Continuous Performance Improvement Research Articles

The Impact of Stagflation on Small and Medium –Sized Enterprise Performance in Champasak Province, LAO PDR

This study investigates the impact of stagflation on SME performance in Champasack Province, Lao PDR. Utilizing data from 450 SME’s owners, acquired via structured questionnaires, the research employed the structural equation modeling technique for data analysis. The result showed that stagflation have positive impact and significant on marketing management capability, Consumers, and SME performance. According to Marketig management capability has a positive impact and significant on Consumers, but its negative impact and significant on SME performances, and Consumers has a positive impact and significant on SME performances. The insights derived from this research provide essential directives for SME managers striving to business survival in economic uncertenty. Therefore, Medium-sized and small-sized enterprises in ten districts of Champasack Province, Lao PDR, need to effectively develop their marketing management capability to stimulate consumer spending during periods of stagflation. This will lead to a continuous improvement in SME performance and high business survival during the period of stagflation.

The Influence of CSR Programs on Financial Performance in the Banking Sector

The basis of the study is the idea that corporate social responsibility, or CSR, suggests that doing this kind of activity will help the company by establishing its reputation and generating positive community feedback. The purpose of the study is to determine how a company's financial performance is impacted by its commitment to Corporate Social Responsibility (CSR). This report includes research conducted in China, India, Indonesia, and Pakistan. This research indicates that Corporate Social Responsibility (CSR) activities lead to continual performance improvement. Using a meta-analysis methodology, this study investigates the connection between financial success and corporate social responsibility (CSR). Based on theoretical assertions and real facts from the literature, ascertain whether there is a favorable association between financial performance and corporate social responsibility (CSR). If the research results are specified correctly, researchers find that although Corporate Social Responsibility (CSR) does not have much positive impact on short-term financial performance, Corporate Social Responsibility (CSR) offers extraordinary long-term fiscal benefits.

Simulation-Based Algorithm for Continuous Improvement of Enterprises Performance

Simulation-Based Algorithm for Continuous Improvement of Enterprises Performance

TINJAUAN TEORITIS MANAJEMEN MUTU PENDIDIKAN

When discussing the results of educational provision, of course you will question the quality of educational provision, and to measure the quality of education you can look at the graduates or output. And this can be realized if it is done with proper management of the quality of education, and that is the domain of education quality management studies. So a comprehensive understanding of education quality management is needed so that it can be implemented in educational institutions appropriately and can run effectively and efficiently. In this study the author uses qualitative research with a library research approach and is descriptive in nature, where the author wants to reveal a deeper theoretical review of education quality management." The result of this study is that quality management, which is more popularly known as TQM, is a way of continuously improving performance (continuous performance improvement) at every level or process, in every functional area of an organization by using available human resources and capital. The main goal of TQM in education is to improve the quality of education in a sustainable, continuous and integrated manner. Meanwhile, the success of TQM is largely determined by its five supporting pillars, namely: 1). Product, 2) process, 3) organization, 4) leadership, and 5) commitment. And the implementation of quality management in schools, among other things, focuses on a). Implementation of institutional management, b). Implementation of curriculum quality management, c). Implementation of learning management and graduates

Protocol for a scoping review study on learning plan use in undergraduate medical education

BackgroundThe current paradigm of competency-based medical education and learner-centredness requires learners to take an active role in their training. However, deliberate and planned continual assessment and performance improvement is hindered by the fragmented nature of many medical training programs. Attempts to bridge this continuity gap between supervision and feedback through learner handover have been controversial. Learning plans are an alternate educational tool that helps trainees identify their learning needs and facilitate longitudinal assessment by providing supervisors with a roadmap of their goals. Informed by self-regulated learning theory, learning plans may be the answer to track trainees’ progress along their learning trajectory. The purpose of this study is to summarise the literature regarding learning plan use specifically in undergraduate medical education and explore the student’s role in all stages of learning plan development and implementation.MethodsFollowing Arksey and O’Malley’s framework, a scoping review will be conducted to explore the use of learning plans in undergraduate medical education. Literature searches will be conducted using multiple databases by a librarian with expertise in scoping reviews. Through an iterative process, inclusion and exclusion criteria will be developed and a data extraction form refined. Data will be analysed using quantitative and qualitative content analyses.DiscussionBy summarising the literature on learning plan use in undergraduate medical education, this study aims to better understand how to support self-regulated learning in undergraduate medical education. The results from this project will inform future scholarly work in competency-based medical education at the undergraduate level and have implications for improving feedback and supporting learners at all levels of competence.Scoping review registration:Open Science Framework osf.io/wvzbx.

The Impact of Training and Development on Job Performance: A Comprehensive Analysis

This study examines the profound impact of training and development initiatives on employee job performance within organizational settings. By synthesizing existing literature and empirical evidence, the research explores the multifaceted dynamics influencing how training and development programs enhance employee skills, knowledge, and performance outcomes. Key aspects investigated include the design and implementation of training programs, the role of employee engagement and motivation, and the alignment of training objectives with organizational goals. Furthermore, the study assesses the efficacy of various training methods in fostering skill acquisition, knowledge transfer, and behavioral change among employees. Additionally, the research delves into organizational factors such as culture, leadership support, and learning climate, elucidating their influence on the success of training and development initiatives. It examines feedback mechanisms, coaching, and mentoring as pivotal components reinforcing learning and driving continuous improvement in job performance. Furthermore, the study explores the repercussions of training and development on employee engagement, job satisfaction, and retention, highlighting the significance of investing in employee development to cultivate a skilled, motivated workforce and bolster organizational performance. This comprehensive analysis contributes nuanced insights into the strategic importance of training and development, offering actionable recommendations for organizations aiming to optimize employee performance, foster a culture of learning, and attain sustained success in today's competitive landscape.

Exploring the effect of dynamic capabilities on operational excellence, moderated by environmental dynamism in the apparel industry

PurposeThe effect of dynamic capabilities on operational excellence and the moderating effect of environmental dynamism on the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka were investigated while developing new psychometric scales to assess operational excellence and dynamic capacities constructs.Design/methodology/approachWe followed the exploratory sequential research design with a mixed-method research approach, aligning with the pragmatic research philosophy. Thus, both qualitative and quantitative research methods were followed.FindingsDynamic capabilities positively affect operational excellence, and environmental dynamism moderates the relationship between operational excellence and dynamic capabilities in the apparel industry in Sri Lanka such that when a higher environmental dynamism exists, a weaker positive relationship exists between dynamic capabilities and operational excellence. The two main dimensions of the operational excellence construct are continuous improvement of sustainable operational performance and sustainable competitive advantages. It empirically confirmed that sensing, seizing and reconfiguring capabilities are the three main dimensions of the dynamic capabilities construct.Research limitations/implicationsThis study was limited to the apparel industry in Sri Lanka. This research phenomenon should be explored in other industrial sectors worldwide to generalize the findings. The practitioners in the apparel sector may improve the organizational dynamic capabilities to achieve operational excellence and keep a strong positive relationship between dynamic capabilities and operational excellence in a highly dynamic environment if they address out-of-family situations with out-of-the-box thinking.Originality/valueWe generated two new empirical findings: (1) dynamic capabilities positively affect operational excellence, and (2) environmental dynamism moderates the relationship between dynamic capabilities and operational excellence. Also, we introduced validated new scales for assessing operational excellence and dynamic capabilities.

Technology empowerment: Digital transformation and enterprise ESG performance-Evidence from China's manufacturing sector.

In light of the long-term constraints posed by the "dual carbon" objective, can digital technology emerge as a transformative solution for enterprises to embark on a sustainable development trajectory? The existing body of research has yet to reach a consensus. In order to shed further light on the intricate relationship between digital transformation and ESG performance of enterprises, this study empirically examines the mechanisms and boundaries through which digital transformation influences ESG performance, based on observational data from A-share manufacturing listed companies in Shanghai Stock Exchange and Shenzhen Stock Exchange spanning from 2011 to 2021. The findings demonstrate that digital transformation exerts a significant positive impact on the ESG performance of manufacturing enterprises. Mechanism analysis reveals that the enabling effect of digital transformation primarily enhances company transparency, thereby fostering continuous improvements in ESG performance among manufacturing enterprises. The performance expectation gap will give rise to the phenomenon of "stop-loss in time" and impede the promotional impact of digital transformation. Further investigation into industrial characteristics and industry competition intensity indicates that state-owned enterprises and those operating within highly competitive environments experience more pronounced effects of digital transformation on their ESG performance. This study expands the mechanism and boundary of digital transformation on ESG performance of manufacturing enterprises, and provides a new perspective for manufacturing enterprises to realize the collaborative transformation of digital and green.

Institutional Pressures and Environmental Management Accounting Adoption: Do Environmental Strategy Matter?

This paper examines the impact of institutional pressures (IPs) on Environmental Management Accounting adoption (EMA). The current research also aims to examine the moderating effect of environmental strategy (ES) on the relationship between IP and EMA. Data were collected from managers working in all registered Egyptian manufacturing companies (N = 491). The collected data were analyzed using smart partial least squares (Smart-PLS) software. The results revealed that there is a positive significant relationship between IP’s three components, namely, coercive, normative, and mimicry pressures, and EMA. The results also revealed that ES was found to moderate the relationship between IP and EMA. The study model was able to explain 68.9% of the variance in EMA adoption. The findings of this study serve as a pivotal yardstick for guiding corporate policy formulation, offering valuable insights to drive continuous improvements in EMA, environmental performance, and sustainable development. The present investigation extends the discourse on the role of IP and ES by revealing a substantial influence on EMA adoption. Positioned as one of the initial studies to delve into the moderating role of ES in the relationship between IP and EMA adoption, this research offers insights within an emerging market context.

LEAN MANUFACTURING IMPLEMENTATION THROUGH DMAIC APPROACH: A CASE STUDY IN THE GLASS TRANSFORMATION COMPANY

Manufacturing companies are constantly driven to minimize costs, maximize efficiency, and enhance production quality, as these factors are pivotal for maintaining a competitive edge in the market. Lean manufacturing is a pivotal strategy that plays a crucial role in assisting companies for achieving significant continuous improvement in performance through the elimination of all wastes of resources and time in the total business process.The main objective of this paper is to validate the results of implementing Lean manufacturing through the DMAIC concept. This study focuses on a case study conducted in a glass transformation company that aims to improve the flow within the production workshop by implementing pull systems and minimizing waste.To achieve this objective, a comprehensive literature review was conducted to examine different frameworks for applying the Lean method.The implementation of DMAIC has provided a better structure for the entire project, enabling the selection of appropriate improvement solutions and the use of the right Lean tools. This approach offers several advantages that are not present in other frameworks. The results of this implementation have shown a significant improvement in production planning, flow efficiency, and a substantial financial gain for the company.

비즈니스 트리즈를 활용한 식품제조업체 성과 향상 방안에 관한 연구

This study was conducted using business TRIZ to develop strategies for the continuous growth, development, and performance improvement of 6th industry certified food manufacturing companies located in the Gyeongbuk region. For this purpose, a survey was conducted targeting 6th industry certified companies in Gyeongsangbuk-do, through which the problems faced by food manufacturing companies and their causes were identified. Based on these problems and causes, key tasks were established, and core issues were derived. Subsequently, these core issues and causes were matched with business TRIZ parameters and applied to a matrix to derive solutions. Based on these solutions, specific measures for improving the performance of 6th industry certified food manufacturers in the Gyeongbuk region were proposed. Firstly, there is a need to expand the organic and eco-friendly product lines and diversify the product lineup to cater to different consumer segments. Secondly, establishing a consumer review and feedback system is crucial, and it is important to improve and re-launch products based on the feedback obtained. Thirdly, launching new products in colors based on market trend analysis and developing product packaging designs that reflect regional characteristics are necessary. Fourthly, expanding product sales through collaboration and joint marketing with local enterprises, as well as partnerships with large franchise corporations, is essential. Lastly, the development of mobile applications for product ordering and promotion, along with promoting and boosting sales through temporary stores, is intended to foster the development of 6th industry food manufacturers in the Gyeongbuk region.

Hyperventilation-Induced Nystagmus in Acute Unilateral Vestibulopathy: A Correlation with Vestibulo-ocular Reflex Gain and Clinical Implication.

Hyperventilation-induced nystagmus test (HINT) is capable of generating a response in 77.2% of cases of acute unilateral vestibulopathy (AUVP); both nystagmus toward the affected side (excitatory pattern) and toward the healthy side (inhibitory pattern) have been described. The aim of the study is to investigate the clinical and prognostic role of the test by evaluating its correlation with vestibulo-ocular reflex (VOR) gain. We evaluated 33 AUVP patients by performing the HINT and video head impulse test (V-HIT) during the acute phase and then at 15 and 90 days after the onset of the symptoms. The correlation between the VOR gain of the affected side and test responses was evaluated first, phase by phase, and then considering the pattern shown during the first assessments. Patients with a negative HINT had a higher mean VOR gain than patients with a positive test at both 15 and 90 days. Patients who showed an inhibitory pattern at the first assessment had a continuous improvement in V-HIT performance, while patients with an initial excitatory response had a transient decrease in gain at the subsequent evaluation (P=.001). No difference between these 2 groups emerged at 90 days (P=.09). The finding of a negative HINT during the follow-up correlates with good V-HIT performance and could be an indicator of good recovery. The inhibitory pattern is associated with a subsequent improvement; and it would be indicative of compensation. but, despite this, the prognostic value of the test is limited.

Comparison of three machine learning algorithms for retrieving soil moisture information from Sentinel-1A SAR data in northwest Shandong plain, China

Soil moisture (SM) plays a critical role in the growth and management of grain in semi-humid regions. However, little is known about how to integrate satellite data with machine learning to accurately retrieve SM information in these areas. This study compares the capability of three machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), to extract SM information over the Northwest Shandong Plain using multi-phase dual-polarized Sentinel-1A satellite data. The backscattering coefficients were obtained through standard intensity and phase processing to calculate the SAR indices, and several characteristic parameters were extracted as impact factors using the Cloude-Pottier decomposition. The importance of these factors was analyzed, while the performance of each machine learning algorithm was comprehensively evaluated using the K-fold cross-validation method. The best-performing model was utilized to retrieve the spatio-temporal changes in SM in the study area. The findings indicate the following: (1) The first eigenvalue has the greatest impact on retrieval accuracy, followed by entropy, where the intensity component of Shannon's entropy is more important than its polarization component; (2) The addition of more impact factors does not bring a continuous improvement in model performance, but the optimal factor combinations differ for different machine learning retrieval models; (3) The RF model trained using the IM12 combination demonstrates better performance than SVM and ANN in retrieving SM information, with a coefficient of determination (R2) of 0.55 and a root mean square error of 6.12 vol% on the validation set. The level of SM in the Yellow River National Wetland Park is higher than that of the surrounding areas, with substantial seasonal changes. Precipitation, temperature, and vegetation significantly influence the regional variations in SM at the macroscopic level.

Design and Development of Model to Detect Garbage from CCTV

Urban waste management is a pressing concern globally, necessitating innovative solutions for effective sanitation and sustainability. In response, this project introduces a Garbage Detection System leveraging deep learning techniques and web-based monitoring for real-time surveillance of city-wide Garbage accumulation. Utilizing the YOLOv7 object detection model, the system achieves an accuracy rate of 81% in identifying Garbage objects across diverse environmental conditions and camera angles. The accompanying web application dashboard provides intuitive access to live camera feeds, empowering stakeholders with proactive monitoring capabilities and facilitating prompt response to sanitation issues. Integration with MongoDB ensures efficient data storage and retrieval, enabling seamless access to historical Garbage detection data for analysis and optimization. The iterative refinement process, guided by periodic model updates and user feedback loops, contributes to continuous improvement in system performance. Through its comprehensive approach, this project demonstrates the feasibility and effectiveness of utilizing advanced computer vision technology for enhancing urban cleanliness and sustainability. Future endeavours will focus on further enhancing accuracy, scalability, and integration with existing waste management infrastructure to realize holistic urban waste management solutions. Key Words: YOLOv7, neural network, object detection, deep learning, web-based monitoring

Method for GPU-based spectral data cube reconstruction of integral field snapshot imaging spectrometers.

In this paper, the principles of spectral data cube reconstruction based on an integral field snapshot imaging spectrometer and GPU-based acceleration are presented. The primary focus is on improving the reconstruction algorithm using GPU parallel computing technology to enhance the computational efficiency for real-time applications. And the computational tasks of the spectral reconstruction algorithm were transferred to the GPU through program parallelization and memory optimization, resulting in significant performance gains. Experimental results indicate that the average processing time of the GPU-based parallel algorithm is approximately 29.43 ms, showing a substantial acceleration ratio of about 14.27 compared to the traditional CPU serial algorithm with an average processing time of around 420.46 ms. The study aims to refine the GPU parallelization algorithm for continued improvement in computational efficiency and overall performance. The anticipated applications of this research include providing crucial technical support for the perception and monitoring of crop growth traits in agricultural production, contributing to the modernization and advancement of intelligence in the field.

A Multifunctional, Low Cost and Sustainable Neonatal Database System.

Continuous improvement in the clinical performance of neonatal intensive care units (NICU) depends on the use of locally relevant, reliable data. However, neonatal databases with these characteristics are typically unavailable in NICUs using paper-based records, while in those using electronic records, the inaccuracy of data and the inability to customize commercial data systems limit their usability for quality improvement or research purposes. We describe the characteristics and uses of a simple, neonatologist-centered data system that has been successfully maintained for 30 years, with minimal resources and serving multiple purposes, including quality improvement, administrative, research support and educational functions. Structurally, our system comprises customized paper and electronic components, while key functional aspects include the attending-based recording of diagnoses, integration into clinical workflows, multilevel data accuracy and validation checks, and periodic reporting on both data quality and NICU performance results. We provide examples of data validation methods and trends observed over three decades, and discuss essential elements for the successful implementation of this system. This database is reliable and easily maintained; it can be developed from simple paper-based forms or used to supplement the functionality and end-user customizability of existing electronic medical records. This system should be readily adaptable to NICUs in either high- or limited-resource environments.

Tibia-YOLO: An AssistedDetection System Combined with Industrial CT Equipment for Leg Diseases in Broilers

With the continuous improvement of broiler production performance, the frequent occurrence of leg problems has caused serious economic losses in many factories. In order to more efficiently detect and prevent broiler leg diseases, we propose an auxiliary detection system for broiler leg diseases based on deep learning. The system is divided into two parts. First, a digital radiography (DR) image of a broiler is taken through industrial computed tomography (CT), and then the improved deep-learning network Tibia-YOLO is used to detect the tibia; the detected results are then extracted and measured. Our improved Tibia-YOLO network uses the Content-Aware ReAssembly of Features (CARAFE) upsampling operator to avoid checkerboard artifacts and increase the generalization capabilities. Efficient multi-scale attention (EMA) and parallel network attention (ParNet) were added to the Tibia dataset at multiple scales (COCO2016), and there were improvements when testing on the three VOC2012 datasets. The mean average precision of tibia detection reached 90.8%, and the root mean square error (RMSE) for the tibia length was 3.37 mm.

Reliable image dehazing by NeRF.

Image dehazing is a typical low-level visual task. With the continuous improvement of network performance and the introduction of various prior knowledge, the ability of image dehazing is becoming stronger. However, the existing dehazing methods have problems such as the inability to obtain real shooting datasets, unreliable dehazing processes, and the difficulty to deal with complex lighting scenes. To solve these problems, we propose a new haze model combining the optical scattering model and the computer graphics rendering. Based on the new haze model, we propose a high-quality and widely applicable dehazing dataset generation pipeline that does not require paired-data training and prior knowledge. We reconstruct the three-dimensional fog space with array camera and remove haze by thresholding voxel deletion. We use the Unreal Engine 5 to generate simulation datasets and the real shooting in laboratory to verify the effectiveness and the reliability of our generation pipeline. Through our pipeline, we can obtain wonderful dehaze results and dehaze datasets under various complex outdoors lighting conditions. We also propose a dehaze dataset enhancement method based on voxel control. Our pipeline and data enhancement are suitable for the latest algorithm model, these solutions can obtain better visual effects and objective indicators.

MOODLE MANUAL UPGRADE METHODOLOGY

This abstract outlines a systematic four-step methodology for upgrading Moodle from version 3.6.5 to 3.9.9. The process begins with comprehensive backup and preparation, followed by the replacement of the Moodle codebase and execution of the upgrade script. Post-upgrade checks are performed to ensure the smooth functioning of core and third-party components. Additionally, the abstract explores the actions and functions of the cron script, emphasizing its role in executing scheduled tasks critical for Moodle's operation.
 Furthermore, the document sheds light on common reasons behind slow Moodle operation and provides practical tips for enhancing system speed. Recommendations include optimizing server resources, database maintenance, strategic caching implementation, and the judicious use of content delivery networks (CDNs). The importance of upgrading PHP and database versions is highlighted, alongside the benefits of disabling unused features and employing server monitoring tools. The abstract emphasizes the significance of staying abreast of Moodle updates for continued performance improvements and bug fixes, offering a holistic guide for upgrading and optimizing Moodle systems.

Distillation, Ensemble and Selection for Building a Better and Faster Siamese Based Tracker

Visual object tracking has witnessed continuous improvements in performance, thanks to deep CNN learning that recently emerged. More complex CNN models invariably offer better accuracy. However, there is a conflict between the tracking efficiency and model complexity, which poses a challenge in balancing speed against accuracy. To optimize the trade-off between these two performance criteria, a distillation-ensemble-selection framework is proposed in this paper. Without any modification to the baseline network architecture, the proposed approach enables the construction of a Siamese-based tracker with improved capacity and efficiency. Specifically, multiple student trackers are designed by means of knowledge distillation from a given teacher tracking model. To manage the varying granularity of unknown targets, an ensemble module combines the outputs of the student trackers with the help of a learnable fine-grained attention module. Besides, in the online tracking stage, a selection module adaptively controls the complexity of the tracker by identifying an appropriate subset of the candidate tracker models. We verify the effectiveness of the proposed method in both anchor-based and anchor-free paradigms. The experimental results obtained on standard benchmarking datasets demonstrate the effectiveness of the proposed method, with an outstanding and balanced performance in both accuracy and speed.