Automatic Monitoring System Research Articles

Automatic animal monitoring systems based on AutoML technologies

In contemporary agricultural contexts, the sector is experiencing active processes of automation, underscoring the need for effective tools to develop such automated systems. The utilization of automation tools enhances the efficiency of numerous processes, including those in the domain of animal monitoring. This article examines the application of the AutoML approach as a means for automating the process of generating deep learning models employed in automatic monitoring systems. The VGG19 architecture has been chosen as a testbed for demonstrating the capabilities of the developed technologies. This well-established architecture for deep learning models is designed for object recognition in images.The present study implements a technology for automated structural-parametric synthesis of VGG19 models and the optimization of their hyperparameters. Such an approach allows for the automated creation of models tailored to specific applied problems, even for users lacking specialized knowledge in deep learning.The system delineated in this work is developed on the AutoGenNet software platform, which embodies the No-Code development concept. This concept conceals complex aspects of model creation and training processes from users, significantly lowering the entry barrier for newcomers. Additionally, the AutoGenNet platform incorporates a mechanism for the automatic generation of software wrappers, facilitating efficient interaction with trained models.All aforementioned aspects have contributed to the effective implementation of the AutoML approach for automating the generation and training processes of the VGG19 model. Consequently, the processes associated with solving automatic monitoring tasks reliant on deep learning models have been significantly simplified and expedited.The developed system has been tested on the task of recognizing individual cows. Test results indicated that the system possesses a high degree of scalability and can be adapted for the automated generation of other object recognition models, thereby opening avenues for addressing a diverse array of applied challenges related to the monitoring of various animal species.

Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules

Investigation of the Automatic Monitoring System of a Solar Power Plant with Flexible PV Modules

ENERGY AUTOMATION SUPPORTS ENERGY CONSUMPTION INTENSITY IN CLASSROOMS

Automation of energy use to control energy consumption intensity (IKE) in classrooms is carried out using an Arduino Nano-based system. This system utilizes an IR transmitter to send signals to the air conditioning unit, a servo motor to position the IR transmitter to the appropriate air conditioning unit, a relay to control the lights, and a Real-Time Clock (RTC) module for scheduling based on a specified schedule. The main goal is to prevent energy waste and ensure comfort by turning on and off the air conditioning unit and lights according to a preset schedule. This automatic scheduling includes time setting, transmitter IR control, servo motor, relay control, and LCD. Programmed scheduling ensures that the air conditioner and lights only turn on when needed, while the LCD provides real-time information about the status of the air conditioner and lights. With this automation, the system helps reduce excessive energy consumption, drives significant energy savings, and creates a comfortable and energy-efficient learning environment. The automatic monitoring and regulation system also allows users to focus more on teaching and learning activities without having to worry about manual settings for air conditioning and lights.

New plant health monitoring framework

With the continuous advancement of technology and the expanding demands of the agricultural market, plant health monitoring holds significant importance in the agricultural sector. However, according to statistics, most current monitoring models exhibit low detection efficiency and inadequate accuracy. To address these issues and specifically enhance the efficiency and accuracy of plant health monitoring, this paper proposes a recognition strategy and plan composed of two parts: (1) identification of plant species and (2) health monitoring, diagnosis, and treatment recommendations. During the health monitoring process, issues such as plant diseases, pests, nutrient deficiencies, and inadequate environmental conditions can be detected. This strategy, implemented through a systematic automated monitoring system, will greatly benefit the large-scale development of agriculture.

Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography

An important parameter in the monitoring and surveillance systems is the probability of detection. Advanced wildlife monitoring systems rely on camera traps for stationary wildlife photography and have been broadly used for estimation of population size and density. Camera encounters are collected for estimation and management of a growing population size using spatial capture models. The accuracy of the estimated population size relies on the detection probability of the individual animals, and in turn depends on observed frequency of the animal encounters with the camera traps. Therefore, optimal coverage by the camera grid is essential for reliable estimation of the population size and density. The goal of this research is implementing a spatiotemporal Bayesian machine learning model to estimate a lower bound for probability of detection of a monitoring system. To obtain an accurate estimate of population size in this study, an empirical lower bound for probability of detection is realized considering the sensitivity of the model to the augmented sample size. The monitoring system must attain a probability of detection greater than the established empirical lower bound to achieve a pertinent estimation accuracy. It was found that for stationary wildlife photography, a camera grid with a detection probability of at least 0.3 is required for accurate estimation of the population size. A notable outcome is that a moderate probability of detection or better is required to obtain a reliable estimate of the population size using spatiotemporal machine learning. As a result, the required probability of detection is recommended when designing an automated monitoring system. The number and location of cameras in the camera grid will determine the camera coverage. Consequently, camera coverage and the individual home-range verify the probability of detection.

Acoustic insights into the corn extrusion process for enhanced quality control

In industrial extrusion processes, a solid material is pressed through a die to obtain products of the desired shape and dimension. Fluctuations in the process parameters have a significant impact on the product quality. In food extrusion, the expansion noise at the die can serve as an indicator of the stability of the process. This study employs microphones to characterize the corn extrusion process, focusing on correlating acoustic emissions with predefined process parameters such as feed intake and water content. Experimental data from laboratory and industrial settings reveal distinct domain shifts, yet consistent findings confirm the distinguishability of various extrusion process parameters by analyzing acoustic emissions. Employing machine learning models, including support vector machines and convolutional neural networks, in conjunction with audio features such as log Mel spectrograms, yields promising accuracies above 90\% in discriminating between standard and non-standard process parameters. The proposed acoustic quality control approach has the potential to enhance the stability of extrusion processes and contributes to the development of automated monitoring systems in the field of food extrusion. This ensures consistent quality and reduced waste, ultimately leading to significant cost savings in production.

Sensor-Based and Visual Behavioral Profiling of Dry Holstein Cows Presenting Distinct Median Core Body Temperatures

The consequences of heat stress during the dry period can extend into the postpartum period, affecting health and productivity in the subsequent lactation. We hypothesized that cows with distinct core body temperatures (CBTs) would exhibit disparate behaviors associated with different degrees of heat generation or dissipation. The primary objective was to investigate behavioral differences of dry Holstein cows (n = 50) classified as high-temperature (HT) or low-temperature (LT), based on median CBT during the summer months using visual observations and accelerometer technology. A secondary objective was to investigate the transcriptome of white blood cells (WBCs) collected from a subgroup of HT and LT cows (n = 5; per group). Minor behavior differences were observed during the visual observations (performed for a total of 16h/cow). Based on automated monitoring system (AMS) data, collected 24/7 over a period of 42 days per cow, HT cows displayed higher periods of high activity and lower periods of inactivity prepartum and diminished rumination time postpartum than LT cows. There were 16 differently expressed genes (DEGs) in WBCs of HT compared to LT cows. Several of the identified DEGs have been previously associated with heat stress. The observed trends in the AMS data indicate that CBT and patterns of activity prepartum may serve as valuable predictors for identifying dairy cows with distinct tolerance to heat stress.

Large scale analysis of the SARS-CoV-2 main protease reveals marginal presence of nirmatrelvir-resistant SARS-CoV-2 Omicron mutants in Ontario, Canada, December 2021-September 2023.

In response to the COVID-19 pandemic, a new oral antiviral called nirmatrelvir-ritonavir (PaxlovidTM) was authorized for use in Canada in January 2022. In vitro studies have reported mutations in Mpro protein that may be associated with the development of nirmatrelvir resistance. To survey the prevalence, relevance and temporal patterns of Mpro mutations among SARS-CoV-2 Omicron lineages in Ontario, Canada. A total of 93,082 Mpro gene sequences from December 2021 to September 2023 were analyzed. Reported in vitro Mpro mutations were screened against our database using in-house data science pipelines to determine the nirmatrelvir resistance. Negative binomial regression was conducted to analyze the temporal trends in Mpro mutation counts over the study time period. A declining trend was observed in non-synonymous mutations of Mpro sequences, showing a 7.9% reduction (95% CI: 6.5%-‬9.4%; p<0.001) every 30 days. The P132H was the most prevalent mutation (higher than 95%) in all Omicron lineages. In vitro nirmatrelvir-resistant mutations were found in 3.12% (n=29/929) Omicron lineages with very low counts, ranging from one to 19. Only two mutations, A7T (n=19) and M82I (n=9), showed temporal presence among the BA.1.1 in 2022 and the BQ.1.2.3 in 2022, respectively. The observations suggest that, as of September 2023, no significant or widespread resistance to nirmatrelvir has developed among SARS-CoV-2 Omicron variants in Ontario. This study highlights the importance of creating automated monitoring systems to track the emergence of nirmatrelvir-resistant mutations within the SARS-CoV-2 virus, utilizing genomic data generated in real-time.

Narrowband IoT in Livestock Farming: A Technological Innovation for Productivity and Sustainability

The integration of technology in livestock farming is crucial for enhancing production efficiency and animal welfare. This study aimed to develop and evaluate the implementation of a Narrowband IoT (NB-IoT)-based automated monitoring system in poultry farming. Using an experimental design, the research involved 30,000 day-old chicks at PT. Anugerah Teknologi Ternak in Central Java, Indonesia. The NB-IoT system collected real-time data on environmental parameters and poultry activity. Time-series analysis revealed non-stationary data, while correlation analysis showed a strong negative relationship between temperature and humidity (r = -0.8521). Anomaly detection identified 13.33% of observations as anomalous, demonstrating the system's capability for early issue detection. Regression modeling (R-squared = 0.7261) indicated that temperature and humidity significantly influence poultry productivity. The study concludes that NB-IoT implementation in poultry farming has significant potential for enhancing productivity through real-time monitoring and early anomaly detection, supporting more efficient and sustainable precision farming practices. However, limitations in data stationarity and sample generalizability suggest the need for further research to improve long-term predictions and broaden applicability across diverse farming contexts.

SISTEMA DE MONITORAMENTO: BIODIGESTOR

This paper presents the development of an automated monitoring system for biodigesters, aiming to maximize operational efficiency, safety, and sustainability in biogas production processes. The system integrates sensors to measure critical parameters such as temperature, pressure, and volume, enabling real-time monitoring and remote control. The proposal seeks not only to ensure the proper functioning of the biodigester but also to optimize waste utilization and the generation of clean, renewable energy.

Effect of automated health monitoring based on rumination, activity, and milk yield alerts versus visual observation on herd health monitoring and performance outcomes

A primary objective of this randomized trial was to compare the percentage of cows that underwent clinical examination and were diagnosed with clinical health disorders (CHD) with a health monitoring program that relied only on automated monitoring system alerts vs a program that relied only on visual observation of clinical signs of disease to select cows for clinical examination. Another objective was to compare the effects of these health monitoring programs on milk yield, the herd exit dynamics (i.e., cows sold and dead), and first service reproductive outcomes. Lactating Holstein cows (n = 1,204) enrolled in the experiment were fitted with a neck-attached sensor of an automated monitoring system (HR Tags; Merck & Co., Inc.) that generated health alerts based on rumination time and activity. Milk yield was monitored 3 times per day by automated milk meters (MM27BC, DeLaval). Cows were blocked by parity, close-up period diet, and stratified by previous lactation milk yield, and then were randomly assigned within block to different programs for monitoring health from 3 to 21 d in milk (DIM). Cows in the visual observation group (VO; n = 597) were selected for clinical examination exclusively based on visual observation of clinical signs of disease, whereas cows in the automated health monitoring group (AHM; n = 607) were selected for clinical examination based on health alerts consisting of the following: a Health Index Score < 86 arbitrary units, daily rumination < 250 min, or a reduction of > 20% in daily milk yield. Once selected for examination, the clinical exam was the same for both treatment groups. Binary data such as the occurrence of CHD, herd exit, and pregnancies per AI were analyzed with logistic regression. Daily and weekly milk yield were analyzed using ANOVA with repeated measurements. More cows underwent a clinical examination, more cows were diagnosed with at least one CHD, and more cows received treatment in the AHM than the VO treatment group. Cows in the AHM treatment had more accumulated milk than cows in the VO treatment from 2 to 21 DIM. Cows in the AHM treatment diagnosed with at least one CHD produced more milk from 3 to 18 and 20 to 21 DIM than cows diagnosed with a CHD in the VO treatment. Fewer cows left the herd up to 21 DIM for the AHM than the VO treatment. Pregnancies per AI at first service were greater for the VO than the AHM treatment at 30 d but not at 50 d after AI and no difference in pregnancy loss was detected. In conclusion, a health monitoring strategy that used automated health alerts increased the risk of undergoing clinical examination and having CHD diagnosed compared with a program that selected cows for clinical examination based exclusively on visual observation. Cows monitored with the program that relied on automated alerts also had greater milk yield in the first 21 DIM. Thus, monitoring cow health based on automated behavior and milk yield alerts might be a more effective alternative for health monitoring than exclusive use of visual observation of clinical signs of disease.

Actual problems of remote patient monitoring

Remote patient monitoring (RPM) is an innovative technology that provides continuous monitoring of patients' health from a distance. The RPM system is versatile because it can be used in the treatment of acute and chronic conditions, helping doctors keep their patients under control between ward rounds or the absence of personal care. However, the implementation of this system is accompanied by numerous problems: from technical to hospital staff management problems. The object of this study is the technical challenges associated with the use of the RPM system. The problem lies in the imperfection of many elements of RPM, including: data management, sensor accuracy, compatibility and user acceptance of the technology. The research found that these technical challenges arise from insufficient standardization, poor sensor data quality, and interoperability issues between medical platforms. These results are explained by the complexity of the technological infrastructure and the need to improve the qualifications of medical workers. The main goal of the current article is to solve the scientific and technical problem of RPM through the proposal of an effective practical development - an automated system for remote monitoring of patients. Since most processes are moving towards their automation, by using the latest scientific developments, among which we can recall machine learning and neural networks and Internet of Things (IoT) technology, it was decided to consider the factors of their application in remote patient monitoring systems. The results of this study can be used to optimize RPM systems in hospitals, as well as to improve the reliability and efficiency of new automated monitoring systems, which is extremely important in the context of war, which causes a shortage of medical personnel and an increase in the number of potential patients per doctor.

СТРАТЕГІЧНІ АСПЕКТИ УПРАВЛІННЯ ЕНЕРГЕТИЧНОЮ БЕЗПЕКОЮ ПРОМИСЛОВИХ ПІДПРИЄМСТВ В УМОВАХ МІНЛИВОСТІ ЗОВНІШНЬОГО СЕРЕДОВИЩА

The study is devoted to strategic planning and management of energy security of industrial enterprises in the conditions of dynamic changes in global energy markets and modern challenges. The relevance of the topic is due to the critical importance of energy security for ensuring the stability of production processes, optimizing costs, increasing energy efficiency and reducing the negative impact on the environment. At the theoretical level, the study analyzes the key concepts and methodologies underlying the formation of energy security management strategies. Special attention is paid to theoretical approaches to risk forecasting, threat identification, and the integration of the latest technologies, such as artificial intelligence and automated monitoring systems, into the strategic planning process. Theoretical analysis helps to reveal the main principles and strategies that are critical for adapting to a rapidly changing external environment and new threats. At the practical level, the study considers real cases and examples of the implementation of energy security strategies at industrial enterprises. Analyzes of best practices, emergency planning, crisis recovery, and resource management are included. The issue of coordination between different levels of management and integration of the energy security strategy with other corporate plans and state regulatory requirements is considered separately. The study emphasizes the need to create adaptive management systems that not only ensure the reliability of energy supply, but also respond promptly to various threats, including natural disasters, man-made accidents, terrorist acts and cyber attacks. The practical aspect of the research also includes evaluating the effectiveness of monitoring and feedback systems for adapting strategies based on changes in the external environment and monitoring results. The conducted research makes a significant contribution to the scientific discussion on the strategy and planning of energy security management, combining theoretical foundations with practical recommendations. Scientifically based methods of forecasting threats and developing adaptive strategies ensure effective management of energy security of industrial enterprises in the context of modern global challenges and technological changes.

Does Pelleted Starter Feed Restriction and Provision of Total Mixed Ration Ad Libitum during Weaning Influence the Behavior of Dairy Calves?

Currently, in dairy farming, there is growing concern for the welfare of calves during the critical period between the separation from their dams and weaning. During weaning, rationed feeding is a practice used to improve feed efficiency and control the calves’ growth, but it could also have negative consequences associated with hunger and feed restriction behavior. One such consequence could be the performance by calves of stereotyped behaviors indicative of poor welfare, such as non-nutritive oral behaviors. We hypothesized that making a Total Mixed Ration (TMR) available to calves, in addition to the standard pelleted starter diet, thanks to its structural and nutritional characteristics, could help to focus the oral behavior of the subjects towards nutritional activities and therefore limit the development of stereotyped behaviors, even when the amount of starter is restricted. To test this hypothesis, 30 female Holstein calves (equipped with an accelerometer based on an ear tag), were randomly assigned to one of three treatments: a control diet (CTR) with an ad libitum calf starter but without TMR; Treatment 1 (TRT1) with both ad libitum calf starter and ad libitum TMR; Treatment 2 (TRT2) with ad libitum TMR and a restricted amount of a calf starter (50% of the intake starter of the control group day by day). All animals were kept in individual cages equipped with a slow-flow teat bucket apparatus for milk feeding and with access to separated buckets (one for drinking water, one for the starter, and one for the TMR) placed on the outside of the front gate of each cage. Sucking behavior, as well as resting, ruminating and activity behavior, was recorded individually from 7 days of life to weaning (63 d of age) by an automated monitoring system based on ear-tag accelerometers (SCR eSense, Allflex, Irving, TX, USA). The results showed that in the CTR group, there was a greater sucking activity compared to the TRT1 group (26.25 min/head/day vs. 16.83 min/head/day, p = 0.0181), while the TRT2 group showed intermediate values (20.41 min/head/day). We hypothesized that this increased sucking activity may be explained by the oral activity directed at sucking the Milk Bar tube observed only in CTR calves after complete consumption of available milk and could indicate a higher stress level than in the TRT1 group. The time spent resting was significantly lower for the CTR group compared to the TRT1 group (9.20 h/day vs. 11.15 h/day, p = 0.0049) while the TRT2 group was in an intermediate situation (10.65 h/day). Furthermore, the increase in time dedicated to rest observed in TRT1, in light of the good vitality of the calves shown by the daily patterns of resting behavior and daily activity, also seems to suggest an improvement in welfare conditions compared to calves receiving pelleted starter alone. Based on these results, we can deduce that providing TMR ad libitum in the diet of weaning calves could be an effective strategy to improve calf welfare due to the reduction in stereotyped oral activity. However, further studies are needed to establish threshold values useful for distinguishing physiological from pathological resting times in weaning calves raised in individual cages.

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

Employing automated electrical resistivity tomography for detecting short- and long-term changes in permafrost and active-layer dynamics in the maritime Antarctic

Abstract. Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze–thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable time-lapse ERT surveys in Antarctica, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it at the Crater Lake CALM-S site on Deception Island, Antarctica, to collect quasi-continuous ERT measurements. We developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active-layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at the Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted results shows that the A-ERT system can detect the active-layer freezing and thawing events with high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost 1 decade at this site. The evolution of thaw depth exhibits a similar pattern in both years, with the active-layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT datasets, combined with the new processing workflow that we developed, are an effective tool for studying permafrost and active-layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the spatiotemporal progression of thaw depth in two dimensions, and potentially in three dimensions, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. An A-ERT monitoring setup with a longer profile length can investigate greater depths, offering effective monitoring at sites where boreholes are costly and invasive techniques are unsuitable. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and to work towards establishing an early warning system for the consequences of climate change.

Development of an IoT-enabled smart baby cradle for enhanced infant monitoring and parental control

raditional infant monitoring systems are evolving into intelligent solutions, offering real-time notifications and automation to cater to the demands of multitasking parents. However, most IoT solutions in parental care focus solely on monitoring, lacking automatic smartphone control. In response to this gap, we developed an IoT-based system for automatic cradle monitoring. This system not only enables smartphone-based monitoring but also allows parents to control the cradle's swing speed and receive notifications through a mobile application. Our solution integrates PIR and sound sensors, an ESP8266 microcontroller, and a Wi-Fi module to create a robust monitoring system that tracks a baby's movements and sounds, sending instant notifications to the smartphone. The user can activate the system and adjust the cradle's motion using a slider based on sensor data. This IoT-enabled smart baby cradle enhances convenience and reduces parental stress, ensuring infant well-being and safety. A survey conducted among Universiti Teknologi MARA instructors revealed that 70% preferred automatic swing cradles over traditional ones, and 80% expressed the need for IoT integration in infant cradles, highlighting the growing demand for such advanced solutions.

Review on solid wastes incorporated cementitious material using 3D concrete printing technology

The substitution of cement, natural sand, and synthetic fibers in concrete with solid wastes substantially reduces CO2 emissions. Incorporating solid wastes into 3D printed concrete, which reduces CO2 emissions from labor and formwork, can achieve innovative low-carbon construction materials. However, the previous studies lack a comprehensive review on the inherent characteristics of various solid wastes and their impacts on the printability, mechanical performance, and functionality of 3D printed concrete. This paper reviews the present state of research and introduces feasible methods for the sustainable use of solid wastes in 3D printed concrete. Numerous types of solid wastes are investigated comprising their benefits, shortages, and fit dose to guide in selecting the optimal solid waste to enhance the performance of printed concrete. Additionally, the printability, functionality, and mechanical strength of printed concrete with solid waste addition are quantified and summarized. The preliminary exploration to achieve a balance between multi-functionality and mechanical properties using mixed solid wastes is conducted for 3D concrete printing. Finally, machine learning, broadening raw material sources, automated monitoring systems, multi-functionalized composite, and BIM are proposed to address current challenges.

Population Dynamics of Bactrocera dorsalis (Diptera: Tephritidae) in Four Counties of Yunnan, China, by Electronic Monitoring System.

Bactrocera dorsalis (Hendel) (Diptera: Tephritidae) is a global economic pest that poses a serious threat to the fruit industry. In the southwest of China, Yunnan Province sustains a severe infestation of B. dorsalis. An automated monitoring system designed for B. dorsalis was employed in this study to elucidate the annual population dynamics of B. dorsalis in four counties: Yuanjiang, Huaping, Guangnan, and Ludian in Yunnan. The system utilizes sex parapheromone and image recognition technology. The data uploaded by the device are used to analyze the annual population dynamics of B. dorsalis in different regions. The results showed that the populations of adult B. dorsalis in all four counties peaked twice annually, with Yuanjiang experiencing the earliest peak periods, followed by Huaping, Guangnan, and Ludian. Adult B. dorsalis occurred in Yuanjiang throughout the year, and Yuanjiang had the highest number of B. dorsalis monitored. In Huaping, adult B. dorsalis occurred in March-December and was highly active, with a high population density in 2019. Bactrocera dorsalis did not occur in December in Guangnan but only in May-October in Ludian. Bactrocera dorsalis abundance was correlated with temperature in all four areas. The outcomes of this experiment provide a practical foundation for developing control strategies targeting B. dorsalis in various orchards across each county.

Rancang Bangun Green Portable Cooler Box

Freon is one of the refrigerants used in refrigeration systems. The refrigeration system has a very important role, namely to preserve food ingredients so that food can last longer. The use of freon is not only used in ships but is widely found in home appliances in refrigeration systems used in freezers, air conditioners, refrigerators, and dispensers. If more freon is used, the negative impact that arises is greater, namely the depletion of the ozone layer and global warming which can have a bad influence on health, the life of living things and the environment. The purpose of this researcher is to create a tool to reduce the negative impact of freon use, namely by using peltier as a cooling medium with environmentally friendly thermoelectric modules. This study uses trial and error methods. Where the trial process is carried out by turning on one of the working push buttons and observing the temperature value. Then it is processed by the control system, namely Arduino Mega which will later provide a PWM output signal to the IRF540 mosfet when the output temperature value is in line with the reference value and vice versa. The test of this tool was carried out by comparing measurements between the author's prototype tool and the standard measuring tool, namely the digital thermometer. Based on the test results, this device can produce a minimum temperature of 140C at 30-minute intervals without any media. The trial using 330ml bottled water can reach a temperature of 170C within 30 minutes in the cooler box. This tool is also equipped with an automatic monitoring system using a PWM signal in the form of a change in fan speed, which is 255 RPS maximum speed and when getting a PWM signal it changes to 50 RPS. The percentage of errors resulting from the comparison of standard measuring instruments and sensor values had the highest and lowest percentages of 2.37% and 0.84% in cold dispersion conditions without charge. Then the highest and lowest error percentages were 1.69% and 0.72% in the state of containing a load of 1 330ml mineral bottle. This ptototype uses data recording using Micro-SD as a data logger.