Improve Cost Efficiency Research Articles

Negative pressure wound therapy with instillation in lower extremity wounds may contribute to cost-savings: 4 case reports

Background: Use of negative pressure wound therapy with instillation and dwell time (NPWTi-d) can assist with wound bed preparation for successful closure. The authors present their experience using NPWTi-d to manage lower extremity wounds in 4 patients and discuss the feasibility of improving cost efficiency. Methods: NPWTi-d involved instillation of normal saline with an 8-to 10 minute dwell time, followed by 3 to 3.5 hours of -125 mm Hg. Therapy continued for 6 to 7 days with dressing changes every 2 to 3 days. Results: Patients were all male, between the ages of 24 and 83 years old. Wound etiologies included chemical burn, deep tissue laceration, compartment syndrome with hematoma, and diabetic foot osteomyelitis. All wounds required cleansing. Prior to NPWTi-d, surgical debridement and antibiotics were administered as necessary. After NPWTi-d, the wounds exhibited healthy granulation and reduced in size, allowing for discharge to outpatient care. Upon follow-up 2 to 6 months later, no patients experienced wound complications or required readmission to the operating room, potentially saving on time and cost. Conclusion: In these patients, use of NPWTi-d assisted in cleansing the wound surface and producing a positive healing outcome. Despite higher initial costs of NPWTi-d over standard dressings, a wound management protocol including NPWTi-d may help mitigate expenses incurred by delayed healing.

Improvement of Response Spectrum Analysis Method of Crane Pile Mooring Facilities

In this study, we analyzed the problems of the conventional response spectrum analysis method. Thereafter, we derived methods to improve it by considering the dynamic behavioral characteristics of a pile-type mooring facility with a crane. The applicability of the standard design response spectrum analysis method of crane pile mooring facilities was analyzed, and the results of previous studies on dynamic behavioral characteristics were reviewed to derive improvements and the applicability of the derived improvements in practice. when the analysis was performed with the application of the improvement, the stress of the pile during the earthquake was 70% of that of the conventional method. As a result of the study, in the recent trend where more stringent seismic design standards are required, applying the derived improvements will enable a more precise analysis than the conventional method, and reduce unnecessary costs of seismic retrofitting, contributing to the improvement of cost efficiency.

Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management

Ant-inspired metaheuristic algorithms known as ant colony optimization (ACO) offer an approach that has the ability to solve complex problems in both discrete and continuous domains. ACOs have gained significant attention in the field of water resources management, since many problems in this domain are non-linear, complex, challenging and also demand reliable solutions. The aim of this study is to critically review the applications of ACO algorithms specifically in the field of hydrology and hydrogeology, which include areas such as reservoir operations, water distribution systems, coastal aquifer management, long-term groundwater monitoring, hydraulic parameter estimation, and urban drainage and storm network design. Research articles, peer-reviewed journal papers and conference papers on ACO were critically analyzed to identify the arguments and research findings to delineate the scope for future research and to identify the drawbacks of ACO. Implementation of ACO variants is also discussed, as hybrid and modified ACO techniques prove to be more efficient over traditional ACO algorithms. These algorithms facilitate formulation of near-optimal solutions, and they also help improve cost efficiency. Although many studies are attempting to overcome the difficulties faced in the application of ACO, some parts of the mathematical analysis remain unsolved. It is also observed that despite its popularity, studies have not been successful in incorporating the uncertainty in ACOs and the problems of dimensionality, convergence and stability are yet to be resolved. Nevertheless, ACO is a potential area for further research as the studies on the applications of these techniques are few.

A suggestive approach for assessing item quality, usability and validity of Automatic Item Generation

Automatic Item Generation (AIG) refers to the process of using cognitive models to generate test items using computer modules. It is a new but rapidly evolving research area where cognitive and psychometric theory are combined into digital framework. However, assessment of the item quality, usability and validity of AIG relative to traditional item development methods lacks clarification. This paper takes a top-down strong theory approach to evaluate AIG in medical education. Two studies were conducted: Study I—participants with different levels of clinical knowledge and item writing experience developed medical test items both manually and through AIG. Both item types were compared in terms of quality and usability (efficiency and learnability); Study II—Automatically generated items were included in a summative exam in the content area of surgery. A psychometric analysis based on Item Response Theory inspected the validity and quality of the AIG-items. Items generated by AIG presented quality, evidences of validity and were adequate for testing student’s knowledge. The time spent developing the contents for item generation (cognitive models) and the number of items generated did not vary considering the participants' item writing experience or clinical knowledge. AIG produces numerous high-quality items in a fast, economical and easy to learn process, even for inexperienced and without clinical training item writers. Medical schools may benefit from a substantial improvement in cost-efficiency in developing test items by using AIG. Item writing flaws can be significantly reduced thanks to the application of AIG's models, thus generating test items capable of accurately gauging students' knowledge.

From Godown to Warehouse Management: Transformation at eKart, An Illustrative Case Study

The growth in Ecommerce has transformed warehouse and distribution centers to mega-fulfilment centers. The focus is increasingly on innovative storage solutions, better tracking mechanisms coupled with technology and training. Companies who do not realize this have little chance of surviving (Rachid Fergati, MD Indian subcontinent, UPS). The purpose of this case is to highlight the process, people, and operations improvements undertaken by an Indian e-commerce giant with the help of the research team at the CII School of Logistics for enhanced efficiency and customer service. The researchers spent 50 days onsite (at the company’s warehouse located in Binola) observing the processes, identifying issues, providing solutions, and tracking and recording improvements as per the action research methodology (Reason, 2001). Thus, two-fold objectives were achieved: (a) improved cost efficiency and material handling processes in the warehouse and (b) knowledge building in the strategic operations management area. The case is a representative illustration of how the Indian logistics industry is moving from fragmented, unorganized, and inefficient systems to a more organized, structured sector in an ultra-competitive global space.

How does pollution heterogeneity affect the role of cleaner production regulations? Evidence from Chinese enterprises' domestic value-added in exports

Cleaner production has been emphasized more in stimulating green transformations of enterprises at the source, while its diversified impacts related to the heterogeneity of pollution levels on exporting enterprises' behaviors are still under-discussed. This study describes an empirical model to show how pollution heterogeneity affects the role of cleaner production standards (CPSs) in promoting Chinese enterprises' domestic value-added rate (DVAR) of exports. We find that the relation between the CPSs and enterprises' DVAR is subject to pollution levels. The positive impact of CPSs on DVAR tends to be weaker for high-polluting enterprises. Mechanism analysis shows that CPSs increase the enterprises' DVAR by stimulating technological innovations and improving cost efficiency, which are also affected by pollution heterogeneity. The heavy pollution would undermine the positive effect of technological innovations and cost efficiency, and consequently a weaker promotion effect of CPSs on high-polluting enterprises' DVAR. This study enriches the knowledge of the heterogeneous effects of environmental regulations, which has great significance in formulating specific policies for green development.

Predictors of Discharge Destination After Stroke

Background Determining the discharge destination after acute stroke care is important to prevent long-term disabilities and improve cost efficiency. Objective The aim of this study was to investigate where stroke patients are discharged to after acute treatment and to identify personal, social, stroke-related, and clinical predictors of discharge destination. Methods The present study included a secondary exploratory analysis of a prospective observational study. Patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage were recruited consecutively over a 15-month period. A hierarchical multinomial logistic regression was performed to identify predictors of the primary outcome of discharge destination. Results We included 1026 stroke patients (48.7% female) with a mean age of 73.3 years (standard deviation 12.9 years) in the analysis. Overall, 55% of the patients were discharged home, 33% to a rehabilitation center, 3% to a residential facility, and 8% to another acute care hospital. Predictors that statistically significantly influenced the odds of the discharge destination were age, living situation pre-stroke, living location pre-stroke, stroke type, stroke severity, treatment type, and length of stay. Higher stroke severity was associated with discharge to all four inpatient facilities. Conclusions In line with previous research, predictors such as stroke severity and living situation pre-stroke significantly influenced the odds of the discharge destination. In contrast, pre-existing conditions and functional impairment pre-stroke had no significant impact on the primary outcome. This discrepancy could be due to a rather functional study sample before stroke and the use of clinical and patient-reported outcome measures.

Employees’ perception of value-added activity increase of Robotic Process Automation with time and cost efficiency: a case study

Implementation of Robotic Process Automation (RPA) is ubiquitous in the financial industry. However, the consequences are not clear enough in terms of strategic perspective. Drawing upon the theory of Task-Technology Fit, this study explores the emerging consequences with the exploratory sequential method. Data related to the time and cost of processes before and after the RPA implementation were collected and descriptively analyzed. Even though time and cost efficiency improvements occurred in 50 out of 54 of the processes, the results indicated no labor reduction after the RPA implementation and no cost reduction in some business units, contrary to reports in the literature. To investigate what happened to the human resource environment, we surveyed 106 employees who were affected by the implementation of RPA. No variance was found between the characteristics of the employees and the changes in the working environment. However, the descriptive results of the survey revealed that employees’ perception of value-added activities increased. These results provided that considering RPA as a routine process without calculating the strategic value creates process-oriented transformation with a lack of time and cost-efficiency.

A cost‐efficient and QoS‐aware adaptive placement of applications in fog computing

SummaryWith the rapid development of the Internet of Things (IoT), fog computing has emerged as a complementary solution to address the issues faced in cloud computing. However, it is a challenging issue to ensure both of the high Quality of Service (QoS) and low cost for different requests when dealing with computing resources. In this article, we propose a new approach on adaptive cost‐efficient and QoS‐aware application placement for fog computing called DATSS. Specifically, this approach consists of a QoS state driven strategy and a credibility rating mechanism. The algorithm divides the QoS into three different states by calculating the QoS violation rate and adaptively selects the best scheduling strategy. Then evaluates the stability of the node based on the task history, and prioritize the task on the node with a higher rating. Performance results show that the cost, delay and energy consumption of the proposed algorithm can be reduced by 20.9%, 15.7%, and 12.6% respectively compared with other algorithms at most. The proposed algorithm can effectively improve cost efficiency and stability under QoS constraints in fog computing environments.

Trajectory Optimization of a Partially Reusable Rocket–Scramjet–Rocket Launch System Including Fly-Back

The integration of high-speed airbreathing engines into dedicated small satellite launchers may allow for some of the stages of the launch system to be reused, resulting in improved cost efficiency and turnaround times. Designing a trajectory of this type is challenging, due to the high number of competing performance drivers and design constraints, which are often in opposition with each other between the different modes of flight (i.e. airbreathing, rocket, and glide). In this work the design of the trajectory for a partially airbreathing rocket–scramjet–rocket multistage launch system is studied by generating optimal trajectories using the pseudospectral method of optimal control for maximum payload-to-orbit. This optimization simultaneously determines the ascent of all three stages, as well as the return trajectory of the scramjet stage within the trajectory optimization. It is found that the scramjet accelerator is able to achieve fly-back to its initial launch site, but that it must ignite its scramjet engines during its return flight, causing this to become an important consideration in the optimal trajectory design. This work shows that the return of the scramjet stage of a rocket–scramjet–rocket launch system may be feasible, and indicates the trajectory features that may heavily influence future partially airbreathing launch system designs.

Optimal integration design of sustainable ethylene glycol production process considering economic benefit and environmental impact

The requirement for ethylene glycol (EG) continues to surge owing to the booming demand for indispensable platform materials. Unfortunately, there is limited insight into engineering modeling, process enhancement, and process optimization to achieve carbon neutrality in ethylene glycol production. Herein, four biomass-to-ethylene glycol (BtEG) processes that integrate renewable energy for hydrogen production and CO2 utilization are proposed to find the best strategy for the carbon-neutral and cost-effective development of the ethylene glycol industry. Rigorous steady-state modeling and simulation of the proposed BtEG processes with various gasification agents are conducted to investigate the effect of different gasification agents on the performance of the BtEG processes. The technoeconomic and environmental performance of the proposed processes is assessed and compared with the conventional process. Results show that the proposed BtEG processes can significantly boost the system performance of the conventional process. The carbon utilization efficiency, exergy efficiency, and internal rate of return of four proposed BtEG processes are improved by 38.96%–43.2%, 0.98%–7.13%, and 1.94%–11.28%, respectively. The environmental performance of the proposed BtEG processes is greatly superior to that of the conventional process since the life cycle GHG emissions of the proposed processes are negative, which are lower than −1.47 t/t. Therefore, this study provides a sustainable and cost-efficient improvement direction for the carbon-intensive ethylene glycol industries.

Research on process-oriented standard requirements generation technique for equipment research & development

Standard requirement generation method is a core technique of standard formation. Due to the lack of systems approach, missing of standard technical elements, weak correlation between standards and research and development(R & D) activities are appeared frequently. Deficiencies in integrity, applicability and traceability of standards can be observed noticeably, which makes the equipment standards cannot bring into full play of cost reduction and efficiency improvement, cannot promote the equipment technology development effectively. According to the positive development requirements of independent and controllable standards, an activity-based standard requirement capture scene model is defined, a mechanization of standard requirement identification is described, a standard configuration, standard element definition and standard requirement integration methods are provided, a mapping matrices of activities, tools, database and standard requirement are established, a process-oriented standard requirements generation method for equipment R & D is finally proposed. By applying in aircraft engine compressor aerodynamic design field, the proposed method shows that the effectiveness of the building relationships between the standards and equipment R & D activities, which can ensure the integrity, availability and uniqueness of standard requirements, support equipment standard systems independent and controllable development.

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency.

The impact of transshipment on an integrated platelet supply chain: A multi-stage stochastic programming approach

This paper presents a multi-stage stochastic optimization model to minimize total cost under centralized control for a multi-echelon platelet supply chain which takes into account transshipment, demand uncertainty, stock age information, two types of supply, and three types of patients. The objective is to study the impact of transshipment between hospitals on performance improvement using a multi-stage stochastic framework to capture dynamic demand so as to explore approaches to improve an integrated platelet supply chain. Applicability of the model is tested through a real-world platelet supply chain with one blood center, three collection facilities, and five hospitals. The proposed multi-stage stochastic model is shown to perform better than a two-stage stochastic program in handling random daily demand and it also shows transshipment helps with alleviating shortage and wastage and reallocating stock at hospitals to improve system cost efficiency. Further, sensitivity analysis indicates that recruiting more apheresis platelet donors at hospitals and maintaining an initial inventory level that is close to the daily demand of hospitals are efficient strategies to improve performance of the platelet supply chain. In addition, we observed that imposing a FIFO issuing policy is not necessary for achieving optimal performance. These findings suggest that administrators and decision makers can improve cost efficiency in the platelet supply chain by implementing transshipment between hospitals, recruiting more donors, maintaining a suitable inventory level, and making collection and production plans in a shorter stage length.

Cost-effectiveness of social media advertising as a recruitment tool: A systematic review and meta-analysis.

Recruitment of study participants is challenging and can incur significant costs. Social media advertising is a promising method for recruiting clinical studies and may improve cost efficiency by targeting populations likely to match a study's qualifications. Prior systematic reviews of social media as a recruitment tool have been favourable, however, there are no meta-analyses of its cost-effectiveness. Studies evaluating recruitment costs through social media and non-social media methods were identified on MEDLINE and EMBASE. Articles were screened through a two-step process in accordance with PRISMA guidelines. Cost data were extracted from selected articles and meta-analyzed using the Mantel-Haenszel method. The primary outcome was the relative cost-effectiveness of social media compared to non-social media recruitment, defined as the odds ratio of recruiting a participant per US dollar spent. The secondary outcome was the cost-effectiveness of social media recruitment compared to other online recruitment methods only. In total, 23 studies were included in the meta-analysis. The odds ratio of recruiting a participant through social media advertising compared to non-social media methods per dollar spent was 1.97 [95% CI 1.24-3.00, P = 0.004]. The odds ratio of recruiting a participant through social media compared to other online methods only was 1.66 [95% CI 1.02-2.72, P = 0.04]. Social media advertising may be more cost-effective than other methods of recruitment, however, the magnitude of cost-effectiveness is highly variable between studies. There are limited data on newer social media platforms and on difficult-to-reach populations such as non-English speakers or older individuals.

Real‐Time Medical Image Classification with ML Framework and Dedicated CNN–LSTM Architecture

In the domain of modern deep learning and classification techniques, the convolutional neural network (CNN) stands out as a highly successful and preferred method for image classification in artificial intelligence. Especially in the medical field, CNN has proven to be an ideal approach for analyzing medical data and accurately identifying diseases. Over the recent years, CNN has demonstrated significant potential and success in various computer vision tasks, with medical image classification being one of the prominent applications. In our study, we introduce a novel custom CNN model called MedvCNN, designed for classifying different types of classes. We conduct experiments with various image sizes to explore their versatility. In addition, long short‐term memory (LSTM), a type of recurrent neural network (RNN), is incorporated into our approach. LSTM is specifically tailored to handle sequential data, making it ideal for time series analysis. However, its capabilities extend beyond time series data and are effectively applied to various sequential data types, including sequential vectors derived from image data. One of the key advantages of utilizing LSTM for image classification is its ability to effectively memorize and capture important features in the image data. This feature is particularly advantageous in medical image processing, where precise and accurate identification of key attributes is crucial for successful diagnosis and analysis. Furthermore, our experiments reveal that the hybrid custom LSTM model, MedvLSTM, a RNN algorithm, surpasses other methods in the domain of medical image classification. Our study places significant emphasis on attaining robust classification performance for medical image data through a sophisticated, parameter free approach, complemented by an ablation study, and comprehensive statistical analysis. This comprehensive analysis and evaluation allow us to gain a deeper understanding of the model’s effectiveness and its potential impact in the field of medical image analysis. We compare these two approaches to a baseline CNN architecture, aiming to streamline the classification process, reduce time consumption, and improve cost efficiency. Additionally, we present a real‐time web‐based AutoML framework along with a practical demonstration. Ultimately, our research provides a thorough investigation of the current state‐of‐the‐art in medical image analysis accuracy, focusing on the utilization of neural networks and LSTM.

On introduction of institution of probation, improving cost efficiency of criminal trials by broadening access to technologies of remote participation in criminal proceedings and other novelties of criminal procedure

On introduction of institution of probation, improving cost efficiency of criminal trials by broadening access to technologies of remote participation in criminal proceedings and other novelties of criminal procedure

A general health economics review of the hidden costs involved in discharging coeliac patients from hospital-based specialty clinics to community-based management.

The aim of this work was to highlight the impact and hidden costs incurred by the NHS in supporting this management process. Coeliac disease (CD) is a common auto-immune condition which affects around 1% of the general population. In 2005 there was a drive by the government to discharge patients with CD from specialist hospital follow up to community-based management to improve cost efficiency. A retrospective analysis of 1317 CD patients collected from a local coeliac database created between 2005 and 2016. During these 12 years, CD patients accounted for 1965 hospital admissions with a total 5716 days spent within the hospital setting. There were 33150 adult and paediatric OPAs attended equating to 25.17 per coeliac patient, or 2.29 per person per year. The cost to the CCG totalled £5,167,396. A total of 527 lower GI procedures were undertaken with findings of microscopic colitis, melanosis coli, inflammatory bowel disease and colon cancer. 420 (29%) of the coeliac cohort were found to have IDA with just 4% (17/420) receiving an intravenous (IV) iron infusion. It would appear that the government's attempts to reduce the cost of CD care within the NHS was not particularly effective, from a financial, or patient care perspective. A hospital-based, specialist nurse led, virtual management system (with consultant over-view) may prove to be a more efficient compromise, to help reduce down waiting times and costs, whilst still providing coeliac patients with the specialist and holistic input they require and deserve.

Strategy for SME Business Development at the SME Catering Academy Center in Bandung

This study aims to formulate a SME business development strategy at the SME Catering Academy Center in Bandung City. The research was conducted with a descriptive-qualitative approach. Analysis techniques using internal-external situation analysis, merging using the IE matrix, and formulating strategies using SWOT A series of situational analyses of the business environment and incorporation using the IE matrix led to the understanding that Bandung City's SME Catering Academy is in the 2nd quadrant of the IE matrix, with recommendations for a general hold and maintenance strategy. While the SWOT analysis yielded several more specific strategic recommendations, namely improving corporate image in the eyes of consumers, strengthening digital capabilities, strengthening existing networks and adding new networks with various parties, increasing working capital for operational financing, expanding distribution, improving the quality of company human resources and employee development, expanding existing markets, carrying out production planning, and improving cost efficiency.
 

AI-Driven Predictive Maintenance in Indian Manufacturing: Enhancing Operational Efficiency

This paper explores the application of Artificial Intelligence (AI) in predictive maintenance within the Indian manufacturing sector. Predictive maintenance leverages AI techniques like machine learning (ML) and deep learning (DL) to predict equipment failures, thus reducing downtime and operational costs. In India, where manufacturing plays a critical role in the economy, the adoption of AI-driven predictive maintenance is still in its early stages. The paper discusses various ML models, such as Decision Trees and Recurrent Neural Networks (RNN), used to predict machinery failures based on historical data and real-time monitoring. The research includes case studies from Indian manufacturing firms that have implemented AI-based predictive maintenance systems, showcasing improvements in machine uptime, maintenance scheduling, and cost-efficiency. The study also highlights the challenges of integrating AI into traditional manufacturing processes, such as data quality, infrastructure limitations, and resistance to change. Finally, the paper offers insights into how Indian manufacturers can scale these solutions to achieve long-term benefits and maintain global competitiveness.