precipitation within R. songarica's water tolerance range is favorable for its growth and reproduction. With changes in the suitable cultivation area for R. songarica, priority should be given to exploring and utilizing its germplasm resources. Introduction and cultivation can be conducted in expanding regions, while scientifically effective measures should be implemented to protect germplasm resources in contracting regions. The findings of this study provide a theoretical basis for addressing desertification resulting from climate change and offer practical insights for the development, utilization, introduction, and cultivation of R. songarica germplasm resources.

Unveiling hidden bacterial players: A closer look at the succession characteristics and co-occurrence patterns of abundant, intermediate and rare bacteria in biological soil crusts

The bacterial communities of biological soil crusts (BSCs) often comprise abundant, intermediate and rare taxa that form complex ecosystems through interspecies interactions and play essential roles in regulating the process of vegetative restoration. However, the community structure of nonabundant bacteria and the interactions among these abundance categories are still largely unknown. In light of this, we used Illumina MiSeq sequencing to investigate the succession characteristics and co-occurrence patterns of abundant bacteria (ABs), intermediate bacteria (IBs) and rare bacteria (RBs) in the three developmental stages of BSCs (cyanobacteria-, lichen- and moss-dominated crusts) on the southeastern edge of the Tengger Desert. The results showed that the changes in the Chao richness and taxonomic composition of ABs, IBs and RBs were significantly different during BSC succession. The results of the co-occurrence network analysis revealed that most of the observed associations were between RBs (more than 80 %), positive correlations were predominant (more than 77 %), and the vast majority of the keystone taxa in the BSC bacterial networks were RBs (only one belonged to IBs). Moreover, the driving effects of environmental variables on ABs, IBs and RBs were not exactly the same, and their diversity was a key driver of the co-occurrence network properties of the BSCs. These findings are crucial for understanding the establishment and maintenance of BSC bacterial diversity and provide a new perspective for utilizing microbial resources and/or regulating environmental factors to achieve the sustainable development of desert ecosystems.

Application of Lock-in Amplifier Technique in Signal Blind Source Separation

Abstract In the realm of signal processing, frequency error stands as a critical challenge that cannot be overlooked, particularly in scenarios characterized by noise disturbances and constantly changing environmental variables, as it significantly undermines the precision of waveform separation. Faced with a spectrum of frequency error phenomena stemming from hardware instability, such as clock drift, transmission distortion, and environmental changes, this paper proposes a waveform separation scheme centered around a core closed-loop control architecture aimed at addressing such challenges. Leveraging the high-performance STM32F407 microcontroller platform, the system harnesses the powerful algorithmic advantages of Fast Fourier Transform (FFT) to precisely locate and delineate the spectral characteristics of signals. Furthermore, we creatively integrate lock-in amplifier technology into the meticulously designed closed-loop control system, coupled with state-of-the-art zero-crossing detection algorithms for in-depth optimization. The research findings demonstrate outstanding signal-to-noise ratio performance within predefined target frequency bands for the separation device based on lock-in amplifier technology, showcasing not only remarkable resilience against interference but also the ability to accurately separate and reconstruct target signal components with high fidelity from complex and dynamic signal environments. This significantly enhances the robustness and accuracy of the entire system.

Forecasting of flash flood susceptibility mapping using random forest regression model and geographic information systems

Flash floods, rapid and devastating inundations of water, are increasingly linked to the intensifying effects of climate change, posing significant challenges for both vulnerable communities and sustainable environmental management. The primary goal of this research is to investigate and predict a Flood Susceptibility Map (FSM) for the Ibaraki prefecture in Japan. This research utilizes a Random Forest (RF) regression model and GIS, incorporating 11 environmental variables (involving elevation, slope, aspect, distance to stream, distance to river, distance to road, land cover, topographic wetness index, stream power index, and plan and profile curvature), alongside a dataset comprising 224 instances of flooded and non-flooded locations. The data was randomly classified into a 70 % training set for model development, with the remaining 30 % used for model validation through Receiver Operating Characteristics (ROC) curve analysis. The resulting map indicated that approximately two-thirds of the prefecture as exhibiting low to very low flood susceptibility, while approximately one-fifth of the region is categorized as high to very high flood susceptibility. Furthermore, the RF model achieved a noteworthy validation with an area under the ROC curve of 99.56 %. Ultimately, this FSM serves as a crucial tool for policymakers in guiding appropriate spatial planning and flood mitigation strategies.

Fire detection and risk assessment via Support Vector Regression with Flattening-Samples Based Augmented Regularization

We propose a Hybrid Support Vector Regression (SVR) with Flattening-Samples Based Augmented Regularization (Hybrid FSR-SVR) architecture for multi-sensor fire detection and forest fire risk assessment. The Hybrid FSR-SVR is a lightweight architecture built upon the novel Flattening-Samples Based Augmented Regularization (FSR) approach and temporal trends of environmental variables. The FSR approach augments l2 norm based smoothing term into an l1-l2 combination, facilitating the integration of l1 regularization into the SVR method, thereby enhancing generalization with minimal computational load. We evaluate the performance of Hybrid FSR-SVR using two distinct datasets covering indoor and forest fires, benchmarking against 15 machine learning models, including state-of-the-art techniques, such as Recurrent Trend Predictive Neural Network (rTPNN), Long-Short Term Memory (LSTM), Multi-Layer Perceptron (MLP), Gated Recurrent Unit (GRU), and Gradient Boosting. Our findings demonstrate that Hybrid FSR-SVR effectively assesses the risk of forest fire, enabling early preventive measures. Notably, it achieves a remarkable accuracy of 0.95 for forest fire detection and ranks third with 0.88 accuracy for indoor fire detection. Importantly, it exhibits computation times significantly lower – by 1 to 2 orders of magnitude – than the majority of compared techniques. The superior generalization ability of Hybrid FSR-SVR, facilitated by flattening-samples based augmented regularization, allows for high detection performance even with smaller training sets.