Hasil untuk "Management information systems"

A. Huang, M. Crow, G. Heydt et al.

Kurt M. Dresner, P. Stone

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers' actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology--traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.

J. Labadie

F. Nah, J. Lau, Jinghua Kuang

M. Earl

Chun Zhang, J. Naughton, D. DeWitt et al.

M. Franklin, A. Halevy, D. Maier

Bashair Althani

Automated legacy code modernization using Large Language Models lacks rigorous evaluation frameworks and multi-objective quality assessment methodologies. Existing research suffers from three critical deficiencies: single-metric evaluation paradigms creating pathological optimization incentives, statistical validation limited to <i>p</i>-values without effect size analysis, and absence of systematic transformation pattern taxonomies explaining what works and why. We present a novel multi-objective statistical framework that jointly assesses Cyclomatic Complexity (CC) and Maintainability Index (MI) while providing comprehensive effect size analysis addressing software engineering research gaps. Applied to 47 legacy Java samples from Apache Ant (version 1.10.x, commit rel/1.10.14), our framework achieves 97.9% metric-level improvement with very large practical effects (Cohen’s <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>d</mi><mo>=</mo><mn>1.86</mn></mrow></semantics></math></inline-formula>, 95% CI [1.36, 2.35], <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.0001</mn></mrow></semantics></math></inline-formula>) for maintainability—substantially exceeding prior work and conventional significance thresholds. We note that this success rate reflects quality metric improvement; functional equivalence was verified through syntactic validation and manual inspection of a 20% random sample, while comprehensive automated test-based verification remains a limitation addressed in future work. We contribute: (1) first multi-objective quality assessment framework for code modernization with weighted composite scoring and sensitivity analysis, (2) rigorous statistical methodology with effect size analysis beyond <i>p</i>-values, (3) systematic transformation pattern taxonomy identifying four successful patterns and three failure modes with predictive value (inter-rater agreement <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>κ</mi><mo>=</mo><mn>0.82</mn></mrow></semantics></math></inline-formula>), and (4) negative result showing iterative refinement provides no benefit (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>d</mi><mo>=</mo><mn>0.08</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo>=</mo><mn>0.179</mn></mrow></semantics></math></inline-formula>), saving community resources. Our transformation taxonomy enables practitioners to predict success likelihood from code characteristics, while our statistical framework provides replicable methodology for evaluating LLM-based software engineering tools. The very large effect size indicates metric-level improvements are materially meaningful for real-world software maintenance, not merely statistically detectable.

Mahtab Khanyabzadeh, Alireza Emamifar, Nikoo Emtiazi et al.

ABSTRACT Background Skin cancer is one of the most prevalent malignancies worldwide, characterized by the abnormal growth of skin cells and significant clinical challenges. Conventional treatments—including surgery, chemotherapy, and radiotherapy—often suffer from limitations such as adverse side effects, tumor resistance, and inadequate efficacy. In this context, natural compounds have garnered attention as alternative therapeutic agents. Quercetin, a flavonoid widely distributed in fruits and vegetables, is recognized for its anti‐cancer, anti‐inflammatory, and antioxidant properties. However, its clinical application is hindered by poor solubility, low bioavailability, and limited skin permeation. Recent advances in nanotechnology have led to the development of nano‐based formulations that can enhance the pharmacological performance of quercetin, offering promising avenues for skin cancer management. Objective This review aims to provide a comprehensive analysis of skin cancer pathogenesis and to evaluate the mechanistic insights and therapeutic potential of quercetin—alone and in nano‐formulated systems—in preclinical models. The novelty of this review lies in its integrated approach, combining an overview of natural compound therapy with the latest cutting‐edge nanotechnology strategies to overcome current treatment challenges. Methods This literature review was performed by searching related words, including “Skin cancer,” “Melanoma,” “Basal cell carcinoma,” “Squamous cell carcinoma,” “Quercetin,” “Nanotechnology,” in different databases like Google Scholar, Scopus, PubMed, Web of Science, and Scientific Information Databases until 2025. Results Preclinical studies indicate that quercetin modulates multiple cellular and molecular pathways—such as those regulating apoptosis, cell cycle progression, mitochondrial function, and DNA repair—to inhibit proliferation, migration, and invasion of skin cancer cells. Nano‐based delivery systems (e.g., titanium dioxide nanoparticles, nanogels, and lipid carrier gels) have been shown to further enhance these therapeutic effects by improving quercetin's stability, skin permeability, and bioavailability. Conclusion Integrating quercetin with nano‐based formulations presents a novel and promising approach for targeted skin cancer therapy. While preclinical results are encouraging, further experimental and clinical investigations are necessary to fully validate these findings and facilitate translation into clinical practice.

Ege Yuceel, Daniel Liberzon, Sayan Mitra

We study localization and control for unstable systems under coarse, single-bit sensing. Motivated by understanding the fundamental limitations imposed by such minimal feedback, we identify sufficient conditions under which the initial state can be recovered despite instability and extremely sparse measurements. Building on these conditions, we develop an active localization algorithm that integrates a set-based estimator with a control strategy derived from Voronoi partitions, which provably estimates the initial state while ensuring the agent remains in informative regions. Under the derived conditions, the proposed approach guarantees exponential contraction of the initial-state uncertainty, and the result is further supported by numerical experiments. These findings can offer theoretical insight into localization in robotics, where sensing is often limited to coarse abstractions such as keyframes, segmentations, or line-based features.

Saqlain Muhammad, Kumam Poom, Kumam Wiyada

Because of the quantitative and qualitative uncertainty and complexity, it is not sufficient to use just Multicriteria Decision Making (MCDM), but also Multi-Criteria Decision Analysis (MCDA). The MCDM relates to the methods by which decisions are taken (i.e., selection of alternatives, ranked or ordered, and for what is analyzed being objective values. While MCDA offers a comprehensive approach for systematic assessment of criteria considering its impact on decision outcomes. Given that both methods have their own strengths, it is necessary to apply both MCDM and MCDA in agricultural economics which has a lot of uncertainty because of market price variability, increasing input costs and changing weather patterns. In this paper, Fuzzy Hypersoft Sets (FHSs), is used to model this problem and a case study is solved with Stable Preference Ordering Towards Ideal Solution (SPOTIS), Random Forest (RF), and Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) to identify the most favorable crop for Jane's farm in terms of weather, costs required during agricultural production process like water or land usage, pesticide resistance against pests as well as market demand. Maize as an alternative 𝐴7 = 0.526 was identified as the best choice by all three methods with Tomatoes and Rice scoring second, based on calculated score values. Thus, it enables us to study both quantitative and qualitative data, making it extremely able for agriculture uncertainties. This unique usage of sophisticated mathematics integrated with machine learning allows the decision-makers to find more accurate results, meaning it can manage economic risks better and allocate resources intelligently in agriculture. The comparative analysis with existing studies highlights the superiority of proposed work. Thus, it is significantly superior in accuracy. Hence farmers can harness farm economics to address these challenges by managing economic risks using mathematical decision-making tool, thereby leading them towards sustainability of livelihoods, food security and a resilient agricultural sector.

Xue Hou

Abstract With the rapid development of science and technology, various types of industrial robots occupy an important role in industrial production. Therefore, the performance testing of industrial robots is very important. In response to the low accuracy in performance testing of industrial robots operating under extreme working conditions using traditional testing devices, a performance testing device for industrial robots is designed using a dedicated servo motor model and programmable logic controller. To test the proposed testing device, comparative experiments are conducted. The results showed that when the temperature varied between − 40 ℃ and 80 ℃, the motor operating speed of the proposed device was around − 2,993 r/min, which was better than the comparative device. It was not significantly affected by temperature. Under over-speed conditions of 1.0 times, 1.2 times, 1.4 times, and 1.6 times, the motor temperatures of the proposed device after operating 20 min were 36 ℃, 40 ℃, 45 ℃, and 53 ℃, respectively, which were significantly lower than those of the comparative device. In summary, the proposed industrial robot performance testing device based on ECMA servo motor and PLC can maintain a stable state under extreme conditions, providing an appropriate idea for the performance testing of industrial robots and ensuring a guarantee for the design of industrial robots.

Chetan Gohil, Oliver M Cliff, James M. Shine et al.

Mutual information (MI) is a useful information-theoretic measure to quantify the statistical dependence between two random variables: $X$ and $Y$. Often, we are interested in understanding how the dependence between $X$ and $Y$ in one set of samples compares to another. Although the dependence between $X$ and $Y$ in each set of samples can be measured separately using MI, these estimates cannot be compared directly if they are based on samples from a non-stationary distribution. Here, we propose an alternative measure for characterising how the dependence between $X$ and $Y$ as defined by one set of samples is expressed in another, \textit{cross mutual information}. We present a comprehensive set of simulation studies sampling data with $X$-$Y$ dependencies to explore this measure. Finally, we discuss how this relates to measures of model fit in linear regression, and some future applications in neuroimaging data analysis.

Amin Haeri, Jonathan Vitrano, Mahdi Ghelichi

Risk management in finance involves recognizing, evaluating, and addressing financial risks to maintain stability and ensure regulatory compliance. Extracting relevant insights from extensive regulatory documents is a complex challenge requiring advanced retrieval and language models. This paper introduces RiskData, a dataset specifically curated for finetuning embedding models in risk management, and RiskEmbed, a finetuned embedding model designed to improve retrieval accuracy in financial question-answering systems. The dataset is derived from 94 regulatory guidelines published by the Office of the Superintendent of Financial Institutions (OSFI) from 1991 to 2024. We finetune a state-of-the-art sentence BERT embedding model to enhance domain-specific retrieval performance typically for Retrieval-Augmented Generation (RAG) systems. Experimental results demonstrate that RiskEmbed significantly outperforms general-purpose and financial embedding models, achieving substantial improvements in ranking metrics. By open-sourcing both the dataset and the model, we provide a valuable resource for financial institutions and researchers aiming to develop more accurate and efficient risk management AI solutions.

Abdul Q. Ebrahim, Carolien L. Van den Berg

Background: Despite being significant contributors to the economy, informal businesses operate with limited resources. In South Africa, the informal sector is substantial, accounting for approximately 30% of total employment and around 6% of gross domestic product (GDP). These businesses often struggle to adopt and leverage technology constraining their capacity for growth and innovation, ultimately limiting their contribution to economic development and the alleviation of socio-economic challenges. Objectives: The objective of this study was to investigate the factors that influence the barriers to adopting digital technologies in South Africa’s informal economy. Method: This study adopted a qualitative research approach, using semi-structured interviews and purposive sampling to collect data from 14 informal business owners in Cape Town. Participants provided informed consent and thematic analysis was conducted using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Results: Findings revealed barriers including cash preference, load-shedding, crime and digital incompetency obstructing technology adoption. Despite these hurdles, the potential benefits of digital technology for informal businesses were underscored. Conclusion: The study suggests untapped potential in enhancing technology adoption among informal businesses through targeted interventions. By addressing identified barriers, such initiatives could significantly bolster the informal economy’s impact on South Africa’s socio-economic landscape. Contribution: This research contributes to understanding the complexities surrounding technology adoption in South Africa’s informal economy. It offers insights for policymakers, practitioners and stakeholders seeking to promote digital inclusion and economic empowerment within marginalised sectors.

Margherita Lasorella, Pasquale de-Dato, Elena Cantatore

Considering the variety of architectural Cultural Heritage typologies, systemic architectures require specific attention in the recovery process. The dimensions of "extension" and "recurrence" at geographic and technological levels affect the complexity of their knowledge process; they require systematic ways for their categorisation and comprehension to guarantee correct diagnosis and suitable rehabilitation. Recent applications involving Internet of Things (IoT) for the built Cultural Heritage have demonstrated the potentialities of three-dimensional (3D) geographic information system (GIS) models and structured databases in supporting complex degrees of knowledge for technicians, as well as management for administrators. Starting from such experiences, the work presents the setting up of a web-based platform to support the knowledge and management of systemic architectures, considering the geographical distribution of fabrics, natural and anthropic boundary conditions, and technical and administrative details. The platform takes advantage of digital models, machine and deep learning procedures and relational databases, in a GIS-based environment, for the recognition and categorisation of prevalent physical and qualitative features of systemic architectures, the recognition and qualification of dominant and recurrent decays and the management of recovery activities in a semi-automatic way. Specifically, the main digital objects used for testing the applied techniques and setting up the platform are based on Red-Green-Blue (RGB) and mapped point clouds of the historical Telegraphic Towers located along the Madrid-Valencia path, resulting from the on-site investigations. Their choice is motivated by the high level of knowledge about the cases reached in the last years by the authors, allowing them to test rules within the decision support systems and innovative techniques for their decay mapping. As the experience has demonstrated, the systematisation of technical details and operative pipeline of methods and tools allow the normalisation and standardisation of the intervention selection process; this offers policymakers an innovative tool based on traditional procedures for conservation plans, coherent with a priority-based practice.

Peng Wu, Lei Yu, Ruifeng Zhang et al.

The grid integration of large-scale photovoltaic and other distributed energy sources is an effective solution for addressing power supply shortages and environmental pollution. However, the widespread adoption of photovoltaics and grid integration presents various technological and management challenges. To fulfill the demands of grid management and ensure safe operations, the exchange of information between different terminals is continuously escalating. Use of diverse communication standards creates the problem of “information islands” among terminals. Hence, a standardized information model is crucial for describing the photovoltaic grid integration business and enhancing the efficiency of related software platform research and development. This study extends the IEC 61970/61968 standards and presents a common information model for the integration of photovoltaic systems into the grid. Initially, the operational procedures for integrating photovoltaic systems into the grid are analyzed, and UML modeling tools are employed for business modeling purposes. Subsequently, leveraging the outcomes of the business modeling and the content of the IEC 61970/61968 standards, the development of the common information model is executed. Lastly, causal analysis is conducted along with the modeling of communication standard extensions specifically targeted for the integration of photovoltaic systems into the grid, culminating in the finalization of the construction of the common information model for photovoltaic grid connection.

Kadek Indah Melanie Dewi, I Wayan Gede Narayana, Rifky Lana Rahardian

One of the problems that exist in the Hospitality Engineering Indonesia Professional Certification Institute is in terms of administration management which still uses conventional data and is inefficient in time, effort and cost in filling out the APL form and also the frequent loss of participant competency certificate archives because there is no specific system for managing data certification. The purpose of conducting this research is to help the management of the Engineering Hospitality Indonesia Professional Certification Institute find solutions to existing problems by building and implementing a Professional Certification Management Information System at the Indonesian Hospitality Engineering Professional Certification Institute which has a website platform. This research was conducted using the Waterfall Software Development Life Cycle (SDLC) method. This system is designed using Context Diagrams, Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD) and also this system is built with a website platform that uses the php programming language with the Laravel framework. In testing the system is done by blackbox testing and also distributing questionnaires to respondents as measured by a Likert scale to determine user acceptance of the information system. This system gets an average value of 85.8% based on measuring user acceptance of the system and it can be concluded that the Professional Certification Institute Certification Management Information System has been designed with "Very Good".

S. V. Shabaeva, A. I. Shabaev

Under conditions of uncertainty caused by sanctions and geopolitical changes, the correct choice of both strategic development priorities and management tools for their implementation becomes important. Given the global trend towards digitalization and the growing supply from software developers, it is necessary to understand clearly, which software products and for what purposes can be used effectively at specific enterprises. The challenge here is that due to instability, sanctions pressure, and withdrawals of foreign software from the Russian market, enterprises are forced to change business and technological processes, and to look for new tools for implementing development strategies.   The purpose of the article is to determine the basic principles for choosing strategic management tools and information technologies as elements of strategic management at enterprises in the context of digital transformation.   The article shows that due to the ongoing digitalization of industrial enterprises any tools for implementing the strategies must be considered within the framework of an automated process management model. It can be based on various information technologies or platform solutions, depending on the coverage of enterprise business processes, and the readiness to adopt digital technologies in strategic and operational management. The modern trend is to implement strategic development directions, relying on the internal ecosystem of the enterprise. Making the right choice of information technology within an enterprise ecosystem requires that technology investments are aligned with the company’s business goals, adaptable to changing needs, can integrate with existing systems, are user-friendly, secure, and provide a short-term return on investment. Ultimately, these principles enable businesses to successfully navigate the digital transformation journey and leverage technology as a strategic enabler for sustainable growth.

Chan Xu, Shuowen Zhang

In this paper, we consider a multiple-input multiple-output (MIMO) radar system for localizing a target based on its reflected echo signals. Specifically, we aim to estimate the random and unknown angle information of the target, by exploiting its prior distribution information. First, we characterize the estimation performance by deriving the posterior Cramér-Rao bound (PCRB), which quantifies a lower bound of the estimation mean-squared error (MSE). Since the PCRB is in a complicated form, we derive a tight upper bound of it to approximate the estimation performance. Based on this, we analytically show that by exploiting the prior distribution information, the PCRB is always no larger than the Cramér-Rao bound (CRB) averaged over random angle realizations without prior information exploitation. Next, we formulate the transmit signal optimization problem to minimize the PCRB upper bound. We show that the optimal sample covariance matrix has a rank-one structure, and derive the optimal signal solution in closed form. Numerical results show that our proposed design achieves significantly improved PCRB performance compared to various benchmark schemes.