Automated grading has become an important component of digital transformation in K-12 education, yet the structured recognition of handwritten responses on answer sheets remains a practical challenge. General-purpose vision-language models often show limited robustness when applied directly to school assessment materials, particularly in the presence of fixed answer regions, mixed Chinese-English content, and diverse handwriting styles. To address this issue, this study develops a task-oriented fine-tuning framework for automated recognition of handwritten answer sheets in K-12 educational settings. A multimodal dataset was constructed from Chinese and English answer sheets, with region-level annotations designed to support structured text extraction. Based on this dataset, the Qwen2.5-VL-7B-Instruct model was adapted through LoRA-based fine-tuning under a dual-A16 GPU environment to reduce computational cost while preserving practical deployment feasibility. An end-to-end workflow covering data preparation, model training, weight merging, and inference was then established for structured JSON output. Experimental results show that the fine-tuned model achieved stable convergence in both small-sample and medium-sample settings and improved the extraction quality of handwritten responses within predefined answer regions. The proposed framework provides a practical and reproducible solution for deploying vision-language models in school grading scenarios with limited computing resources. The study also offers an application-oriented reference for the integration of multimodal large models into educational assessment systems.
This study aimed to demonstrate the application of environmental activity-based costing (EABC) and its impact on supporting environmental sustainability, in accordance with ISO 14001 and 14051 standards for material flow cost accounting (MFCA) and GRI 300 standards for materials, energy, water, compliance, waste, and environmental performance improvement. EABC is an environmental accounting tool that identifies activities and allocates environmental costs to those activities, then to products, thereby assigning each product its actual costs and providing more accurate data. The research was conducted at the General Company for Fertilizer Industries in the Southern Region of Basra, Iraq. The researcher employed a practical approach by comparing the system implemented in the company under study with EABC. The main reason for using this technique is the inefficient use of resources and the resulting environmental pollution and fines imposed for exceeding permissible pollution limits. These costs have come to constitute a large percentage of the company’s total costs, thus impacting its profitability. The research contributed to identifying areas of waste resulting from the inefficient use of available resources and assisted management in making sound and accurate decisions related to environmental and economic aspects. It also helped improve environmental performance and enable the allocation of environmental costs to products based on their resource consumption. This, in turn, leads to the sustainability of resources through optimal use, thus achieving environmental sustainability. The study concluded that adopting cash flow statements helps improve various administrative decision-making processes, including pricing decisions, by allocating environmental costs to products and the activities that generate them. Furthermore, some reasons for waste in raw materials are attributed to the poor quality of those materials and the manual addition of materials. Therefore, the model directs management’s attention and efforts towards purchasing less environmentally damaging materials and using a pump for material application.
This paper presents a genetic algorithm (GA) tuned Mamdani type fuzzy logic control (FLC) framework for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) using a nonlinear rigid body model. The proposed architecture adopts a cascaded structure in which an outer loop position controller generates attitude and thrust references $(\phi_{\mathrm{ref}},\theta_{\mathrm{ref}},T_{\mathrm{ref}})$, while an inner loop attitude controller generates body torques $(\tau_\phi,\tau_\theta,\tau_\psi)$. Both loops employ a shared Mamdani fuzzy inference system with normalized inputs (tracking error and error-rate) and a normalized control output. The GA automatically tunes scaling gains $(K_e,K_d,K_u)$ across all axes to minimize a robust objective that averages tracking error, control effort, and constraint violations over multiple scenarios with mass uncertainty and wind disturbances. Simulation results on a three dimensional figure eight trajectory indicate that GA tuning can reduce position and attitude errors while respecting actuator saturation and tilt safety limits, demonstrating a practical route to performance enhancement without requiring a high fidelity aerodynamic model. The methodology leverages the interpretability of fuzzy rules and the global search capabilities of evolutionary optimization within a UAV modeling framework consistent with established quadrotor dynamics literature.
Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments.
Rolling bearings are critical components of marine shafting power transmission systems, and accurate prediction of their vibration signal trends is essential for predictive maintenance. To address the limited adaptability of conventional time-series forecasting models under varying operating conditions and their insufficient ability to capture strong noise and abrupt changes, this study proposes a vibration signal prediction method that integrates particle swarm optimization (PSO) with an improved Informer model. PSO is used to adaptively optimize key Informer hyperparameters for different operating conditions, while a rolling time-window mechanism is introduced to enhance the capture of abrupt signal variations. In addition, a mixture of sparse attention (MoSA) encoder with a collaborative dense-head/sparse-head structure is designed to balance global temporal dependency modeling and local fault feature extraction. Experimental results on the Case Western Reserve University (CWRU) bearing fault dataset show that the proposed model outperforms Long Short-Term Memory (LSTM), Transformer, Informer, iTransformer, and Flowformer in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Erro (RMSE). The model achieves an MSE of 0.2015, which is 25.5% lower than that of the second-best iTransformer model. It also demonstrates robust performance under four different bearing operating states, confirming its adaptability to complex operating conditions. The proposed method provides a promising technical route for the predictive maintenance of rolling bearings in marine shafting systems.
Hospital infrastructure systems represent one of the most complex categories of engineered systems, characterized by the tight integration of system configuration, technical subsystems, operational processes, and governance structures. Despite their structural durability, such systems—particularly in institutionally unstable environments—are prone to early functional and operational obsolescence, leading to performance degradation over the lifecycle. This challenge highlights the need to conceptualize hospitals not as static built assets, but as dynamic socio-technical systems requiring systematic performance-oriented management. This study develops a system-level analytical framework to examine future-proofing as an emergent outcome of interactions among institutional and contextual drivers, planning mechanisms, innovation, and design capabilities. The empirical analysis is conducted using data collected from professionals engaged in hospital infrastructure projects in Iraq. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach is employed to evaluate both direct and indirect relationships within the proposed system model. The results demonstrate that institutional and contextual drivers significantly influence planning mechanisms, which in turn act as a central structuring layer affecting both innovation and design capabilities. Innovation does not exhibit a statistically significant direct effect on long-term system adaptability, indicating that technological advancement alone is insufficient to ensure sustained performance. In contrast, design capabilities constitute the primary determinant of future-proofing, with a strong mediating effect on lifecycle system performance. The findings provide important implications for engineering management by emphasizing that long-term adaptability in hospital infrastructure systems depends on the alignment between planning structures and implementation-oriented design capabilities, rather than on innovation intensity alone.
This study examines how internal organizational arrangements shape the way financial institutions respond to broader community needs. The analysis focuses on Islamic banking in Indonesia and considers whether strategic performance measurement systems (SPMS) are associated with the development of more community-centered service practices, as well as the role of organizational learning (OL) in this relationship. The empirical evidence is based on a survey of 142 middle managers from Islamic commercial banks and is analyzed using a partial least squares approach. The results suggest that SPMS are positively associated with both OL and community-centered service strategy (CCSS). More importantly, this relationship appears to operate largely through learning processes, indicating that the influence of formal systems depends on how organizations interpret and make use of performance-related information in practice. This study does not treat service strategy purely in market terms, but instead considers Islamic banks as institutions embedded within broader social and economic contexts. From this perspective, CCSS reflects the ability of banks to respond to issues such as access, service relevance, and trust in local financial systems. The findings point to the importance of internal alignment and learning in supporting this form of responsiveness. The analysis does not directly measure community-level outcomes, and the results should therefore be interpreted as evidence of organizational capacity rather than realized development impact. Nevertheless, this study provides a useful link between management systems and the broader question of how financial institutions may support community-centered development processes.
Decentralization is often justified on the grounds that local governments are closer to citizens and therefore better able to respond to local needs. Yet, much of the existing literature has approached decentralization mainly in terms of administrative performance and service delivery, leaving its implications for community development less clearly understood. This study revisits the issue by bringing together empirical findings from a wide range of contexts. Rather than asking whether decentralization performs better than centralization in general terms, attention is directed to the conditions under which it makes a difference at the community level. The evidence points to a pattern that is far from uniform. Where local authorities operate with sufficient resources, administrative competence, and room for decision-making, decentralization tends to support more responsive and locally grounded forms of service provision. In contrast, where these conditions are weak, especially in smaller or under-resourced jurisdictions, similar arrangements often produce uneven access, limited participation, and fragile outcomes. Taken together, the findings suggest that decentralization cannot be treated as a universally beneficial reform. Its contribution depends on how responsibilities are matched with local capacity, how different scales of governance are organized, and whether institutional arrangements allow communities to exercise meaningful influence over local affairs.
Retailers frequently face stockouts and overstocking due to inaccurate demand forecasting, leading to financial losses and reduced customer satisfaction. This study proposes a data-driven framework to improve weekly sales forecasting at both aggregate and store levels using Walmart’s historical sales data. A hybrid methodology integrating time series models, regression techniques, deep learning, and a hierarchical structure is developed to capture temporal patterns and external demand factors. The proposed approach achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 306,361.11, Root Mean Square Error (RMSE) of 528,096.34, and an R² of 0.99, outperforming traditional models. Beyond accuracy, the study emphasizes the role of forecasting as a decision-support tool. The results demonstrate that improved forecasts enable better operational decisions such as replenishment planning and safety stock optimization, while also supporting tactical and strategic decisions related to distribution, workforce planning, and supply chain design. Overall, the findings highlight that integrating hybrid forecasting models with decision-making processes can reduce inventory costs, enhance service levels, and support more efficient and sustainable retail operations.
As environmental concern increases and fossil fuel reserves dwindle, biodiesel has emerged as an alternative sustainable, renewable, and biodegradable fuel to supply diesel engines. Amongst the various sources of raw materials used in biodiesel production, locally sourced sunflower oil presents a viable alternative, especially in countries experiencing problems with energy security and emission reduction, such as Iraq. In this work, the performance and emissions of four-cylinder direct injection diesel engine fueled with locally made Iraqi sunflower oil biodiesel were investigated. The biodiesel was produced with a transesterified reaction, and it was evaluated in blends as B20, B50 and pure (B100) in comparison to diesel fuel under several operating conditions of speed (1250–3000 rpm) and load (4.3–90 kN/m²). Experimental results showed that the environmental impact of water injection was significant: CO emissions decreased by almost 50%, unburned hydrocarbons by 45% and carbon dioxide by 33%, without neglecting reduction of exhaust temperature and engine noise. On the other hand, the calorific value of biodiesel is lower than that for diesel and caused high Brake specific fuel consumption (BSFC) up to its peak at 12% for B100. $\mathrm{NO}_{\mathrm{x}}$ increased by about 21% as a result of improved oxygen availability and higher in cylinder temperatures. Among the blends studies, B20 demonstrated promising balancing of emissions reductions and thermal efficiency with no mechanical modifications. However, some limitations remain and should be explored in further studies. It is recommended to combine durability testing, techno-economic analysis and on-road tests in the future in order to fulfill international emission control legislations and for environment-friendly application of biodiesel in Iraq power and transportation services.
The integration of variable renewable energy sources has driven research into the flexibility capabilities of power systems, which are characterized by high variability and uncertainty. Flexibility refers to a power system's ability to respond to changes in demand and generation across different time frames. This concept has been extensively studied in the literature, so the great variety of flexibility definitions and market approaches is a challenge for new stakeholders interested in the field. Establishing a market design that promotes the participation of flexible sources and ensures proper compensation is essential. This paper provides a comprehensive review of market designs proposed in the literature to enhance power system flexibility and approaches for quantifying its economic value. The study follows the PRISMA methodology for the identification, screening, and inclusion of documents, using Web of Science (WOS) and Scopus databases. After analyzing 102 papers, including 50 literature reviews, common approaches and concepts were identified and categorized into demand response, storage, market design, and other general frameworks. Among the market design proposals, the Flexible Ramp Products and Local Flexibility Markets are highlighted, along with an analysis of how to value this flexibility. This study complements existing literature by grouping the most relevant literature on power system flexibility and its valuation in energy markets, clarifying how market designs contribute to addressing renewable integration challenges—essential for future energy system planning with increased renewable energy penetration.
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