Development Costs Research Articles

Rational construction of a novel fluorescent probe for imaging peroxynitrite in the endoplasmic reticulum during drug-induced liver injury

Drug-induced liver injury (DILI) not only poses a serious health threat to patients, but is also a major obstacle to the development of new drugs. The liver toxicity of many drugs is not detected until late in the development process or even after marketing, which significantly increases the cost of new drug development. This may be limited by the lack of tools to detect DILI. Studies have shown that abnormal changes in peroxynitrite (ONOO-) levels in the endoplasmic reticulum of liver cells may be an early manifestation of DILI. Therefore, the development of fluorescent probes to detect ONOO- in the endoplasmic reticulum may provide assistance in the detection of DILI and the screening of new drugs. Herein, we report a fluorescent probe, ER-N, capable of detecting ONOO- in the endoplasmic reticulum with high selectivity and sensitivity. The probe selects di(trifluoromethyl)benzene as the targeting group of the endoplasmic reticulum, which possesses excellent targeting to the endoplasmic reticulum, and provides a new design method for the establishment of other endoplasmic reticulum-targeting probes. In addition, the probe ER-N successfully realized the detection of endogenous and exogenous ONOO- in cells and zebrafish. More importantly, the probe ER-N was able to be used for tracing changes in the levels of ONOO- in the endoplasmic reticulum of cells and tissues under drug stimulation. Together, these results suggest that the probe ER-N has the potential to be a tool for DILI detection and new drug screening.

Comparing invitro cytotoxic drug sensitivity in colon and pancreatic cancer using 2D and 3D cell models: Contrasting viability and growth inhibition in clinically relevant dose and repeated drug cycles.

In vitro drug screening that is more translatable to the invivo tumor environment can reduce both time and cost of cancer drug development. Here we address some of the shortcomings in screening and show how treatment with 5-fluorouracil (5-FU) in 2D and 3D culture models of colorectal cancer (CRC) and pancreatic ductal adenocarcinomas (PDAC) give different responses regarding growth inhibition. The sensitivity of the cell lines at clinically relevant 5-FU concentrations was monitored over 4 days of treatment in both 2D and 3D cultures for CRC (SW948 and HCT116) and PDAC (Panc-1 and MIA-Pa-Ca-2) cell lines. The 3D cultures were maintained beyond this point to enable a second treatment cycle at Day 14, following the timeline of a standard clinical 5-FU regimen. Evaluation after one cycle did not reveal significant growth inhibition in any of the CRC or PDAC 2D models. By the end of the second cycle of treatment the CRC spheroids reached 50% inhibition at clinically achievable concentrations in the 3D model, but not in the 2D model. The PDAC models were not sensitive to clinical doses even after two cycles. High content viability metrics point to even lower response in the resistant PDAC models. This study reveals the limitations of testing drugs in 2D cancer models and short exposure in 3D models, and the importance of using appropriate growth inhibition analysis. We found that screening with longer exposure and several cycles of treatment in 3D models suggests a more reliable way to assess drug sensitivity.

Artificial intelligence for drug repurposing against infectious diseases

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated drug repurposing. AI allows researchers to analyze massive datasets, revealing hidden connections between existing drugs, disease targets, and potential treatments. This approach boasts several advantages. First, repurposing existing drugs leverages established safety data and reduces development time and costs. Second, AI can broaden the search for effective therapies by identifying unexpected connections between drugs and potential new targets. Finally, AI can help mitigate limitations by predicting and minimizing side effects, optimizing drugs for repurposing, and navigating intellectual property hurdles. The article explores specific AI strategies like virtual screening, target identification, structure base drug design and natural language processing. Real-world examples highlight the potential of AI-driven drug repurposing in discovering new treatments for infectious diseases.

Assessment of low-cost housing provision as government social responsibility towards achievement of sustainable development goal 11: stakeholders’ perspective

Studies show that the government’s role can improve low-cost housing (LCH) to achieve SDGs linked with housing. LCH provision as a government social responsibility to achieving Goal 11 may raise critical issues in Malaysia. Therefore, this study investigated the factors hindering Malaysia’s Government from providing LCH as a social responsibility to achieving Goal 11. The research adopted a qualitative approach via phenomenology. It covered four major cities in Malaysia and achieved saturation at the 35th selected participant. Findings reveal that housing provision for all Malaysia’s low-income earners (LIEs) is an issue. The complexity of the problems, such as government inadequate funding, housing market financialisation, faulty LCH registration system, LCH high development and construction cost, under-declaration of household income by an unqualified person, LIEs collateral lacking, inability to provide 10% down payment, reluctance by mortgage institutions to lend loans to LIEs, entitlement syndrome, and inadequate LCH data sharing between federal and states governments may hinder Malaysia’s Government alone from LCH provision as a social responsibility to the low-income groups. Findings show that achieving Goal 11 may be threatened if these issues are not addressed and suggest measures to improve LCH to achieve Goal 11 via all-inclusive mechanisms.

Development and Application of 3D Printing Manufacturing Technology in Intelligent Vehicle Design

With the rapid development of intelligent vehicle technology, traditional vehicle design and manufacturing methods are facing many challenges. Traditional vehicle design and manufacturing methods have problems such as high cost, long development cycle and lack of personalized design. This paper took advantage of the high degree of flexibility, rapid customization and cost savings of 3D printing technology, which can provide entirely new solutions for intelligent vehicle design. This paper mainly analyzed the application of three-dimensional (3D) printing and manufacturing technology in intelligent vehicle design. In intelligent vehicle design, 3D printing can carry out 3D printed body structure design to make parts lightweight. The displacement sensor in the intelligent sensor can measure the displacement, deformation and vibration of the body components, and detect whether the body structure exceeds the safety range, which can provide safety feedback to the owner. 3D printing technology has been involved in automotive dashboard, interior design and customization. In this paper, the effects of vehicles designed under traditional methods and vehicles designed under 3D printing manufacturing technology were compared. The research and development cycle of class A, Class B, Class C and Class D in brand 1 under traditional manufacturing was 3.85 years, 3.13 years, 4.27 years and 4.32 years respectively. The R&D (research and development) cycle of grade A, B, C and D in brand 1 under 3D printing technology was 2.43 years, 2.05 years, 1.64 years and 1.73 years respectively. Under the traditional method, the average R&D cost of different levels of vehicles in brand 1, brand 2 and brand 3 was 191,000 yuan, 20900 yuan and 227,200 yuan respectively. Under 3D printing technology, the average R&D cost of different levels of vehicles in brand 1, brand 2 and brand 3 was 132,600 yuan, 145,300 yuan and 157,500 yuan respectively. The results of this paper emphasize that the application of 3D printing manufacturing technology shortens the development cycle of intelligent vehicles, reduces the development cost, and can be customized, which can be more in line with the needs of users.

Socio-cultural risks of multimodal large generative models of "artificial intelligence" (GenAI)

The article is devoted to the study of the conditions for ensuring the information security of Russian citizens when using generative "artificial intelligence" technologies in the socio-cultural sphere. The relevance of the topic is due to the modern high rates of development of computer neural networks that generate multimedia content: texts, images, sounds and videos. The developers classify generative technologies as "artificial intelligence", position them as a "new nuclear project" capable of radically increasing the productivity of socio-cultural creativity, and receive significant government, corporate and investment financing. The object of the study is modern multimedia generative models, the subject of the study is the possibility of their use in the socio-cultural sphere of creativity and the associated risks of information security. The purpose of the study is to determine the conditions for ensuring the information security of Russian citizens when using multimodal generative technologies in the socio-cultural sphere. The research materials are scientific publications of recent years (2021–2024) in Russian journals of the HAC list (categories K1, K2) and international Scopus publications (quartiles Q1, Q2) devoted to research and critical analysis of the possibilities of multimodal generative models, associated risks and security tools. The philosophical methodology is applied: theoretical and cultural analysis, synthesis. The scientific novelty of the article is due to the application of a philosophical theoretical and cultural methodology for a critical comparison of the declarations of developers and the actual potential of applications of multimodal generative technologies. The result of the study is an assessment of how greatly exaggerated the risks predicted based on the positioning of the technologies in question as "artificial intelligence". The real risks are proposed to include: the incompatibility of development costs with the usefulness of the results; lowering the cultural level of professional and amateur creativity and worsening the tastes of the mass audience; use in "social engineering", fraud, mass disinformation, fake news, manipulation of public consciousness, "cancellation culture", destruction of traditional values and substitution of socio-cultural identity. The means of ensuring the safety of Russian citizens in the development and use of multimedia generative technologies in the socio-cultural sphere are recommended.

Research on Smart City Platform Construction Technology for Digital Twins

Abstract Urban digital twin is a key step to build a smart city, digital twin is an important application scenario for smart city platform, and the relationship between the two are both current research hotspots. In this paper, we will start from the demand of digital twin on smart city platform, study the architecture method and key technology of smart city platform, in this paper's platform construction method compared to the traditional construction method, reduces the difficulty of digital twin smart city construction, and also reduces the coupling degree between smart city platform modules, and use a smart city platform for engineering verification. Finally compared with the traditional smart platform construction techniques, the techniques in this paper are better than the traditional ones in terms of coupling, difficulty and cost. Through engineering verification and experimental results show that this paper on the digital twin-oriented smart city construction technology, the coupling degree of each module is the lowest, and in the development efficiency experiments, this paper by comparing with the traditional technology, the experimental development cycle compared to the traditional technology can be shortened by 61.7% of the development cycle, greatly reducing the development cost and improving the construction efficiency.

Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound.

A transfer function approach was recently demonstrated to mitigate data mismatches at the acquisition level for a single ultrasound scanner in deep learning (DL)-based quantitative ultrasound (QUS). As a natural progression, we further investigate the transfer function approach and introduce a machine-to-machine (M2M) transfer function, which possesses the ability to mitigate data mismatches at a machine level. This ability opens the door to unprecedented opportunities for reducing DL model development costs, enabling the combination of data from multiple sources or scanners, or facilitating the transfer of DL models between machines. We tested the proposed method utilizing a SonixOne machine and a Verasonics machine with an L9-4 array and an L11-5 array. We conducted two types of acquisitions to obtain calibration data: stable and free-hand, using two different calibration phantoms. Without the proposed method, the mean classification accuracy when applying a model on data acquired from one system to data acquired from another system was 50%, and the mean average area under the receiver operator characteristic (ROC) curve (AUC) was 0.405. With the proposed method, mean accuracy increased to 99%, and the AUC rose to the 0.999. Additional observations include the choice of the calibration phantom led to statistically significant changes in the performance of the proposed method. Moreover, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view. Lastly, the proposed method proved effective when a different transducer was used in the test machine.

Development of low-cost air particulate IoT measurement using HM-3301 sensor

Abstract Air quality measurement is essentially important to create a safe and healthier environment. In this study, the accuracy and precision of the Grove HM-3301 sensor were evaluated and compared with the data taken from a standard air quality monitoring meter. Using the low-cost air particulate measuring sensor, the measuring results of the PM1, PM2.5, and PM10 concentration was fairly accurate, with a relative bias smaller than 30 %. The accuracy of the HM-3301 sensor can be improved using single-point calibration, with relative bias reduced to the limit of 13 % as tested in the particle mass concentration limit within 500 μg/m3. With improved accuracy, the air particulate IoT system using the HM-3301 sensor can be employed in practical applications to assess air quality. With a development cost of 90 USD, the PM measurement IoT system is suitable to be applied in monitoring outdoor and indoor air quality.

Innovative Systems Engineering Solutions for Power-Positive Operations: Navigating the Multi-Constraint Challenges of the SWARM-EX CubeSat Mission

Although the small stature of CubeSats and their standardized deployer options help to lower unit development cost and facilitate launch opportunities, the physical size limits of CubeSats prove to be a double-edged sword vis-à-vis sustaining a stable power state while hosting instruments with high power demands and often strict pointing requirements. For the Space Weather Atmospheric Reconfigurable Multiscale-EXperiment (SWARM-EX), this issue is magnified by the mission’s ambitious goals; to comply with mission requirements, a SWARM-EX spacecraft is required to concurrently (1) point the science instruments no more than 30° off ram when they are operational, (2) point the GPS patch antenna no more than 30° off zenith at least once per orbit, (3) point the boresight of the X-Band patch antenna no more than 18° from the ground station during downlink, (4) maximize the differential cross-sectional area during differential drag maneuvers, and (5) maximize solar array power generation at all times. Consequently, leading-edge CubeSat missions like SWARM-EX require innovative systems engineering solutions to remain power-positive during on-orbit operations. Through the design of a comprehensive module-based concept of operations, orbital power generation simulations, intricate constrained attitude profiles, and a configurable battery state of charge simulation tool, the SWARM-EX mission designers have conceived a plan to retire the risk of not maintaining a power-positive state while successfully meeting all mission requirements; it is the aim of the authors to illuminate these strategies as a case study.

Predicting drug-target interactions using matrix factorization with self-paced learning and dual similarity information.

Drug repositioning (DR) refers to a method used to find new targets for existing drugs. This method can effectively reduce the development cost of drugs, save time on drug development, and reduce the risks of drug design. The traditional experimental methods related to DR are time-consuming, expensive, and have a high failure rate. Several computational methods have been developed with the increase in data volume and computing power. In the last decade, matrix factorization (MF) methods have been widely used in DR issues. However, these methods still have some challenges. (1) The model easily falls into a bad local optimal solution due to the high noise and high missing rate in the data. (2) Single similarity information makes the learning power of the model insufficient in terms of identifying the potential associations accurately. We proposed self-paced learning with dual similarity information and MF (SPLDMF), which introduced the self-paced learning method and more information related to drugs and targets into the model to improve prediction performance. Combining self-paced learning first can effectively alleviate the model prone to fall into a bad local optimal solution because of the high noise and high data missing rate. Then, we incorporated more data into the model to improve the model's capacity for learning. Our model achieved the best results on each dataset tested. For example, the area under the receiver operating characteristic curve and the precision-recall curve of SPLDMF was 0.982 and 0.815, respectively, outperforming the state-of-the-art methods. The experimental results on five benchmark datasets and two extended datasets demonstrated the effectiveness of our approach in predicting drug-target interactions.

Market segmentation using predictive technology and shared-service contracts

‘Shared-service contracts’ provided by Original Equipment Manufacturers (OEMs) are a standard feature of capital-intensive, long-life products. In such contracts, OEMs assume a predetermined portion of the products’ long-term failure opportunity costs by conducting routine and breakdown maintenance tasks for their customers. Recently, OEMs have been utilizing predictive technology to improve service productivity. It enables OEMs to reduce failure opportunity costs mentioned while using the data generated to develop products at lower costs. We extend the adverse selection framework to model the market segmentation of products offered with shared-service contracts and predictive technology. Our model includes: (i) the cost of product development and operation, (ii) the failure opportunity cost driven by long-term product failures, (iii) the cost of designing product reliability, and (iv) the cost of incorporating predictive technology, which reduces the first two cost components. Here, the OEM targets a basic and a premium offering at their respective customer types. We find that using predictive technology improves the quality and price of OEM products, thereby increasing their profitability. However, predictive technology enables the OEM to design a product line with lower reliability. In addition, the OEM is more likely to choose a ‘premium-only’ strategy when predictive technology is present.

Features of State Support for the Agricultural Sector in Russia and Other Countries in the Context of Structural Changes in the Economy

The implementation of structural changes in the economy is conditioned by modern conditions characterized by numerous challenges of a geopolitical, socio-economic and climatic nature, endangering the national security of States. This is manifested in changes in the distribution of factors of production, in the production of gross value added by sectors, the search for new markets for products, which causes the transformation of conditions that determine economic dynamics, that is, an increase in the importance of some sectors and a decrease in the importance of other sectors. Thus, in Russia in 2023 there was a large-scale structural shift, with the manufacturing industry becoming the main driver of the economy. There is a potential for economic growth in the country based on structural changes caused by a change in the technological level of production and the transformation of foreign economic relations, which is true for all sectors of the economy, including the agricultural sector. Currently, this sector is particularly faced with issues of ensuring food security, maintaining agricultural producers’ incomes, technological sovereignty, introducing resource-saving technologies, establishing new transport and logistics routes, diversifying production in accordance with changing demand, increasing the competitiveness of agri-food products on the world market, and exporting products with high added value. In the current circumstances, government regulation is especially necessary aimed at improving the institutional conditions for the development of the agricultural sector of the economy and the agri-food market, creating an effective management apparatus at the governmental and regional levels for the successful implementation of strategic directions for the development of the sector, including increasing costs for basic science and long-term technological developments and the development of industrial cooperation both within the country and with foreign partners.

#1343 Microfluidic kidney tubuloids-on-a-chip for improved drug translation

Abstract Background and Aims Current 2D in vitro models of renal epithelium lack key features of the in vivo setting, such as tubular structure and perfusion, resulting in low translatability to human situations and failure in clinical translation. Advanced renal in vitro models are of great importance to study kidney function under healthy and diseased conditions. Kidney tubular epithelial organoids, called tubuloids, are three-dimensional multicellular structures that recapitulate tubular function and have been used to model genetic, metabolic, and infectious renal diseases. To increase physiological relevance of the tubuloids, they can be integrated into an organ-on-a-chip system. This microfluidic technique is becoming increasingly recognized as a valuable tool for adding physiologically relevant cues to traditional cell culture including long-term gradient stability, continuous perfusion, and interaction with other cell types such as vasculature. Method Here, we introduce the OrganoPlate microfluidic platform, which can accommodate up to 64 independent microfluidic chips in a microtiter plate format, allowing the growth of 64 independent kidney tubuloid-derived barrier tissues in the form of perfused tubules. To this end, tubuloids derived from adult kidney tissue were plated as single cells in the OrganoPlate against an extracellular matrix (ECM). These renal tubules can be formed in just four days of culture in the device showing rapid and reproducible cell polarization, tight junction formation, proper expression of renal markers and functional transport. Results When integrated into an OrganoPlate system, kidney tubuloids form leak-tight, perfusable tubes with stable Trans Epithelial Electrical Resistance (TEER) and are suitable for high-throughput screening of compound effects through assessment of barrier integrity by use of OrganoTEER and by real-time imaging of transport. OrganoPlate grown kidney tubes treated with Pgp inhibitor Verapamil show significant reduction of Rhodamine123 transport through kidney tubule barrier which confirms show stable activity of Pgp transporter and usability of the model in studying renal drug clearance. Conclusion Our results demonstrate the suitability of our in vitro microfluidic kidney tubuloid-on-a-chip model in mimicking key physiological aspects of the kidney and offer new ways for studying organ physiology and renal disease mechanisms, toxicity screening, and personalized medicine. Additionally, the use of animal models, as well as the costs of drug development, can be reduced.

MiRAGE: mining relationships for advanced generative evaluation in drug repositioning.

Drug repositioning, the identification of new therapeutic uses for existing drugs, is crucial for accelerating drug discovery and reducing development costs. Some methods rely on heterogeneous networks, which may not fully capture the complex relationships between drugs and diseases. However, integrating diverse biological data sources offers promise for discovering new drug-disease associations (DDAs). Previous evidence indicates that the combination of information would be conducive to the discovery of new DDAs. However, the challenge lies in effectively integrating different biological data sources to identify the most effective drugs for a certain disease based on drug-disease coupled mechanisms. In response to this challenge, we present MiRAGE, a novel computational method for drug repositioning. MiRAGE leverages a three-step framework, comprising negative sampling using hard negative mining, classification employing random forest models, and feature selection based on feature importance. We evaluate MiRAGE on multiple benchmark datasets, demonstrating its superiority over state-of-the-art algorithms across various metrics. Notably, MiRAGE consistently outperforms other methods in uncovering novel DDAs. Case studies focusing on Parkinson's disease and schizophrenia showcase MiRAGE's ability to identify top candidate drugs supported by previous studies. Overall, our study underscores MiRAGE's efficacy and versatility as a computational tool for drug repositioning, offering valuable insights for therapeutic discoveries and addressing unmet medical needs.

Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder for which current treatments are limited and drug development costs are prohibitive. Identifying drug targets for ASD is crucial for the development of targeted therapies. Summary-level data of expression quantitative trait loci obtained from GTEx, protein quantitative trait loci data from the ROSMAP project, and two ASD genome-wide association studies datasets were utilized for discovery and replication. We conducted a combined analysis using Mendelian randomization (MR), transcriptome-wide association studies, Bayesian colocalization, and summary-data-based MR to identify potential therapeutic targets associated with ASD and examine whether there are shared causal variants among them. Furthermore, pathway and drug enrichment analyses were performed to further explore the underlying mechanisms and summarize the current status of pharmacological targets for developing drugs to treat ASD. The protein-protein interaction (PPI) network and mouse knockout models were performed to estimate the effect of therapeutic targets. A total of 17 genes revealed causal associations with ASD and were identified as potential targets for ASD patients. Cathepsin B (CTSB) [odd ratio (OR) = 2.66 95, confidence interval (CI): 1.28-5.52, P = 8.84 × 10-3], gamma-aminobutyric acid type B receptor subunit 1 (GABBR1) (OR = 1.99, 95CI: 1.06-3.75, P = 3.24 × 10-2), and formin like 1 (FMNL1) (OR = 0.15, 95CI: 0.04-0.58, P = 5.59 × 10-3) were replicated in the proteome-wide MR analyses. In Drugbank, two potential therapeutic drugs, Acamprosate (GABBR1 inhibitor) and Bryostatin 1 (CASP8 inhibitor), were inferred as potential influencers of autism. Knockout mouse models suggested the involvement of the CASP8, GABBR1, and PLEKHM1 genes in neurological processes. Our findings suggest 17 candidate therapeutic targets for ASD and provide novel drug targets for therapy development and critical drug repurposing opportunities.

Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs.

Artificial intelligence in drug repurposing for rare diseases: a mini-review.

Drug repurposing, the process of identifying new uses for existing drugs beyond their original indications, offers significant advantages in terms of reduced development time and costs, particularly in addressing unmet medical needs in rare diseases. Artificial intelligence (AI) has emerged as a transformative force in healthcare, and by leveraging AI technologies, researchers aim to overcome some of the challenges associated with rare diseases. This review presents concrete case studies, as well as pre-existing platforms, initiatives, and companies that demonstrate the application of AI for drug repurposing in rare diseases. Despite representing a modest part of the literature compared to other diseases such as COVID-19 or cancer, the growing interest, and investment in AI for drug repurposing in rare diseases underscore its potential to accelerate treatment availability for patients with unmet medical needs.

Empowering software reliability: Leveraging efficient fault detection and removal efficiency

Advancements in science and technology have led to the widespread use of computer systems in various applications, emphasizing the importance of software reliability. Software failures can have severe consequences, making thorough testing crucial. Software reliability growth models (SRGMs) play a significant role in enhancing reliability by predicting improvement over time. This article introduces a comprehensive approach to software reliability that incorporates a dynamic fault detection rate, along with fault removal efficiency. The fault detection rate measures the rate at which faults are identified during testing, reflecting the effectiveness of the testing process. By incorporating this dynamic component, the model provides a more accurate estimation of software reliability and enables adaptive testing strategies and resource allocation. Achieving a high fault detection rate is desirable, but organizations must consider the cost implications and strike a balance between reliability and time-to-market constraints. This article extends the analysis to calculate the optimal release time and optimal warranty period that minimize development costs, subject to the desired reliability. By considering these factors, development teams can make informed decisions regarding the timing of software release and the duration of the warranty period, optimizing both reliability and cost.

A high-performance dataflow-centric optimization framework for deep learning inference on the edge

Edge computing has been emerging as a popular scenario for model inference. However, the inference performance on edge devices (e.g., Multi-Core DSP, FGPA, etc.) suffers from inefficiency due to the lack of highly optimized inference frameworks. Previous model inference frameworks are mainly developed in an operator-centric way, which provides insufficient acceleration to edge-based inference. Besides, the operator-centric framework incurs significant costs for continuous development and maintenance.Targeting the existing drawbacks of operator-centric frameworks, we design Xenos, which can automatically conduct dataflow-centric optimization of the computation graph and accelerate inference in two dimensions. Vertically, Xenos develops operator linking technique to improve data locality by restructuring the inter-operator dataflow. Horizontally, Xenos develops DSP-aware operator split technique to enable higher parallelism across multiple DSP units. Our evaluation demonstrates the effectiveness of vertical and horizontal dataflow optimization, which reduce the inference time by 15.0%–84.9% and 17.9%–89.9% , respectively. Besides, Xenos also outperforms the widely-used TVM by 1.1×–1.9×. Moreover, we extend Xenos to a distributed solution, which we call d-Xenos. d-Xenos employs multiple edge devices to jointly conduct the inference task and achieves a speedup of 3.68×–3.78× compared with the single device.