Errors In Judgment Research Articles

Behavioral biases and the rational decision-making process of financial professionals: significant factors that determine the future of the financial market

PurposeThis paper describes how financial professionals' behavioral biases influence their financial forecast and decision-making process. Most of the earlier studies are focused on well-developed financial markets, and little is researched about financial professionals, such as institutional investors, portfolio managers, investment advisors, financial analysts, etc., in emerging markets.Design/methodology/approachAn expert-validated questionnaire measure four prominent behavioral biases and Indian financial professionals' rational decision-making process. The final sample consists of 274 valid responses using the purposive sampling technique. IBM SPSS and AMOS structural equation modeling (SEM) software are used to build measurement and structural models, multivariate analysis including regression, factor analysis, etc.FindingsThe results provide empirical insights into the relationship between behavioral biases and the decision-making process. The results suggest that the structural path model closely fits the sample data. The presence of behavioral biases indicates that financial professionals' forecasting and decision-making is not always rational but bounded rational or irrational due to these factors. Furthermore, these biases (except overconfidence bias) have a markedly significant and positive relationship with irrational decision-making.Research limitations/implicationsIt is critical to eradicate these psychological errors, but awareness and attentiveness toward behavioral biases may help financial professionals to make informed decisions. Investors can improve their portfolio decisions and investments by recognizing their judgment errors and focusing on specific investment strategies to mitigate the impact of these biases. It is necessary to incorporate behavioral insights while developing training techniques for financial professionals. Rules of thumb, visual tools, financial coaching and implementing social-cultural elements in training programs enable financial professionals to develop simple, engaging, appealing and customized approaches for their clients.Originality/valueThis novel study is the first of this kind of research that examines the relationship between financial professionals' behavioral biases and rational decision-making process. This study significantly and remarkably provides insights into irrationality in financial professionals' decision-making.

Development of an Artificial Intelligence Tool for Intraoperative Guidance During Endovascular Abdominal Aortic Aneurysm Repair

Adverse events during surgery can occur in part due to errors in visual perception and judgment. Deep learning is a branch of artificial intelligence (AI) that has shown promise in providing real-time intraoperative guidance. This study aims to train and test the performance of a deep learning model that can identify inappropriate landing zones during endovascular aneurysm repair (EVAR). A deep learning model was trained to identify a "No-Go" landing zone during EVAR, defined by coverage of the lowest renal artery by the stent graft. Fluoroscopic images from elective EVAR procedures performed at a single institution and from open access sources were selected. Annotations of the "No-Go" zone were performed by trained annotators. A 10-fold cross-validation technique was used to evaluate the performance of the model against human annotations. Primary outcomes were intersection-over-union (IoU) and F1 score and secondary outcomes were pixel-wise accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The AI model was trained using 369 images procured from 110 different patients/videos, including 18 patients/videos (44 images) from open access sources. For the primary outcomes, IoU and F1 were 0.43 (standard deviation ±0.29) and 0.53 (±0.32) respectively. For the secondary outcomes, accuracy, sensitivity, specificity, NPV, and PPV were 0.97 (±0.002), 0.51 (±0.34), 0.99 (±0.001). 0.99 (±0.002), and 0.62 (±0.34) respectively. AI can effectively identify sub-optimal areas of stent deployment during EVAR. Further directions include validating the model on datasets from other institutions and assessing its ability to predict optimal stent graft placement and clinical outcomes.

Legal knowledge management for prosecutors based on judgment prediction and error analysis from indictments

Legal AI aims to provide improved knowledge management services based on legal documents. Existing legal judgment prediction datasets mainly use court verdicts. However, for prosecutors, the use of indictments for judgment predictions can help detecting inconsistencies between predictions and prosecution, providing prosecutors with more accurate references to laws and charges through error analysis. In this study, we collect a dataset called TWLJP, which contains 342,754 indictments. We compared three possible messaging passing architectures among the law, regulation, and accusation cause prediction tasks, i.e. independent, topological, and interactive. The result shows that interactive message passing among the three tasks achieved the best Macro-F1 performance of 95.2 %, 79.62 %, and 65.84 % for laws, regulations, and accusation cause prediction, respectively. We further improve the prediction of accusation cause from 8.8 % macro-F1 to 62.3 % for underperformed accusation causes via Prompt-Based Learning. Finally, in view of the situation where the charge prediction are written in various ways, we adopted a lenient approach to assess the accusation and improved the accusation performance to 77.2 %.

Naïve information aggregation in human social learning

To glean accurate information from social networks, people should distinguish evidence from hearsay. For example, when testimony depends on others’ beliefs as much as on first-hand information, there is a danger of evidence becoming inflated or ignored as it passes from person to person. We compare human inferences with an idealized rational account that anticipates and adjusts for these dependencies by evaluating peers’ communications with respect to the underlying communication pathways. We report on three multi-player experiments examining the dynamics of both mixed human–artificial and all-human social networks. Our analyses suggest that most human inferences are best described by a naïve learning account that is insensitive to known or inferred dependencies between network peers. Consequently, we find that simulated social learners that assume their peers behave rationally make systematic judgment errors when reasoning on the basis of actual human communications. We suggest human groups learn collectively through naïve signaling and aggregation that is computationally efficient and surprisingly robust. Overall, our results challenge the idea that everyday social inference is well captured by idealized rational accounts and provide insight into the conditions under which collective wisdom can emerge from social interactions.

IPA를 활용한 의과대학생의 연구 역량 강화를 위한 요구 분석

Objectives The purpose of this study is to investigate the level of research competencies and requirements resulting from the completion of a research competency enhancement education program for individual medical students. Based on this investigation, the study aims to provide foundational data for educational support in strengthening research competencies within medical schools.
 Methods A survey was conducted targeting a total of 101 participants, including 54 first-year medical students (Group 1) who were enrolled in the E-Medical School, Department of Medicine, and had completed a two-year research education program in the Department of Medical Education, and 47 third-year medical students (Group 2) who had participated in a two-year research education program. Data was collected using a research competencies scale, and paired-sample t-tests and Importance-Performance Analysis were conducted.
 Results First, the results of the needs analysis for the entire student group revealed the need for short-term focused improvement in “interpretation of results” and “presentation skills,” long-term progressive improvement in “acquisition of specialized knowledge,” “experimental execution,” and “communication skills,” and improved awareness in “research ethics,” “laboratory safety,” “communication abilities,” “experimental design,” and “data organization/ analysis,” as well as the need for sustained maintenance in “topic comprehension” and “error judgment.” Second, the needs analysis based on participation in the educational programs revealed the following results. In Group 1 (first-year medical students), there was a need for short-term focused improvement in “laboratory safety,” “error judgment,” “interpretation of results,” and “presentation skills,” long-term progressive improvement in “research ethics,” “acquisition of specialized knowledge,” “experimental execution,” and “communication skills,” and a demand for “communication skills.” In Group 2 (third-year medical students), there was a need for short-term focused improvement in “interpretation of results” and “presentation skills,” long-term progressive improvement in “acquisition of specialized knowledge,” “experimental design,” “experimental execution,” and “communication skills.”
 Conclusions This study provided empirical data related to enhancing the research competencies of medical students, serving as foundational information for the revision of education programs that promote learner-centered research competencies.

SCAN for Abuse: Electronic Health Record-Based Universal Child Abuse Screening

BackgroundIdentification of physical abuse at the point of care without a systematic approach remains inherently subjective and prone to judgement error. This study examines the implementation of an electronic health record (EHR)-based universal child injury screen (CIS) to improve detection rates of child abuse. MethodsCIS was implemented in the EHR admission documentation for all patients age 5 or younger at a single medical center, with the following questions.1) “Is this patient an injured/trauma patient?”2) “If this is a trauma/injured patient, where did the injury occur?”A “Yes” response to Question 1 would alert a team of child abuse pediatricians and social workers to determine if a patient required formal child abuse clinical evaluation. Patients who received positive CIS responses, formal child abuse work-up, and/or reports to Child Protective Services (CPS) were reviewed for analysis. CPS rates from historical controls (2017–2018) were compared to post-implementation rates (2019–2021). ResultsBetween 2019 and 2021, 14,150 patients were screened with CIS. 286 (2.0 %) patients screened received positive CIS responses. 166 (58.0 %) of these patients with positive CIS responses would not have otherwise been identified for child abuse evaluation by their treating teams. 18 (10.8 %) of the patients identified by the CIS and not by the treating team were later reported to CPS. Facility CPS reporting rates for physical abuse were 1.2 per 1000 admitted children age 5 or younger (pre-intervention) versus 4.2 per 1000 (post-intervention). ConclusionsIntroduction of CIS led to increased detection suspected child abuse among children age 5 or younger. Level of EvidenceLevel II. Type of StudyStudy of Diagnostic Test

Exploring Perceptual Intensity Properties Using Electrotactile Stimulation on Fingertips.

Understanding electrotactile parametric properties is a crucial milestone in achieving intuitive haptics. Perceptual intensity is a primary property, but its exploration remains challenging due to subjectivity. To address this problem, this study conducted two experiments on fingertips and proposed two metrics based on significant findings. Experiment 1 found a significant linear relationship (R 2 = 0.981) between pulse amplitude (PA) and pulse width (PW) in the logarithmic plane, and proposed a metric of parameter intensity (PI) to estimate the intensity of parameters. In Experiment 2, subjective intensity (SI) was defined and measured using a scale of 0 to 10. A metric model of SI (SI model) was derived based on the linear relationship (R 0.78) between PI and measured SI. A calibration method was proposed and its prediction accuracy has been verified. An average RMSE of 11.2 % indicated an accuracy close to the subjective judgment error of 8.7 %. Results are consistent across subjects and four different electrode-skin conditions (ESC). The findings of this study provide theoretical support for SI prediction and regulation, which is significant for electrotactile feedback.

19세기 영국의 중국차 인식과 차 시장의 변화

This study aims to examine the perception of Chinese tea in Britain in and around the 19th century and its effect on the tea market. The findings are summarized as follows: First, in the 19th century, an error of judgment by the Chinese tea producers and immoral British tea merchants brought about the import of adulterated tea into Britain. In the wake of the abolition of the monopoly of the British East India Company, there was a widespread negative view of Chinese tea and China itself due to the lack of quarantine facilities and the outbreak of the Opium War. Second, to eradicate the import of adulterated tea, the British government introduced a new custom quarantine system, along with amendments. Scientific research and publications, and the advertising of tea by merchants, helped to improve consumer awareness regarding food safety and establish good tea standards. Third, after the discovery of tea trees in Assam in India, the tea industry in India and Sri Lanka grew and saw better productivity due to the invention of the tea rolling machine and tea drier. Advertisements promoting pureness gained the widespread trust of consumers, and the tea industry in both these countries surpassed China in the 1880s and led the global tea trade.

A New Method for Reconstructing Tree-Level Aboveground Carbon Stocks of Eucalyptus Based on TLS Point Clouds

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation.

Development and evaluation of a user friendly android application for advocacy of “Organ Donation” among residents of Western Rajasthan, 2022

Introduction: Organ donation is either when a person allows healthy transplantable organs/tissues to be removed after death, or when the donor is alive. Digitization in various aspects of healthcare is replacing humans, eliminating biases and judgement errors. The use of an android application reduces subjectivity in need assessment, accessing basic information and contacting the right facilities to register for organ donation. Objectives: To develop an android application to increase awareness among participants. To assess knowledge, attitudes and practices related to organ donation through the application. To evaluate satisfaction levels regarding the android application. Methods: A cross-sectional study was conducted among 384 participants of urban and rural areas of Pali district over four months. A predesigned pretested questionnaire was used for data collection by trained volunteers and analyzed using Epi info (version 7.2). Results: The study was conducted among 192 urban and 192 rural participants of Pali district whose average age was 27.5 years in urban and 24 years respectively ,60.2% were females ,29.2% were married, 96% belonged to Hindu religion and 43% were medical students .Although 82.8% of the participants had heard about organ donation only 17.2% had pledged to donate organs. The rural and urban differences were marked stating religion as a determinant of their attitude for organ donation (p-value 0.009), that infants and elders could not donate organs (p-value 0.014), that organ donation was costly (p -value 0.00), that doctors should not advocate organ donation (p-value 0.014). The median agreement was above 5 on the Likert’s scale regarding the application being better than print media, increasing insight to organ donation and giving a sense of social responsibility. Conclusions: Awareness about organ donation is marred by myths, but the community had a positive attitude to use the application for increasing awareness levels.

Vision based deformation inspection system for automotive glass using Hough circle detectors

Production of automotive glass with the accurate shape and form is a challenge that fabricates the glass products with properties of adequate transparency and lack of imaging deformations and optical flaws. An auto windscreen with deformation flaws will likely distort the driver’s view of surrounding objects, leading to errors in visual judgment that may be dangerous to other road users. In the traditional method of examining vehicle glass in the manufacturing process, human inspectors perform the bulk of the work. This study proposes a frequency reconstruction method established on computer vision to automatically detect deformation flaws in automotive glass. To quantify the deformation level of an outwardly curving glass product, we exploit the digital imaging of a known standard pattern with base dots through a testing sample to capture a transmitted and reflected deformation image of that sample. Then, the proposed method applies the circular Hough transform voting scheme to find the peak points of the base dots in parameter space and reconstructs an image with the base dots of the captured image. The binary testing image subtracts the binary reconstructed image to obtain a binary difference image that displays the detected deformation areas. Experimental outcomes present that the proposed approach using dots pattern reaches a high 82.76% probability of exactly discriminating deformation flaws and a low 1.14% probability of wrongly investigating regular regions as deformation flaws on transmitted appearances of transpicuous glass.

374 Cognitive Biases and Heuristics in Surgical Settings: A Systematic Review

Abstract Aim Cognitive biases and heuristics represent mental shortcuts that can lead to errors in judgement. Both are noted in surgical settings and risk patient safety. Yet, up till now, the extent of biases in surgical settings has not been systematically reviewed. Method This PROSPERO registered (CRD42022334828) review was conducted in accordance with PRISMA guidelines. MEDLINE and four other major databases were searched from their inception to 28th August 2022 using “surg*” with “cognitive bia*” and “heuristics” terms. Original primary research studies in English were included, with blinded full text screening. Relevant risk of bias (RoB) tools were employed for each study. Results The search identified 26,640 papers. Following de-duplication and screening, 16 final papers were included. 34 biases were identified including three prospective experiments, three retrospective analyses, and 10 questionnaire studies. Confirmation and anchoring biases were the most represented. RoB analysis found broad heterogeneity, with four studies being deemed of high risk. All identified cognitive biases were found to influence relevant surgical outcomes and seven studies cited a negative impact on patient care. Conclusions Cognitive biases will continue to contribute to surgical errors and never events unless they are described and recognised with greater precision and de-biasing strategies are implemented. Of note, mindfulness-based training and deliberate reflection tested by two studies demonstrated reduced rates of surgical error. Nevertheless, our review demonstrates a lack of experimental studies which are crucial to understanding why and how biases contribute to negative outcomes and we suggest avenues for further research and systems change.

The Impact of Judgment Variability on the Consistency of Offline Effectiveness Measures

Measurement of the effectiveness of search engines is often based on use of relevance judgments. It is well known that judgments can be inconsistent between judges, leading to discrepancies that potentially affect not only scores but also system relativities and confidence in the experimental outcomes. We take the perspective that the relevance judgments are an amalgam of perfect relevance assessments plus errors; making use of a model of systematic errors in binary relevance judgments that can be tuned to reflect the kind of judge that is being used, we explore the behavior of measures of effectiveness as error is introduced. Using a novel methodology in which we examine the distribution of “true” effectiveness measurements that could be underlying measurements based on sets of judgments that include error, we find that even moderate amounts of error can lead to conclusions such as orderings of systems that statistical tests report as significant but are nonetheless incorrect. Further, in these results the widely used recall-based measures AP and NDCG are notably more fragile in the presence of judgment error than is the utility-based measure RBP, but all the measures failed under even moderate error rates. We conclude that knowledge of likely error rates in judgments is critical to interpretation of experimental outcomes.

Integration of Deep Learning for Automatic Recognition of 2D Engineering Drawings

In an environment where manufacturing precision requirements are increasing, complete project plans can consist of hundreds of engineering drawings. The presentation of these drawings often varies based on personal preferences, leading to inconsistencies in format and symbols. The lack of standardization in these aspects can result in inconsistent interpretations during subsequent analysis. Therefore, proper annotation of engineering drawings is crucial as it determines product quality, subsequent inspections, and processing costs. To reduce the time and cost associated with interpreting and analyzing drawings, as well as to minimize human errors in judgment, we developed an engineering drawing recognition system. This study employs geometric dimensioning and tolerancing (GD&T) in accordance with the ASME (American Society of Mechanical Engineers) Y14.5 2018 specification to describe the language of engineering drawings. Additionally, PyTorch, OpenCV, and You Only Look Once (YOLO) are utilized for training. Existing 2D engineering drawings serve as the training data, and image segmentation is performed to identify objects such as dimensions, tolerances, functional frames, and geometric symbols in the drawings using the network model. By reading the coordinates corresponding to each object, the correct values are displayed. Real-world cases are utilized to train the model with multiple engineering drawings containing mixed features, resulting in recognition capabilities surpassing those of single-feature identification. This approach improves the recognition accuracy of deep learning models and makes engineering drawing and image recognition more practical. The recognition results are directly stored in a database, reducing product verification time and preventing errors that may occur due to manual data entry, thereby avoiding subsequent quality control issues. The accuracy rates achieved are as follows: 85% accuracy in detecting views in 2D engineering drawings, 70% accuracy in detecting annotation groups and annotations, and 80% accuracy in text and symbol recognition.

Exploration of advanced computer technology to address analytical and noise improvement issues in machine learning

Computer-based visual recognition technology combined with deep learning enables accurate image matching through interactive, multi-level comparison. Currently, popular search methods such as RNN, Faster RNN, etc. are used in computer vision to compute image information by performing hierarchical comparison of image objects. In today’s machine learning is used to construct training curves to predict corresponding results, but the accuracy rate will cause some data distortion due to overformation. Therefore, to solve the problem of interference is now physical, the abandonment method is developed to extract certain neurons and reduce the number of feature values. In this study, a visual image recognition framework is designed that uses advanced computer technology to mark and compare images, and to mark and eliminate blurred images. The experimental method successfully improves the prediction of accuracy after a judgment error by comparing the results of training with the results of deep learning verification. The accuracy of matrix formation and result prediction can reach 0.9812 without eliminating the image produced by the light-emitting elements. We remove noisy images based on the same sampling information. After re-training and re-predicting, the accuracy can reach 0.9847.

Judgment of blast furnace iron-tapping status based on data differential processing and dynamic window analysis algorithm

Ironmaking plants hold a core position in steel production. Efficient level detection and iron-tapping status judgment can accurately measure the liquid level and predict the amount of molten iron in the ladle, thereby ensuring production safety and improving production efficiency. At present, the judgment of the start and end statuses of blast furnace iron-tapping mainly relies on manual visual inspection. However, due to the high-temperature environment on-site and interference from iron powder, gunning mud, and other dust, operators find it challenging to make accurate judgments. To address these issues, this study proposes a robust and accurate method for judging the iron-tapping status of blast furnaces. This study collected four million on-site liquid level data, underwent interpolation, noise reduction, normalization, and other preprocessing steps, and then used differential processing to calculate the iron-tapping rate. Combined with dynamic window feature analysis, it effectively captured the change characteristics of the liquid level in the ladle under different iron-tapping statuses. In simulated on-site experiments, the method achieved a 96.6 ​% accuracy rate for iron-tapping status judgment. The real-time iron-tapping status judgment method based on differential processing of liquid level data and dynamic window feature analysis proposed in this study demonstrates high precision and robustness in the complex environment of ironmaking plants. This technology not only improves the production efficiency of the iron and steel link but also frees on-site operators from the harsh working environment, eliminating human errors in judgments and omissions, further promoting the intelligent process of ironmaking plants.

Self-Assured Deep Learning With Minimum Pre-Labeled Data for Wafer Pattern Classification

Data quality plays an important role during the training stage of machine/deep learning models. The annotation hinges on the experiences of domain experts. To acquire the experts knowledge in the context of machine learning, manual data labeling, a tedious and time-consuming task in supervised learning, should be given a top priority. However, the domain experts in the line of plentiful manual annotation may easily get distracted or fatigued after long-time work, causing judgment errors, mislabeling, etc. The pattern recognition of wafer defect map is investigated in this paper, the primary goal of which is to train the convolutional neural network (CNN) model through a very limited number of manually labeled data so that the trained model is capable of performing pseudo labeling. Subsequently, a self-assured adaptive ensemble learner in terms of a series of shallow neural networks is proposed to filter wafer map samples with untrusted pseudo-labels. In the result, the amount of human annotations is significantly reduced by 61% for training a highly accurate classifier. A minimum number of manually labeled data is suggested while the equally high classification performance of wafer defect pattern is maintained. For the evaluation purpose, the proposed self-assured learning is compared with the confidence learning.

The Design of an Automatic Temperature Compensation System through Smart Heat Comparison/Judgment and Control for Stable Thermal Treatment in Hyperthermic Intraperitoneal Chemotherapy (HIPEC) Surgery.

After surgery for ovarian cancer or colorectal cancer, residual tumors are left around. A practical way to treat residual tumors is to destroy them with heat by injecting high-temperature drugs into the abdominal cavity. The injected medicinal substances are induced to flow out of the abdominal cavity; then, the spilled drug flows back into the abdominal cavity through feedback. During this process, the heat starts to decrease; thus, the treatment performance reduces. To overcome this problem, this study compares and assesses the temperature needed to maintain the heat for treatment and transmits a command signal to the heat exchanger through a look-up table (LUT). When the temperature decreases during the circulation of medications leaking out of the abdominal cavity, the LUT transmits a control signal (Tp) to the heat exchanger, which increases or vice versa. However, if the temperature (To) is within the treatment range, the LUT sends a Ts signal to the heat exchanger. This principle generates a pulse signal for the temperature difference (Tdif) in TC by comparing and determining the temperature (To) of the substance flowing out of the abdominal cavity with the reference temperature (Tref) through the temperature comparator (TC). At this time, if the signal is 41 °C or less, the LUT generates (heats) a Tp signal so that the temperature of the heat exchanger can be maintained in the range of 41 °C to 43 °C. If the Tdif is 44 °C or higher, the LUT generates (cools) the Ta signal and maintains the temperature of the heat exchanger at 41-43 °C. If the Tdif is maintained at 41-43 °C, the LUT generates a Tx signal to stop the system performance. At this time, the TC operation performance and Tdif generation process for comparing and determining the signal of To and Tref for drugs leaking out of the abdominal cavity is very important. It was observed that the faster the response signal, the lower the comparison and judgment error was; therefore, the response signal was confirmed to be 0.209 μs. The proposed method can guarantee rapid/accurate/safe treatment and automatically induce temperature adjustment; thus, it could be applied to the field of surgery.

Short‐term electricity load forecasting based on improved sparrow search algorithm with optimized BiLSTM

AbstractShort‐term electricity load forecasts (STELF) is an essential part of power system and operation, capable of balancing electricity demand and is vital to the safety and efficient operation of the power system. The research improves the Long short‐term memory (LSTM), combines it with Bidirectional recurrent neural network (BIRNN), and obtains the improved Bidirectional Long Short‐Term Memory Network (BiLSTM) forecasting model. The Sparse Search Algorithm (SSA) can provide a new solution to more difficult global optimization problems and has been improved due to the shortcomings of the search and detection mechanisms. and a simplex mechanism is introduced to obtain an improved Search Mechanism Sparse Search Algorithm (SMSSA) optimized pathfinding algorithm. And constructs the SMSSA‐based optimized BiLSTM for STELF model. By choosing actual data, the model's prediction behavior is confirmed. The results showed that, in descending order, BiLSTM, LSTM, and Recurrent Neural Network (RNN) had the best fitting effects between the predicted and actual values. BiLSTM also had the highest prediction accuracy, with error values of 95.7059 for Root Mean Square Error (RMSE), 79.1575 for Mean Absolute Error (MAE), and 2.1260% for Mean Absolute Percent Error (MAPE). After SMSSA optimized the parameters, SMSSA‐BiLSTM had the best fit and had errors that were much lower than those of the other two models. According to the three error judgment metrics of RMSE, MAE, and MAPE, the errors were 82.6298, 71.9029, and 2.0952%, respectively. This showed that SMSSA‐BiLSTM performed well in short‐term power load forecasting, offering security for the power system's safe operation.

Effect of posture on total hip arthroplasty through direct anterior approach

To investigate the effect of different postures on direct anterior approach(DAA) total hip arthroplasty. Total of 94 patients who underwent DAA total hip arthroplasty from July 2016 to June 2020 were retrospectively analyzed. They were divided into two groups according to different positions during the operation, including 45 cases in lateral position and 49 cases in supine position (with the aid of stent). The general data such as gender, affected limb, body mass index(BMI), incision length, operation time, intraoperative bleeding volume, drainage volume 24 hours after operation, hemoglobin difference before and after operation, first landing time after operation, postoperative hospitalization time, postoperative complications, visual analogue scale(VAS) at 1 day, 1, 2 weeks, 1, 3 and 6 months after operation, Harris score at 1, 2 weeks, 1, 3 and 6 months after operation were observed and compared between the two groups. Patients in both groups were followed up for 6 to 12 months with an average of (8.31±2.22) months. There was no significant difference between two groups in gender, affected limb, age, height, weight, body mass index(BMI), preoperative VAS score and preoperative Harris score(P>0.05). The incision length, operation time, intraoperative bleeding volume, 24-hour drainage volume, hemoglobin difference before and after operation, first time to the ground and postoperative hospitalization time of patients in supine position (assisted by stent) group were all better than those in lateral position group(P<0.05);There was no significant difference in the number of blood transfusions during and after operation(P=0.550). There was no significant difference in anteversion angle and abduction angle in the supine position(with the aid of stent) group during and after operation (P=0.825, P=0.066);There was significant difference in anteversion angle and abduction angle in the lateral position group during and after operation(P<0.05). VAS of patients in supine position (assisted by stent) group were lower than those in lateral position group at 1 day, 1, 2 weeks and 1 month after operation(P<0.05), and there was no statistical difference between two groups at 3 and 6 months after operation(P>0.05). Harris scores of patients in supine position(assisted by stent) group were higher than those in lateral position group at 1 week, 1 month and 3 months after operation(P<0.05), and there was no significant difference between two groups at 6 months after operation(P>0.05). Compared with the lateral position, the supine position DAA total hip arthroplasty has the advantages of small incision, short operation time, less bleeding, early landing time, short hospitalization time, and small intraoperative acetabular cup position judgment error. It has the advantage of fast postoperative recovery, but the recovery of hip joint function is the same after 6 months.