Efficient Software Research Articles

Optimizing multi-tenant database architecture for efficient software as a service delivery

Optimizing multi-tenant database architecture for efficient software as a service delivery

Gridder-HO: Rapid and efficient parallel software for high-order curvilinear mesh generation

The advancement in high-order computational methods is reshaping the landscape of mesh generation in Computational Fluid Dynamics (CFD), steering the focus towards curvilinear mesh techniques to meet the escalating accuracy demands. Gridder-HO, the software designed to generate high-order curvilinear mesh efficiently and rapidly, has been developed. Gridder-HO supports the elevation of meshes to P2 (quadratic-order) or P3 (cubic-order). It features a layered architecture and utilizes the concurrent hash table and the Alternating Digital Tree (ADT) data structure, supporting thread-level parallelism to convert straight-edge mesh into high-order curvilinear mesh seamlessly. Gridder-HO utilizes the projection method based on a thread pool to precisely preserve geometry, and employs a novel localized RBF method with ADT for volume node interpolation to untangle the mesh, which aims to achieve a satisfactory balance between efficiency and accuracy. Validated through CFD simulations using the GPU-accelerated Python Flux Reconstruction (PyFR) solver, the practicality of Gridder-HO is demonstrated across various Reynolds numbers in typical cases such as sphere, cylinder, and SD7003 airfoil. These results confirm the high-order curvilinear meshes generated by Gridder-HO meet the high-order requirements of emerging computational methods. Moreover, Gridder-HO exemplifies its effectiveness in generating large-scale, high-order curvilinear meshes for the DLR-F6 transport aircraft configuration standard test cases. It elevates a mesh with 5 million elements to P2 in 3 min 39 sec at 68% parallel efficiency on 16 threads, and another with 14 million elements to P3 in 52 min 39 sec at 60% efficiency, illustrating its efficiency and potential in satisfying the demands of complex geometries in engineering applications.

Transportation Vibration Effects on Apple Bruising

ABSTRACTVibrations during transportation often cause mechanical damage to fresh produce. Addressing or mitigating this damage requires efficient packaging solutions that maintain fresh produce integrity during transit, thereby preserving produce quality and market value. This study investigates the effects of various transportation profiles and packaging designs on apple bruising using simulated truck vibrations. Two apple varieties, Fuji and Jonagold, were packaged in corrugated paperboard boxes with paper pulp trays, plastic crates with paper pulp trays, corrugated paperboard boxes with volume packing and plastic crates with volume packing, all commercial apple packaging. The packaged apples were exposed to two random vibration profiles: leaf spring and air ride suspensions, both set at four different intensities ranging from 0.2 to 0.7 Grms for 1, 3 and 5 h. The findings revealed that both packaging type and vibration parameters significantly influenced apple bruising. Especially, plastic crates with paper pulp trays led to the most severe bruising, whereas corrugated paperboard boxes with volume packing afforded the most protection. Regardless of the packaging design, leaf spring suspension resulted in more apple bruising compared to air ride suspension. The findings of this study provide practical insights for packaging engineers to mitigate the apple bruising during transportation.

Evaluation of Taiwan's IC industry production and market efficiencies under the consideration of corporate social responsibility

AbstractTaiwan has a complete semiconductor industry chain and is important in global semiconductor manufacturing. In addition to considering operational conditions, companies have also attached importance to corporate social responsibility (CSR) in recent years. This research thus takes 60 integrated circuit (IC) companies in Taiwan as a research sample and adopts the Meta Two‐stage dynamic RDM DDF (range directional model directional distance function) under an exogenous CSR model to explore their market stage efficiency and production stage efficiency. This study aimed to analyze whether incorporating a company's CSR performance into performance evaluations affects the production and profitability efficiency of Taiwanese IC companies. The findings are as follows: Most IC companies practice CSR and demonstrate good CSR performance. The market stage efficiency and production stage efficiency of the IC packaging and testing group are better than those of other groups.

Prediction of HPC compressive strength based on machine learning

There is a complex high-dimensional nonlinear mapping relationship between the compressive strength of High-Performance Concrete (HPC) and its components, which has great influence on the accurate prediction of compressive strength. In this paper, an efficient robust software calculation strategy combining BP Neural Network (BPNN), Support Vector Machine (SVM) and Genetic Algorithm (GA) is proposed for the prediction of compressive strength of HPC. 8 features were extracted from the previous literature, and a compressive strength database containing 454 sets of data was constructed. The model was trained and tested, and the performance of 4 Machine Learning (ML) models, namely BPNN, SVM, GA-BPNN and GA-SVM, was compared. The results show that the coupled model is superior to the single model. Moreover, because GA-SVM has better generalization ability and theoretical basis, its convergence speed and prediction accuracy are better than GA-BPNN. Then Grey Relational Analysis (GRA) and Shapley analysis were used to verify the interpretability of the GA-SVM model, which showed that the water-binder ratio had the most significant influence on the compressive strength. Finally, the combination of multiple input variables to evaluate the compressive strength supplemented this research, and again verified the significant influence of water-binder ratio, providing reference value for subsequent research.

FnR: R package for computing inbreeding and numerator relationship coefficients

BackgroundInbreeding and relationship coefficients are essential for conservation and breeding programs. Whether dealing with a small conserved population or a large commercial population, monitoring the inbreeding rate and designing mating plans that minimize the inbreeding rate and maximize the effective population size is important. Free, open-source, and efficient software may greatly contribute to conservation and breeding programs and help students and researchers. Efficient methods exist for calculating inbreeding coefficients. Therefore, an efficient way of calculating the numerator relationship coefficients is via the inbreeding coefficients. i.e., the relationship coefficient between parents is twice the inbreeding coefficient of their progeny. A dummy progeny is introduced where no progeny exists for a pair of individuals. Calculating inbreeding coefficients is very fast, and finding whether a pair of individuals has a progeny and picking one from multiple progenies is computationally more demanding. Therefore, the R package introduces a dummy progeny for any pair of individuals whose relationship coefficient is of interest, whether they have a progeny or not.ResultsRuntime and peak memory usage were benchmarked for calculating relationship coefficients between two sets of 250 and 800 animals (200,000 dummy progenies) from a pedigree of 2,721,252 animals. The program performed efficiently (200,000 relationship coefficients, which involved calculating 2,721,252 + 200,000 inbreeding coefficients) within 3:45 (mm:ss). Providing the inbreeding coefficients (for real animals), the runtime was reduced to 1:08. Furthermore, providing the diagonal elements of D in A=TDT′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extbf{A} = \ extbf{TDT}^{\\prime }$$\\end{document} (d), the runtime was reduced to 54s. All the analyses were performed on a machine with a total memory size of 1 GB.ConclusionsThe R package FnR is free and open-source software with implications in conservation and breeding programs. It proved to be time and memory efficient for large populations and many dummy progenies. Calculation of inbreeding coefficients can be resumed for new animals in the pedigree. Thus, saving the latest inbreeding coefficient estimates is recommended. Calculation of d coefficients (from scratch) was very fast, and there was limited value in storing those for future use.

Evaluating and Managing the Microbial Contamination of Eye Drops: A Two-Phase Hospital-Based Study.

The microbial contamination of eye drop tips and caps varies between 7.7% and 100%. In seeking patient protection and continuous improvement, the Pharmacy Department in the Sterile Ophthalmological and Oncological Preparations Unit at Cochin Hospital AP-HP, Paris, France, conducted a two-phase study to compare the antimicrobial efficiency and practical use of standard packaging and a marketed eye drop container incorporating a self-decontaminating antimicrobial green technology by Pylote SAS at the tip and cap sites. The first phase was conducted in situ to identify the microbial contaminants of eye drops used in the hospital and community settings. A total of 110 eye drops were included for testing. Staphylococcus species were the most prevalent bacteria. Candida parapsilosis was detected in only one residual content sample and, at the same time, on the cap and tip. The second phase was performed in vitro, according to JIS Z2801. Reductions above one log in Staphylococcus aureus and Pseudomonas aeruginosa counts were noted in Pylote SAS eye drop packaging after 24 h of contact. The practical tests showed satisfactory results. Pylote SAS antimicrobial mineral oxide technology exhibited promising effects that combined effectiveness, safety, and sustainability to protect the patient by preventing infections due to the contamination of eye drop containers.

The effects of energy accommodation of reflected gas molecules on flow structures during expired spacecraft reentry

To study the influence of energy accommodation of scattering gas molecules on flow fields during large expired spacecraft reentry, a more elaborated gas-surface interaction model, compared with full Maxwellian diffuse model, is employed in implicit algorithms based on Boltzmann model equation. The characteristic distributions around cylinder at different fluid regimes are accordingly obtained by implicit algorithms, Navier-Stokes solver and DSMC ((Direct Simulation Monte Carlo) method. And the consistency of these results is verified. It is confirmed that present algorithms are capable of solving external flow problems covering various fluid regimes. Then the simulation results see that under current conditions set in the paper, pressure and temperature are proportional to wall activation (ω=Tw/T∞, Tw is surface temperature, T∞ denotes as free stream temperature), but their amplitudes alter with ω at different fluid regimes. As for the effects of energy accommodation coefficients (αe), both pressure and temperature profiles vary in a linear way with αe. However, the variation ranges of these parameters are diverse with regard to different fluid regimes. These observations are favor to the construction of efficient forecasting software, which could predict the flight path of large defunct spacecraft. In this forecasting software, the external ballistics computations and aerothermodynamic simulations are synchronously carried out.

A Multi-Agent Approach for the Optimized Operation of Modular Electrolysis Plants

In response to the energy transition to renewable resources, green hydrogen production via electrolysis is gaining momentum. Modular electrolysis plants provide a flexible and scalable solution to meet rising hydrogen demand and adapt to renewable energy fluctuations. However, optimizing their operation poses challenges, especially when dealing with heterogeneous electrolyzer modules. In this work, a combination of decentralized Multi-Agent Systems and the Module Type Package concept is presented that enhances the cost-optimized operation of modular electrolysis plants. This approach synergizes the individual strengths of Multi-Agent Systems in handling complex operational dynamics with the efficiency of the Module Type Package for integration and control capabilities. By integrating these technologies, the approach addresses the heterogeneity of electrolyzer modules and increases the adaptability, scalability, and operational flexibility of electrolysis plants. The approach was validated through a case study, demonstrating its effectiveness in achieving cost-optimized load scheduling, dynamic response to demand–supply fluctuations, and resilience against electrolyzer module malfunctions. In summary, the presented approach offers a comprehensive solution for the effective coordination and optimization of modular electrolysis plants.

Influence of Performance Packages on Fuel Consumption and Exhaust Emissions of Passenger Cars and Commercial Vehicles under WLTP

The transportation sector is responsible for about 16% of worldwide greenhouse gas emissions. Despite efforts for a sensible reduction by means of new technologies’ development, the average age of a vehicle fleet is 12.3 years in the European Union. In light of this, actions aiming at improving the efficiency of circulating vehicles can prove effective in the short to mid-term. Introducing performance packages in standard fuels could allow a reduction in the CO2 emissions of whole vehicle fleets without any modification to powertrain. Such a kind of additive is generally used in premium fuels; deposit control additives can reduce or control the deposits at intake valves and at nozzle holes with benefits for the fuel efficiency and exhaust emissions. Further improvements in combustion phasing can be achieved with cetane/octane improver. This paper aims to assess the influence of two performance packages on the exhaust emissions and fuel consumption of five vehicles set to be as representative as possible of circulating Italian passenger cars and light commercial fleet vehicles (LCVs). Based on the literature datasets, three Euro 4 vehicles were selected with a mileage representative of each single vehicle class: two passenger cars (one spark ignition and one diesel) and an LCV. Further, two diesel Euro 6 vehicles, a passenger car and an LCV, were tested to investigate the effect of fuel additives on the combustion of vehicles compliant with current homologation regulation. Exhaust emissions and fuel consumption were experimentally estimated on a chassis dynamometer over a worldwide harmonized light vehicles test cycle (WLTC) in a climate-controlled laboratory. Each vehicle was preliminarily tested when running with base fuel, then a 3000 km clean-up stage was performed using the additive package. Finally, WLTC tests were repeated. Results demonstrated the efficiency of the performance packages with a reduction between 1.2% (diesel Euro 6 passenger car) and 8.1% (diesel Euro 4 passenger car) in fuel consumption. Similar trends were found for CO2 emissions. Further, a sensible reduction in THCs, CO and PM was found for each vehicle class.

The Impact of Teamwork Quality (TWQ) on Agile Software Development Team Performance

Purpose: The study examined the extent to which teamwork quality (TWQ) impacts team performance in the Agile software development space in the U.S. and how the TWQ factors (Communication, coordination of expertise, cohesion, trust, mutual support & value sharing) are ranked in terms of importance to team performance Materials and Methods: The study used correlation and regression analysis to determine the degree of the relationship and the effect of the extended TWQ factors on Agile software team performance. Additionally, the study assessed the relative importance of the TWQ factors to team performance. Participants in the study included IT workers from the software development team in U.S. companies with in-house teams using the Agile methodology. Data was collected using an online survey through survey monkey. The data was presented through descriptive statistics and analyzed using regression analysis. Findings: The study found all the extended TWQ factors were strongly correlated to the team performance. The analysis showed that a statistically significance predictive relationship exists between the extended TWQ factors and Agile software development team performance. However, only mutual support and value sharing contributed statistically significantly to the model. Among these factors, mutual support was ranked as the most important TWQ factor in relation to team performance, while trust was ranked as the least. Implications to Theory, Practice and Policy: This empirical study tested and extended the TWQ model on team performance in the Agile software development space in the U.S. for software development teams and how these TWQ factors are ranked in terms of importance to team performance. The findings provide insight to project teams and team leaders, product owners, scrum masters, and project managers about the TWQ model from the perspective of agile software development teams. The findings also address the critical TWQ factors that are needed for building effective and efficient Agile software development teams.

Cinnamon essential oil induced microbial stress metabolome indicates its active food packaging efficiency when incorporated into poly vinyl alcohol, engineered with zinc oxide nanoparticles and nanocellulose

In the study, Poly Vinyl Alcohol (PVA) films engineered with the nanoparticles and essential oils have been developed as efficient alternative to the currently used food packaging materials. For this, impact of cinnamon essential oil (CEO), on the metabolomic profile of Staphylococcus aureus, Escherichia coli and Aspergillus flavus was analysed. Subsequently, PVA based nanocomposite films CEO, zinc oxide nanoparticles (ZnONPs), and nanocellulose (NC) were synthesised and characterized by FT-IR analysis. By the GC–MS analysis. The presence of ZnONPs enhanced the release of cinnamaldehyde from 31.16 to 44.23 and further enhancement to 71.82 was seen the presence of nanocellulose. The incorporation of NPs was found to enhance the hydrodynamic and mechanical properties of the prepared films. The final developed films, PZNCCEO, showed the least values for WHC and MC which were 56.31 ± 2.12 % and 13.30 ± 0 % respectively. Antimicrobial efficacy could also be demonstrated through the observation on changes in the morphological features of treated S. aureus and E. coli by the FE-SEM. Finally, the developed nanocomposite film was found to have the potential for food packaging as demonstrated through the protection of corn kernals and Vigna unguiculata.

Variable neighborhood genetic algorithm for multi-order multi-bin open packing optimization

In the rapidly evolving e-commerce landscape, efficient packaging and logistics reduce costs and enhance customer satisfaction. This study addresses the problem of dynamic bin size optimization in e-commerce logistics by proposing a series of intelligent algorithms. Considering real-world constraints such as item separation requirements, a Mixed Integer Programming Model for Multi-Order Multi-Box Open-Dimension Rectangular Packing (MOMB-ODRPP) is formulated. The Stacked Clustering Algorithm (SCA) series, One-Dimensional Fixed Stacked Clustering Algorithm (ODF-SCA), Two-Dimensional Fixed Stacked Clustering Algorithm (TDF-SCA), and Variable Neighborhood Descent Spatial Ordering Algorithm (VND-SOA) series are employed to solve the MOMB-ODRPP model and improve order packing rates and optimize bin sizes. Computational experiments using real-world data from JD’s e-commerce operations reveal that the TDF-SCA algorithm series outperforms the ODF-SCA series by approximately 5% in Case 4. In contrast, the VND-SOA-S1 and VND-SOA-S2 algorithms achieve improvements of 0.83% and 0.76%, respectively, over the TDF-SCA-P2 algorithm in Cases 4 and 11. The comparative analysis highlights the practical implications of bin size optimization, with Case 11 providing a more viable option for standardizing bin sizes in e-commerce logistics.

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools.

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce 'Lightning Pose', an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We released a cloud application that allows users to label data, train networks and process new videos directly from the browser.

Electrospun Photodynamic Antibacterial Konjac Glucomannan/Polyvinylpyrrolidone Nanofibers Incorporated with Lignin-Zinc Oxide Nanoparticles and Curcumin for Food Packaging.

Due to the growing concerns surrounding microbial contamination and food safety, there has been a surge of interest in fabricating novel food packaging with highly efficient antibacterial activity. Herein, we describe novel photodynamic antibacterial konjac glucomannan (KGM)/polyvinylpyrrolidone (PVP) nanofibers incorporated with lignin-zinc oxide composite nanoparticles (L-ZnONPs) and curcumin (Cur) via electrospinning technology. The resulting KGM/PVP/Cur/L-ZnONPs nanofibers exhibited favorable hydrophobic properties (water contact angle: 118.1°), thermal stability, and flexibility (elongation at break: 241.9%). Notably, the inclusion of L-ZnONPs and Cur endowed the nanofibers with remarkable antioxidant (ABTS radical scavenging activity: 98.1%) and photodynamic antimicrobial properties, demonstrating enhanced inhibitory effect against both Staphylococcus aureus (inhibition: 12.4 mm) and Escherichia coli (12.1 mm). As a proof-of-concept study, we evaluated the feasibility of applying nanofibers to fresh strawberries, and the findings demonstrated that our nanofibers could delay strawberry spoilage and inhibit microbial growth. This photodynamic antimicrobial approach holds promise for design of highly efficient antibacterial food packaging, thereby contributing to enhanced food safety and quality assurance.

TSAT: Efficient evaluation software for NGS data of phage/mirror-image phage display selections

Phage display and mirror-image phage display are commonly used techniques for the identification of binders that are specific to predefined targets. Recent studies demonstrated the effectiveness of next-generation sequencing (NGS) by increasing the amount of information extracted from selections. This allows for a better analysis and increases the possibility to select effective binders. A potential downside to NGS analysis of phage display selections is the increased workload that is needed to analyze the obtained information. Here, we report on the development of TSAT (target-specific analysis tool), software for user-friendly and efficient analysis of peptide sequence data from NGS of phage display selections.

The Role of Machine Learning in Software Development

In today's rapidly evolving technological era, the role of machine learning in software development has become increasingly vital and influential. Machine learning has revolutionized various aspects of software development, from code analysis and optimization to prediction and decision-making. Moreover, machine learning algorithms have the potential to significantly enhance the software development process by automating repetitive tasks, improving code quality, and reducing the time and effort required for software testing and debugging. By gaining lots of data and powerful computing resources, machine learning algorithms can be able to analyse patterns and make accurate predictions about software performance, identify potential bugs or security issues, and assist in improving software design and development processes. Furthermore, machine learning can facilitate software maintenance and debugging by detecting anomalies and identifying potential causes of software failures. Albeit, using machine learning techniques into the software development process can greatly improve efficiency, productivity, and overall software quality. In this paper it is presenting the tools, techniques and the application of Machine Learning (ML) in different phases of Software Development Life Cycle (SDLC) for enhancing and improving the software development process.

Numerical analysis of high vacuum packaging efficiency for narrow structured devices

High vacuum packaging is the key manufacturing technology of passive vacuum devices. The inherent characteristics of narrow and long space and large surface-to-volume ratio make it challenging to effectively remove the desorption gas inside the device. To this end, the pressure distribution of two standard packaging methods during the pump-down process is investigated using the two-dimensional diffusion equation. Experiments and simulations show that a pressure gradient persists within the slit even when pumping reaches the quasi-equilibrium state. In addition, a Monte Carlo method based on the cosine law reveals that the high-frequency collisions of gas molecules with the walls impede the pumping process. Furthermore, the results indicate that for tube pumping, the number of collisions of gas molecules with the wall during transport within the gap is inversely proportional to the gap height. For edge pumping, the number of collisions is inversely proportional to the square of the gap height. This provides a theoretical basis for efficient high vacuum packaging and long-term pressure maintenance for passive vacuum devices.

SDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software

SDK4ED: a platform for building energy efficient, dependable, and maintainable embedded software

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach

BackgroundOptically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software “Feitian” was developed based on kcat prediction using the deep learning approach.ResultsAccording to empirical design and prediction of “Feitian” software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3.ConclusionsIn this study, an efficient rational design software “Feitian” was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.