Results Of Performance Evaluation Research Articles

Combustion and thermodynamics properties of erythritol tetranitrate-based energetic films composed by electrospinning nano fibers

ABSTRACT High energy composite films made from high explosives has important applications in pyrotechnics. In this study, electrostatic spraying was used to prepare energy-containing films based on 1,2,3,4-butanetetrol tetranitrate (ETN). The results show that all energy containing films are made of nanofibres. They have excellent hydrophobicity and combustion properties. In particular, monolayer films in which ETN is the main energy containing substance. Once ignited, the trace monolayer film generates a maximum pressure of 0.85 MPa and an adiabatic flame temperature of 2,855 °C. For single and double layer films, the heat of combustion can reach 9899 J/g and 11975 J/g respectively. The level of combustion heat can also be controlled by adjustment of the ETN content. The results of the thermo-properties performance evaluation show that by optimizing the nanostructure, the ETN-based energy-containing fiber membranes can meet the requirements of advanced pyrotechnic products. In particular, there is great potential in the metastable intermixed composites (MICs) field.

Comparative analysis of communication strategies impact on the effectiveness of interaction with an AI chatbot

Introduction. The integration of artificial intelligence (AI) into everyday human interactions, particularly through chatbots, has significantly increased in recent years. AI-based chatbots successfully mimic human conversations by understanding user requests and responding appropriately. Improving the effectiveness of educational and professional activities related to text processing in these conditions seems extremely relevant and predetermines the goal of our study: comparing the effectiveness of interaction with ChatGPT when formulating queries in various communication strategies. Procedure and methods. The study on the effectiveness of interacting with ChatGPT was conducted using a natural experiment scheme with preliminary training. The subjects of the study were 34 employees (15 men, 19 women, aged 25-36; M = 31.6; SD = 1.8) from the marketing department of the company "Technologies of Trust". These employees performed rewriting tasks by interacting with ChatGPT within the framework of specially adapted communication strategies (persuasive and cooperative) and evaluated the chatbot's usability and the quality of the text generated by ChatGPT. The Wilcoxon signed-rank test for dependent samples was used to identify the effects of communication strategies on interaction efficiency, and exploratory factor analysis with subsequent varimax rotation was employed to identify latent variables of interaction under different communication strategies. Research Results. Comparison of the results of the ChatGPT interaction performance evaluation when using 2 strategies (cooperative and persuasive) showed that for all 11 usability indicators, there is a significant advantage of the cooperative communicative strategy at the significance level of p<0.05. However, on 6 indicators of text quality obtained through rewriting, the persuasive strategy shows advantages and on 4 of them at a significance level of p<0.05. In the course of the study it was found that the factor solutions for the studied communicative strategies differ significantly: the key factor in using the persuasive strategy is speed and accuracy in achieving goals, while in the cooperative strategy the adequate response of chatbots to objections is critical. Conclusion. The results obtained in the study contribute significantly to the understanding of communication with AI chatbots, highlighting the nuances of interacting through linguistic means typical for human communication. The findings demonstrate the complexity of choosing the optimal strategy for interacting with ChatGPT and uncover the factors that determine the features of subjective evaluation of the interaction.

Design and evaluation of UKRI-MPW0: a monolithic HV-CMOS pixel sensor with backside biasing

High-Voltage CMOS (HV-CMOS) pixel sensor technology is the most promising route for achieving thin, monolithic pixel detectors with excellent radiation tolerance, as required by future tracking applications in physics experiments, such as the High Luminosity LHC. This paper introduces a U.K. Research and Innovation funded Multi-Project Wafer prototype chip UKRI-MPW0, which is an HV-CMOS pixel sensor designed to push the performance parameters of these detectors. Having a novel sensor cross-section optimised for backside biasing to unprecedented high voltages, UKRI-MPW0 demonstrates the capability to withstand bias voltages of up to 600 V, surpassing the current state-of-the-art and promising enhanced radiation tolerance. The paper provides a detailed description of the design and performance evaluation results of UKRI-MPW0.

Hazard Identification and Risk Assessment During Simultaneous Operations in Industrial Plant Maintenance Based on Job Safety Analysis

The risk of accidents during simultaneous operations (SIMOPS) in plant maintenance has been increasing. However, research on methods to prevent such accidents has been limited. This study aims to develop a novel framework, hazard identification and risk assessment of simultaneous operations (HIRAS), for identifying and evaluating potential hazards during concurrent tasks. The framework developed herein is expected to be an effective safety management tool that can help prevent accidents during these operations. To this end, the job location and hazard information in job safety analysis (JSA) were standardized into four attributes. The standardized information was then synchronized spatially and temporally to develop a HIRAS model that identifies and assesses the impact of hazards between operations. The model was tested using 40 JSA documents corresponding to maintenance operations at Company P, a South Korean steel-making company. The model was tested in two scenarios: one with planned operations and the other with unplanned operations in addition to planned operations. The performance evaluation results of the first scenario showed an F1-score of 98.33%. In this case, a recall of 97.52% means that the model identified 97.52% of the hazard-inducing factors. The second scenario was compared with the results of a review by six subject matter experts (SMEs). The comparison of the results identified by the SMEs and the model showed an accuracy of 89.3%. This study demonstrates the potential of JSA, which incorporates the domain knowledge of workers and can be used not only for individual tasks but also as a safety management tool for surrounding operations. Furthermore, by improving the plant maintenance work environment, it is expected to prevent accidents, protect workers’ lives and health, and contribute to the long-term sustainable management of companies.

Performance evaluation of an automatically controlled transcutaneous energy transfer system powering left ventricular assist device.

The survival rate of LVAD recipients may be improved by eliminating the infection failure mode at the percutaneous lead entry site. Wireless powering through a Transcutaneous Energy Transfer System (TETS) is a promising solution for achieving this. However, automatic controls need to be employed to compensate for the variations in efficiency and power transfer due to changes in load and coil-to-coil gaps. This article discusses the results of invitro and animal models performance evaluation of an automatically controlled TETS for powering a Left Ventricular Assist Device. It was found that power up to 20 W could be transferred at an axial gap of 30 mm. A 5% variation in speed and a 2% change in flow rate are observed with the change in tissue thickness, in porcine carcass model. The LVAD could be operated at the required RPM irrespective of the change in axial positions.

SNN eXpress: Streamlining Low‐Power AI‐SoC Development With Unsigned Weight Accumulation Spiking Neural Network

AbstractSoCs with analog‐circuit‐based unsigned weight‐accumulating spiking neural networks (UWA‐SNNs) are a highly promising solution for achieving low‐power AI‐SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA‐SNNs in low‐power AI‐SoCs: (i) the absence of UWA‐SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed‐signal circuit implementation of UWA‐SNN‐based SoCs. This paper argues that, by integrating the proposed solutions, the development of an EDA tool that enables the easy and rapid development of UWA‐SNN‐based SoCs is feasible, and demonstrates this through the development of the SNN eXpress (SNX) tool. The developed SNX automates the generation of RTL code, FPGA prototypes, and a software development kit tailored for UWA‐SNN‐based application development. Comprehensive details of SNX development and the performance evaluation and verification results of two AI‐SoCs developed using SNX are also presented.

Design and construction of the cylindrical drift chamber for the COMET Phase-I experiment

COMET is an experiment at J-PARC that searches for the charged lepton flavor violating process of neutrino-less muon-to-electron conversion (μ→e conversion) in a muonic atom. The COMET Phase-I experiment, which represents the first stage of its staged approach, utilizes the cylindrical drift chamber (CDC) as the main detector to measure the momentum of electrons emitted from μ→e conversion in a 1 T magnetic field. The CDC is designed for optimal performance to achieve the best momentum resolution and effectively separate the μ→e conversion electron signal from background signals. This design entails adapting He:i-C4H10 (90:10) as its chamber gas and employing an all-stereo wire configuration. This document provides a comprehensive overview of the CDC detector, encompassing design considerations, mechanical structure, construction methodologies, and results of performance evaluations conducted using cosmic-rays.

The performance evaluation of homestead exit under the background of rural revitalization

The reform of rural residential land system was piloted in 2015. Then, the scope of the pilot was expanded in 2020. An important part of the reform of the homestead system is the homestead exit, which is also a basic and important front-end link in the reform of the homestead system. In this study, the four pilot area: Pengshan District, Ziyang City, Cuiping District and Luxian District, which are close to Chengdu City of Sichuan Province, were investigated and questionnaires were distributed to the farmers. Based on the research data, from the perspective of farmers, this study selects indicators from three aspects including economic benefits, social benefits and living environment to make sub-objective evaluation and comprehensive evaluation of homestead exit performance. In the calculation process, the entropy weight method is used to calculate the weight of each index and the fuzzy comprehensive evaluation method is used to calculate the performance score. The research results show that the comprehensive performance of homestead exit in the surveyed areas is “good”, and the performance evaluation results are “average”, “good” and “good” respectively from the three aspects of economic benefits, social benefits and living environment.

B2DFL: Bringing butterfly to decentralized federated learning assisted with blockchain

We propose a novel decentralized federated learning framework called B2DFL. It decomposes the aggregation process of vanilla FL into layered and serialized sub-aggregation processes and offloads the communication and computation from a single point to distributed nodes, thus addressing the single point of failure issue in centralized FL. The decentralization of B2DFL is based on the Butterfly, a distributed network topology, to organize and orchestrate the order and rules of node aggregation. Additionally, to mitigate potential risks such as dropouts or tampering, we leverage the blockchain and IPFS systems. Specifically, after each node completes its computation (including training and aggregation), it generates a hash value of the results as proof. We maintain a Tamper-evident Data Structure (TDS) on the blockchain, which records these proofs to ensure tamper-proofing and fast verification. To reduce the storage burden on the blockchain and improve throughput, we store the aggregated results on IPFS, a system that enables quick data location through hash values of data, for data backup. We also design a node replacement mechanism for quick dropout handling. We conduct a comprehensive performance evaluation and experimental results demonstrate that B2DFL presents a significant performance improvement while achieving privacy and decentralization.

Hydrothermal synthesis of SnO2/MoO3-x/rGO ternary nanocomposites as a high-performance anode for lithium ion batteries

The theoretical specific capacity of tin dioxide (SnO2) as a lithium-ion batteries (LIBs) anode material is up to 1494 mAh·g−1, far exceeding that of graphite. However, the low conductivity, volume expansion, crushing failure and particles agglomeration during charge/discharge cycles limit its application in LIBs. In this work, the SnO2/MoO3-x/rGO composite material was synthesized by one-step hydrothermal method, with SnO2 and amorphous MoO3-x tightly and uniformly anchored at the surface of reduced graphene oxide (rGO). The results of structural characterization and electrochemical performance evaluation show that amorphous MoO3-x can increase the reactive active sites, inhibit Sn particles aggregation, and promote the reversible reaction of SnO2. rGO effectively improves the electrical conductivity of the composite and buffers the volume change of Li-Sn alloying/dealloying during cycles. In addition, due to introduction of MoO3-x and rGO a stable SEI film can be formed at the material's surface to improve the cycle stability. SnO2/MoO3-x/rGO exhibits excellent rate performance and cycle performance. When the rGO content is 10.9 wt%, the reversible capacity of the composite reach 813 mAh·g−1 after 800 cycles at current density of 1.0 A·g−1, and 450.6 mAh·g−1 after 1000 cycles at current density of 5.0 A·g−1, with the capacity retention rate of 96.9%.

Smartphone as an alternative to measure chlorophyll-a concentration in small waterbodies

Monitoring chlorophyll-a concentrations (Chl-a, μg·L−1) in aquatic ecosystems has attracted much attention due to its direct link to harmful algal blooms. However, there has been a lack of a cost-effective method for measuring Chl-a in small waterbodies. Inspired by the increase of smartphone photography, a Smartphone-based convolutional neural networks (CNN) framework (SCCA) was developed to estimate Chl-a in Aquatic ecosystem. To evaluate the performance of SCCA, 238 paired records (a smartphone image with a 12-color background and a measured Chl-a value) were collected from diverse aquatic ecosystems (e.g., rivers, lakes and ponds) across China in 2023. Our performance-evaluation results revealed a NS and R2 value of 0.90 and 0.94 in Chl-a estimation, demonstrating a satisfactory (NS = 0.84, R2 = 0.86) model fit in lower Chl-a (<30 μg L−1) conditions. SCCA had involved a realtime-update method with hyperparameter optimization technology. In comparison with the existing methods of measuring Chl-a, SCCA provides a useful screening tool for cost-effective measurement of Chl-a and has the potential for being an algal bloom screening means in small waterbodies, using Huajin River as a case study, especially under limited resources for water measurement. Overall, we highlight that the SCCA can be potentially integrated into a smartphone application in the future to diverse waterbodies in environmental management.

Klasifikasi Kualitas Buah Apel Dengan Algoritma K-Nearest Neighbor (K-NN) Menggunakan Bahasa Pemrograman Python

Fruit is an important intake for the human body, apples are included in the fruit favored by the people of Indonesia. For this reason, it is necessary to provide apples of good quality, so that they can benefit the body. By using the k-NN method that is considered able to train data quickly and effectively for training data and testing data in large quantities. This study began from the collection of datasets obtained from https://www.kaggle.com/, then perform a preprocessing process followed by separating the training data and testing data with a composition of 25% testing data and 75% training data. Then the k-NN method is applied to this study to be classified based on several existing criteria, so as to obtain the results of performance evaluation K-NN with the value of accuracy that has been calculated with python programming. In implementing datamining using Python programming language by utilizing the library that has been provided as a process to facilitate the implementation of machine learning. From The Matrix confution test, there are 441 data predicted with true data, and 440 data predicted incorrectly. As for the 54 and 65 data predicted to be less precise than 1000 testing data. So that the accuracy value obtained by the k-NN method is equal to 0.88 or 88%. It is seen that the k-NN method can work well, quickly and efficiently in training large amounts of data.

EVALUASI SASARAN KINERJA PEGAWAI (SKP) PADA DINAS PERTANIAN DAN KETAHANAN PANGAN PROVINSI JAWA TIMUR

The evaluation of employees’ performance, specifically government employees, gives 2 (two) sides of benefit, namely both for the employees’ performance themselves and for the governmental instances where they work. The evaluation of Employees’ Performance Targets (EPT) on government employees is a form of performance valuation based on The Government Regulation Number 30 of Year 2019 about Performance Valuation which aims to ensure objectivity of guidance for government employees that is done based on the performance system and career system. This study uses the descriptive method along with qualitative approach. According to the study results, it is known that the implementation of EPT on government employees’ working performance focusing on quantity, quality, cost, and time valuation. The results of performance evaluation is also in good state, as indicated by the timely manner of performance reporting by the employees. The obstacling factors of EPT implementation are namely the uneven facilities and infrastructures, and the time-consuming coordination between related institutions. Evaluation of EPT on government employees performance has 3 points of view, namely evaluators, performance, and indicators, and is done periodically. Decision making on human resource management also is in good state, as indicated by obstacles where some employees has their performance level evaluated as below expectation. This study suggests that leaders should be closer to their employees in order to intensify their guidance for their omployees on implementing the EPT, as well asgrowing the sense of bounding with their employees. Therefore, the implementation of employees’ performance evaluation would not be mere formality.

Low-carbon development path based on carbon emission accounting and carbon emission performance evaluation: a case study of Chinese coal production enterprises.

Carbon emission accounting is the basic premise of effective carbon emission reduction and management. This study aimed to establish the carbon emission model and performance evaluation framework of coal mine production enterprises and clarify the low-carbon development path of enterprises. In this study, we took a typical coal production enterprise (K enterprise) in the Shanxi province of China as the research object. We also estimated the carbon emissions of the enterprise mainly according to the Chinese Carbon Emission Accounting Standard (GB/T 32151.11-2018). The triangular model was used to construct the carbon performance evaluation framework. On this basis, we suggested the enterprise's low-carbon development path. The results showed that (1) the carbon emission of K enterprise in 2021 was 36,875.38 tCO2eq; the carbon emission intensity of each ton of coal produced was 0.089 tCO2eq. The critical carbon emissions were electricity consumption and methane fugitive emissions during production. (2) The evaluation indicators for carbon emission performance revealed an imbalance in K enterprise's economic, energy, and environmental development in 2021. The work on energy saving and consumption reduction was relatively weak. (3) Countermeasures for low-carbon development, including a carbon emission ledger, were proposed based on carbon emission accounting and performance evaluation results. This study can help typical underground coal production enterprises in Shanxi province obtain more accurate carbon emission data, providing practical guidance and reference for the same underground coal production enterprises to improve the carbon emission control effect.

Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

AbstractPrediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R2 = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR &gt; FNN &gt; CNN &gt; SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R2 = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R2 = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN &gt; GRB &gt; CNN &gt; SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.

Research on the Performance Evaluation System of Teachers in X University based on Analytic Hierarchy Process

In 2019, the gross enrollment rate of higher education in China was 51.6 %, and higher education has entered a period of universalization. the gross enrollment rate of higher education in China has reached 60.2 % by 2023. The change in education methods has caused problems such as declining teaching quality and shortage of school resources in colleges and universities. College teachers are the disseminators of culture, the backbone of teaching and educating people, and a group of high-quality talents in society and colleges and universities. They are the key to improving the quantity and quality of talent training in colleges and universities [1]. In this context, the performance evaluation of college teachers is particularly important, and the performance evaluation system of college teachers is a weather vane of teacher behavior. A scientific and reasonable performance evaluation system for college teachers can effectively motivate teachers to improve their teaching ability, education ability, scientific research ability, social service ability, etc. This paper uses the Delphi method to screen and determine the evaluation indicators initially extracted, and establishes a local university performance evaluation index system that includes the first-level indicators of teacher ethics, teaching level, scientific research ability and social service, 11 second-level indicators and 29 third-level indicators. In order to verify the effectiveness of the index system, a combination of qualitative and quantitative methods is used to take X University as an example. On the premise of understanding the current status of teacher performance management in X University, the performance of teachers in X University is evaluated. Finally, the performance evaluation results of X University are analyzed, and according to the actual situation of X University, suggestions are put forward for the performance evaluation of teachers in X University from three aspects: strengthening teacher workload management, improving performance evaluation feedback mechanism, establishing teacher performance incentive mechanism, strengthening teacher social service, and improving teacher scientific research ability.

Multidimensional performance of agroecology in mixed and agropastoral farming systems of Ethiopia: empirical evidence based on the tool for agroecological performance evaluation (TAPE)

ABSTRACT This study presents the results of agroecological performance evaluation in Ethiopia. The study utilized the TAPE methodology on a relatively large number of farms (619). The results showed that transition to agroecology had a positive effect on tenure security, agricultural output, gross revenue, and diversity of Income-Generating Activities. More advanced agroecological farms were found to have better soil health and agrobiodiversity, and reduced use of chemical pesticides. They were also associated with better dietary diversity; more empowered youth and women; and created on-farm employment opportunities. These results call for support and public investments on agroecological transition in the country.

Efficient photothermal mineralization of toluene over MnCo2O4 with different exposed facets: Revealing the role of oxygen vacancy and photo-/thermo- synergistic mechanism

Modulating oxygen vacancies through facet engineering is a potential strategy to enhance activity of photo-/thermo- synergistic catalysis for toluene mineralization. Hence, we synthesized MnCo2O4 catalysts with exposed {01−1} facets (MCO-R), {1−1−2} facets MCO-S), and {1−10} facets (MCO-C), in which the MCO-R with the highest oxygen vacancy concentration exhibited the highest toluene conversion (95.8 %) and CO2 yield (85.5 %) under the full-spectrum light irradiation. The results of various characterizations and DFT calculations confirmed that the oxygen vacancy could accelerate the activation of lattice oxygen and molecule oxygen, thereby enhancing the photothermal catalytic toluene activity of MnCo2O4. Meanwhile, the results of performance evaluations identified that the efficient photothermal mineralization activity of toluene was induced by the photo-/thermo- synergistic catalysis. Besides, the reaction pathway studies by In-situ DRIFTS tests revealed the key role of oxygen vacancy and photo-/thermo- synergistic effects in the reaction progress of photothermal mineralization of toluene over MnCo2O4.

ადამიანური კაპიტალის მართვა ისტორიული თეორიის ჭრილში

The historical role of human resource management and its importance in effective organizational performance and continuous development is discussed. To understand the historical role of the human resource management, the article reviews the corresponding literature and analyzes management approach based on human capital theory in relation to effective performance of contemporary organizations. At an early stage of scientific research in economic development a human’s role was evaluated as a special resource (human resource) and as a subject of social development. At a later stage a human was considered as a subject of economic development. This gave origin to terms like an “individual” and a “person”. In this case a human is considered as a subject of social and labor relations. Currently, key elements of the social-economic reform, such as stabilization of social-economic and political environment, invention and establishment of new manufacturing and management technologies, rationalization of the finance and banking systems and improvement of social welfare and quality of lifestyle are still critical challenges facing our country. These challenges can only be addressed by implementation of consistent policy in social and labor relations. Establishment of social market economy in the country and its well-organized functioning is possible with integration into global production. This primarily refers to adaptation of domestic labor relations with the internationally recognized social and labor relation system. This goal can be achieved by continuous organizational development efforts. For this reason, it is essential for each organization to develop and periodically renew its human resource management strategy and action plan that will ensure alignment of its personnel conduct and behavior and entire progress with its organizational needs. For organizational development it is essential to develop and periodically renew employee performance evaluation, motivation, personnel development policy and procedures. Staff motivation policy shall define both monetary and non-monetary incentives and be targeted at maintaining best human capital crucial for the long-term success of any organization. Personnel development policy should reflect the correlation between the results of performance evaluation and personnel development plan. The policy should prescribe rules for developing an individual development plan and setting career development criteria. In addition, a personnel career development policy and procedures should prescribe organizational training needs identification methodology, priority trainings, training opportunities and respective actions. Knowledge gained at the trainings should be expanded throughout the organization. Concentrating on above processes will turn an organization into an attractive employer with desirable working environment and organizational culture.

Exploration and prognostic analysis of two types of high-risk ovarian cancers: clear cell vs. serous carcinoma: a population-based study

BackgroundOvarian clear cell carcinoma (OCCC) is a rare pathological histotype in ovarian cancer, while the survival rate of advanced OCCC (Stage III-IV) is substantially lower than that of the advanced serous ovarian cancer (OSC), which is the most common histotype. The goal of this study was to identify high-risk OCCC by comparing OSC and OCCC, with investigating potential risk and prognosis markers.MethodsPatients diagnosed with ovarian cancer from 2009 to 2018 were identified from the Surveillance, Epidemiology, and End Results (SEER) Program. Logistic and Cox regression models were used to identify risk and prognostic factors in high-risk OCCC patients. Cancer-specific survival (CSS) and overall survival (OS) were assessed using Kaplan-Meier curves. Furthermore, Cox analysis was employed to build a nomogram model. The performance evaluation results were displayed using the C-index, calibration plots, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Immunohistochemically approach was used to identify the expression of the novel target (GPC3).ResultsIn the Cox analysis for advanced OCCC, age (45–65 years), tumor numbers (total number of in situ/malignant tumors for patient), T3-stage, bilateral tumors, and liver metastases could be defined as prognostic variables. Nomogram showed good predictive power and clinical practicality. Compared with OSC, liver metastases had a stronger impact on the prognosis of patients with OCCC. T3-stage, positive distant lymph nodes metastases, and lung metastases were risk factors for developing liver metastases. Chemotherapy was an independent prognostic factor for patient with advanced OCCC, but had no effect on CSS in patients with liver metastases (p = 0.0656), while surgery was significantly related with better CSS in these patients (p < 0.0001) (p = 0.0041). GPC3 expression was detected in all tissue sections, and GPC3 staining was predominantly found in the cytoplasm and membranes.ConclusionAdvanced OCCC and OCCC with liver metastases are two types of high-risk OCCC. The constructed nomogram exhibited a satisfactory survival prediction for patients with advanced OCCC. GPC3 immunohistochemistry is expected to accumulate preclinical evidence to support the inclusion of GPC3 in OCCC targeted therapy.