Perfect Data Research Articles

Sunwoda's Financial Analysis Based on The Harvard Analytical Framework

In order to comply with the strategy of sustainable development, China hopes to further accelerate the rapid development of the new energy vehicle industry and supporting industries. Power battery is the core component of new energy vehicles, and the selection of sunwoda in the battery industry for financial analysis can provide a useful reference for the same type of enterprises.This paper selects sunwoda as an analysis case, applies the Harvard analytical framework to sunwoda's financial analysis, and conducts in-depth discussion and analysis of sunwoda's financial status and existing problems. The analysis found that sunwoda is currently in the growth period,adopting a growth-oriented development strategy to gradually expand the scale of the enterprise, with many projects underconstruction and rapid growth in scale. However, behind the seemingly perfect financial data, there are problems such as low profitability, intensified market competition,and insufficient future development capabilities.In view of these problems,corresponding improvement suggestions are put forward,hoping to help the development of sunwoda.

Constitutive Modeling of Anisotropic Plasticity with ML Algorithms in Metals

Nuclear reactors hold significant promise for mitigating the environmental crisis and achieving a carbon-free world. The advancement of nuclear technology, however, is intricately tied to the materials used in reactors, with metals and alloys forming their core components. Understanding the plasticity and degradation of these materials is crucial to prevent critical reactor failures caused by factors such as radiation-induced embrittlement, creep, and fuel-cladding interactions. While traditional plasticity modeling has relied on constitutive laws and yield functions, the advent of machine learning (ML) and artificial intelligence has opened new avenues for this field. This paper explores the potential of data-driven approaches to enhance plasticity modeling for metals used in nuclear reactors. Recent studies have leveraged ML algorithms, including support vector machines and artificial neural networks, to model yield surfaces and correct theoretical yield functions with experimental data. Despite their accuracy, these models often lack interpretability and generality. To address these challenges, we investigate the applicability of various ML algorithms, i.e. neural networks, logistic regressions, decision trees, K-nearest neighbors, and Gaussian processes (GPs), in developing data-oriented flow rules for plasticity modeling. The paper demonstrates the superiority of GPs in terms of interpretability and generality. Using stress data generated from PyLabFEA, we compared the performance of these algorithms, highlighting the intrinsic uncertainties associated with GP predictions. Our findings indicate that Gaussian process classifiers (GPCs) offer a promising approach for modeling plasticity in metals, providing a balance between precision and physical insight. Additionally, this work presents a deep neural network (DNN) to model anisotropic Hill-type plasticity with perfect test data accuracy (accuracy score: 1.0), which is often hard to achieve with a classification problem. This shows the superiority of DNNs in terms of accuracy in modeling yield functions. The implications of our results for bridging the scale gaps in macrolevel simulations are discussed, and future directions for incorporating microstructural features into ML-based plasticity models are proposed. Overall, the ML framework presented in this paper can be employed for modeling constitutive relations that can be incorporated within traditional Finite Element Method (FEM)–based codes. Specifically, the GPC or DNN developed in this work can be readily swapped with the yield function within the so-called return mapping algorithm of nonlinear FEM-based plasticity subroutines to indicate yielding. Consequently, this merging of ML and FEM will allow us to leverage the geometric representational ability of FEM alongside the superior modeling capabilities of the ML algorithms.

Spatial characteristics and optimization of urban living space carbon suitability index (ULS-CSI) in Tianjin, China

The global climate crisis is escalating, and urban living Space (ULS) is a significant contributor to carbon emissions. How to improve the carbon suitability of ULS while promoting social and economic development is a global issue. This study aims to develop an evaluation system for comparing and analyzing carbon suitability inequality and spatial differences in different areas. To achieve this goal, an urban living space carbon suitability index (ULS-CSI) based on spatial organizational index (SOI) has been proposed. The ULS-CSI was calculated at the area scale in Tianjin using information from the Tianjin Land Use Database in 2021. The carbon emissions coefficient method was used to calculate the urban living space carbon emissions (ULSCE). Moran’I and LISA analysis were used to quantify the spatial differences of ULS-CSI. The results showed that the residential living area (RLA) carbon emissions was the highest at the area scale, with carbon emissions of 1.14 × 1011 kg, accounting for 33.74%. The green space leisure area (GLA) carbon absorption was the highest at the area scale, with carbon absorption of 5.76 × 105 kg, accounting for 32.33%. SOI in different areas have spatial heterogeneity as the SOI such as building area, road network density and land use characteristics are significantly different in different areas. Areas with superior CSI were primarily situated in Heping, Hexi, Nankai, and Beichen, accounting for 83.90%. Conversely, areas under the basic CSI threshold included Xiqing, Jinnan, and Dongli, accounting for 16.10%. Spatial characteristics of ULS-CSI in Tianjin portrayed a significant spatial positive correlation, indicating the highest autocorrelation degree of CSI at 500 m, with a Moran ’I value of 0.1733. Although these findings reflect the spatial characteristics of ULS-CSI and the SOI affecting the ULS-CSI at area scale, more perfect data are needed to reflect the complexity of structural factors affecting ULS-CSI at area scale. This study is helpful for urban planning to develop differentiated carbon reduction strategies and promote low-carbon and healthy urban development.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Orbital Prosthesis Rehabilitation in Biomedical Engineering by Means of Computer Vision-Photogrammetry and 3D Prototyping

Eye loss may be caused as a result of eye trauma, accidents, or malignant tumors, which leads the patient to undergo surgery to remove the damaged parts. This research examines the potential of computer vision represented by Structure from Motion (SfM) photogrammetry in fabricating the orbital prosthesis as a noninvasive and low-cost technique. A low-cost camera was used to collect the data towards extracting the dense 3D data of the patient facial features following Structure from Motion-Multi View Stereo (SfM-MVS) algorithms. To restore the defective orbital, a Reverse Engineering (RE) based approach has been applied using the similarity RE algorithms based on the opposite healthy eye to rehabilitate the defected orbital precisely. Following quality assurance and best-fitting statistical analysis, the digital model of the restored eye was converted into a physical model using 3D prototyping. This is later used to fabricate the mold for casting medical-grade silicone to obtain the final orbital prosthesis. The results show the power of SfM photogrammetry by offering a high-accuracy model of 0.048 mm and 0.186 mm relative errors acquired in the horizontal and vertical directions, respectively. These results boost the RE implementation in medicine to reconstruct the patient's damaged eye by mirroring the image of the healthy eye using RE algorithms. Therefore, the margin matching results claim perfect data capture settings and successful data processing workflow as designed in the first place. Consequently, one can claim this approach effectively rehabilitates maxillofacial deformities as an alternative to invasive restoration approaches. The presented approach provided a low-cost and safe workflow that avoids the patient the risks of exposure to harmful rays or magnetic fields available in other sensors.

Research on Enterprise Human Resource Management Reform Strategy in the Era of Big Data

In the 21st century, with the development of the Internet, mobile devices, and information technology, society has entered a new era: the era of big data. With the help of big data technology, enterprises can obtain massive market and consumer data, realize in-depth analysis of business and market, and enable enterprises to have a deeper understanding of consumer needs, preferences, and behaviors. At the same time, big data technology can also help enterprises carry out human resource management innovation and improve the performance and competitiveness of enterprises. Of course, from another perspective, enterprises in this era are also facing severe challenges. In the face of massive data processing and analysis, it requires superb data processing and analysis capabilities. Secondly, enterprises need to reconstruct their management system to adapt to the changes in the era of big data. Enterprises must treat data as assets and establish a perfect data management system. In addition, enterprises also need to pay attention to protecting customer privacy and data security to avoid data leakage and abuse. In this context, this paper will explore the thinking of enterprise human resource management innovation in the era of big data, and put forward some suggestions on enterprise human resource management innovation.

“TimeOntoImperfection”: An OWL 2 ontology to representing temporal data imperfection using imperfection theories

Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step.

Hospital Is Not the Home: Lessons From Implementing Remote Technology to Support Acute Inpatient and Transitional Care in the Home in the United States and United Kingdom.

The COVID-19 pandemic, patient preference, and economic opportunity are shifting acute care from the hospital to the home, supported by the transformation in remote monitoring technology. Monitoring patients with digital medical devices gives unprecedented insight into their physiology. However, this technology does not exist in a vacuum. Distinguishing pathology from physiological variability, user error, or device limitations is challenging. In a hospital, patients are monitored in a contrived environment. Monitoring at home instead captures activities of daily living alongside patients' trajectory of disease and recovery. Both settings make for "noisy" data. However, we are familiar with hospital noise, accounting for it in our practice and perceptions of normal. Home monitoring as a diagnostic intervention introduces a new set of downstream consequences, dependent on device, cadence of collection, adherence, duration, alarm thresholds, and escalation criteria. We must accept greater ambiguity and contextualize vital signs. All devices balance accuracy with acceptability, so compromises are inevitable and perfect data should not be expected. Alarms must be specific as well as sensitive, balancing clinical risk with capacity for response. By setting expectations around data from the home, we can smooth the adoption of remote monitoring and accelerate the transition of acute care.

Parameter-Free Interval Priority Weight Estimation Methods Based on Minimum Conceivable Ranges Under a Crisp Pairwise Comparison Matrix

Methods for interval priority weight estimation from a crisp pairwise comparison matrix were proposed in the interval analytic hierarchy process assuming the vagueness of human evaluation. The interval priority weights estimated by the conventional method do not reflect the intrinsic vagueness in the given pairwise comparison matrix (PCM). This paper proposes parameter-free methods based on minimal conceivable ranges for estimating interval priority weights from a crisp pairwise comparison matrix. The estimated interval priority weight vectors are required to satisfy (1) the potential reproducibility, (2) the normality, and (3) the preservation of the perfect consistent data. Estimation methods of interval priority weights are proposed based on the minimum possible range. We show those proposed methods satisfy the required three properties. The estimation problem of interval priority weights potentially has multiple solutions with which the associated interval PCMs are identical to one another. To make the further investigation simpler, we use an interval priority weight vector among multiple solutions such that the sum of the center values of interval priority weights is one. We compare the estimation methods of interval priority weights from the viewpoint of estimation accuracy by numerical experiments. Namely, by generating crisp pairwise comparison matrices randomly under true interval PCMs, we evaluate the accuracies of the estimated interval priority weight vectors by comparing the true interval priority weight vectors.

Construction of Curriculum System of Data Science and Big Data Technology under the Background of New Engineering

With the rapid development of science and technology, the construction of new engineering has become a hot topic in today s society. The new engineering Department aims to cultivate composite engineering talents with innovative spirit and practical ability to adapt to and promote the rapid economic development and industrial upgrading. Under the background of new engineering, data science and big data technology, as an important discipline direction, have a wide range of application fields and market prospects. Therefore, it is of great significance to build a scientifi c, reasonable and perfect data science and big data technology curriculum system for training high-quality talents to meet the needs of society. This paper will discuss how to build a reasonable curriculum system from the perspective of the job demands of data science and big data technology.

A Transformer-Based Knowledge Distillation Network for Cortical Cataract Grading.

Cortical cataract, a common type of cataract, is particularly difficult to be diagnosed automatically due to the complex features of the lesions. Recently, many methods based on edge detection or deep learning were proposed for automatic cataract grading. However, these methods suffer a large performance drop in cortical cataract grading due to the more complex cortical opacities and uncertain data. In this paper, we propose a novel Transformer-based Knowledge Distillation Network, called TKD-Net, for cortical cataract grading. To tackle the complex opacity problem, we first devise a zone decomposition strategy to extract more refined features and introduce special sub-scores to consider critical factors of clinical cortical opacity assessment (location, area, density) for comprehensive quantification. Next, we develop a multi-modal mix-attention Transformer to efficiently fuse sub-scores and image modality for complex feature learning. However, obtaining the sub-score modality is a challenge in the clinic, which could cause the modality missing problem instead. To simultaneously alleviate the issues of modality missing and uncertain data, we further design a Transformer-based knowledge distillation method, which uses a teacher model with perfect data to guide a student model with modality-missing and uncertain data. We conduct extensive experiments on a dataset of commonly-used slit-lamp images annotated by the LOCS III grading system to demonstrate that our TKD-Net outperforms state-of-the-art methods, as well as the effectiveness of its key components. Codes are available at https://github.com/wjh892521292/Cataract_TKD-Net.

Tradisi Bongkar Lubuk Larangan Di Muara Sipongi : Perspektif Hukum Islam

This research explains the activity of unloading lubuk ban, namely a tradition of harvesting fish in the Batang Girls River which was previously prohibited for a year by customary rules. This tradition has been going on for decades in the Mandailing Natal community. This research is qualitative and was carried out by collecting, analyzing, and providing a comprehensive interpretation so as to produce perfect data about the tradition of dismantling the lubuk ban. The results of this research conclude that the tradition of dismantling the lubuk ban in Simpang Mandepo Village, Muara Sipongi District, not only prioritizes the principles of benefit and maslahat (goodness), but also uses environmentally friendly tools, thus bringing in a lot of good fortune for the community.

Distributed Subgradient Method With Random Quantization and Flexible Weights: Convergence Analysis.

The distributed subgradient (DSG) method is a widely used algorithm for coping with large-scale distributed optimization problems in machine-learning applications. Most existing works on DSG focus on ideal communication between cooperative agents, where the shared information between agents is exact and perfect. This assumption, however, can lead to potential privacy concerns and is not feasible when wireless transmission links are of poor quality. To meet this challenge, a common approach is to quantize the data locally before transmission, which avoids exposure of raw data and significantly reduces the size of the data. Compared with perfect data, quantization poses fundamental challenges to maintaining data accuracy, which further impacts the convergence of the algorithms. To overcome this problem, we propose a DSG method with random quantization and flexible weights and provide comprehensive results on the convergence of the algorithm for (strongly/weakly) convex objective functions. We also derive the upper bounds on the convergence rates in terms of the quantization error, the distortion, the step sizes, and the number of network agents. Our analysis extends the existing results, for which special cases of step sizes and convex objective functions are considered, to general conclusions on weakly convex cases. Numerical simulations are conducted in convex and weakly convex settings to support our theoretical results.

Noise Imitation Based Adversarial Training for Robust Multimodal Sentiment Analysis

As an inevitable phenomenon in real-world applications, data imperfection has emerged as one of the most critical challenges for multimodal sentiment analysis. However, existing approaches tend to overly focus on a specific type of imperfection, leading to performance degradation in real-world scenarios where multiple types of noise exist simultaneously. In this work, we formulate the imperfection with the modality feature missing at the training period and propose the noise intimation based adversarial training framework to improve the robustness against various potential imperfections at the inference period. Specifically, the proposed method first uses temporal feature erasing as the augmentation for noisy instances construction and exploits the modality interactions through the self-attention mechanism to learn multimodal representation for original-noisy instance pairs. Then, based on paired intermediate representation, a novel adversarial training strategy with semantic reconstruction supervision is proposed to learn unified joint representation between noisy and perfect data. For experiments, the proposed method is first verified with the modality feature missing, the same type of imperfection as the training period, and shows impressive performance. Moreover, we show that our approach is capable of achieving outstanding results for other types of imperfection, including modality missing, automation speech recognition error and attacks on text, highlighting the generalizability of our model. Finally, we conduct case studies on general additive distribution, which introduce background noise and blur into raw video clips, further revealing the capability of our proposed method for real-world applications.

Research on the Innovation Path and Enterprise Adaptation of Green Finance in the Development of Low-Carbon Economy-Strategy Analysis Based on Neural Network Modeling

Abstract Green finance is an economic form formed by the organic combination of finance and environmental awareness, and the long-term development of a low-carbon economy needs to be strongly supported by green finance. Based on the generalized regression neural network model, this paper measures the level of green finance development in a low-carbon economy by constructing a green finance evaluation system. The evaluation system for enterprise adaptability level under green finance is constructed from three dimensions: enterprise green development ability, enterprise green innovation ability, and enterprise green management ability. The entropy weight method is used to empower the indicators in the rating system, and the set-pair theory is used to portray the data of the empowered indicators, while the Markov chain combined with the set-pair linkage is introduced to portray the dynamic trend of the development of enterprise adaptability level. The generalized regression neural network model’s prediction accuracy for green finance development in Guangdong Province is above 85% after the study. Year-on-year, the level of green finance development in Guangdong Province increased by around 57.78%, creating a significant growth trend in the context of a low-carbon economy between 2010 and 2017. The level of business adaptability also leaps from “poor” to “excellent”, with the linkage value changing from −0.8574 in 2018 to 0.8031 in 2022. This paper’s research provides the perfect data support for choosing a green financial innovation path only in a low carbon economy.

Clothing, textile, and fashion industry sustainability impact measurement and management

Data from the Kering Group's 2018 Environmental Profit and Loss (EP&L) statement were examined for their capacity to meet the demand for meaningful and manageable sustainability metrics. Significant resources were invested in creating the data reported in this EP&L statement, as Kering's operations in 104 countries were evaluated in ways separable into almost 1,500 different indicators. The data system was not, however, designed as a measurement system. That is, it was not set up as specifically positing the possibility of estimating separable parameters for comparing company location performances across sustainability challenges. Of particular importance is the lack of information in the EP&L on the overall consistency of the data reported, on the uncertainties associated with the metrics given, and on the meaningfulness of comparisons across environmental impacts, processes, and materials. The results reported here showing far from perfect data consistency and large uncertainties comprise an effort at constructing meaningful measurements that offers important lessons for the redesign of the data and reporting system.

A Novel Approach for Detecting Outliers by Using Isolation Forest with Reducing Under Fitting Issue

The effectiveness of machine learning for a particular activity depends on a variety of parameters. The incident database's description and validity come first and primary. Information retrieval even during the training cycle is more challenging if there is a lot of repetitious, unimportant information or incomplete information available. It is good knowledge that running time for ML tasks is significantly impacted by conditions as follows and sorting stages. To increase the accuracy of any model data cleansing is essential. Without sufficient data scrubbing, no predictive model accuracy can begin. EDA, or exploratory data analysis, is the name of this procedure. In this study, we discussed outlier identification, one of many EDA processes for complete perfect data. In this research, we attempted to use the isolation forest approach to calculate the outlier factor. Then a model known as an outlier finding model is created. The problem of outlier detection leads to a collection of connected supervised learning for binary classification. We carry out in-depth tests on various datasets and demonstrate that in our latest outlier finding technique compare with the old way. Our approach yields superior outcomes in terms of accuracy, precision, recall & F-1 score. Additionally, we successfully lowered the machine learning algorithms' under fitting issue.

A safety framework for flow decomposition problems via integer linear programming.

Many important problems in Bioinformatics (e.g. assembly or multiassembly) admit multiple solutions, while the final objective is to report only one. A common approach to deal with this uncertainty is finding "safe" partial solutions (e.g. contigs) which are common to all solutions. Previous research on safety has focused on polynomially time solvable problems, whereas many successful and natural models are NP-hard to solve, leaving a lack of "safety tools" for such problems. We propose the first method for computing all safe solutions for an NP-hard problem, "minimum flow decomposition" (MFD). We obtain our results by developing a "safety test" for paths based on a general integer linear programming (ILP) formulation. Moreover, we provide implementations with practical optimizations aimed to reduce the total ILP time, the most efficient of these being based on a recursive group-testing procedure. Experimental results on transcriptome datasets show that all safe paths for MFDs correctly recover up to 90% of the full RNA transcripts, which is at least 25% more than previously known safe paths. Moreover, despite the NP-hardness of the problem, we can report all safe paths for 99.8% of the over 27 000 non-trivial graphs of this dataset in only 1.5 h. Our results suggest that, on perfect data, there is less ambiguity than thought in the notoriously hard RNA assembly problem. https://github.com/algbio/mfd-safety.

An efficient IoT-Artificial Intelligence-based Chronic Kidney Diseases Prediction using Temporal Convolutional Network (TCN) Deep Learning Method in Healthcare System

IOT (Internet of Things) can control the remote based patient’s health care monitoring system and here monitoring for the chronic kindney diseases predication levels. When an IoT device collects data from patients, it sends the data to a software application that can be viewed by healthcare professionals and/or patients. Ongoing Chronic Kidney Diseases (CKD) is one of the main supporters of the bleakness and mortality of non-transferable illnesses, influencing 15-20% of the total populace. Early and exact finding of CKD stages is accepted to be fundamental to decrease the effect of unexpected problems on patients, for example, hypertension, sickliness (low blood count), bone mineral issues, poor nourishing wellbeing, and irregularities. Since our requirement is better disease classification for Temporal Convolutional Network (TCN) DL (Deep Learning) methods, the problem of machine learning method is that the existing system cannot detect the level of disease. Our get the Chronic Kidney Disease dataset get from kaggle minimum 1000 and 10000 datas has been used the preprocessing of input datasets. The feature extraction using Latent Dirichlet Allocation (LDA) and this algorithm has been process for specially used for text data or information extraction. This method can be used for perfect extract text data form CKD disease dataset. After extract the data and then approach the classification method of TCN algorithm. In this algorithm to classify the CKD disease level accuracy find out here. Our classification algorithm for TCN examine the extreme performance of pointers being an accuracy.

GRI: General Reinforced Imitation and Its Application to Vision-Based Autonomous Driving

Deep reinforcement learning (DRL) has been demonstrated to be effective for several complex decision-making applications, such as autonomous driving and robotics. However, DRL is notoriously limited by its high sample complexity and its lack of stability. Prior knowledge, e.g., as expert demonstrations, is often available but challenging to leverage to mitigate these issues. In this paper, we propose General Reinforced Imitation (GRI), a novel method which combines benefits from exploration and expert data and is straightforward to implement over any off-policy RL algorithm. We make one simplifying hypothesis: expert demonstrations can be seen as perfect data whose underlying policy gets a constant high reward. Based on this assumption, GRI introduces the notion of offline demonstration agent. This agent sends expert data which are processed both concurrently and indistinguishably with the experiences coming from the online RL exploration agent. We show that our approach enables major improvements on camera-based autonomous driving in urban environments. We further validate the GRI method on Mujoco continuous control tasks with different off-policy RL algorithms. Our method ranked first on the CARLA Leaderboard and outperforms World on Rails, the previous state-of-the-art method, by 17%.