Increase Cost Efficiency Research Articles

Feasibility of telementoring during robot-assisted minimally invasive esophagectomy.

Introduction Telementoring could increase the quality, reduce the time, and increase cost efficiency of the proctoring program for robot-assisted minimally invasive esophagectomy (RAMIE). However, feasibility is unclear as no studies assessed telementoring for RAMIE. Methods The feasibility of telementoring was assessed during the thoracic part of RAMIE procedures in three high-volume centers. RAMIEs were performed by trained surgeons, proctored by two experts. The primary outcome was impact of the technology on conveying and understanding instructions. Results Between December 2021 and December 2022, nine RAMIE procedures were proctored using the telementoring. Overall quality of the telementoring technique was scored good to excellent (median score: good). The vast majority of the 24 proctor instructions were conveyed and understood fluently (n=21, 96%). Most proctor instructions were aimed at improving surgical exposure (n=9, 38%). The major point of criticism was the use of the audio as the communication through the headset of the performing surgeon was not accessible by the complete team. Conclusion Telementoring is deemed feasible for proctoring trained RAMIE surgeons after onsite proctoring. Technical improvements with regard to audio technology are warranted for broad implementation, especially in earlier training settings. The role of telementoring in the training pathway of learning surgeons needs clinical validation. Study registration number: 21/322 Date of registration: 14-5-2021.

Predicting extended hospital stay following revision total hip arthroplasty: a machine learning model analysis based on the ACS-NSQIP database.

Prolonged length of stay (LOS) following revision total hip arthroplasty (THA) can lead to increased healthcare costs, higher rates of readmission, and lower patient satisfaction. In this study, we investigated the predictive power of machine learning (ML) models for prolonged LOS after revision THA using patient data from a national-scale patient repository. We identified 11,737 revision THA cases from the American College of Surgeons National Surgical Quality Improvement Program database from 2013 to 2020. Prolonged LOS was defined as exceeding the 75th value of all LOSs in the study cohort. We developed four ML models: artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor, to predict prolonged LOS after revision THA. Each model's performance was assessed during training and testing sessions in terms of discrimination, calibration, and clinical utility. The ANN model was the most accurate with an AUC of 0.82, calibration slope of 0.90, calibration intercept of 0.02, and Brier score of 0.140 during testing, indicating the model's competency in distinguishing patients subject to prolonged LOS with minimal prediction error. All models showed clinical utility by producing net benefits in the decision curve analyses. The most significant predictors of prolonged LOS were preoperative blood tests (hematocrit, platelet count, and leukocyte count), preoperative transfusion, operation time, indications for revision THA (infection), and age. Our study demonstrated that the ML model accurately predicted prolonged LOS after revision THA. The results highlighted the importance of the indications for revision surgery in determining the risk of prolonged LOS. With the model's aid, clinicians can stratify individual patients based on key factors, improve care coordination and discharge planning for those at risk of prolonged LOS, and increase cost efficiency.

Міжнародний досвід бухгалтерського обліку маркетингових витрат

The international aspects of marketing cost accounting cover various approaches and practices that influence companies’ management decisions and financial reporting worldwide. Implementing International Financial Reporting Standards (IFRS) ensures the unification of methods for recognizing and classifying marketing expenses, contributing to transparency and comparability of financial and non-financial data. Different countries adapt these standards to their economic and regulatory characteristics, leading to variations in accounting practice. Analysis of international experience allows business entities to improve management strategies and increase cost efficiency. Studying these aspects is critical to ensuring competitiveness and stability in the global business environment. The article aims to summarize the priority international approaches to the recognition and measurement of marketing expenses in financial statements. The study used deduction, analysis, synthesis, and generalization methods. It is established that the primary documents regulating the accounting treatment of marketing costs include IAS 38 «Intangible Assets» (for capitalization of marketing costs, if justified) and IAS 15 «Revenue from Contracts with Customers» (for advertising and promotion costs related to contracts). One of the most promising types of marketing in today’s conditions is digital, which is confirmed by the amount of financing in European countries. The international standards governing the accounting of marketing costs are generalized. The results obtained can be used to improve domestic UAS and develop internal regulations for business entities. We see promising areas for further research in studying the issues of consistency between international and national accounting standards to identify common and distinctive features and areas for improving domestic regulations, as well as developing primary and reporting forms that will allow an objective reflection of their financial basis and serve as an essential source of data for management personnel. Keywords: marketing expenses, accounting, IFRS, marketing, advertising.

Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

This paper presents advanced workflows that combine 3D X-ray microscopy (XRM), nanoscale tomography, and deep learning (DL) to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques, such as the integration of DL-based algorithms for 3D image reconstruction to improve scan quality through increased contrast and denoising, are also discussed in this article. In addition, a DL-based tool called DeepScout is presented. DeepScout uses 3D XRM scans in targeted regions of interest as training data for upscaling high-resolution in a low-resolution dataset, of a wider field of view, using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and they will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and machine learning—perhaps complemented with some additional correlative microscopy investigations—can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.

Customer service chatbot enhancement with attention-based transfer learning

Customer service is an important and expensive aspect of business, often being the largest department in most companies. With growing societal acceptance and increasing cost efficiency due to mass production, service robots are beginning to cross from the industrial domain to the social domain. Currently, customer service robots tend to be digital and emulate social interactions through on-screen text, but state-of-the-art research points towards physical robots soon providing customer service in person. This article explores the feasibility of Transfer Learning different customer service domains to improve chatbot models. In our proposed approach, transfer learning-based chatbot models are initially assigned to learn one domain from an initial random weight distribution. Each model is then tasked with learning another domain by transferring knowledge from the previous domains. To evaluate our approach, a range of 19 companies from domains such as e-Commerce, telecommunications, and technology are selected through social interaction with X (formerly Twitter) customer support accounts. The results show that the majority of models are improved when transferring knowledge from at least one other domain, particularly those more data-scarce than others. General language transfer learning is observed, as well as higher-level transfer of similar domain knowledge. For each of the 19 domains, the Wilcoxon signed-rank test suggests that 16 have statistically significant distributions between transfer and non-transfer learning. Finally, feasibility is explored for the deployment of chatbot models to physical robot platforms including “Pepper”, a semi-humanoid robot manufactured by SoftBank Robotics, and “Temi”, a personal assistant robot.

Bank cost efficiency and underground economy: The asymmetric impact of cooperative and non-cooperative banks

We build a model of firms' choice between regular and irregular labor factors which links the cost efficiency of banks to the size of the underground economy. We consider two kinds of credit institutions supplying loans to the firms, cooperative and non-cooperative banks. The theoretical results show that cost-efficiency encourages firms to hire more regular workers. We then test our theoretical predictions using regional data for Italy over the 2004–2017 and assess how the efficiency of cooperative and non-cooperative banks, shapes the underground economy. In line with our theoretical prediction, increased cost efficiency reduces the size of the underground economy, especially in building and agriculture, and the estimated coefficients for the different types of banks are quite similar in size. Our evidence is robust to banks’ size, once we control for the likely simultaneity between cost efficiency and the underground economy and once we include the quality of institutions.

Analysis of the Application of Hollow Core Slab to Cost and Time Efficiency in High Rise Building Construction Project

The floor slab is one of the factors that affect the cost and duration of construction projects because it has the largest volume of work among other jobs. The selection of the method of implementation of floor slab work also affects the efficiency of development. The use of precast slabs can be an alternative to conventional floor slabs because of their potential to increase cost efficiency, speed up implementation time, and increase structural durability. In this study, a comparative analysis of cost efficiency and scheduling time of precast hollow core slab (HCS) method as an alternative to conventional slabs was conducted, using primary data obtained from the project and in accordance with government regulations. Time analysis and scheduling were calculated with the help of Microsoft Project software and determination of plate work items. As for the cost analysis, it is calculated based on the literature and applicable regulations to get real results by field conditions. Based on the analysis, it is known that the implementation of the 4-storey building slab work with the alternative precast slab method takes 8 days and costs Rp. 1,691,887,497. While the conventional slab method takes 38 days with a total cost of Rp. 2,276,920,601.

Taguchi Method for Optimizing Economic Product Selling Prices in MSMEs Concrete Industry

This article examines research related to determining the economic selling prices for concrete brick products at Karunia MSMEs, with the aim of optimization for production cost while still maintaining process quality. The method used in this research is Design of Experiment (DoE) based, namely Taguchi with regression, then continued with cost analysis to determine the optimal cost of production for efficiency. The technique in this method uses 3 variables, there are sand, cement and the length of time for mixing the concrete mixture with the quality parameters and production costs of the concrete bricks. Based on the experimental results, in achieving the goal of increasing cost efficiency, through sensitivity analysis it was obtained that the optimal composition of the cost of production was IDR 5,000 per piece of concrete brick, while maintaining SNI quality standards in optimal results of compressive strength is 117.37 kg/cm2, water permeability is 7.77% and roughness is 52.42 µm. The cost of production is the same as the initial cost of production before composition optimization efforts are carried out, where the initial concrete brick product is indicated as not meeting SNI quality standards because it has a compressive strength value below standard. The results of this research are expected to be able to provide recommendations for economic selling price for concrete brick by production costs optimizing while maintain the SNI standard quality of concrete brick products.

Speeding up UAV-based crop variability assessment through a data fusion approach using spatial interpolation for site-specific management

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.

Lights, Camera, Capital: Analyzing financial tactics in the streaming entertainment landscape

The US video streaming service industry, due to the nature of the service it provides and the everchanging trends in the space, has always been volatile. With numerous competitors present in the space, strategic companies such as Netflix, Hulu, Disney+ and Prime Video have become titans in the space. This paper analyses their decisions regarding investments, financing, dividends, and working capital, and the consequent impact on their financial performance. Aggressive pre-pandemic strategies adopted by these companies, such as rampant content acquisition and production led to significant subscriber growth. However, this led to mounting debt and building pressure to sustain the delivery of such hit content. Amidst this landscape, the onset of the pandemic introduced unprecedented variables to the streaming service industry. The global crisis not only disrupted content production but also altered consumer behaviours, leading to platforms being compelled to reassess their financial strategies. Post-pandemic, platforms shifted towards strategic content spending, diversified revenue streams, and tightened working capital management. While subscriber growth slowed, a focus on profitability emerged, reflected in the introduction of dividends and increased cost efficiency. This paper will delve into this dichotomy of the industry before and after this Covid Era.

Case cancellations and associated factors on the day of surgery in hospitals of Wolaita Zone, South Ethiopia

BackgroundCancellations of elective surgery cases are frequent and have significant negative consequences. It causes wasting of valuable resources, patient unhappiness, and psychological stress of patients. Despite this, little is known about the case cancellation and associated factors on the day of surgery in Ethiopia, particularly in the study area.ObjectiveThis study aimed to assess the magnitude of case cancellation and associated factors on the day of surgery in hospitals in Wolaita zone, South Ethiopia, from May 17 to June 17, 2023.MethodsA hospital-based cross-sectional study involving 322 patients was conducted at Wolaita Sodo Zone, South Ethiopia. All elective surgical cases scheduled during the study period were included. The entire number of participants was selected using a systematic random sampling process. Epidata V.3 was used to enter data, and SPSS V.25 was used to analyze it. Binary logistic regression was used to check for a possible association. P-values < 0.05 and 95% CI were used on multi-variable analysis as the threshold for the significant statistical association.ResultA total of 313 study participants were scheduled for elective surgical procedures during the study period and gave a response rate of 97.2%. The mean (± SD) age of the study participants was 39.18 (± 10.64) years. The two-third of patients, 53(64%) were rural residents, and more than half (178, or 55.3%) of the participants were female. This finding showed that the case cancellation was 22.4% (95% CI: 19.3 -25.9%). Among the total canceled cases, 49(58.3%) were males. Variables like rural residence (AOR = 3.48 95% CI: 1.22–9.95), Lack of lab result (AOR = 2.33, 95%CI:1.20–4.51), ophthalmology dept. (AOR = 2.53 95% CI:1.52–4.49), HTN (AOR = 2.53, 95% CI:1.52–4.49), patient refusal (AOR = 3.01 95% CI:1.22–5.05), and age b/n 31 and 43 (AOR = 1.50, 95% CI:1.02–2.01) were significantly associated factors with cancellation of elective surgical cases.ConclusionIn this study schedule of case cancellation was high. The contributing factors of case cancellation were rural residence, Lack of lab results, ophthalmology dept, HTN, patient refusal, and age. To decrease unnecessary cancellations and increase cost efficiency, hospital administration and medical staff must plan ahead carefully, communicate effectively, and make efficient use of hospital resources.

Rubber-Tracked Forwarders—Productivity and Cost Efficiency Potentials

The extraction of timber is expensive, energy intensive, and potentially damaging to the forest soil. Machine development aims to mitigate risks for environmental impact and decrease energy consumption while maintaining or increasing cost efficiency. The development of rubber-tracked forwarders has gained renewed interest, not least due to climate change leading to unreliable weather in combination with low tolerance for soil damage. The increased cost of rubber tracks compared to wheels is believed to be compensated by higher driving speed enabled by semi-active suspension. Thus, the aim of this study was to theoretically investigate how the productivity and cost efficiency of rubber-tracked forwarders are affected by variations in driving speed and machine costs. The calculations were made with fixed stand parameters, to evaluate performance in well-defined working conditions, and with parameters from 2500 final felling stands in central Sweden, to evaluate performance in varied working conditions. Scenarios were compared to a baseline corresponding to mid-sized wheeled forwarders. The results show higher productivity with the increased driving speed enabled by rubber tracks and suspension at all extraction distances, with larger differences at long extraction distances. Assuming a 15% higher machine price for the rubber-tracked forwarder and a variable cost increase proportional to speed increase, extraction costs break even with the baseline at 400 m and 700 m extraction distance for moderate and fast driving speed, respectively. Furthermore, a rubber-tracked forwarder is likely to enable access to a larger part of the harvest area during longer seasons. For the studied set of stands, the year-round accessible volumes are estimated to increase from 9% to 92% with a rubber-tracked forwarder. With rubber tracks instead of wheels, good accessibility has the potential to be combined with low soil impact and cost efficiency in a favourable way for both industry and ecosystem.

Linking Green Innovation to Firm Success: Cost Leadership’s Mediating Role in Vietnam

ABSTRACT Purpose Although most previous studies proposed a positive relationship between green innovation and firm performance, the mechanisms of different types of competitive advantages in the above relationship have not been examined. The aim of the study is to examine the relationship between green innovation and firm performance with the participation of cost leadership competitive advantage as the role of a mediator. Methodology/Approach The study used structural equation model employing partial least squares method (PLS-SEM) to examine the hypotheses. Data was collected using a questionnaire via a survey. Results based on information provided by 121 middle and senior managers of firms that engage in manufacturing in Vietnam were analyzed using SmartPLS 3 software. Findings The results indicate that both dimensions of green innovation including green product innovation and green process innovation improve performance of manufacturers in Vietnam. Further, cost leadership competitive advantage fully mediates the relationship between green process innovation and firm performance but does not act as a mediator in the relationship between green product innovation and firm performance. Research Implications The study’s findings confirm the positive influence of green innovation on firm performance. Moreover, the study emphasizes the diversity of competitive advantages in the role of mediating the relationship between green innovation and firm performance. Practical Implications The study supports the importance of green innovation in enhancing performance. Also, the findings help manufacturers in selecting which type of green innovation suits best with low-cost strategies and serves as value-creating tools. Specifically, manufacturers can prevent costly pollution and improve productivity, achieve economies of scale, thus attaining increased cost efficiency through green process innovation. Originality/Value/Contribution of the paper The study first distinguished types of competitive advantages, then focused on and attempted to address the value-capturing mechanism in which green innovation serves as a means of value creation via cost leadership competitive advantage. The study provides a plausible explanation for the contradictory findings of previous studies on the relationship between green innovation and firm performance. Moreover, in an effort to examine the relationships between environmental activities and their consequences in emerging markets where related researches are scarce, this research was operated in Vietnam, an emerging country that is a manufacturing hub.

Enhancing Green Supply Chain Resilience and Competitive Performance Through Open Innovation and Digital Innovations

This research delves into how green supply chain practices, open innovation, and digital innovations collectively enhance supply chain resilience and market competitiveness amidst the challenges of the COVID-19 pandemic. The study uses a comprehensive mixed-methods approach to collect qualitative and quantitative data from various supply chain professionals. Through structured interviews, online surveys, and the analysis of relevant literature and case studies, it assesses the impact of these practices on business competitive performance. The research uncovers a significant positive association, showing that organizations implementing these integrated strategies experience greater supply chain resilience, evident in their ability to adapt to disruptions and maintain resource efficiency. Furthermore, these practices lead to better competitive performance, marked by increased cost efficiency, revenue growth, and market share. Despite its insights, the study acknowledges limitations such as the reliance on self-reported data and its primary focus on the post-COVID-19 context, which may affect the generalizability of the findings. However, these limitations highlight avenues for future research, including longitudinal studies and cross industry analyses. This report contributes substantially to the evolving sustainable supply chain management field by offering novel insights on the intersection of green practices, innovation, and digital transformation in enhancing organizational resilience and competitive edge in a rapidly changing global landscape.

Analysis of the Implementation of Activity Based Costing to Increase Cost Efficiency at Hotel Mel's in Manado

Activity Based Costing is a system of accumulating costs and assigning costs to products using various cost drivers, carried out by tracing costs from activities and after that tracing costs from activities to products. The aim of this research is to find out how to apply activity based costing to increase cost efficiency at the Mel's Inn Manado Hotel. The data analysis method used in this research is the descriptive analysis method. Based on the research results obtained, the basic price of the room is a nominal value that is smaller than that determined by the hotel management. With a lower HPP for each room, it can be seen that the cost of goods sold has the lowest difference for the superior room type, this is because the amount of labor used is more than the working hours for other room types

The Role of Buildings in Rural Areas: Trends, Challenges, and Innovations for Sustainable Development

Rural buildings represent the functional relationship between rural communities and agricultural land. Therefore, research on rural buildings has practical repercussions on environmental and socio-economic sustainability. Comprehensive state-of-the-art research on rural buildings may address research activities. We present a systematic review of the scientific research between 2000 and 2022 based on the PRISMA protocol. Five main topics were identified. The results showed that the primary research focus was production (25.1%) and environmental management issues (23.2%). However, construction and efficiency are rapidly taking centre stage (20.6%). Regarding sustainability (20.8%), life cycle assessment, green buildings, recycling and global warming should be the future research focus. Energy efficiency will benefit from studies on thermal energy. More research on engineering and technologies (10.3%), specifically remote and automatic detection and transport in rural areas, will increase cost efficiency. The results may help improve the global efficiency of rural buildings in a modern farming system.

Creating an innovative culture in agribusiness of micro, small and medium-sized enterprises

Purpose. This research aims to assess the motivational factors in creating an innovation culture in agribusiness of micro, small, and medium-sized enterprises (MSMEs) by examining the organizational perspective, including organizational culture, organizational learning, market orientation, and attitude to innovation culture.&#x0D; Methodology / approach. A random cluster sampling method was used to select 100 agribusiness MSMEs in Sukoharjo Regency. This research considered exogenous latent variables, such as organizational culture, organizational learning, market orientation, and attitudes toward intention to innovate. The endogenous latent variables used were innovation intentions and innovation culture. Data were collected through an online survey and analyzed using Structural Equation Modeling (SEM) and Partial Least Square (PLS) analysis tools.&#x0D; Results. The results showed that organizational culture, market orientation, and attitudes influenced the intention to innovate, while the level of organizational learning did not. The intention to innovate, in turn, had a significant effect on innovative behavior, as shown in the performance of agribusiness companies. This innovative behavior was reflected in the emergence of new ideas to enhance the business, including the development of new product variants, the utilization of e-commerce for sales, and the improvement of production technology to increase cost efficiency. Therefore, there was a relationship between cultural innovation and company performance.&#x0D; Originality / scientific novelty. This research model was built based on behavioral theory and organizational culture, taking into account the difficulty and time required to develop an innovative culture within agribusiness MSMEs. These enterprises have organizational features different from medium and large companies. The objective of this research was to change the mindset of agribusiness MSMEs towards innovation, where innovation was no longer viewed as an option but rather a condition for success. Innovation became a value in fostering organizational culture.&#x0D; Practical value / implications. The practical implications of the findings were that agribusiness MSMEs should focus on efforts such as adapting to external changes, involving all employees, consistency, and a clear and long-term business vision to promote innovation. This positive attitude towards innovation could create a culture of innovation within the company, enabling MSME agribusiness to compete and develop into a better company.

A Low-Carbon and Economic Dispatch Strategy for a Multi-Microgrid Based on a Meteorological Classification to Handle the Uncertainty of Wind Power

In a modern power system, reducing carbon emissions has become a significant goal in mitigating the impact of global warming. Therefore, renewable energy sources, particularly wind-power generation, have been extensively implemented in the system. Despite the advantages of wind power, its uncertainty and randomness lead to critical security, stability, and economic issues in the power system. Recently, multi-microgrid systems (MMGSs) have been considered as a suitable wind-power deployment candidate. Although wind power can be efficiently utilized by MMGSs, uncertainty and randomness still have a significant impact on the dispatching and operation of the system. Therefore, to address the wind power uncertainty issue and achieve an optimal dispatching strategy for MMGSs, this paper presents an adjustable robust optimization (ARO) model based on meteorological clustering. Firstly, the maximum relevance minimum redundancy (MRMR) method and the CURE clustering algorithm are employed for meteorological classification in order to better identify wind patterns. Secondly, a conditional generative adversarial network (CGAN) is adopted to enrich the wind-power datasets with different meteorological patterns, resulting in the construction of ambiguity sets. Thirdly, the uncertainty sets that are finally employed by the ARO framework to establish a two-stage cooperative dispatching model for MMGS can be derived from the ambiguity sets. Additionally, stepped carbon trading is introduced to control the carbon emissions of MMGSs. Finally, the alternative direction method of multipliers (ADMM) and the column and constraint generation (C&amp;CG) algorithm are adopted to achieve a decentralized solution for the dispatching model of MMGSs. Case studies indicate that the presented model has a great performance in improving the wind-power description accuracy, increasing cost efficiency, and reducing system carbon emissions. However, the case studies also report that the approach consumes a relative long running time. Therefore, in future research, the solution algorithm will be further improved for the purpose of raising the efficiency of the solution.

Workflows for assessing electronic devices with 3D X-ray microscopy and nanoscale computed tomography

Modern 2D and 3D X-ray technologies are among the most useful non-destructive testing (NDT) methods that allow inspection of an object’s internal features without cutting or disassembling a sample. This paper presents workflows that combine 3D X-ray microscopy and nanoscale tomography to generate a detailed three-dimensional visualization of the interior of electronic devices and assemblies, without destroying them, to enable the study of internal components for failure analysis (FA). These imaging workflows can be run independently or complementary to other multiscale correlative microscopy evaluations and provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging, possibly complemented with additional correlative microscopy investigations, can speed development time, increase cost efficiency, and simplify FA and quality inspection of semiconductor packaging, printed circuit boards (PCBs), and electronic devices assembled with new emerging technologies.

Feasibility Study of Road Improvement Geotextile using Discounted Cash Flow Method: Study Case of PT Berau Coal

Lati Mine Operation is one of the open pit mining operational owned by PT Berau Coal. This area has overburden removal production annual target 134,556,000 BCM and coal getting 8,631,000 MT in year 2022. In the first semester of 2022, actual overburden removal was achieved in 94% under the year to date plan. Based on pareto loss production analysis, the biggest lost contributor of overburden removal was came from hauler productivity parameter. The root cause of hauler productivity problem is material used for road surfacing which has implication to road condition. Road surfacing material was came from redisturb material that cause low bearing capacity on subgrade layer and impacted to road damage on the surface. One of improvement can be done to increase productivity and reduce cost maintenance is investment of geotextile implementation to mine road reinforcement. Therefore, project investment need to be evaluated through a comprehensive financial analysis to get financial feasibility of this project. This research will use capital budgeting analysis to evaluate the financial feasibility, including sensitivity analysis and risk analysis. The baseline of feasibility analysis regarding improvement project shows good result with Net Present Value (NPV) worth IDR 2.232.785.016, Internal Rate Return (IRR) of 42,56%, 1,46 of Profitability Index (PI) , and 1,2 years of payback period (PBP). Based on Monte Carlo Simulation, the probability of this project failing (NPV &lt; 0) is 0%, or in the other words, there is no probability that the NPV project will generate negative value or loss in this project. In conclusion, mine road improvement using geotextile is feasible and potentially give optimum impact on increase productivity and cost efficiency regarding to mine road maintenance.