Problem Of Inaccuracy Research Articles

Research Review of Agricultural Product Traceability System Based on Blockchain Technology

With the frequent occurrence of domestic agricultural product safety accidents in China, consumers' trust in the quality and supply chain of agricultural products is gradually declining. Traceability, as a production control system that can connect all links of product circulation and manage undirected tracking of products, is of great significance to guarantee the quality and safety of products. In the traditional traceability system, there are problems of asymmetry, inaccuracy, non-transparency and insecurity of information. Blockchain can provide powerful technical support for the construction of agricultural products traceability system by virtue of decentralization, transparency and openness, non-tamperability and distributed storage. Applying blockchain technology to agricultural products traceability can effectively protect the rights and interests of all members of the agricultural products supply chain and the general consumers. Based on the analysis of the concept and main features of blockchain, this paper summarizes the research and application progress of blockchain-based traceability system at home and abroad in recent years by studying the traceability mechanism of agricultural products. The advantages and challenges of the implementation of blockchain-based agricultural products traceability system are elaborated. It is hoped that this paper can provide useful references for the research and establishment of blockchain agricultural products traceability system.

Josephson junction parameters extraction method for the RCLSJ model

Current-voltage (I-V) characteristics of shunted Nb/Al-AlOx/Nb Josephson junctions (JJs) can be precisely described in the resistive capacitive inductive shunted junction (RCLSJ) model derived from resistive capacitive shunted junction (RCSJ) model with additional parasitic inductance. However, the methods for extraction of current values in the RCLSJ model are not fully developed, which causes unsatisfactor accuracy of the simulations of superconducting integrated circuits. In this work, we propose an innovative method for extraction of parameters that is capable to solve inaccuracy problem. The enhancement is achieved by including the fluctuation of device parameters due to fabrication process in the analysis. A Josephson comparator is designed in the RCLSJ model to test the performance of the proposed parameter extraction method. Obtained results show that our method ensures 2 to 3 times higher simulation accuracy at frequencies above 60 GHz as compared to the traditional method.

DEVELOPMENT OF MATHEMATICAL MODELS OF GROUP DECISION SYNTHESIS FOR STRUCTURING THE ROUGH DATA AND EXPERT KNOWLEDGE

Context. The problem of aggregating the decision table attributes values formed out of group expert assessments as the classification problem was solved in the context of structurally rough set notation. The object of study is the process of the mathematical models synthesis for structuring and managing the expert knowledge that are formed and processed under incompleteness and inaccuracy (roughness).
 Objective. The goal of the work is to develop a set of mathematical models for group expert assessments structuring for classification inaccuracy problem solving.
 Method. A set of mathematical models for structuring the group expert assessments based on the methods of the theory of evidence has been proposed. This techniques allow to correctly manipulate the initial data formed under vagueness, imperfection, and inconsistency (conflict). The problems of synthesis of group decisions has been examined for two cases: taking into account decision table existing data, only, and involving additional information, i.e. subjective expert assessments, in the process of the aggregation of the experts’ judgments.
 Results. The outcomes gained can become a foundation for the methodology allowing to classify the groups of expert assessments with using the rough sets theory. This make it possible to form the structures modeling the relationship between the classification attributes of the evaluated objects, the values of which are formed out of the individual expert assessments and their belonging to the certain classes.
 Conclusions. Models and methods of the synthesis of group decisions in context of structuring decision table data have been further developed. Three main tasks of structuring decision table data gained through the expert survey has been considered: the aggregation of expert judgments of the values of the decision attributes in the context of modeling of the relationship between the universe element and certain class; the aggregation of expert judgments of the values of the condition attributes; the synthesis of a group decision regarding the belonging of an object to a certain class, provided that the values of the condition attributes are also formed through the expert survey. The proposed techniques of structuring group expert assessments are the theoretical foundation for the synthesis of information technologies for the solution of the problems of the statistical and intellectual (classification, clustering, ranking and aggregation) data analysis in order to prepare the information and make the reasonable and effective decisions under incompleteness, uncertainty, inconsistency, inaccuracy and their possible combination

A Study of Error Reduction in Optical Triangulation for Short-Range Intervehicle Positioning

The long-existing problem of high positioning inaccuracy by triangulation in the far region in short-range vehicle-to-vehicle (V2V) communication environments is investigated in this study. The triangulation is achieved by sensing angle of arrival with two optical signal-direction sensors developed in our previous works. The measured signal-direction error can be categorized into two different kinds, namely systematic error and random error. The former is mainly incurred by imperfect calibration of hardware and misalignment of signal-direction sensors as well as multipath scattering of the signal. The latter is caused by random noises. Both of them lead to positioning error. By utilizing the specific characteristics pertaining to the triangulation measurements, it is possible to extract the target location and increase the accuracy with an optimization algorithm. With the aid of a general-purpose optimization subroutine together with two proper objective functions developed in this study, this work shows the viability of this idea. With the technique proposed in this paper, precise hardware calibration and careful alignment of signal-direction sensors are unnecessary. The problem of multipath scattering can also be mitigated. This work indicates a possible direction to treat the problem of high inaccuracy in noisy and ill-geometric environments in triangulation measurement. The abovementioned signal-direction sensors are currently constructed for infrared light. By changing the signal sources and detectors, they can also be fabricated for visible light. Such an alteration of transmission medium is beneficial for the currently emerging short-range visible-light intervehicular communications.

Analysis of Social Assistance, Social Security and Subsidy Programs in Tabalong Regency

The problem of inaccuracies in target recipient data still occurs in many regions in Indonesia. This is often complained of by the community and is one of the causes of the ineffectiveness of social assistance, social security, and subsidies that have been provided in alleviating poverty. The purpose of writing this article is to analyze the number of poor people and their welfare status based on the October 2020 DTKS data, to map the types and numbers of recipients of social assistance and Social Security and Subsidies in Tabalong Regency, and to analyze the database used for targeting beneficiaries. social security and social security and subsidies in Tabalong Regency. The method used is descriptive exploratory with a qualitative approach through FGD, interviews, comparative studies, and secondary data collection and processing. From the results of the recapitulation and analysis of the data obtained, it is known that there are various social assistance, social security, and subsidies received by the community in Tabalong Regency but there are different databases in determining target recipients in each sector. In addition to differences in the database, another difference found was the difference in the criteria for beneficiaries. So, to improve poverty reduction programs in Tabalong Regency, it is necessary to coordinate and synchronize in determining the criteria for the poor and develop a "One Poverty Data" system that can integrate all existing social assistance, social security, and subsidies programs.Keywords: Poverty, Poverty Data, Database.

The Implementation of Using of Land Rights for Industry

This research aims to know the control over land, either by individuals, collectively or by the state, is only exercised and may only be exercised, if the aim is for the greatest prosperity and justice of the people or for the welfare of the nation and state as much as possible. The research methods was a normative juridical approach and descriptive analytical and highlighted especially in terms of technical legislation. Data collection was carried out with an emphasis on the study of documentation in the library. Field visits were more aimed at checking the application of positive law and to find problems in its implications. The result of the research indicate that there are problems related to the incomplete conceptual preparation in the planning, policy and regulatory stages, caused by the weakness of the quality of planners and legal officials. These weaknesses have caused problems of misunderstanding and inaccuracy in applying the concept, so that the regulatory policies that are drawn up and their implementation deviate a lot from what is expected and have even led to disharmony in society. The product achieved is such that it is not so good, that it will require a difficult effort and take a long time to harmonize it again. Such conditions and problems occur and are found both in the issue of 'state tenure in the National Defense Law, and in the issue of 'land tenure in industrial areas'. The novelty show that in the field of industrial development, the choice of approach is carried out by prioritizing the interests of small companies, medium-sized enterprises and cooperatives in the field of agro-business and agro-industry, without neglecting the presence and role of big entrepreneurs. And in the land acquisition program for the development of an area, a partnership approach should be developed, in such a way that the interests of the people and the good protection of people's rights to land can be paid more attention.

Addressing beta‐lactam allergy: A time for action

Addressing beta‐lactam allergy: A time for action

Hyperspectral Anomaly Detection Based on Improved RPCA with Non-Convex Regularization

The low-rank and sparse decomposition model has been favored by the majority of hyperspectral image anomaly detection personnel, especially the robust principal component analysis(RPCA) model, over recent years. However, in the RPCA model, ℓ0 operator minimization is an NP-hard problem, which is applicable in both low-rank and sparse items. A general approach is to relax the ℓ0 operator to ℓ1-norm in the traditional RPCA model, so as to approximately transform it to the convex optimization field. However, the solution obtained by convex optimization approximation often brings the problem of excessive punishment and inaccuracy. On this basis, we propose a non-convex regularized approximation model based on low-rank and sparse matrix decomposition (LRSNCR), which is closer to the original problem than RPCA. The WNNM and Capped ℓ2,1-norm are used to replace the low-rank item and sparse item of the matrix, respectively. Based on the proposed model, an effective optimization algorithm is then given. Finally, the experimental results on four real hyperspectral image datasets show that the proposed LRSNCR has better detection performance.

The Market in Noninvasive Prenatal Tests and the Message to Consumers: Exploring Responsibility.

The potential for bias in industry-developed information about noninvasive prenatal testing (NIPT), in addition to the lack of regulatory oversight for this type of product, raises questions about clinical communication and adoption. We identify NIPTs marketed globally and analyze their English-language consumer-oriented brochures to determine whether they meet existing policy and ethical guidance from the Nuffield Council on Bioethics on NIPT marketing, how they establish the legitimacy of the test given the lack of regulatory oversight for NIPT, and whether content differs between the brochures from for-profit and nonprofit entities. In many of these brochures, NIPTs are misrepresented as diagnostic tests, claims lack supporting evidence, regulatory bodies that do not evaluate the test itself are referenced, and clinicians are invoked as authorities on specific NIPTs. Our findings substantiate concerns about the extent to which commercial imperatives operating in the absence of market-access regulation could exacerbate problems of misrepresentation and inaccuracy in marketing materials.

Implications of the building system boundary definition to conduct an LCA. A case study comparison of two frameworks for assessing building sustainability: DGNB and Level(s)

The embodied impacts calculation is increasing attention in research, and the use of Life Cycle Assessment (LCA) is the most widely recognised method for that purpose. To support architects and engineers in the use of LCA and to overcome the complexity of calculations in design stage practice, different frameworks for assessing building sustainability propose to conduct simplified LCA methods. Nevertheless, LCA implementation in these frameworks is not completely harmonised, causing problems of inaccuracy and incomplete assessments that generate incomparability among case studies and even possible deviations to achieve carbonneutral scenarios. There, the system boundary definition is a key step. The present paper aimed to illustrate its implications, analysing the implementation of the LCA in a building envelope of a certified passive house located in Italy. Two building sustainability frameworks, DGNB and Level(s), are used to identify how the system boundary definition influences the impact assessment results. The study keeps LCA methodological assumptions (data sources, impact categories, characterisation methods, and indicators) constant to allow a comparison focused on the system boundary implications (such as the modularity principle of LCA). The results show the margins and reduction percentages that can be achieved by the two different assessment frameworks. Finally, limitations and challenges related to methodological aspects in the use of simplified LCA to calculate the impacts of a Passive House building are addressed.

Detection method of heterotropic fiber based on improved YOLOv5

Aiming at the problems of inaccuracy and poor real-time detection of heterosexual fiber in cotton cleaning process, a target detection model of heterosexual fiber based on YOLOv5 network was proposed to realize fast and accurate identification and location of heterosexual fiber in cotton. YOLOv5 was selected as the basic target detection model, and depth separable convolution was introduced to reduce the number of parameters of the detection model and improve the detection speed. Combined with SE module of channel attention mechanism, it can reduce irrelevant information interference and enhance feature expression ability. Comparative ablation tests were performed on YOLOv5 model before and after modification. The experimental results show that the improved YOLOv5 model has a mAP of 91.6% and a frame rate of 83 frames /s. The improved YOLOv5 model can not only improve the detection accuracy but also improve the detection speed, which can better meet the requirements of accuracy and real-time detection of cotton foreign fiber.

STLS-LADMM-Net: A Deep Network for SAR Autofocus Imaging

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method.

Ship Navigation Behavior Prediction Based on AIS Data

Real-time and accurate ship navigation dynamic prediction can effectively improve maritime supervision’s intelligence and precision level and ensure ship navigation safety. To further enhance the accuracy of ship navigation dynamic prediction, this paper uses ship AIS as the data source and proposes an improved LSTM navigation dynamic prediction model based on the attention mechanism. Firstly, a set of pre-processing means, including navigation data extraction, abnormal data processing, and missing data interpolation, is proposed to solve the problems of information loss and inaccuracy in AIS data and incomplete retention of dynamic navigation features; secondly, combining the dynamic characteristics of ship navigation in AIS data with time series, using longitude, latitude, heading, speed, a ship heading and time increment as input to establish a dynamic forecasting model for navigation based on LSTM; The existing navigation sequence coding distortion and space-time data incoherence problem, an optimized Attention-LSTM neural network navigation dynamic prediction method is proposed, and the accuracy and robustness of the model are verified by simulation analysis. The results show that this method can achieve high-precision prediction of the ship’s longitude, latitude, heading, and speed.

Intelligent Evaluation Method of Marine Engine Simulator Based on Combination Weighting

In order to solve the problems of objectivity and inaccuracy in the current evaluation of marine engine simulator, an evaluation model is established based on C-OWA (combination ordered weighted averaging)-the Coefficient of Variation method-minimum discriminant information principle combination weighting method and Vague set. Taking standby of marine generator project as an experiment example, the evaluation index system is established, and the evaluation model is applied to evaluate each evaluation team, and the final operation score of each team is obtained. The results show that (1) the index system is comprehensive and targeted for the operation evaluation of generator standby project; (2) the combination weighting method can improve the rationality of index weighting; (3) Vague set fuzzy decision-making can improve the accuracy of fuzzy evaluation of simulator operation; (4) the model shows rationality and reliability in evaluation results and can provide a reasonable and feasible method for intelligent evaluation of marine simulator.

Which CAM is Better for Extracting Geographic Objects? A Perspective From Principles and Experiments

As a method of deep learning interpretability, class activation mapping (CAM) is efficient and convenient for extracting geographic objects supervised by image-level labels. However, in addition to the inherent problem of inaccuracy and incompleteness of CAM, we have to deal with the spectral and spatial variance of geographic objects when applying CAM methods to remote sensing images. To explore the capabilities of CAM methods on extracting various geographic objects, we make a comprehensive comparison of five commonly-used CAM methods, including original CAM, GradCAM, GradCAM++, SmoothGradCAM++, and ScoreCAM, in four aspects: (1) efficiency, (2) accuracy, (3) effectiveness on dealing with the spectral and spatial variance, and (4) performance of delineating different geographic object categories. The results demonstrate that the original CAM, GradCAM, and GradCAM++ achieves the highest efficiency, accuracy, and integrity for extracting geographic objects, respectively, which can help us choose the appropriate CAM methods according to the specific requirements of different extraction tasks. Benefiting from the capability in extracting various geographic objects and adaptability in complex scenes, GradCAM achieves the best performance in dealing with the spectral and spatial variance problem and shows the advantage of capturing object details and keeping object completeness at the same time. In addition to the comparison experiments and suggestions, we also provide the principle explanations of the performance differences. The findings of this study could contribute to a deep understanding of different CAM methods and benefit to selecting suitable CAM methods for extracting geographic objects from the perspectives of both principles and experiments.

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database.

Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

A Novel Joint Estimation Method of State of Charge and State of Health Based on the Strong Tracking-Dual Adaptive Extended Kalman Filter Algorithm for the Electric Vehicle Lithium-Ion Batteries

In order to enhance the efficiency of electric vehicle lithium-ion batteries, accurate estimation of the battery state is essential. To solve the problems of system noise statistical uncertainty and battery model inaccuracy when using the extended Kalman filter (EKF) algorithm to estimate the battery state, a novel joint estimation algorithm of SOC and SOH based on the strong tracking-dual adaptive extended Kalman filter (ST-DAEKF) is proposed. Based on the extended Kalman filtering algorithm, the fading factor is introduced into it to enhance the tracking ability. Meanwhile, the adaptive filter which can statistics the characteristics of time-varying noise is used to adjust the noise parameters of the system. The BBDST condition and the DST condition at 25 °C are used for simulation and verification in MATLAB. The results of the algorithm simulation show that under the BBDST condition, the maximum SOC error and the average error of the proposed algorithm are 3.41% and 0.99%, respectively, with the corresponding convergence time of 15 seconds. And under the DST condition, the corresponding data is 1.56%, 1.29%, and 20 seconds, respectively. At the same time, compared with the extended Kalman algorithm, the SOH estimation results of this algorithm also have a better estimation effect and reference value. Under the BBDST condition, the maximum SOH error and average error under this algorithm are 0.12% and 0.06%, with the corresponding data of 0.66% and 0.23% under the DST condition. The above data proves the superiority of the joint estimation algorithm.

Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

A successful passage of the blood–brain barrier (BBB) is an essential prerequisite for the drug molecules designed to act on the central nervous system. The logarithm of blood–brain partitioning (LogBB) has served as an effective index of molecular BBB permeability. Using the three-dimensional (3D) distribution of the molecular electrostatic potential (ESP) as the numerical descriptor, a quantitative structure-activity relationship (QSAR) model termed AlphaQ was derived to predict the molecular LogBB values. To obtain the optimal atomic coordinates of the molecules under investigation, the pairwise 3D structural alignments were conducted in such a way to maximize the quantum mechanical cross correlation between the template and a target molecule. This alignment method has the advantage over the conventional atom-by-atom matching protocol in that the structurally diverse molecules can be analyzed as rigorously as the chemical derivatives with the same scaffold. The inaccuracy problem in the 3D structural alignment was alleviated in a large part by categorizing the molecules into the eight subsets according to the molecular weight. By applying the artificial neural network algorithm to associate the fully quantum mechanical ESP descriptors with the extensive experimental LogBB data, a highly predictive 3D-QSAR model was derived for each molecular subset with a squared correlation coefficient larger than 0.8. Due to the simplicity in model building and the high predictability, AlphaQ is anticipated to serve as an effective computational screening tool for molecular BBB permeability.

“Evolusi-Devolusi” Pemerintahan Demokratis dalam Nalar Filosofis Abdolkarim Soroush

This article tries to offer Abdolkarim Soroush answer to problem of inaccuracy of religious attitudes in addressing religion and democracy as a government system. In Soroush’s view, peoples sees ideology as no longer valid, when they keep on a single interpretation as solid idea. They prefer on compound, nonsecterian and charitable ideas. This study uses a philosophical-critical analysis method. The article confirms: Soroush offers a new kalām (theology) approach as intermediary way between Islam and social problems. As an intellectual-mondial, he want to present Islam that can be universally understood, follows world community common sense, is not just traditional textual approach. This option is quite sensitive and full of risk, like his criticism toward Iranian post-revolutionary government.

Precise Simulation of Heat-Flow Coupling of Pipe Cooling in Mass Concrete.

For a long time, temperature control and crack prevention of mass concrete is a difficult job in engineering. For temperature control and crack prevention, the most effective and common-used method is to embed cooling pipe in mass concrete. At present, there still exists some challenges in the precise simulation of pipe cooling in mass concrete, which is a complex heat-flow coupling problem. Numerical simulation is faced with the problem of over-simplification and inaccuracy. In this study, precise simulation of heat-flow coupling of pipe cooling in mass concrete is carried out based on finite element software COMSOL Multiphysics 5.4. Simulation results are comprehensively verified with results from theoretical solutions and equivalent algorithms, which prove the correctness and feasibility of precise simulation. Compared with an equivalent algorithm, precise simulation of pipe cooling in mass concrete can characterize the sharp temperature gradient around cooling pipe and the temperature rise of cooling water along pipeline more realistically. In addition, the cooling effects and local temperature gradient under different water flow (0.60 m3/h, 1.20 m3/h, and 1.80 m3/h) and water temperature (5 °C, 10 °C, and 15 °C) are comprehensively studied and related engineering suggestions are given.