Optimal Size Research Articles

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company’s sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

PLGA nanocapsules as a delivery system for a recombinant LRP-based therapeutic.

Telomerase activity is directly affected by the laminin receptor precursor (LRP) protein, a highly conserved nonintegrin transmembrane receptor, which has been shown to have therapeutic effects in ageing, and age-related diseases. Recently, it has been found that overexpression of LRP-FLAG, by plasmid transfection, leads to a significant increase in telomerase activity in cell culture models. This may indicate that upregulation of LRP can be used to treat various age-related diseases. However, transfection is not a viable treatment strategy for patients. Therefore, we present a nanoencapsulated protein-based drug synthesised using poly(lactic-co-glycolic acid) (PLGA) nanocapsules for delivery of the 37 kDa LRP protein therapeutic. PLGA nanocapsules were synthesised using the double emulsification-solvent evaporation technique. Different purification methods, including filtration and centrifugation, were tested to ensure that the nanocapsules were within the optimal size range, and the BCA assay was used to determine encapsulation efficiency. The completed drug was tested in a HEK-293 cell culture model, to investigate the effect on cell viability, LRP protein levels and telomerase activity. A significant increase in total LRP protein levels with a concomitant increase in cell viability and telomerase activity was observed. Due to the observed increase in telomerase activity, this approach could represent a safer alternative to plasmid transfection for the treatment of age-related diseases.

Influence of grain size on the electrochemical performance of Li7‑3xLa3Zr2AlxO12 solid electrolyte

Contemporary Li‐ion batteries are facing substantial challenges like safety and limited energy density. The development of all‐solid‐state battery cells mitigates safety hazards and allows the use of Li‐metal anodes increasing energy density. Garnet‐type solid electrolytes can be vital to achieving an all‐solid‐state cell and an understanding of the influence of its microstructure on the electrochemical performance is crucial for material and cell design. In this work the influence of grain size on the Li‐ion conductivity of Li7‐3xLa3Zr2AlxO12 (x = 0.22) is presented. The synthesis and processing procedure allows changing the ceramic grain size, while maintaining the same synthesis parameters, eliminating influences of the synthesis on grain boundary composition. Field assisted sintering technology is a powerful method to obtain dense, fine‐grained ceramics with an optimal grain size of 2‐3 µm, where the conductivity is double that of the counterpart (0.7 µm). A total Li‐ion conductivity of 0.43 mS cm‑1 and an activation energy of 0.36 eV were achieved. The oxide‐based all‐solid‐state battery cell combining the garnet‐type electrolyte, a Li‐metal anode and a thin‐film LiCoO2 cathode was assembled and cycled at room temperature for 90 hours. This represents a proof of concept, for the application of oxide‐based electrolytes at ambient temperatures.

Macrofaunal diversity patterns in coastal marine sediments: re-examining common metrics and methods

Complex biodiversity patterns arise in marine systems due to overlapping ecological processes, including organism interactions, resource distribution, and environmental conditions. Despite the importance of documenting these patterns, describing diversity in natural ecosystems remains challenging. Here, we investigate 3 nearshore sub-Arctic sites to describe benthic macroinfaunal taxa and biological traits, with the ultimate aim of determining whether common diversity metrics and typical sampling efforts adequately capture community composition in these systems. First, we assess how diversity relates to sediment depth and examine relationships among commonly used taxonomic and functional diversity indices. Second, using a power analysis, we explore how sampling effort influences the interpretation of diversity patterns in coastal systems. We report significant variation in community composition among sites, even across small spatial scales of km, and find that taxonomically diverse communities do not necessarily correspond to high functional diversity. We further find that although environmental factors such as sediment depth consistently affect macroinfaunal diversity, the direction and magnitude of these relationships are site-dependent. Finally, we demonstrate that typical sampling effort for coastal benthic studies (for example, <5 push cores of ~7 cm diameter) may not capture macroinfaunal community composition adequately, potentially obscuring hotspots in common diversity metrics such as taxonomic or functional richness. However, indices such as Simpson’s diversity may be well-suited to resource-limited studies with restricted sampling capacity. Our results highlight the importance of adopting multi-pronged approaches to biodiversity assessment and determining optimal sample sizes for marine benthic systems, particularly in the context of biodiversity monitoring for conservation purposes.

Designing an optimal multi-energy system with fast charging and hydrogen refueling station under uncertainties

As the demand for battery-powered and hydrogen-based electric vehicles continues to rise, the need for integrated charging and refueling systems with energy systems becomes more critical. Therefore, it is crucial to consider the stations serving diverse vehicle users during the system design process. This paper presents an optimal sizing strategy for a multi-energy system that covers clean energy sources and two energy storage technologies under the presence of a fast-charging station and a hydrogen refueling station for various types of car users. For the considered system, the optimal sizing problem aims to minimize the loss of power supply probability (LPSP) and annualized total life cycle cost (TLCC) through a two-stage stochastic programming-based multi-objective optimization approach. Using a scenario-based approach, the study considers the uncertainties associated with renewables, homeowner’s vehicle demands, and energy demands from homes and stations. Different cases are produced to evaluate the impacts of the different stations with the availability of various electric vehicle profiles under different levels of reliability. The TLCC decreases if LPSP increases in each case. Also, the cost of installing a fast charging station is significantly lower than that of a hydrogen refueling station for all LPSP limits. Moreover, the overall cost of the system design is at its lowest when homeowners’ vehicles provide vehicle-to-grid services. It is worth noting that when the station loads which come from the customers’ vehicle increase by 10%, the TLCC increases by 1.19%. Additionally, the revenue from hydrogen refueling station is approximately 70% less than the fast charging station.

The integration of solar-hydrogen hybrid renewable energy systems in oil and gas industries for energy efficiency: Optimal sizing using Fick's Law optimisation Algorithm

The global demand for sustainable energy solutions in the oil and gas industry has stimulated interest in the integration of renewable energy sources. This paper investigates the techno-economic and environmental aspects of incorporating a solar-hydrogen-based hybrid renewable energy system (SHRES) into oil and gas processing facilities.The proposed SHRES, comprising Solar PV modules, hydrogen production and storage systems, and a gas turbine generator (PV-FC-EL-Tank-GT), aims to enhance energy efficiency and reduce the carbon footprint of energy-intensive operations. The study utilises a multidisciplinary approach, encompassing engineering, economics, and environmental analysis. The techno-economic evaluation employs detailed modelling and optimisation using Fick's Law Algorithm, ensuring efficient energy production and cost-effectiveness. Ecological assessments quantify potential reductions in greenhouse gas emissions and other environmental impacts.The technical and economic evaluation identifies the electrolyser system as the most significant cost contributor (57%). The optimised system balances energy sources with 45% from PV modules, 35% from fuel cells, and 20% from gas turbines, showcasing substantial reliance on renewable energy while maintaining required power generation.Addressing the urgent need for sustainable energy solutions in the oil and gas industry, the study highlights the potential of SHRES integration. By employing Fick's Law Algorithm, alongside with 80 % of renewable energy integration, the research optimises system components to optimise the annualised cost to be $57,539.85, decrease to zero the Loss of Power Supply Probability (LPSP), and mitigate CO2 emissions to a lowest value of 0.147 gCO2eq/kWh (by 80 %). The electrolyser system's pivotal role and the distribution of power generation sources are emphasised.This study presents a novel approach to integrating solar-hydrogen systems into oil and gas processing facilities, aiming for sustainability. The findings underscore the significance of the electrolyser system and the distribution of power generation sources in optimising the SHRES. Recommendations for renewable energy integration and potential reductions in CO2 emissions and costs are discussed, providing valuable insights for future energy policies.

Allocate electric vehicles’ public charging stations with charging demand uncertainty

Nowadays, electric vehicles (EVs) have developed rapidly due to their great advantages in fuel savings, reduced greenhouse gas emissions, and low air pollution. However, charging imbalances and shortages have limited the prevalence of EVs. We optimize the siting and sizing of public charging stations under uncertain urban en-route recharging demand. Considering charging congestion, we introduce the Erlang’s loss formula to represent the service level requirement of each charging station. First, we determine the optimal locations and sizes of those stations by a robust optimization model, where we figure out its deterministic dual following linear approximation of the loss rate in queueing models. We further use a discrete-event simulation to relax the assumption of time-independent charging demand in the optimization approach and model real-time traffic based on real-world traffic and power grid information in Nanjing, China. Thus, we verify the robust optimum outperforming deterministic and stochastic optimums.

Chemically engineered mTOR-nanoparticle blockers enhance antitumour efficacy

Hepatocellular carcinoma (HCC) is a highly prevalent and deadly type of cancer, and although pharmacotherapy remains the cornerstone of treatment, therapeutic outcomes are often unsatisfactory. Pharmacological inhibition of mammalian target of rapamycin (mTOR) has been closely associated with HCC regression. Herein, we covalently conjugated AZD8055, a potent mTORC1/2 blocker, with a small panel of unsaturated fatty acids via a dynamically activating linkage to enable aqueous self-assembly of prodrug conjugates to form mTOR nanoblockers. Cell-based experiments were carried out to evaluate the effects of the nanoblocker against hepatocellular carcinoma (HCC) cells. The orthotopic and subcutaneous HCC mouse models were established to examine its antitumour activity. Among several fatty acids as promoieties, linoleic acid-conjugated self-assembling nanoblocker exhibited optimal size distribution and superior physiochemical properties. Compared with free agents, PEGylated AZD8055 nanoblocker (termed AZD NB) was pharmacokinetically optimized after intravenous administration. Invivo investigations confirmed that AZD NB significantly suppressed tumour outgrowth in subcutaneous HCCLM3 xenograft, Hepatoma-22, and orthotopic Hepa1-6 liver tumour models. Strikingly, treatment with AZD NB, but not free agent, increased intratumour infiltration of IFN-γ+CD8+ T cells and CD8+ memory T cells, suggesting a potential role of the mTOR nanoblocker to remodel the tumour microenvironment. Overall, a single conjugation with fatty acid transformed a hydrophobic mTOR blocker into a systemically injectable nanomedicine, representing a facile and generalizable strategy for improving the therapeutic index of mTOR inhibition-based cancer therapy. The mTOR inhibition by chemically engineered nanoblocker presented here had enhanced efficacy against tumours compared with the pristine drug and thus has the potential to improve the survival outcomes of patients with HCC. Additionally, this new nanosystem derived from co-assembling of small-molecule prodrug entities can serve as a delivery platform for the synergistic co-administration of distinct pharmaceutical agents. This work was supported by the National Natural Science Foundation of China (32171368,81721091), the Zhejiang Provincial Natural Science Foundation of China (LZ21H180001), the Jinan Provincial Laboratory Research Project of Microecological Biomedicine (JNL-2022039c and JNL-2022010B), State Key Laboratory for Diagnosis and Treatment of Infectious Diseases (zz202310), and Natural Science Foundation of Shandong Province (ZR2023ZD59).

Renewable hydrogen production through electrolysis: an analysis of the cost gap for its economic competitiveness in Italy

With global policies targeting greenhouse gas emission reduction, renewable hydrogen produced by water electrolysis powered with renewable electricity appears a promising alternative to fossil fuels. However, still high capital costs, reliance on intermittent renewable electricity sources, and high electricity cost hinder the economic viability of electrolyzers. This work develops a comprehensive analysis for assessing the need of an incentive to make renewable hydrogen economically viable, identifying different archetypes of electrolysis plant representative of a wide range of applications. A techno-economic optimization model is set up that minimize the average hydrogen production cost and compute the cost gap that makes hydrogen economically competitive with the fossil alternative, defining the optimal size and operation of the plant components while satisfying the hydrogen demand. The model is applied to the Italian case study. Sensitivity analyses investigate the impact of economy of scales, renewable electricity availability and introduction of the emission trading system. Cost gaps ranging between 8.1 €/kg and 29.9 €/kg highlights the need of an incentive to allow the deployment of this technology, thus meeting the hydrogen penetration targets.

TRIPS: Trilinear Point Splatting for Real‐Time Radiance Field Rendering

AbstractPoint‐based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [KKLD23] struggles when tasked with rendering highly detailed scenes, due to blurring and cloudy artifacts. On the other hand, ADOP [RFS22] can accommodate crisper images, but the neural reconstruction network decreases performance, it grapples with temporal instability and it is unable to effectively address large gaps in the point cloud. In this paper, we present TRIPS (Trilinear Point Splatting), an approach that combines ideas from both Gaussian Splatting and ADOP. The fundamental concept behind our novel technique involves rasterizing points into a screen‐space image pyramid, with the selection of the pyramid layer determined by the projected point size. This approach allows rendering arbitrarily large points using a single trilinear write. A lightweight neural network is then used to reconstruct a hole‐free image including detail beyond splat resolution. Importantly, our render pipeline is entirely differentiable, allowing for automatic optimization of both point sizes and positions.Our evaluation demonstrate that TRIPS surpasses existing state‐of‐the‐art methods in terms of rendering quality while maintaining a real‐time frame rate of 60 frames per second on readily available hardware. This performance extends to challenging scenarios, such as scenes featuring intricate geometry, expansive landscapes, and auto‐exposed footage. The project page is located at: https://lfranke.github.io/trips

Advances in nanofiber technology for biomedical application: A review

The application of nanofiber technology in the biomedical field has garnered significant interest due to its potential to revolutionize areas such as tissue engineering, wound healing, and antimicrobial treatments. This paper provides a comprehensive review of the recent advancements in nanofiber technology, particularly focusing on electrospinning and 3D printing methods that enable the fabrication of scaffolds mimicking the native extracellular matrix. These technologies have facilitated the development of nanofibers with high surface-to-volume ratios, adjustable porosity, and enhanced mechanical properties, tailored to meet specific biomedical needs. Despite their promising features, challenges such as the optimization of pore size for effective cell infiltration and the mechanical robustness required for hard tissue regeneration remain. The review also explores the evolution of sustainable polymers from natural resources, highlighting their potential to create biodegradable and biocompatible scaffolding materials. Future directions emphasize the need for cross-disciplinary collaboration to overcome current limitations and scale production from laboratory to industrial levels. The ongoing research and development efforts aim to refine the properties of nanofibers to achieve optimal performance in clinical applications, underlining the dynamic and evolving nature of this field.

Deep-Learning-Based Segmentation of Predefined Chunks in Connected Speech: A Retrospective Analysis

Background and Objectives In institutional settings, manually segmenting connected speech is a time-consuming and labor-intensive process. This study aims to develop a deep-learning model for automating this process, evaluating its accuracy, and determining the minimum dataset size for effective performance.Materials and Method Voice data from 524 individuals with pathological conditions and 502 individuals with normal conditions, totaling 1026 samples, were used. Each voice sample had 17 chunks, including a “summer” sentence (15 chunks) and vowels /α/ and /i/. The deep-learning model employed in this study is based on the multi-layer perceptron-mixer architecture. This study evaluated performance using the Intersection over Union (IoU) metric, commonly employed in artificial intelligence-based image detection for chunk segmentation.Results The accuracy of chunk identification at the frame level was 96.47%. Using IoU metrics, chunk segmentation accuracy was 98.15% at IoU ≥0.6, 96.03% at IoU ≥0.7, and 89.78% at IoU ≥0.8. Optimal dataset size exploration indicated that more than 700 connected speech datasets were needed for successful training, maintaining F1-scores up to 95% at IoU ≥0.7.Conclusion The artificial intelligence model is suitable for the development of an automated system that efficiently divides segments in the institutional collection of voice data. This suggests its potential utility in advancing voice research using connected speech.

Coal burst mechanism in large-scale panel under extra-thick key strata

Extra-thick key strata (ETKS) are a key factor causing coal bursts. In this study, field monitoring, physical and numerical modelling, and theoretical analysis were comprehensively adopted to investigate the effect of ETKS on the static and dynamic stress and its impact on coal bursts. Firstly, the coal bursts and the stability of ETKS were investigated. The roof strata structure and the mining-induced stress in a large scale panel under ETKS were analyzed. Then, the coal burst mechanism under ETKS is discussed. The results show that when the panel reaches the 4th and 5th longwall faces, the inferior ETKS and the main ETKS break, respectively, and large-scale cantilever-articulated structures involving the ETKS are formed. This moment, the overburden load is transferred downwards massively, and the coal seam becomes overstressed and accumulates massive elastic energy. Meanwhile, the collaborative collapse of multiple strata in the large cantilever-articulated structures causes the sudden release of elastic energy and high-energy seismicity, which exerts strong dynamic disturbances on the overstressed coal mass and leads to coal burst. Finally, strategies for coal burst control under ETKS including deep-hole pre-splitting blasting and panel size optimization are proposed and validated.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Experimental Research of Ultrasonic Cavitation Evolution Mechanism and Model Optimization of RUREMM on Cylindrical Surface

Micro-pits are widely used in the aerospace and tribology sectors on cylindrical surfaces and electrochemical micromachining which are of great significance for the high material removal rate, absence of tool wear, and mechanical stress, while facing significant challenges such as stray corrosion and low machining efficiency. Aiming at the above problems, this paper proposes a comprehensive method called radial ultrasonic rolling electrochemical micromachining (RUREMM) in which an ultrasonic field has been added onto the cylindrical surface. First, a theoretical model was created to gain the rules of the formation and collapse of bubbles in the liquid medium. Second, to analyze the optimal size of the cathode electrode, the COMSOL5.2 simulation software was proposed to research the influence of the electric field on the different dimensions, and the influences of different parameters in RUREMM on material depth/diameter ratio and roughness are explored through processing experiments. Research results found that the cavitation bubble undergoes expansion, compression, collapse and oscillation, where the max deviation is less than 12.5%. The optimized size was chosen as 200 × 200 μm2 and an electrode spacing of 800 μm through a series of electric field model simulation analyses. Relevant experiments show that the minimum pits with a width of 212.4 μm, a depth of 21.8 μm, and a surface roughness (Ra) of 0.253 μm were formed due to the optimized parameters. The research results can offer theoretical references for fabricating micro-pits with enhanced surface quality and processing precision on cylindrical surfaces.

Sustainable energy transition in Bangladeshi academic buildings: A techno-economic analysis of photovoltaic-based net zero energy systems

Bangladesh faces challenges with global carbon emissions and energy security due to its heavy reliance on imported fossil fuels. This paper investigates the techno-economic viability of photovoltaic (PV) systems for achieving net-zero energy in academic buildings as a sustainable alternative. The study evaluates the performance of PV systems based on factors including annual energy production, capacity factor, net-zero energy potential, self-consumption/sufficiency ratio, renewable fraction, levelized cost of energy (LCOE), net present value (NPV), payback period, profitability index (PI), and carbon emission reduction. The results indicate that optimally sized PV systems can generate net positive energy: 6.18 MWh for universities, 4.04 MWh for high schools, and 3.62 MWh for primary schools. Economic viability was assessed under two billing methods (net metering and all energy buy/sell), and all cases showed positive NPV and PI values greater than 1. Decarbonization analysis revealed significant reductions in CO2 emissions: 2026.5 tons for universities, 18.9 tons for high schools, and 9.7 tons for primary schools. Finally, a sensitivity analysis assessed the optimal PV system size for seven other regions in Bangladesh. These findings suggest that the widespread adoption of PV systems in academic buildings can play a crucial role in achieving net-zero energy, contributing significantly to the government and policymakers' pursuit of sustainable development goals.

An automated mineralogy derived criterion for clustering ore samples for mineral liberation studies

Correctly determining the optimal grinding size is crucial for mineral processors, yet it poses several challenges. Still popular among practitioners, the empirical determination of the grinding target involves time-consuming and resource-intensive laboratory routines employed for generating quality, recovery and energy curves from which the target size is set. This approach becomes even more challenging for mines with diverse ore textures, requiring extensive testing on numerous samples to accurately capture the behaviour of the entire ore body. Consequently, a more efficient experimental method is highly desirable. A solution for reducing the laboratory effort is clustering samples according to the mineral liberation characteristics. This paper introduces a novel multivariate criterion for statistical clustering of ore samples for mineral liberation studies, derived from SEM-based automated mineralogy data. The criterion considers three variables: two coefficients (named A and k) derived from the exponential correlation equation between degree of liberation and the top size of the size fraction, and the overall degree of liberation, both easily obtained from the liberation spectrum for a top size of 1 mm. The effectiveness of the proposed criterion has been experimentally demonstrated. It was concluded that the clustering process correctly grouped samples with similar mineral liberation characteristics. The clustering approach can be used to significantly reduce the laboratory effort on liberation studies involving a great number of samples of different types of ore. Although developed using iron ore samples, the criterion's theoretical basis suggests its potential for adaptation to other types of ore.

Optimal sample size division in two-stage seamless designs.

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

Impact of Design Decisions on Performance of Embarrassingly Parallel .NET Database Application

The implementation of parallel applications is always a challenge. It embraces many distinctive design decisions that are to be taken. The paper presents issues of parallel processing with use of .NET applications and popular Database Management Systems (DBMSes). In the paper, four design dilemmas are addressed: how efficient is the auto-parallelism implemented in the .NET TPL library, how do popular DBMSes differ in serving parallel requests, what is the optimal size of data chunks in the data parallelism scheme, and how the TPL auto-parallelism behaves in the public clouds. They are analyzed in the context of the typical and practical business case originated from IT solutions which are dedicated for the energy market participants. The paper presents the results of experiments conducted in a controlled, on-premises and cloud environments. The experiments allowed to compare the performance of the TPL auto-parallelism with a wide range of manually set numbers of worker threads. They also helped to evaluate four DBMSes: Oracle, MySQL, PostgreSQL, and MSSQL in the scenario of serving parallel queries. Finally, they showed the impact of data chunk sizes on the overall performance.

Numerical Analysis of Non-Fourier Model-Based Bio-Heat Transfer in the Laser-irradiated Axisymmetric Living Tissue.

The current work is related to the numerical investigation of non-Fourier heat transfer inside the short-pulsed laser-irradiated axisymmetric soft tissue phantom. It utilizes the modified discrete ordinate method to solve the transient radiative transfer equation (TRTE) for determining the intensity field. The laser energy absorbed by the soft tissue phantom behaves like a source in the Fourier/non-Fourier heat conduction model based-bio-heat transfer equation (BHTE), which is solved by employing the finite volume method (FVM) to determine the temperature distribution. Despite the prevalent use of non-Fourier BHTE for this purpose, a second law analysis is considered crucial to detect any potential anomalies. Equilibrium entropy production rates (EPR) are initially computed based on classical irreversible thermodynamics (CIT), which may yield negative values, possibly contravening the second law. Consequently, the EPR based on CIT is adjusted using the extended irreversible thermodynamics (EIT) hypothesis to ensure positivity. After that, the current research findings are compared with the results from the literature, and found good agreement between them. Then, the independent study is performed to select the optimum grid size, control angle size, and time step. A comparative analysis of results between the traditional Fourier and non-Fourier models has been performed. The impact of different parameters on the temperature fields and EPRs, is discussed. The effect of the optical properties of the inhomogeneity on the temperature distribution has been investigated. This study may help to enhance the effectiveness of the laser-based photo-thermal therapy.