Reliable Results Research Articles

The use of a modern robotic system for the treatment of severe knee deformities.

Robotic-assisted total knee arthroplasty (TKA) have shown promising results in recent years with improved clinical outcomes using standard primary implants. The purpose of this study was to assess the experience of a single center in correcting severe coronal deformities with the use of a robotic-assisted TKA system and an increased constrained implant. Between July 2020 and December 2022, 30 knees in 28 patients with a major deformity and an associated ligament laxity requiring an increased constrained implant treated using an imageless robotic-assisted TKA were prospectively enrolled. Patients included in the study showed a minimum 15 degrees varus or 10 degrees valgus deviation. 20 cases were varus knees and 10 cases were valgus knees. Postoperative neutral alignment was defined as 0∘± 2.5∘. A CCK implant was used in 20 cases while a Constrained Posterior Stabilized implant was used in 10 cases. A neutral alignment was achieved in all patients. At a minimum 6 months follow up (f-u 6-30 months) clinical outcomes including ROM, KSS, HSS, OKS and WOMAC showed significant improvement and no major complications were registered. The robotic system showed the achievement of a mechanical alignment with reliable radiographic outcomes and clinical results in the treatment of major deformities of the lower limb with the use of higher constrained implants at short term follow up. Further follow up and studies are necessary to confirm and verify these promising outcomes.

Phytoestrogens in Menopausal Hot Flashes: A Review Article

Background: Menopause is a biopsychosocial phenomenon in a woman’s life, and it occurs at about the age of 48-55 years. Factors such as smoking, the number of children, early puberty, and social class can cause early natural menopause. Symptoms that occur during menopause include headache, mood disorders, insomnia, distraction, hot flashes, vaginal dryness, and sweating. The most common symptom is hot flashes, which affect 85% of women with menopause. Recently, people are showing more tendencies toward alternative therapies and herbs phytoestrogens. Objective: This review aims to introduce the mechanism of herbal phytoestrogens controlling hot flashes during menopause. Methods: Our searches were performed in the databases of PubMed, Scopus, and Cochrane. English clinical trials, that investigated the effects of phytoestrogen plants individually or in combination on the treatment of hot flashes in menopause were analyzed. After reviewing articles and meeting the inclusion and exclusion criteria, 18 articles were selected. Results: In this study, 18 clinical trials of 2351 female patients were analyzed. The phytoestrogen plants studied include soy, red clover, cohosh, hops, flax, pomegranate, anise, and Vitex Agnus. Conclusion: Studies have shown the beneficial effects of phytoestrogens in controlling hot flashes, but in some cases, including soybeans, there are contradictory effects. Therefore, more clinical trials are needed to achieve reliable results to confirm the phytoestrogen effects of herbal medicines.

HPLC method for the determination of thymoquinone in growth cell medium.

Preclinical drug testing requires in vitro and in vivo assessments that are vital for studying drug pharmacokinetics and toxicity. Distinct factors that play an important role in drug screening, such as hydrophobicity, solubility of the substance and serum protein binding can be challenging by inducing result inconsistencies. Hence, establishing accurate methods to quantify drug concentrations in cell cultures becomes pivotal for reliable and reproducible results important for in vivo dosing predictions. This research focuses on developing an optimized analytical approach via high-pressure liquid chromatography (HPLC) to determine thymoquinone (TQ) levels in monolayer cell cultures. The method's validation adheres to the International Council for Harmonisation (ICH) guideline M10, ensuring its acceptance and applicability. Using an HPLC system with a Diode Array Detector (DAD), the study fine-tuned various parameters to achieve an efficient separation of TQ. Validation covered specificity, sensitivity, matrix effects, linearity, precision, and accuracy, alongside assessing TQ stability in RPMI-1640 medium. The HPLC method exhibited remarkable TQ specificity, free from interfering peaks at the analyte retention. Sensitivity analysis at the lower limit of quantification (LLOQ) revealed 5.68% %CV and 98.37% % mean accuracy. Matrix effect evaluation showcased accuracy within 85-115%. Linearity spanned in the concentration range of 2-10 μM with a correlation coefficient (r2) of 0.9993. Precision and accuracy were aligned with acceptance criteria. The proposed method was found to be greener in terms of usage of persistent, bioaccumulative, and toxic chemicals and solvents, corrosive samples, and waste production. The developed HPLC-DAD method emerges as specific, accurate, sensitive, and reliable for TQ determination in cell cultures. It ensures robust TQ quantification, enhancing precise in vitro assessments and dependable dosing predictions for in vivo studies. Further research is advocated to investigate TQ's stability across diverse environmental conditions.

On a Mixed Transient–Asymptotic Result for the Sequential Interval Reliability for Semi-Markov Chains

In this paper, we are concerned with the study of sequential interval reliability, a measure recently introduced in the literature. This measure represents the probability of the system working during a sequence of nonoverlapping time intervals. In the cited work, the authors proposed a recurrent-type formula for computing this indicator in the transient case and investigated the asymptotic behavior as all the time intervals go to infinity. The purpose of the present work is to further explore the asymptotic behavior when only some of the time intervals are allowed to go to infinity while the remaining ones are not. In this way, we provide a unique indicator that is able to describe the process evolution in the transient and asymptotic cases as well. It is important to mention that this is not a straightforward result since, in order to achieve it, we need to develop several mathematical ingredients that generalize the classical renewal and Markov renewal frameworks. A numerical example illustrates our theoretical results.

Development and validation of a UV spectrophotometric method for biowaiver studies in pyrazinamide tablets

Development and validation of a UV spectrophotometric method for biowaiver studies in pyrazinamide tablets

The Role of PHREEQC Model and Sensor Analysis in Chemical Coagulation Processes Supported by Online Sensors

Population growth and industrial development have led to an increasing demand for water and wastewater treatment in Turkey and around the world. To ensure sustainable treatment, it is necessary to have real-time control and monitor the system. Therefore, this study aims to reveal the removal mechanism and control of the coagulation process using the PHREEQC modeling software, which has a promising potential for simulating the chemical equilibrium and reactions of water. The sensor effectiveness determined by the model was confirmed by experimental tests in the laboratory. This was done to identify the shortcomings and differences of the model, to understand and develop mechanistic structure. To observe the effects of temperature changes in the treatment, PHREEQC software was run for each of the temperatures (T) 1, 9, and 25.3oC, with the addition of FeCl3. The data obtained from pH, conductivity, temperature, and Eh sensors were evaluated. As a result of the study, it was found that different temperatures affect the solubility of the ions, with higher temperatures leading to increased solubility and conductivity. With increasing temperature, the solubility of oxygen in water decreases, while pH, Cl-, and the precipitate Fe(OH)3 are not affected by the temperature change. In general, the modeling results are in line with the analytical results of the samples taken in the laboratory. This highlights the attractiveness of using online sensors for sustainable wastewater treatment. PHREEQC has produced more reliable results by using actual chemical equilibrium constants as it considers equilibrium conditions and includes the effects of ionic bonds and ion pairs.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Probing pharmaceutically important amino acids L-isoleucine and L-tyrosine Solubilities: Unraveling the solvation thermodynamics in diverse mixed solvent systems

The study specifically investigates the solubilities of L-isoleucine and L-tyrosine in water-mixed solvent systems (DMF, DMSO, and ACN), exploring the behaviour of amino acids in complex environments. The experimental methods prioritize meticulous solvent purification to ensure reliable results. The work explores solubility data, uncovering temperature-dependent trends and intricate interactions influencing solubility in the chosen mixed solvent systems. The study emphasizes the impact of thermodynamic properties, solvent-solvent interactions, and amino acid structure on solubility patterns. The broader implications highlight the relevance of understanding amino acid behaviour in diverse solvent environments, offering potential applications in cosmetics and pharmaceutical industries. The distinct solubility patterns contribute valuable insights, enhancing on the understanding of the solution stability and interactions of L-isoleucine and L-tyrosine in different solvent systems. In conclusion, work suggests the enhanced utilization of L-isoleucine and L-tyrosine in various industries, driven by a profound understanding of their solubility in mixed solvent systems. The research expands our knowledge of amino acid behaviour, paving the way for advancements in industries relying on protein-based products and technologies.

Development of a Follow-Up Measure to Ensure Complete Screening for Colorectal Cancer

The current quality performance measure for colorectal cancer (CRC) screening is limited to initial screening. Despite low rates, there is no measure for appropriate follow-up with colonoscopy after receipt of an abnormal result of a stool-based screening test (SBT) for CRC. A quality performance measure is needed. To develop and test a quality performance measure for follow-up colonoscopy within 6 months of an abnormal result of an SBT for CRC. This retrospective quality improvement study examined data from January 1, 2016, to December 31, 2020, with 2018 plus 6 months of follow-up as the primary measurement period to verify performance rates, specify a potential measure, and test for validity, reliability, and feasibility. The Optum Labs Data Warehouse (OLDW), a deidentified database of health care claims and clinical data, was accessed. The OLDW contains longitudinal health information on enrollees and patients, representing a diverse mixture of ages and geographic regions across the US. For the database study, adults from 38 health care organizations (HCOs) aged 50 to 75 years who completed an initial CRC SBT with an abnormal result were observed to determine follow-up colonoscopy rates within 6 months. Rates were stratified by race, ethnicity, sex, insurance, and test modality. Three HCOs participated in the feasibility field testing. Data were analyzed from June 1, 2022, to May 31, 2023. The primary outcome consisted of follow-up colonoscopy rates following an abnormal SBT result for CRC. Reliability statistics were also calculated across HCOs, race, ethnicity, and measurement year. Among 20 581 adults (48.6% men and 51.4% women; 307 [1.5%] Asian, 492 [7.2%] Black, 644 [3.1%] Hispanic, and 17 705 [86.0%] White; mean [SD] age, 63.6 [7.1] years) in 38 health systems, 47.9% had a follow-up colonoscopy following an abnormal SBT result for CRC within 6 months. There was significant variation between HCOs. Notably, significantly fewer Black patients (37.1% [95% CI, 34.6%-39.5%]) and patients with Medicare (49.2% [95% CI, 47.7%-50.6%]) or Medicaid (39.2% [95% CI, 36.3%-42.1%]) insurance received a follow-up colonoscopy. A quality performance measure that tracks rates of follow-up within 6 months of an abnormal SBT result was observed to be feasible, valid, and reliable, with a median reliability statistic between HCOs of 94.5% (range, 74.3%-99.7%). The findings of this observational study of 20 581 adults suggest that a measure of follow-up colonoscopy within defined periods after an abnormal result of an SBT test for CRC is warranted based on low current performance rates and would be feasible to collect by health systems and produce valid, reliable results.

Effects of textile type on the flexural properties of prefabricated hollow-core slabs strengthened with PVA fibers-improved TRM

In this study, the utilization of textile-reinforced mortar (TRM) in the strengthening and rehabilitation field is investigated. To regulate crack width, polyvinyl alcohol (PVA) fibers were incorporated into the TRM matrix. Nine prefabricated hollow-core slabs strengthened with TRM, along with one reference slab, were subjected to static loading using a 4-point bending apparatus. The experimental variables consisted of the type of textile (glass, basalt, and carbon) and the number of textile layers (one, two, and three layers). The experimental findings demonstrated that the inclusion of PVA fibers improved the TRM by effectively controlling crack width and increasing energy absorption capacity. Among the textile types, carbon TRM-strengthened slabs exhibited the highest flexural capacity. This capacity increased by 74%, 119%, and 132% for slabs strengthened with one, two, and three layers of textile, respectively. Glass TRM-strengthened slabs demonstrated superior deformability, with an increase rate ranging from 202% to 331%. Finally, to predict the flexural capacity and deflection of the tested slabs, a theoretical analysis was conducted and validated by comparing the theoretical predictions to the actual test results for accuracy and reliability.

Assessing Between- and Within-Person Reliabilities of Items and Scale for Daily Procrastination: A Multilevel and Dynamic Approach.

Intensive longitudinal data (ILD) has been collected to capture the dynamic fluctuations of procrastination; however, researchers have typically measured daily procrastination by modifying trait measures (e.g., adding a time reference "today") without adequately testing their reliabilities. The main purpose of this study was to use an advanced approach, dynamic structural equation modeling, to assess the between- and within-person reliabilities of a widely used six-item measure of daily procrastination. A total of 252 participants completed retrospective measures of various types of trait procrastination and daily measures of procrastination over 34 consecutive days. The results showed that the entire scale for daily procrastination and five of its six items had high between- and within-person reliabilities, but one item had much lower reliabilities, suggesting that this item may be inappropriate in everyday contexts. Furthermore, we found moderate to strong associations between the latent trait factor of procrastination and trait measures of procrastination. In addition, we identified substantial between-person variation in person-specific reliabilities and explored its relevant factors. Overall, this study assessed the reliabilities of a daily measure of procrastination, which facilitated future studies to obtain more reliable and consistent results and to better estimate the reliability of ILD.

Surrogate model‐aided global sensitivity analysis framework for seismic consequences estimation in buildings

AbstractSeismic consequences estimation for individual buildings is valuable for various stakeholders, including government entities, building owners, and insurers. The robustness of estimation results in the presence of incomplete input information can typically be investigated through sensitivity analysis. However, the estimation process's complexity and the sensitivity analysis's computational burden hinder its practical application, which requires a more efficient procedure to facilitate broader use. This paper proposes a novel framework for sensitivity analysis of seismic consequences estimation to improve the efficiency and reliability of such analysis. The proposed approach encompasses three key components: (1) stochastic ground motion modeling (SGMM)‐based seismic consequences estimation to evaluate the economic, environmental, and social consequences given specific buildings by considering different hazard levels, (2) the training of surrogate model (Gaussian process model) for structural analysis to reduce the computational cost of the evaluation process, and (3) variance‐based global sensitivity analysis to investigate the importance of parameters of concern in the estimation process. The entire procedure is implemented in Python, adhering to object‐oriented programming, and does not rely on external software. Then, the proposed methodology is applied to two distinct three‐story steel moment‐resistant frames (SMRFs) subjected to four different hazard levels to demonstrate its effectiveness. The SGMM method can generate specific ground motions for each hazard level, mitigating the potential for result bias from using ground motions with unrealistic characteristics. Furthermore, the SGMM method is particularly suitable for automated analysis processes reducing the laborious task of screening ground motions from the database. Comparative analysis with surrogate‐free estimation reveals that the surrogate‐based analysis delivers reliable results with significantly reduced computational cost. The results of analyzing different structures under varying hazard levels reflect the variability in sensitivity analysis of consequences estimation, highlighting the necessity of the proposed flexible and efficient framework. Furthermore, the proposed framework's advantages, limitations, and future research needs are discussed.

Detection of arsenic and lead ions in water through validation of the electrothermal atomic absorption method

The validation of the electrothermal spectrometry method for Arsenic and lead determination in water samples guarantees the quality of analytical data. The study was carried out at AQLAB. A theoretical and technical investigation was applied to ensure the reliability and accuracy of the analytical method. Parameters such as INEN standards, Eurachem international standards, AQLAB internal documents and environmental standards were used to validate the process. Several readings were performed on different samples to make calibration curves for As and Pb, evaluate the method's linearity, and obtain information on the slope, intercept and determination coefficient. Statistical calculations were used to determine the limit of detection and quantification, repeatability, reproducibility, trueness and uncertainty. The results show that the hypotheses were fulfilled, calibration curves with determination coefficient higher than 0.995, repeatability standard deviation lower than 16%, calculated F < critical F (4.96), calculated t student < theoretical (2.23), trueness was between 100.29-110.18 and 99.64-107.92%, uncertainty was 20% range 0.005 to 0.10 mg/L and 15% range 0.01-0.20mg/L, limit of detection was 0.001 and 0.005 mg/L and limit of quantification was 0.01 and 0.02mg/L, respectively. Therefore, the validation method was robust and accurate. Keywords: water samples, analytical data, reliable results, Arsenic, Lead, validation.

Proactive safety assessment of urban through-roads based on GPS data

Road safety is a worldwide issue, while urban roads account for a high share of serious road injuries, especially involving vulnerable road users, such as pedestrians or cyclists. Specifically, the safety of major roads through built-up areas (through-roads) is insufficient due to mixed traffic conditions including vulnerable road users, varying driving behaviour, and many disruptions, which are combined with excessive speed.. In this context, various traffic calming measures have been implemented to improve road safety, such as gateways or pedestrian refuge islands. However, the specific safety impacts of traffic calming combined with specific characteristics of through-roads are often unknown, since most traditional evaluations have been limited by small sample sizes of crash data, as well as wide variations in physical and road characteristics. To overcome the limitations of crash-based evaluations, we used the GPS-based data from a sample of 21 Czech and 12 Polish through-roads to develop the Speed-Safety Index, which combines speed, speed variance, and traffic volume. Our study has three novelty features: (1) To assess safety, we used speed and speed variance simultaneously. (2) To complete the missing link between specific traffic calming measures and safety, we validated the statistical relationship between the developed Speed-Safety Index and crash history. (3) To prove the usefulness of the developed index, we also showed its practical interpretation by proving the effect of spacing between traffic calming measures on safety. The index proved to be well correlated to crash frequency and it also proved the effect of spacing between traffic calming measures: the longer spacing, the smaller speed-reducing effect. The paper concludes with a discussion on the limitations, which we plan to address in further research, by moving from the current macro-perspective (Speed-Safety Index on the level of through-roads) to the micro-perspective (focusing on individual directions, locations, and traffic calming measures). We also plan to investigate the method’s applicability in different contexts. If the approach proves feasible, with reliable and valid results, it may become an alternative for a proactive network-wide road assessment, as called for by the European Road Infrastructure Safety Management Directive.

Development and Optimization of a Multiplex Real-Time RT-PCR to Detect SARS-CoV-2 in Human Samples.

PCR and its variants (RT-PCR and qRT-PCR) are valuable and innovative molecular techniques for studying nucleic acids. qPCR has proven to be highly sensitive, efficient, and reproducible, generating reliable results that are easy to analyze. During the COVID-19 pandemic, qPCR became the gold standard technique for detecting the SARS-CoV-2 virus that allowed to confirm the infection event, and those asymptomatic ones, and thus save millions of lives. In-house multiplex qPCR tests were developed worldwide to detect different viral targets and ensure results, follow the infections, and favor the containment of a pandemic. Here, we present the detailed fundamentals of the qPCR technique based on fluorogenic probes and processes to develop and optimize a successful multiplex RT-qPCR test for detecting SARS-CoV-2 that could be used to diagnose COVID-19 accurately.

Low Observable Uncrewed Aerial Vehicle Wind Tunnel Model Design, Manufacturing, and Aerodynamic Characterization

Developing wind tunnel models is time consuming, labor intensive, and expensive. Rapid prototyping for wind tunnel tests is an effective, faster, and cheaper method to obtain aerodynamic performance results while considerably reducing acquisition time and cost for the models. Generally, the rapid prototyping models suffer from insufficient stiffness or strength to withstand the loads generated during a wind tunnel test. In the present study, a rapid prototype model reinforced with metallic inserts was produced to experimentally investigate the aerodynamic characteristics of an uncrewed aerial vehicle with various wingtip deflections. The fused deposition modeling process was used to make the outer mold, whereas the metallic parts were produced using laser cutting and the computer numerical control machining process. Then, the model was evaluated both experimentally and numerically. The test campaign presented in this work was conducted in the de Havilland low-speed wind tunnel facility at the University of Glasgow. For better characterization of flow patterns dominated by leading edge vortices, numerical simulations were run using OpenFOAM 8.0 and validated with experimental data. The experimental data obtained from the hybrid rapid-prototyped model agreed well with the numerical results. This demonstrates the efficacy of hybrid rapid-prototyped models in providing reliable results for initial baseline aerodynamic database development within a short period and at a reduced cost for wind tunnel tests.

Optimal Configuration of Multi-Energy Storage in an Electric–Thermal–Hydrogen Integrated Energy System Considering Extreme Disaster Scenarios

Extreme disasters have become increasingly common in recent years and pose significant dangers to the integrated energy system’s secure and dependable energy supply. As a vital part of an integrated energy system, the energy storage system can help with emergency rescue and recovery during major disasters. In addition, it can improve energy utilization rates and regulate fluctuations in renewable energy under normal conditions. In this study, the sizing scheme of multi-energy storage equipment in the electric–thermal–hydrogen integrated energy system is optimized; economic optimization in the regular operating scenario and resilience enhancement in extreme disaster scenarios are also considered. A refined model of multi-energy storage is constructed, and a two-layer capacity configuration optimization model is proposed. This model is further enhanced by the integration of a Markov two-state fault transmission model, which simulates equipment defects and improves system resilience. The optimization process is solved using the tabu chaotic quantum particle swarm optimization (TCQPSO) algorithm to provide reliable and accurate optimization results. The results indicate that addressing severe disaster situations in a capacity configuration fully leverages the reserve energy function of energy storage and enhances system resilience while maintaining economic efficiency; furthermore, adjusting the load loss penalty coefficients offers a more targeted approach to the balancing of the system economy and resilience. Thus, new algorithmic choices and planning strategies for future research on enhancing the resilience of integrated energy systems under extreme disaster scenarios are provided.

Wireless Strain Gauge for Monitoring Bituminous Pavements

This paper introduces the implementation of a new device for measuring deformations at the surface layers of bituminous pavement. Using wireless technology, rechargeable remotely, low cost, and easily positioned in a layer by coring after pavement construction, this sensor makes it possible to obtain measurements of the deformation when a vehicle passes by. The development of the wireless sensor is presented as well as its advantages and limitations. It was then tested in the laboratory under a hydraulic press and in situ using a full-scale test of the mobile load simulator (MLS10 type). This system allows simple measurement, gives reliable results, and could be a useful device for the structural monitoring of pavement structures.

Research on a Vibrationally Tuned Directional Seed Supply Method Based on ADAMS-EDEM Coupling and the Optimization of System Parameters

We have combined the theory of bulk dynamics and the agronomic requirements of precision sowing with the aim of resolving the technical problems of poor seed mobility and the difficulty in controlling suction posture, which leads to an increase in the leakage rate and a reduction in seed qualification index scores. In this study, a vibrationally tuned directional seed supply method and system are proposed. We carried out a force analysis of seeds, constructed kinematic equations for seeds and seed boxes to specify the state of the seed motion, and determined the structural parameters and the range of structural parameters that affect the seed suction posture. In addition, we coupled the ADAMS-EDEM simulation of the motion process of the seed and seed boxes and analyzed the vibrational tuning process of the seeds and the angle of inclination of the bottom surface of the seed box. The speed of the eccentric wheel and the eccentric distance were used as test factors. Three-factor and three-level Box–Behnken central combination testing with a single-grain rate, multiple-grain rate, and cavity rate were used as response indicators. Mathematical models were obtained between the experimental factors and the response indicators. Multi-objective optimization of mathematical regression models was carried out with Design-Expert 10.0.4 software. The optimal parameter combination obtained was a tilt angle of 14.27°, an eccentric wheel speed of 4.48 rad/s, and an eccentricity of 1.94 mm. The rate of single grains was 90.75%, the rate of multiple grains was 3.63%, and the rate of cavities was 5.62%. In bench performance tests, using an angle of inclination of 14°, the speed of the eccentric wheel was 4.50 rad/s and the eccentricity was 2 mm. The mean value of the single-grain rate was 89.28%, the mean value of the multiple-grain rate was 3.89%, and the mean value of the cavity rate was 6.83%. The test error was within permissible limits, and reliable results were achieved for parameter optimization. The results met the technical requirements for precision sowing. The results of the study can provide academic references for theoretical research on the methodology of posturing and directional seed supply. They can also provide ideas for the design and development of seed supply systems for precision sowing machinery.

Application of ARIMA agorithm and genetic algorithm based on MATLAB language in finance

The price changes of bitcoin and gold are frequent, as observed in previous studies, and market participants can base their daily investment plans on historical data. Market traders can maximise their investment returns by buying low and selling high on volatile assets based on the day's market conditions, as markets fluctuate every day. Gold and bitcoin are two of the most common volatile investments. In this project, we are trying create an algorithm of high degree of integration which can provide advice to traders before they made their decisions. We used ARIMA algorithm based on the Genetic Algorithm to predict the data and let the algorithm to make based on the predicted data. The complete code could be seen in appendix. The codes were mostly based on MATLAB language, for which it integrated the features of simple and easy to get started; dispatchable sensitivity; adaptable and reliable. Which allows the traders to use simple operations to get reliable and visualizable results. Which can let the traders to have the opportunity to get suggestions on when they should trade.