Rule Of Thumb Research Articles

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

Effects of tag mass on the physiology and behaviour of common noctule bats

BackgroundExternal tags, such as transmitters and loggers, are often used to study bat movements. However, physiological and behavioural effects on bats carrying tags have rarely been investigated, and recommendations on the maximum acceptable tag mass are rather based on rules of thumb than on rigorous scientific assessment.MethodsWe conducted a comprehensive three-step assessment of the potential physiological and behavioural effects of tagging bats, using common noctules Nyctalus noctula as a model. First, we examined seasonal changes in body mass. Second, we predicted and then measured potential changes in flight metabolic rate in a wind tunnel. Third, we conducted a meta-analysis of published data to assess effects of different tag masses on the weight and behaviour of bats.ResultsIndividual body mass of common noctules varied seasonally by 7.0 ± 2.6 g (range: 0.5–11.5 g). Aerodynamic theory predicted a 26% increase in flight metabolic rate for a common noctule equipped with a 3.8 g tag, equating to 14% of body mass. In a wind tunnel experiment, we could not confirm the predicted increase for tagged bats. Our meta-analysis revealed a weak correlation between tag mass and emergence time and flight duration in wild bats. Interestingly, relative tag mass (3–19% of bat body mass) was not related to body mass loss, but bats lost more body mass the longer tags were attached. Notably, relatively heavy bats lost more mass than conspecifics with a more average body mass index.ConclusionBecause heavy tags (> 3 g) were generally used for shorter periods of time than lighter tags (~ 1 g), the long-term effects of heavy tags on bats cannot be assessed at this time. Furthermore, the effects of disturbance and resource distribution in the landscape cannot be separated from those of tagging. We recommend that tags weighing 5–10% of a bat’s mass should only be applied for a few days. For longer studies, tags weighing less than 5% of a bat's body mass should be used. To avoid adverse effects on bats, researchers should target individuals with average, rather than peak, body mass indices.

Abstract PO4-19-07: Tailored Axillary Surgery (TAS) in Patients with Clinically Node-Positive Breast Cancer in the Upfront Surgery Setting: A Prospective, Single-Arm, Multicenter Trial

Abstract A brief background discussion: Axillary lymph node dissection (ALND), which can induce lymphedema, has been omitted in clinically node-negative(cN0) patients with positive sentinel lymph nodes (SLNs) if they meet the eligibility criteria of ACOSOG Z0011. Furthermore, the omission of ALND has been attempted through targeted axillary dissection (TAD) in patients whose clinically node-positive(cN+) status converts to ycN0 after neoadjuvant chemotherapy. However, ALND remains the standard of care in patients with cN+ who undergo upfront surgery. Trial design: This is a prospective, single-arm, multicenter phase II feasibility trial with the participation of 41 hospitals belonging to the breast cancer study group of Japan Clinical Oncology Group (JCOG). Tailored axillary surgery (TAS) removes labeled lymph node (LN) with clip, wire, or tattoo, palpable LNs, and SLNs. ALND is performed after TAS. These LNs removed by ALND other than TAS are defined as non-TAS LNs. Eligibility criteria: The eligibility criteria are as follows: 1) histologically-proven invasive breast cancer, 2) upfront surgery is planned, 3) pathologically diagnosed metastatic LN (cytology or core needle biopsy), 4) 1-3 LN metastases in level I by imaging, 5) cT1-3, and 6) females aged ≥18 and ≤74 years on the enrollment date. Specific aims: This trial aims to establish a surgical method of tailored axillary surgery (TAS) among patients with cN+ who undergo upfront surgery and to determine the appropriate criteria for the next phase III TAS trial, which omits ALND by TAS safely. The primary endpoint is the non-TAS LNs positive rate. Clinicopathological factors (the number of suspected metastases by imaging, the number of metastases in LNs resected by TAS, tumor size, and invasive ductal/lobular carcinoma) are analyzed to predict the non-TAS LN metastasis rate (e.g., < 10%). The secondary endpoints are the TAS LNs identification rate, marked LN resection rate, arm edema incidence rate, and QOL (FACT-B, QuickDASH). Statistical methods: In this trial, among the combinations of factors involved in treatment selection, the objective is to narrow down the combinations that will result in a non-TAS metastasis-positive rate of less than 10% when TAS is performed. For a single combination, 167 cases need to be considered to reject a non-TAS metastasis-positive rate of 10% or more (5% one-sided alpha error, 70% power, and 5% expected value). However, performing these studies for all combinations lacks feasibility. Therefore, we decided to improve the estimation accuracy by using a regression model with the non-TAS metastasis-positive rate as the outcome variable and the factors involved in treatment selection as explanatory variables and to search for combinations of factors with a non-TAS metastasis-positive rate less than 10%. Based on the rule of thumb that a sample size of at least ten times the number of factors is required when using regression methods, a regression model is used when 60 cases are accumulated. The combinations with a 90% confidence upper limit of less than 10% of non-TAS metastasis positivity as the predictive value of the regression model will be selected as candidate combinations that fulfill the conditions. If the accuracy of the predictive value is considered insufficient, 60 cases will be added sequentially up to a maximum of 300 cases. Present accrual and target accrual: The patient recruitment was started in April 2023. Up to 300 patients will be enrolled over a 2-year recruitment period. Twenty-one patients were already enrolled until July 2023. Citation Format: Kaori Terata, Yasuaki Sagara, Tadahiko Shien, Takehiko Sakai, Shin Takayama, Dai Kitagawa, Tsuguo Iwatani, Takahiro Tsukioki, Mami Ogita, Naoko Sanuki, Masayuki Yoshida, Hitoshi Tsuda, Seiichiro Yamamoto, Hiroji Iwata. Tailored Axillary Surgery (TAS) in Patients with Clinically Node-Positive Breast Cancer in the Upfront Surgery Setting: A Prospective, Single-Arm, Multicenter Trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-19-07.

Rate-dependent tensile properties of paperboard and its plies

Abstract Tensile properties of paperboard have been characterized, and it has been shown that paper tensile properties are dependent on the strain rate. Tensile testing was done using strain rates in the range 10−4–3 s−1, which corresponds to crosshead movements ranging from 1 up to 24,000 mm/min, using an electro-mechanical testing machine. Two paperboards, and its free-laid top, middle and bottom plies were characterized in MD and CD. The testing was limited by the maximum crosshead speed of the testing machine. Initially 50 mm (grip to grip) long samples were tested, but to test even higher strain rates also short samples with length of 5 mm were tested. The results showed that ultimate strength increased by 9 % per decade increasing of testing rate, and Young’s modulus increased by 7 %. This shows that the previously reported rule of thumb of 10 % increase of in-plane strength per decade increase of strain rate holds. The testing here shows that this is valid also at strain rates as high as 3 s−1. Moreover, the strain at break in CD for long tensile specimens was observed to decrease when the strain rate exceeded 0.1 s−1, which resulted in straighter crack paths.

ОПТИМІЗАЦІЯ КИСНЕВО-КОНВЕРТЕРНОГО ПРОЦЕСУ ЗАСОБАМИ АЛГОРИТМІВ МАШИННОГО НАВЧАННЯ

Traditional methods for optimising the BOF process in steelmaking are based on experience and rules of thumb, but do not always achieve maximum efficiency. As part of the Industry 4.0 concept, the use of machine learning algorithms is becoming a promising way to optimise production processes. They allow you to analyse large amounts of data collected by a variety of different sensors that operate during an industrial process and find complex relationships between parameters to achieve the best results. The created models can work automatically, analysing data in real time and responding to changes in the process. The use of machine learning algorithms can improve the accuracy of results and the quality of steel production by optimising the BOF process with a large number of parameters and their interrelationships.

A Better Estimate of Internally Generated Intangible Capital

Internally developed intangibles are not included in reported assets under U.S. generally accepted accounting principles. The omission of this increasingly important class of assets reduces the usefulness and relevance of financial statement analysis, conducted with reported values of equity and assets. Recent studies overcome this deficiency by capitalizing research and development (R&D) expenses to estimate the value of knowledge capital and by capitalizing selling, general, and administrative (SG&A) expenses to estimate the value of organization capital. Those two estimates are then added to reported values for financial statement analysis. For convenience, many studies rely on two rules of thumb and assume them to be equally applicable in all instances: (1) 30% of SG&A and 100% of R&D expenses are investments, and (2) the useful life of SG&A and R&D investments is three and five years, respectively. We propose a more flexible approach by estimating the capitalization and amortization parameters on an industry–year–specific basis. Our modified values of total assets and equity, inclusive of the value of capitalized intangibles, exhibit greater association with future returns and investments compared with as-reported values and values estimated with uniform rules of thumb. We provide a better estimate of intangible capital for the consumers of financial statements. This paper was accepted by Ranjani Krishnan, accounting. Funding: A. Srivastava and R. Zhao acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. A. Srivastava acknowledges financial support from the Canada Research Chairs Program of the Government of Canada. A. Iqbal acknowledges financial support from the Canadian Securities Institute Research Foundation and Chartered Professional Accountants (Alberta) Education Foundation. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01703 .

Multiply by 37 (or Divide by 0.027): A Surprisingly Accurate Rule of Thumb for Converting Effect Sizes From Standard Deviations to Percentile Points

Multiply by 37 (or Divide by 0.027): A Surprisingly Accurate Rule of Thumb for Converting Effect Sizes From Standard Deviations to Percentile Points

Determining Sample Size Requirements in EFA Solutions: A Simple Empirical Proposal

In unrestricted or exploratory factor analysis (EFA), there is a wide range of recommendations about the size samples should be to attain correct and stable solutions. In general, however, these recommendations are either rules of thumb or based on simulation results. As it is hard to establish the extent to which a particular data set suits the conditions used in a simulation study, the advice produced by simulation studies is not direct enough to be of practical use. Instead of trying to provide general and complex recommendations, in this article, we propose to estimate the sample size that is needed to analyze a data set at hand. The estimation takes into account the specified EFA model. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating data sets from a pseudo-population in which the parent correlation holds exactly, and the criterion for determining the size required is a threshold that quantifies the closeness between the pseudo-population and the sample reproduced correlation matrices. The simulation results suggest that the proposal works well and that the determinants identified agree with those in the literature.

Evaluating and strengthening low-rise reinforced concrete buildings constructed in Nepal

In Nepal, the reinforced concrete (RC) frame system is commonly used to construct low-rise buildings. In last three decades, a significant number of such buildings were proportioned and constructed in accordance with Nepal National Building Code NBC 205:1994 and NBC 205:2012—also known as the Mandatory Rule of Thumb (MRT). In the aftermath of 2015 Gorkha Earthquake (Mw 7.8) which resulted in large scale social and economic losses, the efforts to formulate improved seismic design provisions led to the development of NBC 105:2020. Considering that MRT-designed low-rise RC frame buildings still constitute a significant part of existing building stock, this study is aimed to conduct a comparative seismic performance evaluation of several case study structures under a comprehensive set of ground motions. Using the nonlinear analysis and seismic fragility assessment of four low-rise RC frame buildings (designed using all three code versions), it is shown that the structural performance and seismic losses can be significantly reduced by following NBC 105:2020 provisions. Several retrofitting solutions are also explored to improve the seismic performance of buildings designed using MRT. It is shown that the concrete or steel jacketing of RC columns can significantly increase the lateral strength and energy dissipation, and reduce the damage probability of such buildings. Lastly, based on the developed results, a machine learning approach is employed to correlate the peak ground acceleration with structural drifts for convenient practical applications.

Calculation of Oxygen Uptake during Ambulatory Cardiac Rehabilitation.

Background: Cardiopulmonary exercise testing is not used routinely. The goal of this study was to determine whether accurate estimates of VO2 values can be made at the beginning and at the end of a rehabilitation program. Methods: A total of 91 cardiac rehabilitation patients were included. Each participant had to complete cardiopulmonary exercise testing at the beginning and at the end of a rehabilitation program. Measured VO2 values were compared with estimates based on three different equations. Results: Analyses of the means of the differences in the peak values showed very good agreement between the results obtained with the FRIEND equation or those obtained with a combination of rules of thumb and the results of the measurements. This agreement was confirmed with the ICCs and with the standard errors of the measurements. The ACSM equation performed worse. The same tendency was seen when considering the VO2 values at percentage-derived work rates. Conclusions: The FRIEND equation and the more easily applicable combination of rules of thumb are suitable for estimating the peak VO2 and the VO2 at a percentage-derived work rate in cardiac patients both at the beginning and at the end of a cardiac rehabilitation program.

Weather based thumb rule models for formulating the crop insurance schemes for wheat in Punjab

<p>Weather based crop insurance schemes play an important role in helping the farmers recovering their financial losses incurred due to aberrant meteorological parameters. Wheat is a major winter season crop grown in Punjab state. To formulate the weather based “weekly and monthly thumb rule models” for predicting the high yield of wheat, a study with meteorological and crop data (2007–2008 to 2021–2022) was conducted for three major wheat growing locations in the state. The results revealed that ideally the monthly maximum/minimum temperatures/rainfall/sunshine duration during the months of December, January, February and March in the range of 20–23 ℃/5–9 ℃/0–38 mm/5–8 h, 17–20 ℃/3–8 ℃/2–57 mm/4–6 h, 19–25 ℃/5–11 ℃/0–79 mm/5–8 h and 25–30 ℃/10–15 ℃/0–56 mm/8–9 h, respectively are optimum for high yield of wheat. The ideally humid (maximum/minimum relative humidity between 90%–97%/36%–63%) weather from November to February is favourable for optimum growth and development of wheat crop. Similarly, the maximum/minimum temperatures/rainfall/sunshine duration for anthesis and grain filling stage in the range of 14–23 ℃/3–10 ℃/0–55 mm/2–9 h and 18–30 ℃/5–15 ℃/0–26 mm/4–10 h are favourable for high yield of wheat crop. The maximum temperature of >18 ℃ during grain filling stage is optimum for potential yield of wheat. While the abiotic stresses like heavy rainfall, heat stress during grain filling stage are not favourable for the productivity of the crop. So, these critical limits of weather parameters are the basis for providing the weather based insurance to the farmers of the region.<strong></strong></p>

Capturing innovation opportunities: Learning from growth leaders

AbstractFirms that capture the benefits of innovation opportunities ahead of their rivals achieve superior rates of organic growth. These growth leaders don't wait for opportunities to appear before reacting. Instead, they systematically search for opportunities to select for development. Qualitative case analyses of four growth leaders found that each used two types of heuristics or rules of thumb while capturing innovation opportunities. Their top‐down strategy heuristics were revealed with a wide‐spectrum framework that reimagined and stretched each dimension of their strategy. Growth leaders also used bottom‐up process heuristics to routinize and share their approaches to capturing innovation opportunities throughout their organization. These heuristics are a useful lens for studying innovation practices and suggest fruitful avenues for further research.

The strain demand of reinforced concrete bridge columns under seismic loading

The steel in reinforced concrete (RC) members that form plastic hinges must possess sufficient strain capacity to dissipate seismic deformation demands. Unfortunately, there is limited information on the seismic strain demands of bridge column plastic hinges. Instead, designers rely on a perception of cyclic strain capacity that is an approximate rule of thumb. A standard methodology needs to be established for quantifying the strain demand on these structural members as a function of the expected seismic hazard. To develop this methodology, 1944 columns were analyzed with nonlinear time-history analyses (NLTHAs) using ground motions from a range of earthquakes. This study evaluates the strain demand on RC bridge columns by defining the relationship between the strain demand and earthquake intensity. The results of the model are defined in terms of the peak tensile strain of the reinforcing bar, [Formula: see text]. The earthquake intensity with the highest correlation to the [Formula: see text] was determined to be the elastic spectral displacement at the optimal period ([Formula: see text]), which is defined as 75% of the effective period. The relationship between [Formula: see text] and [Formula: see text] can be used to predict the strain demand for an RC bridge column at a given geographic location. Results are presented as a probability density function (PDF), representing strain demand, compared to a PDF of the column capacity. The intersection of the capacity curve and demand curve represents the maximum acceptable strain given as a function of [Formula: see text]. This methodology can help understand the demand placed on a structural system given a region’s seismicity.

A Macroeconomic Model of Central Bank Digital Currency

We develop a quantitative New Keynesian DSGE model to study the introduction of a central bank digital currency (CBDC): government-backed digital money available to retail consumers. At the heart of our model are monopolistic banks with market power in deposit and loan markets. When a CBDC is introduced, households benefit from an expansion of liquidity services and higher deposit rates as bank deposit market power is curtailed. However, deposits also flow out of the banking system and bank lending contracts. We assess this welfare trade-off for a wide range of economies that differ in their level of interest rates. We find substantial welfare gains from introducing a CBDC with an optimal interest rate that can be approximated by a simple rule of thumb: the maximum between 0% and the policy rate minus 1%.

State of the Art of Modelling and Design Approaches for Ejectors in Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) has a promising future in the power generation and transportation fields. Recirculation of unused anodic gases is fundamental to achieve a high-performance energy system, and this is usually attained employing ejectors or pumps. With respect to the latter, ejectors present no moving parts, thus resulting in both higher overall efficiency of the system and lower maintenance cost. Their main drawback is represented by the narrow optimal operative range: the entrainment ratio (ER) greatly depends on primary pressure, working pressure, and operative condition in general. In the last decade, numerous authors focused their efforts on fully comprehending and correctly simulating their working principles and analyzing how geometrical parameters influence ER and design different geometries to enlarge the operative range. The aim of this paper is to present in an ordered and clear manner the state of the art of ejector design, both from simulative (turbulence model, single or multiphase stream, etc.) and empirical (commonly used “rule of thumb”) points of view.

Sample Size Calculation in Clinical Trial and Animal Studies


 
 
 Objective: This review aims to address the critical role of sample size calculation in designing and conducting animal studies related to diabetes and obesity, particularly focusing on experimental comparison methods like ANOV A design.
 Materials and Methods: We discuss the factors influencing sample size decisions, including type I and type II errors, effect size, and standard deviation. We emphasize the importance of avoiding common pitfalls, such as using rules of thumb or arbitrary choices, and advocate for utilizing established formulas to ensure accurate and reliable sample size determination.
 Results: This review presents relevant equations for calculating sample size in animal studies with an ANOV A design, providing researchers with a framework for determining the appropriate number of animals needed to achieve robust and ethical research.
 Conclusion: Accurate sample size calculation is essential for achieving powerful and statistically sound animal studies in diabetes and obesity research. Utilizing established formulas and avoiding arbitrary choices ensures reliable and ethical research practices while minimizing resource waste and maximizing the validity of collected data.
 
 

Management accounting practice as understanding, supporting and advancing local epistemic methods

Purpose The purpose of this paper is to gain insight into how management accountants can become relevant business partners out of respect for existing locally developed accounts of economic performance for decision-making. Design/methodology/approach The paper is based on qualitative semi-structured interviews with local business actors, in this case, families from seven financially successful Danish dairy farms. The casework and the analysis have been informed by pragmatic constructivism. Findings The local business actors do not use the official accounting system for ongoing cost-management-related decision-making. Instead, they use several epistemic methods that include locally developed decision models, experiences, rules of thumb and intuition. The farmers use these vernacular accountings to compensate for the cost management illusion that the formal accounting system tends to create. What the study suggests is that when management accountants engage as business partners, they are likely to enter a space where accounting is already present. Originality/value This paper argues that local business actors practice epistemic methods where they develop and use vernacular accountings to support their managerial practice, also in the absence of a professional management accountant. These vernacular accountings may lead the local actors into an illusion because the vernacular accountings do not necessarily have an inherent economic logic and theoretical reliability. The role of the management accountant in such a setting is hence to understand, support and advance local epistemic methods. Becoming a business partner requires a combination of management accounting analytical skills and a sense of empathy and sensitivity regarding what is already at play and how this can become an object of discussion without violating the values of the other.

Qualität des Posteditierens von maschinell erstellten Übersetzungen technischer Texte aus dem Slowakischen ins Deutsche durch erstsprachig slowakische Posteditoren

Summary While the rule of thumb that translators translate into their first language (L1) is often desirable, the demand for translations from lesser used into widely used languages often forces translators into this practice because it outstrips supply. The aim of this study is to show readers how the quality of the reference human translation (REF) produced by translators with Slovak as their L1 can influence the assessment of the quality of machine translation output (MT output), using the example of the light post-edited machine translation output (MTPE output) of technical documentation (TD) from Slovak into German by post-editors (PE) with Slovak as their L1. We therefore use the percentage difference between the TER and HTER scores to compare those segments of the studied TD that the machine translated differently than the human, whereby the PE nonetheless adopted such MT output or only partially changed it. We have divided the results into 6 categories of segments based on the differences between TER and HTER, where the difference between TER and HTER was below 50 %. We found that inappropriate REF and different, but not always wrong, solutions adopted by the PE compared to the REF translator can also result in poor evaluation of the MTPE output.

Sivelestat (Neutrophil Elastase Inhibitor) as an Anti-inflammatory and Anti-viral Agent: An In Silico Study.

Background Sivelestat is a potent and specific neutrophil elastase inhibitor. It is clinically used in treating lung injury and respiratory distress syndrome. This engaged us to undertake the present study in which sivelestat was studied as an anti-inflammatory and anti-viral agent. Methodology The docking study of sivelestat on matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), chikungunya virus nonstructural protein-2 (CVnsP2) protease, and influenza A (H1N9) virus neuraminidase was assessed using the Chemistry at Harvard Macromolecular Mechanics (CHARMM) Dock (CDOCK) method. Furthermore, molecular physicochemical; bioactivity; absorption, distribution, metabolism, and excretion (ADME); toxicity; and Search Tool for Interacting Chemicals (STITCH) analyses were performed by using the Molinspiration (Molinspiration Cheminformatics, Slovensky Grob, Slovak Republic), SwissADME SwissADME (Swiss Institute of Bioinformatics, Quartier Sorge - Bâtiment Amphipôle, Switzerland), pkCSM (University of Melbourne, Melbourne, Australia), and STITCH-free online tools. Results The molecular physicochemical assessment of the ligand (sivelestat) showed no (zero) violation and agreed with the thumb rule of five, otherwise known as Lipinski's rule of five. ADME prediction of the ligand (sivelestat) is shown to possess a low gastrointestinal absorption (GIA) property. Similarly, toxicity analysis of the ligand (sivelestat) is predicted to have a hepatotoxicity effect. STITCH analysis reveals that the ligand (sivelestat) has exhibited interactions with the three human proteins. Conclusions The present molecular docking studies showed that the ligand (sivelestat) has successfully docked with all four enzymes of interest. Hence, the current finding has provided a good understanding of sivelestat as an effective suppressor activity against all four enzymes: MMP-2, MMP-9, CVnsP2 protease, and influenza neuraminidase.

Factors Influencing the Application of the Code of Nursing Ethics at Indonesia

Background: Nurses are health workers who are vulnerable to lawsuits because the scope of services performed carries a risk of malpractice and negligence. Aim: This study was aimed to determine the application of a code of ethics in health services and analyze the influencing factors, as well as analyze the factors with the strongest influence. Methods: The study used an explanatory research method with a cross sectional approach. The population in this study were nurses who worked in health services, both in hospitals and health centers in the Surabaya area. The sample size in this study was calculated using the rule of thumb, amounting to 150 respondents. Samples were taken by means of purposive sampling, data were analyzed by path analysis with a significance level of α <0.005. Result: Nurses apply a code of ethics in the good and excellent categories. The influencing factors were individual characteristics (p=0.000), work environment characteristics (p=0.000), experience (p=0.000), knowledge (p=0.000). The biggest influencing factor is experience with a large influence on knowledge is 0.803 and the influence on the application of the code of ethics is 0.823. Conclusion: Most nurses in Indonesia have implemented a code of ethics in the good and excellent categories. Factors that influence the application of the code of ethics in health services are individual characteristics, characteristics of the work environment, experience. and knowledge. The most influential factor is experience. Nurses should be active in the activities of professional organizations. Workplace rotation is needed for nurses to gain experience in decision making, legal ethics