Hasil untuk "Management information systems"

A. Scheer

Michael Zeifman, K. Roth

K. Fan, Shangyang Wang, Yanhui Ren et al.

D. M. Lambert, J. Stock, L. Ellram

William J. Kettinger, Choong-C. Lee

Y. Agarwal, Bharathan Balaji, Rajesh E. Gupta et al.

G. Dimitrakopoulos, P. Demestichas

The increasing need for mobility has brought about significant changes in transportation infrastructures. Inefficiencies cause enormous losses of time, decrease in the level of safety for both vehicles and pedestrians, high pollution, degradation of quality of life, and huge waste of nonrenewable fossil energy.The scope of this article is to introduce novel functionality for providing knowledge to vehicles, thus jointly managing traffic and safety. This will be achieved through the design of the proposed functionality, which, at a high level, will comprise (1) sensor networks formed by vehicles of a certain vicinity that exchange traffic-related information, (2) cognitive management functionality placed inside the vehicles for inferring knowledge and experience, and (3) cognitive management functionality in the overall transportation infrastructure. The goal of the aforementioned three main components shall be to issue directives to the drivers and the overall transportation infrastructure valuable in context handling.

Soonhee Kim, H. Lee

B. Ramdani, P. Kawalek, O. Lorenzo

Yuyan Fan, Wen Li, Limin Zhang et al.

No-tillage (NT) has been widely recognized for significantly enhancing crop yield and nitrogen (N) use efficiency in dryland agricultural systems globally. However, in irrigated fields, NT has demonstrated adverse effects on wheat yield, and limited information is available regarding its impact on N uptake and use efficiencies, and grain protein characteristics. Previous studies concluded that drip fertigation (DF) achieved superior yield gain over the conventional N fertilizer broadcasting with flood irrigation (BF) under NT compared to rotary tillage (RT) and intensive tillage (PRT; first plowing followed by rotary tillage). This study measured tissue N concentration, grain protein content and composition, dough processing quality traits, and the activities of N metabolism enzymes in flag leaves and developing grains. The objectives were to (1) evaluate the response of N use traits and grain quality to DF, and (2) elucidate the relationship between gains in yield and N uptake across varying tillage methods. Results revealed that DF significantly increased N uptake by 35.4–38.0%, 22.1–22.2%, and 16.0–16.6% over BF under NT, RT, and PRT, respectively. This boosted N uptake predominantly contributed to enhanced N use efficiency (grain production per unit of total soil mineral and fertilizer N input). Regression analysis indicated that increased N pre-anthesis uptake was the primary driver of yield improvement by DF (<i>r</i>² > 0.99, <i>P</i> < 0.01). Furthermore, NT demonstrated superior improvements by DF in N nutrition index, grain protein content, gliadin content, wet gluten content, and water absorption rate compared to RT and PRT. In conclusion, wheat N use and grain protein under NT responded greater to DF than intensive tillage. Therefore, our findings emphasize that transitioning from conventional water and N management to DF is an effective and practical strategy for enhancing N uptake, achieving high yield, improving N use efficiency, and enriching grain protein content, particularly under NT conditions.

Wilonotomo Wilonotomo, Mochamad Ryanindityo, Koesmoyo Ponco Aji et al.

Technological transformation not only changes the way we communicate and do business but also changes the way the government provides public services to the community. One manifestation of this transformation is the implementation of Information Systems (IS) for public services, to provide services that are more efficient, transparent, and responsive. By implementing an IS, the administration and data management process becomes more efficient. This research method uses a quantitative method approach, The research data are obtained by distributing online questionnaires via the Google Form platform and the respondents for this research were 576 senior employees of the immigration department in Indonesia who were determined using a simple random sampling method. Research data analysis uses structural equation modeling (SEM). The variables in this research are the dependent variables, namely information Technology System integration and Information Technology System Security (ITSS). The dependent variable is the Efficiency and Accuracy of Immigration Documents (EAID) and User Acceptance and Satisfaction (UAS). Based on data analysis, it is concluded that TSS integration had a positive and significant relationship with EAID, Information technology system integration had a positive and significant relationship with UAS, ITSS had a positive and significant relationship with EAID, ITSS had a positive and significant relationship with UAS and user acceptance and satisfaction had a positive and substantial relationship with the EAID. Implementing IS opens the door to more efficient, transparent, and responsive public services. By leveraging technology, governments can streamline administrative processes, increase citizen participation, and create an environment where every citizen can benefit from better public services. In carrying out this transformation, the government must remain focused on data security, privacy, and community empowerment so that people truly feel the positive impact of technological developments in public services.

Muhammad Ihsan Zul, Suhaila Mohd. Yasin, Dadang Syarif Sihabudin Sahid

Evaluating the quality of student-generated user stories is important in software engineering education, but only a limited number of industry practitioners can assist. The integration of generative AI can facilitate this process. To do so, the INVEST quality evaluation framework is widely recognized for assessing user story quality; however, prior research has not explored its use in conjunction with generative AI. This study investigated ChatGPT's ability to evaluate user stories using the INVEST framework. This study compares two ChatGPT-based evaluation approaches with those of experienced practitioners, focusing on student-generated user stories. Discrepancies between ChatGPT and practitioner evaluations were measured using Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Statistical significance was tested using the Mann-Whitney U Test. The results indicate that ChatGPT’s 1st approach yielded lower discrepancies than practitioner evaluations. Moreover, significance testing showed no statistically significant differences between the ChatGPT and practitioner results for the two INVEST criteria- Independent and Estimable. These findings suggest that the 1st approach can assist in the evaluation process, although practitioners must ensure comprehensive and accurate evaluations. ChatGPT can provide preliminary evaluations in educational contexts, enabling students to receive formative feedback and allowing educators to streamline evaluation processes. Although practitioner validation is still required, their role may shift toward verifying AI-generated results, thus reducing the overall workload and accelerating quality evaluation

Toto Raharjo, Yudi Prayudi

With increasing threats in cyberspace, maintaining the integrity of electronic medical data is crucial. This study aims to develop a method that integrates encryption using Advanced Encryption Standard (AES) and compression with the Lempel-Ziv-Markov Algorithm (LZMA) to protect DICOM files containing sensitive information. This method is designed to address two main challenges: the growth of file sizes after the encryption process and the efficiency in data storage. In this study, an experimental design with random sampling was applied, testing 427 DICOM files from open libraries ranging in size from 513.06 KB to 513.39 KB to evaluate the implementation of this method in reducing file size, encryption time, and maintaining data integrity. The results show that this method is able to reduce file size by between 40-50% with an average encryption time of about 0.2-0.3 seconds per file. In addition, the data remains intact before and after the encryption process, which indicates that the integrity of the data is well maintained. Further analysis revealed that CPU usage during the encryption process reached 94.05%, while memory usage was recorded at 92.95 KB. In contrast, in the decryption process, CPU usage decreased to 78.16% with a much lower memory consumption, which was 31.07 KB. The findings have significant implications for medical information systems, allowing developers to easily implement these methods through APIs. This research is expected to be a reference for future studies that focus on data security in health information systems and provide new insights into the combination of encryption and compression in the context of medical data.

夏文超1，2，徐婧1，2，周星光1，2，吴伟华3，赵海涛1，2

工业物联网在实现自动化和智能化生产方面具有巨大的潜力，但现有无线网络难以满足工业控制场景中的低时延、高可靠通信需求。基于此，研究了工业物联网下行短包传输场景中叠加导频（superimposed pilot，SP）功率分配问题，推导了可达传输速率在非完美信道状态信息和最大比发送下的闭合表达式的下限。进一步建立了下行加权和速率最大化问题，并利用逐次凸逼近法将该问题转化为几何规划问题来优化导频和数据功率分配。仿真结果表明所提SP模式下功率优化方案在短包传输中的优越性。

Yoga Pristyanto, Dipa Wirantanu

The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company, so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the WDC dataset with 30 features and 569 data points, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy and the use of weighted macros on precision, recall, and f1 score, using GA selection features has slightly higher results than RFE, so it is concluded that GA selection features are better at solving problems in high-dimensional data.

Wojciech Dzieza MD, Hailey Hampton MD, Kevin Farmer MD et al.

Category: Sports; Trauma Introduction/Purpose: Artificial intelligence (AI) chatbots have recently gained popularity as a source of information that can be easily accessed by patients given their human-like responses to prompts and questions. Within orthopaedics, the treatment of acute Achilles tendon ruptures is not uniform due to varying surgical repair techniques, postoperative protocols, and nonoperative treatment options dependent on surgeon preference and patient factors. Given that patients are increasingly turning toward AI for questions about medical diagnoses and treatment options, our study looked to compare the adequacy of AI chatbot responses to frequently asked questions regarding acute Achilles tendon ruptures. Methods: Three popular AI platforms (ChatGPT, Google Gemini, and Microsoft Bing AI) were prompted for a concise response to ten commonly asked questions regarding Achilles tendon rupture management (Table 1). Four board-certified subspecialty-trained orthopaedic surgeons (two in foot and ankle, two in sports medicine) were asked to assess the value of the AI response using a four-point scale (1 – satisfactory; 2 – satisfactory requiring minimal clarification; 3 – satisfactory requiring substantial clarification; 4 – unsatisfactory). A Kruskal-Wallis test was used to compare the responses between the three AI platforms using the scores assigned by the surgeons. Results: All three AI chatbots provided comparable answers to 7 of 10 questions (70%). Of all the responses (30 total), only two (6.7%) had a mean rating of 3 or higher. Significant differences were noted between the AI systems for questions 4 [H(2) = 7.258, p = .027], 7 [H(2) = 6.308, p = .043], and 10 [H(2) = 6.796, p = .033]. Post hoc analyses revealed Bing AI had significantly worse scores as compared to ChatGPT for all three of these questions. Conclusion: AI chatbots can appropriately answer concise prompts about the diagnosis and management of acute Achilles tendon ruptures often sought out by patients prior to or after evaluation by an orthopaedic surgeon. The responses provided by the three AI chatbots analyzed in our study were uniform and satisfactory, with only one of the platforms scoring worse on three of the ten questions. As AI chatbots advance, they will become a valuable tool for patient education in orthopaedics. Future studies will be needed to assess performance as new AI chatbots develop and large language models continue to evolve. Table 1: List of 10 selected frequently asked questions regarding acute Achilles tendon ruptures

Abderrahman Chekry, Jamal Bakkas, Mohamed Hanine et al.

In the context of decision-making, the DEMATEL (Decision Making Trial and Evaluation Laboratory) method stands out for its systematic approach to complex systems. By incorporating fuzzy logic, the DEMATEL fuzzy method takes traditional techniques a step further, effectively managing the uncertainties and imprecision inherent in expert assessments. This hybrid method has proved useful in a variety of fields, including business, engineering, healthcare, environmental management, and education. Its ability to refine subjective judgments into actionable information enables decision-makers to improve organizational performance, optimize resource allocation, and achieve more accurate results. The development of software tools for these methods makes them more accessible and practical, enabling more effective analysis and application. In this paper, we propose a flexible implementation that integrates seamlessly into Python-based applications, offering full access to all parameters, matrices, and intermediary calculations of the method. Additionally, the tool also provides a user-friendly graphical interface.

Zhenhan Huang, Fumihide Tanaka

On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively.

Hao Guo, Wanxin Li, Mark Nejad et al.

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients' anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA's signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios' requirements while safeguarding EHR and is robust against unauthorized retrievals.

Ya YU, Yusun FU

The development of the discrete manufacturing shows a trend of intelligence, openness and collaboration.As a result, many heterogeneous devices are connected to the industrial internet, which brings serious challenges to the security.Therefore, it is particularly important to introduce trust management and trusted access to devices for trusted measurement.In order to more timely and accurately evaluate the trustworthiness of the edge terminal initially accessing the system, a trustworthiness measurement method based on the device vulnerability database was innovatively proposed.This method adopted the architecture of cloud-edge collaboration, established a device information database and a vulnerability database in the central cloud, and then calculated the terminal risk factor at the edge.Finally, the trust initialization of the access terminal was completed.The simulation results show that the method can well balance the efficiency and security of the system.