Hotspot Locations Research Articles

Development of a Mechanical Vehicle Battery Module Simulation Model Combined with Short Circuit Detection

In recent years, electric vehicles (EVs) have gained significant traction within the automotive industry, driven by the societal push towards climate neutrality. These vehicles predominantly utilize lithium-ion batteries (LIBs) for storing electric traction energy, posing new challenges in crash safety. This paper presents the development of a mechanically validated LIB module simulation model specifically for crash applications, augmented with virtual short circuit detection. A pouch cell simulation model is created and validated using mechanical test data from two distinct out-of-plane load cases. Additionally, a method for virtual short circuit prediction is devised and successfully demonstrated. The model is then extended to the battery module level. Full-scale mechanical testing of the battery modules is performed, and the simulation data are compared with the empirical data, demonstrating the model’s validity in the out-of-plane direction. Key metrics such as force-displacement characteristics, force, deformation, and displacement during short circuit events are accurately replicated. It is the first mechanically valid model of a LIB pouch cell module incorporating short circuit prediction with hot spot location, that can be used in full vehicle crash simulations for EVs. The upscaling to full vehicle simulation is enabled by a macro-mechanical simulation approach which creates a computationally efficient model.

A novel framework for fatigue hotspot localization and damage assessment of multi-defect marine structures under random vibration

In ocean engineering, assessing vibration-induced fatigue under random loads is critical, particularly for ocean structures with multiple defects, as fatigue is highly sensitive to local structural flaws. Recently, incorporating structural dynamic characteristics into vibration fatigue assessments has emerged as a significant trend and formidable challenge, involving three key obstacles: precise localization of fatigue hotspots, extraction of key modes, and determination and integration of modal damage contributions. This paper introduces a novel framework for fatigue hotspot localization and damage assessment in the context of random vibration fatigue in ocean structures. By initially identifying fatigue hotspots and hot regions using stress mode shapes, refining the finite element mesh in hot regions, and conducting reanalysis, the exact locations of fatigue hotspots can be determined, enabling structural dimensionality reduction. The introduction of a modal damage contribution factor allows for the evaluation of each mode's damage contribution and the identification of key modes, further facilitating modal reduction. For the precisely localized fatigue hotspots, the proposed method combines the damage contributions of key modes, enabling rapid and accurate assessment of fatigue damage under random vibration loads. Finite element case studies and vibration fatigue test results demonstrate that the proposed framework effectively addresses the challenges of random vibration fatigue evaluation, achieving structural and modal reduction while significantly enhancing the efficiency of damage assessment without compromising accuracy.

Recursive Bayesian estimation of wind load on a monopile-supported offshore wind turbine using output-only measurements

Offshore wind turbine structures experience combined wind and wave loading during their lifetime, and the cyclic characteristics of these loads significantly impact the fatigue life of the support structure. Continuous monitoring of stress-related quantities, such as strain time histories at hotspot locations of the structure, can help to estimate the fatigue life. However, sparse measurements from the substructure necessitate using a digital twin as a tool for structural health monitoring by creating a virtual model. This virtual model relies on real-time input loads for virtual sensing and stress-related quantities prediction. However, the exact loading on these structural systems is often unknown or estimated with considerable uncertainty. This paper implements a recursive window-based Bayesian estimator for input load estimation and virtual sensing of acceleration and strain time history at unmeasured critical locations using output-only measurements. The estimator is numerically and experimentally validated in the context of input load estimation for an offshore wind turbine, and the results are compared with a traditional augmented Kalman filter and a linear regression approach for the quasi-static component of loading. The method is first demonstrated through a numerical study using a finite element model of a 6 MW offshore wind turbine, where input wind load time histories are accurately estimated. Then, the framework is applied to the real data measured from an offshore wind turbine in the North Sea. The study demonstrates the window-based Bayesian input estimator as a promising tool for the digital twinning of offshore wind turbines, demonstrating superior accuracy in input load estimations and full response predictions compared to the augmented Kalman filter and linear regression methods without the constraint of collocated measurements at input load locations.

Multiple losses of aKRAB from PRDM9 coincide with a teleost-specific intron size distribution

BackgroundPrimary transcripts are largely comprised of intronic sequences that are excised and discarded shortly after synthesis. In vertebrates, the shape of the intron size distribution is largely constant; however, most teleost fish have a diverged log-bimodal ‘teleost distribution’ (TD) that is seen only in teleosts. How the TD evolved and to what extent this was affected by adaptative or non-adaptive mechanisms is unknown.ResultsHere, we show that the TD has evolved independently at least six times and that its appearance is linked to the loss of the aKRAB domain from PRDM9. We determined intron size distributions and identified PRDM9 orthologues from annotated genomes in addition to scanning 1193 teleost assemblies for the aKRAB domain. We show that a diverged form of PRDM9 (β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta$$\\end{document}) is predominant in teleosts whereas the α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} version is absent from most species. Only a subset of PRDM9-α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} proteins contain aKRAB, and hence, it is present only in a small number of teleost lineages. Almost all lineages lacking aKRAB (but no species with) had TDs.ConclusionsIn mammals, PRDM9 defines the sites of meiotic recombination through a mechanism that increases structural variance and depends on aKRAB. The loss of aKRAB is likely to have shifted the locations of both recombination and structural variance hotspots. Our observations suggest that the TD evolved as a side-effect of these changes and link recombination to the evolution of intron size illustrating how genome architectures can evolve in the absence of selection.

Deciphering Temporal Metabolome Dynamics in Response to MYMV: Contrasting Patterns in Resistant and Susceptible Blackgram (Vigna mungo L. Hepper) Cultivars.

Blackgram (Vigna mungo L. Hepper) production is hindered by mungbean yellow mosaic virus (MYMV) with disease incidence up to 85% in hot spot locations of Tamil Nadu, India. Field screening of 50 genotypes identified Mash 114 as resistant and CO 5 as susceptible cultivars. To understand the resistance mechanism, temporal metabolome variations of resistant (RC) and susceptible (SC) cultivars were assessed at 3, 6, and 12 days post-inoculation with MYMV via whitefly transmission using GC-MS. The study included aviruliferous whitefly and healthy controls. Data analysis identified 98 differential compounds across various metabolic groups. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed distinct metabolic profiles, with RC accumulating antiviral terpenoids such as ursolic acid, betulin, and umbelliprenin, while SC upregulated lipids and fatty acids favoring virus replication. These findings provide insights for breeders to identify resistant sources and pave the way for improved MYMV management strategies using antiviral products.

The Global Forest Fire Emissions Prediction System version 1.0

Abstract. The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that estimates biomass burning in near-real time for global air quality forecasting. The model uses a bottom-up approach, based on remotely sensed hotspot locations, and global databases linking burned area per hotspot to ecosystem-type classification at a 1 km resolution. Unlike other global fire emissions models, GFFEPS provides dynamic estimates of fuel consumption, fire behaviour and fire growth based on the Canadian Forest Fire Danger Rating System, plant phenology as calculated from daily global weather and burned-area estimates using near-real-time Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-detected hotspots and historical burned-area statistics. Combining forecasts of daily fire weather and hourly meteorological conditions with a global land classification, GFFEPS produces fuel consumption and emission predictions in 3 h time steps (in contrast to non-dynamic models that use fixed consumption rates and require a collection of burned area to make post-burn estimates of emissions). GFFEPS has been designed for use in operational forecasting applications as well as historical simulations for which data are available. A study was conducted showing GFFEPS predictions through a 6-year period (2015–2020). Regional annual total smoke emissions, burned area and total fuel consumption per unit area as predicted by GFFEPS were generated to assess model performance over multiple years and regions. The model's fuel consumption per unit area results clearly distinguished regions dominated by grassland (Africa) from those dominated by forests (boreal regions) and showed high variability in regions affected by El Niño and deforestation. GFFEPS carbon emissions and burned area were then compared to other global wildfire emissions models, including the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED4.1s) and the Fire INventory from NCAR (FINN 1.5 and 2.5). GFFEPS estimated values lower than GFAS and GFED (80 % and 74 %) and had values similar to FINN 1.5 (97 %). This was largely due to the impact of fuel moisture on consumption rates as captured by the dynamic weather modelling. Model evaluation efforts to date are described – an ongoing effort is underway to further validate the model, with further developments and improvements expected in the future.

Repetitive Transcranial Magnetic Stimulation on Individualized Spots Based on Task functional Magnetic Resonance Imaging Improves Swallowing Function in Poststroke Dysphagia.

Background: Functional magnetic resonance imaging (fMRI) has not previously been used to localize the swallowing functional area in repetitive transcranial magnetic stimulation (rTMS) treatment for poststroke dysphagia; Traditionally, the target area for rTMS is the hotspot, which is defined as the specific region of the brain identified as the optimal location for transcranial magnetic stimulation (TMS). This study aims to compare the network differences between the TMS hotspot and the saliva swallowing fMRI activation to determine the better rTMS treatment site and investigate changes in functional connectivity related to poststroke dysphagia using resting-state fMRI. Methods: Using an information-based approach, we conducted a single case study to explore neural functional connectivity in a patient with poststroke dysphagia before, immediately after rTMS, and 4 weeks after rTMS intervention. A total of 20 healthy participants underwent fMRI and TMS hotspot localization as a control group. Neural network alterations were assessed, and functional connections related to poststroke dysphagia were examined using resting-state fMRI. Results: Compared to the TMS-induced hotspots, the fMRI activation peaks were located significantly more posteriorly and exhibited stronger functional connectivity with bilateral postcentral gyri. Following rTMS treatment, this patient developed functional connection between the brainstem and the bilateral insula, caudate, anterior cingulate cortex, and cerebellum. Conclusion: The saliva swallowing fMRI activation peaks show more intense functional connectivity with bilateral postcentral gyri compared to the TMS hotspots. Activation peak-guided rTMS treatment improves swallowing function in poststroke dysphagia. This study proposes a novel and potentially more efficacious therapeutic target for rTMS, expanding its therapeutic options for treating poststroke dysphagia.

Stability and reliability of regional cumulative impact mapping: A Canadian Pacific case study

Cumulative impact mapping is a commonly applied spatial representation of the impacts of human activities on the ocean. This spatial model has been applied at varying scales around the world, in part due to its ability to visually communicate complicated information in a simple way. The application of the model to decision-making processes requires an understanding of the reliability and stability of its outputs. This paper represents both an updated regional application in Pacific Canada and an evaluation of the tool over two iterations, 2015 and 2023. The regional application utilised higher model resolution and new and improved data inputs including 38 habitats and 46 activities across five sectors. High impact hotspots were found around major population centres and ports, as well as in fishing and shipping areas along the continental shelf. Commercial fishing, shipping, and climate change (sea surface temperature change) remained the major drivers of cumulative impacts in the region, and cumulative impacts per unit area continued to be highest in kelp and seagrass. Results suggest evidence of decreased impacts where improved protection for sponge reefs were implemented but show an overall increase in cumulative impacts for the region. Results showed consistency and reliability in the location of cumulative impact hotspots. Refinements in data quality and resolution of spatial data inputs contributed towards increasing the spatial precision of hot spot areas identified. Confidence in the cumulative impact mapping outputs and applications for marine spatial planning, marine protected area designation and management, research, and decision support are discussed.

Spatial patterns of vascular plant species richness in Poland: Relations among species group richness and hot spot distribution

Knowledge of spatial patterns of species richness (SR) is highly relevant for theoretical research in ecology and the development of conservation plans. In Poland, despite a long tradition of botanical surveys, vascular plant SR has not been mapped, nor have the correlations in richness among different plant species groups been explored at the entire country scale. Here, we used a recently published data set to examine spatial patterns and relationships among the joined SR of vascular plant species, including native species, archaeophytes, neophytes, and species with high conservation value (i.e., red-list species). The basic spatial unit employed was a 10 × 10 km grid from the Atlas of Distribution of Vascular Plants in Poland (ATPOL). We found that SR of native species and archaeophytes, neophytes, and red-list species were positively correlated. The main patterns of SR and the percentage of particular groups in the joined SR were based on three components: (1) gradient of overall SR, (2) invasion level, and (3) peculiarity of flora in some regions resulting from the high number and proportion of rare species that often have high conservation value (red-list species). In general, northeastern Poland was species-poor, while the Carpathian Mountain range, the uplands in southern Poland, and some parts of Wisła River valley had the highest SR concentrations. e location of SR hotspots usually did not overlap with the existing national parks system. The correlations among native SR, high conservation value species, and neophyte SR suggest that biological invasions are among the most important threats to vascular plant diversity in Poland. Finally, despite likely biases in SR assessments, we demonstrated that the presented maps seem to reflect general ecological gradients influencing vascular plant distribution in Poland.

Switchable RDX‐Based Rubberized Explosive with Thermally‐Expandable Microspheres

AbstractImproving the safety of explosive materials through the synthesis of insensitive explosives has been studied extensively. However, little work has focused on creating switchable explosives. A switchable explosive is normally insensitive to detonation, and therefore safe to handle and transport, but can be sensitized when needed to create a functional explosive. Similarly, it may be desired to desensitize an explosive to prevent its function. This study examined the ability to create a switchable 1,3,5‐trinitro‐1,3,5‐triazinane (RDX)‐based rubberized explosive using thermally‐expandable microspheres (TEMs). The addition of TEMs to the explosive formulation allowed for microstructural changes and potential hot spot locations to form as the microspheres expanded. Small voids (less than about 10 μm) are more likely to be critical hot spots when shocked, and likewise larger voids are less likely to ignite successfully (sub‐critical) when shocked. Consequently, both sensitization and desensitization are possible. The rubberized explosive considered here with unexpanded microspheres was unable to sustain a detonation for the size used, but after specific heating followed by cooling to produce small voids, a detonation was achieved. The TEMs addition to the RDX‐based rubberized explosive resulted in an explosive that is detonation insensitive when unheated but becomes a functional explosive after it is sensitized through heating. This paves the way to create insensitive explosive formulations with on‐demand switchable detonation function through the incorporation of thermally‐expandable microspheres. Desensitization was also demonstrated with specific heating of TEMs in an initially detonable explosive charge. Finally, we also demonstrated that deflagration can be affected by heating TEMs.

Discovering Sericulture Clusters Through the Use of Local Indicators of Spatial Association (LISA)

The aim of this study is to analyze the spatial pattern of sericulture in Türkiye with spatial analysis approaches, taking into account the data of 81 provinces, by creating a database based on Geographic Information Systems (GIS). This study employed data from the TURKSTAT, which included the amount of fresh cocoon produced and the silkworm egg boxes opened, between 2004-2022. In order to evaluate the relationship between the cocoon yield in each province and the cocoon yield in neighboring provinces, Moran's I measurements were made for the relevant time periods, and each period's Moran's I scatter plot was created. The Local Spatial Association Indicator (LISA) was used to assess the spatial structure of fresh cocoon yield. There was determined spatial clustering and a positive spatial association for cocoon yield, and all of the Moran's I values were found to be more than 0. The LISA maps were demonstrated the distinct local clustering patterns in the cocoon yield. More local clusters were identified (High-High or Low-Low) compared to local spatial outliers (High-Low or Low-High). High-High clusters were generally identified in the Marmara Region. Important implications have been obtained in terms of sustainability, such as how to take advantage of hotspot spillover effects and prioritize hotspot locations when allocating resources. As a result, spatial relations should be taken into account in the policies to be developed for sericulture and more activities should be carried out on sericulture.

Identification of false smut – resistant donors in Rice (Oryza sativa L.) and analysis of their morpho-molecular diversity for resistance breeding

False smut disease of rice caused by the pathogen Ustilaginoidea virens is a growing threat to the rice farmers as it affects both quality and quantity. Development of resistant variety becomes difficult, since very few resistant donors were available for false smut resistant breeding programme. Therefore, to identify potential donors for resistance breeding a total of 60 genotypes were screened at hotspot location (Gudalur) during kharif 2023 which led to identification of 12 highly resistant genotypes and the notable ones are Koolavalai, Periya chandikar, Kapikar selection and Earapalli. Genetic variability studies indicated the presence of additive gene action for all the agronomic and disease related traits. Principal component analysis revealed the first 5 principal components collectively contributing 78.79 % of the total variance with disease-related traits contributing significantly to divergence. Ten clusters were delineated using Mahalanobis D2 statistics with clusters IX and V showing higher inter cluster distance (3453.64). Forty-one polymorphic markers were used to analyse the genotypes and The Unweighted Pair Group method with Arithmetic Mean (UPGMA) clustering by Jaccard distance formed 6 clusters. The Bayesian clustering classified the entire population into 2 subpopulations. False smut linked marker RM336 and RM218 were found to be the most informative marker with high Polymorphism Information Content (0.71, 0.69) and Heterozygosity Index (0.76, 0.73). The resistant genotypes such as IG71, Thulasi vasanai sambha, Arupatham vellai, Kaltikar and Chinna aduku nel can be used in the future breeding programmes to develop the resistant cultivar and to identify the candidate genes governing resistance.

Effects of charge motion on knock and thermal efficiency under high load condition of a heavy-duty natural gas engine

Nowadays, the carbon-neutral trend and the crude oil crisis have brought natural gas into increasing focus. However, heavy-duty engines fueled by natural gas have been plagued by thermal efficiency and knocking limitation problems. Well-structured charge motions can enhance thermal efficiency and concurrently mitigate knock, however, there is a lack of comprehensive work involving charge motions. In this paper, numerical simulation is employed to investigate the impact of charge motion on knock and thermal efficiency under high load conditions for a heavy-duty spark-ignition natural gas engine. The findings indicate that in the configuration with intense swirl, the diminished turbulent kinetic energy (TKE) at the combustion chamber’s base lessens the convective heat exchange between the fresh charge and the heated exhaust gases, resulting in a noticeable high-temperature zone and triggering hot spots. In configurations with inclined swirl and strong tumble motion, the extensive tumble motion near the spark plug region close to top dead center leads to an asymmetric flame front shape, determining the location of hotspots. The research also revealed that excessive tumbling motion and TKE can increase the likelihood of engine knock, while appropriate cylinder charge motion can improve the knock resistance of the engine to some extent, the maximum reduction in knocking intensity can reach 92.5%. The optimized combustion systems can significantly improve the initial combustion rate, shorten the ignition delay period, and advance the combustion center. With the combined effects of ignition timing and charge motion, the total indicated specific power per unit of fuel mass is increased in the optimized systems. Compared to the original combustion system, the indicated thermal efficiency within the knocking boundary can be improved by 1.11%.

Thermal management for microelectronic chips under non-uniform heat flux with supercritical CO2

With the rapid growth of information technology and the evolution in the use of microelectronic chips, these components are facing major challenges of high heat dissipation and inhomogeneous heat flux. To address the hotspot issues, a combination of microchannel heat sinks (MCHS) with cavity-rib designs and supercritical fluids is a potential solution. This study compares the thermo-hydraulic properties of water and supercritical carbon dioxide (sCO2) under various boundary conditions and hotspot locations during non-uniform heating. The results showed that sCO2 can effectively improve temperature uniformity along the heat sink in the presence of hotspots as compared to water. Considering a mass flow rate of 600 kg/(m2·s), the inhomogeneity index of the basal temperature of sCO2 is found to be 0.24, significantly lower than 4.22 for water. This indicates that sCO2 eliminates hot spots by rapidly increasing specific heat capacity near the pseudo-critical temperature. Furthermore, it is noted that maintaining the operating temperature of sCO2 near the pseudo-critical temperature can increase the specific heat capacity of the fluid in a short time, in addition to reducing the corresponding density and viscosity. Consequently, the entropy generation rate of sCO2 under ideal conditions is 46.6 % of that of water. Therefore, the proposed combination of a microchannel of complex structure and sCO2 in this study is demonstrated to effectively reduce the influence of hot spots and improve the overall thermal performance of heat sinks.

Dioxin-like and estrogenic activity screening in fractionated sediments from a German catchment after the 2021 extreme flood

BackgroundThe flood in July 2021 is considered one of the largest flood disasters in Western Europe in decades, with massive socio-economic consequences. The potential emission and remobilization of anthropogenic pollutants can lead to additional environmental consequences, which need to be addressed in long-term mitigation strategies. The Inde River and its tributary, the Vichtbach River, form a catchment located at the transition from the low mountain ranges of the Eifel to the lowlands of the Lower Rhine Embayment in Germany. The area has been an industrial and mining hotspot for centuries, making it a high-risk area for flood sediment pollution. The present study provides an ecotoxicological screening of flood sediments of the Vicht–Inde catchment to gain an impression of the degree of contamination by organic pollutants by means of in vitro effect-based method. Sediment samples were collected within days after the flood and fractionated prior to biotesting, and supportive instrumental geochemical analysis was performed.ResultsFlood sediments did not reveal estrogenic potential, which was included in the testing strategy as a relevant endpoint for industrial chemicals and untreated wastewater. In contrast, moderate-to-high dioxin-like activity was observed in 70% of the sediment samples with a peak dioxin-like potential at the restored section of the Inde. Overall, four hotspot samples were identified as at risk, which aligned mostly with the high concentration of organic pollutants including PAHs and PCBs. The fractionation allowed the identification of PAHs and their derivates as the most likely toxicity drivers for dioxin-like activity in the sediments of the Vicht–Inde catchment.ConclusionThe results provide first information on the prioritization of hotspot locations at risk for a detailed ecotoxicological profiling and a post-flood monitoring of organic contamination. The identified sinks of contamination in the floodplain areas can be considered a source for remobilization of pollution in future flood events, which is highly relevant for the receiving Rur River.

Unveiling the hidden impact: Wildlife roadkill assessment in the Paraguayan Chaco

AbstractThe incidence of wildlife roadkill significantly threatens the persistence of wildlife populations and disrupts the ecological functionality of ecosystems. This study investigates the impact of roadkills on wildlife in the Paraguayan Chaco, focusing on a 250‐km segment of Route 9 ‘Dr. Carlos Antonio López’ between Villa Hayes and Pozo Colorado. We conducted a road survey for 15 months and recorded 2338 carcasses, identifying 87 species, with mammals (41.3%), reptiles (32.3%) and birds (19.8%) being the most observed groups. The species most frequently killed included Cerdocyon thous, Caracara plancus, Thamnodynastes hypoconia and Procyon cancrivorus. We also recorded species with conservation concern. Additionally, we estimated mortality rates by accounting for sampling errors such as carcass removal and searcher efficiency, revealing annual roadkill rates of 5183 mammals, 19 402 birds and 5020 reptiles on the 250 km per year. Spatial analysis using Ripley's K statistic and HotSpot Identification highlighted significant variation in roadkill distribution across different taxonomic groups and seasons, with 51 km of road identified as hotspots when analysing all groups together. Notably, there was minimal overlap in hotspot locations between seasons and taxonomic groups, emphasizing the need for targeted mitigation strategies. Our findings challenge previous macroecological assessments suggesting low roadkill rates in Paraguay, underscoring the importance of local studies in accurately assessing ecological impacts. This study provides critical baseline data for conservation efforts and calls for further research to develop and implement effective roadkill mitigation strategies in Latin America, especially in Chaco region.

Water quality hotspot identification using a remote sensing and machine learning approach: A case study of the River Ganga near Varanasi

Turbidity (Turb) and Chlorophyll-a (Chl-a) are crucial indicators of water quality because they can reveal the presence of suspended particles and algae, respectively. Understanding the health of rivers and spotting long-term water quality changes can both benefit from monitoring these measures. Traditional methods of monitoring these parameters, like in-situ measurements, is time-consuming, expensive, and inconvenient in some places. Sentinel-2, a multispectral satellite, might offer a more workable and economical option for monitoring water quality, though. This study used 100 in-situ data collected from the Ganga River near Varanasi in the pre-monsoon season (pre-MS) and post-monsoon season (post-MS) in order to create a model for the prediction of optically active water quality parameters by combining Multispectral Instrument (MSI) data and machine learning method (Random Forest). To create spatial distribution maps for Chl-a and Turb, 14 spectral indices and band ratios were employed as independent variables. The results showed that the prediction accuracy for Turb (R2 = 0.91, MAE = 1.13 and MAPE=7.76 % during pre-MS and R2 = 0.93, MAE = 0.88 and MAPE=2.29 % during post-MS) and for Chl-a (R2 = 0.97, MAE = 0.59, and MAPE=2.07 % during pre-MS and R2 = 0.95, MAE = 0.61, and MAPE = 2.71 % during post-MS). The Ganga near Varanasi abruptly turned green due to an increase in algal bloom in May and June 2021. This study not only revealed the reasons behind the green appearance but also identified potential areas of concern or hotspots. In order to identify hotspot locations, drainage networks, point source discharge locations and LU-LC were used.

New Modeling Approaches for Ethylene Oxychlorination in Fluidized Bed Reactors: Industrial and Low Flow Rate Conditions

A simple model (Continuous Stirred-Tank Reactor) has been developed to predict the behavior of industrial ethylene oxychlorination fluidized beds operating in a turbulent regime. The approach showed good agreement both with results from industrial reactors and with those corresponding to the (Simple two phases-Plug bubble-Mixed flow emulsion approach) validated in the literature. For low flow rates, the use of the (Simple two phases - Plug bubble - Plug emulsion model) adapted to these conditions enabled us to highlight the location and extent of undesirable thermal hot spots for the process, and to propose actions to control them by acting on the temperature and/or on the feed gas flows. By comparing this model with the plug approach, the significant slowdown in ethylene conversion caused by resistance to mass transfer when feed flow rates are low is highlighted. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Identification of new sources of resistance against late leaf spot disease of groundnut using molecular and field screenings

ABSTRACT Late leaf spot (LLS), caused by the fungus Nothopassalora personata, is an important foliar disease in groundnut (Arachis hypogaea L.) production. The present study used previously reported three single nucleotide polymorphism (SNP) markers to screen a set of groundnut germplasm and compared them with phenotypic values of three hotspot locations. The results indicated that marker-identified genotypes had LLS-resistant or moderately resistant phenotypes. About 56% of the phenotypically confirmed genotypes were confirmed with the SNP genotype. Some of the phenotypically resistant genotypes were not confirmed with marker genotypes, indicating some other quantitative trait locus (QTL) responsible for the LLS resistance might have been involved. The findings in this study are relevant to future LLS resistance breeding programs.

A framework for rapid fatigue hotspot localization and damage assessment of plate with multiple holes based on the fatigue damage response spectrum method

AbstractThis paper proposes a novel framework for the random vibration fatigue assessment of thin‐walled plate with multiple holes based on the fatigue damage response spectrum method. Compared with other frequency‐domain evaluation methods, this framework fully exploits the dynamic characteristics of complex structures, decoupling the external excitation characteristics from the spatial characteristics of the structural response. This approach significantly enhances evaluation efficiency by avoiding the complex calculations associated with stress response spectral moments. The proposed method is employed to evaluate the contribution of each mode to the overall damage, additionally, the stress mode shapes are used to identify and refine the mesh around fatigue hotspots. Modal damage contribution factors are proposed to identify the key modes. By leveraging both structural dimension reduction and modal reduction techniques, the proposed framework can swiftly and accurately locate fatigue hotspots within complex structures and conduct precise fatigue assessments using the Absolute Sum ‐ Square Root of the Sum of Squares hybrid method. Finite element simulation analysis is conducted on a single‐lap structure containing numerous circular openings, validating the accuracy and efficiency of the proposed stochastic vibration fatigue assessment.