SQL competency development for technical interviews: A framework for query practice, mock testing, and portfolio building

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This paper presents technical aspects and management issues of wood waste reuse and disposal. It refers to the Greek and European legislation which determines the framework for rational and environmental friendly practices for wood waste management. It refers also to the wood waste classification systems and the currently applied methods of wood waste disposal and reuse. Emphasis is given to the wood waste-to-energy conversion systems, particularly to the pretreatment requirements, the combustion techniques, and the environmental constrains. Finally, the decision-making process for investments in wood waste fired thermal units is discussed.

Developing a Decision-Making Framework for Collaborative Practices in Long-Short Distance Transport Interconnection

Objective: The objective of this study is to investigate the gap between academic experiences and professional success by examining various dimensions of graduates' journeys. By investigating graduation rates across diverse programs, the research sheds light on patterns that may inform institutional improvements and highlight areas where students excel or struggle. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Various career development theories, such as Super's Life-Span and Life-Space theory (Super,1980), can offer insights into the lifelong impact of educational experiences on career trajectories. This theory posits that career development is not a linear process but a series of stages influenced by personal circumstances and environmental contexts. In the context of this tracer study, Super's framework provides a valuable lens through which to analyze the experiences of Bulacan State University Graduate School alumni as they navigate their educational and professional journeys.stand out, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research is a mixed-methods approach, integrating both quantitative and qualitative methodologies to comprehensively understand graduates' career trajectories. The research design followed a cross-sectional framework, collecting data at a specific time to capture graduates' current employment landscape from 2020 to 2024. Data collection was carried out through surveys and actual interview. Results and Discussion: This tracer study reveals significant insights into graduate outcomes and recommendations for program enhancement. The Master of Arts in Education (MAEd) dominates the graduate distribution at 51%, driven by the educational sector's need for advanced qualifications. Alumni shared the importance of postgraduate education for career advancement, particularly emphasizing research, leadership, and management skills. Key recommendations from PhD alumni focus on enhancing academic excellence through two sub-themes: Academic Administration and Support, and Innovative Learning and Development. Improving administrative processes, faculty hiring, and research support is needed in creating a more efficient and supportive academic environment. This approach aims to increase faculty satisfaction and retention, fostering a stronger academic community. Innovative Learning and Development emphasizes diverse educational methods, including extracurricular activities and online courses tailored to different learning styles. The integration of technology and a research-based teaching approach are seen as essential for preparing students for modern challenges. Additionally, alumni from Master's programs advocate for holistic academic development through experiential learning and mentorship, enhancing students' confidence and competence in real-world applications. Research Implications: The tracer study's implications include enhancing curricula with experiential learning, streamlining administrative processes, and fostering industry collaborations to better align education with job market needs. It emphasizes the importance of flexible program structures and student empowerment to improve engagement and motivation. Theoretically, it contributes to competency-based education models by highlighting the integration of practical skills into academic frameworks. Overall, the study call the need for adaptive educational practices that prepare graduates for real-world challenges while enriching existing educational theories. Originality/Value: This tracer provides empirical evidence on how specific skills acquired in graduate programs influence career advancement and emphasizes the need for aligning curricula with industry demands. The study introduces innovative frameworks for student empowerment and experiential learning, offering practical insights for educators and policymakers. It contributes to the theoretical understanding of graduate education's impact on workforce readiness and socio-economic development.

• Analysised the probable potential of MXenes is distinct areas. • Summarized the current MXene synthesis methods. • Covered the challenges in commercial scale MXene production. • A comprehensive feasibility analysis was performed on MXene synthesis. MXenes, a class of two-dimensional materials, exhibit exceptional electrical conductivity, catalytic activity, and rapid charge-discharge characteristics, making them highly promising for energy-related applications, such as supercapacitors, batteries, water splitting, and fuel cell catalysis. Despite extensive research on the physicochemical properties and laboratory-scale synthesis of MXenes, a critical gap remains in understanding the feasibility and economic viability of commercial-scale MXene production. This study addresses this gap by systematically evaluating precursor materials, synthesis routes, scalability constraints, and environmental impacts associated with the manufacturing of MXenes. Through a comparative analysis of synthesis parameters and process economics, critical factors influencing consistent large-scale production were identified. The results demonstrate that optimized acid etching and post-processing methods can achieve high yield and reproducible MXene synthesis, effectively addressing significant scalability challenges. Compared with previous laboratory-focused studies, these findings provide more substantial evidence that industrial-scale MXene production can be both technically feasible and economically viable. The accompanying economic assessment further indicates cost-effectiveness, while market analysis reveals increasing demand across the energy, environmental, and electronic sectors. By integrating insights from technical optimization, cost modeling, and market trends, this work establishes a practical framework for advancing the industrialization of MXene. Overall, the study bridges the gap between laboratory research and real-world application, offering actionable guidance for achieving scalable, sustainable, and commercially competitive MXene manufacturing.

Landscape Character Assessments (LCAs) support planning decisions by offering structured descriptions of landscape character. However, producing these texts is often resource-intensive and shaped by subjective judgement. This study explores whether Generative Artificial Intelligence (GenAI), specifically ChatGPT, can support the drafting of LCA descriptions using a structured, prompt-based framework. Applied to Harku Municipality in Estonia, the method integrates spatial input, reference material, and standardised prompts to generate consistent descriptions of landscape character areas (LCAs) and facilitate scenario building. The results show that ChatGPT outputs align with core LCA components and maintain internal coherence, although variations in terminology and ecological specificity require expert review. A stakeholder role play using ChatGPT highlighted its potential for enhancing early-stage planning, education, and participatory dialogue. The limitations include the reliance on prompt quality, static inputs, and the absence of real-time community validation. Recommendations include piloting AI-assisted workflows in education and practice, adopting prompt protocols, and prioritising human oversight, both experts and stakeholders, to ensure contextual relevance and build trust. This research proposes a practical framework for embedding GenAI into planning processes while preserving the social and interpretive dimensions central to landscape governance.

With the continuous development of information technology, various industries continue to use information technology to promote industrial upgrading and optimize the industrial structure. Agricultural informatization, as the only way for future agricultural development, will become an inevitable direction for the rejuvenation of traditional industries. The proposal of the development concept of “Mass Entrepreneurship, Mass Innovation” marks that my country has gradually entered an innovative society, and the development of public services and service products will become a powerful guarantee for promoting the construction of the agricultural informatization industry chain. This paper starts from the application of cloud computing and 5G in agriculture, considers the impact of big data on agriculture, and combines the business requirements and functional requirements of agricultural social services. The design of agricultural socialization service platform is realized, and finally, the service effect of the platform is evaluated by neural network. The following is a summary of the work done in this paper: (1) The research background and significance of the development concept of agricultural informatization services are expounded. Finally, the research content and method of this paper are discussed. (2) The technical principle and optimization algorithm of BPNN are introduced, and the evaluation index of the service effect of agricultural information platform is designed. (3) Select the appropriate parameters to build the experimental model and then train it, then compare the experimental effects of the traditional BP algorithm and the optimized three algorithms, and comprehensively evaluate the LM‐BP model with the best performance.

The Fisher–Neyman–Pearson theory of hypothesis testing was a triumph of mathematical elegance and practical utility. It was never designed, though, to handle 10,000 tests at once, and one can see contemporary statisticians struggling to develop theories appropriate to our new scientific environment. This paper is part of that effort: starting from just the two-groups model (2.1), it aims to show Bayesian and frequentist ideas merging into a practical framework for largescale simultaneous testing. False discovery rates, Benjamini and Hochberg’s influential contribution to modern statistical theory, is the main methodology featured in the paper, but I really was not trying to sell any specific technology as the final word. In fact, the discussants offer an attractive menu of alternatives. It is still early in the large-scale hypothesis testing story, and I expect, and hope for, major developments in both theory and practice. The central issue, as Carl Morris makes clear, is the combination of information from a collection of more or less similar sources, for example from the expression levels of different genes in a microarray study. Crucial questions revolve around the comparability and relevance of the various sources, as well as the proper choice of a null distribution. Technical issues such as the exact control of Type I errors are important as well, but, in my opinion, have played too big a role in the microarray literature. The discussions today are an appealing mixture of technical facility and big-picture thinking. They are substantial essays in their own right, and I will be able to respond here to only a few of the issues raised. I once wrote, about the jackknife, that good simple ideas are our most precious intellectual commodity. False discovery rates fall into that elite category. The two-groups model is used here to unearth the Bayesian roots of Benjamini and Hochberg’s originally frequentist construction. In a Bayesian framework it is natural

The key aspects of this work were the enhancement of the repeatability of the melt flow index (MFI) measurement and improvement of reproducibility for testing certain hygrothermal-sensitive materials, which are sensitive to temperature and water content. The present work was structured into two main parts. Firstly, several test variables affecting MFI were evaluated using a blocked response surface methodology (RSM), and multivariable linear regression was applied to assess the influence of test load, test temperature, drying time, and pre-heat time on MFI of nylon-based thermoplastic. The impact of test variables was analyzed by 20 randomized experiments, including 8 “two-level full factorial run” with 6 “centre run” replicates. The test parameters were optimized based on the RSM result and the ANSYS® simulation-based prediction. Later, these process variables were chosen to evaluate repeatability and reproducibility of the test of PA and 20 wt% vCF/PA for verification, which was diagnosed by measurement system analysis (MSA) for consistency and reliability assessment. The optimization test parameters for these composites were pre-heat time 1–3 min and drying time 6.6 h. By this means, the repeatability and reproducibility of these composites were up to 3.179% and 12.208%, respectively. The findings provide a practical testing framework that reduces uncertainty, enhances credibility, and ensures meaningful use of MFI in process optimization and quality control for polymer composite manufacturing.

The evolution of cloud computing requires immediate focus on security testing multi-tenant cloud systems. A security challenge emerges from the shared infrastructure that multi-tenant environments present when different users and organizations operate from the same system. The necessary elements for protecting sensitive data and maintaining system integrity include both data isolation along with access control and vulnerability management with continuous monitoring. Traditional security testing techniques struggle to handle the rapid resource handling processes typical of cloud platform deployments. Advanced security testing frameworks are needed to back the scalability and fluidity of contemporary cloud systems. The multi-tenant model generates multiple security issues because it permits data leakage between customers and permits authorization breaches because of improper permission settings and it lacks adequate isolation between isolated virtual environments. Modern security testing needs to adapt because cloud-native technologies including containers microservices and serverless computing have complicated the security environment. The risk vulnerabilities of multi-tenant environments need improved detection and resolution through comprehensive security testing solutions beyond what role-based access control (RBAC) and encryption methods currently provide. The study surveys academic research on multi-tenant cloud system security challenges and highlights essential issues before presenting a novel security testing framework. A proposed solution combines ongoing security monitoring together with proactive vulnerability assessment along with embedding security practices into DevOps pipelines. Centre of focus stands robust access controls and tenant data separation as key elements of providing security across all platforms. A practical framework seeks to resolve multi-tenant cloud system security challenges by providing proactive measures for identifying and blocking potential security risks.