Improve Work Efficiency Research Articles

Monitoring of Broccoli Flower Head Development in Fields Using Drone Imagery and Deep Learning Methods

For different broccoli materials, it used to be necessary to manually plant in a large area for the investigation of flower ball information, and this method is susceptible to subjective influence, which is not only time-consuming and laborious but may also cause some damage to the broccoli in the process of investigation. Therefore, the rapid and nondestructive monitoring of flower heads is key to acquiring high-throughput phenotypic information on broccoli crops. In this study, we used an unmanned aerial vehicle (UAV) to acquire hundreds of images of field-grown broccoli to evaluate their flower head development rate and sizes during growth. First, YOLOv5 and YOLOv8 were used to complete the position detection and counting statistics at the seedling and heading stages. Then, UNet, PSPNet, DeepLabv3+, and SC-DeepLabv3+ were used to segment the flower heads in the images. The improved SC-DeepLabv3+ model excelled in segmenting flower heads, showing Precision, reconciled mean F1-score, mean intersection over union, and mean pixel accuracy values of 93.66%, 95.24%, 91.47%, and 97.24%, respectively, which were 0.57, 1.12, 1.16, and 1.70 percentage points higher than the respective values achieved with the DeepLabv3+ model. Flower head sizes were predicted on the basis of the pixel value of individual flower heads and ground sampling distance, yielding predictions with an R2 value of 0.67 and root-mean-squared error of 1.81 cm. Therefore, the development rate and sizes of broccoli flower heads during growth were successively estimated and calculated. Compared with the existing technology, it greatly improves work efficiency and can help to obtain timely information on crop growth in the field. Our methodology provides a convenient, fast, and reliable way for investigating field traits in broccoli breeding.

An Approach to Checking Map Tiles by Annotation Recognition

Abstract. As the capabilities of geographic information services increase, web geographic information services represented by internet mapping have become increasingly popular, widely used in various location-based services. It is precisely because of the surge in demand that higher requirements have been put forward for the updating and quality control of internet map data. The quality inspection of geographic vector data is relatively well-developed, with many inspection rules and automated processes, but there is a clear deficiency in the quality inspection of map tiles, which mainly relies on manual visual inspection, restricting the rapid updating and release of map tiles. To solve this problem, this paper proposes an automated map tile inspection method. First, web crawling technology is used to capture map tiles, and to avoid the problem of incomplete display of annotations across tiles, adjacent tiles are all captured and merged into one image; second, OCR technology is used for image text detection and text recognition, and finally, the newly added recognition results are stored in the database and compared and confirmed with the results in the database. The experiment proves that the method proposed in this paper is feasible and may greatly improve work efficiency, especially as the updating speed of online maps accelerates, an automated tile quality inspection method can greatly improve the updating speed.

Establishment and application of autoverification system for HbA1c testing.

This study aimed to determine autoverification rules for routine glycated hemoglobin (HbA1c) analysis based on high-performance liquid chromatography (HPLC) principle. Laboratory information system (LIS) and Bio-Rad D-100 Advisor software (Bio-Rad, Hercules, USA) with graphics recognition function were carriers for the autoverification system. A total of 105,126 HbA1c results, including 98,249 HbA1c matching fast plasma glucose (FPG) results of real-world data from May 2019 to June 2020, were collected to determine autoverification rules including flags, delta checks, reporting limits, and logical rules. The validation database was composed of 48,045 HbA1c results and 41,083 matching FPG results. Autoverification passing rate and the reduction of turnaround time (TAT) were evaluated. Four autoverification systems (A, B, C, D) were established by two types of delta check rules, 28 flags, one reporting limits, and two kinds of logical rules. The autoverification passing rates were 80.6%, 78.8%, 83.7%, and 81.3%, and the average time saved in TAT were 117.5 min, 116.7 min, 121.1 min, and 121.7 min, respectively. Autoverification system C was the optimal one. Application of distribution of FPG corresponding to HbA1c groups had better performance as logical rules. Established HbA1c autoverifcation system shortened the auditing report time and improved work efficiency.

Design and Testing of an Integrated Corn Stubble Residual Film-Recycling Machine

The existing residual film-recycling machines struggle to efficiently recover and separate film stubble in a single operation. With roller-type film-rolling device unloading difficulties and other problems, in order to improve the recovery efficiency of film stubble and the separation effect while reducing human labor and to improve work efficiency, we designed an automatic hydraulic unloading film stubble-recycling integrated residual film-recycling machine. The angle of the membrane lifting device was determined by theoretical calculations using the method of coupled simulation of EDEM and ANSYS Workbench. We analyzed the amount of resistance as well as the maximum stress and deformation during the working process of the membrane-lifting device and focused on the design of the membrane–soil separating device and membrane-rolling device. The depth of the film shovel, the forward speed of the machine, and the rotational speed of the driving wheel of the jogging chain were selected as the test factors, and the residual film recovery rate was taken as the evaluation index. A three-factor, three-level test was designed by applying the principles of the Box–Behnken experimental design. The results show that when the forward speed is 1.36 m/s, the soil depth is 147.16 mm, and the rotational speed of the driving wheel of the shaking chain is 77.89 r/min, the recovery rate of the residual film is 87.56%, and the relative error between the experimental value and the optimized value is 2.73%. The experimental results can provide a theoretical basis for the design of the residual film-recycling machine.

LEADERSHIP BEHAVIORAL DEVELOPMENT OF MILLENNIAL LEADERS AS A CHANGE AGENT IN THE DIGITAL TRANSFORMATION ORGANIZATION

<p>The digital transformation era and Industry 4.0 provide ample opportunities for this generation to thrive with their rapid technological adaptation and openness to new ideas. This research examines millennial leadership behavioral development to enable them to effectively become change agents in digital organizations, using the case study of William Tanuwijaya at Tokopedia. Through a review of literature from international sources during 2020–2023, this study discusses millennial leadership theories and change management, highlighting the urgency and importance of adaptive leadership capable of managing change in the face of digital organizational challenges. The research findings indicate that millennial leadership styles vary according to organizational contexts. Millennials are confronted with change management, where organizational readiness to adapt is critical to success. Adaptive leadership, a key strategy in managing change, is crucial. Through William Tanuwijaya's leadership at Tokopedia, millennial leadership characteristics prioritizing social and environmental issues, sustainability, and the implementation of sustainable values are evident, alongside collaboration with the government to create an inclusive environment and focus on innovation and digital transformation in the economy. Leaders like William Tanuwijaya start by building teams consisting of celebrities or public figures in Indonesia to promote Tokopedia and strengthen the company's image while formulating the company's vision and initiatives, motivating employees, overcoming obstacles such as improving work efficiency, implementing successful minor changes, ensuring smooth change integration, and making change an integral part of the organizational culture and system.<strong></strong></p>

Application of Mobile Technology in Enterprise Management: From Mobile Office to Intelligent Decision Support

With the rapid advancement of information technology, the application of mobile technology in enterprise management has become increasingly widespread. From mobile office solutions to intelligent decision support systems, the proliferation of mobile technology has not only enhanced operational efficiency but also provided more flexible and convenient management methods. Existing research indicates that the adoption of mobile office solutions enables enterprises to overcome limitations of time and space, improving work efficiency and employee satisfaction. Additionally, the implementation of intelligent decision support systems allows enterprises to make faster and more accurate business decisions. However, current studies often focus on individual mobile technologies or application scenarios, lacking a systematic exploration of mobile office data sharing and intelligent decision-making collaboration. Moreover, most research overlooks privacy protection issues in the enterprise management decision-making process. To address these gaps, this paper proposes a scheme for mobile office data sharing and intelligent decision-making collaboration and examines the integration of privacy protection in the decision-making process. The findings not only enrich the theoretical framework of management studies but also provide enterprises with a systematic and practical solution for mobile technology applications, enhancing management efficiency and intelligence while effectively safeguarding data privacy and security.

Minimum area of primitive broad-leaved Korean pine forest community based on biomass

Based on the survey data of nine primitive broad-leaved Korean pine forest plots ranging from 1 to 10.4 ha in Heilongjiang Province, this study used the moving window method and GIS technology to analyze the variation characteristics of the spatial distribution pattern of forest biomass in each plot. We explored the minimum area that can reflect the structural and functional characteristics of the primitive broad-leaved Korean pine forest, and used computer simulation random sampling method to verify the accuracy of the minimum area. The results showed that: (1) Through the analysis of the spatial distribution raster map of biomass deviation in the plots at various scales of 10 − 100 m, there is a minimum area (0.64 ha) for the critical range of biomass density variation in the primitive broad-leaved Korean pine forest. This minimum area based on biomass density can indirectly reflect the comprehensive characteristics of productivity level per unit area, structure, function, and environmental quality of the primitive broad-leaved Korean pine forest community. (2) Using computer simulation random sampling, it was found that only by sampling in a specific plot larger than or equal to the minimum area can equivalent or similar results be achieved as random sampling within the plot, indicating that the minimum area determined by the moving window method is accurate. (3) The minimum area determined in this paper is an excellent indicator reflecting the complexity of community structure, which can be used for comparing changes in community structure and function before and after external disturbances, and has a good evaluation effect. This minimum area can also be used as a basis for scientific and reasonable setting of plot size in the investigation and monitoring work of broad-leaved Korean pine forests in this region, thereby achieving the goals of improving work efficiency and saving work costs.

Multi-robot collision avoidance method in sweet potato fields.

Currently, precise spraying of sweet potatoes is mainly accomplished through semi-mechanized or single spraying robots, which results in low operating efficiency. Moreover, it is time-consuming and labor-intensive, and the pests and diseases cannot be eliminated in time. Based on multi robot navigation technology, multiple robots can work simultaneously, improving work efficiency. One of the main challenges faced by multi robot navigation technology is to develop a safe and robust collision avoidance strategy, so that each robot can safely and efficiently navigate from its starting position to the expected target. In this article, we propose a low-cost multi-robot collision avoidance method to solve the problem that multiple robots are prone to collision when working in field at the same time. This method has achieved good results in simulation. In particular, our collision avoidance method predicts the possibility of collision based on the robot's position and environmental information, and changes the robot's path in advance, instead of waiting for the robot to make a collision avoidance decision when it is closer. Finally, we demonstrate that a multi-robot collision avoidance approach provides an excellent solution for safe and effective autonomous navigation of a single robot working in complex sweet potato fields. Our collision avoidance method allows the robot to move forward effectively in the field without getting stuck. More importantly, this method does not require expensive hardware and computing power, nor does it require tedious parameter tuning.

WEB-BASED BPS PARTNER MONITORING INFORMATION SYSTEM FOR SOUTH COAST BASED

In the digital era, effective and efficient data management is very important, especially in government organizations such as the Central Statistics Agency (BPS). This project aims to design and implement a web-based BPS Pesisir Selatan Partner Monitoring Information System, which aims to facilitate monitoring and management of BPS partner data in Pesisir Selatan Regency. This system is built using the Waterfall method, which allows gradual software development starting from the analysis, design, implementation, to testing stages. For system design, the Unified Modeling Language (UML) is used to visually model system requirements and workflows. The PHP programming language was chosen for the development of this system because of its flexibility in developing web applications and its support for various databases. The result of this system development is a web application that can be used by BPS to monitor partner performance and activities in real-time, accelerate the decision-making process, and improve work efficiency. With this system, it is hoped that the management of BPS Pesisir Selatan partners can be carried out in a more structured and accurate manner.

Various aspects of the doctor's quality of life

Nowadays, more and more attention is paid to the quality of life. Both private and work-related. More and more actions are taken to improve the quality of life in order to reduce the phenomenon of professional burnout. This significantly improves work efficiency and allows employees to work longer. It is no different in medical professions, including doctors. Fortunately, there is more and more scientific research on this topic. This allowed us to write an illustrative article about various aspects of the doctor's quality of life. The article will provide information about what quality of life really is and how it is defined. We will present the impact of various factors influencing this, such as length of work, sleep time, age, shift work and work at irregular intervals. We will also show how occupational factors influence health, which translates quite well into life expectancy. It is surprising that doctors have higher than expected risk factors for cardiovascular diseases such as hypertension, hypercholesterolemia, body mass. The cause is usually high stress and lack of time for physical activities after work. Specialization is also important when it comes to life expectancy. Charts with this information about expected age will be presented along with discussion in the text. We hope that in the future there will be further research expanding knowledge on this very important topic. And employers will have developed appropriate standards to improve the quality of life among their employees.

Techno-economic analysis on a hybrid system with carbon capture and energy storage for liquefied natural gas cold energy utilization

For cold energy utilization in the liquefied natural gas (LNG) regasification process, integrated cryogenic CO2 capture using high-grade cold energy is a mainstream method. However, high-grade cold energy for carbon capture may lead to a decrease in cold energy utilization efficiency. This paper aims to propose a hybrid system in which high-grade cold energy is used for liquid air energy storage (LAES) and post-combustion amine scrubbing technology is considered to replace cryogenic carbon capture for improved working efficiency. The medium and low-grade cold energy is utilized for similar working processes, e.g., organic Rankine cycle (ORC), carbon dioxide liquefaction, and data center cooling for comparison. Results show that the proposed hybrid system can produce 437.7 MW of power, 28.8 t·h−1 of CO2 capture capacity, and 23.0 MW of cooling capacity, respectively. The levelized cost of electricity and the annual revenue are 112.3 $·MWh−1 and 2.17 million $, respectively. LNG cold energy utilization efficiency, system energy efficiency, and cold exergy efficiency are 53.4 %, 24.0 %, and 51.5 %, higher than similar processes using cryogenic CO2 capture. It is demonstrated that the proposed system could be a promising method for cold energy utilization and post-combustion capture.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

A novel combined method for conveyor belt deviation discrimination under complex operational scenarios

Conveyor belt can enhance transportation efficiency and reduce labor intensity, which is extensively applied in material transportation. However, the deviation of conveyor belt commonly leads to malfunctions in the transmission system, affecting routine transportation function. Therefore, it is crucial to accurately identify whether the conveyor belt deviates and rectify anomalies promptly. Previously, the on-site camera have to be arranged in the appropriate position to ensure conducive recognition results for deviation discrimination, which is probably constrained by environmental conditions during implementation. In this paper, a combined method of detection and segmentation with deblurring function (CDSwD) is presented for conveyor belt deviation discrimination, which can avoid the impact of the camera position on the deviation discrimination results by combining roller information and conveyor belt contour information. Initially, the deblurring approach is adopted to remove the motion blur from the images. Subsequently, the object detection technology and the semantic segmentation technique are utilized to generate the detection results along the lower line of the rollers and the segmentation results of the overall conveyor belt contour. Finally, the outcomes will be combined for conveyor belt deviation discrimination. Comparisons have demonstrated that the proposed method can accurately determine the deviation of the conveyor belt, which is beneficial for maintaining the normal transportation and improving work efficiency.

Artistic sense of interior design and space planning based on human machine intelligent interaction

The rapid development of artificial intelligence technology is gradually penetrating into multiple fields such as interior design and spatial planning. The aim of this study is to integrate artificial intelligence with interior design, enhance design artistry and user experience, and address the interactive needs of interior space design choices. A set of indoor space design recognition system has been designed by introducing artificial intelligence networks and attention mechanisms. This study first optimizes the CenterNet algorithm based on attention mechanism and feature fusion to improve its accuracy in identifying complex components. Afterwards, the long short-term memory network and convolutional neural network are trained to complete the task of spatial layout feature recognition and design. The performance test results showed that after testing 100 images, the software could recognize indoor design space images and create corresponding vector format space maps in about 5 minutes, providing them to the 3D modeling interface to generate 3D scenes. Compared to the approximately 25 minutes required by manual methods, the design efficiency has been significantly improved. The research and design method has a fast convergence speed and low loss during the retraining process. In simulation testing, its mAP value reached 91.0%, higher than similar models. It performs better in detecting walls, doors and windows, bay windows, double doors, and two-way doors. Moreover, it has outstanding ability when facing structures such as short walls and door corners, and can recognize and create vector format spatial maps within 5 minutes, which is accurate and efficient. The system designed in this project has optimized the interaction between designers and clients in interior design, accurately capturing user intentions and assisting designers in improving work efficiency.

Building information modeling-based production process optimization model

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency.

Individualized generation of women's prototype based on the classification of body shape

The traditional prototype method only relies on the basic dimensions of the human body such as bust girth and back length to make the prototype pattern, ignoring the differences of human body details dimensions. In order to improve the fit of garments, the research on intelligent generation technology of individualized garment patterns has become one of the hot spots in the field of clothing. Based on the 3D body scanning technology, we proposed a method for generating prototype pattern based on body shape classification. The proposed method takes as inputs the body parameters (width, thickness and angle), while the output of the method is one of the four pattern generation rules-"Y" shape, "V" shape, "I" shape, or "X" shape. 207 subjects were divided into four categories based on the body angle parameters, namely "Y" shape, "V" shape, "I" shape and "X" shape, and a shape recognition model was built. The pattern database is required to obtain pattern generation rules by 3D point cloud reconstruction and flattening. Combined with the human body shape parameters, the mathematical formulas of the feature points on the pattern landmarks are built. The accuracy of body shape recognition model is 97.4%. The error analysis of the predicted pattern parameters shows that 90% of the pattern feature points have a goodness of fit above 0.7. In the 5 main landmarks, the proportion of pattern within the absolute error range is more than 80%, indicating that the prediction effect of the pattern is good. This method can be applied to the process of automatic pattern generation system based 2D measurement to improve work efficiency.

Analisis Pengelolaan Kearsipan di SMAN 1 Parigi

This study aims to analyze the management of archives in SMAN 1 Parigi, focusing on the effectiveness of the archival system used and its impact on the efficiency of school administration. The research method used is a case study with a qualitative approach. Data was collected through in-depth interviews with administrative staff, direct observation, and analysis of school archive documents. The results of the study show that archive management at SMAN 1 Parigi still faces various challenges, including the lack of human resources trained in the field of archives, as well as the limitations of supporting infrastructure such as storage space and information technology equipment. Nonetheless, efforts have been made to improve the archival system, such as digitizing some of the archives and increasing the capacity of staff through training. The study also found that better archive management can improve work efficiency and accelerate access to information needed for decision-making. For this reason, it is recommended that schools allocate more resources for the development of the archival system, including increasing staff training, adding storage facilities, and applying more sophisticated information technology. Thus, it is hoped that archive management at SMAN 1 Parigi can run more effectively and support improving the overall quality of school administration.

The Differences in Time Perception between China and Japan: An Analysis based on Hofstede's Cultural Dimensions Theory

In response to President Xi Jinping's "Belt and Road" initiative, it is crucial to enhance relationships with neighboring trade partners, especially Japan, to further promote the healthy development of the global economy and build a community with a shared future for mankind. Despite the profound impact of World War II on Sino-Japanese relations, the trade ties between China and Japan have become increasingly close since the reform and opening up, demonstrating the potential and necessity for cooperation between the two countries. Therefore, this paper will utilize the Uncertainty Avoidance Dimension from Hofstede's Cultural Dimensions Theory to analyze the cultural differences in time management between China and Japan. Through this analysis, the paper aims to explore how to optimize communication between Chinese and Japanese enterprises, improve work efficiency, and eliminate cultural misunderstandings, thereby fostering the deepening of Sino-Japanese relations.

Research on multi-path optimization problem based on particle swarm optimization algorithm

The problem of finding the optimal path within a certain range exists in various scenarios in our lives, such as the traveling salesman problem, robot automatic path selection problem, vehicle and pedestrian navigation, game path navigation, communication routing, logistics and transportation planning problems, etc. , through the optimization of path problems, we can help people improve resource utilization, improve work efficiency, reduce production costs, or complete the goal of a specific scenario, etc. Therefore, solving the path optimization problem is a very important task in our reality. In order to improve the game experience of the players in the game, this paper studies how to use the particle swarm optimization method to solve the optimal route of NPC.

The Application of Generative AI for Nursing Education.

Building a nursing education platform through Microsoft Power Apps. The main purpose is to provide convenience, real-time access, and comprehensive knowledge in nursing, as well as to improve work efficiency.