Carbon Dioxide Concentration Research Articles

Periodic open and closed resonators as a biosensor using two computational methods

The volatile particles and molecules in our dry exhaled breath can reveal enormous information about the health of any person, such as the person’s respiratory and metabolic functioning. Beyond the carbon dioxide level is an indicator of life, it provides important health-related data like people’s metabolic rate. This study considers periodic open and closed resonators for measuring carbon dioxide concentration in dry exhaled breath. Transfer matrix and green methods are used to simulate the interaction between acoustic waves and the proposed sensor. The band gaps using the green method coincide with the transmittance spectra by the transfer matrix. The suggested sensor recorded a sensitivity of 5.3Hz.m-1.s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.3 Hz.{m}^{-1}.s$$\\end{document}, a figure of merit of 10,254 m-1.s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${m}^{-1}.s$$\\end{document}, a detection limit of 5×10-6m.s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5\ imes {10}^{-6} m.{s}^{-1}$$\\end{document}, and a quality factor of 3×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes {10}^{6}$$\\end{document}. Furthermore, the efficiency shows that the proposed design is appropriate as a diagnostic sensor for different diseases such as chronic obstructive pulmonary. Besides, cylindrical-adapted sensors are urgently needed in medicine, industry, and biology because they can simultaneously be used for fluid transport and detection.

Monitoring the ventilation of living spaces to assess the risk of airborne transmission of infection using a novel Pocket CO2 Logger to track carbon dioxide concentrations in Tokyo.

We employed carbon dioxide (CO2) concentration monitoring using mobile devices to identify location-specific risks for airborne infection transmission. We lent a newly developed, portable Pocket CO2 Logger to 10 participants, to be carried at all times, for an average of 8 days. The participants recorded their location at any given time as cinema, gym, hall, home, hospital, other indoors, other outgoings, pub, restaurant, university, store, transportation, or workplace. Generalized linear mixed model was used for statistical analysis, with the objective variable set to the logarithm of CO2 concentration. Analysis was performed by assigning participant identification as the random effect and location as the fixed effect. The data were collected per participant (seven males, four females), resulting in a total of 12,253 records. Statistical analysis identified three relatively poorly ventilated locations (median values > 1,000 ppm) that contributed significantly (p < 0.0001) to CO2 concentrations: homes (1,316 ppm), halls (1,173 ppm), and gyms (1005ppm). In contrast, two locations were identified to contribute significantly (p < 0.0001) to CO2 concentrations but had relatively low average values (<1,000 ppm): workplaces (705 ppm) and stores (620 ppm). The Pocket CO2 Logger can be used to visualize airborne infectious transmission risk by location to help guide recommendation regarding infectious disease policies, such as restrictions on human flow and ventilation measures and guidelines. In the future, large-scale surveys are expected to utilize the global positioning system, Wi-Fi, or Bluetooth of an individual's smartphone to improve ease and accuracy.

Improving Tree Seedling Quality Using Humates Combined with Bacteria to Address Decarbonization Challenges through Forest Restoration.

Improving the quality of tree planting material for carbon sequestration through reforestation can help solve environmental problems, including the need to reduce the concentration of carbon dioxide in the atmosphere. The purpose of this study was to investigate the possibility of using humic substances in combination with rhizosphere microorganisms Pseudomonas protegens DA1.2 and Pseudomonas sp. 4CH as a means to stimulate the growth of seedlings of pine, poplar, large-leaved linden, red oak, horse chestnut, and rowan. Humic substances stimulated the growth of shoots and roots of pine, large-leaved linden, and horse chestnut seedlings. The effects of bacteria depended on both plant and bacteria species: Pseudomonas protegens DA1.2 showed a higher stimulatory effect than Pseudomonas sp. 4CH on pine and linden, and Pseudomonas sp. 4CH was more effective in the case of chestnut. An additive effect of humates and Pseudomonas protegens DA1.2 on the growth rate of pine and linden saplings was discovered. Poplar, red oak, and rowan seedlings were unresponsive to the treatments. The growth-stimulating effects of the treatments are discussed in connection with the changes in carbon, chlorophyll, and nitrogen contents in plants. The results show the need for further research in bacterial species capable of stimulating the growth of plant species that were unresponsive in the present experiments.

Evaluation of Ambient Air Quality Levels at Various Locations within Lead City University, Ibadan

Universities are big metropolitan institutions with sizable populations of students, employees, and visitors. However, university settings can contribute to air pollution, with diverse activities such as lab work, cooking in dorms, and vehicle traffic, among others, causing interior and ambient air pollution. It is impossible to overestimate how much air pollution affects the health and happiness of students. Based on this, evaluation of ambient air quality (i.e., temperature, relative humidity (RH), CO2, and particulate matter (PM2.5)) at various locations within Lead City University, Ibadan, is essential. Ambient concentrations of carbon monoxide (CO), carbon dioxide (CO2), relative humidity (RH), temperature (TEMP), and particulate matter (PM) were recorded from 15 locations across Lead City University over a two-week period. Morning temperatures in all the locations measured ranged from 23.7°C to 29.2°C, while afternoon temperatures fluctuated more significantly, with the lowest recorded at 27.2°C and the highest at a notably warmer 35.8°C in all the locations measured. The morning RH levels ranged from 63.8% to 74.7%, while afternoon RH values ranged from 58.2% to 63.4%. The findings also show that afternoon CO2 levels range from 468.5 ppm to 971.6 ppm, with Location 13 having an unusually high average. Morning CO levels ranged from 4.1 µg/m³ to 49 µg/m³, with location 13 showing an outlier at 184.2 µg/m³. CO2 and CO levels are mostly within acceptable ranges. Conclusively, variability in these parameters has implications for human health; therefore, adequate ventilation and pollution control measures are thereby recommended for the university management in order to improve indoor air quality.

Rapid conversion of carbon dioxide into titanium carbide by atmospheric microwave plasma

The accelerating climate warming requires fast methods to reduce atmospheric carbon dioxide levels. Here, we converted carbon dioxide into titanium carbide using four magnetrons which were sequentially operated to emit microwave on titanium swarf. Carbon dioxide molecules dissociated in the plasma to react with ionized titanium atoms to form a stable titanium carbide product, using a microwave frequency is 2.3 gigahertz and 800 watts electrical power for each magnetron. Results show a reduction of carbon dioxide concentration from 2000 to 385 ppm within 30 s. Titanium carbide could be further functionalized as a three-dimensional printed gas sensor.

Assessment of Ecofriendly Zeotropic Working Fluids in Organic Rankine Cycle for Renewable Energy Harvesting

The shift toward renewable energy has drawn significant attention to zeotropic mixtures in the organic Rankine cycle (ORC). However, synthetic zeotropic mixtures are detrimental to the environment due to high global warming potential. In this context, this study explores the potential of natural zeotropic binary mixtures as an alternative to synthetic zeotropic mixtures in a transcritical ORC system. The performance of two natural zeotropic mixtures and three synthetic zeotropic mixtures (hydrofluorocarbons, hydrofluoroolefins) are analyzed and compared using turbine inlet pressure and carbon dioxide (CO2) concentration as objective functions. The comprehensive thermodynamic analysis focuses on energy (first‐law) and exergy (second‐law) efficiencies, heat transfer, environmental performance, and economic aspects. The thermal and exergy efficiencies of the natural zeotropic mixture consistently outperform other mixtures by a maximum of 8% and 18%, respectively, for the conditions studied. The net power‐to‐environmental impact ratio of natural zeotropic mixture outweighs other zeotropic mixtures by more than 50%. The heat transfer analysis also favors natural zeotropic mixture at CO2 concentrations below 60% while maintaining cost parity with synthetic zeotropic mixtures. The results emphasize a holistic approach involving performance, heat transfer, environmental impact, and cost as parameters while choosing zeotropic mixtures for sustainable energy harvesting.

Land use modified impacts of global change factors on soil microbial structure and function: A global hierarchical meta-analysis

Nitrogen cycling in terrestrial ecosystems is critical for biodiversity, vegetation productivity and biogeochemical cycling. However, little is known about the response of functional nitrogen cycle genes to global change factors in soils under different land uses. Here, we conducted a multiple hierarchical mixed effects meta-analyses of global change factors (GCFs) including warming (W+), mean altered precipitation (MAP+/−), elevated carbon dioxide concentrations (eCO2), and nitrogen addition (N+), using 2706 observations extracted from 200 peer-reviewed publications. The results showed that GCFs had significant and different effects on soil microbial communities under different types of land use. Under different land use types, such as Wetland, Tundra, Grassland, Forest, Desert and Agriculture, the richness and diversity of soil microbial communities will change accordingly due to differences in vegetation cover, soil management practices and environmental conditions. Notably, soil bacterial diversity is positively correlated with richness, but soil fungal diversity is negatively correlated with richness, when differences are driven by GCFs. For functional genes involved in nitrification, eCO2 in agricultural soils and the interaction of N+ with other GCFs in grassland soils stimulate an increase in the abundance of the AOA-amoA gene. In agricultural soil, MAP+ increases the abundance of nifH. W+ in agricultural soils and N+ in grassland soils decreased the abundance of nifH. The abundance of the genes nirS and nirK, involved in denitrification, was mainly negatively affected by W+ and positively affected by eCO2 in agricultural soil, but negatively affected by N+ in grassland soil. This meta-analysis was important for subsequent research related to global climate change. Considering data limitations, it is recommended to conduct multiple long-term integrated observational experiments to establish a scientific basis for addressing global changes in this context.

Precision Regulation and Forecasting of Greenhouse Tomato Growth Conditions Using an Improved GA-BP Model

Tomatoes are annual herbaceous plants of the family Solanaceae. They have very stringent requirements for their growing environment and climate conditions. To precisely control the greenhouse environment for tomato growth, this project designed and implemented a monitoring system utilizing programmable logic controllers and a data acquisition system for monitoring. Sensors are installed at key locations in the greenhouse, such as near the ground, in the plant canopy, and under the roof, to monitor environmental parameters such as temperature, humidity, light intensity, and carbon dioxide concentration in real time. A three-layer feedforward GA-BP neural network model was established using soil temperature, soil humidity, air humidity, and illumination as predictive parameters. This technology predicts optimal environmental parameters and performs real-time monitoring and intelligent control, overcoming the traditional BP neural network’s drawbacks of slow convergence, susceptibility to disturbance, and poor generalization ability, with the current model’s average prediction error being less than 5%. It provides a theoretical basis and decision support for precision control and related environmental forecasting in agricultural projects. Based on the growth characteristics, physiological and morphological features of tomatoes, and the comprehensive impact of planting conditions, the technology enhances tomato yield and quality while reducing production costs and labor inputs. It mainly includes the establishment of tomato growth models, measurement and control of environmental parameters, and the design and optimization of intelligent decision control systems. Research outcomes in this field are significant for improving agricultural production efficiency, ensuring food security, and promoting sustainable agricultural development.

CROP IMPROVEMENT THROUGH DIFFERENT MEANS TO ADDRESS CLIMATE CHANGE AND FOOD SECURITY

This study evaluated several crop development techniques in addition to the effects of climate change on productivity in agriculture. Climate affects agricultural productivity and the distribution of crops worldwide. Given the current and projected variations in temperature, precipitation, and carbon dioxide concentrations, it is unclear whether agroecosystems will be able to produce enough food to meet the world's needs. Crop output, water productivity, and soil water balance are frequently altered by climate change. Soil moisture content and groundwater levels will be affected by the immediate effects of global warming-induced changes in temperature and precipitation patterns. Agricultural output is affected by several factors, such as crop variety, growing circumstances, soil deterioration, and water availability. Modern agricultural systems use genetics, biotechnology, plant breeding, environmental monitoring, and adjustments to management techniques to adapt to climate change. On the other hand, without a more thorough approach to agricultural systems, development cannot proceed. A strategy like this would mean growing amounts of a wider variety of superior crops and advancing equity, sustainability, nutrition, and food security in the future.

Predicting CO2 production of lactating dairy cows from animal, dietary, and production traits using an international dataset

Automated measurements of the ratio of concentrations of methane and carbon dioxide, [CH4]:[CO2], in breath from individual animals (the so-called "sniffer technique") and estimated CO2 production can be used to estimate CH4 production, provided that CO2 production can be reliably calculated. This would allow CH4 production from individual cows to be estimated in large cohorts of cows, whereby ranking of cows according to their CH4 production might become possible and their values could be used for breeding of low CH4-emitting animals. Estimates of CO2 production are typically based on predictions of heat production, which can be calculated from body weight (BW), energy-corrected milk yield, and days of pregnancy. The objectives of the present study were to develop predictions of CO2 production directly from milk production, dietary, and animal variables, and furthermore to develop different models to be used for different scenarios, depending on available data. An international dataset with 2,244 records from individual lactating cows including CO2 production and associated traits, as dry matter intake (DMI), diet composition, BW, milk production and composition, days in milk, and days pregnant, was compiled to constitute the training dataset. Research location and experiment nested within research location were included as random intercepts. The method of CO2 production measurement (respiration chamber [RC] or GreenFeed [GF]) was confounded with research location, and therefore excluded from the model. In total, 3 models were developed based on the current training dataset: model 1 ("best model"), where all significant traits were included; model 2 ("on-farm model"), where DMI was excluded; and model 3 ("reduced on-farm model"), where both DMI and BW were excluded. Evaluation on test dat sets with either RC data (n = 103), GF data without additives (n = 478), or GF data only including observations where nitrate, 3-nitrooxypropanol (3-NOP), or a combination of nitrate and 3-NOP were fed to the cows (GF+: n = 295), showed good precision of the 3 models, illustrated by low slope bias both in absolute values (-0.22 to 0.097) and in percentage (0.049 to 4.89) of mean square error (MSE). However, the mean bias (MB) indicated systematic overprediction and underprediction of CO2 production when the models were evaluated on the GF and the RC test datasets, respectively. To address this bias, the 3 models were evaluated on a modified test dataset, where the CO2 production (g/d) was adjusted by subtracting (where measurements were obtained by RC) or adding absolute MB (where measurements were obtained by GF) from evaluation of the specific model on RC, GF, and GF+ test datasets. With this modification, the absolute values of MB and MB as percentage of MSE became negligible. In conclusion, the 3 models were precise in predicting CO2 production from lactating dairy cows.

Lung Volume and Ventilation Distribution After Bariatric Surgery: High-Flow Nasal Cannula Versus CPAP.

Patients with obesity are at increased risk of postoperative pulmonary complications. CPAP has been used successfully to prevent and treat acute respiratory failure, but in many clinical scenarios, high-flow nasal cannula (HFNC) therapy is emerging as a possible alternative. We aimed to compare HFNC and CPAP in a sequential study measuring their effects on gas exchange, lung volumes, and gas distribution within the lungs measured through electrical impedance tomography (EIT). We enrolled 15 subjects undergoing laparoscopic bariatric surgery. Postoperatively they underwent the following oxygen therapy protocol (10 min/step): baseline air-entrainment mask, HFNC at increasing (40, 60, 80, and 100 L/min) and decreasing flows (80, 60, and 40 L/min), washout air-entrainment mask and CPAP (10 cm H2O). Primary outcome was the change in end-expiratory lung impedance (ΔEELI) measured by EIT data processing. Secondary outcomes were changes of global inhomogeneity (GI) index and tidal impedance variation (TIV) measured by EIT, arterial oxygenation, carbon dioxide content, pH, respiratory frequency, and subject's comfort. Thirteen subjects completed the study. Compared to baseline, ΔEELI was higher during 10 cm H2O CPAP (P = .001) and HFNC 100 L/min (P = .02), as well as during decreasing flows HFNC 80, 60, and 40 L/min (P = .008, .004, and .02, respectively). GI index was lower during HFNC 100 compared to HFNC 60increasing (P = .044), HFNC 60decreasing (P = .02) HFNC 40decreasing (P = .01), and during 10 cm H2O CPAP compared to washout period (P = .01) and HFNC 40decreasing (P = .03). TIV was higher during 10 cm H2O CPAP compared to baseline (P = .008). Compared to baseline, breathing frequency was lower at HFNC 60increasing, HFNC 100, and HFNC 80decreasing (P = .01, .02, and .03, respectively). No differences were detected regarding arterial oxygenation, carbon dioxide content, pH, and subject's comfort. HFNC at a flow of 100 L/min induced postoperative pulmonary recruitment in bariatric subjects, with no significant differences compared to 10 cm H2O CPAP in terms of lung recruitment and ventilation distribution.

Fusarium mycotoxins: The major food contaminants.

Mycotoxins, which are secondary metabolites produced by toxicogenic fungi, are natural food toxins that cause acute and chronic adverse reactions in humans and animals. The genus Fusarium is one of three major genera of mycotoxin-producing fungi. Trichothecenes, fumonisins, and zearalenone are the major Fusarium mycotoxins that occur worldwide. Fusarium mycotoxins have the potential to infiltrate the human food chain via contamination during crop production and food processing, eventually threatening human health. The occurrence and development of Fusarium mycotoxin contamination will change with climate change, especially with variations in temperature, precipitation, and carbon dioxide concentration. To address these challenges, researchers have built a series of effective models to forecast the occurrence of Fusarium mycotoxins and provide guidance for crop production. Fusarium mycotoxins frequently exist in food products at extremely low levels, thus necessitating the development of highly sensitive and reliable detection techniques. Numerous successful detection methods have been developed to meet the requirements of various situations, and an increasing number of methods are moving toward high-throughput features. Although Fusarium mycotoxins cannot be completely eliminated, numerous agronomic, chemical, physical, and biological methods can lower Fusarium mycotoxin contamination to safe levels during the preharvest and postharvest stages. These theoretical innovations and technological advances have the potential to facilitate the development of comprehensive strategies for effectively managing Fusarium mycotoxin contamination in the future.

Low–carbon regulation method for greenhouse light environment based on multi–objective optimization

Controlled environment agriculture is uniquely positioned to contribute to the decarbonization of cities due to its low carbon emissions and high energy efficiency in the food supply process. The energy consumed by the horticultural industry to supplemental lighting is enormous, but improvements in the light environment of greenhouse plants are essential. To make light regulation compatible with low carbon and environmentally friendly while improving photosynthetic production, experiments on tomato plant measurements with nested light intensity, spectra, carbon dioxide concentration and temperature were carried out in a solar greenhouse based on an infrared gas analyzer and machine learning. Based on the photosynthetic response to the photosynthetic photon flux density and red–blue ratio under multi–environmental conditions, the photosynthesis prediction model was developed using the least squares support vector regression, and model hyperparameter optimization was achieved by genetic algorithm nested cross–validation. A calculation method of carbon emissions from supplemental lighting was proposed, and the low–carbon light regulation was abstracted into multi–objective optimization for the solution. Based on the comprehensive objective evaluation method, low–carbon targets for light regulation were obtained through decision–making. The results showed that the developed prediction model with root mean square error of 1.069 and determination coefficient of 0.976, which is both accurate and fast, realizes the description and expression of the net photosynthetic rate in response to the environments. The optimal targets contain two–dimensional information of light intensity and spectra, and reduces carbon emissions by a maximum of 14.85% compared to the light saturation objective based on single–objective optimization at 95.49 % of the net photosynthetic rate. The method is energy efficient and suitable for supporting multi–spectral light environment regulation applications and guiding the greenhouse environment management under the demand of carbon reduction.

Forests high on CO2: A glimpse into how scientists study the biochemical machinery of forest ecosystems

Are forests across the planet storing more carbon as a result of rising atmospheric carbon dioxide (CO2) concentration? In this text, I propose to reflect on the work of researchers who are asking that very question. The focus is on a particular approach called free-air CO2 enrichment (FACE) experiments, in which forests in their local environment are subjected to a CO2 concentration higher than in the ambient air, to test the CO2 fertilization hypothesis. This hypothesis predicts that forests store more carbon in those conditions. FACE studies held the promise of establishing cause–effect relations and transcending the here and now. They emerged in the United States in the late 1980s, at a time when ecology scaled up to make itself relevant to the then nascent Earth System science. FACE experiments in forests are now conducted in Australia, the United Kingdom, and Brazil, and their results are closely scrutinized by a small research community. The text, first, presents the engineering feat that underpins FACE experiments in forests and discuss two problems highlighted by scientists because they impede hasty generalization from the data: the timescale needed to witness forests change and the spatial heterogeneity of forest soils. It then shows how scientists contend with their incomplete understanding of forest ecosystems as they bring together computer models and empirical data to build consensus around the sources of epistemic uncertainty. To conclude, I propose to rework the idea of “the macroscope” to conceptualize what scientists do when they seek to study forests as complex, layered, multi-scale systems. I wish to capture how researchers, who willingly endorse a planetary gaze, problematize their own predicament. This, as I suggest, has to do with the immanence of forests in the world.

CH4 emissions from runoff water of Alaskan mountain glaciers

Recent studies have observed high methane concentrations in runoff water and the ambient air at various glacier sites, including the Greenland Ice Sheet, the glacier forefield in Svalbard, and the ice cap in Iceland. This study extends these findings to smaller mountain glaciers in Alaska. Methane and carbon dioxide concentrations in the ambient air near the meltwater outlet, fluxes of these gases at the surface of runoff water and riverbank sediments, and dissolved methane content in the runoff water were measured at four glaciers. Three of the four glaciers showed conspicuous signals of methane emissions from runoff water, with the Castner Glacier terminus exhibiting a methane concentration three times higher than background levels, along with elevated dissolved methane levels in the runoff water. This study marks the detection of significant methane emissions from small mountain glacier runoff, contributing to the understanding that mountain glaciers also release methane into the atmosphere.

A review of g‐C3N4‐based photocatalytic materials for photocatalytic CO2 reduction

AbstractCurrently, the concentration of carbon dioxide (CO2) has exceeded 400 ppm in the atmosphere. Thus, there is an urgent need to explore CO2 reduction and utilization technologies. Photocatalytic technology can convert CO2 to valuable hydrocarbons (CH4, CH3OH, and C2H5OH, etc.), realizing the conversion of solar energy to chemical energy as well as solving the problems of fossil fuel shortage and global warming. Graphitic carbon nitride (g‐C3N4), as a two‐dimensional nonmetallic semiconductor material, shows great potential in the field of CO2 photoreduction due to its moderate bandgap, easy synthesis method, low cost, and visible light response properties. This review elaborates the research progress of g‐C3N4‐based photocatalysts for photocatalytic CO2 reduction. The modification strategies (e.g., morphology engineering, elemental doping, crystallinity modulation, cocatalyst modification, and constructing heterojunction) of g‐C3N4‐based photocatalysts for CO2 reduction application have been discussed in detail. Finally, the challenges and development prospects of g‐C3N4‐based photocatalytic materials for CO2 reduction are presented.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries

BackgroundIn low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data.MethodsWe implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software.ResultsThe model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%.ConclusionsWe demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Potential of Methane (CH4), Nitrogen (N2), and Carbon Dioxide (CO2) from Eco-Enzyme with the Addition of Cow Feces Starter

Eco-enzyme solutions are generally used in the manufacture of disinfectants, floor cleaners, liquid fertilizers, preservatives and others. In this study, eco-enzyme was used as the main ingredient in the manufacture of biogas fermentation with the addition of cow feces as an additional starter in the fermentation. The aim of this study was to determine the content of CH4, CO2, N2, pH, temperature, and pressure in eco-enzyme fermentation with the addition of cow feces starter. his research is a quantitative research with the type of experimental research as well as direct observation data collection techniques and data analysis techniques using descriptive statistical analysis. The results showed that the treatment that produced the highest methane (CH4) gas was the P5 treatment with a concentration of 2.889%. 6,1. The highest volume pressure value was produced in the P5 treatment which was 70 ml. Nitrogen gas (N2) and carbon dioxide gas (CO2), the concentration of nitrogen gas (N2) from reactor P0 - P4 is in the range of 95.875-99.669 % and the concentration of carbon dioxide (CO2) from reactor P0 - P4 is in the range of 0.237 - 4.125 %.

Improving the Fuel Economy and Energy Efficiency of Train Cab Climate Systems, Considering Air Recirculation Modes

Current developments in vehicles have generated great interest in the research and optimization of heating, ventilation, and air conditioning (HVAC) systems as a factor to reduce fuel consumption. One of the key trends for finding solutions is the intensive development of electric transport and, consequently, additional requirements for reducing energy consumption and modifying climate systems. Of particular interest is the optimal functioning of comfort and life support systems during air recirculation, i.e., when there is a complete or partial absence of outside air supply, in particular to reduce energy consumption or when the environment is polluted. This work examines numerical models of airfields (temperature, speed, and humidity) and also focuses on the concentration of carbon dioxide and oxygen in the cabin, which is a critical factor for ensuring the health of the driver and passengers. To build a mathematical model, the Navier–Stokes equations with energy, continuity, and diffusion equations are used to simulate the diffusion of gases and air humidity. In the Ansys Fluent finite volume analysis package, the model is solved numerically using averaged RANS equations and k-ω turbulence models. The cabin of a mainline locomotive with two drivers, taking into account their breathing, is used as a transport model. The problem was solved in a nonstationary formulation for the design scenario of summer and winter, the time of stabilization of the fields was found, and graphs were constructed for different points in time. A comparative analysis of the uniformity of fields along the height of the cabin was carried out with different locations of deflectors, and optimal configurations were found. Energy efficiency values of the climate system operation in recirculation operating modes were obtained. A qualitative assessment of the driver’s blowing directions under different circulation and recirculation modes is given from the point of view of the concentration of carbon dioxide in the breathing area. The proposed solution makes it possible to reduce electricity consumption from 3.1 kW to 0.6 kW and in winter mode from 11.6 kW to 3.9 kW and save up to 1.5 L/h of fuel. The conducted research can be used to develop modern energy-efficient and safe systems for providing comfortable climate conditions for drivers and passengers of various types of transport.

Entropy Drives Accelerated Ion Diffusion upon Carbon Dioxide Expansion of Electrolytes.

Carbon dioxide-expanded liquids, organic solvents with high concentrations of soluble carbon dioxide (CO2) at mild pressures, have gained attention as green catalytic media due to their improved properties over traditional solvents. More recently, carbon dioxide-expanded electrolytes (CXEs) have demonstrated improved reaction rates in the electrochemical reduction of CO2, by increasing the rate of delivery of CO2 to the electrode while maintaining facile charge transport. However, recent studies indicate that the limiting behavior of CXEs at higher CO2 pressures is a decline in solution conductivity due to reduced polarity, leading to poorer charge screening and greater ion pairing. In this article, we employ molecular dynamics simulations to investigate the energetic driving forces behind the diffusive properties of an acetonitrile and tetrapropylammonium hexafluorophosphate (TPrAPF6) CXE with increasing CO2 concentration. Our results indicate that entropy drives solvent and electrolyte diffusion with increasing CO2 pressure. The activation energy of ion diffusion increases with higher concentrations of CO2, indicating that increasing the temperature may improve solution conductivity in these systems. This trend in the activation energies is traced to stronger cation-anion Coulombic interactions due to weaker solvent screening at high CO2 concentrations, suggesting that the choice of ion may provide a route to diminish this effect.