Effective Mitigation Strategies Research Articles

Las Bolitas Syndrome in Penaeus vannamei Hatcheries in Latin America.

In September 2023, several hatcheries in Latin America experienced significant mortality rates, up to 90%, in zoea stage 2 of Penaeus vannamei. Observations of fresh mounts revealed structures resembling lipid droplets, similar to those seen in a condition known as "las bolitas syndrome". Routine histopathological examinations identified detached cells and tissues in the digestive tracts of affected zoea, contrasting with the typical algal cell contents seen in healthy zoea. Polymerase chain reaction (PCR) testing for over 20 known shrimp pathogens indicated minimal differences between diseased and healthy batches. Both groups tested negative for acute hepatopancreatic necrosis disease (AHPND) but positive for Vibrio species and Rickettsia-like bacteria in the diseased samples. Histological analyses of the affected zoea revealed characteristic tissue degeneration in the hepatopancreas, forming spheres that eventually migrated into the upper gut, midgut, and midgut caeca, a pathology identified as bolitas syndrome (BS). Microbiological assessments revealed Vibrio species at concentrations of 106 CFU zoea/g in affected zoea, approximately two orders of magnitude higher than in healthy zoea. Bacterial isolation from both healthy and BS-affected zoea on thiosulphate-citrate-bile salts-sucrose (TCBS) agar and CHROMagar™ (Paris, France), followed by identification using API 20E, identified six strains of Vibrio alginolyticus. Despite similarities to "las bolitas syndrome" in fresh mounts, distinct histopathological differences were noted, particularly the presence of sloughed cells in the intestines and variations in hepatopancreatic lobes. This study highlights the critical need for further research to fully understand the etiology and pathology of bolitas syndrome in zoea stage 2 of P. vannamei to develop effective mitigation strategies for hatchery operations.

Performance enhancement in hydroponic and soil compound prediction by deep learning techniques

The soil quality plays a crucial role in providing essential nutrients for crop growth and ensuring a bountiful yield. Identifying the soil composition, which includes sand, silt particles, and the mixture of clay in specific proportions, is vital for making informed decisions about crop selection and managing weed growth. Furthermore, soil pollution from emerging contaminants presents a substantial risk to water resource management and food production. Developing numerical models to comprehensively describe the transport and reactions of chemicals within both the plants and soil is of utmost importance in crafting effective mitigation strategies. To address the limitations of traditional models, this paper devises an innovative approach that leverages deep learning to predict hydroponic and soil compound dynamics during plant growth. This method not only enhances the understanding of how plants interact with their environment but also aids in making more informed decisions about agriculture, ultimately contributing to more sustainable and efficient crop production. The data needed to perform the developed hydroponic and soil compound prediction model is acquired from online resources. After that, this data is forwarded to the feature extraction phase. The weighted features, deep belief network (DBN) features, and the original features are achieved in the feature extraction stage. To get the weighted features, the weights are optimally obtained using the Iteration-assisted Enhanced Mother Optimization Algorithm (IEMOA). Subsequently, these extracted features are fed into the Multi-Scale feature fusion-based Convolution Autoencoder with a Gated Recurrent Unit (MS-CAGRU) network for hydroponic and soil compound prediction. Thus, the hydroponic and soil compound prediction data is attained in the end. Finally, the performance evaluation of the suggested work is conducted and contrasted with numerous conventional models to showcase the system’s efficacy.

Luxury application of biochar does not enhance rice yield and methane mitigation: a review and data analysis

PurposeIt is unclear whether a higher biochar (BC) application rate enhances rice (Oryza sativa L.) yield and reduces CH4 emissions. This study investigated changes in rice yield and CH4 emissions with varying BC application rates.MethodsData on rice yield and CH4 emission from paddies amended with or without BC were collected from the literature, and the biochar effects were analyzed using the data set.ResultsAcross the biochar application rate from 2 to 48 t ha-1, the rice yield increased (by 10.8%) while the area-scaled (by 14.4%) and yield-scaled CH4 emission (by 22.2%) decreased. However, the correlation of BC application rates with rice yield and CH4 mitigation was not significant, implying that a higher BC application rate did not enhance rice yield and CH4 reduction. Interestingly, for a data set showing increased rice yield and decreased CH4 emission by BC, the magnitude of change in the rice yield and CH4 mitigation per unit weight of BC (1 t ha-1) decreased with an increase in the BC application rate. These results suggest that BC effects on rice yield and CH4 mitigation are not additive, probably because of the decreases in the inherent capacity of unit weight of BC to enhance rice yield and reduce CH4 emission, which might be caused by the adverse effects of toxic compounds contained in BC, losses of BC, and a higher degree of nutrient immobilization by BC.ConclusionsAnnual BC application at a low rate (e.g., 2 t ha-1) rather than a luxury application may be an effective and economical strategy for long-term rice yield enhancement and CH4 mitigation using BC.

How do the 3D urban morphological characteristics spatiotemporally affect the urban thermal environment? A case study of San Antonio

Urban landscapes, characterized by intricate interactions within a three-dimensional (3D) framework, give rise to complex mechanisms that underlie the Surface Urban Heat Island phenomenon. While global case studies have emphasized the impact of 3D urban morphological components on urban thermal environments, conducting quantitative assessments to identify spatial and seasonal variations in these relationships poses significant challenges. Based on Lidar and land cover dataset, this study first investigated the 3D urban morphology characteristics across nine components, and then assessed their spatial and seasonal impact on the urban thermal environment through Geographically Weighted Regression (GWR). We found that Impervious Surface Fraction (ISF) has the greatest warming effect, whereas the Vegetation Coverage Fraction (VCF) exerts the strongest cooling influence on the thermal environment across all seasons. Building Height (BH) and Street Aspect Ratio (SAR) exhibit spatially varying cooling effects, while Building Number (BN) and Standard Deviation of Building Height (BH_SD) have minimal warming impacts. Additionally, the influence of BH on the thermal environment is significant primarily in areas characterized by higher building coverage. VCF has a stronger cooling effect in central regions with limited vegetation during spring and winter, but this effect diminishes compared to suburban ones in summer and autumn. These findings provide valuable insights into developing effective heat mitigation strategies at both city and community levels, ultimately fostering sustainable urban development.

Phytochemical Profiling and Bioactive Potential of Grape Seed Extract in Enhancing Salinity Tolerance of Vicia faba.

Salinity stress poses a significant threat to crop productivity worldwide, necessitating effective mitigation strategies. This study investigated the phytochemical composition and potential of grape seed extract (GSE) to mitigate salinity stress effects on faba bean plants. GC-MS analysis revealed several bioactive components in GSE, predominantly fatty acids. GSE was rich in essential nutrients and possessed a high antioxidant capacity. After 14 days of germination, GSE was applied as a foliar spray at different concentrations (0, 2, 4, 6, and 8 g/L) to mitigate the negative effects of salt stress (150 mM NaCl) on faba bean plants. Foliar application of 2-8 g/L GSE significantly enhanced growth parameters such as shoot length, root length, fresh weight, and dry weight of salt-stressed bean plants compared to the control. The Fv/Fm ratio, indicating photosynthetic activity, also improved with GSE treatment under salinity stress compared to the control. GSE effectively alleviated the oxidative stress induced by salinity, reducing malondialdehyde, hydrogen peroxide, praline, and glycine betaine levels. Total soluble proteins, amino acids, and sugars were enhanced in GSE-treated, salt-stressed plants. GSE treatment under salinity stress modulated the total antioxidant capacity, antioxidant responses, and enzyme activities such as peroxidase, ascorbate peroxidase, and polyphenol oxidase compared to salt-stressed plants. Gene expression analysis revealed GSE (6 g/L) upregulated photosynthesis (chlorophyll a/b-binding protein of LHCII type 1-like (Lhcb1) and ribulose bisphosphate carboxylase large chain-like (RbcL)) and carbohydrate metabolism (cell wall invertase I (CWINV1) genes) while downregulating stress response genes (ornithine aminotransferase (OAT) and ethylene-responsive transcription factor 1 (ERF1)) in salt-stressed bean plants. The study demonstrates GSE's usefulness in mitigating salinity stress effects on bean plants by modulating growth, physiology, and gene expression patterns, highlighting its potential as a natural approach to enhance salt tolerance.

Modelling the rainfall threshold for shallow landslides considering the landslide predisposing factors in Portugal

Rainfall-triggered landslides pose a significant threat to both infrastructure and human lives, making it crucial to comprehend the factors that contribute to their occurrence. Specifically, understanding the relationship between these factors and the amount of rain that is necessary for triggering such events is essential for effective prediction and mitigation strategies. To address this issue, our study proposes a statistical modelling approach using machine learning, specifically the Random Forest algorithm, to investigate the connection between landslide predisposing factors and the daily rainfall intensity threshold necessary for the initiation of shallow landslides in Portugal. By leveraging a comprehensive dataset comprising historical landslide events, associated critical rainfall, and ten distinct landslide predisposing factors, we developed several models and used cross-validation technique to evaluate their performance. Our findings demonstrate that the Random Forest model effectively captures a relationship among landslide predisposing factors, critical daily rainfall intensity, and landslide occurrences. The models exhibit a satisfactory accuracy in assessing the spatial variation of critical daily rainfall intensity based on the predisposing factors, with a mean absolute percentage error (MAPE) of around 17%. Furthermore, the models provide valuable insights into the relative importance of various predisposing factors in landslide triggering, highlighting the significance of each factor. It was found that it takes higher rainfall intensity to trigger shallow landslides in the north region of Portugal when considering critical rainfall events of 3 and 13 days. Slope aspect, slope angle, and clay content in the soil are among the main predisposing factors used for defining the spatial variation of the daily rainfall intensity threshold.

Alleviation of fluoride-induced colitis by tea polysaccharides: Insights into the role of Limosilactobacillus vaginalis and butyric acid

Endemic fluorosis has gained increasing attention as a public health concern, and the escalating risk of colitis resulting from excessive fluoride intake calls for effective mitigation strategies. This study aimed to investigate the potential mechanisms underlying the alleviation of fluoride-induced colitis by Tea polysaccharides (TPS). Under conditions of excessive fluoride intake, significant changes were observed in the gut microbiota of rats, leading to aggravated colitis. However, the intervention of TPS exerted a notable alleviating effect on colitis symptoms. Antibiotic intervention and fecal microbiota transplantation (FMT) experiments provided evidence that TPS-mediated relief of fluoride-induced colitis is mediated through its effects on the gut microbiota. Furthermore, TPS supplementation was found to modulate the structure of gut microbiota, enhance the relative abundance of Limosilactobacillus vaginalis in the gut microbiota, and promote the expression of short-chain fatty acid (SCFAs) receptors in colonic tissue. Notably, L. vaginalis played a significant role in alleviating fluoride-induced colitis and facilitating the absorption of butyric acid in the rat colon. Subsequent butyric acid intervention experiments confirmed its remarkable alleviating effect on fluoride-induced colitis. Overall, these findings provide a potential preventive strategy for fluoride-induced colitis by TPS intervention, which is mediated by L. vaginalis and butyric acid.

Assessing CMIP6 models in simulating meteo-oceanographic variability on Spanish continental coasts

The ocean is a key player in the Earth's climate, absorbing heat and carbon dioxide from the atmosphere. The ocean's temperature and salinity are influenced by climate change, which can significantly impact marine ecosystems. Reliable data sets of atmospheric and oceanographic parameters are of special interest in coastal productive areas to adequately monitor their variability at regional and local levels. It is therefore essential to continue monitoring and studying the ocean to develop effective mitigation and adaptation strategies. In this context, the main aim of this study is to identify the Earth System Models (ESMs) from the Coupled Model Intercomparison Project 6 (CMIP6) that best capture the variability of water temperature, salinity, and wind speed along the continental Spanish coasts, using them to estimate future impacts in these regions. To achieve this, a multifaceted approach is used, encompassing a historical (2000−2014) assessment comparing ESM outputs to in situ observations from Puertos del Estado (PdE) oceanographic buoys, an examination of the present period (2015−2022) under three IPCC scenarios (SSP1−2.6, SSP2−4.5, and SSP5−8.5), and a projection of future (2023−2100) trends using the same emission scenarios. Results showed that the ESMs from CMIP6 can reproduce the historical patterns of meteo-oceanographic properties, rendering them valuable tools for climate change studies. In the future (2100), considering the most pessimistic scenario (SSP5−8.5), the water temperature may increase by 2.8°C, salinity may decrease by -1.6, and wind speed may decrease by -0.4 m·s−1. These projected changes can significantly impact the Spanish coasts, jeopardizing the growth, reproduction, survival, abundance, and distribution of some marine species.

Contemporary Farming and Associated Consequences of Climate Change

Aim: This review aims to synthesize current understanding of the multifaceted impacts of climate change on agriculture, examine adaptive strategies to maintain food security, and offer insights into sustainable farming practices. Objectives: The objectives are to: (1) identify the potential causes of climate change, (2) assess its effects on agriculture, and (3) discuss mitigation strategies and sustainable farming practice. Methodology: A systematic review of the literature has been performed. We conducted an in-depth study of previous research, reports, and related literature. Sources were selected based on relevance and credibility to provide a thorough examination of the impacts of climate change on agriculture. Analysis: The analysis focused on categorizing the direct and indirect effects of climate change on agricultural systems, understanding changes in crop yield, plant physiology and metabolism, and evaluating the effectiveness of various mitigation strategies. Result Findings: The findings indicate that the greenhouse effect, driven by gases such as CO2, CH4, and H2O, leads to global temperature increases. The concentration of these gases is rising, with the global average temperature expected to increase by 2°C by 2100, causing significant economic losses. While this increase has boosted plant growth and productivity through enhanced photosynthesis, the associated rise in temperature counteracts these benefits. Therefore, mitigation strategies such as nutrient management, drip and sprinkler irrigation, and sustainable agricultural practices are essential. Natural farming is a sustainable agricultural practice that offers chemical-free, healthy food while promising to increase farmers' income, improve environmental health, restore soil fertility, and reduce greenhouse gas emissions. Conclusion: Climate change poses a significant threat to global food and nutritional security by altering agricultural productivity and sustainability. Understanding these impacts and developing effective mitigation strategies is crucial. This review provides valuable insights for policymakers, researchers, and practitioners to address these challenges and ensure resilient agricultural systems capable of sustaining future food security.

Quantifying Visual Differences in Drought-Stressed Maize through Reflectance and Data-Driven Analysis

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

Modeling multi-source plastic pollution yield and transport driven by catchment hydrometeorological processes

Plastic pollution has emerged as a global environmental concern, impacting both terrestrial and marine ecosystems. However, understanding of plastic sources and transport mechanism at the catchment scale remains limited. This study introduces a multi-source plastic yield and transport model, which integrates catchment economic activities, climate data, and hydrological processes. Model parameters were calibrated using a combination of field observations, existing literature, and statistical random sampling techniques. The model demonstrated robust performance in simulating both plastic yield and transport from 2010 to 2020 in the upper and middle Mulan River Catchment, located in southeast China. The annual average yield coefficients were found to closely align with existing estimations, and the riverine outflow exhibited a high correlation coefficient of 0.97, with biases ranging from -63.0 % to -21.4 % across all monitoring stations. The analysis reveals that, on average, 12.5 ± 2.5 % of the total plastic yield is transported to rivers annually, with solid waste identified as the primary source, accounting for 37.8 ± 20.7 % of the total load to rivers, followed by agricultural film (26.4 ± 9.8 %), impermeable surfaces (21.5 ± 10.3 %), urban and rural sewage (10.4 ± 5.0 % and 3.0 ± 1.5 %, respectively), and industrial wastewater (0.9 ± 0.7 %). The annual average outflow was estimated to between 9.3 and 43.0 ton/year (median: 23.1) at a 95 % confidence level. This study not only provides insights into the primary sources and transport pathways of plastic pollution at the catchment scale, but also offers a valuable tool for informing effective plastic pollution mitigation strategies.

An effective DDoS attack mitigation strategy for IoT using an optimization-based adaptive security model

The Internet of Things enables the creation of transmitted use cases for interconnected devices and complementary channels. The varied structure of it creates additional security needs and problems. In particular, the safeguards used in the IoT should adjust to the changing environment. One of the major dangers to the World Wide Web (WWW) things is Distributed Denial of Service (DDoS). Therefore, in this work, an intelligent Game Theory-based Adaptive security (GT-AS) mathematical model was developed to maximize the effectiveness of DDoS attack mitigation. Moreover, this strategy can strongly derive the five parameters such as energy channel, memory, intruder, and hybrid. These all can achieve a stronger defense posture against DDoS attacks from the newly designed IoT. Consequently, the Recurrent Bat (RB) framework is developed to classify the nodes into two classes such as trusted node and malicious node. In addition, the proposed frameworks analyze how protection effectiveness and energy consumption interact when evaluating adaptive security techniques. To analyze the effectiveness of the suggested paradigm, researchers also give the outcomes of simulation experiments. Researchers demonstrate that, in comparison to existing models, the developed approach has increased the lifespan of the connected objects by 47 %. Also, the developed strategy has attained better accuracy and lower error rates when comparing traditional strategies. Moreover, the packet delivery ratio is 60 KB, energy consumption is 116 KJ, Mean Location Error is 0.078 and resource usage is 148.

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202–2050) and FP2 (2051–2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

Balancing Hormone Therapy: Mitigating Adverse Effects of Androgen-Deprivation Therapy and Exploring Alternatives in Prostate Cancer Management.

Androgen-deprivation therapy (ADT) is well established as the standard of care in metastatic prostate cancer (PCa) management; however, ADT has significant adverse effects (AEs) that must be addressed. This review aims to highlight opportunities to mitigate AEs of ADT and explore alternatives in PCa management. Specifically, we discuss behavioral and pharmacologic strategies for mitigating ADT AEs as well as ADT-sparing approaches for hormone-sensitive and castration-resistant PCa. Equipped with effective mitigation strategies and possible alternatives, clinicians and researchers can optimize health-related quality of life for patients currently receiving ADT for PCa and consider treatments that spare patients from AEs of ADT.

Statistical‐Physical Adversarial Learning From Data and Models for Downscaling Rainfall Extremes

AbstractQuantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models.

Comprehensive risk assessment of Pakistan railway network: A semi-quantitative risk matrix approach

Pakistan Railway (PR) is vital to the country's transportation infrastructure, facilitating passenger and freight transportation. However, the growing number of accidents associated with PR has raised concerns about its overall safety. In addition, there have been limited research efforts to investigate PR accidents and their underlying causes The present study conducts a comprehensive risk assessment and safety management of PR using a semi-quantitative risk matrix approach. The study combines historical accident data and expert evaluations to assess the likelihood and consequences of different railway accident types and potential contributing factors. The descriptive statistics analysis has revealed varying degrees of severity for different types of railway accidents in Pakistan. For instance, accidents like passenger and goods train derailments and collisions at unmanned level crossings were identified as extreme and intolerable, whereas train fire accidents were categorized as high and undesirable. Moreover, accidents attributed to human error are classified as extreme and intolerable, while those caused by negligence of road users and track defects are classified as high and undesirable. The study utilized the risk matrix approach and identified critical risk areas that can help the decision-makers prioritize effective risk mitigation strategies. In light of the present study's findings, policy implications, such as investment in infrastructure to mitigate risks associated with aging or deteriorating tracks, bridges, and tunnels, and human resource development for railway personnel to improve their skills, decision-making abilities, and awareness of safety protocols, are recommended.

Recent Update on the Prevalence of Sarcopenia in Koreans: Findings from the Korea National Health and Nutrition Examination Survey.

As recognized by the World Health Organization in 2016 with its inclusion in the International Classification of Diseases, Tenth Revision as M62.84, and by South Korea in 2021 as M62.5, the diagnostic guidelines for sarcopenia vary globally. Despite its prevalence in older populations, data on sarcopenia in Koreans aged 60 and above is scarce, highlighting the need for research on its prevalence in this demographic. Utilizing the 2022 Korea National Health and Nutrition Examination Survey dataset, sarcopenia was assessed among 1,946 individuals aged 60 or older according to the Asian Working Group for Sarcopenia 2019 criteria, incorporating grip strength and bioelectrical impedance analysis measurements. Statistical analyses were performed to differentiate categorical and continuous variables using logistic regression and Student's t-tests, respectively. The prevalence of sarcopenia was found to increase with age, with the highest prevalence observed in the oldest age group (80 years and older). The overall prevalence of sarcopenia in our study population was 6.8%. Among men, the prevalence of sarcopenia was 5.5% in the 60 or older age group, 9.6% in the 70 or older age group, and 21.5% in the 80 or older age group. Among women, the prevalence of sarcopenia was 7.9%, 10.5%, and 25.9%, respectively. This study highlights the significant burden of sarcopenia in elderly Koreans, particularly among the oldest individuals. These findings call for targeted interventions to manage and prevent sarcopenia, along with further research on its risk factors, consequences, and effective mitigation strategies.

Reducing the fatal attraction of nocturnal insects using tailored and shielded road lights

The attraction of insects to artificial light is a global environmental problem with far-reaching implications for ecosystems. Since light pollution is rarely integrated into conservation approaches, effective mitigation strategies towards environmentally friendly lighting that drastically reduce insect attraction are urgently needed. Here, we tested novel luminaires in two experiments (i) at a controlled experimental field site and (ii) on streets within three municipalities. The luminaires are individually tailored to only emit light onto the target area and to reduce spill light. In addition, a customized shielding renders the light source nearly invisible beyond the lit area. We show that these novel luminaires significantly reduce the attraction effect on flying insects compared to different conventional luminaires with the same illuminance on the ground. This underlines the huge potential of spatially optimized lighting to help to bend the curve of global insect decline without compromising human safety aspects. A customized light distribution should therefore be part of sustainable future lighting concepts, most relevant in the vicinity of protected areas.

The heterogeneity of human flood adaptation characteristics in Central Asia based on human-flood distance

The Central Asian region is subject to frequent seasonal floods, resulting in substantial losses. These recurrent floods have induced certain changes in human flood adaptation characteristics in Central Asia, an area that is currently under-researched. This study, grounded in meticulously simulated flood inundation outcomes, multisource population distribution data, and flood protection infrastructure data, introduces an improved approach to calculating the distance between human and flood (HFD) and investigates human flood adaptation characteristics in Central Asia. Our findings indicate that residents in Central Asia get close to flood-prone areas from 1970 to 2007, with the exceptions of Turkmenistan (TKM) and Kyrgyzstan (KGZ). Notably, this trend is particularly pronounced in regions such as Xinjiang, China, and Tajikistan (TJK). An intricate exponential increase is observed in the population exposed to floods as the HFD decreases. Under future scenarios, KGZ exhibits the most significant trend of staying away from flood-prone areas, while TJK presents the opposite trend, and TKM experiences minimal changes. Additionally, compared with urban areas, rural areas of Central Asia exhibit a trend towards approaching floods. This study underscores the optimum nature of human flood adaptation when the HFD reaches 4.23 km, showing the potential for substantial benefits by implementing population evacuation within this threshold buffer zone. These insights highlight the complex interplay between human behaviours and floods and can inform the formulation of effective flood risk mitigation strategies.

FLOOD VULNERABILITY AND ADAPTATION ASSESSMENT IN PADANG TERAP DISTRICT, KEDAH, MALAYSIA

Padang Terap, Kedah in Malaysia experienced frequent floods, incurring financial losses to the flood victims and the government for flood mitigation. For an effective mitigation strategy, there is a need for a reliable database on the vulnerability and adaptation levels of flood victims. Therefore, this study is important to provide the data. The objectives of this paper are to analyse the factors that cause flood vulnerability and to determine and assess the existing adaptation to flood in Padang Terap. The research utilised a quantitative approach, through a household survey of 680 respondents. The data were analysed using descriptive and crosstabulation tests. The findings show that topography, distance between houses and rivers, and flood relief centres affect the vulnerability of the flood victims. The vulnerability to flood is higher than the adaptation level, implying that the Padang Terap flood victims are still not adapting to flood and prone to losses incurred related to flood.