Technology Accuracy Research Articles

Crispr and plant pathology: Revolutionizing disease resistance in crops

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology has revolutionized plant pathology by providing a precise and efficient tool for enhancing disease resistance in crops. This gene-editing technique enables scientists to modify specific DNA sequences in plants, allowing for the targeted improvement of traits related to pathogen defense. By altering genes responsible for susceptibility to diseases, CRISPR facilitates the development of crops that are more resistant to bacterial, fungal, and viral pathogens, thereby reducing the reliance on chemical pesticides. Additionally, CRISPR can be used to boost plant immunity and improve resilience against emerging plant diseases that threaten global food security. The technology's accuracy and speed have accelerated breeding programs, enabling researchers to respond rapidly to agricultural challenges posed by climate change and the evolution of new pathogens. This article explores the breakthroughs achieved through CRISPR in plant pathology, highlighting case studies of its application in major crops such as rice, wheat, and tomatoes. Furthermore, it discusses the ethical, regulatory, and environmental considerations of using CRISPR for crop improvement, alongside its potential to revolutionize sustainable agriculture by reducing disease-induced yield losses and promoting food security.

Optimal fusion‐based localization method for tracking of smartphone user in tall complex buildings

AbstractIn the event of a fire breaking out or in other complicated situations, a mobile computing solution combining the Internet of Things and wearable devices can actually assist tracking solutions for rescuing and evacuating people in multistory structures. Thus, it is crucial to increase the positioning technology's accuracy. The sequential Monte Carlo (SMC) approach is used in various applications such as target tracking and intelligent surveillance, which rely on smartphone‐based inertial data sequences. However, the SMC method has intrinsic flaws, such as sample impoverishment and particle degeneracy. A novel SMC approach is presented, which is built on the weighted differential evolution (WDE) algorithm. Sequential Monte Carlo approaches start with random particle placements and arrives at the desired distribution with a slower variance reduction, like in a high‐dimensional space, such as a multistory structure. Weighted differential evolution is included before the resampling procedure to guarantee the appropriate variety of the particle set, prevent the usage of an inadequate number of valid samples, and preserve smartphone user position accuracy. The values of the smartphone‐based sensors and BLE‐beacons are set as input to the SMC, which aids in fast approximating the posterior distributions, to speed up the particle congregation process in the proposed SMC‐based WDE approach. Lastly, the robustness and efficacy of the suggested technique more accurately reflect the actual situation of smartphone users. According to simulation findings, the suggested approach provides improved location estimation with reduced localization error and quick convergence. The results confirm that the proposed optimal fusion‐based SMC‐WDE scheme performs 9.92% better in terms of MAPE, 15.24% for the case of MAE, and 0.031% when evaluating based on the R2 Score.

厚板のラウンド曲げによる加工方法についての最適化手法

Analysis technology of FEM is a very versatile numerical analysis. Recently these Analysis technology's accuracy is enhanced, in the field of structural analysis and metal processing have received a great benefit. Simulate with FEM, The introduction of the FEM analysis process and simulate with FEM, it has become possible to reduce the cost of the prototyping. But Current state, often error between the actual workpiece and the analysis results is increased. In this paper, propose a reasonable shape optimization using the FEM and RBF network.

Human factors affecting the interpretation of fetal heart rate tracings

Human factors can have an important impact on cardiotocography (CTG) interpretation and management decisions, and therefore may directly affect obstetrical outcomes. It has been well demonstrated that there is wide observer disagreement over CTG interpretation, particularly in the evaluation of variability, decelerations, and overall tracing classification. The reasons behind this are still incompletely understood, but poor reproducibility can have a profound impact on the technology's accuracy and on its efficacy. Some scientific societies have recently revised their guidelines for CTG interpretation, but no up-to-date universally accepted recommendation exists. In spite of some approximation between the major guideline sets, important differences still exist between them, and they remain complex and prone to memory decay. Regular training in CTG interpretation appears to result in increased knowledge, better observer agreement, and improved quality of care. Computer analysis has also been developed, but remains heavily dependent on staff to confirm interpretation and to decide clinical management. An international consensus, comprising simpler and more objective interpretation guidelines, together with regular staff training, and improved decision support systems seem to be the way forward for this technology.