Flow Technique Research Articles

Machine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research

In experimental research, animal welfare should always be of the highest priority. Currently, physical in-person observations are the standard. This is time-consuming, and results are subjective. Video-based machine learning models for monitoring experimental pigs provide a continuous and objective observation method for animal misthriving detection. The aim of this study was to develop and validate a pig tracking technology, using video-based data in a machine learning model to analyze the posture and activity level of experimental pigs living in single-pig pens. A research prototype was created using a microcomputer and a ceiling-mounted camera for live recording based on the obtained images from the experimental facility, and a combined model was created based on the Ultralytics YOLOv8n for object detection trained on the obtained images. As a second step, the Lucas–Kanade sparse optical flow technique for movement detection was applied. The resulting model successfully classified whether individual pigs were lying, standing, or walking. The validation test showed an accuracy of 90.66%, precision of 90.91%, recall of 90.66%, and correlation coefficient of 84.53% compared with observed ground truth. In conclusion, the model demonstrates how machine learning can be used to monitor experimental animals to potentially improve animal welfare.

Valuation Considerations of Stock Based Compensation: A Case Study of Semiconductor Firms

Equity research analysts and investors remain divided on the treatment of stock-based compensation (SBC) in firm valuation, particularly in discounted cash flow (DCF) and earnings multiples techniques. Many firms and analysts exclude SBC, viewing it as a non-cash expense, while others argue this leads to overvaluation. SBC usage is especially high in the tech sector. This study examines five large semiconductor firms, analyzing SBC levels, its treatment in adjusted earnings, and its impact on share dilution, valuation multiples, and free cash flow calculations. Findings indicate that SBC treatment significantly affects valuation, particularly within the semiconductor industry.

System-level modeling with temperature compensation for a CMOS-MEMS monolithic calorimetric flow sensing SoC

We present a system-level model with an on-chip temperature compensation technique for a CMOS-MEMS monolithic calorimetric flow sensing SoC. The model encompasses mechanical, thermal, and electrical domains to facilitate the co-design of a MEMS sensor and CMOS interface circuits on the EDA platform. The compensation strategy is implemented on-chip with a variable temperature difference heating circuit. Results show that the linear programming for the low-temperature drift in the SoC output is characterized by a compensation resistor Rc with a resistance value of 748.21 Ω and a temperature coefficient of resistance of 3.037 × 10−3 °C−1 at 25 °C. Experimental validation demonstrates that within an ambient temperature range of 0–50 °C and a flow range of 0–10 m/s, the temperature drift of the sensor is reduced to ±1.6%, as compared to ±8.9% observed in a counterpart with the constant temperature difference circuit. Therefore, this on-chip temperature-compensated CMOS-MEMS flow sensing SoC is promising for low-cost sensing applications such as respiratory monitoring and smart energy-efficient buildings.

Scalable and Precise Synthesis of Structurally Colored Bottlebrush Block Copolymers: Enabling Refined Color Calibration for Sustainable Photonic Pigments.

Self-assembled bottlebrush block copolymers (BBCPs) offer a vibrant, eco-friendly alternative to traditional toxic pigments and dyes, providing vivid structural colors with significantly reduced environmental impact. Scaling up the synthesis of these polymers for practical applications has been challenging with conventional batch methods, which suffer from slow mass and heat transfer, inadequate mixing, and issues with reproducibility. Precise control over molecular weight and dispersity remains a significant challenge for achieving finely tuned color appearances. Here, we present an alternative strategy to overcome the challenges by integrating a rapid continuous flow technique with an in-line self-assembly procedure. This strategy enables the rapid, stable and large-scale synthesis of narrow-dispersed BBCPs, exceeding 2 kg/day, a significant improvement over conventional gram-scale methods. Furthermore, precise control over the degree of polymerization is achieved with an unprecedented interval accuracy of four repeat units. This level of precision enables refined color calibration in the resulting photonic pigments, effectively eliminating the need for labor-intensive and costly multiple batch syntheses.

Scalable and Precise Synthesis of Structurally Colored Bottlebrush Block Copolymers: Enabling Refined Color Calibration for Sustainable Photonic Pigments

Self‐assembled bottlebrush block copolymers (BBCPs) offer a vibrant, eco‐friendly alternative to traditional toxic pigments and dyes, providing vivid structural colors with significantly reduced environmental impact. Scaling up the synthesis of these polymers for practical applications has been challenging with conventional batch methods, which suffer from slow mass and heat transfer, inadequate mixing, and issues with reproducibility. Precise control over molecular weight and dispersity remains a significant challenge for achieving finely tuned color appearances. Here, we present an alternative strategy to overcome the challenges by integrating a rapid continuous flow technique with an in‐line self‐assembly procedure. This strategy enables the rapid, stable and large‐scale synthesis of narrow‐dispersed BBCPs, exceeding 2 kg/day, a significant improvement over conventional gram‐scale methods. Furthermore, precise control over the degree of polymerization is achieved with an unprecedented interval accuracy of four repeat units. This level of precision enables refined color calibration in the resulting photonic pigments, effectively eliminating the need for labor‐intensive and costly multiple batch syntheses.

High-resolution mapping of sea surface currents in the Iroise sea from MSG/SEVIRI satellite observations: validation with HF radar and model data

ABSTRACT Sea surface currents are crucial for regulating ocean circulation, global climate, and marine ecosystem. This study uses the robust optical flow technique to map surface tidal and flow currents in the Iroise Sea and adjacent waters (ISAS) by analysing sequential sea surface temperature images from SEVIRI (Spinning Enhanced Visible and Infrared Imager) satellite. Comparisons with high-frequency (HF) radar show good agreement in total surface currents, with a mean bias of 0.15 m/s and a root mean square error (RMSE) of 0.50 m/s. Additionally, SEVIRI-derived M2 tidal currents agree well with both the Oregon State University regional tidal model and radar observations. The detided daily mean currents reveal strong concordance with the radar and Cooperative Marine Environment Monitoring Service daily (CMEMS) model, which exhibits high sensitivity to rapid wind changes. This study highlights the potential of SEVIRI satellite in expanding coastal current monitoring and forecasts and understanding coastal ocean dynamics, particularly in regions with limited in-situ measurements.

SCHEME FOR CALCULATING UNSTEADY FLOWS OF HEAT-CONDUCTING GAS IN A THREE-TEMPERATURE APPROXIMATION

A numerical modeling technique for unsteady flows of heat-conducting gas in a three-temperature approximation is presented. The methodology is based on the foundational principles of S.K. Godunov. For time integration, each time step is computed by splitting the governing equations into hyperbolic and parabolic subsystems. The first subsystem is solved using a generalized Godunov scheme, while the second uses an explicit-iterative Chebyshev scheme. Adaptive, curvilinear moving grids are used for discretization, and the discrete scheme is formulated in curvilinear coordinates, preserving the symmetries of the differential problem. The methodology is implemented as a parallel code for multiprocessor computers. Its primary purpose is to support computational studies in controlled thermonuclear fusion, though it can also be applied to other areas of computational aerogas dynamics.

SYSTEMATIC LITERATURE REVIEW: DETERMINANTS OF WOMEN'S EMPOWERMENT IN STUNTING PREVENTION

Stunting has become a global priority program. In 2024 Indonesia intends to reduce the prevalence of stunting. Studies show that there is a relationship between women's empowerment and children's nutritional status. This study was conducted to identify women's empowerment variables that are used as assessment indicators in several scientific publications that have been carried out. This study uses a systematic literature review from electronic data sources that can be accessed and published from the Scopus database portal, a systematic review is carried out using the Prisma flow technique. The variables used to assess women's empowerment include women's ability to make decisions, including decisions to buy something, access health services, and other productive decisions, education level, economic level, employment, acceptance of violence, age, participation in the community, ability to communicate with partners, and confidence to speak and express opinions. Reducing the prevalence of stunting can start by building a society that is aware of equality from the individual and family level, providing opportunities for women to have the ability to make strategic decisions, and having sufficient knowledge and resources for children's nutritional needs.

Laplace transformation technique for free convective time-dependent MHD flow over a vertical porous flat plate with heat sink and chemical reaction: An analytical approach

Laplace transformation technique for free convective time-dependent MHD flow over a vertical porous flat plate with heat sink and chemical reaction: An analytical approach

A chemoenzymatic cascade for sustainable production of chiral N-arylated aspartic acids from furfural and waste

Both biomass valorization and waste upcycling are important routes to sustain the circular bioeconomy. In this work, we present a chemoenzymatic cascade for selective synthesis of chiral N-arylated aspartic acids from biomass-derived furfural and waste nitrophenols (NPs) by merging robust photo- and electrocatalysis with stereoselective biocatalysis. Concurrent photoelectrocatalytic oxidation of furfural into maleic acid (MA) and fumaric acid (FA) was significantly enhanced by combining catalyst and reaction engineering strategies including identification of a powerful photocatalyst meso-tetra(4-carboxyphenyl)porphyrin, continuous flow technique, enhancing dissolved O2 and paired electrosynthesis. The overall space-time yield (STY) approached 2.8 g L−1 h−1 in a fed-batch process, with the product titer of 28.3 g L−1. Besides, photoelectrosynthesis of MA/FA was effectively fueled by sunlight, with the STY of up to 3.6 g L−1 h−1. Both MA selectivity and yield could be facilely improved to around 89% by reducing the buffer concentrations. Paired electrosynthesis strategy not only resulted in greatly improved MA production at the anode, but also enabled NPs upcycling into value-added aminophenols (APs) at the cathode. The products formed in the two electrode chambers were converted into N-arylated (S)-aspartic acids by a bienzymatic cascade. This work presents a multicatalytic approach for integrating selective biomass valorization and waste upcycling towards sustainable manufacture.

Towards carbon‐free electricity: A flow‐based framework for power grid carbon accounting and decarbonization

AbstractThis study introduces a comprehensive framework aimed at advancing research and policy development in the realm of decarbonization within electric power systems. The framework focuses on three key aspects—carbon accounting, carbon‐aware decision making, and carbon‐electricity market design—and proposes solutions to existing problems. In contrast to traditional pool‐based emission models, this framework proposes a novel flow‐based emission model that incorporates the underlying physical power grid and power flows. Thus, the framework allows accurate carbon accounting at both the temporal and spatial scales, thereby facilitating informed decision‐making to achieve grid decarbonization goals. The framework is built on a flow‐based carbon accounting methodology and utilizes the carbon‐aware optimal power flow technique as a theoretical foundation for decarbonization decision‐making. Additionally, this study explores the potential design of carbon‐electricity markets and pricing mechanisms to incentivize decentralized decarbonization actions. Critical issues of data availability, infrastructure development, fairness and equity considerations are also discussed.

A new analytical technique for obtaining the optimal sizing, location, and scheduling of BESS in a grid-connected microgrid with multiple cases of heavy DG penetration

This paper aims to provide an optimal location, power, and energy rating for a battery energy storage system (BESS) in a grid-connected microgrid. The microgrid is pre-installed with heavy renewable distributed generations like solar and wind power plants. The paper suggests a straightforward optimal operating schedule for BESS, considering real-time data on solar irradiance, wind speed, load profile, and electricity tariff obtained from the US Energy Information Administration. The proposed method analytically identifies the optimal size and location of the storage system using the modified Q-PQV load flow technique. The method also proposes incorporating seasonal variations of the real-time data to obtain the optimal BESS size. A detailed cost-benefit analysis is exhibited to validate the economic feasibility. The methodological superiority is established by comparing the proposed algorithm with other soft computing techniques like PSO, GA, and JAYA algorithms. The novel technique is executed on 33-bus and 136-bus test systems with multiple cases of solar and wind-distributed generations. The proposed method provided a significant reduction in annual energy loss with a definite improvement in the voltage profile and overall profit of the system.

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

The climate crisis - actions to prioritize for anaesthesiologists.

Climate change is the biggest threat to human health and survival in the twenty-first century. Emissions associated with healthcare contribute to climate change and there are many personal and professional actions that can reduce carbon emissions. This review highlights why action is necessary and what anaesthetists and healthcare workers can do. Encouraging continuing research regarding sustainable anaesthesia and expanding education at all levels to include climate action is key. Professionally, actions include limiting use of single-use equipment, reducing reliance on volatile gas inhalational anaesthesia, and adopting low fresh gas flow techniques. Personal actions such as climate-conscious travelling, spending, and eating are important, especially when shared to create climate positive movements. This article shows that, while patient safety and quality of care must remain healthcare's top priority, considering the climate implications of care is part of that duty. Many actions that reduce the carbon impact of care simultaneously improve the quality of care and reduce financial cost. More research into sustainable healthcare is needed. Departments and hospitals and must create environments in which climate conversations are welcomed and can result in positive advancements.

Impact of light polarization on laser speckle contrast imaging with a custom phantom for microvascular flow

Laser speckle contrast imaging (LSCI) is a non-invasive, powerful, and cost-effective imaging technique that has seen widespread adoption across various medical fields, particularly for blood flow imaging. While LSCI provides physicians with valuable insights into changes or occlusions in blood flow, the technique is susceptible to various factors and parameters that can impact measurement sensitivity and signal-to-noise ratio (SNR). These include the scattering of light, which can affect the quality and reliability of the LSCI data. The polarization of light holds significant promise to enhance the performance of LSCI. In this study, we employed polarization manipulation of light to investigate its impact on the performance of LSCI for measuring flow. Focusing on the application of LSCI in microcirculation within capillaries, we examined the effect of polarization control on the technique's flow measurement capabilities using a custom-designed phantom system. This phantom consisted of three tubes with inner diameters of 1.1 mm, 1.6 mm, and 2.8 mm, embedded in a polydimethylsiloxane (PDMS) matrix with optical properties similar to biological tissue. By manipulating the polarization of both the incident and reflected light, alternating between parallel and perpendicular states, we compared the performance of our LSCI system in detecting flow for different tube diameters and depths within the phantom. Our study revealed that while depth is a critical parameter influencing flow detection using LSCI, employing perpendicular polarization (between incident and reflected light) resulted in the lowest measurement error and highest SNR compared to parallel polarization and the absence of polarization control.

Rapid biofabrication of cell-free, anisotropic collagen tissues using a novel horizontal shear flow technique

Ultrastructure and organisation of collagen fibres is essential to tissue function, due to the loadbearing properties of collagen. Current techniques used to create aligned collagen tissue equivalents use the contractile ability of cells to remodel and align collagen fibres or utilise highly specialised pieces of equipment. The aim of this study is to develop a novel and rapid method to produce acellular aligned collagen sheets by combining horizontal shear flow (HSFlow) and the established RAFT method to remove excess fluid from a hydrogel.Force applied to the gel during the HSFlow process was measured to allow replication of the method. Quantification of fibres and cellular alignment revealed a significant difference between HSFlow and control samples, where both cells and collagen fibres showed alignment in the direction of shear flow, compared to the randomly aligned RAFT controls. Mechanical properties were also measured and revealed that HSFlow does not appear to improve the strength of the constructs despite the improved alignment, therefore further optimisation is needed to strengthen the constructs.In conclusion, we developed a novel and rapid technique to generate flat sheets of aligned collagen without relying on the contractile ability of cells to rearrange collagen fibres. This rapid method has potential to be used in the fabrication of a scaffold to mimic anisotropic tissues for regenerative medicine.

Synthesis and Docking Studies of Novel Spiro[5,8-methanoquinazoline-2,3'-indoline]-2',4-dione Derivatives.

In this study, a set of spiro[5,8-methanoquinazoline-2,3'-indoline]-2',4-dione derivatives 3a-p were synthesized starting from unsubstituted and N-methyl-substituted diendo- and diexo-2-aminonorbornene carboxamides, as well as various substituted isatins. The typical method involves a condensation reaction of alicyclic aminocarboxamide and isatin in the presence of a catalyst, using a solvent and an acceptable temperature. We developed a cost-effective and ecologically benign high-speed ball milling (HSBM), microwave irradiation (MW), and continuous flow (CF) technique to synthesize spiroquinazolinone molecule 3a. The structures of the synthesized compounds 3a-p were determined using 1D and 2D NMR spectroscopies. Furthermore, docking studies and absorption, distribution, metabolism, and toxicity (ADMET) predictions were used in this work. In agreement with the corresponding features found in the case of both the SARS-CoV-2 main protease (RCSB Protein Data Bank: 6LU7) and human mast cell tryptase (RCSB Protein Data Bank: 2ZA5) based on the estimated total energy and binding affinity, H bonds, and hydrophobicity in silico, compound 3d among our 3a-g, 3i-k, and 3m derivatives was found to be our top-rated compound.

Modeling of hybrid renewable resources and comprehensive feasibility analysis of grid interactive energy system for commercial building: a case study

ABSTRACT The growing energy demand in commercial buildings and businesses poses significant environmental challenges due to the dependence on fossil fuel-based energy. To address this issue, increasing the use of renewable energy sources and decreasing reliance on fossil fuels is crucial. Commercial buildings, in particular, have many opportunities to harness energy from renewable resources, such as solar, wind, and biomass. This study analyzed historical energy consumption data from a commercial building using MATLAB to forecast future energy demands. A range of machine learning algorithms were employed to accurately predict energy consumption, including Ensemble Boosting, Ensemble Bagging, Decision Trees, Support Vector Machines (SVM), and Neural Networks (NN). A Hybrid Renewable Energy (HRE) system was designed to meet the building’s energy needs, integrating renewable sources with grid-interactive inverters. Performance metrics for the forecasting models were calculated and documented, followed by a detailed techno-economic and environmental feasibility analysis of the HRE system. Different levels of renewable energy penetration were explored to optimize HRE designs using load-following dispatch algorithms tailored to the commercial building’s specific needs. The study revealed that achieving a renewable energy proportion of 12.6% resulted in the lowest cost of electricity (COE) at $0.135 and a Net Present Cost (NPC) of $8.84 million. Additionally, a reliability study using load flow techniques confirmed the stability of the optimized Hybrid Renewable Energy system. The energy costs for varying levels of renewable penetration, ranging from 10% to 40% is also explored in this research. In summary, transitioning to renewable energy sources in commercial buildings can reduce environmental impact while ensuring a sustainable energy supply.

Impacts of Light Exposure and Soil Covering on Sweet Potato Storage Roots in a Novel Soilless Culture System

Soilless culture systems, which promote plant growth and enable the precise control of the root-zone environment, have yet to be fully established for sweet potatoes. In this study, we developed a soilless culture system and examined the effects of soil covering and light exposure on the storage roots of sweet potatoes. Sweet potato seedlings with induced storage roots were transplanted into five systems: a previously developed pot-based hydroponics system (Pot), an improved version with storage roots enclosed in a plastic box and covered with a soil sheet (SS), the SS system without the soil sheet (SD), the SD system with light exposure to storage roots after 54 days (SL), and a deep flow technique (DFT) hydroponics system. Our study enabled the time-course observation of storage root enlargement in the SS, SD, and SL systems. In the SL system, light exposure suppressed the storage root enlargement and reduced epidermal redness. No storage root enlargement was observed in the DFT system, even at 151 days after transplantation. Light exposure in the SL system increased the chlorophyll and total phenolic contents in the cortex beneath the epidermis, while the starch content was the lowest in this system. These findings indicate that the developed system can induce normal storage root enlargement without soil. Additionally, the observed changes in growth and composition due to light exposure suggest that this system is effective for controlling the root-zone environment of sweet potatoes.

Hybrid Extreme Learning for Reliable Short-Term Traffic Flow Forecasting

Reliable forecasting of short-term traffic flow is an essential component of modern intelligent transport systems. However, existing methods fail to deal with the non-linear nature of short-term traffic flow, often making the forecasting unreliable. Herein, we propose a reliable short-term traffic flow forecasting method, termed hybrid extreme learning, that effectively learns the non-linear representation of traffic flow, boosting forecasting reliability. This new algorithm probes the non-linear nature of short-term traffic data by exploiting the artificial bee colony that selects the best-implied layer deviation and input weight matrix to enhance the multi-structural information perception capability. It speeds up the forecasting time by calculating the output weight matrix, which guarantees the real usage of the forecasting method, boosting the time reliability. We extensively evaluate the proposed hybrid extreme learning method on well-known short-term traffic flow forecasting datasets. The experimental results show that our method outperforms existing methods by a large margin in both forecasting accuracy and time, effectively demonstrating the reliability improvement of the proposed method. This reliable method may open the avenue of deep learning techniques in short-term traffic flow forecasting in real scenarios.