Development Costs Research Articles

Perfusion Process Intensification for Lentivirus Production Using a Novel Scale‐Down Model

ABSTRACTProcess intensification has become an important strategy to lower production costs and increase manufacturing capacities for biopharmaceutical products. In particular for the production of viral vectors like lentiviruses (LVs), the transition from (fed‐)batch to perfusion processes is a key strategy to meet the increasing demands for cell and gene therapy applications. However, perfusion processes are associated with higher medium consumption. Therefore, it is necessary to develop appropriate small‐scale models to reduce development costs. In this work, we present the use of the acoustic wave separation technology in combination with the Ambr 250 high throughput bioreactor system for intensified perfusion process development using stable LV producer cells. The intensified perfusion process developed in the Ambr 250 model, performed at a harvest rate of 3 vessel volumes per day (VVD) and high cell densities, resulted in a 1.4‐fold higher cell‐specific functional virus yield and 2.8‐fold higher volumetric virus yield compared to the control process at a harvest rate of 1 VVD. The findings were verified at bench scale after optimizing the bioreactor set‐up, resulting in a 1.4‐fold higher cell‐specific functional virus yield and 3.1‐fold higher volumetric virus yield.

Excessive Pharmaceutical Pricing and Repercussions on Paediatric Health Services

Context: Paediatric pharmaceutics have a significant share in expensive drugs. Recent years have seen significant calls for intervention against high prices for pharmaceutical products. The prohibition of prices that are excessive as a form of abuse of dominance is considered as Competition law violation in the European Union Law and national legal orders of its Member States. Majority of the Organization for Economic Cooperation and Development (OECD) member countries have similar prohibitions in their respective Competition laws with important exemption of United States (US) Antirust Law, although there is some indication of change in US exactly in the area of pharmaceutical sector. Regulation of excessive prices also exists in Competition laws of all BRICS (Brazil, Russian Federation, India, China, and South Africa) countries. The problem of excessive prices in pharmaceutics is one of the emerging themes in Competition law literature and case law development. This is the area where intellectual property rights, usually patent protection, collide with competition rules. Costs of research and development for such pharmaceutics are often very high emphasizing the need for balance between colliding legitimate interests: access to health and research development. Aim: To present regulatory and case law development of application of excessive prices as form of abuse of dominance in pharmaceutical sector with emphasis on paediatric health services. Data source: OECD, PubMed, Google Scholar, Scopus. Conclusion: Balance between competition rules on excessive prices as form of abuse of dominance and intellectual property rights in the area of expensive paediatric pharmaceutics can be regarded as suboptimal. Thus, there is a need for further regulatory development and application of rules on excessive prices in that field.

Construction Method of a Digital-Twin Simulation System for SCARA Robots Based on Modular Communication

Due to the high cost of robots, the algorithm testing cost for physical robots is high, and the construction of motion control programs is complex, with low operation fault tolerance. To address this issue, this paper proposes a low-cost, cross-platform SCARA robot digital-twin simulation system based on the concept of digital twins. This method establishes a 5D architecture based on the characteristics of different platforms, classifies data and integrates functions, and designs a data-processing layer for motion trajectory calculation and data storage for a virtual-reality robot. To address the complexity of data interaction under different cross-platform communication forms, an editable, modular, cross-platform communication system is constructed, and various control commands are encapsulated into simple programming statements for easy invocation. Experimental results showed that, based on modular communication control, users can accurately control data communication and synchronous motion between virtual and physical models using simple command statements, reducing the development cost of control algorithms. Meanwhile, the virtual-robot simulation system, as a data mapping of the real experimental platform, accurately simulated the physical robot’s operating state and spatial environment. The robot algorithms tested using the virtual simulation system can be successfully applied to real robot platforms, accurately reproducing the operating results of the virtual system.

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.

Repairs and Breaks Prediction for Deep Neural Networks

With the increasing prevalence of software incorporating deep neural networks (DNNs), quality assurance for these software systems has become a crucial concern. To this end, various methods have been proposed to repair the misbehavior of DNNs by modifying their weights. However, these repair methods may not meet the developer's needs for a given dataset and model. In this study, we build prediction models for repair outcomes (i.e., repairs and breaks) to help determine whether the repair method is likely to work. By using our prediction models, developers and operators of DNNs can decide whether or not to apply a repair method, and if so, which method to use. Our prediction models utilize four metrics as explanatory metrics that represent the confidence or ambiguity in the DNN predictions. We experimented with four repair methods and 10 datasets. The experimental results demonstrate that our prediction models successfully select a repair method that meets developers’ needs in 16 out of 24 cases, resulting in an average time saving of 16.29% compared to the naive method. Based on these results, our prediction models can reduce costs for developers and operators when deciding whether to employ repair methods for real-world applications of DNNs.

An Attempt to Predict Transparent Armor Ballistic Performance through Quasi-Static Punch Shear Test

Abstract Background Transparent armor systems are traditionally designed following a trial-and-error approach, which involves high development costs associated with ballistic testing. This research article presents a novel methodology, termed quasi-static multi-punch shear testing, within the domain of transparent armor systems. Objective The primary aim is to establish a correlation between multi-hit ballistic tests at Level III-A according to the NIJ 0108.01 standard, achieved through an adaptation of the single-shot ballistic limit methodology, and the quasi-static multi-punch shear testing. The objective is to utilize a simple experimental methodology that provides insights into the multi-hit ballistic behavior of transparent armors. Methods Parameters such as absorbed energy and observed damage mechanisms were utilized to assess the potential relationship between these tests. Transparent armor samples that underwent testing using the quasi-static multi-punch shear test were subsequently cross-sectioned using a water jet cutting machine to facilitate visualization of material damage. In addition, drawing on insights from quasi-static multi-punch shear testing results, the K-means clustering algorithm was employed to predict the likelihood of a specific transparent armor system passing a multi-hit ballistic test. Results Various damage mechanisms were observed as a function of the punch displacement, and correlations were made with the load–displacement curves. Furthermore, the implementation of the K-means clustering algorithm successfully classified transparent armor into two groups: those that passed the ballistic test and those that did not. Conclusions This research significantly advances understanding of transparent armor system behavior under multi-hit conditions and offers a promising predictive tool for evaluating their performance through straightforward and cost-effective experimentation.

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Nanotherapeutics, which merge nanotechnology with targeted drug delivery, exemplify this advancement by offering improved precision and reduced side effects in cancer therapy. The development of these nanotherapeutic agents depends critically on understanding nanoparticle (NP) properties and their biological interactions, often analyzed through molecular dynamics (MD) simulations. This study enhances these analyses by integrating ML with MD simulations, significantly improving both prediction accuracy and computational efficiency. We introduce a comprehensive three-stage methodology for predicting the solvent-accessible surface area (SASA) of NPs, which is crucial for their therapeutic efficacy. The process involves training an ML model to forecast the many-body tensor representation (MBTR) for future time steps, applying data augmentation to increase dataset realism, and refining the SASA predictor with both augmented and original data. Results demonstrate that our methodology can predict SASA values 299 time steps ahead with a 40-fold speed improvement and a 25% accuracy increase over existing methods. Importantly, it provides a 300-fold increase in computational speed compared to traditional simulation techniques, offering substantial cost and time savings for nanotherapeutic research and development.

A supervised machine learning model to select a cost-effective directional drilling tool

With the increased directional drilling activities in the oil and gas industry, combined with the digital revolution amongst all industry aspects, the need became high to optimize all planning and operational drilling activities. One important step in planning a directional well is to select a directional tool that can deliver the well in a cost-effective manner. Rotary steerable systems (RSS) and positive displacement mud motors (PDM) are the two widely used tools, each with distinct advantages: RSS excels in hole cleaning, sticking avoidance and hole quality in general, while PDM offers versatility and lower operating costs. This paper presents a series of machine learning (ML) models to automate the selection of the optimal directional tool based on offset well data. By processing lithology, directional, drilling performance, tripping and casing running data, the model predicts section time and cost for upcoming wells. Historical data from offset wells were split into training and testing sets and different ML algorithms were tested to choose the most accurate one. The XGBoost algorithm provided the most accurate predictions during testing, outperforming other algorithms. The beauty of the model is that it successfully accounted for variations in formation thicknesses and drilling environment and adjusts tool recommendations accordingly. Results show that no universal rule favors either RSS or PDM; rather, tool selection is highly dependent on well-specific factors. This data-driven approach reduces human bias, enhances decision-making, and could significantly lower field development costs, particularly in aggressive drilling campaigns.

Comprehensive Survey of Image Steganography Systems based on FPGA Implementation

Steganography is the art of concealed communication. The basic idea of steganography is to hide secret data inside a cover media in an undetectable manner. Cover media and Secret data can be any popular digital media such as image, video, audio, or text. Various types of image steganography methods have been proposed according to the cover image domain, i.e. spatial domain and transform domain. The spatial domain methods are easy and simple while the transform domain methods are more complex. However, the transform domain methods are more robust against image processing operations. Besides, they are more secure and less detectable in an unsecured channel. The recent steganography methods are based on sophisticated algorithms that include several computational time-consuming tasks, and hence they are unable to embed and extract the hidden data in real-time. Therefore, the trend is to implement the steganography methods in the hardware to speed up their processing time and so improves the efficacy of steganography techniques. FPGA is used in various fields in the modern era due to lower development cost, flexibility, and reconfigurability. Most of the published steganography techniques carried out on FPGA are based on the spatial domain. In this paper, it has comprehensively studied and reviewed various existing implementations of image steganography on FPGAs. This includes studying their general operations, requirements, and performance evaluations. This review would assist researchers in finding the research gaps in FPGA implementation of steganography methods for real-time applications.

The Surveillance Strategy for Intraductal Pancreatic Mucinous Neoplasms: Criteria for Discontinuation.

To review the surveillance of IPMN, the risk of pancreatic cancer and the cost of surveillance. The increasing IPMN prevalence and low pancreatic cancer associated with IPMN question the necessity and cost-effectiveness of surveillance. Guidelines favour a 'watch and wait' approach, lacking clarity on stopping surveillance. This study aims to identify patients with pancreatic cancer risk equivalent to their age group, create guidelines for stopping surveillance and reduce NHS costs. Retrospective analysis of IPMN patients on surveillance in the WoS. Clinicopathological data were collected. Endpoints included pancreatic cancer development and surveillance pathway cost estimation. Age-matched controls were used for comparison using standardised incidence ratios (SIRs) for pancreatic cancer. Of 746 patients, 27 (3.62%) were resected. 3 (0.402%) developed pancreatic cancer and 44 (5.90%) developed worrisome features/ high-risk stigmata after a median surveillance of 48 (IQR 48) months. 221 (29.6%) had a stable cyst for at least 5 years and their SIR was 1.56 (95% CI 0.04-8.71). Patients ≥75 years with stable cysts for ≥5 years, SIR was 1.71 (95% CI 0.03-3.42). Patients ≥65 years with stable cysts of <15mm for ≥5 years and patients with stable cysts of <10mm for ≥5 years, had SIRs of 0. The cost of surveillance was £6,330.36 ($8,105.65) per resected patient and £2,032.78 ($2,602.85) per non-resected patient. Patients with stable IPMNs have similar pancreatic cancer risk as the general population. Surveillance discontinuation can be considered after 5 years in a cohort of patients, saving £106,211.19 ($136,020.42) per year.

ANALISIS BIAYA INVESTASI AIR CURAH PADA SISTEM PENYEDIAAN AIR MINUM (SPAM) BENDUNGAN TAMBLANG DI KABUPATEN BULELENG

Buleleng Regency has sub-districts that still experience a shortage of raw water. Tamblang Dam is one of the sources of raw water that will be utilized to meet the needs of raw water in Buleleng Regency. Given the extreme topographic conditions in Buleleng Regency, an economic feasibility study is needed which is used as a reference for the implementer of development and becomes a standard for the feasibility of this infrastructure development. Tariff determination can be done by calculating the capex and opex. Capex is a component of the operational costs required and opex is the investment cost of development. Raw Water consists of a Transmission Unit consisting of Preparatory work for permits, with the source of funds from the APBN, Consulting Services and Stages of Supervision services from the source of funds from the APBN, Construction Work Civil Works, Mechanical Electrical Works IPA and JDU, Mechanical Electrical Works Distribution Reservoirs with the source of funds from the APBN while in the Production unit the preparation work, planning and supervision of the Distribution Reservoir uses the APBD budget. The total value of the overall SPAM Tamblang Reservoir RAB in Buleleng Regency is Rp299,887,860,916.13 plus a tax of Rp332,875,526,000.00.

Determining the influence of data on working with video materials on the accuracy of student success prediction models

The object of this study is models for predicting students’ success, constructed on the basis of machine learning methods. The paper reports results of research into the problem of improving their accuracy by expanding the data set for training the specified models. The most available are data on student actions, which are automatically collected by learning management systems. Entering additional information about students’ work increases time and resources but allows the improvement of the accuracy of the models. In the study, information about students’ work with video materials, particularly the number and duration of views, was entered into the original data set. To automate the collection of this data, the plugin for the Moodle system has been developed, which stores information about user’s actions with the video player and the duration of watching video materials in the database. Model training was carried out using Naive Bayes (NB), logistic regression (LR), random forest (RF), and neural networks (NN) algorithms with and without video data. For the models using video viewing data, accuracy increased by 10 %, balanced accuracy by 15 %, and overall performance, expressed as area under the curve (AUC), increased by 14 %. The highest prediction accuracy, with a difference of 1.8 %, was obtained by models built using RF algorithms – 87.1 % and NN – 85.3 %. At the same time, the accuracy of the models obtained by the NB and LR algorithms was 70.7 % and 76.5 %. The increase in accuracy for them was 2.3 % and 8.1 %, respectively. Analysis of calculations confirms the assumption that students’ work with educational video materials is correlated with their success. The results make it possible to find a reasonable compromise between model development costs and its accuracy at the stage of data preparation for model training.

AC thin-film electroluminescence: A historical overview with a look ahead

50 years ago, television screens used bulky cathode ray tubes (CRTs) with a size limited to about 70 cm in diagonal (28 in.), while flat panel displays belonged to the realm of science fiction. Although the performance of CRTs in terms of energy consumption, size, and color rendering slowly improved, there were numerous applications where flat panel displays would present a tremendous advantage, notably for confined spaces and in portable equipment. Starting from the 1970s, AC thin-film electroluminescence (ACTFEL) was investigated as a high performance alternative to the—also emerging—liquid crystal displays (LCDs) and plasma display panels (PDPs). For a number of years, ACTFEL developed from the phase of fundamental research to commercial products (for monochrome displays) and full color prototype displays. However, due to the fast development and strong cost reduction of LCDs, ACTFEL did not remain competitive and most of the research activities were ceased. The current overview sketches a brief history of the ACTFEL technology, its merits and limitations, and the reasons why such displays still have their use in some niche applications.

The Application of Virtual Reality and Augmented Reality Technology in Digital Marketing

This study investigates the utilization of Virtual Reality (VR) and Augmented Reality (AR) technologies in digital marketing, particularly their impacts on user engagement, brand awareness, conversion rates, and customer experience. With the progression of digital marketing, VR and AR have emerged as significant tools that create immersive and interactive experiences, aiding brands in distinguishing themselves within a competitive market. By analyzing applications such as virtual product demonstrations, interactive advertising campaigns, virtual showrooms, and AR shopping applications, this research underscores the substantial advantages these technologies confer. The findings reveal that VR and AR not only improve user engagement, brand visibility, but also boost customer satisfaction and drive sales. Despite obstacles such as high development costs and technical challenges, the outlook for VR and AR in digital marketing remains promising. This study provides strategic insights for businesses on effectively incorporating these technologies into their marketing frameworks to ensure long-term success and competitive advantage.

Novel dual-pathogen multi-epitope mRNA vaccine development for Brucella melitensis and Mycobacterium tuberculosis in silico approach.

Brucellosis and Tuberculosis, both of which are contagious diseases, have presented significant challenges to global public health security in recent years. Delayed treatment can exacerbate the conditions, jeopardizing patient lives. Currently, no vaccine has been approved to prevent these two diseases simultaneously. In contrast to traditional vaccines, mRNA vaccines offer advantages such as high efficacy, rapid development, and low cost, and their applications are gradually expanding. This study aims to develop multi-epitope mRNA vaccines argeting Brucella melitensis and Mycobacterium tuberculosis H37Rv (L4 strain) utilizing immunoinformatics approaches. The proteins Omp25, Omp31, MPT70, and MPT83 from the specified bacteria were selected to identify the predominant T- and B-cell epitopes for immunological analysis. Following a comprehensive evaluation, a vaccine was developed using helper T lymphocyte epitopes, cytotoxic T lymphocyte epitopes, linear B-cell epitopes, and conformational B-cell epitopes. It has been demonstrated that multi-epitope mRNA vaccines exhibit increased antigenicity, non-allergenicity, solubility, and high stability. The findings from molecular docking and molecular dynamics simulation revealed a robust and enduring binding affinity between multi-epitope peptides mRNA vaccines and TLR4. Ultimately, Subsequently, following the optimization of the nucleotide sequence, the codon adaptation index was calculated to be 1.0, along with an average GC content of 54.01%. This indicates that the multi-epitope mRNA vaccines exhibit potential for efficient expression within the Escherichia coli(E. coli) host. Analysis through immune modeling indicates that following administration of the vaccine, there may be variation in immunecell populations associated with both innate and adaptive immune reactions. These types encompass helper T lymphocytes (HTL), cytotoxic T lymphocytes (CTL), regulatory T lymphocytes, natural killer cells, dendritic cells and various immune cell subsets. In summary, the results suggest that the newly created multi-epitope mRNA vaccine exhibits favorable attributes, offering novel insights and a conceptual foundation for potential progress in vaccine development.

Facilitating the transfer and implementation of practices across health and care systems

Abstract The THCS partnership has developed the initial draft of a framework designed to facilitate the transfer and implementation of solutions across health and care systems. During the workshop, we will present this framework along with the preliminary results of its real-world testing in transfer and implementation activities. The goal of this presentation is to gather feedback on the framework and generate ideas for its further development. Choosing to transfer existing solutions rather than developing new ones can offer several advantages in terms of efficiency, reliability, and cost-effectiveness. Adopting existing solutions can save time and reduce development costs significantly. The THCS framework aims to support, promote, and accelerate the transfer and implementation of solutions across health and care systems. The THCS framework is primarily targeted at individuals involved in planning, coordinating, and executing development activities within health and care organizations. This framework includes: 1) Paradigmatic commitments that underpin its understanding of development activities, transferability, and implementation; 2) Principles for conducting the activities of transferring and adapting solutions developed elsewhere; 3) Guidelines for describing original solutions; and 4) Key development tasks that outline the necessary steps for transferring, adapting, and implementing solutions developed elsewhere.

R&amp;D Expenditures and Their Impact on Innovation in the Digital Era: A Kazakhstan Perspective

The article aims to assess the relationship between research and development costs and the level of innovation activity of enterprises in Kazakhstan. The research methodology involves correlation analysis, applying quantitative methods to analyze the collected data from official statistical yearbooks from 2007 to 2022. Correlation analysis with five variables on innovation activity and the impact of research and development costs on that, using Excel and Smart PLS 4 programs were used. According to the results of research, there was a positive impact of R&amp;D expenditure on the level of innovation activity of enterprises mostly, secondly on the ratio of innovative product to gross domestic product, volume of innovative products (goods, services), and showing not so strong correlation on costs of product and process innovations. Digital hubs perform as an ecosystem with a network approach, including organizations in a region to undergo digital transformation in the era of the digital economy and digital technologies. The results of current research might be helpful to academicians and public administration. The originality of the study consists in assessing the impact of R&amp;D expenditure on innovation management in a particular level of innovation activity, the share of innovative products about GDP, costs of product and process innovations, the volume of innovative products (goods, services) to a proper subsequent further contribution. As a result, it is recommended that policy measures focus on enhancing digital infrastructure and supporting R&amp;D investments to ensure sustained innovation performance and economic development.

Novel electrical characteristic measurement system of semiconductor films for estimating device characteristics

We proposed a novel measurement method and system to measure the electrical characteristics of non-patterned semiconductor films to estimate the final device’s properties. The proposed method is based on measuring bottom-gate structure thin-film transistor (TFT) using the probe pins as source and drain electrodes. We carefully designed the array structure and material of the probe pin to effectively detect extremely low-level currents without patterned electrodes on the semiconductor film. The contact pressure was also carefully controlled through continuous monitoring using an implemented pressure sensor to ensure the reliability and reproducibility of the measured data. In addition, we developed a method of extracting device parameters, such as the threshold voltage and field-effect mobility, from the measured data, considering the geometric factor of the proposed method. Comparative analysis between the measurement results of the film sample using the proposed system and that of the final TFT sample exhibited similar results with slight deviations. Furthermore, the proposed system provided similar results under repetitive measurements. Consequently, the proposed method and system successfully demonstrated the estimation of the final TFT’s properties by measuring the electrical characteristics of the non-patterned semiconductor film with excellent reliability, highlighting the possibility of effectively reducing costs for development and fabrication.

Identifying the proximal cue(s) for pupal color variation in the bordered patch butterfly, Chlosyne lacinia (Lepidoptera: Nymphalidae)

Abstract Coloration is a multifaceted trait that serves various functions, including predator defense, thermoregulation, and immune response, among others. We investigated pupal color variation in Chlosyne lacinia pupae, focusing on identifying the cue that influences variation in melanization. Through laboratory experiments, we demonstrated that pupae reared on black backgrounds exhibited significantly higher melanization compared to those on white backgrounds. Additionally, black pupae experienced longer developmental periods, suggesting a trade-off between defense and developmental time. Our findings support crypsis as a likely evolutionary driver for increased melanization in response to substrate color. We discuss potential implications for predator avoidance, immune response, and developmental costs associated with melanization. This study provides insights into the adaptive significance of pupal melanization in response to environmental cues, shedding light on the complex interplay between life history traits in butterflies.

Pricing policies of dual-channel green supply chain: considering manufacturers’ dual behavioural preferences and government subsidies

As the green economy and e-commerce evolve, some manufacturers begin to engage in green production and use online direct channels to enhance competitiveness. However, the additional cost of green production makes manufacturers often show dual behavioural preferences of fairness concern and risk aversion when making decisions. Based on this, this study constructs the optimal pricing model of a dual-channel green supply chain, considering dual behavioural preferences of manufacturers’ fairness concern and risk aversion. In addition, no government subsidy, government research and development cost subsidy and green degree subsidy are also considered. This paper uses the Stackelberg game to find the optimal solution for the model. The findings illustrate that (1) Government subsidy mechanisms cannot coordinate the effects of manufacturers’ dual behavioural preferences on pricing policies of a dual-channel green supply chain. (2) Under the three different government subsidy conditions, manufacturers’ fairness concern and risk aversion will all reduce the green degree of products and online direct price of products. (3) The government research and development cost subsidy and green degree subsidy can both improve the green degree of products, offline retail price, market demand and members’ expected profits.