Improve Process Efficiency Research Articles

A high-precision and efficient method for badminton action detection in sports using You Only Look Once with Hourglass Network

Standardized striking movements are essential in badminton for enhancing player techniques and minimizing sports-related injuries. However, accurately detecting these movements against complex backgrounds while balancing precision and speed remains a significant challenge. To address this, we propose a novel model that synergizes You Only Look Once (YOLO) with the Hourglass Network (HGNet), called YOLO-HGNet, to enhance feature learning across multiple levels. By replacing traditional convolutional modules with Depth-Wise Convolution (DWConv), we achieve significant improvements in data processing efficiency. Additionally, our model incorporates a combination of self-attention and convolution mechanisms (ACmix) and FocalModulation to improve object localization and recognition accuracy in complex backgrounds. Our method leverages action vectors and machine learning techniques to accurately detect and classify six key badminton strokes: backhand push, backhand net shot, forehand clear, forehand push, forehand lift, and forehand net shot. Empirical evaluations demonstrate that our approach achieves a mean Average Precision (mAP) of 96.1% for detecting badminton player postures, outperforming existing advanced methods by at least 8.8%. Furthermore, our method achieves an average accuracy of 95.4% in classifying the six badminton strokes. These results underscore the superior capability of YOLO-HGNet for precise and efficient pose detection, recognition, and classification of badminton strokes, contributing significantly to advancements in sports science and athlete training methodologies.

AI Technology Integrated Education Model for Empowering Fashion Design Ideation

The rapidly increasing importance of technology integration and generative AI in the fashion industry is prompting changes in fashion design education. This study explored a new design methodology utilizing AI for sustainable and future-oriented fashion design education. The methodology involved consulting practitioners to select ChatGPT and Midjourney as AI tools and technology, pedagogy, and content knowledge (TPACK) as the theoretical framework. An AI prompt guide was developed based on specialized books, from which an educational program was created. An experiment with 30 third- and fourth-year fashion design students showed that their overall satisfaction with AI through TPACK was 4 out of 5, suggesting that TPACK enhances students’ creativity and efficiency through generative AI. Prompt guides received a satisfaction score of 4.7, indicating their usefulness for creative and efficient design outputs. AI-powered educational programs, like ChatGPT and Midjourney, also improved student creativity and learning efficiency, with ChatGPT scoring 4.5. However, concerns about technology dependency were noted. This study offers insights into integrating the latest technology into fashion design to improve process efficiency and creative output. This study not only provides a foundation for future research on AI design methodology but also explores practical directions for sustainable design in the fashion industry.

Reducing the Rebound Effect in Microscale Laser Dynamic Forming through Multi-Pulse Laser Shock Loading

Microscale laser dynamic forming, as a novel high-speed microforming technique, can overcome the shortcomings of traditional microforming methods. However, in practical applications, laser dynamic microforming technology is often affected by the rebound behavior of the workpiece, limiting the further improvement of processing quality and efficiency. This paper aims to reduce the rebound effect in laser dynamic forming by using multi-pulse laser shock loading. The forming results of workpieces under different laser energies and laser impact numbers were studied using experimental and numerical simulation methods. After multiple laser shocks, numerical simulations of the forming results were conducted using ANSYS/LS-DYNA software. These numerical simulation results were then experimentally validated and compared. The surface morphology of the workpieces was characterized using a confocal microscope and a scanning electron microscope (SEM). Energy-dispersive spectroscopy (EDS) was used to analyze the chemical element content changes in the collision regions at the bottoms of the workpieces after multi-pulse loading. The SEM and EDS results revealed the collision behavior patterns during the forming process. Finally, the forming laws of workpieces under multiple laser shocks were summarized.

FRKVF: High-accuracy motion interpolation for polishing operations using fourth-order Runge-Kutta and velocity flexibility planning

Traditional interpolation algorithms often fail to meet the precision requirements of ultra-precision machining when applied to super-precision polishing machines. Moreover, the processing of fused silica glass optical components is frequently threatened by mechanical impacts due to their fragility. In order to address this issue, this paper proposes a high-precision motion interpolation method based on fourth-order Runge-Kutta and velocity-flexible planning. This method is designed for open-architecture small multi-axis optical polishing machines to polish quartz glass. The algorithm initially employs composite Simpson's rule to calculate the lengths of sub-paths within the polishing trajectory. Based on these length values, flexible velocity planning is executed to ensure the smooth continuity of velocity, acceleration, and jerk during motion interpolation. This reduces the risk of mechanical impacts that could damage the components during the machining process. The introduction of the adaptive fourth-order Runge-Kutta method significantly enhances the parameter point calculation accuracy of NURBS curves. The incorporation of adaptive principles also maintains a higher processing speed, thereby greatly improving processing efficiency. This method comprehensively addresses both the precision of curve interpolation and execution efficiency. Finally, experimental validation is conducted on an open-architecture small multi-axis optical polishing machine. The proposed method based on FRKVF not only mitigates mechanical impacts resulting from discontinuous acceleration, thereby ensuring the machining quality of optical components, but also satisfies the high-precision requirements for processing fused silica optical elements.

Marketing Strategies of CV. Kopi Panas Event Organizer Bali in Enhancing Brand Awareness

Purpose: This study examines the 7P marketing strategies of CV. Kopi Panas Event Organizer Bali in enhancing brand awareness. The issues faced include intense competition and a lack of innovation in services and promotions. The objective is to identify and implement effective marketing strategies to boost brand awareness. Method: This study employs a qualitative approach through interviews with vendors, clients, and event organizers, as well as SWOT analysis. Data was collected and analyzed using thematic analysis and SWOT techniques. Result: The results of the study indicate that CV. Kopi Panas has implemented various marketing strategies, including product development, pricing review, diversification of event locations, promotion optimization, team skill development, process efficiency improvement, and physical facility maintenance. These strategies have helped the company enhance brand visibility and market competitiveness. However, further adjustments are needed in digital promotion and product innovation to achieve more optimal results. Contribution: This study contributes by evaluating the effectiveness of CV. Kopi Panas’s marketing strategies, which include product development, pricing review, diversification of event locations, promotion optimization, team skill development, process efficiency improvement, and physical facility maintenance. These strategies have enhanced the company’s brand visibility and market competitiveness. However, the findings also highlight the need for further adjustments in digital promotion and product innovation to achieve more optimal results.

Machine Learning Analysis Using the Black Oil Model and Parallel Algorithms in Oil Recovery Forecasting

The accurate forecasting of oil recovery factors is crucial for the effective management and optimization of oil production processes. This study explores the application of machine learning methods, specifically focusing on parallel algorithms, to enhance traditional reservoir simulation frameworks using black oil models. This research involves four main steps: collecting a synthetic dataset, preprocessing it, modeling and predicting the oil recovery factors with various machine learning techniques, and evaluating the model’s performance. The analysis was carried out on a synthetic dataset containing parameters such as porosity, pressure, and the viscosity of oil and gas. By utilizing parallel computing, particularly GPUs, this study demonstrates significant improvements in processing efficiency and prediction accuracy. While maintaining the value of the R2 metric in the range of 0.97, using data parallelism sped up the learning process by, at best, 10.54 times. Neural network training was accelerated almost 8 times when running on a GPU. These findings underscore the potential of parallel machine learning algorithms to revolutionize the decision-making processes in reservoir management, offering faster and more precise predictive tools. This work not only contributes to computational sciences and reservoir engineering but also opens new avenues for the integration of advanced machine learning and parallel computing methods in optimizing oil recovery.

Investigation of cellulosic fines removal in dissolving pulp production with small-holed screen plates

This study investigates the influence of hole diameter and hole spacing on the fractionation performance using an industrial scale pressure screen. The objective is to increase the quality of the long fiber fraction during dissolving pulp production by separating cellulosic fines from unbleached softwood sulfite pulp containing 8% (w/w) primary fines (fibrous particles shorter than 0.200 mm fiber length). Different screen basket designs were applied, using the same hole spacing while varying hole diameter, and vice versa. This allowed a comparison of the separate influence of hole diameter and hole spacing on the separation efficiency. Production parameters of screening devices are always a compromise between separation efficiency, runnability/plugging, and throughput. High aperture velocity, high feed consistency and low reject rate were identified as the crucial operating parameters for high cellulosic fines removal efficiency, which turned out to be positive in terms of throughput. Considering the different hole diameters, the industrial trials showed that smaller holes yielded better fractionation results. Whereas reduced hole spacing resulted in a denser fiber network on the screen, reducing the amount of fines and short fibers passing, thereby leading to worse fractionation results. By choosing suitable screening conditions, we found that the weight-weighted fines content was reduced more than 50 percent using a one stage fiber fractionation screen set up under industrial conditions. The lowered fines content in the long fiber fraction is expected to allow a reduced purification effort and a lower chemical demand in the subsequent processes of dissolving pulp production. By reducing the fines fraction, consisting of short fibers, fiber fines, as well as mainly short-chain alkali-soluble carbohydrates (hemicelluloses), improvements in process efficiency and dissolving pulp quality can be achieved.

Rapid surface patterning to strengthen adhesive bonding of carbon fiber reinforced polymer by spatial shaping femtosecond laser

Carbon fiber reinforced polymer (CFRP) have become gradually important in the aerospace industry due to their outstanding strength-to-weight ratio. However, traditional mechanical surface treatment methods are challenging to apply to CFRP because of their anisotropic and nonhomogeneous properties. Femtosecond laser offers unique advantages for surface treatment, as it allows processing with very low thermal load due to the extremely short interaction time. This study investigates the effect of different surface structures resulting from surface treatment using a femtosecond laser on adhesive properties of CFRP. The experimental results show that: 1) beam shaping can be realized by using the plano-convex cylindrical mirror, which improves the quality of laser processing and greatly improves processing efficiency. It only takes 20 s to complete the laser processing of a 1 cm*1 cm area, which increases the work efficiency by 49 times. 2) pre-bonding surface treatment significantly enhances the tensile shear strength of the single lap joint, and the shear strength of samples with low spatial frequency LIPSS (LSFL) (14.57 ± 1.58 MPa) is 2.96 times higher than that of the untreated sample (US) (4.92 ± 1.34 MPa). 3) LSFL structure exhibits the best results, because the surface of CFRP with LSFL exhibits a relatively higher surface polarity and surface energy. This study provides an efficient, high-precision, and low-damage surface treatment method for preparing CFRP for adhesive bonding, which may promote the application of femtosecond laser technology in difficult-to-process composite materials and provide new methods and technical support for its application in aerospace, vehicle manufacturing, and other fields.

Patient Experience of Complex Genomic Sequencing Exploring Patient Preference, Barriers, and Enablers for Delivery.

Despite increasing evidence of benefit supporting complex genomic sequencing (CGS) in personalizing cancer therapy, its widespread uptake remains limited. This mixed-methods, prospective cross-institutional demonstration study was designed to evaluate implementation of CGS in the care of patients with advanced cancer. DNA sequencing was undertaken on formalin-fixed paraffin-embedded tumor and matched blood was completed with the Peter MacCallum Cancer Centre Comprehensive Cancer Panel; 391 genes via central laboratory. Oncologists performed consent and result delivery. Patients completed pre- and post-test surveys, including validated and study-specific questions and, if eligible, semistructured interviews. Qualitative interviews were undertaken with study clinicians to evaluate processes. One hundred ninety-nine (63%) had ≥1 finding with the potential to affect management, including 172 (55%) whose finding could affect their treatment options, 25 (8%) whose test led to the resolution of diagnostic ambiguity, and 49 (16%) with a pathogenic germline variant. In 6-month follow-up, 50 (16%) participants had their subsequent therapy changed on the basis of their CGS results. Two hundred ninety-three (88% of adult patients) completed surveys at three time points. At consent, patients cited multifaceted value in testing, showed good understanding of basic concepts, but most (69%) overestimated the likelihood of result-led change. Post-test patients remained consistently satisfied with accessing CGS. 21% struggled with understanding results but there were low levels of decisional regret after participation (89% had nil/mild regret). Clinicians cited collaboration and communication as critical to delivery. Patients undergoing CGS are generally satisfied and place value on its use beyond potential therapeutic benefit. Our results suggest that to improve test utility and delivery of CGS with value to patients and investing institutions, focus must be placed on addressing the additional barriers to its wider implications including efforts to improve process efficiencies, clinician genomic literacy, and decision-making support.

Magnetic field-assisted electrochemical additive manufacturing of nickel structure: Growth mechanism and microstructural evolution

The microstructure and crystal texture of magnetically assisted electrochemical additive manufacturing parts play crucial roles in their mechanical properties and applications. This paper elucidates the electrocrystallization process of electrochemical additive manufacturing by constructing a magnetically assisted setup and exploring both nucleation mechanisms and growth kinetics. The electrochemical behavior of the system was analyzed using electrochemical methods under different magnetic field strengths. Nickel structures with aspect ratios of 7.0 and 8.2 were fabricated using the experimental setup, and their morphology, crystal texture, and microstructure were characterized. The results showed that without applying a magnetic field, the nucleation mechanism followed three-dimensional instantaneous nucleation under diffusion control. Under magnetic field conditions, forced convection, double layer charging, and hydrogen evolution reactions were combined to modify the S-H nucleation model, demonstrating that the magnetic field can significantly enhance the nucleation rate and number of nuclei. The initial growth of the microstructure did not exhibit preferential solid orientation, primarily forming refined equiaxed grains. Subsequently, a characteristic pentagonal structure developed, followed by spiral dislocation growth, with a significant transformation of equiaxed grains into columnar crystals. The crystal orientation eventually evolved to <110>, with many twin boundaries, among which Σ3 grain boundaries accounted for more than 55 %. Applying a magnetic field improved processing efficiency, altered the morphology and texture of the material, refined the grains, and increased the types of twins. These results provide theoretical and experimental support for electrochemical additive manufacturing.

Novel sensorized additive manufacturing-based enlighted tooling concepts for aeronautical parts

This paper presents lightweight tooling concepts based on additive manufacturing, with the aim of developing advanced tooling systems as well as installing sensors for real-time monitoring and control during the anchoring and manufacturing of aeronautical parts. Leveraging additive manufacturing techniques in the production of tooling yields benefits in manufacturing flexibility and material usage. These concepts transform traditional tooling systems into active, intelligent tools, improving the manufacturing process and part quality. Integrated sensors measure variables such as displacement, humidity and temperature allowing data analysis and correlation with process quality variables such as accuracy errors, tolerances achieved and surface finish. In addition to sensor integration, additive manufacturing by directed energy arc and wire deposition (DED-arc) has been selected for part manufacturing. The research includes the mechanical characterisation of the material and the microstructure of the material once manufactured by DED-arc. Design for additive manufacturing" principles guide the design process to effectively exploit the capabilities of DED-arc. These turrets, equipped with sensors, allow real-time monitoring and control of turret deformation during clamping and manufacturing of aeronautical parts. As a first step, deformation monitoring is carried out within the defined tolerance of ± 0.15, which allows a control point to be established in the turret. Future analysis of the sensor data will allow correlations with process quality variables to be established. Remarkably, the optimised version of the turret after applying DED technology weighed only 2.2 kg, significantly lighter than the original 6 kg version. Additive manufacturing and the use of lightweight structures for fixture fabrication, followed by the addition of sensors, provide valuable information and control, improving process efficiency and part quality. This research contributes to the development of intelligent and efficient tool systems for aeronautical applications.

Digital nonlinear self-interference cancellation using SW-GRU-TPA

In order to enhance spectrum utilization and decrease transmission delay, the 6th generation mobile networks (6G) system adopts full-duplex (FD) mode. Simultaneous operation of the same frequency inevitably causes the receiver to receive the signal sent by the transmitter, leading to serious nonlinear self-interference due to the nonlinear distortion generated by the power amplifier (PA). This paper proposes hybrid layer structures SW-GRU-TPA for the implementation of digital self-interference cancellation (SIC), combined with Sliding Window (SW), Gated Recurrent Unit (GRU), and Temporal Pattern Attention (TPA). The model can not only effectively utilize the influence of historical information on the signal, but also capture the spatial and temporal dependencies in the data to fit the nonlinear characteristics of the PA and the delay effect in multipath transmission. Meanwhile, by utilizing the L1 norm model pruning, the model parameters can be optimized, leading to improved processing efficiency. The model is verified using two methods: injection and antenna reception. Compared to the SW-GRU model with Attention Mechanism (AM), the Interference Cancellation Ratio (ICR) of injection and antenna increases by 3.13 dB and 2.01 dB, respectively. When the TPA is compressed by 42.17%, the ICR of injection and antenna decreases by 1.58% and 2.6%.

Optimization of green spherical agglomeration process based on response surface methodology for preparation of high-performance spherical particles

Spherical agglomeration (SA) is a processing technique that enhances the physical properties of particles, reduces the number of unit operations in pharmaceutical manufacturing, and improves process efficiency. However, one of the limitations of SA is its high nonlinearity, which makes scalability a challenge. This prospective study was designed to realize the optimization of SA process parameters of aspirin, the world’s first and most widely used nonsteroidal anti-inflammatory drug, by developing a green SA model through response surface methodology. First, Plackett-Burman experiments were conducted to identify the key operating variables affecting SA, and Sustainability Index (STI) was defined to evaluate the effects of these operating variables on the SA and the energy input to the environment during the post-processing process. Furthermore, the effects of three independent variables on mean size, yield, and STI were investigated based on Box-Behnken design. A second-order regression equation with response values was developed to optimize the above three objectives. As a result, the spherical products were obtained with excellent powder properties, including anti-caking property, filtration property, and tableting performance compared to the raw materials. This work provides an experimental and modelling basis for the further application of this environmentally-friendly SA technology.

Energy recovery from syngas and pyrolysis wastewaters with anaerobic mixed cultures

The anaerobic digestion of aqueous condensate from fast pyrolysis is a promising technology for enhancing carbon and energy recovery from waste. Syngas, another pyrolysis product, could be integrated as a co-substrate to improve process efficiency. However, limited knowledge exists on the co-fermentation of pyrolysis syngas and aqueous condensate by anaerobic cultures and the effects of substrate toxicity. This work investigates the ability of mesophilic and thermophilic anaerobic mixed cultures to co-ferment syngas and the aqueous condensate from either sewage sludge or polyethylene plastics pyrolysis in semi-batch bottle fermentations. It identifies inhibitory concentrations for carboxydotrophic and methanogenic reactions, examines specific component removal and assesses energy recovery potential. The results show successful co-fermentation of syngas and aqueous condensate components like phenols and N-heterocycles. However, the characteristics and load of the aqueous condensates affected process performance and product formation. The toxicity, likely resulting from the synergistic effect of multiple toxicants, depended on the PACs’ composition. At 37 °C, concentrations of 15.6 gCOD/gVSS and 7.8 gCOD/gVSS of sewage sludge-derived aqueous condensate inhibited by 50% carboxydotrophic and methanogenic activity, respectively. At 55 °C, loads between 3.9 and 6.8 gCOD/gVSS inhibited by 50% both reactions. Polyethylene plastics condensate showed higher toxicity, with 2.8 gCOD/gVSS and 0.3 gCOD/gVSS at 37 °C decreasing carboxydotrophic and methanogenic rates by 50%. At 55 °C, 0.3 gCOD/gVSS inhibited by 50% CO uptake rates and methanogenesis. Increasing PAC loads reduced methane production and promoted short-chain carboxylates formation. The recalcitrant components in sewage sludge condensate hindered e-mol recovery, while plastics condensate showed high e-mol recoveries despite the stronger toxicity. Even with challenges posed by substrate toxicity and composition variations, the successful conversion of syngas and aqueous condensates highlights the potential of this technology in advancing carbon and energy recovery from anthropogenic waste streams.Graphical

Implementasi SOP Account Payable Pada Pelaporan Penerimaan Barang di Vasa Hotel Surabaya

This research aims to analyze the implementation of Standard Operating Procedures (SOP) for accounts payable in reporting goods receipts at the Vasa Hotel Surabaya. Quantitative methods are used with data collection techniques through observation, interviews and document analysis. The research results show that the implementation of SOP accounts payable still faces several challenges, especially in terms of completeness of Receiving Report documents and coordination between departments. This research provides recommendations for improving process efficiency and compliance with existing SOPs.

Improving processing efficiency through workflow process reengineering, simulation and value stream mapping: a case study of business process reengineering

PurposeBusiness Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.Design/methodology/approachThe BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.FindingsThe findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.Research limitations/implicationsThe single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.Originality/valueThe experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Mathematical modeling of the enzymatic hydrolysis of lignocellulosic waste in membrane bioreactor considering transport phenomena

Enzymatic hydrolysis is a crucial step in processing lignocellulosic biomass into biofuels and other valuable products in biorefineries. Applying a membrane reactor to realize this process allows for continuous separation of inhibitory end-products, thus improving process efficiency. Developed so far models have generally been dedicated to describing the reaction kinetics without considering transport phenomena and flux decline prediction. In the present paper, a new mathematical model simulating the hydrolysis process course in membrane bioreactor was established. Compared to previous reports, our study presents a novel approach to modeling the process accounting for the reaction course and permeate flux decline depending on reaction hydrodynamics. A surface renewal theory and empirical reaction kinetic model were implemented to simulate the influences of the operating parameters on the permeate flux and the monosaccharide concentration in the permeate. A procedure to determine the model parameters with experimental data was also presented. The proposed model simulated experimental data with good accuracy (R2>0.97). The total mass of glucose and xylose produced during 10 h of the hydrolysis performed under mixing conditions equaled 2.37 g and 2.39 g for the pressure of 0.4 and 0.6 bar, respectively. They correspond to the high hydrolysis yield, equaled in average to 99.1 %.

Exploring and understanding the microwave-assisted pyrolysis of waste lignocellulose biomass using gradient boosting regression machine learning model

The production of bio-oil is a complex process influenced by various parameters. Optimizing these parameters can significantly enhance bio-oil yield, thus improving process efficiency. This study aims to develop a predictive model for bio-oil yield using the Gradient Boosting Regression (GBR) technique. It also seeks to identify the key factors affecting bio-oil yield and determine the optimal conditions for maximizing production. The GBR model was constructed using data collected from the literature. The model's performance was evaluated based on its determination coefficients for training and testing datasets. Optimization studies were conducted to identify the best conditions for bio-oil production. The GBR model demonstrated high precision, with determination coefficients of 0.983 and 0.913 for the training and testing datasets, respectively, indicating its effectiveness in predicting bio-oil yield. The optimal conditions for maximizing bio-oil yield were identified as 20 min of pyrolysis time, a temperature of 771 °C, and 524W of microwave power. The two-way PDP analysis provided valuable insights into the interactive effects of temperature with other factors, enhancing the understanding of the dynamics of the bio-oil production process. This study not only identifies the most impactful variables for bio-oil yield but also offers critical guidance for optimizing the production process.

Design framework of a computer-generated hologram that performs volumetric beam shaping for advanced laser processing

Axial beam shaping is very effective for material laser processing, typically laser cutting, drilling, and grooving. We demonstrate a framework for designing a computer-generated hologram (CGH) that performs volumetric beam shaping. The procedure performs axial beam shaping with a continuous intensity distribution, unlike our previous research in which only discrete focal points were arranged three-dimensionally. This research is the more general approach for volumetric beam shaping. An important point in this research is finding an optimal interval in the optical axis direction and in calculating the CGH design. The design interval is half of the focusing length (the full width at half-maximum of the laser beam profile in the axial direction) given by the diffraction limit of the optical system. The optimal value is obtained using an axially shaped beam that is the reconstruction of the CGH calculated from Zernike polynomials. We also demonstrate that the optimal interval for evaluating the axially shaped beam is also half of the beam length. Following the CGH design procedure, we demonstrate CGHs that generate long-focus beams with an arbitrary axially shaped beam. We found a tradeoff relation between the focusing length and the intensity of the long-focus beam, suggesting that the use of a focused beam with an appropriate length according to the purpose will lead to improved processing efficiency.

Integrating Change Management with a Knowledge Management Framework: A Methodological Proposal

This study proposes the integration of change management with a knowledge management framework to address knowledge retention and successful change management in the context of Industry 5.0. Using the ADKAR model, it is suggested to implement strategies for training and user acceptance testing. The research highlights the importance of applying the human capital life cycle in knowledge and change management, demonstrating the effectiveness of this approach in adapting to Industry 5.0. The methodology includes a review of the state of the art in intangible asset management, change management models, and the integration of change and knowledge management. In addition, a case study is presented in a food production company that validates the effectiveness of the ADKAR model in implementing digital technologies, improving process efficiency and increasing employee acceptance of new technologies. The results show a significant improvement in process efficiency and a reduction in resistance to change. The originality of the study lies in the combination of the ADKAR model with intangible asset and knowledge management, providing a holistic solution for change management in the Industry 5.0 era. Future implications suggest the need to explore the applicability of the ADKAR model in different industries and cultures, as well as its long-term effects on organisational sustainability and innovation. This comprehensive approach can serve as a guide for other organisations seeking to implement successful digital transformations.