Analysis Tools Research Articles

A Quick Calculation Method for Radiation Pattern of Submillimeter Telescope with Deformation and Displacement

Radiation pattern captures the electromagnetic performance of reflector antennas, which is significantly affected by the deformation of the primary reflector due to gravity and the displacement of the secondary reflector. During the design process of large reflector antennas, a substantial amount of time is often dedicated to iteratively adjusting structural parameters and validating electromagnetic performance. To improve the efficiency of the design process, we first propose an approximate calculation method of optical path difference (OPD) for the deformation of the primary reflector under gravity and the displacement of the secondary reflector. Then an OPD fitting function based on the modified Zernike polynomials is proposed to capture the phase difference of radiation over the aperture plane, based on which the radiation pattern will be obtained quickly by the aperture field integration method. Numerical experiments demonstrate the effectiveness of the proposed quick calculation method for analyzing the radiation pattern of a 10.4 m submillimeter telescope antenna at its highest operating frequency of 856 GHz. In comparison with the numerical simulation method based on GRASP (which is an antenna electromagnetic analysis tool combining physical optics (PO) and physical theory of diffraction (PTD)), the quick calculation method reduces the time for radiation pattern analysis from more than one hour to less than two minutes. Furthermore, the quick calculation method exhibits excellent accuracy for the figure of merit (FOM) of the radiation pattern. Therefore, the proposed quick calculation method can obtain the radiation pattern with high speed and accuracy. Compared to the time-consuming numerical simulation method (PO and PTD), it can be employed for quick analysis of the radiation pattern for the lateral displacement of the secondary reflector and the deformation of the primary reflector under gravity in the design process of a reflector antenna.

The Dynamics of School Environment and the Impact of Parental Attention on the Academic Achievement of Students at SMA Negeri 1 Ketapang Sampang

In the current era of globalization, students are faced with demands to consider the factors that influence the achievement of their learning outcomes. This research aims to reveal the role of an effective school environment and parental attention in improving the quality of student education. Quantitative methods were used in this research. In this research, this approach was chosen to provide a more measurable understanding of the role of the school environment and parental attention on student learning outcomes. The research population includes all students of SMA Negeri 1 Ketapang Sampang so that the research results can be more generally applied to this group. The use of multiple linear regression as a data analysis tool allows researchers to understand the extent to which certain variables, such as school environment and parental attention, contribute to variability in student learning outcomes. The findings show that the school environment has a fairly large role in determining student learning outcomes. Apart from that, emotional involvement and parental support also play a role in developing student academic achievement. With a school environment that supports learning and positive contributions from parents, it is hoped that student learning outcomes will improve.

A Compositional Semantics of Boolean-Logic Driven Markov Processes

Boolean-logic driven Markov processes (BDMPs) is a prominent dynamic extension of static fault trees to model repairable and complex dynamic systems. While BDMPs are intensively used in an industrial context for dependability analysis of energy systems, its formal semantics has not been systematically treated. To date, BDMPs are defined as a library of the domain-specific dependability-modelling language Figaro, a library that is neither open source nor publicly available. A rigorous semantic underpinning of BDMPs is indispensable for (1) developing BDMP analysis tools and (2) comparing its expressive power to other related reliability modelling languages. This paper presents a formal semantics to BDMPs using Markov automata (MA), an extension of continuous-time Markov chains (CTMCs) with action transitions that can be used to compose complex MA from smaller MA. This enables us to provide a compositional semantics. That is, we express the semantics of each individual BDMP element as an MA and obtain the MA for the entire BDMP by combining the MA of its elements. This makes the semantics comprehensible, for those who are familiar with automata theory, and easily extensible with new BDMP elements, e.g., to model security aspects. After the entire BDMP is considered, the actions in its MA that were used to “glue” the MA of BDMP elements, are ignored. This results in a CTMC that is amenable to exact numerical analysis by, e.g., efficient probabilistic model-checking techniques. We report on a prototypical implementation of our semantics and empirically show that our semantics yields dependability metrics that correspond to the interpretation by the Figaro knowledge base of BDMPs.

The Influence of Green Marketing and Brand Awareness on Purchase Intention Through Brand Image as a Mediation Variable

Marketing using the concept of green marketing should create positive emotions so that consumers can influence product evaluation. Objectives: The problems of the study are: Does green marketing influence brand image? Does green marketing influence buying interest through brand image as a mediation variable? Does brand awareness influence buying interest? Does brand awareness influence brand image? Does brand awareness influence purchasing attention through the perception of a brand as a mediation variable? Does brand image influence buying interest? Meanwhile, the objectives of the study are to find out the influence of green marketing on buying interest, the influence of green marketing on brand image, the influence of green marketing on buying interest through brand image as a mediation variable, the brand awareness's impact on consumers' purchase intentions, the impact of brand awareness on brand image, the influence of buying interest through brand image as a mediation variable, and the influence of brand image on buying interest.Methodology: This research is part of exploration research. Participants in the research are unknown; the quantity of samples used in this research is 110. The analysis tool used is PLS.Finding: green marketing does not influence buying interests; green marketing does not influence brand image; brand image does not moderate the impact of green advertising on buying interests; brand awareness influences buying interests; brand awareness influences brand image; brand image does not mediate the impact of brand recognition on purchasing interests; and trademark image influences purchasing interests. Conclusion: Based on the findings from the conclusions of this study, these are some suggestions that can be used as research recommendations: this research focuses on the variables green marketing, brand awareness, brand image, and buying interest, so other variables such as brand trust, social media marketing, perceived quality, price, and customer satisfaction are needed to find out the buying interest; Future studies are anticipated to increase the sample size, expand the research object, and add relevant topics; future research is expected to process data with different analysis tools.

The Role of Influencer Marketing and Sales Promotion on the Purchase Decision of Scarlett Whitening Products

This research is a quantitative research that examines social phenomena with the aims to determine the effect of influencer marketing and sales promotion on purchase decision of scarlett whitening products. The population of this research is scarlett whitening consumers. This research took a sample of 100 respondents through a non-probability sampling technique using purposive sampling method. The data collection for this research was in the form of distributing questionnaires to consumers who had bought and used scarlett whitening products. The data analysis technique used is descriptive analysis and inferential analysis by the Partial Least Square (PLS) 3.0 analysis tool. The results of this research state that the influencer marketing has a significant effect on purchasing decisions and sales promotion has a significant effect on purchasing decisions. The implications of this research, can be a reference for business owners in determining a marketing multichannel system to increase sales of scarlett whitening products, especially in sales promotion and marketing through influencers.

Antecedent Factors of Intention to Use Digital Wallets and the Moderating Role of Consumer Trust

The Covid 19 (Coronavirus Disease That Was Discovered In 2019) pandemic has encouraged people to have high digital literacy. Since entering the New Normal Era, people as buyers must also avoid and make transactions using cash and make more use of digital payments, one of which is through digital wallet platforms. The aim of this research is to explore the antecedent factors that influence the intensity of digital wallet use and also to look at the moderating role of customer trust. This research was conducted on 194 respondents who are digital wallet users. This research uses quantitative methods using covariance based structural equation modeling (SEM) analysis tools. The research sample was Gopay/OVO/Dana digital wallet users aged 17 to 45 years and had used digital wallets for at least 6 months. Several variables in this research were confirmed to be antecedent factors for the intensity of digital wallet use, where attitude was proven to have a positive influence on intention to use and of all the factors that influence attitude, perceived usefulness is the most dominant. Some of the recommendations from this study are to continue to increase the benefits that digital wallet users can experience so that they will improve attitudes to increase the intensity of payments using digital wallets

Fusion Analyser: a novel measurement method and software tool for dental model analysis in orthodontics

Accurate analysis of dental models is crucial for diagnosing and treating malocclusion successfully. Traditional measurement techniques, however, often fail to accurately measure teeth in crowded conditions. In this study, we introduce Fusion Analyser (FA), an innovative software tool capable of calculating key dental indicators, thereby advancing the scope of digital dental model analysis. Our unique FA framework employs segmentation discs on teeth to enhance the accuracy of the anterior Bolton’s ratio, outperforming the widely-used 3Shape software when dealing with crowded teeth. The FA framework exhibits performance commensurate with the established gold standard, as evidenced by high scores across a range of evaluation metrics, thereby validating its accuracy, stability, and efficiency. The potential of the FA framework as an auxiliary tool for dentists, particularly in diagnosing and strategising treatment plans for malocclusion, is underscored.

“You Know No Rules of Charity”: A Corpus Analysis of ‘Love’ in William Shakespeare’s Richard III Using Voyant Tools

This essay conducts a corpus analysis of ‘love’ in Shakespeare’s Richard III using Voyant Tools, a free web-based application for analyzing digital texts. While Voyant Tools have been used in Shakespeare classes for educational purposes, it is hard to find any books or journal essays that incorporate the online program in Shakespeare drama research. It is hoped that the paper serves as an example that suggests academic potential of using digital tools in drama text analysis. Although Richard III is rarely discussed with reference to ‘love,’ the high frequency of the word in the text confirms its thematic importance in the play. My analysis of the corpus data makes the following discoveries. First, ‘love’ in Richard III has a broad spectrum of meaning ranging from erotic and familial love to loyalty and self-love. Second, the majority of references to ‘love’ come from or concern Richard and his chief assistant Buckingham, both of whom use the word for purposes of deception. Third, the type of love that is absent from all characters, including antagonist Richmond and those who display sincere love for their family or monarch, is ‘charity,’ the highest form of love modeled on God’s unconditional love for humanity. From these observations, it can be concluded that Shakespeare explores the broad spectrum of human love in order to satirize the absence of charity from the powerful figures that inhabit the world of Richard III as well as to expose the limitations and vulnerability of human love. If Richard III is a play of ‘love,’ as the corpus data suggests, it is not a critique of the title character but of the lack of charity in the world he was born into.

Using automated indices of cohesion to explore the growth of cohesive features in L2 writing

Abstract While the use of cohesive devices has received considerable attention in L2 writing research, scant attention has been devoted to exploring local, global, and textual cohesive features across two genres at different time points using indices generated by the computational tool for automatic analysis of cohesion (TAACO). Inspired by this gap, this study attempted to: (a) identify TAACO indices that are predictive of human ratings of essay organization, (b) investigate which of the identified TAACO indices differ between narrative and argumentative genres, and (c) explore which of the identified TAACO indices might change over time. To achieve these purposes, 270 narrative and argumentative essays were collected from 45 L2 writers in counterbalanced order at six different time points. The results of mixed-effects modeling revealed textual cohesive devices to be significant predictors of human ratings of essay organization regardless of genre and indicated sustained development in the use of textual cohesive devices over time. This study has important implications for L2 writing assessment including the potential to enhance assessment practices by incorporating computational tools like TAACO. The findings emphasize the importance of considering textual cohesive devices as significant predictors of essay organization, regardless of genre.

P2X7 receptors: a bibliometric review from 2002 to 2023.

For many years, there has been ongoing research on the P2X7 receptor (P2X7R). A comprehensive, systematic, and objective evaluation of the scientific output and status of P2X7R will be instrumental in guiding future research directions. This study aims to present the status and trends of P2X7R research from 2002 to 2023. Publications related to P2X7R were retrieved from the Web of Science Core Collection database. Quantitative analysis and visualization tools were Microsoft Excel, VOSviewer, and CiteSpace software. The analysis content included publication trends, literature co-citation, and keywords. 3282 records were included in total, with the majority of papers published within the last 10years. Based on literature co-citation and keyword analysis, neuroinflammation, neuropathic pain, gastrointestinal diseases, tumor microenvironment, rheumatoid arthritis, age-related macular degeneration, and P2X7R antagonists were considered to be the hotspots and frontiers of P2X7R research. Researchers will get a more intuitive understanding of the status and trends of P2X7R research from this study.

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

Matlab based graphical user interface for the monitoring and early detection of keratoconus

The rapid and extensive growth in medical imaging modalities and their applications is creating a pressing need for computers and computing in image processing, visualization, archival, and analysis. In this article, a Matlab-based graphical user interface (GUI) program is proposed for the monitoring and early detection of keratoconus. The findings show the efficiency of the proposed to detect the early stage of keratoconus. The proposed neural network model produces accuracy, ranging from 96% to 92%. It considers, respectively, 2 classes (normal cornea and keratoconus) and 3 classes (keratoconus, suspected keratoconus or normal) which will increase to 99% with respect to the 2 classes of keratoconus and 94% to the 3 classes when combining topography parameters with OCT image corneal pachymetry measurements and clinical judgments. 
 The compatibility of the graphical interface components with common medical data and image analysis tools facilitates the involvement of the ophthalmologist in the digitization of the medical records, the image processing and the conception of multimodal artificial intelligence applications for medical imaging.

The Effect of Good Corporate Governance (GCG) on Financial Performance with Corporate Social Responsibility (CSR) as an Intervening Variable (Empirical Study on Primary Consumer Sub Sector Companies Listed on The IDX 2018 - 2022)

This research aims to examine the influence of Good Corporate Governance (GCG) including institutional ownership and the board of commissioners on financial performance with Corporate Social Responsibility (CSR) as an intervening variable. This research uses financial performance as the dependent variable and Good Corporate Governance as a proxy for institutional ownership and the board of commissioners as the independent variable and Corporate Social Responsibility (CSR) as the intervening variable. This type of research is associative research. This research uses secondary data in the form of annual financial reports originating from the Indonesia Stock Exchange (BEI). The population in this research is Primary Consumer Sub-Sector Companies listed on the Indonesia Stock Exchange 2018-2022. The sampling technique in this research used a purposive sampling method, 7 companies were obtained as research samples. The data analysis technique in this study uses the multiple linear regression analysis method and the data analysis tool in this study uses the Eviews version 12 software program. The results of the study show that GCG and CSR together have a positive effect on the company's financial performance, while the proxies for institutional ownership and the board commissioner is partially insignificant. Likewise, CSR does not have a significant impact on financial performance. In addition, the board of commissioners proxy influences financial performance through CSR as a moderation, while the institutional ownership proxy does not have a similar effect.Keywords: Good Corporate Governance, Institutional Ownership, Board of Commissioners, Corporate Social Responsibility, Financial Performance

Green Competence Building, Green Employee Involvement and Green Work-Life Balance to Improve Environmental Performance through Green Organizational Culture

This research is motivated by environmental performance. The aim of this study is to determine the influence of the relationship between green competence building, green employee involvement, and green work-life balance on environmental performance, mediated by the green organizational culture of the Head of Study Program at PGRI Higher Education Institutions in East Java. The grand theory of this research is the AMO theory (Ability, Motivation, Opportunity). The sampling technique uses a saturated or census sample. The sample in this study consists of 185 respondents, the Head of Study Program at PGRI Higher Education Institutions in East Java, through a self-administered survey. The research instrument uses a questionnaire and is analyzed using SEM analysis, employing the Warp PLS Version 7.0 analysis tool to analyze the outer model, inner model, and hypothesis testing using p-value. The results show a significant direct influence of green competence building and green employee involvement on green organizational culture. Subsequently, green organizational culture significantly influences environmental performance. The relationship with green work-life balance does not significantly affect green organizational culture.
 This research indicates that there is an intervening variable effect, namely green organizational culture, on the relationship between green competence building, green employee involvement, and environmental performance. However, green organizational culture cannot act as an intervening variable in the relationship between green work-life balance and environmental performance. This study demonstrates that the practices of green competence building, green employee involvement, and green organizational culture play a crucial role in the implementation of environmental policies to promote environmental performance at PGRI Higher Education Institutions in East Java.

A Review - Factors Affecting on Time, Cost and Quality of Construction

Abstract: The primary goal of this study is to assess and explore time, cost, and quality management within the construction industry. The purpose of the study is to identify factors that impact time, cost, and quality management in construction projects. Quality stands out as a crucial aspect of all projects, and the success of construction projects hinges significantly on the performance of quality. However, accomplishing this objective is undeniably a challenging task. This study specifically investigates the factors that influence the control of cost and time in construction projects. The aim is to propose recommendations that can assist stakeholders in achieving enhanced cost and time performance in construction projects. The study acknowledges the intricate nature of balancing these key elements and strives to contribute valuable insights for improving overall project outcomes in the construction industry. This study is limited to the perspective of an appropriate sample of professionals in the construction industry. The data collection method involves the use of questionnaires and interviews, which will be subject to descriptive statistical analysis utilizing percentages, mean scores, and frequency of collected data. The primary factors influencing time, cost, and quality in a construction project were identified as planned and scheduled deficiencies, fraudulent methods, kickbacks, and the absence of clear evaluation standards. The research aims to scrutinize the factors that have both positive and adverse effects on construction projects. The study recommends that the construction team work diligently to ensure effective management of time, cost, and quality in construction projects. The objective of this study is to analyze how those involved in project teams perceive time, cost, and quality management on construction projects. Conclusions are drawn, and recommendations are made regarding time, cost, and quality management estimates for construction projects. In the construction industry, the concept of risk management is a less popular technique. There are three main stages in the systematic approach to risk management in the construction industry. A questionnaire, developed based on identified factors, was used to gather the opinions of construction experts. Quality is a critical aspect of any project, especially in construction. The success of construction projects is largely determined by the quality of work. Unfortunately, the construction industry is facing quality issues that result in ineffective and inefficient projects, leading to cost overruns, delays, and excessive rework. To address this issue, a research study was conducted to examine the factors that have positive and negative impacts on construction projects. A questionnaire was developed to gather feedback from construction experts, and statistical analysis tools such as chisquare and weighted mean method (WMM) were used to rank the significance of these factors. The definition of quality can vary depending on the perspective of those defining it. Some see it as meeting specific requirements, while others view it as delivering value for the price paid. For construction companies, quality is about satisfying customers and fulfilling their needs while staying within budget. Quality is an important factor in construction projects, as it can lead to cost overruns and delays. Quality control (QC) and quality assurance systems are established to meet specific requirements. In the construction industry, QC can be seen as achieving client satisfaction, managing costs, and allowing enough time to achieve the desired level of quality. An international standard organization (ISO) has developed a quality assurance system to improve quality and address quality-related issues. The ISO 9001 series has been created to apply to all types of organizations.

Vulnerable JavaScript functions detection using stacking of convolutional neural networks.

System security for web-based applications is paramount, and for the avoidance of possible cyberattacks it is important to detect vulnerable JavaScript functions. Developers and security analysts have long relied upon static analysis to investigate vulnerabilities and faults within programs. Static analysis tools are used for analyzing a program's source code and identifying sections of code that need to be further examined by a human analyst. This article suggests a new approach for identifying vulnerable code in JavaScript programs by using ensemble of convolutional neural networks (CNNs) models. These models use vulnerable information and code features to detect related vulnerable code. For identifying different vulnerabilities in JavaScript functions, an approach has been tested which involves the stacking of CNNs with misbalancing, random under sampler, and random over sampler. Our approach uses these CNNs to detect vulnerable code and improve upon current techniques' limitations. Previous research has introduced several approaches to identify vulnerable code in JavaScript programs, but often have their own limitations such as low accuracy rates and high false-positive or false-negative results. Our approach addresses this by using the power of convolutional neural networks and is proven to be highly effective in the detection of vulnerable functions that could be used by cybercriminals. The stacked CNN approach has an approximately 98% accuracy, proving its robustness and usability in real-world scenarios. To evaluate its efficacy, the proposed method is trained using publicly available JavaScript blocks, and the results are assessed using various performance metrics. The research offers a valuable insight into better ways to protect web-based applications and systems from potential threats, leading to a safer online environment for all.

Analysis of The Role of Store Atmosphere in Influencing Consumer Purchasing Decisions at XYZ Cooperative

A cooperative is a business entity consisting of individuals and legal entities that is based on the principles of kinship and economic democracy and is a means of increasing economic progress for cooperative members and the community. This research aims to determine the influence of Store Atmosphere on consumer purchasing decisions at the XYZ Bandung Cooperative. Several members explained that the reason for reducing purchases of goods at the XYZ Cooperative was because they felt that some goods were not given enough attention, such as whether the goods on display were still suitable for consumption or not, communication of information either through social media or information boards was still not working well, the prices of goods were not high. expensive and less affordable for members and other consumers to buy. The research method used was quantitative, the sampling technique used probability sampling and a sample of 43 domestic and foreign tourist respondents was obtained. The analytical tool used was simple regression analysis and the hypothesis used a significance test using the SPSS analysis tool. The results of this research explain that the Store Atmosphere has a positive and significant influence on purchasing decisions at the XYZ cooperative and several dimensions of the Store Atmosphere such as the exterior, store layout and interior appearance have a positive and significant influence on purchasing decisions at the XYZ cooperative. There needs to be more attention from the management of the XYZ cooperative in paying attention to the shop atmosphere so that consumers, especially members, can increase their purchasing decisions at the XYZ Cooperative.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). This perspective aims to (1)justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2)recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

The Influence of Internet Marketing on Purchase Intention in E-Commerce Blibli

This research is a quantitative study that examines social phenomena with the aim of knowing the effects of internet marketing on purchasing intentions. The population of this study is Blibli consumers. This study took a sample of 100 respondents through non -probability sampling techniques for purposive sampling methods. Data collection in this study in the form of distributing questionnaires to users and those who have shopped using the Blibli site. The data analysis technique used is descriptive analysis and inferential analysis with data processing calculation analysis tools using partial least square (PLS) applications. After analyzing the data, the results show that there is a positive and significant influence on the internet marketing variable on the intention of purchasing.

The bi-objective multi-depot split delivery location routing problems under uncertain conditions

This research focuses on addressing the multi-depot location-routing problem with split delivery and multiple objectives under uncertain conditions. A mathematical model is proposed to effectively tackle this problem by considering various constraints and considerations. The paper emphasises the use of split delivery as an effective approach for reducing transportation costs. Furthermore, the study explores bi-objective optimisation, specifically cost minimisation and distance balancing. To assess the robustness of the proposed model under different scenarios, sensitivity analysis tools are employed. Numerical examples are presented to demonstrate the effectiveness of the model. Bi-objective optimisation is conducted using the augmented ε-constraint method, enabling decision-makers to analyse trade-offs between cost minimisation and distance balancing objectives. The findings of the study reveal that an increase in demand leads to higher cost minimisation and lower distance balancing. Conversely, an increase in transportation costs results in higher distance balancing. The research also highlights the robustness of the model and its potential for cost reduction through split delivery compared to scenarios without uncertainty.