Multi level perspectives in stock price forecasting: ICE2DE-MDL.

This study proposes a novel hybrid model, called ICE2DE-MDL, integrating secondary decomposition, entropy, machine and deep learning methods to predict a stock closing price. In this context, first of all, the noise contained in the financial time series was eliminated. A denoising method, which utilizes entropy and the two-level ICEEMDAN methodology, is suggested to achieve this. Subsequently, we applied many deep learning and machine learning methods, including long-short term memory (LSTM), LSTM-BN, gated recurrent unit (GRU), and SVR, to the IMFs obtained from the decomposition, classifying them as noiseless. Afterward, the best training method was determined for each IMF. Finally, the proposed model's forecast was obtained by hierarchically combining the prediction results of each IMF. The ICE2DE-MDL model was applied to eight stock market indices and three stock data sets, and the next day's closing price of these stock items was predicted. The results indicate that RMSE values ranged from 0.031 to 0.244, MAE values ranged from 0.026 to 0.144, MAPE values ranged from 0.128 to 0.594, and R-squared values ranged from 0.905 to 0.998 for stock indices and stock forecasts. Furthermore, comparisons were made with various hybrid models proposed within the scope of stock forecasting to evaluate the performance of the ICE2DE-MDL model. Upon comparison, The ICE2DE-MDL model demonstrated superior performance relative to existing models in the literature for both forecasting stock market indices and individual stocks. Additionally, to our knowledge, this study is the first to effectively eliminate noise in stock item data using the concepts of entropy and ICEEMDAN. It is also the second study to apply ICEEMDAN to a financial time series prediction problem.

Sequence comparison of spoiled gradient echo and balanced steady-state free precession for pulmonary free-breathing proton MRI in patients and healthy volunteers: Correspondence, repeatability, and validation with dynamic contrast-enhanced MRI.

Phase-resolved functional lung (PREFUL) MRI is a proton-based, contrast agent-free technique derived from the Fourier decomposition approach to measure regional ventilation and perfusion dynamics during free-breathing. Besides the necessity of extensive PREFUL postprocessing, the utilized MRI sequence must fulfill specific requirements. This study investigates the impact of sequence selection on PREFUL-MRI-derived functional parameters by comparing the standard spoiled gradient echo (SPGRE) sequence with a lung-optimized balanced steady-state free precession (bSSFP) sequence, thereby facilitating PREFULs clinical application in pulmonary disease assessment. This study comprised a prospective dataset of healthy volunteers and a retrospective dataset of patients with suspected chronic thromboembolic pulmonary hypertension. Both cohorts underwent PREFUL-MRI with both sequences to assess the correspondence of PREFUL ventilation and perfusion parameters (A). Additionally, healthy subjects were scanned a second time to evaluate repeatability (B), whereas patients received dynamic contrast-enhanced (DCE)-MRI, considered the perfusion gold standard for comparison with PREFUL-MRI (C). Signal-to-noise ratio (SNR), calculated from the unprocessed images, was compared alongside median differences of PREFUL-MRI-derived parameters using a paired Wilcoxon signed rank test. Further evaluations included calculation of the Pearson correlation, intraclass-correlation coefficient for repeatability assessment, and spatial overlap (SO) for regional comparison of PREFUL-MRI and DCE-MRI. bSSFP showed a clear SNR advantage over SPGRE (median: 23 vs. 9, p < 0.001). (A) Despite significant differences, parameter values were strongly correlated (r ≥ 0.75). After thresholding, binary maps showed high healthy overlap across both cohorts (SOHealthy > 86%) and high defect overlap in the patient cohort (SODefect ≥ 48%). (B) bSSFP demonstrated slightly higher repeatability across most parameters. (C) Both sequences demonstrated comparable correspondence to DCE-MRI, with SPGRE excelling in absolute quantification and bSSFP in spatial agreement. Although bSSFP showed superior SNR results, both sequences displayed spatial defect concordance and highly correlated PREFUL parameters with deviations regarding repeatability and alignment with DCE-MRI.

One-step inverse generation network for sparse-view dual-energy CT reconstruction and material imaging.

Objective.Sparse-view dual-energy spectral computed tomography (DECT) imaging is a challenging inverse problem. Due to the incompleteness of the collected data, the presence of streak artifacts can result in the degradation of reconstructed spectral images. The subsequent material decomposition task in DECT can further lead to the amplification of artifacts and noise.Approach.To address this problem, we propose a novel one-step inverse generation network (OIGN) for sparse-view dual-energy CT imaging, which can achieve simultaneous imaging of spectral images and materials. The entire OIGN consists of five sub-networks that form four modules, including the pre-reconstruction module, the pre-decomposition module, and the following residual filtering module and residual decomposition module. The residual feedback mechanism is introduced to synchronize the optimization of spectral CT images and materials.Main results.Numerical simulation experiments show that the OIGN has better performance on both reconstruction and material decomposition than other state-of-the-art spectral CT imaging algorithms. OIGN also demonstrates high imaging efficiency by completing two high-quality imaging tasks in just 50 seconds. Additionally, anti-noise testing is conducted to evaluate the robustness of OIGN.Significance.These findings have great potential in high-quality multi-task spectral CT imaging in clinical diagnosis.

Efficient Cardiovascular Parameters Estimation for Fluid-Structure Simulations Using Gappy Proper Orthogonal Decomposition.

As full-scale detailed hemodynamic simulations of the entire vasculature are not feasible, numerical analysis should be focused on specific regions of the cardiovascular system, which requires the identification of lumped parameters to represent the patient behavior outside the simulated computational domain. We present a novel technique for estimating cardiovascular model parameters using gappy Proper Orthogonal Decomposition (g-POD). A POD basis is constructed with FSI simulations for different values of the lumped model parameters, and a linear operator is applied to retain information that can be compared to the available patient measurements. Then, the POD coefficients of the reconstructed solution are computed either by projecting patient measurements or by solving a minimization problem with constraints. The POD reconstruction is then used to estimate the model parameters. In the first test case, the parameter values of a 3-element Windkessel model are approximated using artificial patient measurements, obtaining a relative error of less than 4.2%. In the second case, 4 sets of 3-element Windkessel are approximated in a patient's aorta geometry, resulting in an error of less than 8% for the flow and less than 5% for the pressure. The method shows accurate results even with noisy patient data. It automatically calculates the delay between measurements and simulations and has flexibility in the types of patient measurements that can handle (at specific points, spatial or time averaged). The method is easy to implement and can be used in simulations performed in general-purpose FSI software.

Evaluating theories of neural information integration during visual search.

The brain routes and integrates information from many sources during behavior. A number of models explain this phenomenon within the framework of mixed selectivity theory, yet it is difficult to compare their predictions to understand how neurons and circuits integrate information. In this work, we apply time-series partial information decomposition [PID] to compare models of integration on a dataset of superior colliculus [SC] recordings collected during a multi-target visual search task. On this task, SC must integrate target guidance, bottom-up salience, and previous fixation signals to drive attention. We find evidence that SC neurons integrate these factors in diverse ways, including decision-variable selectivity to expected value, functional specialization to previous fixation, and code-switching (to incorporate new visual input).

Decomposing the gaps in healthy and unhealthy life expectancies between Indigenous and non-Indigenous Australians: a burden of disease and injury study.

BACKGROUND: The gaps in healthy life expectancy (HLE) between Indigenous and non-Indigenous Australians are significant. Detailed and accurate information is required to develop strategies that will close these health disparities. This paper aims to quantify and compare the causes and their relative contributions to the life expectancy (LE) gaps between the Indigenous and non-Indigenous population in the Northern Territory (NT), Australia. METHODS: The age-cause decomposition was used to analyse the differences in HLE and unhealthy life expectancy (ULE), where LE = HLE + ULE. The data was sourced from the burden of disease and injury study in the NT between 2014 and 2018. RESULTS: In 2014-2018, the HLE at birth in the NT Indigenous population was estimated at 43.3 years in males and 41.4 years in females, 26.5 and 33.5 years shorter than the non-Indigenous population. This gap approximately doubled the LE gap (14.0 years in males, 16.6 years in females) at birth. In contrast to LE and HLE, ULE at birth was longer in the Indigenous than non-Indigenous population. The leading causes of the ULE gap at birth were endocrine conditions (explaining 2.9-4.4 years, 23-26%), followed by mental conditions in males and musculoskeletal conditions in females (1.92 and 1.94 years, 15% and 12% respectively), markedly different from the causes of the LE gap (cardiovascular disease, cancers and unintentional injury). CONCLUSIONS: The ULE estimates offer valuable insights into the patterns of morbidity particularly useful in terms of primary and secondary prevention.

Implications of intracochlear decomposition gas formation in non-putrefied cadavers.

Introduction: Postmortem computed tomography (pmCT) prior to forensic autopsy has become increasingly important in recent decades, especially in forensic documentation of single injuries, injury patterns, and causes of death. Postmortem decomposition gas formation can also be detected in pmCT scans, which might affect cochlear implant research in postmortem human temporal bones (TBs). Material and methods: Fifty non-putrefied hanging fatalities within a 2-year period (January 2017 to December 2019) were included with 100 TBs. Each body underwent whole-body pmCT prior to forensic autopsy. PmCT scans were analyzed with respect to the presence of intracochlear gas despite the lack of putrefaction at autopsy by an experienced fellow neurotologist. Results: PmCT revealed gas formation in two individuals despite the lack of head trauma and putrefaction at postmortem examination and autopsy. Both individuals showed enclosed gas in the vestibule and the cochlea on both sides. Discussion: Intracochlear gas formation, most likely related to decomposition, may occur despite the lack of putrefaction at postmortem examination and autopsy and can be detected by pmCT. This finding seems to be rather rare in non-traumatic death cases but might affect cochlear pressure research in postmortem human TB.

Mechanism, thermochemistry, and kinetics of formation of PhC(O)H and HOPhC(O)H during unimolecular decomposition of P-PhC(O2•)HPhOH.

CONTEXT: Bisphenols are one of the main components of bio-oil, produced during the pyrolysis of lignin-containing biomass. Synthetic bisphenols are used in polycarbonate plastics, epoxy resins, and thermal papers. Their mechanism of oxidation is important for the determination of the fire safety of these materials and the possibility of using them as additives in fuels for the decrease and description of ignition delays, as well as for the determination of their health risk assessment in medicine. One representative of bisphenols is p-benzylphenol (p-PhCH2PhOH), which is formed during the fast pyrolysis of lignine-containing biomass. Its thermochemistry of oxidation has been partially studied previously. It is shown that the reaction of chain oxidation of p-PhCH2PhOH is thermochemically favorable at low temperatures. However, these studies consider only two pathways of this reaction: (1) the chain oxidation of RH by RO2• and (2) the tautomerization of R'HO2• to R'O2H with following production of R'O• and OH radicals. At the same time, the reactions of intramolecular rearrangement of RO2•, produced PhC(O)H and •PhOH or HOPhC(O)H and •Ph, are not reported but can be an important part of its oxidation mechanism. METHODS: The five DFT (M06-2X (i = 1), B3LYP (i = 2), wB97XD (i = 3), M08HX (i = 4), MN15 (i = 5)) approaches with 6-311 + + G(d,p) basis set are used for the determination of standard enthalpies of atomization (ΔraH°(Xi)) of considered compounds (molecules, radicals, and transition states). These values of ΔraH°(Xi) are corrected using the empirical linear calibration dependencies, reported previously. The different calibration dependencies are used for the hydrocarbons (including the aromatics and simple oxygenated derivatives) and for the peroxides. The corrected values of ΔraH°(Xi, CORR) are used according to Hess's law for the determination of ΔfH°(Xi, CORR). The most consistent values of ΔfH°(X, MEAN) are derived from the coordination of the values of ΔfH°(Xi,CORR) using the intersection of their values of standard deviations (3SDi). These values of ΔfH°(X, MEAN), as well as the B3LYP values of S°(X), which are accounting the frequencies correction and internal rotations, as well as their temperature dependencies, are used for the determination of thermochemistry of considered reactions and of the calculation, within transition state theory (TST), of the values of high pressure limits of the rate constant. The values of H°(Xi), S°(Xi), and G°(Xi) are calculated using the Gaussian 16w program. The considered mechanism is prepared using ISIS/Draw package. The temperature dependencies of thermochemical properties and the values of rate constants are determined using the ChemRate program (v.1.5). The optimized structures are visualized using the Chemcraft package.

Fire impacts on the biology of stream ecosystems: A synthesis of current knowledge to guide future research and integrated fire management.

Freshwater ecosystems host disproportionately high biodiversity and provide unique ecosystem services, yet they are being degraded at an alarming rate. Fires, which are becoming increasingly frequent and intense due to global change, can affect these ecosystems in many ways, but this relationship is not fully understood. We conducted a systematic review to characterize the literature on the effects of fires on stream ecosystems and found that (1) abiotic indicators were more commonly investigated than biotic ones, (2) most previous research was conducted in North America and in the temperate evergreen forest biome, (3) following a control-impact (CI) or before-after (BA) design, (4) predominantly assessing wildfires as opposed to prescribed fires, (5) in small headwater streams, and (6) with a focus on structural and not functional biological indicators. After quantitatively analyzing previous research, we detected great variability in responses, with increases, decreases, and no changes being reported for most indicators (e.g., macroinvertebrate richness, fish density, algal biomass, and leaf decomposition). We shed light on these seemingly contradicting results by showing that the presence of extreme hydrological post-fire events, the time lag between fire and sampling, and whether the riparian forest burned or not influenced the outcome of previous research. Results suggest that although wildfires and the following hydrological events can have dramatic impacts in the short term, most biological endpoints recover within 5-10 years, and that detrimental effects are minimal in the case of prescribed fires. We also detected that no effects were more often reported by BACI studies than by CI or BA studies, raising the question of whether this research field may be biased by the inherent limitations of CI and BA designs. Finally, we make recommendations to help advance this field of research and guide future integrated fire management that includes the protection of freshwater ecosystems.

The determinants of mental health inequalities between Chinese migrants and non-migrants during the Shanghai 2022 lockdown: a Blinder-Oaxaca decomposition.

BACKGROUND: The mental health inequality between migrants and non-migrants was exacerbated by the COVID-19 pandemic. Identifying key determinants of this inequality is essential in promoting health equity. METHODS: This cross-sectional study recruited Shanghai residents by purposive sampling during the city-wide lockdown (from April 29 to June 1, 2022) using an online questionnaire. Migration statuses (non-migrants, permanent migrants, and temporary migrants) were identified by migration experience and by household registration in Shanghai. Mental health symptoms (depression, anxiety, loneliness, and problematic anger) were assessed by self-report scales. The nonlinear Blinder-Oaxaca decomposition was used to quantify mental health inequality (i.e., differences in predicted probabilities between migration groups) and the contribution of expected correlates (i.e., change in predicted probability associated with variation in the correlate divided by the group difference). RESULTS: The study included 2738 participants (771 [28.2%] non-migrants; 389 [14.2%] permanent migrants; 1578 [57.6%] temporary migrants). We found inequalities in depression (7.1%) and problematic anger (7.8%) between permanent migrants and non-migrants, and inequalities in anxiety (7.3%) and loneliness (11.3%) between temporary migrants and non-migrants. When comparing permanent migrants and non-migrants, age and social capital explained 12.7% and 17.1% of the inequality in depression, and 13.3% and 21.4% of the inequality in problematic anger. Between temporary migrants and non-migrants, age and social capital also significantly contributed to anxiety inequality (23.0% and 18.2%) and loneliness inequality (26.5% and 16.3%), while monthly household income (20.4%) and loss of monthly household income (34.0%) contributed the most to anxiety inequality. CONCLUSIONS: Significant inequalities in depression and problematic anger among permanent migrants and inequalities in anxiety and loneliness among temporary migrants were observed. Strengthening social capital and economic security can aid in public health emergency preparedness and promote mental health equity among migrant populations.

Metagenomic insights into microbial variation and carbon cycling function in crop rotation systems.

The decomposition and utilization of plant-derived carbon by microorganisms and carbon fixation are crucial pathways for enhancing soil organic carbon (SOC) storage. However, a gap remains in our understanding of the impact of microorganisms on the decomposition of plant-derived carbon and their capacity for carbon fixation in crop rotation systems. Based on a 12-year experiment with wheat-maize (WM), wheat-cotton (WC), and wheat-soybean (WS) rotations, the microbial communities and carbon cycle function were investigated. The results indicated that WS rotation significantly increased SOC content compared to WM and WC. In addition, a significant increase was observed in microbially available carbon and microbial biomass carbon in the WS soil compared with those in the others. Further analysis of the microbial community factors that influenced SOC content revealed that WS rotation, in contrast to WM rotation, enhanced the diversity and richness of bacteria and fungi. Analysis of microbial carbon decomposition functions revealed an increase in starch, lignin, and hemicellulose decomposition genes in the WS soil compared to the others. The changes in carbon decomposition genes were primarily attributed to six bacterial genera, namely Nocardioides, Agromyces, Microvirga, Skermanella, Anaeromyxobacter, and Arthrobacter, as well as four fungal genera, namely Dendryphion, Staphylotrichum, Apiotrichum, and Abortiporus, which were significantly influenced by the crop rotation systems. In addition, microbial carbon fixation-related genes such as ACAT, IDH1, GAPDH, rpiA, and rbcS were significantly enriched in WS. Species annotation of differential carbon fixation genes identified 18 genera that play a role in soil carbon fixation variation within the crop rotation systems. This study highlights the impact of crop rotation systems on SOC content and alterations in specific microbial communities on carbon cycle function.

Raman Spectroscopy Reveals Photobiomodulation-Induced α-Helix to ß-Sheet Transition in Tubulins: Potential Implications for Alzheimer's and Other Neurodegenerative Diseases.

In small clinical studies, the application of transcranial photobiomodulation (PBM), which typically delivers low-intensity near-infrared (NIR) to treat the brain, has led to some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, despite the extensive literature detailing the mechanisms of action underlying PBM outcomes, the specific mechanisms affecting neurodegenerative diseases are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and thus provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer's and other neurodegenerative diseases. Thus, we investigated the effects of PBM on these cellular structures in the quest for insights into the underlying therapeutic mechanisms. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, 22.5 J/cm2 dose). Peaks in the Raman fingerprint region (300-1900 cm-1)-in particular, in the amide I band (1600-1700 cm-1)-were used to quantify the percentage of protein secondary structures. Under this band, hidden signals of C=O stretching, belonging to different structures, are superimposed, producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of polymerized NIR-exposed tubulin, characterized by a notable decrease in α-helix content and a concurrent increase in ß-sheets compared to the control samples. This PBM-induced α-helix to ß-sheet transition connects to reduced microtubule stability and the introduction of dynamism to allow for the remodeling and, consequently, refreshing of microtubule structures. This newly discovered mechanism could have implications for reducing the risks associated with brain aging, including neurodegenerative diseases like Alzheimer's disease, through the introduction of an intervention following this transition.

Self-Association and Microhydration of Phenol: Identification of Large-Amplitude Hydrogen Bond Librational Modes.

The self-association mechanisms of phenol have represented long-standing challenges to quantum chemical methodologies owing to the competition between strongly directional intermolecular hydrogen bonding, weaker non-directional London dispersion forces and C-H⋯π interactions between the aromatic rings. The present work explores these subtle self-association mechanisms of relevance for biological molecular recognition processes via spectroscopic observations of large-amplitude hydrogen bond librational modes of phenol cluster molecules embedded in inert neon "quantum" matrices complemented by domain-based local pair natural orbital-coupled cluster DLPNO-CCSD(T) theory. The spectral signatures confirm a primarily intermolecular O-H⋯H hydrogen-bonded structure of the phenol dimer strengthened further by cooperative contributions from inter-ring London dispersion forces as supported by DLPNO-based local energy decomposition (LED) predictions. In the same way, the hydrogen bond librational bands observed for the trimeric cluster molecule confirm a pseudo-C3 symmetric cyclic cooperative hydrogen-bonded barrel-like potential energy minimum structure. This structure is vastly different from the sterically favored "chair" conformations observed for aliphatic alcohol cluster molecules of the same size owing to the additional stabilizing London dispersion forces and C-H⋯π interactions between the aromatic rings. The hydrogen bond librational transition observed for the phenol monohydrate finally confirms that phenol acts as a hydrogen bond donor to water in contrast to the hydrogen bond acceptor role observed for aliphatic alcohols.

Evaluation of Terpene Decomposition in Kaffir Lime Juice during Storage Using Gas Chromatography-Mass Spectrometry and Proton Transfer Reaction-Mass Spectrometry.

Kaffir lime juice, often treated as production waste, can be a good source of terpenes. These compounds undergo various decomposition processes under the influence of external factors, especially during transportation and storage. In this paper, it was possible to monitor changes in the terpene profile of kaffir lime juice under different storage conditions, namely, 4 °C, 20 °C, and 35 °C. The identification of key decomposition products was achieved using gas chromatography-mass spectrometry (GC-MS) and a data mining protocol. It was followed by tracing those products in different storage conditions using a high-throughput proton transfer reaction mass spectrometry (PTR-MS) approach. Based on our findings, degradation pathways were presented, showing that the main products resulting from storage are p-cymene, p-cymenene, terpinene-4-ol, and α-terpineol. It was shown that conversion to p-cymenene occurs after 5 days of storage. Terpinene-4-ol and α-terpineol were found to be the final products of the conversion at all temperatures. Changes in the composition of terpenes are important from the point of view of their bioactive properties.

Chemical Composition of Volatile and Extractive Organic Compounds in the Inflorescence Litter of Five Species of Woody Plants.

The decomposition of plant litter, most of which is found in forests, is an important element of the global carbon cycle, as a result of which carbon enters the atmosphere in the form of not only CO2 but also volatile organic compounds (VOCs). Although the formation of litter is associated with autumn cooling, in the spring, there is a very intense fall of faded inflorescences of woody plants. This study examined the chemical composition of the litter and VOCs emitted from decaying inflorescences of four species of forest-forming trees: silver birch, European hornbeam, black alder and aspen. All litter emissions consisted of 291 VOCs, mainly terpenes actively participating in atmospheric processes. The detection of a number of typical mushroom metabolites, such as 1-octen-3-ol, known as "mushroom alcohol", and alkyl sulphides, suggests that inflorescence-derived VOCs are a mixture of components of plant and microbial origin. In methanol extracts of the fallen inflorescences of all types, 263 organic compounds were identified, the majority of which were related to carbohydrates. Their share in the extracts was 72-76%. In general, the composition of the extractive compounds indicates the easy availability of this material for assimilation by various types of destructors.

The Roles of the Anthraquinone Parietin in the Tolerance to Desiccation of the Lichen Xanthoria parietina: Physiology and Anatomy of the Pale and Bright-Orange Thalli.

Lichens are symbiotic organisms that effectively survive in harsh environments, including arid regions. Maintaining viability with an almost complete loss of water and the rapid restoration of metabolism during rehydration distinguishes lichens from most eukaryotic organisms. The lichen Xanthoria parietina is known to have high stress tolerance, possessing diverse defense mechanisms, including the presence of the bright-orange pigment parietin. While several studies have demonstrated the photoprotective and antioxidant properties of this anthraquinone, the role of parietin in the tolerance of lichens to desiccation is not clear yet. Thalli, which are exposed to solar radiation and become bright orange, may require enhanced desiccation tolerance. Here, we showed differences in the anatomy of naturally pale and bright-orange thalli of X. parietina and visualized parietin crystals on the surface of the upper cortex. Parietin was extracted from bright-orange thalli by acetone rinsing and quantified using HPLC. Although acetone rinsing did not affect PSII activity, thalli without parietin had higher levels of lipid peroxidation and a lower membrane stability index in response to desiccation. Furthermore, highly pigmented thalli possess thicker cell walls and, according to thermogravimetric analysis, higher water-holding capacities than pale thalli. Thus, parietin may play a role in desiccation tolerance by stabilizing mycobiont membranes, providing an antioxidative defense, and changing the morphology of the upper cortex of X. parietina.

Chalcogen Noncovalent Interactions between Diazines and Sulfur Oxides in Supramolecular Circular Chains.

The noncovalent chalcogen interaction between SO2/SO3 and diazines was studied through a dispersion-corrected DFT Kohn-Sham molecular orbital together with quantitative energy decomposition analyses. For this, supramolecular circular chains of up to 12 molecules were built with the aim of checking the capability of diazine molecules to detect SO2/SO3 compounds within the atmosphere. Trends in the interaction energies with the increasing number of molecules are mainly determined by the Pauli steric repulsion involved in these σ-hole/π-hole interactions. But more importantly, despite the assumed electrostatic nature of the involved interactions, the covalent component also plays a determinant role in its strength in the involved chalcogen bonds. Noticeably, π-hole interactions are supported by the charge transfer from diazines to SO2/SO3 molecules. Interaction energies in these supramolecular complexes are not only determined by the S···N bond lengths but attractive electrostatic and orbital interactions also determine the trends. These results should allow us to establish the fundamental characteristics of chalcogen bonding based on its strength and nature, which is of relevance for the capture of sulfur oxides.

A Water Environment-Based Simulated Method for Ultrasonic Testing of Slag Inclusion Weld Defects Based on Improved VMD.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.

Research into Prediction Method for Pressure Pulsations in a Centrifugal Pump Based on Variational Mode Decomposition-Particle Swarm Optimization and Hybrid Deep Learning Models.

Centrifugal pump pressure pulsation contains various signals in different frequency domains, which interact and superimpose on each other, resulting in characteristics such as intermittency, non-stationarity, and complexity. Computational Fluid Dynamics (CFD) and traditional time series models are unable to handle nonlinear and non-smooth problems, resulting in low accuracy in the prediction of pressure fluctuations. Therefore, this study proposes a new method for predicting pressure fluctuations. The pressure pulsation signals at the inlet of the centrifugal pump are processed using Variational Mode Decomposition-Particle Swarm Optimization (VMD-PSO), and the signal is predicted by Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model. The results indicate that the proposed prediction model combining VMD-PSO with four neural networks outperforms the single neural network prediction model in terms of prediction accuracy. Relatively high accuracy is achieved by the VMD-PSO-CNN-LSTM model for multiple forward prediction steps, particularly for a forward prediction step of 1 (Pre = 1), with a root mean square error of 0.03145 and an average absolute percentage error of 1.007%. This study provides a scientific basis for the intelligent operation of centrifugal pumps.

Impact of Particle Size on the Nonlinear Magnetic Response of Iron Oxide Nanoparticles during Frequency Mixing Magnetic Detection.

Magnetic nanoparticles (MNPs), particularly iron oxide nanoparticles (IONPs), play a pivotal role in biomedical applications ranging from magnetic resonance imaging (MRI) enhancement and cancer hyperthermia treatments to biosensing. This study focuses on the synthesis, characterization, and application of IONPs with two different size distributions for frequency mixing magnetic detection (FMMD), a technique that leverages the nonlinear magnetization properties of MNPs for sensitive biosensing. IONPs are synthesized through thermal decomposition and subsequent growth steps. Our findings highlight the critical influence of IONP size on the FMMD signal, demonstrating that larger particles contribute dominantly to the FMMD signal. This research advances our understanding of IONP behavior, underscoring the importance of size in their application in advanced diagnostic tools.