Margin Of Error Research Articles

Fault Detection Technology for Ultrapure Water Plant using Virtual Sensors for Pressure Control Valve and Indicators in Return Line

Objectives:This study aims to propose a technology that utilizes virtual sensors to quickly and accurately diagnose faults in valves and measurement devices that may occur in the return line of an ultrapure water plant.Methods:The empirical equations were derived, and the error range was calculated using operational data collected from three pieces of equipment (pressure control valve, pressure transmitter, and flow transmitter) in the recovery system of the ultrapure water process. Based on this, a virtual sensor is established.Results and Discussion:It was confirmed that the utilization of 24-hour data collected from the operation of the ultrapure water plant was enough to establish virtual sensors and could also estimate monthly data with high accuracy. In Phase 1, the fault diagnosis technology was found to estimate values more accurately and had a meaningful margin of error compared to Phase 2.Conclusion:It was possible to develop a fault detection technology using the virtual sensor derived from the empirical equation. The technology showed high performance in detecting anomalies in the flow meter and pressure control valve opening but had limitations in estimating consistently pressure values. Additionally, considering that there was no issue in estimating monthly data using the equations derived using 24 hours operational data. The fault detection algorithm could be used continuously if the empirical equations are updated by collecting a day's data when operating conditions change.

HUBUNGAN MODAL SOSIAL PETANI TERHADAP KEBERLANJUTAN USAHATANI JAGUNG KUNING DI DESA WAKADIA KECAMATAN WATOPUTE KABUPATEN MUNA

The objective of this study is to ascertain the correlation between farmers' social capital and the sustainability of yellow corn farming in Wakadia Village, Watopute Subdistrict, Muna Regency. The study population consisted of all yellow corn farmers in Wakadia Village, Watopute Subdistrict, Muna Regency, comprising a total of 103 households. The number of samples was determined using the Slovin formula with a 10% error margin, resulting in a sample size of 51 respondents. The sampling technique employed was simple random sampling. The data collection techniques employed included surveys, interviews, and document analysis, with data collected using questionnaires. This study employs two variables: social capital, comprising trust, participation, networks, and social norms, and farm sustainability, encompassing economic, social, and environmental sustainability. The data were subjected to descriptive analysis using a class interval formula and a Rank Spearman correlation formula. The findings indicate that the social capital of farmers engaged in yellow corn farming in Wakadia Village, Watopute Subdistrict, Muna Regency is in an optimal category, suggesting that their social capital is capable of sustaining yellow corn farming over the long term. The sustainability of yellow corn farming in Wakadia Village is sufficient, indicating that farmers have implemented business sustainability indicators but still require improvement. There is a significant and positive relationship between farmers' social capital and yellow maize business sustainability in Wakadia Village.

METODE PENYULUHAN KELOMPOK DALAM BUDIDAYA PADI SAWAH DI DESA LEBO JAYA KECAMATAN KONDA KABUPATEN KONAWE SELATAN

The implementation of extension activities is confronted with a multitude of challenges and difficulties. It is therefore imperative for extension workers to possess a comprehensive understanding of the most appropriate and productive extension methods in the context of agricultural extension activities. The objective of this study was to ascertain the efficacy of group extension methods in paddy rice cultivation and to elucidate the relationship between the two. The study population consisted of all rice paddy farmers in Lebo Jaya Village, Konda Subdistrict, with a total of 104 farmers. The sample size was determined using the Slovin formula, with a degree of error or margin of error of 15%, resulting in a research sample of 31 respondents. The sampling technique employed was simple random sampling. The data were collected via survey, interview, and document analysis, using various media in the form of questionnaires. The research employed a quantitative approach. The research variables were twofold, comprising group extension methods and paddy rice cultivation activities. The data were subjected to quantitative descriptive analysis, employing the class interval formula and the Spearman rank correlation equation. The findings indicated that the implementation of group extension methods in paddy rice cultivation in Lebo Jaya Village was classified as "good." The aforementioned activities, which pertain to paddy rice cultivation specifically, land preparation, seed selection, irrigation, fertilization, pest and disease control have been classified as "good." The level of application of group counseling methods is found to be significantly and positively related to the cultivation of wet rice in Lebo Jaya Village. The correlation value is 0.542, and the significance of 0.002 is less than 0.05. The strength of the relationship between group extension methods and paddy rice cultivation activities is moderately strong. This indicates that an improvement in the method of agricultural counseling will result in an improvement in rice paddy farming activities in Lebo Jaya Village, Konda Subdistrict, South Konawe District.

The health profile of Lithuanian beekeepers and use of bee products for health purposes

Abstract Background There is little research about the health profile and attitude of beekeepers themselves who are the main suppliers of apitherapy products to the general public. The study aims to describe the health profile of Lithuanian bee keepers and identify their attitudes towards use of bee products for health purposes. Methods A cross-sectional survey was carried out from November 2023 to February 2024 among beekeepers in Lithuania. A representative sample of N = 369 (5% margin of error) has been enrolled in the study. A mixed-model approach was used for data collection. Research instrument consisted of a two-part questionnaire covering socio-demographic variables and questions on the use of bee products. Results 369 respondents participated in the study: 73% (n = 270) males and 27% (n = 99) of females. The mean age was 57.8 years (SD = 14.4). Beekeepers assessed their health as good and only 2% assessed it as bad, while the Lithuanian rate is 12%. 95% of the respondents used bee products for health purposes, 5% did not use at all. Large beekeepers (>100 beehives) were more likely not to choose bee products (p < 0.001). No correlation was found between chronic diseases and consumption of bee products, except for diabetes mellitus (p = 0.001). The most common reasons for choosing apitherapy products included respiratory (62%), digestive (31%) diseases, immunity boosting (77%), dermatological conditions (53%). Propolis was pointed out in the survey as being used by 74%. Elder beekeepers (>65 years) were found to be more likely to use propolis (p < 0.001). They were also more likely to use it for prevention (p < 0.001) and in case of toothache (p = 0.001). Conclusions The majority of beekeepers use bee products for health purposes. The most common reasons for consuming honey and other bee products are immunity boosting and colds. It was found that beekeepers over 65 years of age are more likely to use propolis for prevention and toothache treatment. Key messages • Although the consumption of bee products is high among beekeepers, elder generation and owners of smaller apiaries are more likely to use bee products for health purposes. • Chronic diseases are not associated with consumption of bee products, except for beekeepers with diabetes mellitus who do not consume it.

A Non-Contacted Height Measurement Method in Two-Dimensional Space

Height is an important health parameter employed across domains, including healthcare, aesthetics, and athletics. Numerous non-contact methods for height measurement exist; however, most are limited to assessing height in an upright posture. This study presents a non-contact approach for measuring human height in 2D space across different postures. The proposed method utilizes computer vision techniques, specifically the MediaPipe library and the YOLOv8 model, to analyze images captured with a smartphone camera. The MediaPipe library identifies and marks joint points on the human body, while the YOLOv8 model facilitates the localization of these points. To determine the actual height of an individual, a multivariate linear regression model was trained using the ratios of distances between the identified joint points. Data from 166 subjects across four distinct postures: standing upright, rotated 45 degrees, rotated 90 degrees, and kneeling were used to train and validate the model. Results indicate that the proposed method yields height measurements with a minimal error margin of approximately 1.2%. Future research will extend this approach to accommodate additional positions, such as lying down, cross-legged, and bent-legged. Furthermore, the method will be improved to account for various distances and angles of capture, thereby enhancing the flexibility and accuracy of height measurement in diverse contexts.

Kalibrasi Wire Feeder Flux Core Arc Welding (FCAW) Mengacu Pada Standar British Standard (BS) 7570:2000 Dan British Standard (BS) European Standard (EN) 50504:2008

In the fabrication industry, the welding process plays a crucial role in both engineering and repair production processes. Continuous use of welding machines can lead to a decline in their effectiveness. Suboptimal machines may produce low-quality products. A common issue encountered is the poor quality of welds, even when each step has been aligned with the Welding Procedure Specification (WPS). One source of this problem is the welding equipment itself. The objective of this study is to calibrate arc welding equipment to ensure it remains within machine standards. Calibration is performed in accordance with BS EN 50504:2008 and BS 7570:2000. The calibration focuses on measuring constant voltage and wire feed speed (WFS). The measurement results for WFS variation indicate that it does not exceed a 10% error margin. This suggests that the WFS complies with the established standards. Consequently, the Lincoln LN-25X wire feeder speed is ready for use in fabrication and production processes.

Numerical Modeling of Tsunamis Generated by Subaerial, Partially Submerged, and Submarine Landslides

Using the existing two-dimensional experimental data and Open-source Fields Operation and Manipulation (OpenFOAM) software, this study performs a comprehensive comparative analysis of three types of landslide-generated tsunamis (subaerial, partially submerged, and submarine). The primary objective was to assess whether numerical simulations can accurately reproduce the experimental results of each type and to compare the predictive equations of the tsunami amplitudes derived from experimental and simulated data. The mesh size and dynamic viscosity parameters were initially optimized for a specific partially submerged landslide tsunami scenario and then applied across a broader range of experimental scenarios. Most of the simulated wave amplitudes remained within the 50% error margin, although significant discrepancies were observed between landslide types. When focusing on the crest amplitude of the first wave, the simulations of subaerial landslides least deviated from the experimental data, with a mean absolute percentage error of approximately 20%, versus approximately 40% for the partially submerged and submarine landslides. The predictive equations derived from the simulations closely matched those from the experimental data, confirming that OpenFOAM can effectively capture complex landslide–tsunami dynamics. Nonetheless, variations in the coefficients related to slope angles highlight the need for further calibration to enhance the simulation fidelity.

Integrated substrate and adsorbent layer additive manufacturing for asymmetrically operating minichannel adsorbent bed

Adsorption heat pumps (AHP) have conventionally used packed bed design, making their operation sluggish leading to difficulty in using heat for the adsorbent regeneration. While thin adsorbent coatings improve the performance of AHPs when implemented in microscale geometries like minichannels, integrating the adsorbent coating procedure into the scaled-up adsorbent bed manufacturing has been impractical because of the finite time required for the coating process. Meanwhile, separately coated plates manifest sealing issues. This research details an innovative avenue to fabricate a unified adsorbent bed integrated with thin adsorbent coatings. Here, a compact adsorbent bed was fabricated through the integration of additive manufacturing using a fused deposition modeling (FDM) 3D printer for the substrate and resin curing for the MIL-101 (Cr) adsorbent layer. The multi-layered bed was designed to exchange heat between heat transfer fluid (HTF) and the passages through which the refrigerant vapor flows. The vapor channels were coated with a MIL-101 (Cr) adsorbent. The research incorporated both experiments and computational models to evaluate the operation of this adsorbent bed through the measurement of pressure during the adsorption and desorption stages for this component. Various HTF temperatures (80 °C − 90 °C) and flow rates (0.125 kg min−1 – 0.25 kg min -1) were tested. The findings revealed the much-desired asymmetrical operation with long adsorption (75–80 % of the cycle time) and short desorption stages (20 %-25 % of the cycle time), opening the possibility of employing a single bed in adsorption cooling systems to produce a near continuous cooling effect leading to more compact AHPs. The computational model developed for this test setup using MATLAB strongly aligned with experimental findings, with an error margin of 4–12 %.

Optimizing neural network models for predicting nuclear reactor channel temperature: A study on hyperparameter tuning and performance analysis

This study emphasizes how important accurate prediction of channel temperatures in nuclear reactors is for safety and operational efficiency. While traditional methods require long and complex processes such as kernel modeling and mathematical simulations, artificial neural networks (ANN) provide more efficient predictions by accelerating this process. The superior ability of ANNs to process large data sets is intended to demonstrate that this study will provide a valuable alternative compared to conventional methods and increase the accuracy of reactor temperature predictions. In this study, the training performances of Artificial Neural Network (ANN) developed to determine the nuclear reactor channel temperature with different hyperparameter combinations were analysed. It was conducted several experimental studies to assess the influence of hyperparameters on our model for nuclear reactor parameter data prediction. The training and validation results indicates that learning rate, hidden layer sizes and number have critical effects for the more precisive prediction. It was observed that models with a learning rate of 0.05 and 0.5 achieved successful learning with less fluctuation in training and validation errors. When looking at hidden layer sizes, networks with 32 and 64 neurons performed better than networks with 16 neurons. For the test phase our model can successfully predict data with slight error margin. As a result, we demonstrated that neural networks are a powerful tool in nuclear reactor channel temperature prediction through our proposed model.

Determining an Applicable Sample Size in Research Studies

Determining a proper sample size is a critical aspect of research design as it influences the reliability and validity of study findings. The key objective of writing this article is to suggest some rudimentary formulae, easy software tools and a general sample size table to determine an applicable sample size to be used in research studies. To attain this objective, relevant books and journal articles have been taken as the sources of secondary data. After a comprehensive overview of the literature regarding the sample size, this article concludes that the confidence level, the margin of error, the population size, and the population proportion statistically determine the sample size in quantitative research studies. Similarly, the nature of population, and types of research studies are the vital factors that confer us the basic idea about the sample size before computing the ideal or required sample size that homogeneous population and qualitative research studies generally require a smaller sample size, whereas heterogeneous population and quantitative research studies necessitate a larger sample size. Researchers, practitioners, and students will find this article valuable in guiding their decisions on sample size determination for robust and meaningful research outcomes.

Development of a 6-degrees-of-freedom measurement system using a robotic total station for a free-running model test

The measurement of a model ship's six degrees of freedom (6-DOF) using sensors such as Marvelmind beacons and robotic total stations (RTS) has previously demonstrated substantial error margins. To address these limitations, this study introduces a refined approach for accurate and reliable 6-DOF measurement through free-running model tests (FRMT). The tests were meticulously designed, with three RTS strategically positioned to meet the specific requirements of the measurement scenario. Given the potential constraints in an FRMT environment, we proposed feasible installation ranges for the RTS and prisms, ensuring the stable measurement of the model ship's 6-DOF. To achieve high-precision motion measurement, even in complex environments, we employed the prism queue algorithm, which identifies and processes overlapping or untracked prisms monitored by the RTS. The proposed methodology was validated through simulations and FRMT conducted in the ocean engineering basin of the Korea Research Institute of Ships and Ocean Engineering and by developing an actual implementation program. Comparative analysis using high-precision navigation equipment, such as fiber optic gyros, demonstrated that the method provided unbiased and stable data. Consequently, the proposed method shows significant promise as a viable alternative for precision navigation in real-world applications.

A hybrid predictive modeling approach for catalyzed polymerization reactors

Polymerization reactions are characterized by complex, nonlinear behaviors that pose significant challenges for conventional modeling techniques. Accurate and reliable models are crucial for advancing material science and enabling technological innovations across various industries. Conventional first-principles models often fall short in capturing the intricate dynamics of polymeric systems, leading to limitations in predictive accuracy. In this work, we propose a novel hybrid modeling approach that combines a conventional first-principles model with the strengths of a data-driven multi-layer perceptron (MLP) model and also a linear regression (LR) model to enhance the predictability of polymerization processes. Utilizing this hybrid approach significantly reduces the mean absolute error for predicting the concentrations of main reagents by 84% and 86%, respectively, in experiments with significantly deviant outcomes. Our results indicate that the model is capable of predicting the concentrations of both the main and side products with a maximum error margin of 3.5%.

Study on the coupled hydro-mechanical model of gas-induced dilation effects in bentonite

IntroductionGas migration in low-permeability buffer materials is a crucial aspect of nuclear waste disposal. This study focuses on Gaomiaozi bentonite to investigate its behavior under various conditions.MethodsWe developed a coupled hydro-mechanical model that incorporates damage mechanisms in bentonite under flexible boundary conditions. Utilizing the elastic theory of porous media, gas pressure was integrated into the soil's constitutive equation. The model accounted for damage effects on the elastic modulus and permeability, with damage variables defined by the Galileo and Coulomb–Mohr criteria. We conducted numerical simulations of the seepage and stress fields using COMSOL and MATLAB. Gas breakthrough tests were also performed on bentonite samples under controlled conditions.ResultsThe permeability obtained from gas breakthrough tests and numerical simulations was within a 10% error margin. The experimentally measured gas breakthrough pressure aligned closely with the predicted values, validating the model's applicability.DiscussionAnalysis revealed that increased dry density under flexible boundaries reduced the damage area and influenced gas breakthrough pressure. Specifically, at dry densities of 1.4 g/cm³, 1.6 g/cm³, and 1.7 g/cm³, the corresponding gas breakthrough pressures were 5.0 MPa, 6.0 MPa, and 6.5 MPa, respectively. At a dry density of 1.8 g/cm³ and an injection pressure of 10.0 MPa, no continuous seepage channels formed, indicating no gas breakthrough. This phenomenon is attributed to the greater tensile and compressive strengths associated with higher dry densities, which render the material less susceptible to damage from external forces.

КОПІНГ-СТРАТЕГІЇ В КОНТЕКСТІ КРИЗОВОГО УПРАВЛІННЯ

The ongoing humanitarian crisis in Ukraine, intensified by the conflict that began in February 2022, has resulted in 14.7 million individuals requiring humanitarian aid by 2024. This crisis has led to significant socioeconomic challenges, with households adopting various coping strategies to manage economic stress. Understanding these strategies and their regional variations is crucial for effective crisis response and resource allocation. The purpose of the article. To analyze and present findings on the coping strategies adopted by Ukrainian households in response to the ongoing humanitarian crisis and conflict, with a particular focus on regional differences and the most severely affected areas. The object of the research. Household coping strategies across different regions of Ukraine, including selling assets, spending savings, purchasing food on credit, seeking additional employment, reducing health and education expenditures, and using degrading income sources. The methods of the research. The study employed a quantitative data collection methodology developed by REACH, utilizing both face-to-face household surveys and computer-assisted telephone interviews. The dataset encompassed 10,434 interviews conducted at the household level across 23 oblasts, targeting a 95% confidence level and an 8% margin of error. The hypothesis of the research. The research appears to hypothesize that there are significant regional variations in the adoption of coping strategies, with areas closer to the conflict zone experiencing more severe economic stress and resorting to more extreme coping measures. The statement of basic materials. The research identified ten distinct coping strategies and analyzed their adoption rates across different regions of Ukraine. Eastern and southern regions, particularly Donetska, Kharkivska, Zaporizka, Khersonska, and Sumska oblasts, consistently demonstrated higher adoption rates of various coping strategies. Donetska oblast emerged as the epicenter of economic stress, with 50% of households reporting the sale of assets or spending of savings. Zaporizka oblast led in purchasing food on credit (18.63%) and reducing health expenditures (55.28%). Significant regional disparities were observed, with some oblasts showing coping strategy adoption rates far exceeding national averages. The originality and practical significance of the research. This study provides a comprehensive, region-specific analysis of household coping strategies in Ukraine during an ongoing crisis, offering valuable insights for targeted interventions. The research's practical significance lies in its potential applications for socio-economic management, including informing the design of targeted social safety nets and economic support programs, guiding efficient resource allocation based on region-specific needs, and supporting the development of tailored healthcare and education policies. Conclusions and prospects for further research. It provides a baseline for monitoring and evaluating the effectiveness of future interventions and informs long-term regional development strategies and crisis response planning. By offering a nuanced understanding of how different regions are coping with the crisis, this research enables policymakers to develop more effective, localized approaches to addressing Ukraine's complex humanitarian challenges.

Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Developing biomimetic PVA/PAA hydrogels with cellulose nanocrystals inspired by tree frog structures for superior wearable sensor functionality

This study developed a wearable sensor device featuring biomimetic structures inspired by tree frog toe pads to measure shoulder joint movements effectively. A PVA/PAA double-network hydrogel was formulated by mixing PVA and PAA in a 3:7 ratio, resulting in a Young's modulus of 7.2 kPa, closely matching human skin's mechanical properties. To enhance moisture retention, the hydrogel was supplemented with 20 mL glycerin, increasing its weight retention rate to 95 % after 28 days, a 5.6-fold improvement over glycerin-free hydrogels. Additionally, the incorporation of 0.2 wt% cellulose nanocrystals (CNC) increased the cross-linking density, reducing the swelling ratio from 124 % to 106 % and minimizing the impact of swelling cycles on mechanical properties from 22 % to 4 %. Treefrog-inspired microstructures were fabricated on the sensor surface using laser cutting and casting techniques, significantly enhancing adhesion under humid conditions, with normal, lateral, and peel-off adhesion forces improving by 500, 700, and 700 %, respectively. The sensor demonstrated accurate measurement of shoulder joint movements with an error margin of 6.3 % for polar angles (45°-135°) and 7.8 % for azimuth angles (0°-45°). The accompanying smartphone application provided real-time feedback, helping to prevent exercise injuries and improve user awareness and control of shoulder movements, making it suitable for both rehabilitation and athletic training.

Detection of body shape changes in obesity monitoring using image processing techniques

Body measurements are primarily made with a tape measure. In measurements taken with a tape measure, the inability to take measurements from the same part of the body each time, incorrect positioning of the tape measure, the occurrence of incorrect measurements, and the need for a person to take the measurements are significant problems in the traditional measurement method. Due to the social distancing rule that must be followed during the Covid-19 pandemic, the close contact between the person to be measured and the person taking the measurement became the starting point of this study. This study focuses on the detecting body shape changes using image processing techniques with 2D imaging. The novelty of the work is that non-contact body measurements are taken more accurately and reliably using the cosine theorem. Regular monitoring of obese patients is important in combating obesity, which is also the source of many health problems. In the monitoring of obese patients, it is necessary to determine the rate of slimming in areas where fat accumulation is intense. The error margin between the real measurements of human models and the calculated measurements was calculated as an average of ± 5.16% for waistline and an average of ± 4.58% for hip size. The cosine theorem was used instead of the ellipse formula used in the literature, and it was observed that the cosine theorem obtained results closer to reality. It is also thought that the developed system will be beneficial not only for extracting body measurements but also for extracting body measurements contactless in the textile sector. The study demonstrates the feasibility of image processing for non-contact body anthropometry and shape tracking.

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

Pharmacists' Knowledge, Perception, and Prescribing Practice of Probiotics in the UAE: A Cross-Sectional Study.

The human body is a complex and interconnected system where trillions of microorganisms, collectively known as the gut microbiota, coexist with these cells. Besides maintaining digestive health, this relationship also impacts well-being, including immune function, metabolism, and mental health. As frontline healthcare providers, pharmacists are pivotal in promoting the benefits of probiotics for immune support. This study explored pharmacists' knowledge, perception, and practice behavior in the UAE towards the implication of probiotic application beyond digestive health, such as cardiovascular and mental health impacts and their diverse dosage forms. An online self-administered survey was distributed among pharmacists in the UAE. Data were collected through personal visits to pharmacies, where pharmacists were approached and asked to complete the questionnaire. The sample size included 407 pharmacists, determined using the formula for proportions with a 95% confidence level and a 5% margin of error. Statistical analysis was performed using SPSS version 29. Descriptive statistics were used to summarize demographic characteristics and survey responses. The knowledge levels were categorized into poor, moderate, and good. Chi-square analysis was employed to investigate associations between demographic factors and knowledge levels, with a significance level set at p < 0.05, enhancing the robustness of the study's findings. This study included 407 completed eligible responses. About 63.56% of participants were female, with 52.1% employed in pharmacy chains. While 91.2% of pharmacists recognized probiotics' role in immune support, only 30% were aware of their cardiovascular benefits. Moreover, chewing gum was the least known dosage form of probiotics, recognized by only 16.7% of respondents. Additionally, only 57% of the participants recognized liposomes as a dosage form. In practice, most pharmacists recommended storing probiotics at room temperature, accounting for 66.6%. The most prevalent misconception encountered in the pharmacy setting was the belief that probiotics are primarily intended for gastrointestinal tract problems, at 79.1% of the respondents. Regarding perception, the agreement was observed regarding the safety of probiotics for all ages. Perceived barriers included the high cost of probiotics, with the majority (86.5%) indicating this as a significant obstacle, while lack of demand was identified as the minor barrier by 64.6%. Additionally, an association was found at a significance level of p < 0.05 with knowledge, gender, educational level, type and location of pharmacy, and source of information. The study highlights knowledge gaps in pharmacists' understanding of probiotic applications beyond digestive health, particularly cardiovascular health and depression. Targeted educational interventions are necessary to address these gaps. The findings underscore the importance of ongoing professional development for pharmacists, enhancing their role in patient education and the promotion of probiotics for overall health.

Investigation of heat leakage calculations and experiments for a wide-mouth liquid nitrogen tank

ABSTRACT With the continuous development of biomedicine, the long-term low-temperature storage of biological samples has become essential. In order to meet the demand for long-term storage of liquid nitrogen to keep samples safe, a study on the evaporated gas recycling storage of liquid nitrogen tanks will be carried out. This research utilizes new approach to accurately calculate the heat leakage of the liquid nitrogen tank. The experimental results show that the theoretical heat leakage is 6.16 W, and the actual heat leakage is 7.07 W. By incorporating the insulating plug, the actual heat leakage was reduced by 61.2%, decreasing to 2.74 W, and the calculated heat leakage is 2.47 W. The error margin in both cases was approximately 10%. Furthermore, in contrast to existing investigations, this experiment with the 8W@77K pulse tube cryocooler for recovering nitrogen is conducted under environmental pressure rather than overpressure, and it also takes into account the sensible heat issues caused by ambient pressure. The ratio of 1.81 W of sensible heat to 2.74 W of latent heat is 2:3, indicating that under ambient pressure conditions, the impact of sensible heat cannot be ignored. The key improvement methods are given to improve the success rate of the experiment. The research results provide a useful reference for the re-cycling and storage of liquid nitrogen.