Changes In Environmental Parameters Research Articles

Use of an Ecological Compensation Model in Water Resource Development: A Case Study from Shaanxi Province, China

This study aims to analyze the current situation of water resource management in Shaanxi Province, study the basic principles of ecological compensation, evaluate the impact of water conservation projects on the ecological environment by establishing a model, and propose a sustainable water resource management model. Hanzhong City has certain typicality and representativeness within Shaanxi Province, and the problems it faces in water resource management and ecological environment may represent the entire province or similar regions. At the same time, Hanzhong City has rich data and research foundations. Therefore, by conducting a detailed analysis of the current water resource status in the Hanzhong City area of Shaanxi Province, the problems in current water resource management are revealed, and the basic principles of ecological compensation are intensely studied. The original ecological compensation plan in Shaanxi Province has been summarized. Guided by the concept of sustainable development, an ecological compensation model is established using algorithms, and the model is applied to sustainable water resource management. Establish a model for water conservation and water resource management through data collection, preprocessing, and cleaning, and apply it to practical cases in Hanzhong City. Through simulation and analysis of Hanzhong City, the new water resource management model effectively mitigates the adverse effects of water conservation projects on the ecological environment while improving water resource utilization efficiency. The changes in various environmental parameters indicate that the new plan has improved the ecological environment. Through the application of the model, the ecological compensation plan formulated has achieved sustainable protection of the ecological environment while promoting economic development. This study proposes a sustainable water resource management model through a comprehensive study of water resource management and ecological compensation in Shaanxi Province and verifies it in practical cases, demonstrating that the model has not only good applicability but also has significant effects in promoting economic growth and ecological environment protection.

Time and Environmental Conditions for Heat Stress Recovery Based on Human Physiological Responses: Developing a Predictive Model

Breaks can protect workers exposed to high temperatures from heat stress and other health risks. However, the most suitable recovery conditions and how the body physiologically responds to them remain uncertain. The aim of this study was to provide scientific evidence of the human physiological response to step changes in environmental parameters through climate chamber experiments and to develop a predictive model. Volunteer subjects experienced 60 min of walking exercise in air temperatures of 32 and 36 °C, RH 60%, and an air velocity of 0.2 m/s to form two heat stress levels, and then moved into a resting space under designated environmental conditions, such as a standard effective temperature (SET) range of 15.6–26.0 °C. The results showed that the recovery time for physiological parameters decreased with decreasing SET, accompanied by a simultaneous increase in thermal discomfort rate. Correlation analysis suggested that recovery time was related to the physiological stress index at the beginning of the recovery phase (PSI0; r = 0.86) and the SET (r = 0.75) of the recovery environment. A predictive model of heat stress recovery time was developed based on PSI0 and SET, with a determination coefficient of 0.9. According to the model, a SET of 18.8–24.9°C was suitable for people with high heat stress requiring full recovery in 15–30 min. The research findings contribute to the regulation of health and safety in high-temperature working environments and the operation of restroom environmental conditions.

Alteration in butterfly community structure along urban–rural gradient: with insights to conservation management

BackgroundEcosystem services rendered by the butterflies are important for the sustenance of community interaction. Butterfly species have also coevolved with the host and nectaring plants. In the adult condition, they mostly rely on nectar, while in the larval condition, they feed on the leaves of their host plants. Butterfly species are sensitive to changes in environmental parameters and are considered excellent indicators of ecosystem health. The study of species diversity and richness indices aids in better ecosystem management. The present study's goal was to determine butterfly diversity in the urban–rural gradient of Purulia district, West Bengal, India, a part of the Chota Nagpur Plateau. We aim to complement crucial information on butterfly conservation management in Purulia, West Bengal, India, and other similar geographical areas with the findings of this study.ResultsIt was found that out of 3809 sampled butterflies, the individual contribution of the family Nymphalidae was the highest (51.24%), followed by Lycaenidae (18.40%), Pieridae (17.32%), Papilionidae (9.74%), and Hesperiidae (3.12%). A total of 54 butterfly species were observed in the urban–rural gradient, out of which the urban region contained 49 species, the suburban region had 32 species, and the rural region had 30 species. Significant differences were observed in butterfly abundance for the sites, seasons, and families during the study period. PERMANOVA and ANOSIM for species abundance and species presence-absence data show that all three sites are significantly different. Results Both PCoA and NMDS revealed clear differences among sites (groups) in terms of species abundance and presence-absence data. According to the findings of this study, the urban region has the highest species richness, followed by the suburban and rural regions. We discovered that urban areas have the highest butterfly abundance, followed by suburban and rural areas. Numerous butterfly species prefer the bushes dominated by Lantanacamara in the urban region with the highest species richness. Aside from this invasive weed, the site also contains Tridaxprocumbens, Catharanthusroseus, Synedrellanodiflora, and Ocimumamericanum, which are well known for being butterfly nectaring plants. In the case of the suburban region, members of the Lycaenidae family contributed the highest percentage after Nymphalidae, which was dominated by Tridaxprocumbens and Sphagneticolatrilobata, which was preferred by the members of the Lycaenidae family observed during the survey, this site also contained Ixoracoccinea, Catharanthusroseus, and Lantanacamara. This site, in terms of nectaring plants, remains homogeneous in a rural region.ConclusionsOut of 3809 butterfly individuals, the family Nymphalidae contributed the most, followed by Lycaenidae, Pieridae, Papilionidae, and Hesperiidae. Both species richness and butterfly abundance were highest in urban regions, followed by sub-urban and rural regions. The current study has shown that this particular geographic location can sustain a variety of butterfly species. However, it is important to note that conservation planning is necessary not only for the butterfly species but also for the nectaring plant species that contribute to the diversity of these insects. The conservation of butterfly species can also lead to the achievement of ecosystem services they provide.

Changes in ruminal fermentation and rumen bacteria population in feedlot cattle during a high lipid diet adaptation

Abstract This study aimed to investigate changes in feed intake and ruminal environmental parameters during a high-lipid diet transition in cattle. Eight Nellore steers were fed a control diet composed of 30% hay and 70% concentrate for 21 days, followed by the inclusion of 60 g/kg dry matter of soybean oil for 21 days. The DM intake expressed as a percentage of BW 21 days after lipid inclusion was lower (1.75% BW) than that observed during the control diet feeding (1.81% BW) (P<0.01). Steers fed the control diet had a lower pH than the ruminal pH recorded on days 7, 14, and 21 after lipid inclusion (P=0.034). Lower total short-chain fatty acid production in the rumen and lower microbial nitrogen synthesis were observed on day 7 after lipid inclusion compared to values found when steers were fed the control diet and on days 14 and 21 after lipid inclusion (P=0.041). Lipid inclusion in the diet decreased the population of protozoa on days 7, 14, and 21 (P<0.001). The abundances of R. albus and F. succinogenes were higher when steers were fed the control diet than the abundance observed on days 7, 14, and 21 after lipid inclusion in the diet (P<0.05). The first seven days of lipid diet inclusion are considered the most critical for ruminal adaptation, involving reductions in fibrolytic bacteria and changes in fermentation parameters. After 14 days the rumen showed signs of recovery and adaptation.

Numerical study on the thermal environment of the vertical greening system based on infrared thermal imaging

ABSTRACT Since the design and construction of the building envelope affects approximately 20–60% of the energy used to heat and cool the building, it is important to improve the energy efficiency of the building envelope. This study intends to obtain an optimal thermal environment model of vertical greening through software simulation in the later stage. For some countries and cities that are still relatively backward in green development, we can provide technical support and guidance. In this study, infrared imaging equipment was used to obtain the thermal environment parameters of vertical greening, and the corresponding thermal environment characteristics were analyzed to provide support for the construction of the thermal environment model of the vertical greening system. Secondly, a new microclimate numerical simulation software ENVI-met was used to establish a vertical greening system model, quantify the changes of thermal environment parameters under different conditions, and output the numerical simulation analysis results, including temperature and thermal comfort evaluation indicators, to evaluate the role relationship and influence mechanism under different conditions. It provides theoretical support for the development, application and prediction of vertical greening. This will improve the energy efficiency of buildings.

Variability in land surface temperature concerning escalating urban development using thermal data of Landsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India

Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.

Predictive Analysis of a Building’s Power Consumption Based on Digital Twin Platforms

Colleges and universities are large consumers of energy, with a huge potential for building energy efficiency, and need to reduce energy consumption to build a low-carbon, energy-saving campus. Predicting the energy consumption of campus buildings can help to accurately manage the electricity consumption of buildings and reduce the energy consumption of buildings. However, the electricity consumption of a building’s operation is affected by many factors, and it is difficult to establish a model for analysis and prediction. Therefore, in this study, the training building of the BIM education center on campus was selected as the research object, and a digital twin O&M platform was established by integrating IoT, digital twin technology (DDT), smart meter monitoring devices, and indoor environment monitoring devices. The O&M management platform can monitor real-time changes in indoor power consumption data and environmental parameters, and organize data on multiple influencing factors and power consumption. Following training, validation, and testing, the machine learning models (back propagation neural network, support vector model, and multiple linear regression model) were assessed and compared for accuracy. Following the multiple linear regression and support vector models, the backpropagation neural network model exhibited the highest accuracy. Consistent with the actual power consumption detection results in the BIM education center, the backpropagation neural network model produced results. Consequently, the BP model created in this study demonstrated its dependability and ability to forecast campus building power usage, assisting the university in organizing its energy supply and creating a campus that prioritizes conservation.

A new ventilation performance index considering relative changes in environmental parameters: A numerical study

The ventilation index plays a crucial role in evaluating and selecting ventilation systems. However, existing studies focus only on the "effectiveness" of ventilation systems but usually ignore the "extent of the effect" caused by ventilation, and no relevant indexes are available. To address this issue, this study aims to propose a new ventilation index named the equivalent ventilation number (EVN), which considers the relative change in environmental parameters. The application potential of the EVN index was numerically examined in a virtual hospital ward setting with varying air changes per hour (ACH), thermal loads, air distribution patterns, contaminant source intensities, and local air supply rates. The results demonstrate that the highest average temperature-based EVN and CO2-based EVN are obtained when ACH increases from 6 h−1 to 12 h−1 and local supply air velocity increases from 0 m/s to 1 m/s, with values of 0.66 and 1.49 respectively. Furthermore, EVN values differ across different ventilation conditions and locations. The effect of changing ventilation conditions on ventilation performance assessed by heat removal effectiveness and temperature-based EVN, as well as contaminant removal effectiveness and CO2-based EVN, is opposite in 4 out of 10 cases and 3 out of 10 cases, respectively. The average EVN values calculated based on temperature and contaminant changes differ among all cases, with the largest difference of 1.2. The proposed EVN is anticipated to be an effective index for quantifying and comparing the "extent of the effect" caused by ventilation systems under various ventilation conditions.

Neural network PID control of spherical permanent magnet based on external magnetic field driving system

Abstract The neural network PID control method of the external magnetic field driving system solves the shortcomings of the traditional control method. The controlled object is a spherical permanent magnet. Other detections or operation equipment can be attached to the sphere. In the process of external magnetic field driving, the model-based controller is prone to large fluctuations when the environmental parameters change, which reduces the control performance. The neural network algorithm has good control ability for the highly nonlinear magnetic levitation driving system. PID control system combined with a neural network algorithm has high stability and strong adaptability in the driving process. The test results show that the response output overshoot is 9.322%, and the system fluctuation is low. Self-tuning and adaptive environment changes of PID controller parameters can be realized by neural network control.

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.