Humidity Research Articles

Effect of temperature on moisture sorption isotherm characteristics of Thai jasmine paddy (Khao Dawk Mali 105)

Mathematical models of moisture sorption isotherms were evaluated to assess their suitability for long grain paddy (Khao Dawk Mali 105) across various temperature ranges. Paddy samples were placed in sealed containers with relative humidity controlled, ranging from 5.32% to 84.34%, and temperatures maintained from 25 to 70 °C, until equilibrium between the samples and the container's atmosphere was reached. Results showed that for both desorption and adsorption isotherms, equilibrium moisture content (EMC) decreases with an increase in temperature at constant water activity. However, at constant temperature, when water activity increases, EMC also increases. Additionally, the sorption isotherms exhibit a hysteresis effect. At 25 °C, the Henderson model proved to be the most suitable. At 40 °C, the Chung and Pfost model was best suited for adsorption, while the Henderson model was optimal for desorption. At 60 °C, the Peleg model was the most appropriate for adsorption, and the Henderson model remained the most suitable for desorption. At 70 °C, the Peleg model was the most suitable for both adsorption and desorption. The application of mathematical modeling to predict the EMC of jasmine rice at various temperature and relative humidity levels is crucial for efficient storage and processing planning within the rice export industry.

Performance enhancement of triboelectric nanogenerator by embedding tea-leaf powder in waste polystyrene

A triboelectric nanogenerator (TENG), which harvests electrical energy from ambient mechanical vibrations, has emerged as a potential alternative to battery for future energy autonomous sensors and wireless devices. In this paper, we report a very simple and cost-effective strategy to improve the performance of a waste-material-based triboelectric nanogenerator (TENG). Tea-leaf powder is used as the filler in waste polysyrene (WPS), derived from the waste packaging material by using a very simple process. TENGs are fabricated using the composite layer comprising varying concentrations of the tea-leaf powder in WPS (Tea@WPS) as the tribopositive layer and polytetrafluoroethylene (PTFE) as the tribonegative layer. It is found that a low concentration of the tea leaf powder, such as 5%, produces the best performance with a peak voltage of ~1800 V, a surface charge density of ~180 μC/m2, and a power density of 61.25W/m2, which are approximately 4.5×, 2.8×, and 8× higher than that for a similar TENG in which no tea-leaf filler is used in WPS. Possible reasons for such huge performance enhancement are discussed. Although the performance of the device is degraded when exposed to high humidity level, it is found that the device restores it original performance when the humidity is reduced again. No degradation of performance of the device is observed for over 110 days in the same ambient condition.

An efficient strategy based on proper orthogonal decomposition for rapid determination of the location and rate of water vapor humidification in aircraft cabins

Currently, the air in commercial aircraft cabins during flights routinely exhibits extremely low relative humidity. But none of the previously published studies have addressed optimization of the humidification parameters in order to, create the desired humidity distributions in aircraft cabins at minimal cost. This study proposed an efficient strategy to accomplish the task. Under this strategy, a database of various potential humidification locations for aircraft cabins is first constructed. One scenario in the database is then selected by computational fluid dynamics (CFD)-based enumeration. Next, with the humidification location as prior information, the humidification rate and the air-supply temperature from the main ventilation system can be inversely designed by proper orthogonal decomposition. The above strategy was applied to a three-dimensional aircraft cabin model for the validated CFD cases. The results showed that an appropriate humidification for the cabin might both improve the thermal comfort of passengers and increase the humidity levels in the cabin; a decrease of the air-supply temperature from the main ventilation system would be beneficial in reducing the water vapor humidification rate required for passengers and allowing a higher humidification rate to be tolerated for the cabin walls. The parameters that improved the cabin air environment without significant moisture accumulation on walls were also provided by the proposed strategy. Compared with the full CFD simulation method, the proposed strategy is capable of providing accurate ranges of parameters while reducing the computing time by more than 95%, which demonstrates its competitive prospects.

Facile synthesis of silver and copper modified graphitic carbon nitride for volatile organic compounds sensing

Volatile organic compounds (VOCs) pose significant health risks when inhaled or ingested in large quantities. Metal oxide-based solid-state gas sensors are commonly utilized for VOCs detection, including methanol, however their high operating temperature and selectivity are both biggest challenges. In this context, graphitic carbon nitride (g-C3N4) has emerged as a promising alternative for VOCs sensing due to its superior sensing properties. In this work, Cu and Ag doped g-C3N4 was synthesized via the polycondensation method for VOCs sensing applications. All the samples showed a selective response to methanol at room temperature. Notably, the Ag/g-C3N4 sensor exhibited a significantly enhanced response (~27.5) compared to both undoped g-C3N4 (~3.58) and Cu/g-C3N4 (~9.82) sensors towards 200 ppm methanol. The Ag/C3N4 based sensor showed rapid response (21 s) and recovery (17 s) times, along with excellent short-term and long-term stability. It was found that Ag/C3N4 sensor exhibited a good response to humidity levels ranging from 9 % to 93 % RH, without any significant variation observed when deposited on the ceramic and flexible polyimide substrates. Further, considering its practical applicability, the Ag/C3N4 sensor showed successful detection of alcohol in human breath, highlighting its potential for real-world applications.

Fresh water production via atmospheric water harvesting using solar energy for green hydrogen production

Abstract Solar energy is used to drive an atmospheric water harvesting unit to produce fresh water needed for the electrolysis process to produce hydrogen, as well as to drive the electrolyzer. So, the system can be considered as a solar powered hybrid system for fresh water harvesting and green hydrogen production implementing the electrolysis concept. Implementation of the system specially in arid regions with high levels of water scarcity and high relative humidity levels helps in achieving SDGs 3, 7, 9, and 13 progressively, improving health and wellbeing, reducing water scarcity, using clean renewable energy, and limiting climate change. The system is proposed and investigated for the production of both fresh and clean water and green hydrogen gas. To improve the productivity of the overall system, performance enhancement of the atmospheric water harvesting subsystem is experimentally investigated to assure the reliability and continuous operation of the overall green hydrogen hybrid system. It was investigated in summer and winter using different number of porous sheet metals with the silica gel bed. The results show that the productivity of the atmospheric water harvesting system can be increased by a minimum of 6% with one mesh metal sheet and 30% with two meshes, and increases by a maxinmum of 60% with one mech and 260% with two meches.

Comprehensive numerical and experimental analysis the impact of employing vortex generators on evaporative cooling process

Reduced energy usage is an important concern in vapor compression cooling systems, particularly in hot climates. These systems function poorly in hot weather and consume a lot of electricity. Evaporative condensers use the cooling impact of evaporation to facilitate the process of heat rejection and thereby increase energy efficiency. This work evaluates the impact of employing vortex generators on the performance of an evaporative condenser. The analysis is performed using a validated numerical model, a commercial CFD code. The proposed vortex generators are parallel plates with sinusoidal and zigzag patterns. Dry air enters a channel equipped with four vortex-generating plates and water spray. The incoming dry air cools due to the two-phase heat transfer during air contact with the sprayed water droplets, and the cooled air with the proper humidity level exits the channel. The investigated parameters include longitudinal nozzle position, inlet air flow rate, pitch, and the height of the vortex generator plates in two sinusoidal and zigzag modes and the obtained results are compared with the plain channel model. Greater inlet air velocity resulted in superior output temperatures and pressure drops, yet lower outlet relative humidity and water mass percentage. According to the findings, the air pressure drop and outlet dry temperature are raised by 152 % and 5 %, respectively, when the incoming air velocity is altered from 0.4 to 0.6 m. s−1 (50 % growth), and the nozzle position is moved from X = 1.35 m to X = 1.85 m (37.04 % change) from the channel entrance, while the relative humidity and water mass fraction are decreased by 19 % and 11 %, respectively. Additional research revealed that the vortex flow level is lower when the nozzle is positioned farther away from the plates, and the water spray's impact on lowering the local air temperature in vortex-flowing areas is more substantial when it is positioned closer to the plates. Furthermore, increasing wave height and pitch values of vortex generators result in a more extensive air pressure drop in the channel, and vice versa. This means that, at a fixed pitch, a higher plate wave height raises the amount of vortex flows. According to the findings, sinusoidal vortex plates have better efficiency than the zigzag ones, and in a channel equipped with water spray, the use of vortex plates before the water spray nozzles lowers the dry temperature at the outlet by about one °C compared to that of the plain channel.

Ultrasensitive and durable borophene-based humidity sensors for advanced human-centric applications

Borophene, a novel two-dimensional (2D) nanomaterials with high surface-to-volume ratio and abundant active sites, show great promise for humidity sensing applications, which are crucial for improving human comfort, public health, and device safety. However, traditional fabrication methods, such as drop-coating, face limitations including uncontrollable sensing layer thickness, poor water-damage resistance, and inherent variability between devices. In this work, we present a high-performance humidity sensor fabricated using borophene glass. The sensor demonstrates an impressive detection range (11–97 % RH), exceptional sensitivity (up to 1.83×105% at 97 % RH), low hysteresis (1.245 %), excellent repeatability, and relatively fast response (28.8 s)/recovery (2.6 s) times. Notably, the sensor exhibits thickness-dependent sensing performance, long-term stability, high selectivity, and water-damage resistance. First-principles calculations show that borophene exhibits an adsorption energy of 0.208 eV for H2O molecules, with the hole doping effect following adsorption driving the sensor’s response to varying relative humidity levels. The sensor’s sustained heightened sensitivity under high humidity conditions is attributed to the Grotthuss mechanism. Additionally, the sensor has been successfully integrated into various human-centric applications, including speech recognition, wireless monitoring of respiratory patterns, and non-contact switches. These advancements pave the way for the integration of borophene humidity sensors into smart devices and non-contact control systems, driving innovation in human–computer interaction and healthcare technologies.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

γ- WO3 decorated MXene: An advanced nanomaterial for room temperature operable enhanced ammonia sensor

The proliferation of Internet of Things (IoT) applications necessitates the deployment of high-performance ammonia (NH3) sensors. In recent studies, Ti3C2Tx, a novel two-dimensional (2D) material, has been extensively investigated for its potential as a room temperature NH3 sensor. However, pristine MXene-based sensors have exhibited deficiencies in long-term stability and reproducibility. In response, an MXene/γ-WO3 composite has been synthesized using an ultra-sonication method to address these challenges. The resulting composite sensor has demonstrated remarkable performance, attributable to, increased active sites from defects induced by WO3, and the presence of a p-n heterojunction. Notably, electron transfer from WO3 to Ti3C2Tx has been identified as a critical contributing factor to the enhanced characteristics of these nanohybrids, as evidenced by observed alterations in binding energies for the Ti 2p and W 4f core levels. These findings have expanded our understanding of the electrical interactions in Ti3C2/WO3 and their potential applications in diverse domains. The pristine MXene sensor exhibited a response of 59.39% at 300 ppm at room temperature, while the composite MXene/γ-WO3 displayed a response of 92.3%. Additionally, the response recovery time was recorded at 22seconds and 9seconds, respectively. The chemiresistive ammonia sensor's performance was evaluated across a range of concentrations (25–300 ppm) and relative humidity levels (11–94% RH). The composite sensor has demonstrated reproducibility and long-term stability, coupled with high sensitivity to NH3. This manuscript aims to highlight the enhanced ammonia sensing properties and selectivity of MXene/γ-WO3 composite materials.

Evaporation temperature prediction of the refrigerant-direct convective-radiant cooling system based on LSTM neural network

The existing radiant cooling systems are effective at handling indoor sensible heat load. However, the indoor latent load needs to be treated with an additional dehumidification system, which will increase the complexity and initial investment of the system. To settle this dilemma, a novel aluminum column-wing type refrigerant-direct convective-radiant cooling (ACT-RCC) system was proposed, which adopted refrigerant as direct medium and provided cooling and independent dehumidification simultaneously. Experiments were conducted in an enthalpy difference laboratory with the indoor air temperature of 22.0 °C − 30.0 °C and relative humidity of 60.0%. A numerical model of the ACT-RCC terminal was established and validated with the experimental data. To meet the indoor heat and latent load of residential buildings, a heuristic approach based on Long Short-Term Memory (LSTM) neural network for predicting the evaporation temperature of this system was proposed. Results showed that the predicted values were accurate enough by measured data and the maximum deviation of the evaporation temperature was 0.7 °C. The relationship between the evaporation temperature, indoor air temperature and relative humidity, and heat moisture ratio was derived. Besides, the indoor thermal comfort of the selected bedroom was also evaluated. Results showed that when the indoor air temperature and relative humidity remained 26.0 °C and 40.0% − 70.0%, the thermal comfort of this bedroom can achieve Level Ⅰ by adjusting the evaporation temperature of the ACT-RCC system of 2.0 °C − 19.8 °C.

DrYFiT: An Excel Freeware Tool to Describe Thin Layer Drying of Foods

ABSTRACTWe introduced DrYFiT (drying data fitting tool), a Microsoft Excel freeware tool to be used for modeling thin‐layer drying of foods, which is available at https://drive.google.com/drive/folders/1ouompmNkmdmw1KMTJUnY0t8dJqSJ9iKv?usp=drive_link. There are 12 models in the tool and it can be used without any modeling and programming skills. Time and moisture ratio data can be entered and one of models available (one at a time) can be selected to describe the drying data. Parameter values, standard error of the parameters, p value and a statement that indicates whether the parameter is statistically significant or not (α = 0.05) are reported. Moreover, R2, adjusted R2 and root mean square error values are calculated for each model. Users can instantaneously observe the experimental data and the model fit on the same graph. Residual plot is given next to this graph. It is possible for the users to have the results of all models applied to drying data within a couple of minutes. The results of DrYFiT were compared with some popular software programs used for nonlinear regression and identical values (parameters, standard errors, p values, goodness‐of‐fit statistics) were obtained for 40 datasets.

Experimental analysis and development of novel drying kinetics model for drying grapes in a double slope solar dryer

This experimental study aimed to evaluate the efficacy of natural and forced convective drying techniques in reducing the moisture content of the grapes in comparison with the conventional open sun drying method. Moisture effective diffusivity and activation energy were graphically determined using the Arrhenius equation. In the initial three-day period, forced convection drying significantly reduced moisture from 2000 g to 353 g, with a minimum loss of 14 g. In the next three days, natural convection reduced moisture from 2787 g to 468 g, with a minimum loss of 11 g. These outcomes were then compared to the results of open-sun drying. The investigation showed that open drying and natural convection methods eliminated 25.05 % and 82.35 % of the moisture content, respectively. However, for three days, open-sun drying removed 30.5 % of grape moisture, while forced convection achieved an impressive 83.21 % reduction. The study's innovative mathematical model explained drying curve characteristics, supported by correlation coefficients and parity plots. The comparison shows that the experimental moisture ratios and those predicted by the new correlation exhibit R2 values ranging from 0.984 to 0.994.

Models for the Inactivation of Foodborne Pathogens in Salad Dressing from Challenge Studies

The Association for Dressings and Sauces’ (ADS) members have conducted challenge studies on salad dressing products to assess pathogen survival. Data from 79 different challenge studies provided by ADS were used in this analysis. The acid-moisture ratio, pH, incubation temperature and ingredient details were provided for each study. Linear regression models were used to predict the time to 3-log, 4-log, and 5-log reduction as a function of study parameters. A statistically based approach also was used to estimate the concentration of pathogens in ingredients based on testing history. This was combined with decline modeling to estimate pathogen concentration over time. The time to five log reduction for each of the target pathogens were highly skewed. A logarithmic transformation of time to 5 log reduction resulted in approximately normally distributions. Incubation temperature and formulation pH were highly significant (p < 1E-6), in predicting the number of days to a five-log reduction of Escherichia coli O157:H7, while the percentage of spices in the formulation is also quite significant (p = 0.01). Salmonella modeling showed that the most highly significant parameter was the percentage of water (p < 1E-8). Other parameters in order of descending significance include the percent fruit (p=0.00032), incubation temperature (p=0.00268), followed by percent sugar (p=0.02161) and percent vegetables (p=0.03149). The most significant parameter in predicting Listeria monocytogenes reduction was incubation temperature (p=0.000687), followed by acid moisture ratio (p=0.012423). The next two significant parameters in the Listeria model were percent lipid (p=0.023772) and percent water (p=0.025701). The least significant parameter that meets the minimum criteria for inclusion in the Listeria model (p<0.05) was percent fruit (p=0.047074). Our analysis will be useful in developing risk-based approaches to continue to assure the safety of commercially prepared salad dressings.

Response of Native and Non-Native Subarctic Plant Species to Continuous Illumination by Natural and Artificial Light.

This study addressed the following questions: How does continuous lighting (CL) impact plant physiology, and photosynthetic and stress responses? Does the impact of CL depend on the source of the light and other environmental factors (natural vs. artificial)? Do responses to CL differ for native and non-native plant species in the subarctic region and, if differences exist, what physiological reasons might they be associated with them? Experiments were conducted with three plants native to the subarctic region (Geranium sylvaticum L., Geum rivale L., Potentilla erecta (L.) Raeusch.) and three non-native plant species (Geranium himalayense Klotzsch, Geum coccineum Sibth. and Sm., Potentilla atrosanguinea Loddiges ex D. Don) introduced in the Polar-Alpine Botanic Garden (KPABG, 67°38' N). The experimental groups included three species pairs exposed to (1) a natural 16 h photoperiod, (2) natural CL, (3) an artificial 16 h photoperiod and (4) artificial CL. In the natural environment, measurements of physiological and biochemical parameters were carried out at the peak of the polar day (at the end of June), when the plants were illuminated continuously, and in the second week of August, when the day length was about 16 h. Th experiments with artificial lighting were conducted in climate chambers where plants were exposed to 16 h or 24 h photoperiods for two weeks. Other parameters (light intensity, spectrum composition, temperature and air humidity) were held constant. The obtained results have shown that plants lack specific mechanisms of tolerance to CL. The protective responses are non-specific and induced by developing photo-oxidative stress. In climate chambers, under constant environmental conditions artificial CL causes leaf injuries due to oxidative stress, the main cause of which is circadian asynchrony. In nature, plants are not photodamaged during the polar day, as endogenous rhythms are maintained due to daily fluctuations of several environmental factors (light intensity, spectral distribution, temperature and air humidity). The obtained data show that among possible non-specific protective mechanisms, plants use flavonoids to neutralize the excess ROS generated under CL. In local subarctic plants, their photoprotective role is significantly higher than in non-native introduced plant species.

ВЛИЯНИЕ СОВРЕМЕННОГО ИЗМЕНЕНИЯ КЛИМАТА НА ИСПАРЕНИЕ С ВОДНОЙ ПОВЕРХНОСТИ В ИЛЕ-БАЛКАШСКОМ БАССЕЙНЕ

Using the Ile-Balkash basin as an example, changes in evaporation from the water surface over the last 40 years, as well as changes in the main meteorological elements that affect evaporation, are considered. Within the framework of this work, the changes in evaporation from the water surface for 1996-2020 in relation to the previous period of 1980-1995 were assessed. It was established that over the last 20 years, there was a slight increase in evaporation at many lowland stations of the Ile-Balkash basin and it is about 1-10 %. In mountainous areas and near large water areas, such as Balkash and Ulken Almaty Lakes, there is a decrease in evaporation by about 2-9 %. The main factors influencing the change in evaporation values from the water surface were analyzed. It was concluded that the increase in evaporation from the water surface at the lowland stations in the Ile-Balkash basin is mainly due to the increase in air temperature and some increase in precipitation, and the decrease in evaporation in mountainous areas and near a water area is caused by the decrease of average wind speed and an insignificant increase in air humidity. This study can help support decision making in the agriculture and water resources sector, since evaporation from the water surface is involved in many hydrological, climatic and hydrodynamic models. Therefore, the obtained results can be used in many scientific calculations.

PENERAPAN REGRESI LINEAR UNTUK PREDIKSI SUHU BADAN SAPI MENGGUNAKAN DATA SMART TERNAK DARI PT TELKOM INDONESIA: STUDI KASUS DI DESA PENGENGAT

Abstract West Nusa Tenggara (NTB) is one of the regions in Indonesia with significant potential in the field of livestock farming. In the context of the agricultural industry, livestock is considered a valuable asset, and monitoring their health is crucial for improving livestock productivity. This research aims to determine the relationship between the body temperature of cattle (Y) and environmental factors, namely air humidity (), environmental temperature (), and barometric pressure (), using smart livestock data provided by PT Telkom. The study was conducted in Pengengat Village, NTB, involving correlation and regression analysis. The analysis results indicate a significant positive correlation between the body temperature of cattle and environmental temperature () and a negative correlation with humidity (). Additionally, there is a strong positive correlation between the body temperature of cattle and barometric pressure (). The multiple linear regression equation obtained is as follows: = –26.816842418191094 + -0,0014707 + 0.39772325 + 0.04927779. Model testing shows excellent prediction quality with low Mean Squared Error (MSE) values (0.3849 for training and 0.4623 for testing). Furthermore, the high R-squared (R^2) values (0.7283 for training and 0.7110 for testing) indicate that the linear regression model can predict the body temperature of cattle based on the analyzed environmental factors. Keywords: Cattle Body Temperature, Smart Livestock Data, Prediction, Machine Learning, Linear Regression

Integrated Iot Approaches for Crop Recommendation and Yield-Prediction Using Machine-Learning

In this study, we present an integrated approach utilizing IoT data and machine learning models to enhance precision agriculture. We collected an extensive IoT secondary dataset from an online data repository, including environmental parameters such as temperature, humidity, and soil nutrient levels, from various sensors deployed in agricultural fields. This dataset, consisting of over 1 million data points, provided comprehensive insights into the environmental conditions affecting crop yield. The data were preprocessed and used to develop predictive models for crop yield and recommendations. Our evaluation shows that the LightGBM, Decision Tree, and Random Forest classifiers achieved high accuracy scores of 98.90%, 98.48%, and 99.31%, respectively. The IoT data collection enabled real-time monitoring and accurate data input, significantly improving the models’ performance. These findings demonstrate the potential of combining IoT and machine learning to optimize resource use and improve crop management in smart farming. Future work will focus on expanding the dataset to include more diverse environmental factors and exploring the integration of advanced deep learning techniques for even more accurate predictions.

ANALISIS GEOGRAPHIC INFORMATION SYSTEM FAKTOR RISIKO BAYI BERAT BADAN LAHIR RENDAH (BBLR) PADA IBU HAMIL DENGAN MALARIA: STUDI KASUS

Papua has the highest rate of malaria cases among all provinces in Indonesia. Malaria is a scourge, especially among vulnerable groups, including pregnant women. Pregnant women with malaria increase the risk of giving birth to Low Birth Weight (LBW). This study aimed to analyze geographic information system risk factors for LBW in pregnant women with malaria. This research method was observational analytics using a matched case-control design. The study encompassed pregnant women residing in the Sentani health center working area, who were both afflicted with and unaffected by malaria, and sought outpatient care at the health center. With a population of 104 pregnant woman, and the sample used was the Total sampling technique. This study found that the distribution of LBW cases tends to increase if there are many malaria incidents in the area. The distribution pattern of malaria cases shows a tendency to gather in areas with high rainfall, areas with low air humidity and areas with high air temperatures, namely Nolokla and Asei Villages. To maintain the health of mothers and babies and avoid possible risks, it is necessary to increase supervision and vigilance in the months where the intensity of rain increases, namely from November to March, where rainfall is also related to humidity and temperature so that it affects the sustainability of mosquito larvae habitat

Monitoring of stable isotope composition of precipitation reveals thunderstorm dynamics

ABSTRACT The summer of 2019 is particularly well known for the famous heatwaves that swept across the European continent, with its associated drought and record-breaking air temperatures. This was followed by powerful thunderstorms, characterised by hail and heavy rain that damaged the crops on a regional scale. Here, we investigated one of the largest storm cells, lasting more than 6 h, which struck southwestern Romania. High-temporal resolution sampling of storm precipitation was performed for stable isotope measurements, rainfall and air temperature, to follow the storm dynamics. Hydrogen and oxygen isotope measurements show an abrupt decreasing temporal trend followed by superimposed V-shaped patterns interpreted as reflecting moisture replenishment by successive rain bands. To model the stable isotope values of precipitation in relation to the general trend of decreasing air temperatures, we applied a numerical Rayleigh condensation model for a non-constant α isotopic fractionation factor between liquid water and water vapour. The storm is powered by four consecutive moisture fronts, each following a Rayleigh distribution. About 40 % of the water vapour condenses during the sampled storm due to adiabatic expansion and cooling, which lowers saturation. Condensation ceases when cooling and absolute humidity can no longer sustain the dew point, stopping the rain. The timing of the event, occurring late at night and early in the morning, its duration of over 6 h as well as its synoptic scale may indicate a mesoscale convective complex.