Time Characteristics Research Articles

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

Study on hormone induction of the male parent and embryonic development, gonad differentiation, and growth of “All-female No.1” Culteralburnus

Female Culter alburnus was faster in growth rate than males. In this study, the gynogenetic G2 and the pseudo-male G2' were used as the female and male parents, respectively, to breed a new national variety “All-female No.1” C. alburnus (AFC). Hormone induction, embryonic development, gonadal differentiation, and growth of AFC were studied. The results showed induction with low concentrations of 17α-methyltestosterone in a indoor-net cage culture was not effective. Under the stimulation of 17α-methyltestosterone, some gonads had a tendency to transform into testis, but not completely. There were three types of gonads in 5-month-old and four types of gonads in 12-month-old fishes, however, they all differentiated into ovaries in 15-month-old fishes. Testosterone propionate and high concentrations of 17α-methyltestosterone in pond culture induction had a good effect resulting in ①a functional pseudo-male with normal testis development that could successfully extrude semen during the breeding period, ②a pseudo-male with normal testis development, but could not extrude semen, and ③the appearance of intersexual glands. The second experiment revealed that with common fish, all-female fish embryo had normal embryonic development. The development time and morphological characteristics of each stage were similar, but the development of the all-female embryo was slightly slower than the common embryos. The gonad differentiation of the all-female embryo were normal and none differentiated into testis, which indicated that all-female could ensure the female sex without affecting the normal gonad differentiation. The correlation between body weight, length, and month-age of all-female and common fish was strong. The all-female had faster growth rate and more uniform growth specification than the common fish. Therefore, the use of testosterone propionate and high concentrations of 17α-methyltestosterone in pond culture induction could avoid complete degeneration of gonads into ovaries. The all-female embryo had the advantages of normal embryonic development and gonadal differentiation, faster growth, and uniform growth specification.

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.

Physicochemical and sensory properties of vegetarian pasta produced with pea (Pisum sativum) protein powder

Dry legumes are frequently preferred as a protein source in plant-based diets. In this study, the production of "pea pasta" with the addition of pea protein powder was aimed. Moisture, ash, protein, weight loss, volume, and cooking time characteristics of the pasta were examined. Additionally, the sensory properties of the produced pea pasta and its effect on individuals' satiety were investigated, and the results were evaluated. In all sensory analyses, the pasta sample containing 10% pea protein powder was the most preferred among 8 different criteria. It was observed that the satiety lasted longer when consumers consumed pasta containing 10% pea protein powder compared to the control pasta consumption.

Pore structure characterization and reservoir quality prediction in deep and ultra-deep tight sandstones by integrating image and NMR logs

Pore structure and fracture determine reservoir quality in deep and ultra-deep tight sandstones, however, the limited availability of core data makes it challenging to describe the complexity of pore-throat structure and characterize the wide range of fractures. To fill this gap, a comprehensive analysis was conducted to evaluate pore structure and fractures from multiple perspectives. Integration of core, thin sections, scanning electron microscopy (SEM), high pressure mercury injection (HPMI) and nuclear magnetic resonance (NMR) tests are used to evaluate pore structure and characterize fracture of deep and ultra-deep tight sandstones in the Cretaceous Bashijiqike Formation in the Kuqa Depression. Pore structure and reservoir quality types can be divided into dominantly three types according to characteristics of NMR transverse relaxation time (T2) distribution and HPMI curves. Correlation analysis among HPMI and NMR parameters with petrophysical parameters indicate that maximum pore throat size (rmax), logarithmic mean of T2 (T2gm), irreducible water saturation (Swi), and reservoir quality index (RQI) are the most sensitive parameters for pore structure characterization. The Type Ⅰ pore structure is composed of intergranular and intragranular pores, and NMR T2 spectrum shows the highest magnitude, with the calculated mobile fluid saturation content also being the highest. The Type II and III pore structures are composed of intergranular pores, intragranular dissolved pores and intercrystalline micropores in clay minerals. T2gm of Type II and III are smaller, with the calculated mobile fluid saturation content being the lower.The fracture features and parameters including fracture density, porosity, aperture, and length are analyzed using image logs and core. The pore structure characteristics are quantificationally and qualitatively analyzed by NMR logs. Integration of pore structure and fractures by image logs and NMR logs are used for prediction of high quality reservoir and hydrocarbon productivity. High hydrocarbon productivity is obtained in well intervals with abundant natural fractures, and favorable pore structure will also contribute to high matrix porosity. Therefore, NMR logs and image logs are crucial for evaluating reservoir quality and predicting productivity when calibrated with core and analysis data. The comprehensive analysis integrating core data with geophysical well logs provide important insights for reservoir quality prediction of deep and ultra-deep tight sandstones.

Analyzing the urban–rural divide: the role of location, time, and breach characteristics in U.S. hospital security incidents, 2012–2021

This analysis describes how cybersecurity breaches vary between urban and rural hospitals, how they differ over time, and how they differ across different types and locations of breaches within hospitals. Utilizing data from the U.S. Department of Health and Human Services Office for Civil Rights on incidents from 2012 to 2021 that affected 500 or more individuals, we studied breaches in 237 community hospitals, distinguishing between 185 urban and 52 rural facilities. Using Poisson and Quasi-Poisson regression models, we found a significant yearly increase in data breaches, especially in urban hospitals. Hacking/IT incidents were the most common breaches, particularly prevalent in urban settings, while unauthorized access/disclosure occurred frequently. Email and network servers were the primary locations of breaches in urban and rural hospitals. These findings indicate differences between urban and rural hospitals in data security incidents, suggesting areas for further research.

Intelligent Matching Method for College Dormitory Roommates: Chameleon Algorithm Based on Optimized Partitioning

A chameleon algorithm based on optimized partitioning was studied to solve the intelligent matching problem of college dormitory roommates. Using quantitative research methods, data on personal preferences and lifestyle habits of college students were collected, and the K-center object chameleon algorithm was used for data analysis and roommate matching. Test the algorithm performance on the BBC dataset, compare clustering quality indicators such as entropy, purity, and RI value, and verify the effectiveness of the algorithm. This algorithm can accurately assign students to their respective dormitories, avoiding overlapping situations and achieving excellent matching results. In terms of matching accuracy and running time, the K-center object chameleon algorithm shows superior performance compared to other algorithms. In terms of clustering quality evaluation, comparisons were made from three dimensions: entropy value, purity, and RI value. The experimental results show that the closer the entropy value is to 0, the closer the purity and RI value are to 1, and the better the matching effect. This result further validates the effectiveness of the algorithm in the intelligent matching problem of college dormitory roommates. The matching accuracy of this algorithm on the BBC dataset reached 98.82%, showing better clustering quality than other algorithms in terms of entropy, purity, and RI values. The entropy value approached 0, while the purity and RI values approached 1, verifying the efficiency of matching quality. The chameleon algorithm based on optimized partitioning proposed in the study has shown excellent performance in intelligent matching of college dormitory roommates, with the characteristics of high-precision matching and fast running time. It has important practical significance for improving the quality of life and learning efficiency of college dormitory students, and provides new research methods and ideas for related fields.

Twins transformer: rolling bearing fault diagnosis based on cross-attention fusion of time and frequency domain features

Abstract Current self-attention based Transformer models in the field of fault diagnosis are limited to identifying correlation information within a single sequence and are unable to capture both time and frequency domain fault characteristics of the original signal. To address these limitations, this research introduces a two-channel Transformer fault diagnosis model that integrates time and frequency domain features through a cross-attention mechanism. Initially, the original time-domain fault signal is converted to the frequency domain using the Fast Fourier Transform, followed by global and local feature extraction via a Convolutional Neural Network. Next, through the self-attention mechanism on the two-channel Transformer, separate fault features associated with long distances within each sequence are modeled and then fed into the feature fusion module of the cross-attention mechanism. During the fusion process, frequency domain features serve as the query sequence Q, and time domain features as the key-value pairs K. By calculating the attention weights between Q and K, the model excavates deeper fault features of the original signal. Besides preserving the intrinsic associative information within sequences learned via the self-attention mechanism, the Twins Transformer also models the degree of association between different sequence features using the cross-attention mechanism. Finally, the proposed model's performance was validated using four different experiments on four bearing datasets, achieving average accuracy rates of 99.67%, 98.76%, 98.47%, and 99.41%. These results confirm the model's effective extraction of time and frequency domain correlation features, demonstrating fast convergence, superior performance, and high accuracy.

Application and Research of Multi-Scale Dynamic Diffusion Permeability Model in Different Coal Ranks

The spatial scale of coal mining and rock layer control is distributed between 10-10 and 107, spanning 17 orders of magnitude. There are multi-scale pore cracks from millimeter to micro and nanometers in the coal, and the pore size can reach one million times, which makes the coal permeability also show a multi-scale characteristics of millions and time. For the permeability of the coal seam, it is necessary to test and analyze the distribution of micro and nano pores and cracks in the coal [1]. CBM mining is accompanied by the dynamic change of adsorption pressure in coal and the complex deformation of multi-scale heterogeneous structure of coal, and the multi-scale deformation feature of pore fissure structure plays an important role in the transport of methane [2]. However, existing studies focus on treating coal as a homogeneous isotropic medium, without considering the influence of coal multiscale pore structure on permeability. The variation of heterogeneous degree of coal structure with structural scale and its influence on dynamic diffusion and permeability are rarely reported. Li Zhiqiang et al. [3] conducted an experimental-model-mechanism study of CBM gas micro-nano series multi-scale dynamic diffusion permeability. On this basis, the dynamic diffusion and permeability of different coal scales.

Analysis and prediction of epidemiological characteristics of tuberculosis deaths among Chinese residents from 2006 to 2021

Objective: The epidemiological characteristics of tuberculosis deaths among Chinese residents from 2006 to 2021 were analyzed, and the tuberculosis mortality rate from 2022 to 2027 was predicted to provide a reference for tuberculosis prevention and control in China. Methods: The data set of tuberculosis deaths from 2006 to 2021 was published regularly by the China CDC, and the crude mortality rate (CMR) and age-standardized mortality rates (ASMR) were calculated according to the population structure of China in 2000. The distribution characteristics of age, sex, region, and time of tuberculosis deaths were analyzed, the Joinpoint regression analysis model was used to analyze the changing trend, and the grey model was applied to predict CMR and ASMR from 2022 to 2027. Results: From 2006 to 2021, the CMR and ASMR of tuberculosis showed a downward trend among males and females, urban and rural areas, and all age groups, in a word, all the Chinese residents. Except for the age group ≥85 years old, the mortality trend was insignificant. In the eastern, central, or western regions. CMR and ASMR were significantly higher in males than in females.CMR and ASMR were significantly lower in urban areas than in rural areas. In general, active tuberculosis patients present a higher mortality rate. The CMR and ASMR in the western region were higher than those in the eastern and central regions and lower in the eastern region than in the central region, but the differences were less obvious. The ASMR of the eastern cities was lower than that of the central and western regions, and the ASMR of the central cities was higher than that of the western region from 2006 to 2009 and 2012 and lower than that of the western region in other years. The ASMR in the western countryside was higher than that in the eastern and central regions and lower in the eastern part than in the central region, but the difference was not obvious. The grey model prediction results show that the CMR (/100 000) of Chinese residents from 2022 to 2027 is 1.585, 1.471, 1.360, 1.250, 1.143, and 1.038, and the ASMR (/100 000) is 0.779, 0.653, 0.531, 0.411, 0.295 and 0.181, respectively. Conclusions: The CMR and ASMR of tuberculosis will continue to decline, and extraordinary achievements have been made in tuberculosis prevention and control in Chinese residents from 2006 to 2021 and, presumably, from 2022 to 2027. However, tuberculosis screening and treatment programs in the western region, men, the elderly population, and rural areas should be further strengthened, and targeted prevention and control measures should be formulated to reduce mortality.

Muscari zagricum (Asparagaceae, Scilloideae), a unique new species from Iran

A new species, Muscari zagricum Eker, Alipour & Majidi (Asparagaceae) from Iran, is described and illustrated. It has no closely related species, and is partly similar to M. pseudopallens and M. pallens, but differs from them mainly in morphological characters such as outer tunic color, raceme shape and density, pedicels no extending when in fruit, shape of fertile flowers with shouldered and grooved, color when dry, color of lobes, strongly compressed capsule valves, as well as flowering time and habitat characteristics. In this study, a comprehensive description, diagnostic characters, original photographs, geographical distribution, conservation assessment, and taxonomic comments on the new species are presented.

Effect of 2-hydroxyethylammonium carboxylate protic ionic liquids on the solubility and cytotoxicity of indomethacin

Recently, there is a particular interest to utilize protic ionic liquids (PILs) in drug solubility. This study is exploring the effect of three protic ionic liquids (PILs) based on 2-hydroxyethylammonium carboxylate [2-hydroxyethylammonium acetate (MEAA), 2-hydroxyethylammonium lactate (MEAL), and 2-hydroxyethylammonium propionate (MEAP)] on the solubility of the very poorly soluble drug in water, indomethacin (IMC). The shake flask method was used to measure the experimental solubility of IMC at the different temperatures range (298.15–313.15) K. The results demonstrate significantly enhancment the solubility of IMC in PILs compared to pure water, with an approximate increase of 200 times. The experimental solubility data have been correlated using the empirical models which showed the performance as the order: Modified Apelblat–Jouyban–Acree > Van’t Hoff–Jouyban–Acree > Modified Apelblat equations and also the performance for the Wilson model indicated as the order (absolute relative deviation): 2-hydroxyethylammonium acetate (3.030) > 2-hydroxyethylammonium propionate (3.239) > 2-hydroxyethylammonium lactate (7.665). Then the thermodynamic dissolution properties were obtained by usage of Gibbs and Van’t Hoff equations to investigate the thermodynamic behavior of the IMC in the aqueous solution PILs. Eventually, the cytotoxicity of the co-solvents (PILs) under study was evaluated using a standard MTT assay. The results showed that the cell viability percentage increased in the following order: MEAA < MEAP < MEAL. These findings indicated that these PILs had low to moderate toxicity. It is noteworthy that the functional groups of the anions were not the only determinant factor of the cytotoxicity. Other factors encompassing concentration, exposure time, and cell line characteristics also had significant effects.

Hospital Factors Influencing the Mobility of Patients for Systemic Therapies in Breast and Bowel Cancer in the Metastatic Setting: A National Population-based Evaluation

AimsThis national study investigated hospital quality and patient factors associated with treatment location for systemic anticancer treatment (SACT) in patients with metastatic cancers. Materials and methodsUsing linked administrative datasets from the English NHS, we identified all patients diagnosed with metastatic breast and bowel cancer between 1 January 2016 and 31 December 2018, who subsequently received SACT within 4 months from diagnosis. The extent to which patients bypassed their nearest hospital was investigated using a geographic information system (ArcGIS). Conditional logistic regression models were used to estimate the impact of travel time, hospital quality and patient characteristics on where patients underwent SACT. Results541 of 2,364 women (22.9%) diagnosed with metastatic breast cancer, and 2,809 of 10,050 (28.0%) patients diagnosed with metastatic bowel cancer bypassed their nearest hospital providing SACT. There was a strong preference for receiving treatment at hospitals near where patients lived (p < 0.001). However, patients who were younger (p = 0.043 for breast cancer; p < 0.001 for bowel cancer) or from rural areas (p = 0.001 for breast cancer; p < 0.001 for bowel cancer) were more likely to travel to more distant hospitals. Patients diagnosed with rectal cancer were more likely to travel further for SACT than patients with colon cancer (p = 0.002). Patients were more likely to travel to comprehensive cancer centres (p = 0.019 for bowel cancer) and designated Experimental Cancer Medicine Centres (ECMCs) although the latter association was not significant. Patients were less likely to receive SACT in hospitals with the highest readmission rates (p = 0.046 for bowel cancer). ConclusionPatients with metastatic cancer receiving primary SACT are prepared to travel to alternative more distant hospitals for treatment with a preference for larger comprehensive centres providing multimodal care or hospitals which offer early phase cancer clinical trials.

Mesoscale-coupled Large Eddy Simulation for Wind Resource Assessment

Turbulence, a key driver of wind turbine loads, is central in the assessment of turbine suitability and performance, and consequently impacts the expected energy production of a wind farm. Conventional flow modeling methods for wind resource assessment (WRA) typically lack the ability to resolve turbulence due to key simplifications in their formulation. This work applies Large Eddy Simulation (LES) to address these limitations and deliver high-fidelity wind condition predictions at 24 wind farm sites. The model includes recently improved boundary conditions to downscale information from global weather models via a mesoscale layer. Validation of the model is presented through comparison to over 100 years of measurements, with comparison of the time-series, distributions and spectra. Validation results indicate excellent performance of the model for key flow quantities including wind speed, turbulence and direction. Comparison of spectra indicates a significant improvement in the representation of the atmospheric energy cascade when compared to previous approaches. The results are significant for wind-farm site assessment as they demonstrate the feasibility of applying accurate LES at a commercial scale. LES also provides a step change in the quality of model predictions through resolving time and site-specific turbulence characteristics. The application of the model for WRA is a step towards improved understanding of the wind resource, as well as an improved suitability assessment process itself.

Characterization of a SiPM-based monolithic neutron scatter camera using dark counts

The Single Volume Scatter Camera (SVSC) Collaboration aims to develop portable neutron imaging systems for a variety of applications in nuclear non-proliferation. Conventional double-scatter neutron imagers are composed of several separate detector volumes organized in at least two planes. A neutron must scatter in two of these detector volumes for its initial trajectory to be reconstructed. As such, these systems typically have a large footprint and poor geometric efficiency. We report on the design and characterization of a prototype monolithic neutron scatter camera that is intended to significantly improve upon the geometrical shortcomings of conventional neutron cameras. The detector consists of a 50 mm×56 mm× 60 mm monolithic block of EJ-204 plastic scintillator instrumented on two faces with arrays of 64 Hamamatsu S13360-6075PE silicon photomultipliers (SiPMs). The electronic crosstalk is limited to < 5% between adjacent channels and < 0.1% between all other channel pairs. SiPMs introduce a significantly elevated dark count rate over PMTs, as well as correlated noise from after-pulsing and optical crosstalk. In this article, we characterize the dark count rate and optical crosstalk and present a modified event reconstruction likelihood function that accounts for them. We find that the average dark count rate per SiPM is 4.3 MHz with a standard deviation of 1.5 MHz among devices. The analysis method we employ to measure internal optical crosstalk also naturally yields the mean and width of the single-electron pulse height. We calculate separate contributions to the width of the single-electron pulse-height from electronic noise and avalanche fluctuations. We demonstrate a timing resolution for a single-photon pulse to be (128 ± 4) ps. Finally, coincidence analysis is employed to measure external (pixel-to-pixel) optical crosstalk. We present a map of the average external crosstalk probability between 2×4 groups of SiPMs, as well as the in-situ timing characteristics extracted from the coincidence analysis. Further work is needed to characterize the performance of the camera at reconstructing single- and double-site interactions, as well as image reconstruction.

Effect of the Initial Moisture Content of Cedar Needles on the Time Characteristics of the Ignition of Coal–Water Suspensions

Effect of the Initial Moisture Content of Cedar Needles on the Time Characteristics of the Ignition of Coal–Water Suspensions

Investigating the impact of different solder alloy materials during laser soldering process

PurposeThis study aims to investigate the influence of different solder alloy materials on passive devices during laser soldering process. Solder alloy material has been found to significantly influence the solder joint’s quality, such as void formation that can lead to cracks, filling time that affects productivity and fillet shape that determines the solder joint’s reliability.Design/methodology/approachFinite volume method (FVM)-based simulation that was validated using real laser soldering experiment is used to evaluate the effect of various solder alloy materials, including SAC305, SAC387, SAC396 and SAC405 in laser soldering. These solders are commonly used to assemble the pin-through hole (PTH) capacitor onto the printed circuit board.FindingsThe simulation results show how the void ratio, filling time and flow characteristics of different solder alloy materials affect the quality of the solder joint. The optimal solder alloy is SAC396 due to its low void ratio of 1.95%, fastest filling time (1.3 s) to fill a 98% PTH barrel and excellent flow characteristics. The results give the ideal setting for the parameters that can increase the effectiveness of the laser soldering process, which include reducing filling time from 2.2 s to less than 1.5 s while maintaining a high-quality solder joint with a void ratio of less than 2%. Industries that emphasize reliable soldering and effective joint formation gain the advantage of minimal occurrence of void formation, quick filling time and exceptional flowability offered by this solution.Practical implicationsThis research is expected not only to improve solder joint reliability but also to drive advancements in laser soldering technology, supporting the development of efficient and reliable microelectronics assembly processes for future electronic devices. The optimized laser soldering material will enable the production of superior passive devices, meeting the growing demands of the electronics market for smaller, high-performance electronic products.Originality/valueThe comparison of different solder alloy materials for PTH capacitor assembly during the laser soldering process has not been reported to date. Additionally, volume of fluid numerical analysis of the quality and reliability of different solder alloy joints has never been conducted on real PTH capacitor assemblies.

Hydro Priming Efficacy on Rubber Plant Seeds (Hevea brasiliensis) Growth

Among the plantation products, rubber is critical to economic activity within Indonesia. Rubber is also identified as one of Indonesia's export products, which is significant as an export revenue source apart from the oil and gas sector. Malaysia is the world's second-largest rubber producer and exporter of natural rubber after Indonesia. Besides having more open export opportunities and stimuli, the domestic rubber market is still large. Therefore, this study aims to ascertain the efficiency of hydro priming of rubber seeds on germination time and power as well as sprout height. The research was conducted in the greenhouse at the experimental garden in the Faculty of Agriculture KU IMS in Kuantan Singingi, which are given at geographical coordinates 00 00-10 00 LS 1010 02 – 1010 55 BT. The research was conducted in November-December 2023. The questionnaire survey was conducted in November and December of 2023. The average air temperature of the research location for November and December 2023 was 28. ranges from 8 0C to 32 0C with relative humidity of about 81%. The research approach was conducted in a qualitative manner utilizing mathematical techniques. Data is generally put in a tabular form and analyzed. Various treatment levels were under Control, 12h, 24h, 36h and 48h of hydro priming. In this research, the parameters recorded include time taken for germination, germination strength and sprout height. According to the outcomes of the assessments made in this study, the hydro priming treatment conducted on the rubber plant seeds showed the fastest germination time characteristics in the 48-hour treatment, which is 3. 4 days. The maximum germination capacity was observed in the hydro-priming treatment of 36 hours, which was 90. 0%. On the other hand, the tallest observation parameter of the height of the sprouts was in 48 hrs of Hydro Priming, with a height of 26. 3 cm

Block diagram analysis of speed time characteristics of a simple DC motor and modeling of a PID controller in fuzzy systems engineering

Block diagram analysis of speed time characteristics of a simple DC motor and modeling of a PID controller in fuzzy systems engineering

A novel optical thermometry strategy: Based on the combined effects of red-shift charge transfer band and thermal coupling

The non-contact temperature measurement method based on the fluorescence intensity ratio of thermally coupled energy levels has emerged as a focal point of research in recent years due to its characteristics of short response time, suitability for extreme environments, and high sensitivity. The enhancement of relative sensitivity (Sr) is a key objective in refining ratio-metric optical thermometry. However, the Sr is limited by the thermometry strategies. Regarding excitation and monitoring methods, ratio-metric temperature measurement strategies typically adopt two distinct approaches: single-excitation dual-emission (S-D), and dual-excitation single-emission (D-S). In this study, we developed a dual-excitation dual-emission (D-D) thermometry strategy that theoretically and experimentally demonstrates higher Sr and reduced temperature uncertainty (δT). This advancement was achieved by analyzing thermal coupling energy levels (TCELs) and the red-shift charge transfer band (CTB) properties. YVO4:Er3+ was selected for experimental validation due to its pronounced thermal coupling and red-shift CTB effects. The experimental results show that the Sr(D-D) equal to the sum of Sr(S-D) and Sr(D-S), and 1/δT(D-D) equal to the sum of 1/δT(S-D) and 1/δT(D-S), which well verifies the theoretical expectations. Thus, the D-D strategy emerges as a novel and effective method for ratio-metric thermometry, promising enhanced precision in temperature measurements.