Purpose: This study aims to examine the effect of Environmental Disclosure, Social Disclosure, and Governance Disclosure on stock returns, as well as to investigate the moderating role of audit quality in the relationship between ESG disclosure dimensions and stock returns of companies listed in the LQ45 Index on the Indonesia Stock Exchange during the 2021–2025 period. Method: This research adopted a quantitative approach by utilizing secondary data derived from annual reports, sustainability reports, and stock market information. The study population comprised companies listed in the LQ45 Index of the Indonesia Stock Exchange throughout the 2021–2025 period. Sample selection was carried out through a purposive sampling technique based on specific criteria established by the researcher, resulting in a total of 65 firm-year observations. The collected data were analyzed using descriptive statistical methods, classical assumption testing, multiple linear regression, and Moderated Regression Analysis (MRA). All statistical procedures were performed with the support of SPSS software. Finding: The findings reveal that Environmental Disclosure does not significantly influence stock returns. Conversely, both Social Disclosure and Governance Disclosure demonstrate a significant positive association with stock returns. The analysis also shows that audit quality does not strengthen or weaken the relationship between Environmental Disclosure and stock returns. However, audit quality is found to enhance the positive impact of Social Disclosure and Governance Disclosure on stock returns. These results imply that investors tend to assign greater importance to information related to social and governance aspects than to environmental disclosures when evaluating investment opportunities. In addition, higher audit quality increases the reliability and trustworthiness of ESG-related information, thereby improving its usefulness in investment decision-making. Novelty: This research enriches the existing body of literature by investigating the effects of Environmental Disclosure, Social Disclosure, and Governance Disclosure on stock returns as separate dimensions, rather than evaluating ESG disclosure as a unified construct. Furthermore, the study advances previous research by introducing audit quality as a moderating factor in the relationship between sustainability disclosures and stock returns. By concentrating on firms included in the LQ45 Index over the 2021–2025 period, this study offers more focused empirical evidence regarding the role of ESG disclosures and audit quality within the context of the Indonesian capital market.
Purpose: This study was conducted to investigate the influence of sustainability Report, the proportion of independent commissioners on the board, and institutional ownership on firm value. In this relationship, financial performance was positioned as a mediating variable among energy companies listed on the Indonesia Stock Exchange during the 2022–2024 period. Method: A quantitative research design was employed using secondary data collected from companies’ annual reports and sustainability reports. The research sample comprised 35 energy-sector firms selected through purposive sampling, resulting in a total of 105 firm-year observations. The data were analyzed using panel regression techniques with EViews 13 software, while the mediating role of financial performance was assessed through the Sobel test. Finding: The findings revealed that sustainability Report contributes positively and significantly to both financial performance and firm value. In contrast, the presence of independent commissioners was found to have a significant negative impact on financial performance as well as firm value. Meanwhile, institutional ownership did not demonstrate a statistically significant effect on either financial performance or firm value. Further analysis indicated that financial performance partially mediates the association between sustainability Report and firm value, as well as the relationship between independent commissioners and firm value. However, no mediating effect of financial performance was identified in the linkage between institutional ownership and firm value. Novelty: The originality of this study lies in the adoption of the Global Report Initiative (GRI) 2021 framework, which encompasses 117 disclosure indicators, the inclusion of institutional ownership as an additional corporate governance mechanism, and the application of the Quintuple Bottom Line perspective. This framework was utilized to explain the interconnections among sustainability practices, corporate governance, financial performance, and firm value within the context of Indonesia’s energy industry.
Background: The Department of Business Administration uses several laboratories to support practical learning, including computer, business simulation, typing, competency testing (TUK), and banking laboratories. Objective: This research was conducted to analyze the needs of office administration laboratories in improving the quality of practical learning and competencies of students of the Department of Business Administration at the Sriwijaya State Polytechnic, Palembang. Methods: The equipment used from the entire laboratory is office administration equipment. Learning facilities in the laboratory at the Department of Business Administration are available. However, there is a shortage of equipment such as printers, damage to some software on computers and some office practice equipment and archives and office stationery that are not available to suppobrt practical learning activities and student competencies. Quantitative descriptive approach (survey), which is an approach to using numerical data from questionnaires to measure the level of need, use of tools, or the level of satisfaction of laboratory users statistically. Results: The results of this study show that equipment, equipment, hardware and software in the laboratory need to be revitalized and improved so that practical learning can run well and the output produced is competent and in accordance with industry needs. Conclusion: The findings show that office administration laboratory revitalization is essential for practical learning and competency development, with priorities including equipment, computer maintenance, digital archives, and simulation tools. However, the study was limited to one institution, requiring broader comparative research.
This paper develops an integrated electromechanical position control system with an embedded microcontroller platform, combining signal processing, control, actuation, and mechanical design. The system operates through a sensing, control, and execution loop for automatic position adjustment. For hardware implementation, the system includes a signal conditioning circuit, a power drive circuit, and a mechanical structure for sensor processing, actuator control, and component integration. For control implementation, a proportional controller runs on an Arduino microcontroller for closed-loop position adjustment. Then, its performance is evaluated through circuit, control, and prototype simulations under different inputs. The results indicate that the prototype achieves a relative tracking error below 2% across the tested positions, with an average response time of 0.82 s under the 90° reference input. Moreover, the integrated hardware and software modules achieve basic closed-loop position control in a low-cost electromechanical prototype system.
Shaohang Yuan· Applied and Computational En...· 0 citations
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Context: LLM-based multi-agent systems enable automation and decision support in software development, yet existing studies rely on benchmark datasets offering only binary pass-or-fail results, limiting insight into real-world applicability. Objective: This study empirically investigates the potential and limitations of LLM-based agents in autonomous software development tasks. Method: A two-phase approach was employed: developing a multi-agent system, CodePori, for automated code generation, and conducting participant-based evaluation to assess practical performance. Results: Participant feedback reveals key strengths, challenges, and areas for improvement in LLM-based multi-agent systems, highlighting aspects missed by standard code-generation benchmarks. Conclusions: While LLM-based multi-agent systems show potential for large-scale software development, successful integration requires addressing challenges such as memory limitations, hallucinations, and code smells, alongside a practitioner-centric perspective.
Z. Rasheed, Muhammad Waseem, Kai-Kristian Kemell et al.· 31 citations
Context: Manual qualitative data analysis is time-intensive and can compromise validity and replicability, affecting analysis design, implementation, and reporting. Large Language Models (LLMs) enable human-bot collaboration in Software Engineering (SE), but their potential for qualitative data analysis in SE remains largely unexplored. Objective: The objective of this study is to design and develop an LLM-based multi-agent system that synergizes human decision support with AI to automate various qualitative data analysis approaches. Methods: We used LLM-based multi-agents systems to assist the qualitative data analysis process, deploying 27 agents, each responsible for a specific task, such as text summarization, initial code generation, and extracting themes and patterns. Results: The main findings are: (1) the LLM-based multi-agent system accelerates the qualitative data analysis process, (2) the system effectively automates tasks such as text summarization, initial code generation, and theme extraction, and (3) the publicly accessible code facilitates validation and further evaluation. Conclusion: The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners. Future improvements focus on enhancing multilingual performance and integrating continuous expert feedback. The source code of proposed system and system details can be found here: https://github.com/GPT-Laboratory/Qualitative-Analysis-with-an-LLM-Based-Agentts
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 40 citations
Recent advances in agentic frameworks have enabled AI agents to perform complex reasoning and decision-making. However, evidence comparing their reasoning performance, efficiency, and practical suitability remains limited. To address this gap, we empirically evaluate 22 widely used agentic frameworks across three reasoning benchmarks: BBH, GSM8K, and ARC. The frameworks were selected from 1,200 GitHub repositories collected between January 2023 and July 2025 and organized into a taxonomy based on architectural design. We evaluated them under a unified setting, measuring reasoning accuracy, execution time, computational cost, and cross-benchmark consistency. Our results show that 19 of the 22 frameworks completed all three benchmarks. Among these, 12 showed stable performance, with mean accuracy of 74.6-75.9%, execution time of 4-6 seconds per task, and cost of 0.14-0.18 cents per task. Poorer results were mainly caused by orchestration problems rather than reasoning limits. For example, Camel failed to complete BBH after 11 days because of uncontrolled context growth, while Upsonic consumed USD 1,434 in one day because repeated extraction failures triggered costly retries. AutoGen and Mastra also exhausted API quotas through iterative interactions that increased prompt length without improving results. We also found a sharp drop in mathematical reasoning. Mean accuracy on GSM8K was 44.35%, compared with 89.80% on BBH and 89.56% on ARC. Overall, this study provides the first large-scale empirical comparison of agentic frameworks for reasoning-intensive software engineering tasks and shows that framework selection should prioritize orchestration quality, especially memory control, failure handling, and cost management.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 1 citation
Large Language Models (LLM) and Generative Pre-trained Transformers (GPT), are reshaping the field of Software Engineering (SE). They enable innovative methods for executing many software engineering tasks, including automated code generation, debugging, maintenance, etc. However, only a limited number of existing works have thoroughly explored the potential of GPT agents in SE. This vision paper inquires about the role of GPT-based agents in SE. Our vision is to leverage the capabilities of multiple GPT agents to contribute to SE tasks and to propose an initial road map for future work. We argue that multiple GPT agents can perform creative and demanding tasks far beyond coding and debugging. GPT agents can also do project planning, requirements engineering, and software design. These can be done through high-level descriptions given by the human developer. We have shown in our initial experimental analysis for simple software (e.g., Snake Game, Tic-Tac-Toe, Notepad) that multiple GPT agents can produce high-quality code and document it carefully. We argue that it shows a promise of unforeseen efficiency and will dramatically reduce lead-times. To this end, we intend to expand our efforts to understand how we can scale these autonomous capabilities further.
Z. Rasheed, Muhammad Waseem, Kai-Kristian Kemell et al.· XP Workshops· 35 citations· ⚡2
The growing domain of liquidity in computing extends its boundaries to include advancements like liquid artificial intelligence (AI). Liquid AI leverages liquid software using isomorphic Internet of Things (IoT) architecture to enhance computation at the edge. This innovation unveils vast opportunities yet also introduces significant challenges, particularly around privacy and trust. We explore the vulnerabilities that might hinder the progression of this technological fusion toward achieving trustworthy AI. Through an intensive examination of the literature, this research highlights the heightened threats to data integrity and stakeholder trust in these evolving ecosystems. Four main challenges: Data collection, Data storage and Access, Data utilization and sharing, and Surveillance and profiling were identified and examined under privacy, and two, Algorithms and decision-making and Security of IoT infrastructure under trust. The concerns are further categorized to highlight their impact on the development of trustworthy AI. The study acknowledges the early state of the field. Consequently, this research navigates through the limited available literature, initiating a pioneering discourse emphasizing fostering a foundation for developing secure and trustworthy Liquid AI environments.
M. Agbese, Niko Mäkitalo, Muhammad Waseem et al.· IoT· 6 citations· ⚡1
The rapid adoption of Generative AI (GenAI) in the software development life cycle (SDLC) increases computational demand, which can raise the carbon footprint of development activities. At the same time, organizations are increasingly embedding governance mechanisms into GenAI-assisted development to support trust, transparency, and accountability. However, these governance mechanisms introduce additional computational workloads, including repeated inference, regeneration cycles, and expanded validation pipelines, increasing energy use and the carbon footprint of GenAI-assisted development. This paper proposes Carbon-Aware Governance Gates (CAGG), an architectural extension that embeds carbon budgets, energy provenance, and sustainability-aware validation orchestration into human-AI governance layers. CAGG comprises three components: (i) an Energy and Carbon Provenance Ledger, (ii) a Carbon Budget Manager, and (iii) a Green Validation Orchestrator, operationalized through governance policies and reusable design patterns.
M. Abbasi, T. Mikkonen, Petri Ihantola et al.· 2026 IEEE 23rd International...· 0 citations
Abstract. Carbon dioxide (CO2) emissions from industrial activities remain one of the greatest contributors to global climate change. Hollow fiber membranes (HFMs) have emerged as a promising technology for post-combustion CO2 separation owing to their high surface-area-to-volume ratio and scalability. This work focuses on the fabrication of HFMs with an emphasis on gas separation, particularly CO2, using the non-solvent induced phase separation (NIPS) spinning process for HFMs fabrication. The process allows specific control over dope and bore fluid selection, and flowrates, enabling the formation of asymmetric structures with desirable porosity, mechanical strength and suitable morphology for gas separation. The fabrication of polyethersulfone (PES)-based HFMs via NIPS, with 3 wt% polyethylene glycol (PEG) as a pore-forming additive, served as a foundational and basic study framework to provide an overview of the general hollow fibre membrane fabrication process. Preliminary assessments demonstrated the suitability of the fabricated membranes for gas separation applications as per requirements of membrane-based carbon capture technologies. Scanning electron microscopy (SEM), gas permeability tests, and tensile testing all revealed improvements in morphology, porosity, and mechanical strength, implying that this method for fabricating hollow fibre membranes has significant potential for tuning hollow fibre membranes for gas separation applications. Finally, the potential of HFM-based systems for energy-efficient CO2 capture is highlighted to be explored further.
Muhammad Waseem· Materials Research Proceedin...· 0 citations
Coding agents are increasingly used for software engineering tasks, including bootstrapping projects from third-party repositories whose integrity cannot be assumed. Prior work on repository poisoning largely focuses on attacker-controlled injection and disguise, but developers also shape risk through everyday invocation choices: what task to delegate, how to phrase the request, and which skills or rules to supply. We term these user-side choices Prompt-Level Configurations (PLCs) and introduce CIPR (Coding In Poisoned Repos), the first benchmark that systematically varies PLCs in poisoned real-world repositories. CIPR comprises 1,920 instances across 20 repositories, four task types, three social-media-grounded prompt styles, and three skill/rule conditions, and measures attack success rate (ASR) and agent alert rate (AR) using automated runtime and trace-based oracles. Our evaluation reveals two key insights: (1) Vulnerability is highly context-dependent, with task type creating up to a 4.5-fold difference in ASR, with test-execution task forming a silent attack surface (high ASR, low AR). (2) Prompt expression shifts risk indirectly: underspecified prompts reduce ASR by truncating execution depth; noisy prompts exhibit a directional trend toward suppressing alerts by making malicious content less conspicuous. These findings highlight that coding agent vulnerability is not a static property, but a dynamic outcome shaped by everyday user configurations.
Fu-Kang Zhu, Binbin Zhao, Ruixiao Lin et al.· 0 citations
A USAF cadet and a Lincoln Laboratory researcher found AI chatbots can help nontechnical service members produce viable software applications for their unique problems.
