Search Results (113)

Due to the scarcity of high-quality, specialized datasets for coal mining equipment operation and maintenance (O&M) and the poor adaptability of large language models to domain-specific scenarios, the reliability of actual mining O&M cannot be guaranteed. To address this, this paper investigates the construction of vertical-domain large language models for coal mining equipment O&M scenarios under limited fine-tuning data. First, to tackle the lack of O&M scenario data, a safety-guided evolutionary self-instruction method (SafeEvol-Instruct), is developed by integrating Self-Instruction, Evol-Instruct, and Rule-Based Filtering. This approach achieves the unified fusion of scalable generation, deep evolution, and safety filtering on limited O&M data, resulting in the construction of scenario-specific datasets for system status assessment, equipment fault diagnosis, maintenance plan formulation, and preventive maintenance. Second, to account for the distinct characteristics of different O&M tasks, a hybrid fine-tuning strategy (SynergyLoRA) is proposed based on the Qwen2.5-7B-Instruct foundation model. This strategy incorporates middle-layer LoRA, top-layer LoRA, middle-layer IA³, Prompt Tuning, and Prefix Tuning to enable specialized training of vertical-domain models for each O&M scenario. Finally, the constructed Coal Mining Equipment O&M Large Language Model (CMEOM-LLM) is evaluated through ablation studies across various scenarios, validating the effectiveness of the proposed methods. Experimental results demonstrate that, in the system status assessment scenario, CMEOM-LLM achieves improvements of 4.9%, 1.5%, and 1.4% over the Qwen model in accuracy, recall, and F1-score, respectively. In the equipment fault diagnosis scenario, CMEOM-LLM outperforms Qwen by 7.4% in accuracy, with BLEU-4 and ROUGE-L scores increasing by 6.6% and 6.5%, respectively. In the maintenance plan formulation scenario, CMEOM-LLM surpasses ChatGLM with improvements of 6.6%, 6.5%, and 8.5% in ROUGE-L, BLEU-4, and human evaluation, respectively. In the preventive maintenance scenario, CMEOM-LLM achieves improvements of 7.1% and 8.9% over Qwen in ROUGE-L and BLEU-4, along with a 0.69-point increase in human evaluation scores. This paper provides an effective approach for knowledge management in coal mining equipment O&M. Full article

This study addresses the growing need for structured, measurable organizational development (OD) models amid digital transformation, geopolitical uncertainty, and increasing managerial complexity. Contemporary middle- and top-level managers are expected to ensure productivity, strategic clarity, resilience, and data-driven decision-making; however, existing leadership methodologies are often examined separately and lack integrated evaluation frameworks. The research analyses two prominent approaches: the American Action Science methodology and the Scandinavian (particularly Finnish) consensus-based leadership concept. While Action Science emphasizes explicit reasoning, double-loop learning, accountability, and measurable performance outcomes, the Finnish consensus model prioritizes trust, participation, and relational cohesion. The aim of the study is to develop and empirically test the original digital model LeadWinO (LEADership for WINning Organizations) for evaluating the organizational development activities of middle- and top-level managers. The model was empirically tested on managers in Lithuania. The novelty of the research lies in combining management and informatics perspectives by embedding organizational development evaluation into a digital, indicator-based, and potentially predictive framework. The type of study is quantitative research integrating questionnaire analysis in the case of multi-profile sections. Analytical tool used for data simulation is Feedforward Neural Network for constructing sufficient gapless sets of digitalized data. Research results showed that the American Action Science methodology is most effectively used by managers working in very small and small enterprises in the service and maintenance sectors. The findings are expected to contribute to the operationalization of leadership effectiveness under uncertainty and provide organizations with an auditable structure linking managerial behaviour, decision-making processes, and organizational performance outcomes. Full article

Social media platforms have become critical infrastructures for customer relationship management (CRM), requiring scalable and intelligent solutions to handle high-volume interactions. In the luxury fragrance sector, digital promotion poses a unique challenge because olfactory attributes cannot be experienced online. As a result, physical fragrance samples remain essential, generating large volumes of sample requests or inquiries across social media. However, many requests remain unmanaged due to limitations in manual CRM (i.e., human-driven processes), revealing a design gap that may negatively affect perceived responsiveness and service quality. This study uses qualitative content analysis with NVivo 12 to examine large-scale sample request interactions on the Facebook pages of four luxury fragrance brands. Data was collected via NCapture and analyzed to identify recurring patterns, linguistic structures, and customer expressions related to sample requests. Findings confirm frequent repetitive requests, highlighting inefficiencies in traditional CRM systems under high demand. This research proposes an AI-driven CRM Sample Request Management Architecture (CRM–SRMA) that systematically captures and processes customer sample requests, collects the necessary mailing information, and seamlessly transfers validated data to the final dispatching stage. The proposed system also models individual fragrance preferences by analyzing customers’ interactions with samples, particularly in terms of top, middle, and base notes. By leveraging this information, the architecture enables the targeted promotion of new fragrance releases that closely align with customers’ demonstrated olfactory preferences. The insights of this research provide a scalable, intelligent mechanism that enables luxury social media managers and CRM systems to manage high-volume interactions while maintaining service quality. By automating sample request processing, the mechanism improves responsiveness and reduces operational burden. It also supports long-term relationship building through preference tracking and updating customers with any new relevant-fragrance releases. Although focused on fragrances, the mechanism is adaptable to other luxury cosmetic categories, thereby ideally enhancing overall social media-based customer service. Full article

Learning Management Systems (LMS) generate rich activity and interaction logs that can be exploited using machine learning techniques. This study models temporal engagement patterns, such as early, middle, late, weekend, and night activity, derived from Moodle logs in multiple undergraduate courses. It constructs temporal feature vectors per-student, applies k-means clustering to uncover behavioral patterns, and then uses ANOVA and Kruskal–Wallis tests to assess whether patterns differ in final grades. Results show that the predictive value of temporal patterns is highly course-dependent; in some courses, structured early engagement aligns with higher achievement, whereas in others, heavy weekend and night usage is associated with the best outcomes. To complement the obtained quantitative analyses, a Large Language Model (LLM) (i.e., ChatGPT) is evaluated as a zero-shot classifier that receives only natural-language summaries of temporal behavior and predicts performance tiers. While accuracy is limited, the model produces a coherent approach, indicating value as an interpretable layer on top of statistical analysis. The work demonstrates a generalizable pipeline for temporal feature engineering, unsupervised profiling, and LLM-based reasoning over LMS data for early risk detection in digital learning environments. Full article

This study proposes a systematic methodology to evaluate the energy flexibility and operational performance of air-conditioning systems (ACSs) in residential buildings, leveraging the passive thermal storage capacity of building thermal mass through indoor temperature setpoint adjustment. A comparative analysis was conducted between inverter-controlled and intermittent on-off air conditioners under a baseline indoor temperature of 24 °C. Two additional temperature setpoint scenarios (26 °C and 28 °C) were tested to quantify variations in the building’s electricity consumption demand. To characterize the dynamic thermal response across different floor levels, ground-floor, middle-floor, and top-floor apartments were investigated in a three-story residential building, enabling a controlled, floor-level comparison under identical control logic and climatic conditions. Dymola simulation software was employed to model and calculate ACS energy consumption and energy flexibility under the three temperature setpoint conditions (24 °C, 26 °C, and 28 °C). Results indicate that a strategy of scheduled ACS shutdown and automatic restart, enabled by the thermal inertia capacity of building thermal mass, effectively enhances ACS energy flexibility. Specifically, adjusting the zone temperature setpoint reduced the total ACS load by approximately 40% in two hours of a demand response event. This temperature setpoint adjustment strategy demonstrates significant potential to mitigate grid peak-load demand without compromising indoor thermal comfort and requiring additional building retrofitting investments. The findings provide a technical basis for optimizing residential ACS operation and promoting demand-side management in power systems. Full article

This study aims to investigate the impact of strategic drivers, specifically IT & Business Strategy Alignment (IT-BSA), Transglobal Leadership (TL), and Product Innovation (PI), on the adoption of Green Supply Chain Management (GSCM) and its subsequent effect on Sustainable Performance (SP). A key objective is to examine the moderating role of Risk Management (RM) in the relationship between these drivers and GSCM. This research employs a quantitative methodology, utilizing survey data collected from 216 middle and top Indonesian oil and gas managers. The hypothesized relationships were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that IT-BSA, TL, and PI are significant positive GSCM antecedents. Furthermore, GSCM has a strong, positive impact on SP. The results confirm that RM significantly and positively moderates the influence of all three strategic drivers on GSCM adoption. These findings provide a clear managerial roadmap, highlighting that an active risk management framework is critical for translating internal capabilities into effective sustainability practices, thereby enhancing a firm’s competitive advantage and long-term performance. Full article

Centralized heating systems in severe cold regions suffer from widespread load estimation deviations due to architectural heterogeneity and a lack of construction drawings, leading to substantial energy waste. This study proposes a lightweight load calculation method that facilitates efficient calculation of heating loads for heterogeneous building clusters via spatial thermal pattern decoupling and matrix reorganization. First, a 3 × 3 load characteristic matrix is developed to characterize the spatial variation in thermal demand across different building positions (corner vs. intermediate units × top, middle, and bottom floors), revealing that corner units exhibit higher thermal loads than intermediate units, while top and bottom floors show significantly higher loads than middle floors. Second, two complementary matrices are established: the load characteristic matrix, which represents the building’s thermal behavior, and the structural feature matrix, which encodes the architectural configuration in terms of unit count (a) and floor count (b). Together, they enable rapid hourly load synthesis using only lightweight input parameters. The method is validated on 56 heterogeneous residential buildings in Northeast China. Using a decoupled 4U/6F standard model, the synthesized cluster heating load achieves an R² of 0.88, an RMSE of 24.15 GJ, a MAPE of 4.94%, and a Mean Percentage Error (MPE) of −0.82% against actual heating supply data, demonstrating high accuracy and negligible systematic bias—particularly during cold waves. This approach allows the seasonal variation in heat demand across an entire residential area to be estimated even in the absence of detailed construction drawings, offering practical guidance for operational heating management. Full article

Background: Tirzepatide (Mounjaro or Zepbound), a dual GLP-1/GIP receptor agonist, is approved for type 2 diabetes and weight management. Despite its efficacy, real-world safety data remain limited. This study analyzed post-marketing adverse events (AEs) associated with tirzepatide using the FDA Adverse Event Reporting System (FAERS) to identify emerging safety concerns. Methods: FAERS reports from 2022 to Q1 2025 were analyzed. Disproportionality analyses (proportional reporting ratio [PRR], reporting odds ratio [ROR], empirical Bayes geometric mean [EBGM], and information component [IC]) were performed to detect safety signals. Reports were stratified by year, demographics, and AE type, focusing on cases in which tirzepatide was the primary suspect. Results: Among 65,974 reports, the majority originated from the U.S. (96%), with middle-aged females (40–59 years; 67%) most frequently affected. Incorrect dose administration was the top AE, increasing 8-fold from 1248 (2022) to 9800 (2024), with strong risk signals (ROR 22.15, 95% CI (20.75–23.65), and ROR 23.43, 95% CI (22.82–24.05), respectively, and PRR 16.80, 95% CI (15.74–17.93), and PRR 17.62, 95% CI (17.16–18.09), respectively). Other common AEs included injection-site reactions (e.g., pain [5273 cases in 2024]), gastrointestinal issues (nausea [3602 in 2024]), and off-label use. Class-related AEs (e.g., decreased appetite and blood glucose fluctuations) were also reported. Conclusions: Tirzepatide is associated with significant dosing errors, injection-site reactions, and gastrointestinal AEs in real-world use. The rising trend in reports underscores the need for enhanced provider and patient education, clearer dosing guidelines, and proactive monitoring. Further research is warranted to explore causative factors and optimize risk mitigation strategies. Full article

The aim of this research is to understand the events and experiences that contribute to the development of top leaders who are capable of thinking of their organization pedagogically and strategically to respond to present-day challenges. The uniqueness of the objective itself justified the choice of narrative research based on the interdependent relationship between leaders and institutions. Methodologically, the autobiographical narrative was used as the method and data collection technique. We studied the life stories of two headmasters from two school clusters in Portugal, as well as the dynamics of their leadership. The analysis of the life stories was complemented by a chronotopography, documentary analysis, focus groups with middle managers and interviews with members of the Portuguese Ministry of Education. The analysis of the data collected through the life narratives enabled a series of milestones to be identified that, due to their authors’ ability to reflect, were decisive in their professional development, namely, further education; initial training; experience in management bodies and lifelong learning; the participation in the Educational Territories of Priority Intervention programme, the Pedagogical Innovation Pilot Project and school networks. Full article

Corporate entrepreneurship is critical in determining a firm’s sustainability. Traditionally, middle managers have been believed to lead corporate entrepreneurship by developing relationships between top managers and subordinates. However, the high authority of a top manager can be both a threat and a strength for middle managers. Previous studies have not focused on the role of top managers in developing corporate entrepreneurship. To address this gap, this study aimed to identify the intuition of top managers in facilitating corporate entrepreneurship in China. We also identified the various industries that can develop corporate entrepreneurship, including the manufacturing and financial sectors. Based on a questionnaire survey conducted with 322 top managers in China, the research hypotheses were tested using structural equation modeling and multigroup analysis, respectively, via SPSS and AMOS. Research examined whether the intuition of top managers positively influences corporate entrepreneurship and whether this influence is stronger in a specific industry. Results revealed that the intuition of top managers positively influenced corporate entrepreneurship, and its impact was greater in the manufacturing industry compared to the financial industry. These results implied the need for the development and maintenance of top managers’ intuition. Synthesizing with the current literature, this study has identified new pathways to develop corporate entrepreneurship from the role of top managers rather than only from middle managers. Full article

Organizational performance defines how well an organization achieves its goals and objectives. To fulfill these, the organization should improve its logistical competencies including delivery speed, order accuracy, and returns handling. At the same time, social media plays an important role. Therefore, the main objective of this study is to examine and research the influence of logistical competence on the performance of small and medium enterprises (SMEs) with the moderating effect of the competitive advantages of social media. We used a quantitative, descriptive, cause–effect, and cross-sectional approach to actualize this research. A non-probability convenience sampling method was used as it is cost-effective, practical, easy to access, and time-efficient. The main variables, such as delivery speed, order accuracy, and returns handling, were analyzed to determine their influence on organizational performance. A total of 163 respondents participated, ranging from middle to top management employees in SMEs, specifically in Jordan, who completed a structured Google form. Simple, multiple, and hierarchical regression were used to check the hypotheses in this research. The conclusion shows that logistical competence positively affects organizational performance, with competitive advantages in social media campaigns enhancing this effect significantly; this was evident as social media campaigns emerged as an essential platform for marketing logistical strengths and boosting customer engagement. This study and research give recommendations for SMEs to integrate logistics and E-marketing strategies properly. Regarding the study limitations, we see that the regional focus and the small sample size are acknowledged. In the future, research is highly encouraged which looks into industry-specific dynamics, advancing technologies, and cross-cultural contexts. This research bridges the gap between logistics and marketing, thus showcasing a framework promoting logistical competence to gain a competitive advantage in the SME market. Full article

The premise of this research is to investigate the influence of an ethical climate on the environmentally responsible behavior of employees within the accommodation sector in Pakistan. We further seek to understand this connection through the intermediation of motivation states and contingency of ethical knowledge. For this purpose, we gathered data from a sample of 290 managers serving at middle and top levels in the accommodation sector of Pakistan, employing an adapted version of the quantitative research instrument. We used Structural Equation Modeling (SEM) to test the hypothesis. We found that (1) ethical climate cultivates green behaviors and (2) motivational states partially mediate the association between ethical climate and green behavior. Additionally, (3) the influence of ethical climate on motivational states is found to be stronger in the presence of environmental knowledge. These findings apply to the accommodation sector, where hotel managers can cultivate green behavior by fostering an ethical climate and enhancing motivational states and environmental knowledge. We added empirical justification to social capital theory by enhancing the understanding of ethical climate-driven pro-environmental behavior through intermediation and intensifier. Full article

Lean manufacturing can be considered a socio-technical system integrating both technical tools and human-centered, or soft, practices. While extensive research has examined technical aspects, the contribution of soft Lean practices focused on human behavior to environmental sustainability remains underexplored. This study addresses this gap by examining how soft Lean practices can help overcome barriers to environmental performance in large-scale production systems (LSPSs), using Italy’s food manufacturing sector as a case study. A multi-case study methodology was employed, involving five companies. Data were collected through interviews conducted across top management, middle management, and operational staff levels to capture diverse perspectives. Using variables extracted from the literature and a deductive coding approach, the study identifies (1) the specific soft Lean practices adopted and the perceived environmental performance barriers at each hierarchical level, (2) differences in interpretation of these practices and barriers across hierarchical levels, and (3) how soft practices can mitigate obstacles to sustainable performance. The results demonstrate that soft Lean practices, when aligned with organizational structure and culture, can effectively mitigate barriers to environmental improvement. This research contributes to the Lean and sustainability literature by offering a multi-level perspective and practical insights into integrating human-centered approaches within industrial sustainability strategies. Full article

As the global workforce ages and multiple generations collaborate in workplaces, addressing the unique needs of diverse age groups becomes critical. Grounded in Social Cognitive Theory, this study examines how regulatory emotional self-efficacy in managing negative emotions serves as a crucial personal resource in protecting against interpersonal strain. It also explores whether this relationship varies between middle-aged and senior employees. Age-related improvements in emotional self-efficacy highlight its significance in shaping perceptions of workplace inclusivity, defined by the inclusive behaviors of social drivers: colleagues, supervisors, and top management. A total of 1068 employees from a leading European telecommunication organization completed online questionnaires measuring regulatory emotional self-efficacy, social drivers of inclusive workplaces, and interpersonal strain. Mediation analyses revealed that regulatory emotional self-efficacy is positively associated with perceptions of inclusive social drivers, which, in turn, are negatively related to interpersonal strain. Multi-group analyses demonstrated that the protective effects of regulatory emotional self-efficacy differ by age. While inclusive colleagues mediate the relationship across all age groups, inclusive top management is particularly significant for employees aged 45–54. These findings deepen the understanding of age-specific dynamics in fostering workplace inclusion and underscore the necessity of tailored organizational strategies to support employee well-being across the lifespan. Full article

A jet flame is a common type of flame in fires in the oil and gas industries. At present, research on jet flames is still not comprehensive enough. To systematically investigate the combustion characteristics of vertical methane jet flames, experiments were conducted on vertical methane jet flames, supplementing the existing experimental data on jet fires. The study reveals variations in the flame shape, center temperature, and thermal radiation with different flow rates and nozzle diameters, and the mechanisms of change in the flame center temperature and thermal radiation are discussed. The results show that increasing the gas flow rates and nozzle diameters led to a greater flame height and width. Along the flame axis, the temperature initially rose and then decreased with an increasing vertical distance from the nozzle. For smaller nozzle diameters, the flame temperature increased with the flow rate beyond the peak temperature point. Additionally, higher flow rates and larger nozzle diameters raised the height at which the maximum thermal radiation occurred. The thermal radiation near the flame’s top exceeded that in the middle, while minimal changes were observed near the base. The jet flame’s lift-off height and shape significantly influenced the distribution of the centerline temperature and thermal radiation. These findings provide valuable insights for the effective management and control of gas jet fires. Full article