Greater Ability Research Articles

Decoding the elite soccer player’s psychological profile

Soccer is arguably the most widely followed sport worldwide, and many dream of becoming soccer players. However, only a few manage to achieve this dream, which has cast a significant spotlight on elite soccer players who possess exceptional skills to rise above the rest. Originally, such attention was focused on their great physical abilities. However, recently, a new perspective has emerged, suggesting that being an elite soccer player requires a deep understanding of the game, rapid information processing, and decision-making. This growing attention has led to several studies suggesting higher executive functions in soccer players compared to the general population. Unfortunately, these studies often had small and nonelite samples, focusing mainly on executive functions alone without employing advanced machine learning techniques. In this study, we used artificial neural networks to comprehensively investigate the personality traits and cognitive abilities of a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden. Our findings indicate that elite soccer players demonstrate heightened planning and memory capacities, enhanced executive functions, especially cognitive flexibility, elevated levels of conscientiousness, extraversion, and openness to experience, coupled with reduced neuroticism and agreeableness. This research provides insights into the psychology of elite soccer players, holding significance for talent identification, development strategies in soccer, and understanding the psychological traits and cognitive abilities linked to success.

TIP 4 P 2005 Ice : Simulating water with two molecular states.

Rigid, non-polarizable water models are very efficient from a computational point of view, and some of them have a great ability in predicting experimental properties. There is, however, little room for improvement in simulating water with this strategy, whose main shortcoming is that water molecules do not change their interaction parameters in response to the local molecular landscape. In this work, we propose a novel modeling strategy that involves using two rigid non-polarizable models as states that water molecules can adopt depending on their molecular environment. During the simulation, molecules dynamically transition from one state to another depending on a local order parameter that quantifies some local structural feature. In particular, molecules belonging to low- and high-tetrahedral order environments are represented with the TIP4P/2005 and TIP4P/Ice rigid models, respectively. In this way, the interaction between water molecules is strengthened when they acquire a tetrahedral coordination, which can be viewed as an effective way of introducing polarization effects. We call the resulting model TIP4P2005Ice and show that it outperforms either of the rigid models that build it. This multi-state strategy only slows down simulations by a factor of 1.5 compared to using a standard non-polarizable model and holds great promise for improving simulations of water and aqueous solutions.

A Novel Prognostic Risk Model Based on Oxidative Stress to Predict Survival and Improve Treatment Strategies in Stomach Adenocarcinoma.

Stomach adenocarcinoma (STAD) is the fifth most common tumor worldwide, imposing a significant disease burden on populations, particularly in Asia. Oxidative stress is well-known to play an essential role in the occurrence and progression of malignancies. Our study aimed to construct a prediction model by exploring the correlation between oxidative stress-related genes and the prognosis of patients with STAD. STAD data from TCGA were used to identify the differentially expressed oxidative stress-related genes (OSGs), with data from GEO serving as the validation cohort. Univariate Cox and LASSO regression analyses were performed to select prognosis-related genes for the risk model, which was then integrated with clinical features into a nomogram. The physiological functions and pathways of these identified genes were explored using GO and KEGG analyses. After evaluating the prediction value of the prognostic model in the GEO cohort, drug sensitivity and immune infiltration were comprehensively analyzed using R. Expression levels of the prognostic genes were verified by quantitative real-time PCR in gastric cancer and paired normal tissues. Cox regression and LASSO regression analysis identified SERPINE1, VHL, CD36, NOS3, ANXA5, ADCYAP1, POLRMT and GPX3 as the signature genes from 160 differentially expressed OSGs. Both Kaplan-Meier survival analysis and ROC curve at 5 years in the TCGA and the GEO cohort exhibited great predictive ability of the prognostic model, with the AUC >0.7 in TCGA. Validated as an independent risk factor, the model was integrated with clinicopathological variables (including age, stage, and gender) to build a nomogram for more accurate risk stratification. Moreover, therapy sensitivity analysis between the low- and high-risk categories showed that those who scored higher would benefit more from BEZ235, Dasatinib, Pazopanib, and Saracatinib. Meanwhile, differences in the tumor environment, immune infiltration and response to immunotherapy between the two groups were noted. Finally, qRT-PCR validated the differential expression of these genes in STAD and paired normal tissues. Our study has effectively established an oxidative stress-related prognostic model, providing a promising tool for personalized clinical strategies and improved STAD patient outcomes.

Batch biosorption studies of ammonical nitrogen (NH3-N) ions from aqueous solutions using the ubiquitous bacteria Klebsiella sp.: equilibrium, kinetic, and thermodynamic studies

BackgroundThe ubiquity of ammonical nitrogen (NH3-N) in aquatic habitats is a contradictory phenomenon since it serves a crucial function in maintaining these ecosystems, yet when levels are too high, they can have adverse effects on ecological balance and human welfare. An extensive set of batch tests were used in this study to see how well the bacterial species Klebsiella sp. broke down ammonical nitrogen (NH3-N).ResultsThe research results established that Klebsiella sp. has a remarkable capacity to adapt to ammonical nitrogen concentrations of up to 125 mg/l over a long period of time. The adaptation process depends on several factors such as biomass abundance, ammonical nitrogen concentration, pH, and temperature. This study identified the optimal method for the absorption of ammonical nitrogen (NH3-N) from a solution at a concentration of 100 parts per million (ppm), achieving an efficiency of 89 ± 1.5% mg/g under specified conditions. At a pH of 6.5, the adsorbent dosage was 0.3 g in 50 milliliters of NH3-N at a temperature of 26 degrees C. We used an extensive range of analytical techniques, such as Scanning Electron Microscopy, Xray diffraction, Brunauer-Emmett-Teller analysis, Transmission Electron Microscopy, and Fourier-Transform Infrared Spectroscopy, to confirm the accuracy of our results. The study also showed that the biosorption process closely followed pseudo-second-order kinetics and the Langmuir model, which propose that both physical and chemical processes were involved. The thermodynamic studies also showed that this process can happen on its own and can be used in industry.ConclusionThis study emphasizes the great ability of Klebsiella sp. to reduce NH3-N, providing important knowledge for water quality management and aquatic ecosystem preservation.Graphical

Small peptides from non-edible fish waste with antimicrobial activity

Intestine and gill have remained as by-products in fish processing. They can be used to produce various value-added products such as bioactive peptides. This research produced low molecular weight antimicrobial peptides from Tuna protein hydrolysates which were hydrolyzed via pepsin. The protein hydrolysate was passed through a 3kDa cut-off column. The fraction containing ≤ 3 kDa peptides from Tuna hydrolysate had the great ability to inhibit the growth rate of Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923 and Salmonella typhimurium ATCC 13311. The results of antimicrobial activity tests showed that low molecular weight peptides from both intestines and gills affect bacterial growth like a result of the ampicillin test. OFFGEL electrophoresis and C18 column were done to purify peptides following hydrophobic/hydrophilic properties and the isoelectric point (pI). The result revealed that 11.15 mg/mL of hydrophobic peptide hydrolysate from intestines in pH during 3-10and 11.94 mg/mL of hydrophobic peptide hydrolysate from gill in pH 8-10 were able to inhibit the bacterial growths. 8 Peptide sequences from LCMS/MS were synthesized [GGLGVGGY; GLSGWAS; GAQEGSY; ALMAISL; LYMGLAVPL; VILLVAPAS; GGQSTDY; AFSGVEA]. The results revealed that synthesized peptides; GGLGVGGY GLSGWAS have a great 50% inhibitory activity against S. typhimurium ATCC 13311

Commentary: Ovarian Cancer: Path to Effective Treatments.

Despite advancements in cancer therapeutics such as checkpoint inhibitors and some targeted therapies, we have not achieved success in effectively treating ovarian cancer, since these therapeutics only benefit a subset of patients, and also provide short-term protection. The use of chemotherapy and radiation therapy can cause depletion and/or lack of immune cells' function. Chimeric antigen receptor T (CAR-T) cell therapy is found to be effective against several blood-based cancers, but limited success was seen against solid tumors. Targeting fewer antigens and significant side effects of therapy decreases the efficacy of CAR-T cells as immunotherapeutic in solid tumors, even though there is a great drive and significant effort to establish these therapies around the world. Bispecific and tri-specific antibodies have recently been advocated as effective cancer therapeutics. However, at present, these also suffer the fate of CAR-Ts since the loss of antigen on tumor cells will render these therapeutics ineffective. At present, we should design therapeutics that may have synergistic effects on killing/treating tumors. The only way we can establish that will be by learning the mechanisms of actions of immune therapeutics. Thus, advancement in the knowledge and effective strategies are required to develop cancer immuno-therapeutics. We have dedicated our efforts to understand the immunobiology of natural killer (NK) cells. One of our most important discoveries was demonstration of targeting of cancer stem-like cells (CSCs)/poorly differentiated tumors exhibiting lower major histocompability complex class I expression by the NK cells. In addition, we showed that supercharged NK (sNK) cells had great ability to target both CSCs/poorly differentiated and well differentiated ovarian tumors, whereas activated primary NK cells only targeted CSCs/poorly differentiated tumors. Therefore, the use of sNK cells in immunotherapy should result in effective elimination of heterogeneous populations of ovarian tumors.

Preparation and identification novel angiotensin-converting enzyme-inhibitory peptides derived from fish waste

The purpose of the research is to compare short peptides from different hydrolysates of fish entrails that can inhibit the activity of angiotensin-converting enzyme (ACE). Fish hydrolysates derived from Catfish, Tilapia, and Mackerel entrails were digested by pepsin and passed through the 3kDa cutoff column. The fraction containing peptides shorter than and equal to 3kDa from catfish hydrolysate has the great ability to inhibit ACE activity in converting the substrate (Furanacroloy-Phe-Gly-Gly, FAPGG) and producing FAP and GG as the products of the reaction. The 3kDa filtrate from the catfish hydrolysate had activity like Captopril, a drug for treating hypertension. The 3kDa filtrate derived from the catfish entrail hydrolysate was purified using OFFGEL electrophoresis and then passed through the C18 column. The 3kDa filtrate was separated into two fractions and then these fractions were determined ACE inhibitory activity. The result showed that the fractions containing hydrophilic peptides and others containing hydrophobic peptides possessed inhibitory activity against ACE. Those fractions were analyzed with LCMS/MS for sequencing. The results revealed that synthesized peptides; ASNLHGV, LFKDLR, PGYALQR, and LETAKSR, derived from the catfish hydrolysate showed anti-ACE activity against its substrate.

Self-Powered Triboelectricity-Driven Multiple-Input-Single-Output Occupancy Detection System Using a Triboelectric Nanogenerator for Energy Management.

An energy crisis, resulting from rapid population growth and advancements in the Internet of Things, has increased the importance of energy management strategies. Conventionally, energy management is conducted using sensors; however, additional energy is required to maintain sensor operation within these systems. Herein, an all-fiber-based triboelectric nanogenerator with O2 plasma treatment, graphene oxide/tannic acid solution coating, and hexane/1-octadecanethiol solution coating (AFT-OGH) is fabricated to implement a self-powered sensor, generating a high electrical power density, of 0.35 W/m2, with high stability. Using the AFT-OGH and inductors, self-powered wireless communication in real-time is implemented, achieving a communication distance of 180 cm. Based on these developments, a triboelectricity-driven multiple-input-single-output (T-MISO) system is demonstrated for the first time. An AFT-OGH-driven self-powered T-MISO occupancy detection system (AS-MODS) is implemented to determine the presence of a user in a specific space by developing a unique algorithm for automatically controlling LEDs using triboelectric signals. Considering these results, the proposed AS-MODS is expected to serve as a smart energy management system in the near future, owing to its great ability to control energy consumption.

Reversible Bimetallic Inhibition to Modulate Selectivity During Catalysis.

Bimetallic complexes have demonstrated a great ability to enhance the activity of monometallic systems for bond activation and catalysis. In this work, we explore the opposite approach: using a second metal to passivate the activity of another by reversible bimetallic inhibition. To do so we have synthesized a family of nine electrophilic gold complexes of formula Au(PR3)(NTf2) ([NTf2]- = [N(SO2CF3)2]-) that can act as inhibitors in the semihydrogenation of terminal and internal alkynes catalyzed by the iconic iridium Vaska complex IrCl(CO)(PPh3)2. This behavior parallels the well-known passivation effect of lead over palladium in the heterogeneous Lindlard catalyst. Most gold fragments, except for the most hindered, form metal-only Lewis pairs upon combination with iridium, which have been fully characterized and exhibit distinct dative Ir → Au bonds. When applied to alkyne hydrogenation, these bimetallic structures have a clear tendency toward olefin formation, while the monometallic catalyst unselectively leads to overreduction products. Our computational studies not only provide a feasible mechanism for the Ir-only system, but also evince the active role of gold in passivating iridium by reversibly forming heterobimetallic structures that lead to enhanced selectivity.

Development of a Collection Kit for Efficient Isolation of SARS-CoV-2 RNA from Urine and Stool Samples

Background Information: Consider adding a brief sentence about the significance of detecting SARS-CoV-2 in urine and stool samples. For example, mention how these samples can serve as alternative sources for diagnosis, potentially leading to better patient outcomes. Objective Statement: This study aimed to develop a collection kit for the effective extraction of SARS-CoV-2 RNA from urine and stool samples. The study presents a significant advancement in the field of viral diagnostics by introducing a novel collection kit specifically designed for the extraction of SARS-CoV-2 RNA from urine and stool samples. The demonstrated efficacy of the kit in isolating the virus with high success rates from alternative biological fluids, as well as the superior RNA. Methodology: The kit contains a swab of medical polyester and three tubes, two of them used to inactivate the virus, one contains 3% phenol and the other 1% phenol, and the third tube contains 2 ml of (PBS) Phosphate Buffer Saline added to it 4% of skim milk. Urine or stool samples from Covid-19 patients who reported positive by nasopharyngeal swab during RT-PCR was adde consequently in these three tubes then assessed through RT-PCR testing. Results: The kit succeeded in isolating coronavirus from the urine and stool of patients who reported positive by nasopharyngeal samples. From 20 positive nasopharyngeal samples, there was 18 (90%) of them were positive by using stool sample using this kit, and 15 (75%) of them were give positive result when urine samples were used by this kit. The concentration of RNA of coronavirus which was isolated from urine and feces was higher than in the nasopharyngeal swab during RT-PCR. Mean Ct value of the 20 samples was showed that the mean Ct of nasopharyngeal was 31.5, while urine and stool samples Ct means less concentration of virus. were 25.75 and 24.32 respectively. High The kit was able to serve RNA that was isolated from urine and feces for about 5,10, and 30 days in a percentage of 100%, 94.1%, and 94.1% respectively. Conclusion and Implications: The kit is produce to confirm that the patient is infected with by COVID-19 and to preserve the virus in stool or urine samples of the infected person for a long period until laboratory tests through RT-PCR are conducted on the samples. The kit showed great ability to test SARS-CoV-2, even if it was at low concentrations of virus compared to the examination of nasopharyngeal swabs.

Energy targeting of abandoned mines to supply greenhouse energy demand in cold climates

The combination of a Solar Assisted Geothermal Heat Pump system (SAGHP) with a multi-zone greenhouse is investigated to take advantage of water flooding in abandoned open pit mines in Canada. The envisioned system includes an Air Handling Unit (AHU), Heat Recovery Ventilation (HRV), daily Thermal Energy Storage (TES), and daily Domestic Hot Water (DHW). The main objective is to satisfy the greenhouse heating, cooling, and dehumidification loads, for the considered application, while minimizing energy consumption. This analysis is conducted using data extracted from a case study of a commercial, multi-zone greenhouse, considering different daily weather conditions throughout a year. To reduce the computation time, a clustering approach based on the K-Means method is applied to obtain a small number of typical weather days. Elbow, Dendrogram, and Silhouette approaches confirmed that it is possible to represent a year as six different Typical Days (TD), which can be further categorized as Heating only (TD1 and TD2), Heating/Cooling (TD3 and TD4), and Cooling only (TD5 and TD6). Dynamic Pinch Approach (DPA) showed a great ability to target the minimum energy consumption and maximize the potential heat recovery for each typical day. The study focuses on energy targeting, with discussion of preliminary design considerations, such as the solar hot water (SHW) system, Thermal Energy Storage (TES), and heat pumping. Results revealed that mine water can significantly improve the energy system efficiency, specifically where heating/cooling or only cooling is dominant (TD3, TD4, TD5, and TD6). For instance, by integrating an AHU with the greenhouse for the TDs where heating/cooling is dominant, 22.5% energy saving is achievable. The incorporation of heat pumping, waste heat recovery, and solar thermal collectors through mixed direct/indirect heat recovery (i.e. via TES) can reduce hot utility usage in the considered application by as much as 40%.

Antioxidant activity of gallic acid carbon-based nanomaterials

Reactive oxygen species (ROS) play a role in multiple physiological processes. In contrast, an excessive generation of ROS can harm various biological components, including proteins, lipids, and DNA, leading to accelerated aging, illness, and inflammatory disorders. Carbon-based nanomaterials (CNMs) are well-known nanomaterials widely developed for antioxidant activity because of their great biocompatibility, low toxicity, easy synthesis, unique physicochemical properties, and especially great ROS scavenging ability. The most general method to prepare CNMs is bottom-up synthesis because it is more environmentally friendly and economical than top-down methods. In this study, the antioxidant activity of CNMs was evaluated. A microwave-assisted pyrolysis method was applied at 200°C for 20 min to prepare gallic acid carbon-based nanomaterials (GACNMs) by using 2.5 mg/mL of gallic acid as a carbon source according to the previous research. The morphology, cytotoxicity on normal human fibroblast cell lines by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide assay, and antioxidant activity using a 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay were elucidated. The results revealed that the GACNMs were spherical, with a particle size of 104.43±0.43 nm, and their structure was in line with the previous study. The GACNMs exhibited higher antioxidant activity (50% scavenging capacity (SC50) of 0.99±0.07 μg/mL) than the gallic acid solution (SC50 of 1.25±0.03 μg/mL). The GACNMs were non-toxic to fibroblast cells. Therefore, the GACNMs could be a promising nanomaterial for skin antioxidants.

Recent Advances of Foundation Language Models-based Continual Learning: A Survey

Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neural network models, foundation LMs obtain a great ability for transfer learning by acquiring rich commonsense knowledge through pre-training on extensive unsupervised datasets with a vast number of parameters. Despite these capabilities, LMs still struggle with catastrophic forgetting, hindering their ability to learn continuously like humans. To address this, continual learning (CL) methodologies have been introduced, allowing LMs to adapt to new tasks while retaining learned knowledge. However, a systematic taxonomy of existing approaches and a comparison of their performance are still lacking. In this paper, we delve into a comprehensive review, summarization, and classification of the existing literature on CL-based approaches applied to foundation language models, such as pre-trained language models (PLMs), large language models (LLMs) and vision-language models (VLMs). We divide these studies into offline and online CL, which consist of traditional methods, parameter-efficient-based methods, instruction tuning-based methods and continual pre-training methods. Additionally, we outline the typical datasets and metrics employed in CL research and provide a detailed analysis of the challenges and future work for LMs-based continual learning.

ОСОБЕННОСТИ ТЕЧЕНИЯ ИШЕМИЧЕСКОГО ИНСУЛЬТА ПРИ ВАСКУЛОПАТИИ И КОМПЕНСАТОРНЫЕ ВОЗМОЖНОСТИ МОЗГА. Клинический случай

Abstract. Introduction. Vasculopathies are inflammations of the walls of blood vessels, in which immunopathological processes cause endothelial dysfunction of the vessels. Changes in endothelial dysfunction are a universal pathogenetic mechanism for the development of ischemic circulatory disorders. Given the permanent development of ideas about immunopathological disorders, situations may arise with the need for revising the features of the cause of acute circulatory disorders in a particular case. Aim. To demonstrate the features of ischemic stroke in presence of vasculopathy and the compensatory capabilities of the brain. Materials and methods. A clinical case of female patient F., 77 years old, with vasculitis-associated ischemic stroke is presented. Results and Discussion. In vasculitis, the vascular walls are attacked by the body’s immune system, which leads to structural disorders. Short-term exposure of the vascular wall endothelium to the pathological factor leads to hypercoagulation, hypertonus, and production of inflammatory cytokines. It has been proven that endothelial activation is a favorable reaction that promotes an immune response to the pathogen. Cessation of the pathogenic stimulus leads to a rapid return of the endothelium from the activated state to the physiological one Accordingly, the course of ischemic stroke in immunopathological disorders may be atypical and, therefore, additional diagnostic methods and therapy correction are needed, taking into account the pathogenetic factor, considering the mechanisms of brain plasticity. Conclusions. The increasing frequency of autoimmune diseases necessitates a deeper understanding of the mechanisms, diagnostics and treatment of acute cerebrovascular accidents. Ischemic stroke with some existing brain processes develops with latent symptoms, which indicates the great compensation abilities of the brain, which can be a theoretical basis for neurorehabilitation.

Risk Factors and Prediction Model for Pressure Injuries in Patients Undergoing Da vinci Surgery: Stress Injury in Da vinci Surgery Patients.

Most patients present at high risk of perioperative pressure injury. Patients may experience severe agony from pressure injuries, which can also result in potentially fatal infections and increase hospital stays and medical expenses. This is a critical issue that requires immediate attention and resolution. From December 2019 to August 2023, 335 patients receiving Da vinci surgery in a grade A tertiary hospital were chosen to serve as the participants, and univariate analysis and patients undergoing Da vinci surgery had their risk factors for pressure injuries screened using logistic regression analysis, and in patients having Da vinci surgery, a pressure injury risk prediction model was developed. Among 335 patients, 195 patients acquired pressure injuries, and the results of logistic regression analysis showed that age, length of surgery, BMI, and nasogastric tube (NGT) were independent risk factors for pressure injuries (p<0.05). With great risk prediction ability, the risk prediction model for pressure injuries in patients undergoing Da vinci surgery established in this study can be used as an effective assessment tool.

Role of samarium doping in ZnSe electrode material with increased electrocapacitive properties for supercapacitor energy storage applications

Researchers have recently paid great attention to different transition metals-basedsupercapacitor electrode materials, which haveincreased interest due to their excellent energy storage applications. In this regard, electrodes based on samarium-doped materials have shown impressive promise for higher-performingsupercapacitors and energy-storing devices in many industries. In the present work, Sm-doped ZnSe electrode material was fabricated by using a simple hydrothermal method and then characterized structurally and morphologically by various physical characterizations. When prepared Sm-doped ZnSewasevaluated in three electrode configurations,then displayedan impressive specific capacitance(923.43F/g), high energy density (45.43 Wh/kg) along with power density (297.6 W/kg)at 1 A/g,higher as compared with ZnSe. According to EIS characterization small charge transfer resistance (0.10 Ω) of the doping material was calculated while chronoamperometry data showed that the fabricated electrode exhibited higher stability of 30 h. This work shows that doping of Smto ZnSeenhances energy storage efficiency and suggests great energy storage abilities in supercapacitors and other energy storage devices.

Domain-Adaptive Graph Attention-Supervised Network for Cross-Network Edge Classification.

Graph neural networks (GNNs) have shown great ability in modeling graphs; however, their performance would significantly degrade when there are noisy edges connecting nodes from different classes. To alleviate negative effect of noisy edges on neighborhood aggregation, some recent GNNs propose to predict the label agreement between node pairs within a single network. However, predicting the label agreement of edges across different networks has not been investigated yet. Our work makes the pioneering attempt to study a novel problem of cross-network homophilous and heterophilous edge classification (CNHHEC) and proposes a novel domain-adaptive graph attention-supervised network (DGASN) to effectively tackle the CNHHEC problem. First, DGASN adopts multihead graph attention network (GAT) as the GNN encoder, which jointly trains node embeddings and edge embeddings via the node classification and edge classification losses. As a result, label-discriminative embeddings can be obtained to distinguish homophilous edges from heterophilous edges. In addition, DGASN applies direct supervision on graph attention learning based on the observed edge labels from the source network, thus lowering the negative effects of heterophilous edges while enlarging the positive effects of homophilous edges during neighborhood aggregation. To facilitate knowledge transfer across networks, DGASN employs adversarial domain adaptation to mitigate domain divergence. Extensive experiments on real-world benchmark datasets demonstrate that the proposed DGASN achieves the state-of-the-art performance in CNHHEC.

Efficient ranitidine removal using Si3N4-ZVI/PAA system: Mechanistic insights and environmental implications

In this study, silicon nitride-zero-valent iron (Si3N4-ZVI) powders were obtained through post-milling and employed as a catalyst to activate peracetic acid (PAA) for the removal of Ranitidine (RAN). The system achieved an impressive 90.4 % of degradation efficiency, with a pseudo-first-order kinetic reaction constant 11.3 times higher than that of the ZVI/PAA system. Quenching experiments and Electron Paramagnetic Resonance (EPR) analysis confirmed that both radical and non-radical processes are involved in RAN degradation within the Si3N4-ZVI/PAA system. Moreover, the identified ten degradation products are less toxic compared to the byproducts from alternative oxidation methods reported by prior literature. The proposed degradation pathway of RAN includes CS cleavage, oxygenation/hydroxylation demethylation, and denitrification. Furthermore, the Si3N4-ZVI/PAA system showed effectively in treating RAN-contaminated surface water, artificial wastewater, and dissolved organic matter, demonstrating its great ability in treating diverse organic-contaminated water sources. In conclusion, results of this study suggest that the Si3N4-ZVI/PAA system possess high pollutant removal efficiency, broad applicability, and less environmental and health risks.

Highly sensitive, environmental-friendly and rapid visual detection of uranium using a novel fluorescent fiber-optical sensing system based on aggregation-induced emission with bovine serum albumin as sensitizing agent

In order to achieve the on-site rapid detection of trace uranium under excitation with visible light and avoid the harmful effect of toxic organic solvent to human body and organisms, in this work, a novel water-soluble, environmental-friendly and biocompatible aggregation-induced emission (AIE) fluorescence probe was fabricated by introducing the pyridine quaternary ammonium cation and amide group into the propeller structure of triphenylamine (TPA). And by creatively using bovine serum albumin (BSA) in PBS buffer as the sensitizing agent to replace the toxic organic solvent, the probe can emit intense red fluorescence under excitation with visible light (470 nm), exhibiting very low cytotoxicity and great anti-interference ability against large equivalent of different metal ions and anions over wide pH range. After further integration with the self-constructed portable fiber optical sensing system, the satisfying detection limit as low as 7.44 nM (1.77 ppb) can be achieved. The established method has been applied successfully for sensing uranium in complex real samples such as uranium leachate, seawater and cancer cells, demonstrating excellent analytical performance and great potential for in-situ rapid detection and timely evaluation of the uranium contamination in vivo and in field.

Extended Fourier Neural Operators to learn stiff chemical kinetics under unseen conditions

The solution of stiff chemical kinetics is recognized as the computational bottleneck for direct simulations of reacting flows. In this study, we extend the concept of Fourier Neural Operator (FNO) to learn stiff chemical kinetics. Specifically, element and mass conservation are introduced as the physical constraints in the extended FNO (EFNO). In addition, the training data are transformed by a Box–Cox strategy to rectify the skewed distribution of the species in the stiff problems. Finally, balanced loss functions are formulated to address the unbalanced sampling data points in complex reacting flow problems. The EFNO model is leveraged to forecast the temporal evolution of chemical species, utilizing an iterative approach wherein the prediction outcome from the previous time step is employed as a new input for subsequent time step prediction. The results in this work demonstrate the significant use of an EFNO approach to solving stiff chemical dynamics in reacting flow simulations, with a time step size comparable to the typical flow time step size. Its prediction accuracy and generalization ability are evaluated by comparing with the original FNO, Deep Nueral Network (DNN) and DeepONet models, in solving toy problems, zero-dimensional hydrogen autoignition, and a three-dimensional hydrogen/ammonia turbulent jet flame. The EFNO is shown to be highly accurate. More importantly, compared with other deep learning models, it can be generalized to stiff chemical kinetic states under unseen conditions, which the model has never trained for. The great performance of EFNO in terms of accuracy and generalization ability suggests that EFNO is a promising solution algorithm for stiff chemical kinetics problems in reacting flows.Novelty and Significance Statement: The novelty of this work lies in the newly developed extended Fourier neural operators (EFNO) to learn stiff chemical kinetics. Specifically, we for the first time evaluated and tested the performance of Fourier neural operators in solving stiff chemical kinetics. More importantly, we extended the original Fourier neural operators to accurately solve for stiff chemical kinetics problems under unseen conditions, which was a very challenging problem for deep learning methods in the literature. Our results demonstrated that the EFNO model solves chemical kinetics in both simple 0D autoignition and complex 3D turbulent jet flames with great accuracy and generalization ability, even for conditions which the training dataset has never encompassed. This work is significant because it developed a neural operator-based algorithm that can significantly accelerate the stiff chemical kinetic solution process in reacting flow simulations with great accuracy even for unseen initial conditions.