Series Of Experiments Research Articles

Heat Transfer in Double Pipe Heat Exchangers with Small Tube Spacing

Abstract In this current work, the heat transfer in double pipe heat exchangers with small tube spacing is analysed. This type of heat transfer is generally described in many literary sources, however, in laminar flow, the resulting values of the theoretical equations differ. Moreover, the small spacing of the pipes can affect the fluid flow and subsequent heat transfer. The suitability of using available theoretical methods for the design of double pipe heat exchangers (the Stephan, VDI and Baehr methods) was evaluated. A series of experiments was carried out on a double pipe heat exchanger with a tube spacing of 1.4 mm. The experimentally determined value of the heat transfer coefficient was up to two times higher in comparison with the values calculated according to theoretical methods. The relative increase was more significant for the lower Reynolds number values. At the same time, the difference of the wall temperature along the circumference of the outer tube was detected. This phenomenon was theoretically analysed and can be explained by tube misalignment in the small tube spacing. The procedure of quantification of this effect was proposed. This effect may cause an inhomogeneity of the media flow and temperature distribution and, as a result, increase the performance of the heat exchanger by tens of percent.

Integrative Human Genetic and Cellular Analysis of the Pathophysiological Roles of AnxA2 in Alzheimer’s Disease

Alzheimer’s disease (AD) is an intractable and progressive neurodegenerative disease. Amyloid beta (Aβ) aggregation is the hallmark of AD. Aβ induces neurotoxicity through a variety of mechanisms, including interacting with membrane receptors to alter downstream signaling, damaging cellular or organelle membranes, interfering with protein degradation and synthesis, and inducing an excessive immune-inflammatory response, all of which lead to neuronal death and other pathological changes associated with AD. In this study, we extracted gene expression profiles from the GSE5281 and GSE97760 microarray datasets in the GEO (Gene Expression Omnibus) database, as well as from the Human Gene Database. We identified differentially expressed genes in the brain tissues of AD patients and healthy persons. Through GO, KEGG, and ROC analyses, annexin A2 (AnxA2) was identified as a putative target gene. Notably, accumulating evidence suggests that intracellular AnxA2 is a key regulator in various biological processes, including endocytosis, transmembrane transport, neuroinflammation, and apoptosis. Thus, we conducted a series of cell biology experiments to explore the biological function of AnxA2 in AD. The results indicate that AnxA2 gene knockdown primarily affects oxidative phosphorylation, cell cycle, AD, protein processing in the endoplasmic reticulum, SNARE interactions in vesicular transport, and autophagy. In SH-SY5Y cells secreting Aβ42, AnxA2 gene knockdown exacerbated Aβ42-induced cytotoxicity, including cell death, intracellular ROS levels, and neuronal senescence, altered cell cycle, and reduced ATP levels, suggesting its critical role in mitochondrial function maintenance. AnxA2 gene knockdown also exacerbated the inhibitory effect of Aβ42 on cell migration. AnxA2 overexpression reduced the inflammatory response induced by Aβ42, while its absence increased pro-inflammatory and decreased anti-inflammatory responses. Furthermore, AnxA2 gene knockdown facilitated apoptosis and decreased autophagy. These results indicated potential pathophysiological roles of AnxA2 in AD.

Investigation on the Machinability of Polycrystalline ZnS by Micro-Laser-Assisted Diamond Cutting

Polycrystalline ZnS is a typical infrared optical material. It is widely used in advanced optical systems due to its excellent optical properties. The machining accuracy of polycrystalline ZnS optical elements must satisfy the requirements of high-performance system development. However, the soft and brittle nature of the material poses a challenge for high-quality and efficient machining. In recent years, in situ laser-assisted diamond cutting has been proven to be an effective method for ultra-precision cutting of brittle materials. In this study, the mechanism of in situ laser-assisted cutting on ultra-precision cutting machinability enhancement of ZnS was investigated. Firstly, the physical properties of ZnS were characterized by high-temperature nanoindentation experiments. The result revealed an increase in ductile machinability of ZnS due to plastic deformation and a decrease in microhardness and Young’s modulus at high temperatures. It provided a fundamental theory for the ductile–brittle transition of ZnS. Subsequently, a series of diamond-cutting experiments were carried out to study the removal mechanism of ZnS during in situ laser-assisted cutting. It was found that the mass damage initiation depth groove generated by in situ laser-assisted cutting increased by 57.99% compared to the groove generated by ordinary cutting. It was found that micron-sized pits were suppressed under in situ laser-assisted cutting. The main damage form of HIP-ZnS was changed from flake spalling and pits to radial cleavage cracks. Additionally, the laser can suppress the removal mode difference of different grain crystallographic and ensure the ductile region processing. Finally, planning cutting experiments were carried out to verify that a smooth and uniform surface with Sa of 3.607 nm was achieved at a laser power of 20 W, which was 73.58% better than normal cutting. The main components of roughness were grain boundary steps and submicron pit. This study provides a promising method for ultra-precision cutting of ZnS.

Prediction of thermally induced axial displacement of mechanical components using LightGBM

Abstract The goal of this research is to create a machine learning model that can predict thermally induced axial displacement of machine tool spindles. To achieve this goal, this study applied the LightGBM machine learning framework to predict thermally induced axial displacement of mechanical equipment by a heat source in a model that had an outer structure similar to that of a machine spindle. In the predictions using LightGBM, the time, temperature, and heat flux of equipment surfaces are measured and used to predict displacement. A similar trial study was also conducted for a servomotor. A series of experiments clarified that the thermally induced axial displacement of the equipment can be predicted using a machine learning model created from the measured temperatures and heat fluxes of each surface of the target component and other parameters. Furthermore, the study focused on the feature importance in the prediction process. Through these considerations, the features that are most valuable for prediction among the features used for the trial measurement and subsequent prediction were extracted based on the feature importance. Using the feature importance, only the top-ranked parameters were chosen to create a machine learning model for prediction. As a result, equivalent prediction accuracy is possible, even if the number of features, namely sensors required for the acquisition of sufficient features for the prediction, can be reduced without significantly affecting the prediction accuracy. Concretely, it was confirmed that the number of sensors can be reduced.

A Real-Time Key-Finding Algorithm Based on the Signature of Fifths

The signature of fifths is a special kind of music representation technique enabling acquisition of musical knowledge. The technique is based on geometrical relationships existing between twelve polar vectors inscribed in the circle of fifths, which represent individual pitch-classes detected in a given composition. In this paper we introduce a real-time key-detection algorithm which utilizes the concept of the signature of fifths. We explain how to create the signature of fifths and how to derive its descriptors required by the algorithm, i.e., the main directed axis of the signature of fifths, the major/minor mode axis, the characteristic vector of the signature of fifths, the characteristic angle of the signature of fifths, and the angle of the major/minor mode. We performed a series of experiments to test the algorithm’s effectiveness. The results were compared with those obtained using key-detection approaches based on key-profiles. All experiments were conducted using works composed by J.S. Bach, F. Chopin, and D. Shostakovich. The distinctive features of the presented algorithm, with respect to the considered key-detection approaches, are computational simplicity and stability of the decision-making process.

How Observed Queue Length and Service Times Drive Reneging Behavior in Queues

Customers who renege from a queue signal dissatisfaction with the wait, possibly leading to lost business opportunities. Service organizations need to understand how queue features observed by waiting customers drive decisions to renege. Through a series of experiments run in the lab and online, this article demonstrates a relative progress effect in queues. This behavioral effect is created when a series of fast service times are observed early in a wait, followed by slower service times that make the total wait equal to that in a comparable queue progressing at a steady rate. The observer experiences a fast depletion of the queue and a lower proportion of the original queue length remaining. This generates a sense of relative progress for the observer which leads to a reduction in renege behavior. In one of the studies, a relative progress effect enables participants to wait for a longer queue or longer duration compared to a benchmark queue without this effect. A simulation analysis, where individuals in a queue are modeled as experiencing a similar behavioral effect, shows that the individual-level effects lead to systematic differences in the queue performance. Behavioral effects that increase individual patience for some are shown to reduce the overall renege rate in the queue. Our findings suggest that queue practices that modulate the perception of relative progress can reduce renege behavior.

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

Suppression of Aseptic Inflammation Reduces the Severity of Remodeling of the Pulmonary Artery Branches and Improves Progressing of Experimental Chronic Thromboembolic Pulmonary Hypertension

Chronic thromboembolic pulmonary hypertension (CTEPH) is a complication of pulmonary embolism, characterized by increased pressure in the pulmonary artery and impaired lysis of thromboemboli. Previously, the presence of aseptic inflammation in CTEPH was identified in the wall of the pulmonary artery branches and perivascularly. However, the role of this inflammation in the CTEPH formation is unknown. The aim of the work was to study the effect of aseptic inflammation on the CTEPH formation and progression. The experiments were performed on 54 male rats. The CTEPH model was reproduced by repeated intravenous administration of partially biodegradable microspheres (MS). Immediately after the last administration of MS, all animals were divided into groups: control CTEPH (c.CTEPH) – saline solution was administered intramuscularly (i/m) for 6 weeks; low dose of prednisolone (LD) – prednisolone was administered i/m at a dose of 1.5 mg/kg; high dose (HD) – prednisolone was administered i/m at a dose of 6 mg/kg; healthy animals. After 6 weeks, the following was performed: treadmill test, TTE, cardiac catheterization with manometry, and histological examination of the lungs. In a separate series of experiments, the severity of inflammatory infiltration of the vascular wall and perivascular zone was assessed by immunohistochemical studies (IHC). In the LD group, there was the decreasing of hypertrophy index (HI) and the percentage of collagen fibers in the vascular wall compared to c.CTEPH. There was a significantly greater reduction in HI compared to HD. In the HD group, there was positive effect on the percentage of collagen fibers in the vascular wall, this parameter did not significantly differ from healthy animals. According to IHC data, prednisolone in low dose effectively suppressed inflammatory infiltration of the vascular wall and perivascular space. The results of the study revealed the ability of prednisolone, by suppressing aseptic inflammation, to reduce the severity of remodeling of the pulmonary artery branches.

Determination of Hafnium in Zirconium by Spectrophotometry

Zirconium and hafnium have opposite nuclear properties and are used very differently in the nuclear industry. However, hafnium is a common metal impurity in zirconium, and the chemical properties of the two are very similar except for nuclear properties, and it is difficult to separate and detect them. At present, the detection of hafnium content in zirconium is usually achieved by using an inductively coupled plasma (ICP) spectrometer, but ICP equipment is expensive, and the detection cost is high. Therefore, it is necessary to develop a simple and low-cost method for the determination of hafnium content in zirconium. Based on this, this paper takes the spectrophotometric method as a starting point. Through a series of experiments on the influence of pH and concentrations of the color-developing agent xylenol orange sodium salt on the absorbance of zirconium and hafnium ions, the appropriate variables are selected to detect the content of hafnium in zirconium. Finally, according to the measured absorbance and total ion concentration, by comparing the working curve of zirconium and hafnium ions, the content of hafnium in zirconium is calculated based on the lever principle.

Spectral and optic-metric methods of monitoring parameters of plasma channels caused by discharge currents between metals granules in working liquids

Introduction. Spark-erosion processing of metals and alloys granules in working liquids is the basis of a several technological processes. Efficiency of energy use in them and parameters of the resulting product largely depend on the accuracy of stabilization and regulation of pulse power in each plasma channel between the granules. To achieve this, until now only the voltage and current of the discharge pulses in the entire layer of granules have been controlled. Problem. The measurement methods, which are used, are not effective enough for monitoring the parameters of individual plasma channels and predicting the size distribution of eroded metals particles at the stage of their formation. The aim of the work is to develop a method for determining the volumes of components of plasma channels in layers of metals granules during their spark-erosion treatment to predict the size distribution of eroded metal particles at the stage of their formation, as well as to simplify the method of spectrometric analysis of the elemental composition of substances surrounding plasma channels for the operational prediction of the chemical composition of resulting products. Methodology. A series of experiments were carried out on spark-erosion processing of Al and Ag granules layers in distilled water. Using a digital camera, images of the plasma channels in them were obtained. Based on the theory of pulsed electrical breakdown of liquid dielectrics, an analysis of the components of plasma channels was carried out. Using the specialized ToupView program, the volumes of equivalent ellipsoids of rotation were determined, approximating the halos of colored radiation likely arising from streamers, as well as the spark cores of plasma channels emitting white light. The shades of the resulting radiation were studied for several metals and working liquids. The obtained data were compared with the known results of spectrometric studies for the same elements excited by similar mechanisms. Results. The theory of discharge-pulse systems for spark-erosion processing of granular conductive media has been developed in the direction of new methods for monitoring the parameters of discharge pulses and predicting the chemical composition and size distribution parameters of eroded metal particles at the stage of their production. An optic-metric method has been developed for determining the volumes of halos and cores of plasma channels. A simplified spectral method for determining the chemical composition of erosion particles based on the shade of the resulting radiation was proposed. Originality. The developed new optic-metric method makes it possible to obtain information about almost every plasma channel, which refines predictions of the size distribution of erosion particles. To implement the method, general-purpose hardware and specialized software that is freely available are used. The developed method of simplified spectral analysis of excited atoms makes it possible to make preliminary predictions of the chemical composition of the obtained erosion particles already at the stage of their formation without the use of expensive specialized equipment. Practical significance. The ratio of the volumes of halos and cores of plasma channels between Al and Ag granules in distilled water was measured. An analysis of the emission spectra of plasma channel halos between Al, Ag and Cu granules in distilled water, Fe in ethyl alcohol, Ni-Mn-Ga and Ti-Zr-Ni alloys in liquid nitrogen, and Ti-Zr-Ni in liquid argon was carried out. Based on spectrometry data, the resulting shades of these radiations were substantiated and their description in the RGB system is given. References 56, table 1, figures 4.

Ginsenoside 20(S)-Rg3 Hinders Esophageal Squamous Cell Carcinoma Cells Malignant Behaviors by miR-210-3p/B4GALT5 Axis.

Ginsenoside 20(S)-Rg3 (20(S)-Rg3) belongs to a natural chemical with an anti-tumor function, but its potential function and underlying mechanism in esophageal squamous cell carcinoma (ESCC) are unknown. Several reports have manifested that microRNA (miRNA) miR-210-3p functions as a tumor repressor in tumors, but its biofunction in ESCC remains obscure. Herein, the role and interaction of 20(S)-Rg3 and miR-210-3p in ESCC cells were investigated. We performed a series of functional experiments to validate that 20(S)-Rg3 notably restrained ESCC cell proliferation and migration while promoting cell apoptosis. Besides, miR-210-3p was found to be lowly expressed in ESCC cells. Overexpressing miR-210-3p suppressed the malignant behaviors of ESCC cells. More importantly, 20(S)-Rg3 could upregulate miR-210-3p expression in ESCC cells. MiR-210-3p knockdown offset the inhibitive impacts of 20(S)-Rg3 treatment on ESCC cell growth and migration. Furthermore, through luciferase reporter assay, beta-1,4-galactosyltransferase 5 (B4GALT5) was certified to be targeted by miR-210-3p. B4GALT5 upregulation neutralized the suppressive function of 20(S)-Rg3 on ESCC progression. Overall, 20(S)-Rg3 attenuated malignant behaviors of ESCC cells by modulating miR-210-3p/B4GALT5 axis, indicating 20(S)-Rg3 has therapeutic potential for ESCC.

An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, RIME has certain limitations. For example, it is prone to falling into Local Optima (LO), thus failing to find the Global Optima (GO), and has the problem of slow convergence. To solve these problems, this paper introduces an improved RIME algorithm (PCRIME), which combines the random reselection strategy and the Powell mechanism. The random reselection strategy enhances population diversity and helps to escape LO, while the Powell mechanism helps to improve the convergence accuracy and thus find the optimal solution. To verify the superior performance of PCRIME, we conducted a series of experiments at CEC 2017 and CEC 2022, including qualitative analysis, ablation studies, parameter sensitivity analysis, and comparison with various advanced algorithms. We used the Wilcoxon signed-rank test and the Friedman test to confirm the performance advantage of PCRIME over its peers. The experimental data show that PCRIME has superior optimization ability and robustness. Finally, this paper applies PCRIME to five real engineering problems and proposes feasible solutions and comprehensive performance index definitions for these five problems to prove the stability of the proposed algorithm. The results show that the PCRIME algorithm can not only effectively solve practical problems, but also has excellent stability, making it an excellent algorithm.

Absorption Measurement in Ultrapure Crystalline Quartz with the Eliminated Influence of Ambient Air Absorption in the Time-Resolved Photothermal Common-Path Interferometry Scheme

We demonstrate measurements of the absorption coefficient α ≈ 2.5 × 10−7 cm−1 in synthetic crystalline quartz at a wavelength of 1071 nm with a signal-to-noise ratio of 10/1 using the Time-resolved photothermal common-path interferometry (TPCI) scheme. It utilized cells filled with flowing argon and eliminated the influence of ambient air absorption. The scheme elements limiting the sensitivity of measurements at the level of ≈7.8 × 10−8 cm−1 were revealed. When these elements are replaced by better ones in terms of their thermal influence, the sensitivity of absorption coefficient measurements in crystalline quartz is ~10−8 cm−1. The calculation of the correction due to these optical elements of the values of the measured absorption coefficients is also described, which makes it possible to achieve the same sensitivity without replacing the elements. The improved scheme confirms the presence of the spatial inhomogeneity of absorption with a minimum coefficient of 2.5 × 10−7 cm−1 in synthetic crystalline quartz. The discrepancy of the absorption coefficient values in different regions of the crystal in the presented series of experiments was 2.5 × 10−7 cm−1 to 4 × 10−6 cm−1. Taking into account the ratio of thermo-optical parameters and the heat diffusion effect, the calculation shows that for quartz glasses the corresponding sensitivity of the absorption coefficient measurements equals ≈1.5 × 10−9 cm−1.

Experimental investigation of dissolved oxygen improving aeration efficiency by hydraulic jumps

The dissolved oxygen (DO) level in water is vital for water quality and supporting aquatic life. Hydraulic jumps involve rapid flow changes from super-critical to sub-critical, visible at abrupt bed slope shifts, like at spillway bases in rivers or canals. The hydraulic jump efficiently mixes oxygen from air into water and offers a cost-effective method of aeration by entraining air bubbles in the stream to improve oxygen transfer compared to traditional systems. The objective of this experimental research is to investigate the aeration performance with hydraulic jump parameters and establish correlations crucial to measuring aeration (or transfer) efficiency. Relationships between transfer efficiency, jump height, jump length, sequent depth ratio, discharge, inlet Froude number, and channel bed slope were determined. To investigate the nature of such relationships, a series of experiments were conducted in a rectangular tilt flume to test the aeration performance of forced submerged hydraulic jumps with five different discharges and five different smooth bed slopes. The inlet Froude number before the jump varied from 2.18 to 8.23. Experimental observation confirms a positive relationship between transfer efficiency and jump control parameters. During experimentation, transfer efficiency was found to vary between 9.4 % and 34 %. This research includes estimating the optimal transfer efficiency due to hydraulic jumps, which can help hydraulic engineers in building structures that can revitalize any degraded stream.

UNeXt: An Efficient Network for the Semantic Segmentation of High-Resolution Remote Sensing Images

The application of deep neural networks for the semantic segmentation of remote sensing images is a significant research area within the field of the intelligent interpretation of remote sensing data. The semantic segmentation of remote sensing images holds great practical value in urban planning, disaster assessment, the estimation of carbon sinks, and other related fields. With the continuous advancement of remote sensing technology, the spatial resolution of remote sensing images is gradually increasing. This increase in resolution brings about challenges such as significant changes in the scale of ground objects, redundant information, and irregular shapes within remote sensing images. Current methods leverage Transformers to capture global long-range dependencies. However, the use of Transformers introduces higher computational complexity and is prone to losing local details. In this paper, we propose UNeXt (UNet+ConvNeXt+Transformer), a real-time semantic segmentation model tailored for high-resolution remote sensing images. To achieve efficient segmentation, UNeXt uses the lightweight ConvNeXt-T as the encoder and a lightweight decoder, Transnext, which combines a Transformer and CNN (Convolutional Neural Networks) to capture global information while avoiding the loss of local details. Furthermore, in order to more effectively utilize spatial and channel information, we propose a SCFB (SC Feature Fuse Block) to reduce computational complexity while enhancing the model’s recognition of complex scenes. A series of ablation experiments and comprehensive comparative experiments demonstrate that our method not only runs faster than state-of-the-art (SOTA) lightweight models but also achieves higher accuracy. Specifically, our proposed UNeXt achieves 85.2% and 82.9% mIoUs on the Vaihingen and Gaofen5 (GID5) datasets, respectively, while maintaining 97 fps for 512 × 512 inputs on a single NVIDIA GTX 4090 GPU, outperforming other SOTA methods.

Incorporating concrete waste as additive in acidic fermentation–A novel approach for enhanced biohydrogen production and concrete mass reduction

Acidic fermentation (AF) of organic waste/wastewater generates valuable byproducts, including hydrogen, volatile fatty acids, and ethanol; however, its sensitivity to pH drops limits the stability and efficiency of the process. A reversal process, the acidic chemical treatment of concrete waste (CW) to recycle its aggregates, generates calcium hydroxide, which can serve as buffering agent. Meanwhile, integrating both processes can introduce a sustainable win-win approach, which is the aim of this study. To assess this approach, a series of batch AF experiments were conducted at 50 °C and pH 5.5, using glucose as the substrate. Cementitious specimen (1.5 × 1.5 × 0.8 cm) was supplemented to each 200 mL-fermenter. Unamended fermenters, with and without additional NaHCO3-buffering, were used as controls to compare with the amended ones. Introducing cementitious specimens to AF increased H2-production by 2-fold and 1.7-fold compared to controls with and without NaHCO3-addition, respectively, after three consecutive feed-cycles. The surface analysis of incorporated specimen confirmed the Ca, Al, Mg, and Si leaching. The AF efficiency and resulting cementitious mass reduction were further assessed at different organic loads and specimens’ volume, surface area, and porosity (by changing water-to-cement [W/C] ratio). Increasing the organic load from 10 to 20 g-glucose/L resulted in lower H2-production, higher specimen mass reduction (up to ∼32%), and higher Ca2+ release (up to 2 g/L); however, no significant effect was observed when using specimens with higher W/C ratio or surface area. Moreover, the presence of cementitious specimens significantly influenced the microbial composition, leading to notable developments in the abundant genera Thermoanaerobacterium and Bacillus. This study presents a novel approach to sustainably enhancing AF process using CW as both an additive and a treatable substance, with reusable aggregates as a byproduct. It provides valuable insights for optimizing the process and guiding future practical applications. This includes considering various concrete compositions, adjusting organic load conditions, and evaluating long-term stability in larger-scale systems.

Optimizing H2 production from biomass: A machine learning-enhanced model of supercritical water gasification dynamics

Supercritical water gasification (SCWG) is recognized as an efficient technology for biomass conversion, demonstrating substantial potential in the sustainable energy sector. This paper focuses on the H2 production by SCWG of biomass. The objective is to develop an accurate gasification kinetic model that can facilitate the optimization of industrial reactor designs. A series of SCWG experiments is conducted in a batch reactor system under temperature of 500–600 °C and residence time of 1–20 min. Based on the experimental results, a hybrid-driven model for SCWG reaction is established. Firstly, experimental data is utilized to delineate SCWG reaction mechanistic model and forecast gas yields with an acceptable error margin. Subsequently, a Wasserstein Generative Adversarial Network Gradient Boosting Regression Grid Search (WGAN-GBR-GRID) model is applied to acquire knowledge of the predictive errors from the mechanistic model and to develop a hybrid model. The hybrid-driven model significantly enhances the average relative prediction error from 15.56 % to 0.02 % when compared to the mechanistic model. This result underscores the potential of the hybrid model in optimizing SCWG technology and the Shapley Additive exPlanations (SHAP) values in feature analysis shows the possible shortcomings of mechanistic model, thereby providing a new perspective for SCWG reaction dynamics modeling.

Spectral broadening and vibronic dynamics of the S2 state of canthaxanthin in the orange carotenoid protein

We have performed a series of broadband multidimensional electronic spectroscopy experiments to probe the electronic and vibrational dynamics of the canthaxanthin chromophore of the Orange Carotenoid Protein (OCP) from Synechocystis sp. PCC 6803 in its photoactivated red state, OCPR. Cross-peaks observed below the diagonal of the two-dimensional electronic spectrum indicate that absorption transitions prepare the bright S2 state of the ketocarotenoid canthaxanthin near to a sequence of conical intersections, allowing passage to the dark S1 state via the Sx intermediate in <50 fs. Rapid damping of excited-state coherent wavepacket motions suggests that the branching coordinates of the conical intersections include out-of-plane deformation and C=C stretching coordinates of the π-conjugated isoprenoid backbone. The unusual proximity of the Franck–Condon S2 state structure to the conical intersections with Sx and S1 suggests that the protein surroundings of canthaxanthin prepare it to function as an excitation energy trap in the OCPR–phycobilisome complex. Numerical simulations using the multimode Brownian oscillator model demonstrate that the ground-state absorption spectrum of OCPR overlaps with the fluorescence emission spectrum of allophycocyanin due to spectral broadening derived especially from the intramolecular motions of the canthaxanthin chromophore in its binding site.

Artificial intelligence for predicting arctic permafrost and active layer temperatures along the Alaskan North Slope

AbstractClimate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is accentuated by the scarcity of subsurface thermal data in remote Arctic locales. To alleviate this lack of soil thermal data we set out to combine in situ observed soil temperatures with MERRA-2 reanalysis data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. First, reanalysis features are selected based on surface energy budget physics. Coupled with Julian calendar dates, reanalysis features are preprocessed and then fed to the ML prediction model. We conducted a series of computational experiments and tested the results against performance benchmarks to determine the optimal prediction models, length of look-back period of model inputs, and the training set size. Six conventional ML models (e.g., GBDT, RF, SVR) and five statistical baselines (e.g. ARIMA) using in situ time series of soil temperature data ranging from 0 - 1 m depth are considered for the prediction task. The models are trained using in situ soil temperatures at depths between 0 and 1 m, spanning 16+ years, from field sites at Deadhorse and Toolik Lake, Alaska. All prediction performances are assessed using root mean squared error (RMSE) and mean absolute error (MAE). Results show that locally trained ML models can estimate shallow soil temperatures with an average error of $$RMSE=1.308^{\circ }$$ R M S E = 1 . 308 ∘ C.