Efficiency Benefits Research Articles

Artificial Intelligence Research in Radiation Oncology: A Practical Guide for the Clinician on Concepts and Methods

Abstract The use of AI holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice. Part of the limited clinical uptake of these models may be explained by the need for broader knowledge, among practising clinicians within the medical community, about the processes of AI development. This lack of understanding could lead to low commitment to AI research, widespread scepticism, and low levels of trust. This attitude towards AI may be further negatively impacted by the perception that deep learning is a “black box” with inherently low transparency. Thus, there is an unmet need to train current and future clinicians in the development and application of AI in medicine. Improving clinicians' AI-related knowledge and skills is necessary to enhance multidisciplinary collaboration between data scientists and physicians, ie, involving a clinician in the loop during AI development. Increased knowledge may also positively affect the acceptance and trust of AI. This paper describes the necessary steps involved in AI research and development, and thus identifies the possibilities, limitations, challenges, and opportunities, as seen from the perspective of a practising radiation oncologist. It offers the clinician with limited knowledge and experience in AI valuable tools to evaluate research papers related to an AI model application.

A Hybrid Approach Integrating Physics-Based Models and Expert-Augmented Neural Networks for Ship Fuel Consumption Prediction

Abstract The International Maritime Organization’s recent approval of the 2023 strategy on reduction of greenhouse gas emissions amplifies the pressure on stakeholders to achieve net-zero emissions in shipping by 2050. Considering the anticipated predominance of traditional single-fuel engines into the next decade, due to their high efficiency and economic benefits, the implementation of operational measures stands as the foremost effective method for mitigating emissions and reducing fuel consumption. Accurate fuel consumption prediction is crucial for informed decision-making and operational efficiency. This paper introduces an innovative hybrid model, combining an advanced physics-based model with an expert-augmented neural network, offering superior fuel consumption predictions. Expert knowledge is integrated into the neural network model to enhance its learning capabilities. Performance is validated against DNV Navigator Insight and publicly available fuel consumption reporting data, demonstrating superiority over purely data-driven and physics-based models. This hybrid approach bridges accuracy and scalability for sustainable maritime operations.

Biofertilizer use in the United States: definition, regulation, and prospects

Abstract The increasing demand for sustainable food production has driven a surge in the use and commercialization of biological inputs, including biofertilizers. In this context, biofertilizers offer potential benefits for nutrient use efficiency, crop yield and sustainability. However, inconsistent definition of the term “biofertilizer” and regulations, particularly in the USA, hinder market growth and consumer confidence. While the European Union, and countries like Brazil, India, and China have made progress in this area, the USA market, projected to exceed $1 billion by 2029, lacks clear guidelines for biofertilizer production and sale. The USA market is dominated by Rhizobium genus, Mycorrhizae fungi, and Azospirillum species and based products targeting various crops. Although there is a growing and promising market for the use of biofertilizers, there are still many challenges to overcome, and to fully realize the potential of biofertilizers, future research should focus on modes of action, specific claims, and robust regulations that must be established. Key points • The term “biofertilizer” lacks a universally accepted definition • It is necessary establishing a national regulation for biofertilizers in the USA • The biofertilizer market is growing fast and the biggest one is in America

Gen AI and student perspectives of use and ambiguity

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI. This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings. Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI. Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating. Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct. Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development. Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment). Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work. These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).

Masting ontogeny: the largest masting benefits accrue to the largest trees

Abstract Background and Aims Both plants and animals display considerable variation in their phe- notypic traits as they grow. This variation helps organisms to adapt to specific challenges at different stages of development. Masting, the variable and synchronized seed production across years by a population of plants, is a common reproductive strategy in perennial plants that can enhance reproductive efficiency through increasing pollination efficiency and decreasing seed predation. Masting represents a population-level phenomenon generated from individual plant behaviors. While the developmental trajectory of individual plants influences their masting be- havior, the translation of such changes into benefits derived from masting remains unexplored. Methods and Key Results We used 43 years of seed production monitoring in European beech (Fagus sylvatica) to address that gap. The largest improvements in reproductive efficiency from masting happen in the largest trees. Masting leads to a 48-fold reduction in seed predation in large, compared to 28-fold in small trees. Masting yields an 6-fold increase in pollination efficiency in large, compared to 2.5-fold in small trees. Paradoxically, although the largest trees show the biggest reproductive efficiency benefits from masting, large trees mast less strongly than small trees. Conclusions That apparently suboptimal allocation of effort across years by large plants may be a consequence of anatomical constraints or bet-hedging. Ontogenetic shifts in individual mast- ing behavior and associated variable benefits have implications for the reproductive potential of plant populations as their age distribution changes, with applications in plant conservation and management.

Micro-fracture mechanism of microwave induced fracturing of basalt based on a novel Electromagnetic–Thermal–Mechanical coupling model

Microwave-assisted rock fracturing is recognized for its efficiency, energy savings, and environmental benefits. Investigating microscopic mechanisms of microwave-induced rock fracturing is essential for predicting the weakening effect on rock. A coupled Electromagnetic–Thermal–Mechanical model based on FEM–DEM was established to describe the response of rock under microwave irradiation. This model employs interpolation algorithms and mineral lattices randomization algorithms to establish a 2D cross-sectional representation of rock. A discrete element calculation method is proposed to synchronize computational time with the experimental time. The model can simulate the multi-physical field response of different rocks under various conditions, making it an effective tool for studying microwave-induced rock fracturing. The effectiveness of the numerical model was validated through open-end microwave-induced fracturing experiments on basalt. Additionally, the study elucidates the micro-fracture mechanism of basalt under microwave irradiation. The results indicate that the direction of crack propagation is influenced by microwave power and boundary effects. The patterns of fracture development between minerals are summarized as follows: Initial fractures primarily result from the rapid heating of microwave-absorbing minerals like enstatite,creating a significant temperature gradient. With increased heating time, heat transfers to highly expansive minerals such as olivine, causing fractures due to localized thermal expansion.

Characteristics of Volatile Organic Compounds Emissions and Odor Impact in the Pharmaceutical Industry

Volatile Organic Compounds (VOCs) are not only essential precursors for the formation of ozone and PM2.5, but also hazardous to human health and responsible for unpleasant odors. The pharmaceutical industry has become an important industrial source of VOCs due to China’s large emissions and complex emission chains. In total, 245 VOCs samples were collected and analyzed from 11 typical pharmaceutical companies in Zibo City of the North China Plain, in order to investigate the VOCs emission characteristics and odor impacts. The emission factor for the pharmaceutical industry was 7.97 ± 8.21 g/kg pharmaceuticals, while the main emission links were chimney emissions, equipment sealing leakage, and so on. Finally, considering both purifying efficiency and economic benefits, the multistage absorption (AB) method is most effective for VOCs concentrations below 100 mg/m3, while UV photo-oxygenation combined with adsorption (UVA) is more suitable for concentrations below 300 mg/m3. The Regenerative Thermal Oxidizer (RTO), Catalytic Oxidizer (CO), and Condensation + Adsorption (CA) technologies demonstrated greater stability and efficiency, particularly in the treatment of complex organic pollutants, highlighting their advantages in both VOCs and odor removal at higher concentrations.

Lane-free signal-free intersection crossing via model predictive control

The operation of signal-free intersections, where Connected Automated Vehicles (CAVs) cross simultaneously for all Origin-Destination (OD) movements, has the potential to greatly increase throughput and reduce fuel consumption. Since the intersection crossing areas naturally include no lanes, an extended crossing area, appropriately delineated, can be considered as a lane-free infrastructure so as to enable further efficiency benefits. This paper presents two Model Predictive Control (MPC) schemes to manage CAVs in signal-free and lane-free intersections. In fact, the control inputs of all vehicles are optimized over a time-horizon by online solving of a joint Optimal Control Problem (OCP) that minimizes a cost function including proper terms to ensure smooth and collision-free vehicle motion, while also considering fuel consumption and desired-speed tracking, when possible. Additionally, appropriate constraints are designed to respect the intersection boundaries and ensure smooth vehicle movements towards their respective destinations. A fast Feasible Direction Algorithm (FDA) is employed for the numerical solution of the introduced OCP. Multiple simulations are carried out to assess the efficiency and practicality of the proposed methods. A comparison with signalized intersection operation is provided.

Exploring the paradoxical use of ChatGPT in education: Analyzing benefits, risks, and coping strategies through integrated UTAUT and PMT theories using a hybrid approach of SEM and fsQCA

ChatGPT's impact on education is both significant and inevitable, presenting a paradox of threats and benefits. While studies have explored ChatGPT's usage, intention, and motivation, few have addressed its paradoxical use from a benefit-risk-coping perspective, leaving gaps in practical and theoretical solutions. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) and Protection Motivation Theory (PMT) to elucidate ChatGPT's paradoxical use in education. Using Structural Equation Modeling (SEM) and fuzzy sets Qualitative Comparative Analysis (fsQCA), it aims to provide insights and solutions for managing the benefits, risks, and coping mechanisms associated with ChatGPT in education. Hypotheses and propositions were tested on Taiwanese higher education users (N = 351). Findings from SEM confirm that the perceived threat of ChatGPT's severity decreases the intention to use it, while coping strategies such as self-efficacy and response efficacy strongly predict the intention to use ChatGPT. Additionally, the benefits of performance expectancy and task efficiency significantly increase the intention to use ChatGPT, which in turn significantly increases actual usage behavior. Findings from fsQCA reveal three configurations for both the usage and disusage of ChatGPT in education. The study identifies three constructs with necessary conditions. This research makes a significant theoretical contribution by integrating UTAUT and PMT into a unified framework to elucidate the paradoxical aspects of benefit-risk-coping in the use of ChatGPT. Practical implications for higher educational institutions and their scholars (e.g., students, lecturers, researchers) are also provided through confirmed solutions from the QCA model. These solutions offer strategic insights to leverage the benefits, identify the risks, and cope with the threats associated with using ChatGPT in education.

Giving the prostate the boost it needs: Spiral diffusion MRI using a high-performance whole-body gradient system for high b-values at short echo times.

To address key issues of low SNR and image distortions in prostate diffusion MRI (dMRI) by means of using strong gradients, single-shot spiral readouts and an expanded encoding model for image reconstruction. Diffusion-weighted spin echo imaging with EPI and spiral readouts is performed on a whole-body system equipped with strong gradients (up to 250 mT/m). An expanded encoding model including static off-resonance, coil sensitivities, and magnetic field dynamics is employed for image reconstruction. The acquisitions are performed on a phantom and in vivo (one healthy volunteer and one patient with prostate cancer). The resulting images are compared to conventional dMRI EPI with navigator-based image reconstruction and assessed in terms of their congruence, SNR, tissue contrast, and quantitative parameters. Using the expanded encoding model, high-quality images of the prostate gland are obtained across all b-values (up to 3 ms/μm2), clearly outperforming the results obtained with conventional image reconstruction. Compared to EPI, spiral imaging provides an SNR gain up to 45% within the gland and even higher in the lesion. In addition, prostate dMRI with single-shot spirals at submillimeter in-plane resolution (0.85 mm) is accomplished. The combination of strong gradients and an expanded encoding model enables imaging of the prostate with unprecedented image quality. Replacing the commonly used EPI with spirals provides the inherent benefit of shorter echo times and superior readout efficiency and results in higher SNR, which is in particular relevant for considered applications.

Comprehensive Understanding of Laboratory Evolution for Food Enzymes: From Design to Screening Innovations.

In the field of food processing, enzymes play a pivotal role in improving product quality and flavor, and extending shelf life. However, the exposure of traditional food enzymes to high temperatures during processing often leads to a decrease in activity or even inactivation, limiting the effectiveness of their application under high-temperature conditions. Therefore, the modification of thermostability and activity of enzymes to adapt to extreme conditions through protein engineering has become a key way to improve the efficiency and economic benefits of industrial production. Directed evolution and semirational design strategies in the laboratory have proven to be broadly applicable frameworks for biochemical researchers in the food field, including those who are beginners. In this review, we systematically summarize semirational design strategies and high-throughput screening strategies, and introduce some intuitive computer simulation software to improve the thermostability and enzyme activity of food enzymes. The application of these strategies and techniques provides a comprehensive guide for the optimization of food enzymes. In addition, the latest hot topics of genetically engineered food enzymes in the field of application are discussed.

Boosting Hydrogen Transport in Mixed Matrix Membranes Through Continuous Spillover Via Pd‐Functionalized MOF Gel Networks

AbstractMembrane‐based gas separation offers notable energy efficiency benefits for hydrogen purification, yet it is often hindered by the inherent trade‐off between permeability and selectivity. To address this challenge, a novel mixed matrix membrane (MMM) design is presented to boost H2 separation performance via continuous hydrogen spillover mechanisms for the first time. The MMM incorporates a palladium‐functionalized ZIF‐67 gel (Pd@ZIF‐67 gel) network into a polymer of intrinsic microporosity (PIM‐1) matrix. The ZIF‐67 gel network serves as a uniform dispersion medium for palladium nanoparticles (Pd NPs), thereby generating a multitude of active sites. These exposed sites, in conjunction with the microporous structure of ZIF‐67, facilitate hydrogen dissociation and establish a continuous hydrogen spillover pathway throughout the membrane. This synergistic MMM design leads to substantial improvements in both hydrogen transport and selectivity. At an optimal loading of 28 wt% Pd@ZIF‐67 gel, the MMMs exhibit a H2 permeability of 3620 Barrer and a remarkable 417% enhancement in H2/CH4 selectivity (24.9), surpassing the 2008 upper bound. This approach paves the way for the development of advanced materials tailored for gas separation applications.

Characteristics of climate resource utilization and economic benefits in new planting pattern for fresh faba bean and fresh maize in Yangtze River Delta

ABSTRACT In this research, we aimed to construct a novel planting pattern for faba bean and maize with high-efficiency utilization of climate resources for application in production. We incorporated relay intercropping in two triple-crop planting patterns, namely faba bean/maize-maize and faba bean/maize-soybean. A field experiment was conducted to compare the annual yield, utilization efficiency of climatic resources and economic benefits among these two triple-crop planting patterns and the conventional faba bean – maize double-cropping system at Nantong, Jiangsu, China, from 2016 to 2020. Compared with the conventional double-cropping system, the faba bean/maize-maize triple-crop planting pattern resulted in significantly increased annual yield by 7,392 kg hm−2 to 10,519 kg hm−2, and increased annual biomass by 5,342 kg hm−2 to 7,172 kg hm−2.The triple-crop planting patterns increased the annual economic benefit by 12,195 RMB hm−2 and 15,680 RMB hm−2, respectively. The faba bean/maize-maize triple-crop planting pattern increased the annual radiation production efficiency by 0.18 g·MJ−1 to 0.31 g·MJ−1, the annual temperature production efficiency by 2.70 kg·hm−2·°C−1 to 3.78 kg·hm−2·°C−1, and the annual precipitation production efficiency by 4.55 kg·mm−1·hm−2 to 7.20 kg·mm−1·hm−2. Considering the yields, resource-use efficiency, and economic benefits, the triple-cropping planting patterns show promise as multi-crop systems to achieve high yields and high resource-use efficiency in the Yangtze River Delta region. These results demonstrate the use of a new relay intercropping planting pattern for use in crop production and represent a theoretical contribution to the development of efficient agricultural production.

The positive impact of corporate mergers on consumer welfare: from the perspective of corporate R and D investment

Enterprise mergers are essential for the development and growth of enterprises, and they are also an important mechanism for the market economy to improve the overall efficiency of the market through the survival of the fittest. Compared to gradually growing a company through internal accumulation, merging with other companies can achieve various goals such as scale expansion, business expansion, and financial optimization faster in a short period. Although business mergers are spontaneous actions of enterprises, the merger of enterprises, especially large enterprises, affects their performance and consumer welfare. To better study the potential impact of corporate mergers, this article combines traditional and modern merger theories to investigate the positive effects of corporate mergers on product prices, corporate innovation, and overall consumer welfare. The impact channels of Chinese-listed company mergers on their R&D expenses are evaluated empirically. Through empirical and theoretical analysis, this article proves that business mergers can improve efficiency, innovation, and consumer benefits by increasing research and development expenditures.

3D Controlled‐source electromagnetic modelling in anisotropic media using secondary potentials and a cascadic multigrid solver

AbstractQuantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc.

Enhancing financial reporting and management efficiency through enterprise resource planning (ERP) systems: A theoretical review for large-scale energy operations

Enterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes. Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector.

Energy, environmental footprints and economic benefit of substituting inorganic fertilizer with organic manure for winter wheat in Huanghuaihai Plain

Manure substitution shows promise for nitrogen (N) management, food security, energy balance and environmental costs reduction. However, there is limited research on this practice in the Huanghuaihai Plain. This study aimed to investigate the energy use efficiency, economic benefits, carbon and nitrogen footprint under two types of N fertilizer (U, urea and M, organic manure), two application rates of N (180 kg N ha−1, U1 for 100 % urea and M1 for 100 % organic manure; 90 kg N ha−1, U2 for 50 % urea and M2 for 50 % organic manure) and no fertilizer application treatment (CK) for winter wheat from 2017 to 2019. Results showed that grain yield and agricultural input cost under N application rate of 90 kg N ha−1 was 15.5 % and 7.8 % lower than that of 180 kg N ha−1, respectively, leading to a significant decrease in economic benefit. Under the same N rate, M1 obtained higher grain yield than U1, grain yield of M2 did no differ in that of U2. Total energy inputs and agricultural input costs of M were 9.5 % and 3.6 % lower than U, resulting in higher energy use efficiency and economic benefit. The reduced agricultural input for M was primarily due to a decrease in the application of inorganic fertilizer. Compared with other treatments, U2+M2 obtained higher grain yield, energy use efficiency, and economic benefit. The carbon and nitrogen footprint on unit grain yield of U1 was increased by 13.7 %-24.1 % and 3.9 %-19.6 %, which was attributed to the increase in direct N2O emissions, indirect carbon emission and losses of reactive N from agricultural inputs. Overall, U2+M2 sustained high productivity and reduced the environmental impact. Substituting inorganic fertilizer with organic manure was a promising strategy to improve agricultural production with less agricultural inputs and environmental footprints in the Huanghuaihai Plain.

Agribusiness Prospects of Kachai Lemon Produced in Ukhrul District of Manipur, India: Status, Issues and Way Forward

Kachai lemon (Citrus jambhiri Lush.) is a variety of citrus, known for its rich nutritional and medicinal benefits due to its high flavonoid content and ascorbic acid. It has played a significant role in enhancing the socio-economic development of the tribal communities by generating income and employment opportunities. The Geographical Indication (GI) tag awarded to Kachai lemon has increased its commercial cultivation and market value. This recognition and growing attention have led to the emergence of agripreneurs and farmer producer organizations (FPOs) for production, value addition and marketing of Kachai lemon in an organized manner. The present study is aimed to compile the success stories, challenges of farmers and explore various Kachai lemon brands available on online platforms. A SWOT (Strength, Weakness, Opportunities and Threats) analysis on production and marketing of kachai lemon was performed. For the study, descriptive analysis was carried out to assess its potential, current status and challenges. The study suggests that in order to compete globally, it is crucial to develop strong post-harvest processing and marketing systems with support from agencies and institutions to provide valuable resources and market linkages to enhance efficiency and gain economic benefits.

The Budget Management Supported by the Government Sector to Systemic Private Schools, Nakhon Ratchasima Province

Background and Aims: Budgetary management of educational institutions focuses on management independence. This makes it flexible, transparent, and auditable. By adhering to management principles that focus on achievement and operating results. Budget management in this way also places importance on profiting from the educational institution's assets. Including generating income from various services to support efficient management and maximum educational benefits. This results in better quality for learners. The objective of this research is to study and compare the level of opinions of personnel towards budget management supported by the government sector of private schools in the system, Nakhon Ratchasima Province. Classified according to position and duty education level and experience in budget operations In addition, the objective is to study guidelines for managing budgets supported by the government sector of private schools in Nakhon Ratchasima Province. Methodology: The sample group used in the research was personnel from private schools in the system. In Nakhon Ratchasima Province, a total of 354 people, were obtained by stratified random sampling. The research instrument consisted of a questionnaire using a 5-level rating scale, with an IOC value between 0.80 - 1.00 and a confidence value equal to 0.72. In addition, a structured interview was used. To collect additional information data analysis uses a variety of statistics including: Finding frequency, percentage, mean, standard deviation, t-test, F-test, and testing differences between groups using Scheffe's method. (Scheffé's Method) to compare data in cases where there are many groups. Results: The results of the research found that (1) budget management supported by the government sector of private schools in the system of Nakhon Ratchasima Province Overall is at a high level. When considering each area, it was found that budget management in all areas was at a high level. (2) Results of comparative analysis of budget management supported by the government sector of private schools in the system, Nakhon Ratchasima Province. Classified by position and experience in budget work. Found that there was no difference but when classified according to educational level, it was found that there was a statistically significant difference at the .05 level. (3) Guidelines for managing budgets supported by the government sector of private schools in the system, Nakhon Ratchasima Province found that educational institutions have effective ways to organize and manage budgets. Emphasis is placed on budget allocation that takes into account output and cost of output. There is a procurement system that is orderly, concise, transparent, and can be audited. Moreover, complete accounting documents are used. And there is a continuous internal audit. To manage the budget according to the principles of good management. Conclusion: The research results found that Budget management in private schools in Nakhon Ratchasima Province has been significantly improved thanks to government support which helps to enhance excellence in every aspect. It also emphasizes the importance of consistent policy and strict adherence to procedures in budget allocation. Focusing on work efficiency and responsibility in using resources in a worthwhile and effective manner.

Optimization of Casing Design for Complex-Structure Wells

This study aims to address the technical challenges encountered during drilling and completion of complex-structure wells, improving the efficiency and safety of casing design. Complex-structure wells, such as horizontal wells, multilateral wells, and extended-reach wells, impose higher demands on casing design due to their intricate geological conditions. Traditional design methods, often reliant on empirical formulas and simplified assumptions, struggle to meet the evolving needs of modern drilling technology. Therefore, this research is dedicated to developing a novel optimization method for casing design in complex-structure wells to enhance drilling efficiency and economic benefits. The study employs a combination of numerical simulation and optimization algorithms. Initially, a comprehensive mathematical model is established, factoring in formation properties, drilling fluid performance, and borehole stability, to forecast drilling performance under various casing designs. Genetic algorithms are then utilized to optimize casing parameters, seeking the optimal casing design that meets safety and economic constraints. The effectiveness of the optimized solution is validated through field trial data and compared against conventional design methods. It is found that the optimized casing design significantly reduces drilling risks, accelerates drilling speed, and cuts drilling costs. Specifically, under the premise of maintaining borehole stability, the optimized scheme decreases drilling time by an average of 15% and reduces drilling costs by approximately 10%. Moreover, the proposed method exhibits strong adaptability, capable of being flexibly adjusted according to different well types and geological conditions, providing a robust technical foundation for efficient drilling of complex-structure wells.