Management Techniques Research Articles

Optimizing airway management and anesthetic techniques in maxillofacial procedures

Optimizing airway management and anesthetic techniques in maxillofacial procedures

Hybrid learning strategies: integrating supervised and reinforcement techniques for railway wheel wear management with limited measurement data

Train wheel wear significantly impacts wheel-rail interaction forces and is an unavoidable issue in the railway industry. This study focuses on regular wear, specifically changes in wheel profiles such as tread wear, flange height, and flange thickness. Effective wheel wear management is crucial for maintaining the reliability, safety, and efficiency of rail systems. However, regular measurement of wheel profiles is often limited by constraints such as dense traffic, budget, time, and remote assets, which reduces the effectiveness of traditional maintenance approaches. This study proposes a hybrid learning strategy combining supervised and reinforcement learning techniques to optimize train wheel wear management under these constraints and achieve predictive maintenance. The supervised learning model, developed from validated simulations, predicts wear progression, while reinforcement learning improves maintenance decision-making using basic operational data without regular measurements. Various machine-learning techniques are explored and fine-tuned to identify the best models for preventing faulty wheels without the need for frequent inspections. By integrating these two learning approaches, the framework enhances the accuracy of wear predictions and optimizes maintenance schedules, reducing the risk of over-maintenance or unexpected failures. This integrated model addresses challenges such as system complexity, limited data, and cost-effectiveness in the industry. In terms of supervised learning, the R2 for tread wear prediction improves from 0.94 to 0.95 compared to previous studies, and the model, when integrated with reinforcement learning, significantly reduces defects based on wear and irregular wheel dimensions. This research is the first to integrate supervised and reinforcement learning specifically for train wheel wear management under limited measurement data constraints, offering a breakthrough compared to traditional methods that rely on regular inspections. The study provides significant benefits for the railway industry, including reduced maintenance costs, improved maintenance efficiency, lower defect rates, reduced possession and inspection time, and enhanced passenger comfort and safety.

Effect of Organic Farming Techniques on the Quality of Almond Fat

Almond fruit is characterized by a mature, hardened endocarp enclosing the seed. Despite being a drupe botanically, almonds are commonly referred to as nuts due to their low water content. The edible seed, known as the kernel, is the primary commercial product. Fatty acid composition was determined to value the quality of fat over four years in almonds of the Guara variety, from plots where the production system (organic and conventional), the irrigation system (intense irrigation, support irrigation and dry land), and the use of plant cover varied. The oil content and fatty acid composition were determined over four years in almonds of the Guara variety, using different sustainable agriculture techniques. Oil almond content ranged from 35% to 55% of total fresh weight of the kernel, with strong variability between years. Fatty acid composition was also highly variable, with significant differences depending on agricultural practices. Oleic acid, which ranged from 59% to 78%, was statistically higher with support irrigation. Plant cover also favored the synthesis of oleic acid. Linoleic acid, which ranged from 11% to 25%, had a statistically higher content in almonds from organic farming and dry land conditions. The atherogenic and thrombogenic indices and the value of health-beneficial fat was better in organically prod\uced almonds. The great variability observed in the quality of almond fat represents a very promising basis for establishing practices for adaptation to arid cultivation conditions, irrigation management, and organic and regenerative techniques, satisfying requirements for healthier and more sustainable diets.

Why is it important? Financial literacy in students in entrepreneurship: A systematic literature review

This paper aims to analyze financial literacy in students a foundational understanding of personal finance management in addition to more intricate financial concepts like corporate risk analysis and capital management techniques. A comprehensive evaluation of the literature has been conducted by locating 155 studies from different sources. 36 papers were determined to be pertinent for the current investigation after the 155 research were eliminated based on selection criteria. The results of the study show that financial literacy has a significant impact on the success of students in entrepreneurship, both in planning, management, and business development. This article also provides recommendations for the development of a more comprehensive entrepreneurship education curriculum, which integrates financial literacy as a core competency. This paper develops a research model that can be used for universities or policymakers who need to create financial skills in entrepreneurial students in order to improve financial performance.

Essential Knowledge for English Student Teachers: Core Competencies and Pedagogical Skills Needed for Effective Classroom Teaching

This study aimed to identify the essential knowledge and teaching competencies English student teachers should possess before beginning their in-school teaching practice. The study involved 24 English student teachers from the College of Teacher Education at Phranakhon Rajabhat University. All participants were in their 4th year of study and were actively engaged in their practicum during the academic year 2024. The data was collected using a Google form survey, designed based on the suggestions of three education specialists. The results of the Google form questionnaire were interpreted, categorized and tabulated on computer sheets and were calculated into the statistical values, Mean. The findings revealed that the participants demonstrated the highest confidence in integrating technology into English teaching, with a mean score of 4.34, reflecting their preparedness to utilize digital tools to enhance instruction. Confidence in pronunciation and phonetics (mean 4.27) and classroom management techniques (mean 4.20) also ranked highly, indicating their readiness to model accurate language and maintain an effective learning environment. Moreover, the highest-rated competencies include Classroom Management and Technology Integration (mean 4.37), highlighting confidence in managing classroom environments and integrating digital tools to support learning.

Leveraging Incremental Machine Learning for Reconfigurable Systems Modeling under Dynamic Workloads

Dynamic workload orchestration is one of the main concerns when working with heterogeneous computing infrastructures in the edge-cloud continuum. In this context, FPGA-based computing nodes can take advantage of their improved flexibility, performance and energy efficiency provided that they use proper resource management strategies. In this regard, many state-of-the-art systems rely on proactive power management techniques and task scheduling decisions, which in turn require deep knowledge about the applications to be accelerated and the actual response of the target reconfigurable fabrics when executing them. While acquiring this knowledge at design time was more or less feasible in the past, with applications mostly being static task graphs that did not change at run time, the highly dynamic nature of current workloads in the edge-cloud continuum, where tasks can be deployed on any node and at any time, has removed this possibility. As a result, being able to derive such information at run time to make informed decisions has become a must. This paper presents an infrastructure to build incremental ML models that can be used to obtain run-time power consumption and performance estimations in FPGA-based reconfigurable multi-accelerator systems operating under dynamic workloads. The proposed infrastructure features a novel stop-and-restart resource-aware mechanism to monitor and control the model training and evaluation stages during normal system operation, enabling low-overhead updates in the models to account for either unexpected acceleration requests (i.e., tasks not considered previously by the models) or model drift (e.g., fabric degradation). Experimental results show that the proposed approach induces a maximal additional error of 3.66% compared to a continuous training alternative. Furthermore, the proposed approach incurs only a 4.49% execution time overhead, compared to the 20.91% overhead induced by the continuous training alternative. The proposed modeling strategy enables innovative scheduling approaches in reconfigurable systems. This is exemplified by the conflict-aware scheduler introduced in this work, which achieves up to a 1.35 times speedup in executing the experimental workload. Additionally, the proposed approach demonstrates superior adaptability compared to other methods in the literature, particularly in response to significant changes in workload and to mitigate the effects of model overfitting. The portability of the proposed modeling methodology and monitoring infrastructure is also shown through their application to both Zynq-7000 and Zynq UltraScale+ devices.

Stress Management Techniques and Coping Strategies Mechanism of Teachers in the New Normal Education

This study determined the stress management techniques and coping strategies mechanism of teachers in the new normal education in public elementary schools, Mintal District, Division of Davao City. This study used the non-experimental quantitative research design utilizing correlational method. The respondents of this study were composed of 133 teachers in public elementary school using the universal sampling. The data analysis utilized the mean, pearson r and regression analysis. The findings disclosed that the stress management techniques of teachers in the new normal education in terms of relaxation techniques, time-management skills, counseling, exercise and healthy lifestyle were moderate and sometimes manifested. Moreover, coping strategies mechanism of teachers in the new normal education in terms of problem solving, self-control and responsibility were also moderate and sometimes manifested. It was found out that there was a significant relationship between stress management techniques and coping strategies mechanism of teachers in the new normal. It revealed further that the domains of stress management techniques significantly influence coping strategies mechanism of teachers in the new normal education. Based on the findings, the public school teachers may attend consultations and training from the DepEd for professional growth platform. This may provide skillful progress for teachers with the objective of constructing capabilities thoroughly and reinforced education chances for learners and simultaneously learn the content of better educational practices. Additionally, the findings highlight the need for targeted stress management programs and coping strategy workshops for teachers, as their well-being directly impacts the quality of education. By equipping educators with effective techniques, schools can foster a healthier teaching environment, leading to improved teacher performance and better learning outcomes for students.

Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques

This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore.

New Energy Access Distribution Network Line Loss Management Based on Deep Learning and Fuzzy Algorithm

This paper introduces a novel method for managing line losses in energy access distribution networks, utilizing deep learning and fuzzy algorithms. As new energy sources become more integrated into distribution networks, traditional line loss management techniques struggle to address the complexities and uncertainties that arise. To tackle this issue, a comprehensive model for line loss management is developed, leveraging the predictive strengths of deep learning alongside the fuzzy algorithm’s capability to handle uncertainty. The deep learning model forecasts the operational conditions of the power grid following the integration of new energy. At the same time, the fuzzy algorithm assesses and adjusts potential line losses, facilitating effective management of distribution network line losses. The model is trained and validated using a comprehensive dataset from the IEEE 39-Bus System, with performance metrics like Accuracy, F1-score, and recall rate evaluated against traditional models like CNN and GAT. The results show that the FGAN model achieves an accuracy of 96.32%, an F1-score of 95.17%, and a recall rate of 97.05%, significantly outperforming baseline models. Experimental findings indicate that this proposed method substantially reduces line losses and improves the efficiency and reliability of the distribution network. This research offers a fresh perspective on line loss management for intelligent distribution networks in the context of new energy integration, demonstrating considerable practical value.

A Method to Handle Unbalanced Systems in Branch Current-Based State Estimators

The undergoing energy transition to a sustainable future requires, among other things, the application of demand side management (DSM) techniques to maintain grid stability and allow a smooth performance. To successfully implement DSM strategies, near real-time monitoring of the grid is required. This can be achieved through a distribution system state estimator (DSSE). Conventional approaches to state estimation (SE) typically rely on the assumption of a balanced reference bus, which is reasonable for transmission systems but may not be applicable to low-voltage distribution networks, even more with significant distributed generation (DG) penetration. To address this problem, a branch current-based low-voltage DSSE for unbalanced three-phase systems is developed. The algorithm incorporates a virtual bus to account for highly unbalanced systems, enabling it to obtain a more accurate estimation of the grid state. The proposed method is compared to the conventional balanced reference bus method through multiple simulations under different load conditions in the IEEE European low-voltage test feeder.

Implementation of Conventional and Smart Weed Management Strategies in Sustainable Agricultural Production

ABSTRACTConventional weed management is known for the use of selective herbicides, tackling issues of herbicide‐resistant weeds, and endeavors to reduce reliance on herbicides, such as employing biological control of weeds and incorporating allelopathy. These strategies are integrated with traditional and other conventional weed management techniques. New challenges in agricultural production, such as declining population involved in agriculture, global warming, and climate changes make conventional weed management inadequate. Rapid advancements over the past decade in artificial intelligence (AI), unmanned aerial vehicles (UAVs), cameras, and sensors, necessitate the development and implementation of smart weed management (SWM) approaches for the 21st century. Wise weed management practices can control weeds and reduce emissions of methane (CH4), carbon dioxide (CO2), and nitrogen dioxide (NO2). Minimizing the use of herbicides in tandem with the application of smart technologies, like weed mapping and identifying spatial variability of weeds using proximal and remote sensors, multispectral and RGB cameras, can effectively manage weeds while also reducing labor costs. The SWM strategies also encompass the integrated use of organic herbicides, bioherbicidal products, and the judicious application of both organic fertilizers and inorganic fertilizers for weed management. Some approaches, such as alternate wetting and drying (AWD), can contribute to both SWM and carbon credits to farmers. The utilization of computing power, weed control robotics, and big data, combined with the education of the next generation of weed scientists and efforts to shift growers' attitudes and behaviors in weed management, may vary among countries. It requires active collaboration between agricultural extensionists and policymakers.

Analysis of Time Control Strategies Using The Critical Path Method (CPM) In The Development of The Java Residence Cluster For Mass-Product

The increasing complexity and scale of industrial and construction projects demand efficient management techniques to ensure timely delivery, cost-effectiveness, and adherence to quality standards. This study focuses on the implementation of the Critical Path Method (CPM) in managing the construction of the Cluster Java Residence in Sidoarjo to enhance time efficiency. The research investigates the effectiveness of CPM in identifying and mitigating delays by analyzing project activities, their interdependencies, and schedule flexibility using forward and backward pass calculations. Secondary data from project schedules and primary data from field observations were analyzed to identify critical paths and optimize project timelines. Results indicate that the implementation of product quality control in the construction of housing units in the Java Residence Cluster is still under control, although the average number of defects or complaints per housing unit is relatively high. The issues of defects or complaints in the construction of housing units in the Java Residence Cluster are caused by factors related to humans, machines or tools, materials, and work methods.

A Modified Hybrid Technique for Airway Management in a Patient with an Unusually Large Supraglottic Mass: A Case Report

Background: Supraglottic masses are rare and pose a distinct challenge during airway management due to their inherent risk of severe airway obstruction owing to their large size, compressing the glottic opening. Additionally, they may be friable and may lead to an increased risk of bleeding during airway manipulation. Case: We present the successful airway management of a 65-year-old male patient with a large supraglottic mass, posted for direct laryngoscopy and biopsy, utilizing the inherent characteristics of a hybrid intubation technique using a flexible fibreoptic bronchoscope and videolaryngoscope in conjunction with the SALAD (Suction Assisted Laryngoscopy and Airway Decontamination) technique to obtain the desired results. Conclusion: Managing the airway in patients with a large supraglottic mass requires a diligent selection of airway techniques. The combination of the hybrid technique of using fibreoptic-assisted, videolaryngoscopy-guided intubation along with the SALAD technique in the present case seems to be a plausible option and should be considered.

Evaluation of adherence to the Preferred Reporting of CasE Series in Surgery (PROCESS) 2020 guideline in case series describing endovascular management of vein of Galen malformation demonstrates suboptimal reporting practices.

Interventional neuroradiology is a dynamic field with technological advancements constantly driving evolution in clinical practice. Case series provide interventional neuroradiologists the opportunity to describe the clinical implications of novel equipment and techniques almost in real-time, informing broader adoption and directing future research. Complete reporting in case series is vital in enabling readers to detect bias, determine the generalizability of results and replicate study methodology. The aim of this study was to assess the quality of reporting in case series describing endovascular management of vein of Galen malformations. A systematic search for case series describing endovascular management of vein of Galen malformations in paediatric patients was conducted using a previously published search strategy in Medline, Embase and Web of Science in February 2024. Screening and data extraction were performed by two independent reviewers with disagreements resolved by consensus discussion. Quality of reporting was assessed using adherence to the Preferred Reporting of CasE Series in Surgery (PROCESS) 2020 checklist. We included 19 case series representing a broad range of approaches and endovascular techniques for management of vein of Galen malformations. Overall adherence to the PROCESS 2020 guideline was moderate with a mean of 7.0/13 (54%; range 5.2 - 9.7/13; SD 1.1) items reported. The most consistently reported items related to characteristics of the clinical cohort and details regarding the specific image-guided intervention(s) provided. Items relating to study design, source of funding, pre-intervention patient optimisation, clinical follow-up and potential complications and alternatives for novel techniques/devices were less frequently reported. The quality of reporting in case series describing endovascular management of vein of Galen malformation is suboptimal. Measures to improve the quality of reporting in neurointerventional case series include mandating adherence to reporting guidelines in journal instructions for authors and requiring submission of completed copies of relevant reporting guidelines alongside manuscripts.

Monitoring changes of forest height in California

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carbon sequestration. Here, we use LiDAR measurements of top of canopy height metric (RH98) from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission to map vegetation height across the entire California for two different time periods (2019–2020 and 2021–2022) and explore the impact of disturbance. Exploring the reliability of machine learning methods for temporal monitoring of forest is still a developing field. We train a deep neural network to predict forest height metrics at 10-m resolution from radar and optical satellite imagery. Model validation against independent airborne LiDAR data showed a R2≥0.65 for the top of canopy height outperforming existing GEDI-based height maps and with improved sensitivity for mapping tall trees (RH98 ≥ 50 m) across California. Height showed distinct spatial variations across forest types offering quantitative and spatial information to evaluate forest conditions. The model, trained on data from 2019 to 2020, showed a similar accuracy when applied to satellite imagery acquired in 2021–2022 allowing a robust detection of changes caused by natural and man-made disturbances of forest. Changes of height captured impacts of tree mortality and fire intensity, pointing to the influence of wildfire across landscapes. Fires caused more than 60% of the large forest disturbances between the two time periods. This study demonstrates the benefits of using locally trained ML models to rapidly modernize forest management techniques in the age of increasing climate risks.

Review of Thermal Management Techniques for Prismatic Li-Ion Batteries

This review presents a comprehensive analysis of battery thermal management systems (BTMSs) for prismatic lithium-ion cells, focusing on air and liquid cooling, heat pipes, phase change materials (PCMs), and hybrid solutions. Prismatic cells are increasingly favored in electric vehicles and energy storage applications due to their high energy content, efficient space utilization, and improved thermal management capabilities. We evaluate the effectiveness, advantages, and challenges of each thermal management technique, emphasizing their impact on performance, safety, and the lifespan of prismatic Li-ion batteries. The analysis reveals that while traditional air and liquid cooling methods remain widely used, 80% of the 21 real-world BTMS samples mentioned in this review employ liquid cooling. However, emerging technologies such as PCM and hybrid systems offer superior thermal regulation, particularly in high-power applications. However, both PCM and hybrid systems come with significant challenges; PCM systems are limited by their low thermal conductivity and material melting points. While hybrid systems face complexity, cost, and potential reliability concerns due to their multiple components nature. This review underscores the need for continued research into advanced BTMSs to optimize energy efficiency, safety, and longevity for prismatic cells in electric vehicle applications and beyond.

The Value of a Modified Posterior Thigh Flap with Z-Plasty for Reconstruction of Ischial Tuberosity Pressure Ulcers: A Retrospective Single Center Analysis

Pressure ulcers are a persistent and growing challenge in modern medicine, with prevalence rates ranging from 3.4% to 32.4% globally. Demographic changes suggest an increasing number of patients at risk, emphasizing the need for effective prevention and advanced treatment strategies. These ulcers, particularly stages III and IV, often require surgical intervention due to severe tissue damage. Among the surgical options, the Posterior Thigh Flap has emerged as a reliable fasciocutaneous flap, frequently referred to as a “work-horse flap”, for defect coverage in the ischial region. To improve outcomes and minimize recurrences in weight-bearing areas, our clinic combined this flap technique with a Z-plasty modification to achieve redistribution of scar tissue to reduce localized pressure. In this retrospective study, six patients with seven ischial pressure ulcers were treated between 2003 and 2024 using this approach. Following debridement and wound conditioning with negative pressure therapy, defect coverage was performed. The results show clinically acceptable complication rates (42.9%) and a low recurrence rate (14.3%), with recurrences occurring no earlier than 12 months post-treatment. The Z-plasty effectively shifted scar zones away from high-pressure areas, reducing tension and recurrence risk. This study highlights the potential benefits of combining fasciocutaneous flaps with scar management techniques to improve long-term outcomes for ischial pressure ulcers and provides an innovative approach to treating ischial pressure ulcers.

The Role of Technological Innovations in Improving Oil Extraction Efficiency in South Sudan

Purpose: The purpose of this study was to explore the role of technological innovations in improving oil extraction efficiency in South Sudan, with a specific focus on recovery rates, operational costs, reservoir management, and real-time data accuracy. Methodology: In the research, a descriptive methodology was used to find out the effect of the contributing factors on the oil production results. Findings: Results show that EOR methods, such as reactive injection of water (CW), clearly increase the recovery yield, and reactive injection of gas (GE) technologies obviously enhance the recovery efficiency and the energy security. Further, real-time information systems and modern reservoir management techniques facilitate better decision-making, more efficient production activities and are also scarcity-reducing. Nonetheless, development of South Sudan's oil sector is constrained by the presence of outdated infrastructure, lack of technical ability and insufficient funding for technology. Unique Contribution to Theory, Practice and Policy: The conclusion reached from the study is that these innovations are necessary to help with maximizing resource use, to help with cost reduction and to enable sustainable development in the country's oil industry. Target areas of advice include giving importance to EOR methods, incorporating real-time data technology, and developing technical capacity to modernise the South Sudan oil industry.

Unleashing Enviropreneurship Toward Innovation: Unraveling Frugal Innovation and Green Innovation Through Zero Waste Management in Circular Economy

ABSTRACTFocused on human well‐being and social responsibility, this study explores the complex relationships within sustainable entrepreneurship and explores whether enviropreneurship significantly impacts both frugal innovation and green innovation. The analysis also studies whether there are possible beneficial links between zero waste management, frugal innovation, and green innovation. Additionally, through assessing the level to which these items come together in sustainable strategic actions, the paper shows the correlation among two important topics: Enviropreneurship and zero waste management. The study explores at the relations between zero waste management and frugal innovation as well as green innovation, examining whether these aspects of innovation have a positive relationship with effective waste management techniques in the presences of enviropreneurship. The paper analyses interactions between enviropreneurship, frugal innovation and green innovation, and the mediation role of zero‐waste management. A total of 273 managers in the manufacturing sector completed the survey and the results were analyzed applying SPSS 25.0 and structural equation modeling (SEM). The findings confirmed significant association among enviropreneurship, zero waste management, frugal innovation, and green innovation. Furthermore, findings also revealed that zero waste management have positive intervention role between enviropreneurship and frugal and green innovation.

Potential Approaches and Techniques for Long-term Management of the Human-Elephant Conflict in Western Serengeti, Tanzania

Human-elephant conflict is a ubiquitous feature between Ikorongo-Grumeti Game Reserves (IGGRs) management and the adjacent communities of Bunda and Serengeti Districts, in the Mara Region. This study aimed at analyzing the conflict and then coming up with potential techniques for effective mitigation of the conflict in the area. Data collection involved direct observations, key informant interviews and household surveys using questionnaires. The analysis was done using SPSS computer software. Results revealed and recommended several non-conventional mitigation measures namely the construction of trench (95.3%), electric fencing (92.7%), establishment of buffer zone management units (BZMUs) (92.7%) and geo-fencing system (92.3%). Also were wireless sensing networks (WSN) (85.3%), translocation of problem elephants (11.7%), and evacuation of people adjoining wildlife area boundaries (22%) as HEC prevention and mitigation measures with long-term impacts. Generally, no single solution was considered effective as different approaches must be integrated to address the problem proactively. Hence, it is recommended that community involvement in decision-making and policy formulation be emphasized to effectively implement proposed mitigation measures