Changes In Environmental Parameters Research Articles

Integrating depth-dependent protist dynamics and microbial interactions in spring succession of a freshwater reservoir

BackgroundProtists are essential contributors to eukaryotic diversity and exert profound influence on carbon fluxes and energy transfer in freshwaters. Despite their significance, there is a notable gap in research on protistan dynamics, particularly in the deeper strata of temperate lakes. This study aimed to address this gap by integrating protists into the well-described spring dynamics of Římov reservoir, Czech Republic. Over a 2-month period covering transition from mixing to established stratification, we collected water samples from three reservoir depths (0.5, 10 and 30 m) with a frequency of up to three times per week. Microbial eukaryotic and prokaryotic communities were analysed using SSU rRNA gene amplicon sequencing and dominant protistan groups were enumerated by Catalysed Reporter Deposition-Fluorescence in situ Hybridization (CARD-FISH). Additionally, we collected samples for water chemistry, phyto- and zooplankton composition analyses.ResultsFollowing the rapid changes in environmental and biotic parameters during spring, protistan and bacterial communities displayed swift transitions from a homogeneous community to distinct strata-specific communities. A prevalence of auto- and mixotrophic protists dominated by cryptophytes was associated with spring algal bloom-specialized bacteria in the epilimnion. In contrast, the meta- and hypolimnion showcased a development of a protist community dominated by putative parasitic Perkinsozoa, detritus or particle-associated ciliates, cercozoans, telonemids and excavate protists (Kinetoplastida), co-occurring with bacteria associated with lake snow.ConclusionsOur high-resolution sampling matching the typical doubling time of microbes along with the combined microscopic and molecular approach and inclusion of all main components of the microbial food web allowed us to unveil depth-specific populations’ successions and interactions in a deep lentic ecosystem.

Stability of Immobilized Chemosensor‐Filled Vesicles on Anti‐Fouling Polymer Brush Surfaces

AbstractThe characterization of phospholipid membrane permeability for small molecules is crucial for many applications in drug discovery and biomedical research in general. Here, chemosensor‐laden vesicles offer an attractive platform for permeability assays. In this work, the stability of immobilized chemosensor‐filled vesicles is explored on anti‐fouling polymer brush surfaces for potential use in monitoring small molecule membrane permeability. The study focuses on the development of a method for immobilizing sensor‐loaded vesicles into arbitrary patterns on surfaces and characterizing their stability under changing temperatures and compositions. As substrate to enable intact, long‐term stable vesicle immobilization reactive polymer brushes with anti‐fouling properties are used. Utilizing microchannel cantilever spotting, biotin moieties are introduced on the polymer brush surface, enabling stable tethering of vesicles through streptavidin‐biotin interactions. The immobilized vesicles are monitored through fluorescence microscopy for their response to analytes under changing environmental parameters and vesicle composition. A higher stability of immobilized vesicles compared to free‐floating ones is observed, with permeability only at elevated temperatures. By tuning vesicle compositions, permeability at lower temperatures can be raised again. Overall, the study provides insights into a novel approach for vesicle immobilization, showcasing the potential of surface‐bound vesicles for applications in microfluidic systems and as biosensors in various assays.

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control.

Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials

A physics-guided neural network (PGNN) is proposed to predict the fatigue life of materials. In order to reduce the complexity of fatigue life prediction and reduce the data required for network training, the PGNN only predicts the fatigue performance parameters under a specific loading environment, and calculates the fatigue life by substituting the load into the fatigue performance parameters. The advantage of this is that the network does not need to evaluate the effect of numerical changes in the load on fatigue life. The load only needs to participate in the error verification, which reduces the dimension of the function that the neural network needs to approximate. The performance of the PGNN is verified using published data. Due to the reduction in the complexity of the problem, the PGNN can use fewer training samples to obtain more accurate fatigue life prediction results and has a certain extrapolation ability for the changes in trained loading environment parameters. The prediction process of the PGNN for fatigue life is not completely a black box, and the prediction results are helpful for scholars to further study the fatigue phenomenon.

Dynamics of changes in the temperature coefficient of electrical conductivity of distilled water in conductometric cells during heating and cooling

The problem of taking into account the influence of air on the properties of distilled water is considered, namely the lack of a single generally accepted method for calculating such an influence. The sensitivity of the structure of water to the influence of external factors is described and the possibility of recording and studying such factors by changes in the temperature coefficient of electrical conductivity of water is shown. The dependences of the temperature coefficient of electrical conductivity of distilled water on the rate of change in water temperature, the degree of filling of conductometric cells, as well as on the intensity of the exchange of carbon dioxide between water and air across their interface have been studied. It is noted that these metabolic processes are currently insufficiently studied. A hardware-software measuring complex has been developed and manufactured to study the temperature coefficient of electrical conductivity of water when its temperature changes within the range of 20–55 °C. The temperature coefficient of electrical conductivity of water in sealed conductometric cells was measured at different degrees of filling the cells with distilled water and the rate of heating and cooling of water. The degree of filling of the cells varied within the range of 10–100 %, the rate of change in water temperature varied within the range of 0.04–2.00 °C/min. With a constant heating and cooling time of 15 minutes in all experiments, the change in speed was achieved by changing the temperature of the heating element. The integral temperature coefficient of electrical conductivity is calculated based on the initial and final values of electrical conductivity and water temperature in each measurement cycle. The dependences of the temperature coefficient of electrical conductivity on the rate of change in water temperature at several constant degrees of cell filling were obtained. It has been shown that with a constant ratio of the volumes of water and air in the cell and an increase in the rate of heating of water, the temperature coefficient of electrical conductivity of water decreases by 19–22 %. It has been established that at a constant rate of water heating, with a decrease in the volume of water in the cell, the temperature coefficient of electrical conductivity of water decreases by 40–42 %. The results obtained can be used to quantify the dissociation coefficient of carbonic acid, the mobility of hydrogen ions, as well as the intensity of the gas exchange process under various external influences on water. Refinement of data on the electrical conductive properties of water and processes at the water/air interface is necessary for the development of models of atmospheric phenomena and climate change, as well as for the creation of sensors for weak changes in environmental parameters for the purposes of both environmental monitoring and medical diagnostics.

Cloud Characteristics during Intense Cold Air Outbreaks over the Barents Sea Based on Satellite Data

The Arctic experiences remarkable changes in environmental parameters that affect fluctuations in the surface energy budget, including radiation and sensible and latent heat fluxes. Cold air masses and cloud transformations during marine cold air outbreaks (MCAOs) substantially influence the radiative fluxes, thereby shaping the link between large-scale dynamics, sea ice conditions, and the surface energy budget. In this study, we investigate various cloud characteristics during intense MCAOs over the Barents Sea from 2000 to 2018 using satellite data. We identify 72 intense MCAO events that propagated southward using reanalysis data of the surface temperature and potential temperature at the 800 hPa level. We investigate the macro- and microphysical parameters and radiative properties of clouds within selected MCAOs, their dependence on sea ice concentration, and their initial air mass properties using satellite data. A significant increase in low-level clouds near the ice edge (up to +25% anomalies) and a smooth transition to upper-level clouds is revealed. The total cloud top height during intense MCAOs is generally 500–700 m lower than under neutral conditions. MCAOs induce a positive net cloud radiative effect, which peaks at +20 W m−2 (100 km from the ice edge) and gradually decreases towards the continent (−2.3 W m−2 per 100 km). Our study provides evidence for the importance of changes in the cloud radiative effect within MCAOs, which should be accurately simulated in regional and global climate models.

Influence of water chemistry and operating parameters on PFOS/PFOA removal using rGO-nZVI nanohybrid

Graphene and zero-valent-iron based nanohybrid (rGO-nZVI NH) with oxidant H2O2 can remove perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS) through adsorption-degradation in a controlled aquatic environment. In this study, we evaluated how and to what extent different environmental and operational parameters, such as initial PFAS concentration, H2O2 dose, pH, ionic strength, and natural organic matter (NOM), influenced the removal of PFOS and PFOA by rGO-nZVI. With the increase in initial PFAS concentration (from 0.4 to 50 ppm), pH (3 to 9), ionic strength (0 to 100 mM), and NOM (0 to 10 ppm), PFOS removal reduced by 20%, 30%, 2%, and 6%, respectively, while PFOA removal reduced by 54%, 76%, 11%, and 33% respectively. In contrast, PFOS and PFOA removal increased by 10% and 41%, respectively, with the increase in H2O2 (from 0 to 1 mM). Overall, the effect of changes in environmental and operational parameters was more pronounced for PFOA than PFOS. Mechanistically, •OH radical generation and availability showed a profound effect on PFOA removal. Also, the electrostatic interaction between rGO-nZVI NH and deprotonated PFAS compounds was another key factor for removal. Most importantly, our study confirms that rGO-nZVI in the presence of H2O2 can degrade both PFOS and PFOA to some extent by identifying the important by-products such as acetate, formate, and fluoride.

Метод оптимизации организационной структуры централизованных многоагентных систем в автоматическом режиме

The organizational structure of a multi-agent system (MAS) is a set of roles and relationships of agents, com-ponents that control their behavior, as well as rules governing the interaction of system elements. The effectiveness of the MAS largely depends on the characteristics of the organizational structure used. Existing solutions in this area have a significant disadvantage, namely low adaptability to changes in environmental parameters or adjustment of the conditions of the task, which consists in the need to restart the procedure for synthesizing the organizational structure of the MAS. The problem of automatic optimization of the organizational structure of centralized MAS in the conditions of changed environmental parameters or the task is of particular relevance for centralized MAS with a strict hierarchical structure, whose agents can be divided into two classes – agents-managers, agents-subordinates. The subject of the study is the methods of synthesis of the organizational structure of the MAS. The aim of the work is to develop a method for optimizing the organizational structure of centralized MAS in automatic mode. To achieve this goal, the concepts of primary and secondary agent resources were introduced, based on a biogeographic algorithm, a method was developed that regulates the movement of subordinate agents between groups depending on the degree of attractiveness of the agent manager (depends on the sum of estimates of the effectiveness of subordinate agents managed by him, as well as on the distance from this subordinate agent). The developed method can find its practical application in the implementation of the following tasks: inspection or patrolling (protection) of infrastructure facilities by mobile robots, the implementation of artificial intelligence in computer games.

Investigating environmental sustainability applications using advanced monitoring systems

This study employs an Internet of Things (IoT)-based simulation to investigate environmental parameters critical to sustainability and public health. Over a 10-hour period, temporal variations in air quality parameters, including carbon dioxide (CO2), nitrogen dioxide (NO2), and particulate matter (PM2.5), were monitored. CO2 levels exhibited a decline from 400 ppm to 375 ppm, suggesting improvements in ventilation and reduced emissions. NO2 levels consistently decreased from 25 to 22 ppm, indicative of effective emissions control measures, while PM2.5 levels increased from 10 to 25 μg/m³, possibly influenced by transient factors. Water quality monitoring revealed fluctuations in dissolved oxygen (DO) levels (8.20–8.80 mg/L) and pH levels (7.00–7.35) over 10 hours, emphasizing the dynamic nature of aquatic ecosystems. Soil moisture levels stood at 29.48%, and energy consumption was recorded at 270 units, highlighting the importance of resource-efficient management. The significance of IoT-enabled monitoring in tracking and responding to environmental parameter changes, contributing to environmental sustainability and public health. Continuous data collection empowers stakeholders to make informed decisions for improved air and water quality, sustainable agriculture, and energy efficiency. Further research aims to enhance simulation realism and validate findings against real-world IoT deployments

A Case Study of Using Long Short-Term Memory (LSTM) Algorithm in Solar Photovoltaic Power Forecasting

Solar photovoltaic power plays an important role in distributed energy resources. The number of solar-powered electricity generation has increased steadily in recent years all over the world. This happens because it produces clean energy, and solar photovoltaic technology is continuously developing. One of the challenges in solar photovoltaic is that power generation is highly dependent on the dynamic changes of environmental parameters and asset operating conditions. Solar power forecasting can be a possible solution to maximise the electricity generation capability of the solar photovoltaic system. This study implements the deep learning method, long short-term memory (LSTM) models for time series forecasting in solar photovoltaic power generation forecasting. The data set collected by The Ravina Project from 2010 to 2014 is used as the training data in the simulations. The root mean square value is used in this study to measure the forecasting error. The results show that the deep learning algorithm provides reliable forecasting results.

Changes in basic water environmental parameters of the water intake of brackish water shrimp farming in Northern Vietnam period 2017-2021

This study synthesised and analysed water environmental parameters data of water supply of brackish water shrimp farming in Northern Vietnam over 5 years from 2017 to 2021 to find out the changing patterns of the parameters, thereby making appropriate recommendations for stakeholders. There were 7 analysed and evaluated water environment parameters, including water temperature, dissolved oxygen (DO), salinity, pH, alkalinity, chemical oxygen demand (COD), nitrite (N-NO2), with monitoring frequency of 2 times/month, 10 months/year at 13 monitoring locations for brackish water shrimp farming in 6 provinces: Nam Dinh, Nghe An, Ha Tinh, Quang Binh, Quang Tri, and Thua Thien Hue. Water temperature was low at the beginning of the year, gradually increased to the highest level in June, then gradually decreased to the end of the year. The temperature fluctuated strongly at the beginning and end of the year and tended to increase gradually from the North to the South. DO tended to decrease from the beginning of the year to the end of the year. Salinity was lower in the rainy season, especially in Nghe An and Quang Tri, compared to the others. The pH and alkalinity levels were suitable for brackish shrimp farming. COD in Ha Tinh and Nam Dinh, NO2 in Nam Dinh were often high, indicating that the supply water was contaminated with organic matter and needed a long-term solution.

CLIMATE CHANGES IN THE SEA OF AZOV REGION OVER THE LAST 126 THOUSAND YEARS AND THE PROBLEM OF LOW FLOW

In the last 15 years, in the south of the European part of Russia, including in the Sea of Azov region, there has been a period of low water associated with a decrease of river flow. This leads to a number of negative consequences for the population of the region, agriculture, inland navigation, and transformations of the ecosystems of the Sea of Azov. The article discusses a retrospective analysis of changing of climate and watering of the territory during the Late Pleistocene and Holocene, based both on the results of paleogeographic studies and on archival and historical data. It is shown that changes in environmental parameters, including the amount of precipitation, on which the full flow of the Don river depends, is cyclical. Currently, there is a cycle associated with a significant decrease of the annual precipitation amount and an increase in average annual temperatures.

Effects of different nutrient loadings on planktonic primary production in embayments of Indonesia

A study on planktonic primary production was conducted in three Indonesian embayments subjected to different levels of eutrophication. The three embayments are Jakarta, Lampung and Semangka Bay. A previous study revealed that Jakarta Bay is heavily eutrophied while Lampung Bay is moderately eutrophied and Semangka Bay is close to pristine conditions. The eutrophication state in the three bays studied was closely related with respective nutrient loading of the embayments. Annual primary production in Jakarta Bay ranged from 47 to 503 g C m-2 y-1, in Lampung Bay from 31 to 196 g C m-2 y-1 and in Semangka Bay from 14 to 40 g C m-2 y-1. Spatial gradients in primary productivity were closely related to spatial nutrient gradients in the bays, except in Semangka Bay, due to the high turbidity of the river mouth. Temporal variability in primary productivity was small, consistent with small changes in environmental parameters across seasons. Jakarta Bay which has the most eutrophied waters was the highest in terms of primary productivity, followed by Lampung Bay and Semangka Bay. The primary productivity levels in the three bays was closely related with levels of nutrient loadings. This research is of importance for the development of primary production measurement in Indonesia since it is among that uses the radiocarbon technique.

Exploring the spatial interplay between built-up environments and surface urban heat island phenomena in the main urban area of Shanghai

The urban built-up environment, consisting of buildings, roads, and various facilities, plays a crucial role in forming surface urban heat islands (SUHI). This study aims to address this issue by creating a comprehensive indicator system for the urban built-up environment, drawing from previous research. By employing multi-source data and diverse geospatial processing methods, this article examine the influence of 20 environmental factors on the spatial and temporal variability of SUHI in Shanghai during the summer and winter seasons from 2016 to 2020. This article findings reveal four key observations. (1) The average SUHI intensity in Shanghai's main urban area during the summers of 2016–2020 showed an increasing trend, with the heat island's shape and location differing between seasons. (2) The correlation values (R-values) between the built and road environment indicators and SUHI changed with season and year, reflecting the dynamic interplay among urban morphology, weather conditions, and human activities that affect heat generation and dissipation. (3) This article found that the increasing SUHI trend was not solely dependent on changes in individual built-up environment parameters. (4) This article analysis demonstrated that the built environment factor significantly influenced heat island intensity in summer, while the green space environment factor had a substantial impact during winter. In conclusion, the results emphasize the complex relationship between the urban built-up environment and SUHI, highlighting the need to consider various environmental factors when assessing their impact on SUHI formation. This study offers valuable insights to inform the development of effective strategies for mitigating the negative effects of SUHI on urban environments and promoting sustainable urban development.

Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem

BackgroundViruses impact nearly all organisms on Earth, including microbial communities and their associated biogeochemical processes. In soils, highly diverse viral communities have been identified, with a global distribution seemingly driven by multiple biotic and abiotic factors, especially soil temperature and moisture. However, our current understanding of the stability of soil viral communities across time and their response to strong seasonal changes in environmental parameters remains limited. Here, we investigated the diversity and activity of environmental soil DNA and RNA viruses, focusing especially on bacteriophages, across dynamics’ seasonal changes in a snow-dominated mountainous watershed by examining paired metagenomes and metatranscriptomes.ResultsWe identified a large number of DNA and RNA viruses taxonomically divergent from existing environmental viruses, including a significant proportion of fungal RNA viruses, and a large and unsuspected diversity of positive single-stranded RNA phages (Leviviricetes), highlighting the under-characterization of the global soil virosphere. Among these, we were able to distinguish subsets of active DNA and RNA phages that changed across seasons, consistent with a “seed-bank” viral community structure in which new phage activity, for example, replication and host lysis, is sequentially triggered by changes in environmental conditions. At the population level, we further identified virus-host dynamics matching two existing ecological models: “Kill-The-Winner” which proposes that lytic phages are actively infecting abundant bacteria, and “Piggyback-The-Persistent” which argues that when the host is growing slowly, it is more beneficial to remain in a dormant state. The former was associated with summer months of high and rapid microbial activity, and the latter with winter months of limited and slow host growth.ConclusionTaken together, these results suggest that the high diversity of viruses in soils is likely associated with a broad range of host interaction types each adapted to specific host ecological strategies and environmental conditions. As our understanding of how environmental and host factors drive viral activity in soil ecosystems progresses, integrating these viral impacts in complex natural microbiome models will be key to accurately predict ecosystem biogeochemistry.2RJdjcjTGzDNGfvgvrWr_YVideo

Genomic diversity of Vibrio spp. and metagenomic analysis of pathogens in Florida Gulf coastal waters following Hurricane Ian.

Changing climatic conditions influence parameters associated with the growth of pathogenic Vibrio spp. in the environment and, hence, are linked to increased incidence of vibriosis. Between 1992 and 2022, a long-term increase in Vibrio spp. infections was reported in Florida, USA. Furthermore, a spike in Vibrio spp. infections was reported post Hurricane Ian, a category five storm that made landfall in Florida on 28 September 2022. During October 2022, water and oyster samples were collected from three stations in Lee County in an area significantly impacted by Ian. Vibrio spp. were isolated, and whole-genome sequencing and phylogenetic analysis were done, with a focus on Vibrio parahaemolyticus and Vibrio vulnificus to provide genetic insight into pathogenic strains circulating in the environment. Metagenomic analysis of water samples provided insight with respect to human health-related factors, notably the detection of approximately 12 pathogenic Vibrio spp., virulence and antibiotic resistance genes, and mobile genetic elements, including the SXT/R391 family of integrative conjugative elements. Environmental parameters were monitored as part of a long-term time series analysis done using satellite remote sensing. In addition to anomalous rainfall and storm surge, changes in sea surface temperature and chlorophyll concentration during and after Ian favored the growth of Vibrio spp. In conclusion, genetic analysis coupled with environmental data and remote sensing provides useful public health information and, hence, constitute a valuable tool to proactively detect and characterize environmental pathogens, notably vibrios. These data can aid the development of early warning systems by yielding a larger source of information for public health during climate change. Evidence suggests warming temperatures are associated with the spread of potentially pathogenic Vibrio spp. and the emergence of human disease globally. Following Hurricane Ian, the State of Florida reported a sharp increase in the number of reported Vibrio spp. infections and deaths. Hence, monitoring of pathogens, including vibrios, and environmental parameters influencing their occurrence is critical to public health. Here, DNA sequencing was used to investigate the genomic diversity of Vibrio parahaemolyticus and Vibrio vulnificus, both potential human pathogens, in Florida coastal waters post Hurricane Ian, in October 2022. Additionally, the microbial community of water samples was profiled to detect the presence of Vibrio spp. and other microorganisms (bacteria, fungi, protists, and viruses) present in the samples. Long-term environmental data analysis showed changes in environmental parameters during and after Ian were optimal for the growth of Vibrio spp. and related pathogens. Collectively, results will be used to develop predictive risk models during climate change.

Mammalian odorant-binding proteins are prone to form amorphous aggregates and amyloid fibrils

Odorant-binding proteins are involved in perceiving smell by capturing odorants within the protein's β-barrel. On the example of bovine odorant-binding protein (bOBP), the structural organization of such proteins and their ability to bind ligands under various conditions in vitro were examined. We found a tendency of bOBP to form oligomers and small amorphous aggregates without disturbing the integrity of protein monomers at physiological conditions. Changes in environmental parameters (increased temperature and pH) favored the formation of larger and dense supramolecular complexes that significantly reduce the binding of ligands by bOBP. The ability of bOBP to form fibrillar aggregates with the properties of amyloids, including high cytotoxicity, was revealed at sample stirring (even at physiological temperature and pH), at medium acidification or pre-solubilization with hexafluoroisopropanol. Fibrillogenesis of bOBP was initiated by the dissociation of the protein's supramolecular complexes into monomers and the destabilization of the protein's β-barrels without a significant destruction of its native β-strands.

Slow growing bacteria survive bacteriophage in isolation

The interactions between bacteria and bacteriophage have important roles in the global ecosystem; in turn changes in environmental parameters affect the interactions between bacteria and phage. However, there is a lack of knowledge on whether clonal bacterial populations harbour different phenotypes that respond to phage in distinct ways and whether the abundance of such phenotypes within bacterial populations is affected by variations in environmental parameters. Here we study the impact of variations in nutrient availability, bacterial growth rate and phage abundance on the interactions between the phage T4 and individual Escherichia coli cells confined in spatial refuges. Surprisingly, we found that fast growing bacteria survive together with all of their clonal kin cells, whereas slow growing bacteria survive in isolation. We also discovered that the number of bacteria that survive in isolation decreases at increasing phage doses possibly due to lysis inhibition in the presence of secondary adsorptions. We further show that these changes in the phenotypic composition of the E. coli population have important consequences on the bacterial and phage population dynamics and should therefore be considered when investigating bacteria-phage interactions in ecological, health or food production settings in structured environments.

IMPEDANCE SPECTROSCOPY GENETIC PROGRAMMING (ISGP) ANALYSES SETTING PROPER WORKING CONDITIONS OF A POLYMER ELECTROLYTE MEMBRANE FUEL CELL


 
 
 This study demonstrates application of Impedance Spectroscopy Genetic Program (ISGP) for the investigation of a polymer electrolyte membrane fuel cell (PEMFC). It further demonstrates the procedures to optimize the operating conditions of a single cell in a test station. To do that, the effects of temperature, hydrogen/air, and dew point temperature (DPT) on the cell were examined using an Arbin test station. ISGP followed a two-iteration procedure. First, find an out-of-range peak (at high frequencies) that corresponds to the ohmic (series) resistance of the system. Second, finding the models after subtracting the ohmic resistance from the real part of the measured spectrum. This two- step procedure allows solving a Fredholm equation of the second kind with a reasonable accuracy. The resulting peaks making the distribution function of relaxation time (DFRT) were partially assigned to different physical processes in the PEMFCs. ISGP seeks for a distribution of relaxation times that has the form of a peak or a sum of several peaks, assuming the Debye kernel, where each peak is represented by a known analytic function. As a part of the analysis, the peak areas, which correspond to the contribution of the relevant process to the total impedance, were calculated obtaining tendentious behavior depending on the changing environmental parameters. ISGP of PEMFC results in three peaks. The optimized conditions were found to be the ratio of gas flow fuel to air rate 1:7, fuel cell temperature 60°C and dew point temperature 50°C.
 
 

Development of Web-Based Plant Sensor Tool Data Processing Application

The rapid development of technology in the 4.0 era brings changes in the use of technology. The industrial 4.0 era makes IoT (Internet of Things) media a technology that can support all sectors of activity including the agriculture or farming sector. IoT media takes place in the form of plant sensor device that can be used to measure changes in environmental parameters. These changes include light intensity, temperature, humidity, soil fertility, are the supporting factors for plant growth. This study aims to design a website-based application that can read plant sensor data. Sensor device data is collected and downloaded in CSV (Comma Separated Values) format. Data processing requires the Naïve Bayes algorithm as a method for grouping parameter data. The results of the study are in the form of a website design that can help analyze the ideal environmental parameters for farming activities.