Software Cost Research Articles

Barriers of conducting Effective Online Classes in Undergraduate Medical Education during the COVID-19 Pandemic

Introduction: In March 2020, the Coronavirus disease 2019 (COVID-19) pandemic forced medical colleges in Bangladesh to stop face-to-face learning activities and abruptly shift to an online curriculum. This study aims to identify the barriers to online teaching and learning from the perspective of the students and the teachers at a medical college from a developing country’s perspective. Methodology: This prospective, cross-sectional study was based on an electronic survey that was sent to the medical students and teachers of Mugda Medical College, Dhaka, Bangladesh, between August and December of 2021, to find out the barriers that they were facing in online classes during the COVID-19 pandemic. We used a semi-structured questionnaire with a Likert scale to find out the barriers. Data was obtained regarding perceived barriers to online learning. Results: Perceived barriers to online learning in the COVID-19 situation as identified by the students were poor internet connection specially in the rural or peri urban areas of Bangladesh (67%), high cost of internet package (65%), ineffectiveness of online class regarding practical classes specially anatomy classes (59%), distractions in the home environment disturbs concentration in class (57%), ineffectiveness of online class in solving problems and absence of feedback (41%). On the other hand, according to the teacher's opinion, the most frequently encountered barrier was conducting practical, laboratory or bedside clinical teaching and learning (85%), followed by difficulty in understanding whether students are engaged in learning or not (65%). Conclusion: Medical students and teachers confronted several interrelated barriers as they tried to adapt to online teaching and learning. Most frequently encountered were difficulty in adjusting learning styles, having distraction in home environment, and above all, poor internet connection. By implementing student-centered interventions, medical colleges and educators can play a significant role in addressing those challenges and finding possible solutions.

Quantifying the potential value of entomological data collection for programmatic decision-making on malaria control in sub-Saharan African settings

BackgroundThe availability of many tools for malaria control leads to complex decisions regarding the most cost-effective intervention package based on local epidemiology. Mosquito characteristics influence the impact of vector control, but entomological surveillance is often limited due to a lack of resources in national malaria programmes.MethodsThis study quantified the monetary value of information provided by entomological data collection for programmatic decision-making using a mathematical model of Plasmodium falciparum transmission. The 3-year impact and cost of various intervention packages was simulated in different sub-Saharan African settings, including combinations of scaling-up insecticide-treated nets (ITN), switching to next-generation ITNs, and a treatment and prevention package. The DALYs averted and their net monetary benefit were compared at different cost-effectiveness thresholds and the value of resolving uncertainty in entomological model parameters was calculated.ResultsAcross transmission settings and at cost-effectiveness thresholds over US$170 per DALY averted, the most cost-effective intervention package was switching to and scaling up pyrethroid-pyrrole ITNs combined with the treatment and prevention package. The median expected value of perfect information on the entomological indicators was US$0.05 (range 0.02–0.23) and US$0.17 (range 0.09–1.43) per person at risk at thresholds of US$75 and US$1000 per DALY averted, respectively. This represented less than 2% of the net monetary benefit of implementing the most cost-effective intervention package. Value of information estimates at cost-effectiveness thresholds over US$250 were higher than current investments into entomological monitoring by the US President’s Malaria Initiative.ConclusionsThese results suggest that entomological data collection should not delay implementation of interventions with demonstrated efficacy in most settings, but that sustained investments into and use of entomological surveillance are nevertheless worthwhile and have broad value to national malaria programmes.

The Impact of Tele pharmacy in Rural Healthcare

In many nations, the rapid expansion of information and telecommunication technology use has a significant impact on healthcare delivery. Geographical and demographic characteristics, however, frequently make it difficult for rural areas and residents to access healthcare services. Telepharmacy, a relatively new term for pharmaceutical services, allows a qualified pharmacist to provide healthcare services, including medication reviews, patient counseling, and prescription verification, to patients who live far from a hospital, pharmacy, or healthcare facility. Economic benefits, patient satisfaction due to medication access and information in rural areas, easy access to healthcare services in remote and rural locations, effective patient counseling, and a lack of local pharmacists and pharmacy services are just a few of the well-known advantages of telepharmacy. Although telepharmacy is an excellent idea, its implementation can occasionally be difficult. There are hazards and legal issues that come with implementing these techniques that must be resolved. Significant time, money, and effort are required for the technology, software, connectivity, and operational costs associated with telepharmacy startup. One of the main obstacles to telepharmacy services for rural hospitals with fewer patients seems to be cost.Furthermore, there are still issues with the execution and uniformity of the telepharmacy law. A properly-designed system, however, has the power to transform pharmacy practice in ways that benefit rural communities as well as the hospitals or retail pharmacies that provide these services.

Therapeutic drug monitoring versus Bayesian AUC-based dosing for vancomycin in routine practice: a cost-benefit analysis.

AUC-based dosing with validated Bayesian software is recommended as a good approach to guide bedside vancomycin dosing. To compare treatment and vancomycin-associated acute kidney injury (AKI) costs between Bayesian AUC-based dosing and conventional therapeutic drug monitoring (TDM) using steady-state plasma concentrations of vancomycin administered as continuous infusion in hospitalized non-critically ill patients with severe Gram-positive infection. A cost-benefit analysis presented as a return on investment (ROI) analysis from a hospital perspective was conducted using a decision tree model (TDM versus AUC-based dosing) to simulate treatment cost (personnel, serum sampling and drug cost), vancomycin-associated AKI risk and cost up to 14 days. ROI was calculated against AUC-based software cost. One-way and probabilistic sensitivity analyses (respectively OWSA and PSA) were performed to check for robustness. In base case, an overall cost per patient of €621.0 with TDM and €543.6 with AUC-based dosing resulted in a treatment saving of €77.4 per patient when applying AUC-based dosing. This saving against the software cost (€26.9/patient) generated an ROI per patient of €1.9 per invested € in software [€1.9 (95% CI 1.6-2.2) in PSA]. Enrolling 900 AUC-based dosed patients annually translated to a net saving of €45 469. Software break-even was reached after 313 patients. In OWSA, a higher AKI risk with TDM strongly contributed to a positive ROI. AUC-based dosing appeared a cost-saving strategy compared with conventional TDM when applying base-case settings of vancomycin-associated AKI risk, treatment and AKI costs.

Novel Low Warpage Mold Film for FO WLP/PLP Application

There has been increasing demand for FOPLP package, due to its high performance compared to conventional rigid substrate package and low cost compared to FOWLP package. To achieve high yield using FOPLP process, warpage was the most serious problem. Warpage of the panel results in poor pattern accuracy and reduced yield of package, and warpage of the panel was often severely increased by molding. So low warpage mold was highly needed. To reduce warpage, we have developed novel low warpage mold films. Mold factors related to warpage were mainly curing shrinkage, CTE mismatch and elastic modulus. Conventional powder type mold and liquid type mold showed high cure shrinkage and high elastic modulus thus its often-showed high warpage. We adopted film instead of powder or liquid to reduce mold curing shrinkage. And we reduced elastic modulus of the mold film by considering proper formulation with keeping its CTE low. The film showed very low warpage in case laminating Si wafer, Cu foil, PI film and glass. We have also developed novel photo imageable film for RDL, and it showed very low warpage compared to conventional liquid type photo imageable polyimide. Using these two materials, Taiwan ITRI had made and presented FOPLP on glass substrate which had 5 layers RDL and mold, and it showed very low warpage.

A beginner’s guide to design of experiments for life sciences using Spreadsheet DoE

Life science research frequently involves optimizing experimental conditions to maximize product yield, ensuring the research pipeline can progress efficiently. For instance, in biochemistry, many processes in the protein production and characterization pipeline need optimizing such as cloning, protein expression, protein purification, and a range of structural biology techniques and functional assays. Design of Experiments (DoE) is an approach that designs a set of experiments that varies all factors at the same time for rapid process optimization, however, this approach presents a challenge due to statistical complexities and high software costs. To address this, DoE is introduced with Spreadsheet DoE, an open access user-friendly educational factorial design tool that provides step-by-step guidance and transparent spreadsheet calculations. Protein expression optimization in Escherichia coli is used as an example, and other biochemical optimizations are also explored. This guide along with Spreadsheet DoE aims to encourage researchers with minimal statistical background to effectively employ DoE methodologies in their life science studies.

Purchasing Still Wines: The Influence of Extrinsic Attributes

Aims: Purchasing wine in shops implies that the consumer makes a choice based on the extrinsic attributes of the product (eg packaging, bottle shape and design, labels and written information) rather than extrinsic information such the flavor or the taste of the wine. Various studies were undertaken in different countries and published that provide information regarding the extrinsic attributes that influence this choice. Focusing on studies addressing still wine purchasing in shops, this study aims to summarize the main attributes that intervene in the choice-making of wine for the geographical areas with high levels of consumption. Study Design: The study was based on bibliographic research and its analysis. Place and Duration of Study: The bibliographic research and analysis were conducted at the Laboratory for Research in Science of Agronomy & Biology (France) in November 2024. Methodology: Bibliographic research was conducted using Googlescholar with keywords including influence, purchasing wine, bottle, packaging. Articles addressing sparkling wine, organic wine, online purchase, tourist consumers, and studies focusing on specific generations were excluded. Results: A total of 23 articles were selected, covering the period from 2006 to 2024. The increase in the number of articles over time illustrates growing interest in the topic. A significant correlation was found between the number of articles per country and the importance of the wine market (r=.94). Regardless of the country considered among those selected, the main criterion in consumer choice was the price followed by the label (design and content), and then the bottle shape. Attributes such as the wine’s origin, vintage, and the food pairing - appreciated on the label - also influence the consumer choice. Important features of label design were identified. Conclusion: Despite the undeniable effect of cultural differences across countries, which may influence consumer behavior differently when purchasing wine, a common set of extrinsic attributes was identified for selecting still wines in shops in the selected countries. The information that should be emphasized on the label, along with its design features, was determined. These findings may help wine producers, sellers and marketers reduce packaging costs by focusing on key extrinsic attributes during the design process and emphasizing features important to consumer choice.

Analyzing the Reliability and Cost of the Most Commonly Used Dosimeters for Personal Ultraviolet Radiation Monitoring—A Rapid Review

To identify the most used dosimeters for monitoring ultraviolet radiation (UVR) and analyze their reliability and cost for individual UV exposure monitoring, this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. An extensive search of the PubMed, Scopus, and Web of Science databases, covering 2005–2023, was conducted, including examining reference lists of retrieved studies. Of the 1202 records, 52 were eligible for analysis. Three types of dosimeters were identified: photosensitive, photochromic, and electronic dosimeters. Photosensitive dosimeters were utilized for 1236 samples across the studies, while photochromic dosimeters were employed for 360 samples. Electronic dosimeters, with a sample size of 3632, were the most extensively studied. This study highlights the variety of resources available for UVR assessment and the significance of specific dosimeter types in this field. Although few studies have explored the costs associated with dosimeter use, electronic dosimeters are the most cost-effective for radiation monitoring and provide the highest accuracy for measuring UVR exposure. Electronic dosimeters, known for real-time data and high precision, are reliable but costly, being approximately 16.5 times more expensive than photosensitive dosimeters and 160 times more expensive than photochromic dosimeters. Photosensitive dosimeters suit large-scale personal use, and photochromic sensors such as polysulphone dosimeters are also reliable. Additional costs for data analysis software, laboratory equipment, or external analysis services may be incurred, especially for advanced research-grade sensors.

Optimizing Software Cost Estimation: A Hybrid Approach Integrating Soft Computing Techniques for Enhanced Accuracy and Efficiency

In software engineering, software cost estimate is a necessary process directly affecting project deadlines, budget planning, and resource allocation. Conventional estimating methods could overlook the inherent complexity and uncertainty of software development, therefore causing mistakes and maybe project failures. To optimise software cost estimation and so address these challenges, this work proposes a hybrid framework incorporating fuzzy logic, artificial neural networks (ANN), and genetic algorithms (GA). Although fuzzy logic models imprecise and ambiguous information to control uncertainty, ANN is employed for its ability to learn and generalise patterns from past data. GA guarantees accurate and effective assessment of the process by strengthening already used techniques with strong optimising characteristics. Combining these techniques makes use of their respective benefits to generate a synergistic plan beyond more traditional methods. The proposed framework is investigated using industry-standard datasets exhibiting appreciable increases in prediction accuracy, computational efficiency, and flexibility to varied project situations. Comparatively, for complex and large-scale projects the hybrid approach not only reduces estimating errors but also enhances scalability and dependability. Additionally included in the paper is a case application for a practical project, therefore stressing the relevant usefulness of the framework. By addressing the limitations of present approaches, this work reduces the gap between theoretical breakthroughs in soft computing and their pragmatic use in software engineering. stressing the need of applying contemporary computing techniques to solve evolving challenges in cost estimation, the outcomes provide useful information for scholars and business practitioners. At the end of this work is explored the possibility of new machine learning models and the investigation of domain-specific customisations to increase the applicability of the framework.

ANN-based software cost estimation with input from COCOMO: CANN model

Different project management processes have been used in software engineering to support managers in keeping project costs manageable. One of the essential processes in software engineering is to accurately and reliably estimate the required effort and cost to complete the projects. The domain of software cost estimation has witnessed a prominent surge in research activities in recent years and being an evolving process, it keeps opening new avenues, each with advantages and disadvantages, making it important to work out better options. This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. A publicly available dataset (COCOMONASA 2), various combinations of activation functions, and layer densities have been systematically explored, employing multiple evaluation metrics such as MAE, MRE, and MMRE. In the PCNN model, the ReLU activation function and a 1000-dense layer have demonstrated better performance. While layer density generally correlates with better outcomes, this correlation is not universally applicable for all activation functions and outcomes vary across different combinations. The use of the relationships between 26 key parameters of COCOMO in PCNN produced better results than FCNN by 0.59%, achieving an MRE of 6.55 and an MMRE of 7.04. The results indicated that the CANN model (COCOMO & ANN) presented better results than existing models.

Advancing sustainability in e-commerce packaging: A simulation-based study for managing returnable transport items

As e-commerce grows, tackling its environmental impacts, including those of packaging, becomes increasingly challenging. This study explores the benefits of reusable industrial packaging systems in the e-commerce industry, addressing ecological and financial perspectives. We contribute by formulating a generalized balancing problem for returnable transport items (RTIs) and developing a discrete-event simulation model and a heuristic to solve the problem. One methodological novelty is our modeling of network nodes that are simultaneously senders and recipients of RTIs. By applying the methodology to a case study of a large European e-commerce retailer, we address the need for industry-specific empirical studies comparing single-use and reusable packaging and RTI management in e-commerce.The case study reveals that reusing packaging and balancing its inventory across the network, even with packaging materials designed for single use, coupled with harmonizing packaging solutions, may produce substantial financial and ecological benefits. Furthermore, ours is the first study to include a CO2 price in optimizing RTI management. We find that while this does not have a positive effect on operational decision-making, it does steer strategic decisions towards more ecologically sustainable logistics systems. Our results indicate a savings potential of 30 to 75% of industrial packaging costs and an industry-wide CO2 reduction potential of 200,000 to 600,000 tons annually in Europe alone. Our research contributes to theoretical and practical understanding of sustainable packaging in e-commerce, offering a model and strategies for industry adaptation amidst growing environmental and regulatory pressures.

Cost-Effectiveness of Computer-Assisted Cognitive Behavioral Therapy for Depression Among Adults in Primary Care

Approximately 1 in 5 adults are diagnosed with depression in their lifetime. However, less than half receive help from a health professional, with the treatment gap being worse for individuals with socioeconomic disadvantage. Computer-assisted cognitive behavioral therapy (CCBT) is an effective and convenient strategy to treat depression; however, its cost-effectiveness in a sociodemographically diverse population remains unknown. To evaluate the cost-effectiveness of clinician-supported CCBT compared with treatment as usual (TAU) in a primary care population with a substantial number of patients with low income, limited computer or internet access, and lack of college education. This economic evaluation was a randomized clinical trial-based cost-effectiveness analysis. The trial was conducted at the Departments of Family and Geriatric Medicine and Internal Medicine at the University of Louisville. Enrollment occurred from June 24, 2016, to May 13, 2019. Participants had mild to moderate depression and were followed up for 6 months after treatment completion. The last follow-up assessment was conducted on January 30, 2020. Statistical analysis was performed from August 2023 to August 2024. CCBT intervention was provided for 12 weeks and included 9 modules ranging from behavioral activation and cognitive restructuring to relapse prevention strategies, supported by telephonic sessions with a clinician, in addition to TAU, which included standard clinical management in primary care. The primary health outcome was quality-adjusted life years (QALYs), estimated using the Short-Form 12 questionnaire (SF-12). The secondary outcome was treatment response, defined as at least 50% improvement in the Patient Health Questionnaire. The intervention cost included sessions with mental health clinicians and the cost of the CCBT software, plus the cost of loaner computer and internet data plan for low-resource households. An incremental cost-effectiveness ratio (ICER) was computed, while adjusting for baseline scores, age, and sex. The cost-effectiveness acceptability curve presented the probability of CCBT being cost-effective for a range of willingness-to-pay values. Among the 175 primary care patients included in this study, 148 (84.5%) were female; 48 (27.4%) were African American, 2 (1.2%) were American Indian or Alaska Native, 4 (2.5%) were Hispanic, 106 (60.5%) were White, and 15 (8.6%) were multiracial; and the mean (SD) age was 47.03 (13.15) years. CCBT was associated with better quality of life and higher chance of treatment response at the posttreatment and 6-month time points, compared with the TAU group. The ICER for CCBT was $37 295 (95% CI, $22 724-$66 546) per QALY, with a probability of 89.4% of being cost-effective at a willingness-to-pay threshold of $50 000/QALY. The ICER per case of treatment response was $3623 (95% CI, $2617-$5377). In this trial-based economic evaluation, CCBT was found to be cost-effective, compared with TAU, in primary care patients with depression. As this study included individuals with low income and with limited internet access who are underrepresented in cost-effectiveness studies, it has important policy implications for addressing unmet needs in sociodemographically diverse populations.

Common open source WebGIS platform for spatial and non-spatial data to enhance health services in Sri Lanka

This study explores the transformative potential and current limitations of Geographic Information Systems (GIS) in the context of Sri Lanka's health sector and broader development initiatives. Despite the global GIS market's expected growth to billions by 2025, driven by its adoption in infrastructure, urban planning, and the integration with emerging technologies, its application in critical areas such as public health, disaster management, and citizen services remains underutilized. This underutilization stems from a lack of ground-level information, limited knowledge of GIS capabilities, restricted access to essential geospatial data, and the high cost of GIS software. This paper discusses the significant hurdles in obtaining updated and comprehensive citizen, household, and health-related information, which impedes effective planning and decision-making within the health sector of Sri Lanka. It further examines the initiatives by the Ministry of Health, supported by the Asian Development Bank, to implement a GIS-based Health Mapping System focused on infrastructure and inventory management of health facilities, yet lacking in integrating critical citizen data for demand analysis of health services. The study underscores the necessity for a comprehensive "Geographical Citizen Information System for the Health Sector in Sri Lanka" akin to those in developed countries, to host, process, and analyze spatial and non-spatial information for enhanced policymaking and development planning. Through an analysis of current challenges and the exploration of potential solutions, this paper advocates for an integrated approach to harness the full capabilities of GIS technologies, thereby enabling more informed and effective governance in Sri Lanka’s Health sector.

SOFTWARE COST EFFORT AND TIME ESTIMATION USING DRAGONFLY WHALE LION OPTIMIZED DEEP NEURAL NETWORK

Effective software development depends on the exact estimation of effort, time, cost, and customer satisfaction. Software project management requires an accurate evaluation of software development's effort, time, and cost, often underestimated or overestimated. So far, methodology has yet to accurately and reliably estimate the cost of software development. To overcome this issue, this paper proposed a constructive rapid application development model based on software cost effort and time estimation approach (CORADMO-based CETA) for accurate software cost estimation. The data requirements, cost drivers, constraints, and priorities are given as input to the fuzzy inference system (FIS). The processed output, such as effort, time, and cost for the nominal plan, shortest schedule plan, and least cost plan, is computed in the FIS. To reduce the effort, time, and cost, the output is optimized by dragonfly whale lion optimization (DWLO), which provides the best-estimated effort, time, and cost as an output for software development. The proposed CORADMO-based CETA model is tested in the NASA 93 dataset using MATLAB. The performance of the CORADMO-based CETA method is measured in terms of Pred (25 %), magnitude of relative error, and mean magnitude of relative error, attaining the values of 80.72 %, 87.94 %, and 98.13 %, respectively. Finally, the CORADMO-based CETA model justifies the suitability of dragonfly whale lion optimization with the proposed fuzzy logic.

NHPP Software Reliability Model with Rayleigh Fault Detection Rate and Optimal Release Time for Operating Environment Uncertainty

Software is used in diverse settings and depends on development and testing environments. Software development should improve the reliability, quality, cost, and stability of software, making the software testing period crucial. We proposed a software reliability model (SRM) that considers the uncertainty of software environments and the fault detection rate function as a Rayleigh distribution, with an explicit mean value function solution in the model. The goodness-of-fit of the proposed model relative to several existing nonhomogeneous Poisson process (NHPP) SRMs is presented based on three software application failure datasets. Further, a cost model is also presented that addresses the error-removal risk level and required time. The optimal testing release policy for minimizing the expected total cost (ETC) is also determined for NHPP SRMs. The impact of the software environment is studied by varying it, and the optimal release times and minimum ETCs are compared. The goodness-of-fit comparison confirmed that the proposed model has more accurate prediction values than other models. Further, whereas the existing models applied to the cost model do not change after a certain operation period, the proposed model yields changes in release time even for long operating periods.

Utilization patterns of Chiranjeevi Swasthya Bima Yojana in Rajasthan during financial year 2023 to 2024: a cross-sectional study at a tertiary care hospital

Background: Publicly funded health insurance (PFHI) is considered a key policy tool to provide financial protection to households seeking healthcare. The state government of Rajasthan has launched Mukhya Mantri Chiranjeevi Swasthya Bima Yojana (CSBY), with a cover of 25,00,000 INR per family per year, that makes it one of the largest PFHI in world by cost-coverage. However, there is an evidence-gap on utilization patterns and consequent need of higher-rate packages in state-PFHIs. A study was designed with the objective to describe the utilization pattern of CSBY in Rajasthan. Methods: The cross-sectional study was conducted at Sawai Man Singh Hospital in Jaipur, the highest public tertiary care institute of Rajasthan. The data on CSBY hospitalisations was collected from Chiranjeevi portal for financial year 2023-2024. Summary statistics were used to describe the variables such as age and gender of beneficiaries and medical specialities and cost details of packages utilised. Results: The mean age of patients was 45.9 (±20.0) years and 62.1% were males. The top specialty by volume of admission was general medicine (14.2%) and by cumulative cost of packages utilized was cardiology (22.0%). The median cost of packages utilized per hospitalization was 12,200 INR. 96.8% of beneficiaries utilized the services at a cumulative cost of up to 1 lakh INR and only 0.01% patients utilized more than 5 lakh INR. Conclusions: The study found higher utilisation of highly-specialised services over broad-specialties and an underutilisation of higher-rate packages, demonstrating a gap in perceived need of higher-rate packages in the state.

Implementation Challenges of Building Information Modelling (BIM) in Small to Medium-Sized Enterprises (SMEs) Participating in Public Projects in Qatar

This research investigates the implementation challenges of Building Information Modelling (BIM) in Small to Medium-Sized Enterprises (SMEs) participating in public projects, with a focus on the construction industry in Qatar. Through a combination of literature review, surveys, and interviews, the study identifies key obstacles such as interoperability issues, financial constraints, organisational and cultural resistance, and the lack of comprehensive training programs. The literature highlights the fragmented nature of construction projects and the potential of BIM to improve efficiency through enhanced data handling and collaboration. However, SMEs often struggle with the high initial costs of software and hardware, as well as the need for extensive training to bridge the skill gap. Interviews with industry professionals reveal that while mandatory BIM integration in public projects drives adoption, it also exacerbates these challenges for SMEs, which may lack the necessary resources and expertise. Survey findings indicate that contractors frequently encounter issues with understanding and implementing BIM and meeting the government body standards, suggesting a need for better training and support. The study also emphasises the importance of strategic incremental adoption, where SMEs gradually integrate BIM into their processes, starting with low-level implementation in privately funded projects. The research concludes that while BIM offers significant benefits for the construction industry, its successful adoption requires targeted support for SMEs. This includes financial incentives, improved interoperability standards, comprehensive training programs, and strategic change management approaches. Addressing these challenges will enable SMEs to fully leverage BIM's potential, enhancing project efficiency and competitiveness in the construction sector.

Design Business Offline and Online Based on Website of Pikyeum SME in Bandung

Pikyeum SME created a new business idea to connect sellers and consumers, introduce products,and place online purchases. By creating a website, Pikyeum SME makes it easy for consumersthroughout Indonesia to find Peuyeum chips. This case study was conducted in Pikyeum SME,located in Lot Leuwidulang No. C 02, Sub-district Pameungpeuk, Bandung, Indonesia. Datacollection and analysis were conducted qualitatively and quantitatively. The purpose of thisresearch is to measure the demand, design technical and operational aspects, design a websitebased application, measure the product's financial feasibility, and measure the sensitivity level ofPikyeum food products offline and online sales. Survey data were analyzed using corrected itemtotal correlation as a validity testing technique and Cronbach's Alpha as a reliability testingtechnique with the aid of the SPSS program. Based on this research, Net Present Value (NPV) isIDR 167.006.461, Interest Rate of Return (IRR) at 36,70%, and Payback Period at 3,4 years. It canbe concluded that Pikyeum SME is feasible to run because NPV > 0, payback period < asseteconomic life, and IRR > MARR. The increase of raw materials and packaging costs shouldn’t bemore than 36,76%. Meanwhile, the increase of labor cost shouldn’t be more than 23,51%. Thedemand decreases shouldn’t be more than 15,63% and selling prices shouldn’t exceed 11,34%.

Evolutionary game analysis of government subsidies for reusable express packaging promotion

The increase in e-commerce and online shopping has resulted in a significant amount of packaging waste from express delivery, posing a considerable burden on the environment. To address this issue, reusable express packaging has emerged as one of potential solutions to reduce packaging waste in express delivery. This study analyzes the evolutionary stability strategy of local governments and express delivery enterprises by constructing an evolutionary game model. The model considers the limited rationality and group behavior of decision makers, as well as the economies of scale and diseconomies of scale in operating cost of reusable express packaging. Results indicate that the variability in the cost of reusable express packaging may lead to the simultaneous emergence of two evolutionary stability strategies, contrasting traditional evolutionary game studies. Due to the present low uptake of reusable express packaging among express companies, coupled with the lack of subsidies support from local governments, it is difficult to attain the optimal situation in which businesses actively utilize reusable express packaging. To accomplish this objective, it is crucial to establish subsidy amounts that are balanced and reasonable, rather than merely elevating them to the highest possible levels. The study proposes an optimal range for government subsidy levels and gives corresponding policy suggestions.

Exploring key metrics of sterilization services using cost analysis

ABSTRACT Sterilization services not only impact service delivery processes directly but also account for a substantial portion of total costs. Therefore, accurate resource measurement becomes imperative for efficient resource utilization. This research aims to draw a framework for calculating the package and autoclave unit cost within a sterilization unit and to pinpoint sterilization unit-specific findings distinct from those of other service provider departments. To achieve this, we collected and analyzed cost data from a public hospital in Trabzon, Türkiye, for 2021 and 2022. Subsequently, the unit cost of the package and autoclave of the sterilization unit was calculated based on the collected data. Our analysis considered various cost components, such as equipment maintenance, personnel salaries, and utility expenses. Through our research, we identified key factors influencing the sterilization process. The findings guided decision-makers in prioritizing areas for efficient and sustainable service provision. Furthermore, we emphasize the importance of our developed framework in cost analysis, highlighting its potential impact on resource management practices in healthcare settings. Additionally, insights into decision-making processes regarding in-house versus outsourced provision of sterilization services can inform managerial decisions and resource allocation strategies more effectively.