Inappropriate Operation Research Articles

FSPDD: A double-branch attention guided network for few-shot PCB defect detection

AbstractDuring the production of printed circuit board (PCB), there will be defects due to inappropriate operations, which will affect the use of electronic products. Majority defect detection methods cost a large number of annotated samples to train detection models. However, PCB defect samples are difficult to collect. Moreover, existing few-shot object detection methods tend to extracting low-level features from support and query images via the shared backbone such as ResNet-50. However, it is not sufficient to obtain fine-grained prior guidance. To address the above issues, we propose a few-shot PCB defect detection model with double-branch attention. Specifically, the joint attention enhancement (JAE) module is proposed to fully mine effective information of query PCB images in multiple dimensions to enhance the representation of latent defects. Then, the multi-scale guidance (MSG) module is proposed to integrate prior knowledge within support PCB images into vectors to reweight query PCB images. Experiments on the PCB defect dataset demonstrate that AP of FSPDD outperforms state-of-the-art methods under different shot settings (k=1,2,3,5,10,30) and our proposed FSPDD has a good generalization ability, in which AP reachs 0.273 when $$k=30$$ k = 30 and is 5.28% higher than SOTA methods.

A Review of Modifiable Health Care Factors Contributing to Inpatient Suicide: An Analysis of Coroners’ Reports Using the Human Factors Analysis and Classification System for Healthcare

Inpatient suicides have devastating and long-lasting consequences for patients, families, and health care organizations, posing a major challenge for hospitals. Although many studies have identified patient risk factors for inpatient suicide, the modifiable health care factors are less understood. Failure to understand these modifiable factors weakens organizations' ability to design and implement effective prevention strategies. The Human Factors Analysis and Classification System for Healthcare (HFACS-Healthcare) was used to classify and analyze modifiable health care factors that contributed directly or indirectly to inpatient suicides in Australian hospitals between 2009 and 2018. Comparisons were made between general and psychiatric hospital units to identify context-specific recommendations. Of the 367 cases, 216 (58.9%) had enough information to analyze the contributing factors, and 214 (58.3%) included unit location information. Multiple modifiable health care factors were identified in the cases as contributing to the patients' suicides. Commonly, cases reported decision errors made by individuals (57.4%), problems with the physical environment (56.0%), and unit-level operational decision-making errors (that is, planned inappropriate operations) (48.6%). An association was found between unit type and problems with coordination, mental state, tasks, physical environment, planned inappropriate operations, and organizational culture (p < 0.05). General prevention initiatives may not be effective in addressing inpatient suicides across specialty units. HFACS-Healthcare enabled a deeper understanding of inpatient suicide and the identification of priority areas that, if addressed, could help reduce the number of preventable suicides in hospitals. Hospital suicide prevention initiatives need to be tailored to specific units and target individual and system vulnerabilities to improve safety and reduce inpatient suicide rates.

Multimodal Framework for Smart Building Occupancy Detection

Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving.

Evaluation of Hazards and Operability Study for Optimization Operation of Industrial Processes

Nitrocellulose is a substance that ignites easily when exposed to a hot or humid environment. It is widely used in the military as well as civil industries, most notably the production of explosives and films. Therefore, it was selected as a case study in order to assess the risks in the seven production stages using HAZOP (Hazards and Operability) technology. In this study, the raw material preparation stage investigated. This stage was divided into five identified nodes. The deviation from the design intention for 16 critical parameters for this process was studied.The findings of the study were as follow: Nitric acid and sulfuric acid are stored for a long time under process shutdown resulting in the corrosion of the tank and acids decomposition, Acids measurement depend on operator competence in the process of preparation of the acid mixture, which leads to inappropriate operating conditions, the temperature of the acid mixture increases due to endothermic mixing process resulting in decomposition of the mixture, in the process of feeding RC to the reactor, the flow increases due to the increase in the speed of the belt affecting the degree of nitration. Recommendations are; It should not be turned off until the end of the acids, thermoneutral process was wanted and Adjust the RC flow speed every 10 operations.

Proposal for Real-time Quality Control Method to Enhance Hydrological Application of Radar Rainfall

The primary aim of a rain radar is to observe ground-level rainfall accurately. Effective quality control of rain radar data is necessary to ensure accuracy. In the field of hydrology, various methods have been employed to improve the quality of radar rainfall by correcting or synthesizing it with gauge rainfall. However, it is more important to enhance the quality of radar polarimetric variables in order to improve the quality of radar rainfall. Rain radar systems generate various polarimetric variables through complex radar hardware systems and signal processing algorithms. Measurement errors may arise from equipment abnormalities, inappropriate operating settings, and obstructions in observation during signal processing algorithms and production process of polarimetric variables. Because rain radar is an observation device operating in real time, it entails more frequent and continuous quality control compared to flowmeters that intermittently measure streamflow. This study introduces a series of real-time quality control methods tailored for rain radar, including inspection of radar rainfall accuracy, quality analysis of polarimetric variables (ρ&lt;sub&gt;HV&lt;/sub&gt;, Z&lt;sub&gt;DR&lt;/sub&gt;, Φ&lt;sub&gt;DP&lt;/sub&gt;), error estimation of polarimetric variables, and long-term variability of errors.

Net Power Optimization Based on Extremum Search and Model-Free Adaptive Control of PEMFC Power Generation System for High Altitude

The operating characteristics of proton exchange membrane fuel cell (PEMFC) generation systems will change in high altitude. In order to improve the system output performance, this paper proposes a net power optimization (NPO) based on extremum search and model-free adaptive control (MFAC) of PEMFC power generation system. A PEMFC power generation system model for high-altitude environment is established, the off-line analysis shows that with the increase of altitude, the performance of the air compressor decreases, the optimal oxygen excess ratio (OER) of the system decreases, and surge phenomenon is more likely to occur. Considering the non-minimum phase characteristic of the net power to OER, the OER is optimized online by extremum search strategy and the optimization step and cycle ensure the convergence of the strategy. Combined with a model-free adaptive control strategy based on compact form dynamic linearization (CFDL), the optimal OER is tracked real time, the data model identified online ensures the adaptability of the system. Finally, a hardware-in-the-loop platform is built and comparative experiment is carried out. The results show that: compared with the off-line optimization method (OOM), (1) NPO can improve the net power of the system at different altitudes through the adaptive adjustment of OER and no off-line data is required inappropriate static operating points are avoided. (2) in high altitude areas, NPO has higher operational stability and can effectively avoid surge phenomenon.

COMPARISON OF PROBABILITY IMPACT MATRIX AND FUZZY LOGIC METHODS FOR RISK ANALYSIS OF RURAL INFRASTRUCTURE PROJECTS IN INDONESIA

Through the provision of facilities and infrastructure in the Village, it can be used as an effort to empower the community to accelerate regional economic development that is effective and robust. In its implementation, infrastructure projects have risks that can have an impact on the sustainability of the project. Therefore the research aims to analyze what are the most influential risk factors in Participatory-Based Rural Infrastructure Projects using the PIM (probability impact matrix) and Fuzzy Logic Methods. This research uses a descriptive method with a qualitative and quantitative approach. The collected data will be analyzed using the Fuzzy Logic method. The results of the identification of risk factors for infrastructure projects were identified as many as 23, but after validating the risk identification for rural infrastructure projects, there were 17 identified risk factors. Based on the results of the analysis using the risk matrix, 17 factors have values that tend to be small and overall are at a moderate level. However, after analysis using the Fuzzy Logic method, the risk factor values varied, and there were 8 risk factors that were at a high level, these risk factors included technological failure, land change, negligent social behavior, bad design, inappropriate materials, inappropriate methods, inappropriate Standard Operating Procedures.

Study on the Mode and Characteristics of SSOs in Hybrid AC–DC Transmission Systems via Multitype Power Supply

The impact of subsynchronous oscillation (SSO) on grid security is becoming increasingly prominent with the rapid development of a large new energy base. However, the SSO modes and characteristics in complex power systems where series-complementary AC systems, DC systems, wind farms, and thermal power plants co-exist simultaneously are still not well understood, and relevant research has yet to be conducted. To address these issues, this study aims to investigate the SSO oscillation modes and the participation of specific influencing factors using eigenvalue and participation factor analysis. Additionally, the influence of system operation mode and control parameters on the SSO characteristics is studied through eigenvalue analysis. The findings of this study suggest that multiple oscillation sources and the co-existence of various oscillation patterns in hybrid AC–DC transmission systems cause SSO problems. The SSOs arise due to inappropriate system operation or parameter selection. As the series compensation increases, the system tends to become unstable. The system stability improves when the wind power output increases or the thermal power output decreases while keeping the output power of other sources constant. On the other hand, the system stability decreases as the DC transmission power gradually decreases. In terms of the control parameters, a higher value of the inner-loop proportionality coefficient of the converter current on the rotor side of the wind turbine results in a more unstable system. However, the rotor-side converter outer loop parameters and the stator-side control loop PI parameters have a negligible effect on the oscillation frequency and damping of the system. Matlab time domain simulations are conducted to verify the accuracy of the theoretical analysis.

Involvement of Human and Organizational Factors in Railway Accidents: Application of the Human Factors Analysis and Classification System and Bayesian Network

The occurrence of railway accidents is not always the result of error/violation on the operator’s end; rather, there are many organizational factors involved. This research investigated 97 railway accident reports from the West Zone of Bangladesh Railway between 2015 and 2019 to categorize factors within the institution that contribute to accidents. The accidents were divided into four categories (collision, derailment, locomotive failure, and signal passed at danger [SPAD]) and were applied to the Human Factors Analysis and Classification System (HFACS) framework. The HFACS framework systematically identifies direct and hidden factors that lead to an accident. A total of 225 human and organizational factors were found involved in 97 accidents, of which 106 (47.11%) were organizational-level factors and 119 (52.89%) were front-line/operation-level factors. To understand the association between factors, a Bayesian network was created following the naive Bayes algorithm, keeping “accident type” as the class variable. Analysis revealed that “Collisions” occur because of organizational factors such as the structure of command, planned inappropriate operations, and operation-level factors such as perceptual errors, and personal unreadiness. “Derailment” was influenced by organizational reasons, such as equipment issues, company safety culture, and inadequate supervision, whereas operation-level factors were crew and resource management and skill-based error. “Locomotive failure” was found to be associated with only organizational factors, such as equipment issues and inadequate supervision, and SPAD mostly occurred because of exceptional violations at the operational level. The identified issues, if addressed properly, can help ensure a safer railway industry.

Construction and application of a heterogeneous quality control library for the Xpert MTB/RIF assay in tuberculosis diagnosis.

Proficiency testing based on quality control materials is an important component of the quality assurance system for detection methods. However, in the detection of infectious diseases, it is a challenge to use quality control materials derived from clinical samples or pathogens owing to their infectious nature. The Xpert MTB/RIF assay, endorsed by the World Health Organization, is one of the most widely implemented assays in the detection of Mycobacterium tuberculosis along with rifampicin resistance and its heterogeneity. Clinical isolates are typically used as quality controls for this assay, leading to concerns about biosafety, constrained target sequence polymorphisms, and time-consuming preparation. In this study, a heterogeneous quality control library for the Xpert MTB/RIF assay was constructed based on DNA synthesis and site-directed mutation, which provides sufficient rifampicin resistance polymorphisms, enabling monitoring all five probes of Xpert MTB/RIF and its combinations. Escherichia coli and Bacillus subtilis were used as heterogeneous hosts rather than the pathogen itself to eliminate biosafety risks; thus, preparation does not require a biosafety level III laboratory and the production time is reduced from a few months to a few days. The panel was stable for more than 15 months stored at 4°C and could be distributed at room temperature. All 11 laboratories in Shanghai participating in a pilot survey identified the specimens with corresponding probe patterns, and discordant results highlighted inappropriate operations in the process. Collectively, we show, for the first time, that this library, based on heterogeneous hosts, is an appropriate alternative for M. tuberculosis detection.

Surgical management of duodenal injury: experience from 92 cases.

Duodenal injury increases with traffic accidents, and delayed diagnosis or inappropriate operation increase mortality and complications. This study aimed to explore early recognition and timely surgical intervention. All patients with duodenal injuries treated operatively during the past 10years were reviewed, and the data were analyzed retrospectively regarding the mechanism of injury, diagnostic and therapeutic methods, and outcome. A total of 92 patients with duodenal injuries accounted for 7.3% of 1258 patients with abdominal injury. Of the 92 patients, 71 (77.17%) experienced blunt trauma, with traffic accidents accounting for 59.2% (42/71). In 35 patients, a preoperative diagnosis was obtained by reviewing abdominal signs, peritoneocentesis, and imaging. The remaining 57 patients underwent urgent laparotomy, through which a definitive diagnosis of duodenal injury was confirmed during the operation. In all 92 patients, the surgical procedures involved simple sutures; pedicled jejunal piece coverings; and various anastomoses following resection of the injured duodenal portion, including the Whipple procedure and damage-control surgery principles. The overall mortality rate was 12.0% (11/92) with deaths mainly occurring due to associated injuries. When excluding 2 cases of intraoperative death, there were 47 cases in the double-tube gastrostomy group and 43 cases in the traditional triple-tube group, with mortality rates of 10.64% and 9.30% in the two groups, respectively (χ2 = 0.045, P > 0.05). Postoperative complications occurred in 15 patients (18.5%). There was a high incidence of duodenal (or pancreatic/biliary) leakage. Early diagnosis and operation of duodenal injury are crucial to reducing complications and mortality. Surgical methods should be based on injury grade, associated injuries, and vital signs. Double-tube gastrostomy can reduce complications such as intestinal obstruction.

Assessing humanitarian supply chain operations in the aftermath of the Kermanshah earthquake

PurposeThe main objective of this study is to consider how to benefit efficiently from the significant potential of humanitarian operations by individuals. For this purpose, this study aims to assess failure factors in humanitarian supply chain operations after the Kermanshah earthquake considering the role of all parties, focusing on individuals who did not wish to work with formal organisations on the whole. In the aftermath of the Kermanshah earthquake, which occurred on 12 November 2017, improvised groups of Iranian civilians from all over the country played an important role in humanitarian supply chain operations as individuals. Although most of these groups sincerely intended to help the affected society, victims could not benefit properly from these significant potential humanitarian actions. On the contrary, these potential actions caused some issues during humanitarian operations, such as blocking roads, inappropriate last-mile distribution, wasting resources and so on.Design/methodology/approachThis research study considers mixed methods, including an on-site survey, semi-structured interviewing and a questionnaire designed for statistical analyses. The analysis included 140 responses to the questionnaire, semi-structured interviews with 32 affected families, interviews with 5 emergency managers from the Housing Foundation of the Islamic Republic of Iran and on-site survey reports.FindingsThis study presents a framework for humanitarian supply chain management to deal with future disasters in the same area or areas with similar characteristics to the case study. In general, the results of this study demonstrate that the nature of humanitarian supply chain operations makes it impossible to consider that these operations are free of challenges. However, several influential factors, such as training humanitarian actors and integrated management, might considerably increase the efficiency of humanitarian operations by individuals.Originality/valueThis study highlights the influential factors of inappropriate humanitarian operations by individuals, derived from an analysis of the Kermanshah case and literature review. The authors suggest a framework to benefit from the significant potential of individuals with wide-ranging experiences and proficiency, for future cases similar to the case study.

Practical Rules to Adjust the Numerical Overcurrent Functions of Protective Relays to Avoid Maloperation due to Inrush Currents of Downstream Distribution Transformers

This article shows practical rules which were developed for the adjustment of Numerical Overcurrent Functions (NOCF) of microprocessor-based protective relays in order to avoid their maloperation due to Transformer Magnetizing Inrush Currents (TMIC). The TMIC waveforms are decaying and nonsinusoidal, and their effects are different on instantaneous, definite-time, or inverse-time NOCF; thus, specific rules are necessary for each type of NOCF. Experimental methods, specifically developed to perform tests of NOCF with TMIC, were applied to four commercial relays from different manufacturers. As each relay has different types of curves, a total of 26 inverse-time curves were included in the set of tests. In practice, the specific behavior of a relay cannot be assumed as known during the coordination of protection; therefore, the rules are based on the worst cases of results (i.e., in order to avoid the incorrect trip of any of the analyzed relays). The input data for the developed rules are the estimated cumulative RMS values of TMIC, assumed as known since there is a long tradition of their estimation. The estimated cumulative RMS values of TMIC, at different time intervals, should be divided by the numerical factors which were specifically proposed for each type of NOCF (to obtain suitable limits to avoid their inappropriate operation). In order to illustrate the application of the proposed rules, the solutions to two cases related to a simple example are shown at the end of this article.

A dynamic anomaly detection method of building energy consumption based on data mining technology

Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study proposes a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algorithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algorithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy consumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.

Reason for improper simple hysterectomy in invasive cervical cancer in Northeast India.

The objective of the study was to determine the reasons for improper simple hysterectomy in the presence of invasive cervical cancer in Northeast India. The medical records of 52 patients who had undergone improper simple hysterectomy in the presence of invasive cervical cancer and were referred to a tertiary regional cancer Institute at Guwahati, Assam, between January 2015 and December 2019 were reviewed. Most of the patients presented with abnormal vaginal bleeding (40.4%). The failure to perform cervical cytology before the operation was quite high at 48.1% (25 patients). Interestingly, normal cytologic smear could still be found in 15.4% (8 patients) despite the presence of invasive cervical cancer. Failure to perform preoperative Papanicolaou smear, incomplete evaluation of cervical intraepithelial neoplasia (CIN) on cervical biopsy, and negative Papanicolaou smear accounted for 75% of the patients undergoing inappropriate simple hysterectomy. The most common indications for inappropriate operation were abnormal vaginal bleeding (40.4%) and CIN (19.2%). The reasons for inappropriate simple hysterectomy included lack of preoperative cervical cytology (48.1%), false-negative cervical cytology (15.4%), incomplete evaluation of cervical dysplasia or microinvasion on biopsy (11.5%). failure to perform indicated conization( 5.8%), emergency hysterectomy (3.8%), errors in colposcopic examination (3.8%), incomplete evaluation of an abnormal cervical cytology (3.8%), failure to review slide (3.8%) and failure to biopsy a gross cervica lesion (3.8%). Most improper simple hysterectomy resulted from deviation from guideline for cervical cancer detection protocols. Improper simple hysterectomy in the presence of invasive cervical cancer can be avoided if one sticks to the diagnostic guideline for patients with an abnormal cervical cytology.

Fuzzy hypergraph network for recommending top-K profitable stocks

Stock ranking prediction is an effective method for screening high investment value stocks in the future and can strongly assist investors in making decisions. However, this task is also challenging. In recent years, the role of stock relationship information in ranking prediction has been gradually recognized, and the hypergraph has been introduced to analyze the complex group-wise relationships between stocks. However, the application of hypergraph to stock relationship analysis still faces two major issues: unreasonable hyperedge construction and inappropriate aggregation operation in graph convolution that does not conform to the actual market. To solve these problems, we propose an attribute-driven fuzzy hypergraph network (AFHGN). Compared to the traditional hypergraph, AFHGN provides the following advantages: (1) The incidence matrix is constructed via fuzzy clustering to describe the relationships between stocks more reasonably. (2) An attribute-driven gate unit is introduced in the graph convolution to simulate the influence of stocks in the real market. (3) Trend weights are created to enhance the ability of stock embeddings to represent trends. The effectiveness of our algorithm has been verified by conducting a large number of experiments on real data. In addition, the investment simulation proves that our algorithm has better profitability than the state-of-the-art (SOTA) algorithms.

Gas Flow and Ablation of 122 mm Supersonic Rocket Nozzle Investigated by Conjugate Heat Transfer Analysis

The propellant gas flow of a supersonic rocket in inappropriate operating conditions can cause excessive ablation inside a nozzle. In this research, conjugate heat transfer analysis (CHTA), consisting of computational fluid dynamics (CFD) and finite element analysis (FEA), was applied to investigate the gas flow and ablation of a 122 mm nozzle as a case study in the transient state, based on actual operating conditions. First, the nozzle was tested in a static experiment. Then, the experimental results were employed for CHTA settings and validation. Next, after completing the CFD calculation, the results revealed that the nozzle’s gas flow, temperature, pressure, Mach number, shock, etc. were consistent with theoretical results. Finally, using the CFD results as loads, the FEA results showed the equivalent von Mises stress (σv), which was consistent with the ablation results from the experiment, as expected. The more the σv, the greater the ablation. Both σv and ablation were high near the throat and decreased further away. In addition, increasing the insulators’ thickness reduced σv, leading to ablation reduction. The research findings contribute to an understanding of ablation and the methodology of employing CHTA to improve the design of 122 mm and other nozzles with reduced ablation for higher efficacy.

Islanding power quality detection using lighting search optimization with deep learning model on distributed generation systems

Recently, the significant penetration of distributed generations (DGs) in distribution networks has made new challenging issues in network protection. A major challenge exists in the distribution networks in the identification of islanding conditions, where a portion of the distribution network with DGs is accidentally detached from the central grid due to protection relay and circuit breaker operation. Since it results in damaging the DGs and their equipment, effective islanding condition detection model becomes essential. Besides, non-detected islanding conditions can lead to voltage and frequency deviances from the normal ranges, inappropriate operation of protection, and personnel hazards. Therefore, the recent developments in deep learning (DL) can be applied to perform island power quality detection process. In this view, this article introduces an Islanding Power Quality Detection using Lighting Search Optimization with Deep Learning (IPQD-LSODL) model. The proposed IPQD-LSODL model mainly aims to find the events in islanding power quality (IPQ) and non-islanding power quality (NIPQ). The proposed model initially applies down-sampling empirical mode decomposition (DEMD) approach which effectively filters out the basic signal from the polluted ones. In addition, deep belief network (DBN) model is used to classify the events into IPQ and NIPQ. Moreover, the hyperparameters of the DBN method were optimally chosen by the use of LSO algorithm with an intention of accomplishing maximum classification accuracy. The performance validation of the IPQD-LSODL model is carried out and validated through comparison study with existing models. The IPQD-LSODL method achieved an accuracy of 99.91 percent in the class 5. The results implied the promising performances of the IPQD-LSODL method over recent approaches.

Risk Assessment Model-Guided Configuration Optimization for Free-Floating Space Robot Performing Contact Task

The use of free-floating space robots for contact tasks is very promising in space exploration. However, severe damage or obvious disturbance may occur if inappropriate operation is implemented. In this paper, a novel risk assessment method is first proposed to give a clear description of the risk state before contact happens and provide guidance for configuration optimization to reduce risk on contact tasks. Firstly, the dynamics model of a free-floating space robot is given. On this basis, two important risk assessment indicators, the maximum contact force and the base attitude disturbance caused by contact, are derived. By integrating the risk assessment indicators, a novel risk assessment model is proposed for a free-floating space robot performing a contact task. It is a multidimensional and extensible risk assessment space which could give a clear description of the risk state before contact happens. Thereafter, considering the results given by the risk assessment model, the configuration optimization for a free-floating space robot is implemented based on the design of optimization factor in null space. Finally, numerical simulation for a 7-degree-of-freedom free-floating space robot performing a contact task is carried out, and the simulation results verify the effectiveness of the proposed method.

998 Improving Accuracy of Tumour Site Identification During Colonoscopy; a Retrospective Analysis

Abstract Aim Colonoscopy is essential for accurate pre-operative colorectal tumour localisation. Due to variable colonic length and lack of internal landmarks, tumour site identification can be difficult, posing risks of inappropriate operations being offered. To avoid this, we must correctly identify the tumour site on endoscopy in &amp;gt;95% of patients as per BSG guidelines. In 2019, accuracy levels compared to radiology and histology fell significantly short of this standard. We therefore made improvements to our endoscopy database and displayed relevant guidelines in endoscopy rooms. Here, we evaluate improvement in accuracy of endoscopic tumour localisation after implementation of these initiatives. Method Retrospective study of results of all colonoscopies (214) showing suspicious lesions at our trust in 2020, compared to results of counterpart radiology and histology tests, and audited against the BSG standard of &amp;gt;95% accurate identification of tumour position by colonic segment. Results Accuracy of endoscopic tumour localisation was 92.2% and 92.3% compared to histology and radiology respectively. Although this does not meet the national standard of &amp;gt;95%, it is a significant improvement from the year prior - 80.2% and 78.8% compared to histology and radiology respectively. The greatest degree of error appears in the rectum, often being confused with recto-sigmoid and sigmoid. Conclusions Guideline reminders and database improvements were crucial to increased accuracy. However, clearer definition of colonic territories is required to avoid confusion when attributing localisation. We recommend following the American guidelines in which the term ‘recto-sigmoid’ is abolished in favour of a boundary 15cm from the anal verge that represents the division between rectum and sigmoid.