Hasil untuk "Management. Industrial management"

Joseph F. Hair, Carole L. Hollingsworth, A. Randolph et al.

Ou Tang, Nurmaya Musa

H. Kanchev, D. Lu, F. Colas et al.

J. Meadowcroft

E. Abdelaziz, R. Saidur, S. Mekhilef

R. Hoskisson, M. Hitt, William P. Wan et al.

K. Legge

F. Khan, Samith Rathnayaka, Salim Ahmed

Oscar Lahuerta, Claudio Carretero, Luis Angel Barragan et al.

This article introduces a hybrid variant of a physics-informed neural network (PINN) that is designed to effectively capture both the rapid dynamics of electrical variables and the slower dynamics of state parameters in a domestic induction heating system. By utilizing observable variables, specifically the voltage and current waveforms from the inductor system, the proposed architecture aims to accurately estimate key electrical parameters, i.e., equivalent resistance and inductance, which vary over time due to the nonlinear magnetic properties of the induction load. To assess the performance of the proposed PINN architecture, a comparison with results obtained using an extended Kalman filter was conducted, which serves as a benchmark for this type of task. In addition, the robustness of both approaches was assessed by introducing varying levels of uncertainty in the observable variables. Finally, the effectiveness of both methods was validated through the analysis of experimental measurements collected from a functional prototype.

Gal Engelberg, Konstantin Koutsyi, Leon Goldberg et al.

Identity Security Posture Management (ISPM) is a core challenge for modern enterprises operating across cloud and SaaS environments. Answering basic ISPM visibility questions, such as understanding identity inventory and configuration hygiene, requires interpreting complex identity data, motivating growing interest in agentic AI systems. Despite this interest, there is currently no standardized way to evaluate how well such systems perform ISPM visibility tasks on real enterprise data. We introduce the Sola Visibility ISPM Benchmark, the first benchmark designed to evaluate agentic AI systems on foundational ISPM visibility tasks using a live, production-grade identity environment spanning AWS, Okta, and Google Workspace. The benchmark focuses on identity inventory and hygiene questions and is accompanied by the Sola AI Agent, a tool-using agent that translates natural-language queries into executable data exploration steps and produces verifiable, evidence-backed answers. Across 77 benchmark questions, the agent achieves strong overall performance, with an expert accuracy of 0.84 and a strict success rate of 0.77. Performance is highest on AWS hygiene tasks, where expert accuracy reaches 0.94, while results on Google Workspace and Okta hygiene tasks are more moderate, yet competitive. Overall, this work provides a practical and reproducible benchmark for evaluating agentic AI systems in identity security and establishes a foundation for future ISPM benchmarks covering more advanced identity analysis and governance tasks.

Ivana Valentina Lemmuela, Mewati Ayub, Oscar Karnalim

Background: Communication is important for everyone, including individuals with hearing and speech impairments. For this demographic, sign language is widely used as the primary medium of communication with others who share similar conditions or with hearing individuals who understand sign language. However, communication difficulties arise when individuals with these impairments attempt to interact with those who do not understand sign language. Objective: This research aims to develop models capable of recognizing sign language movements in Bahasa and converting the detected gesture into corresponding words, with a focus on vocabularies related to religious activities. Specifically, the research examined dynamic sign language in Bahasa, which comprised gestures requiring motion for proper demonstration. Methods: In accordance with the research objective, sign language recognition model was developed using MediaPipe-assisted extraction process. Recognition of dynamic sign language in Bahasa was achieved through the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) methods. Results: Sign language recognition model developed using bidirectional LSTM showed the best result with a testing accuracy of 100%. However, the best result for the CNN alone was 86.67 %. The integration of CNN and LSTM was observed to improve performance than CNN alone, with the best CNN-LSTM model achieving an accuracy of 95.24%. Conclusion: The bidirectional LSTM model outperformed the unidirectional LSTM by capturing richer temporal information, with a specific consideration of both past and future time steps. Based on the observations made, CNN alone could not match the effectiveness of the Bidirectional LSTM, but a combination of CNN with LSTM produced better results. It is also important to state that normalized landmark data was found to significantly improve accuracy. Accuracy within this context was also influenced by shot type variability and specific landmark coordinates. Furthermore, the dataset containing straight-shot videos with x and y coordinates provided more accurate results, dissimilar to those comprised of videos with shot variation, which typically require x, y, and z coordinates for optimal accuracy. Keywords: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), MediaPipe, Sign Language

Yang Cai, Yunli Hao, Yongfang Qi

The niche situation can reflect the advantages and disadvantages of biological individuals in the ecosystem environment as well as the overall operational status of the ecosystem. However, higher-order niche systems generally exhibit complex nonlinearities and parameter uncertainties, making it difficult for traditional Type-1 fuzzy control to accurately handle their inherent fuzziness and environmental disturbances in complex environments. To address this, this paper introduces the backstepping control method based on Type-2 T-S fuzzy control, incorporating the niche situation function as the consequent of the T-S backstepping fuzzy control. The stability analysis of the system is completed by constructing a Lyapunov function, and the adaptive law for the parameters of the niche situation function is derived. This design reflects the tendency of biological individuals to always develop in a direction beneficial to themselves, highlighting the bio-inspired intelligent characteristics of the proposed method. The results of case simulations show that the Type-2 backstepping T-S fuzzy control has significantly superior comprehensive performance in dealing with the complexity and uncertainty of high-order niche situation systems compared with the traditional Type-1 control and Type-2 T-S adaptive fuzzy control. These results not only verify the adaptive and self-development capabilities of biological individuals, as well as their efficiency in environmental utilization, but also endow this control method with a solid practical foundation.

Pankaj K Agarwal, H K Pradhan, Konark Saxena

This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India.

Josenaide Alves da Silva, Geilsa Costa Santos Baptista, Nataélia Alves da Silva

A pesquisa é qualitativa e o objetivo propõe a análise da comunicação dos licenciandos para desenvolvimento de um ensino intercultural em aulas de ciências. Os envolvidos no trabalho foram dois licenciandos do curso de Ciências Agrárias, do Instituto Federal de Educação, Ciências e Tecnologia Baiano, do campus de Senhor do Bonfim-BA. Para coleta de dados, utilizou-se gravações em vídeos, procedendo a Análise de Conteúdo e a Estrutura de análise das classes comunicativas, para analisá-los. Este artigo apresenta resultados sobre as análises das aulas de ciências dos licenciandos, as quais direcionaram para o desenvolvimento da abordagem comunicativa dialógica, incluindo os saberes socioculturais dos estudantes e os saberes científicos, a partir de uma relação entre essas formas de conhecer. Considera-se que a abordagem comunicativa dialógica é um alicerce para os licenciandos ministrarem a prática de ciências contextualizada.

Soo Ran Won, Yong Pyo Kim, Misheel Sainjargal et al.

In this study, 34 volatile organic compounds (VOCs) were analyzed using an online VOCs instrument at 30-min intervals from November 16 to November 23, 2023, in Ulaanbaatar (UB), the capital of Mongolia for the first time. The average concentration of the 34 VOCs was 13.0 ± 11.1 ppb, with the top 10 compounds, such as benzene, toluene, ethylbenzene, and xylenes (BTEX), constituting 80 % of the total. The concentrations of n-hexane, n-heptane, and undecane tended to increase significantly during high-concentration episode period (HEP). Compared to other studies, BTEX concentration levels in UB were higher than those in Seoul and Beijing, but lower than in Southeast Asian cities. Positive matrix factorization (PMF) identified four VOCs sources: vehicle exhaust (33.8 %), industrial/coal combustion (25.3 %), secondary formation precursors (21.3 %), and solvent usage (19.6 %). Vehicle exhaust and industrial/coal combustion sources increased during rush hours and were strongly correlated with nitrogen oxides. During HEP, stagnant air mass led to increased contributions from vehicle exhaust and industrial/coal combustion sources, indicating a significant local impact. Solvent usage appeared to be influenced by building materials and exterior painting which increased with high relative humidity. Secondary formation precursors increased in concentration during daytime and were highly correlated with ozone. Among the measured compounds, benzene was assessed for lifetime health risk, showing that adults with the prolonged exposure exhibited higher risk than infants and children. However, during HEP, children were also at increased risk, despite their shorter exposure duration. Based on the concentration levels of VOCs and the associated health risks, the results highlight that the need for a policy on ambient VOCs management in UB, with a particular focus on local source management.

I. A. Danilov

This study explores the functioning of the Yakut language in the conditions of the northern industrial monotown Mirny (Republic of Sakha (Yakutia)). The study identifies the features of linguistic distribution in the speech repertoire of ethnic Sakha based on data from a sociolinguistic survey and interviews (n=279). Descriptive statistical methods and content analysis are employed. The results reveal an asymmetric nature of Yakut-Russian bilingualism with Russian language predominance in public communication. While Sakha individuals exhibit a high level of language competence in the Yakut language, its application is limited. In familial and friendly communication, the Yakut language maintains significant positions; however, its demand is minimal in the professional-business sphere, especially in industry. Among individuals with higher education, the percentage of Russian speakers at work reaches 68.34% compared to 55.77% among those with only secondary vocational education. Russian language dominates in management, service sectors, and law enforcement (75—100%). Only in fields such as healthcare, education, and culture does the Yakut language maintain strong positions. The native language is primarily perceived by Sakha as a symbolic marker of identity, yielding to Russian in social prestige and communicative power. The vitality prospects of the Yakut language in the city are assessed ambiguously and are linked to the effectiveness of language policy in key institutional contexts (education, media, government).

Li Yizhan, Dong Lu, Fan Xiaoxiao et al.

Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

Jinyang Li

In this research paper, we investigate into a paper named "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [arXiv:1706.10059]. It is a portfolio management problem which is solved by deep learning techniques. The original paper proposes a financial-model-free reinforcement learning framework, which consists of the Ensemble of Identical Independent Evaluators (EIIE) topology, a Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL) scheme, and a fully exploiting and explicit reward function. Three different instants are used to realize this framework, namely a Convolutional Neural Network (CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory (LSTM). The performance is then examined by comparing to a number of recently reviewed or published portfolio-selection strategies. We have successfully replicated their implementations and evaluations. Besides, we further apply this framework in the stock market, instead of the cryptocurrency market that the original paper uses. The experiment in the cryptocurrency market is consistent with the original paper, which achieve superior returns. But it doesn't perform as well when applied in the stock market.

Michele Azzone, Emilio Barucci, Davide Stocco

We investigate the portfolio frontier and risk premia in equilibrium when institutional investors aim to minimize the tracking error variance under an ESG score mandate. If a negative ESG premium is priced in the market, this mandate can reduce portfolio inefficiency when the return over-performance target is limited. In equilibrium, with asset managers endowed with an ESG mandate and mean-variance investors, a negative ESG premium arises. A result that is supported by empirical data. The negative ESG premium is due to the ESG constraint imposed on institutional investors and is not associated with a risk factor.

Lei Jihu

Post-pandemic, the chemical sector faces new challenges crucial to national progress, with a pressing need for rapid transformation and upgrading. The pandemic's impact and increasing demand for sustainability have highlighted the importance of green supply chain management. This study used a questionnaire survey and analyzed the data with SPSS and AMOS to investigate the influence of factors like regulatory compliance, green procurement, manufacturing, logistics, sales, competitors, internal environmental protection, and cost control on green supply chain management awareness and implementation in chemical enterprises. The results show that these factors significantly enhance green supply chain management, contributing to economic and environmental benefits. This paper provides a theoretical framework to improve green supply chain efficiency in chemical clusters, promoting sustainable industry growth.