Level Fluctuations Research Articles

Nonlinear Tide‐River‐Surge Interactions and Their Impacts on Compound Flooding During Typhoon Hato in the Pearl River Delta

AbstractIn coastal areas, multiple hydrodynamic processes co‐occur, including the astronomical tide, river discharge, and storm surge. Their propagation and interaction within coastal tidal river result in strong nonlinear behavior in inland areas. This study employed a hydrodynamic model and continuous wavelet transform analysis to investigate the complex tide‐river‐surge interactions and their impacts on compound flooding in the West River of the Pearl River Delta during an extreme typhoon event. Validated model outputs provided insights into the spatial and temporal variations of river discharge, water levels, and currents. Wavelet analysis revealed river discharge modulates tidal properties, causing nonstationary tides and asymmetries, with high flows suppressing tidal ranges but facilitating energy transfers to overtides. During Typhoon Hato, storm surges dominated high‐frequency water level fluctuations that rapidly propagated upstream. Crucially, strong high‐frequency tide‐river‐surge coupling induced significant water level amplifications, with interaction intensities increasing landward. In upstream areas where riverine and coastal drivers converged, flood risks exceeded typical estimates due to this vigorous multi‐driver compounding. Findings highlighted how existing flood mitigation approaches over simplistically superposing individual sources may severely underestimate flood levels by neglecting such nonlinear interactions. A comprehensive accounting of the cumulative, multiplicative effects of tides, discharge, storm surge, and sea level rise is imperative. This quantitative unraveling of key physical drivers offers transferable insights applicable to compound flood risk evaluations and policy guidance for enhancing resilience in other estuary and delta regions. Future work should focus on holistically modeling multivariate extremes and their interactions.

Solar Dryers: Technical Insights and Bibliometric Trends in Energy Technologies

This review article provides a comprehensive analysis of the technical advancements and research trends in solar drying technologies for agricultural products. The study encompasses various innovations in energy storage systems, including phase change materials (PCMs) and the use of computational fluid dynamics (CFD) for optimizing the drying process. Through a bibliometric analysis of 126 scientific papers published between 1984 and 2024, five major research clusters were identified: energy generation, heat transfer, thermal storage, simulation modeling, and the integration of hybrid systems. The results demonstrate a marked increase in scientific output over the past decade, emphasizing a growing interest in the sustainable use of solar energy for drying applications. Key findings highlight that while PCM-based storage solutions significantly enhance the thermal stability of dryers, the high implementation costs and technical complexities limit their adoption, especially in small-scale operations. Similarly, CFD models have proven effective in optimizing air and temperature distribution within dryers; however, their performance is hindered by real-world fluctuations in solar radiation and humidity levels. To address these limitations, future research should focus on the development of cost-effective PCM materials and the improvement of CFD models for dynamic environmental conditions. The review concludes by emphasizing the importance of interdisciplinary collaboration in the design and application of these technologies, recommending the inclusion of real-world case studies to better illustrate the practical implications and economic benefits of solar drying technologies for agricultural production.

Inter-cycle variability of anti-Müllerian hormone: implications for predicting controlled ovarian stimulation cycle outcomes

BackgroundAnti-Müllerian hormone (AMH) is a widely used marker for estimating ovarian reserve, and it may predict response to ovarian stimulation. While AMH is considered a stable, cycle-independent marker, studies have shown it can exhibit significant fluctuations based on factors like age, reproductive stage, and menstrual cycle phase. The fluctuations in AMH levels can make it challenging to predict individual responses accurately, particularly when the AMH is not measured in the COS cycle. The aim of this study was to assess the inter-cycle variability of serum AMH levels in two consecutive menstrual cycles and their correlation with response to controlled ovarian stimulation outcome in the latter.MethodsIn this single-centre retrospective cohort study, data of normal and low responder patients who underwent intracytoplasmic sperm injection following a GnRH antagonist cycle at a university hospital infertility clinic between January 2022 and December 2023 were reviewed. Serum AMH levels were measured in the early follicular phase of two consecutive menstrual cycles with Elecsys-AMH Roche® system (Roche Diagnostics, Meylan, France). Correlations between AMH levels and controlled ovarian stimulation outcomes, including total oocyte and mature oocyte (MII) counts, were assessed. The study included normal and poor responder women to maintain data integrity.ResultsA total of 79 patients were included in the final analyses. Significant cycle-to-cycle variation in serum AMH levels was observed, with a median variation of 44.3%. Normal responders exhibited a mean change of 0.60 ± 0.46 ng/ml, while poor responders had a mean change of 0.28 ± 0.28 ng/ml. Approximately 20% of patients were reclassified between normal and poor responder categories based on the second AMH measurement. The controlled ovarian stimulation cycle AMH levels showed a stronger correlation with both total oocyte count (r = 0.871, P < 0.001) and MII oocyte count (r = 0.820, P < 0.001) compared to preceding cycle AMH levels.ConclusionAMH levels can exhibit significant variations between consecutive cycles, potentially leading to misclassification of patients. Measuring AMH in the early follicular phase of the COS cycle provides a more accurate prediction of the numbers of total and MII oocytes collected. Consistent and repeated AMH measurements can help clinical decision-making.

Impact of CFTR modulatory therapies on liver function and fibrosis indices in cystic fibrosis patients: a retrospective analysis from two Romanian medical centers

Background. Patients with cystic fibrosis (CF) frequently require modulatory therapies such as Lumacaftor/Ivacaftor (LI) and Elexacaftor/Tezacaftor/Ivacaftor (ETI) to manage their condition. Given the potential hepatic complications associated with CF, it is critical to understand the impact of these therapies on liver function and fibrosis indices. This study aimed to evaluate the changes in liver function markers and fibrosis indices in CF patients undergoing LI and ETI therapies, with a specific focus on the influence of underlying hepatic disease. Methods. In this retrospective analysis, liver function markers and fibrosis indices were assessed in CF patients receiving ETI (n=24), LI (n=4), or a combination of both (LI/ETI, n=8). Key liver function markers, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), bilirubin, platelet count, and fibrosis indices (APRI and FIB-4), were measured at baseline and at various time points up to 12 months. Results. In patients receiving LI therapy, ALT and AST levels demonstrated a slight but non-significant decrease over six months, accompanied by significant fluctuations in total bilirubin levels. Among those receiving ETI therapy, ALT and AST levels initially increased but stabilized over time, while total bilirubin levels significantly increased from baseline to 12 months. No significant differences were observed in liver function markers between patients with and without hepatic disease under ETI therapy. Trends in fibrosis indices (APRI and FIB-4) were modest and largely non-significant across both therapies. Conclusions. ETI therapy appears to be safe for CF patients, including those with pre-existing hepatic disease, with no significant deterioration in liver function over a 12-month period. However, the observed fluctuations in bilirubin levels underscore the necessity for ongoing monitoring. Further research is warranted to investigate the long-term hepatic effects of LI and ETI therapies.

A Reactive and Specific Sensor for Activity-Based 19F-MRI Sensing of Zn2.

The rapid fluctuations of metal ion levels in biological systems are faster than the time needed to map fluorinated sensors designed for the 19F-MRI of cations. An attractive modular solution might come from the activity-based sensing approach. Here, we propose a highly reactive but still ultimately specific synthetic fluorinated sensor for 19F-MRI mapping of labile Zn2+. The sensor comprises a dipicolylamine scaffold for Zn2+ recognition conjugated to a fluorophenyl acetate entity. Upon binding to Zn2+, the synthetic sensor is readily hydrolyzed, and the frequency of its 19F-functional group in 19F-NMR is shifted by 12 ppm, allowing the display of the Zn2+ distribution as an artificial MRI-colored map highlighting its specificity compared to other metal ions. The irreversible Zn2+-induced hydrolysis results in a "turn-on" 19F-MRI, potentially detecting the cation even upon a transient elevation of its levels. We envision that additional metal-ion sensors can be developed based on the principles demonstrated in this work, expanding the molecular toolbox currently used for 19F-MRI.

A mitochondria-targeted nitric oxide probe with large Stokes shift for real-time imaging and evaluation of inflammatory bowel disease in situ

BackgroundInflammatory bowel disease (IBD) is a prevalent inflammatory disorder, and the abnormal expression of nitric oxide (NO) produced by biocatalysis of iNOS enzyme in mitochondria is directly associated with the occurrence and progression of IBD. Activatable fluorescent probes offer promising tools for early diagnosis of IBD, however, inadequate biodistribution and limited targeting properties of these probes in vivo severely impede accurate diagnosis of IBD and real-time evaluation of inflammatory levels in situ. Therefore, it is necessary to designed a highly efficient fluorescent probe towards NO to overcome inadequate biodistribution and achieve accurate diagnosis and evaluation of IBD in situ. ResultsWe designed a highly efficient mitochondria-targeted “turn-on” NIR fluorescent probe Cy-OMe which has excellent targeting properties and imaging ability. The response mechanism is probe Cy-OMe rapidly undergoes N-nitrosation reaction resulting in turn-on NIR fluorescence signal when exposed to NO. Cy-OMe exhibits high sensitivity and specificity in detecting NO content in vitro, owing to its large Stokes shift. Furthermore, the probe Cy-OMe not only efficiently targets mitochondria but also enables precise assessment of fluctuations in endogenous NO concertation across various cell types. Importantly, by virtue of large Stokes shift and excellent mitochondrial targeting ability, Cy-OMe has the capability to specifically evaluate dynamic fluctuations of NO in lipopolysaccharide (LPS)‐stimulated IBD mouse models in situ and Cy-OMe was achieved high-contrast imaging and precision diagnosis of intestinal inflammation diseases. SignificanceCy-OMe can accurately assess fluctuations in NO levels and show high signal fidelity in the diseased intestine region, which has prospects in the non-invasive diagnosis of intestinal inflammation in vivo. At the same time, it is expected to serve as a potential diagnose platform for investigating the physiological processes underlying NO-related inflammatory diseases and promoting understanding of the pathological functions of NO across diverse inflammatory diseases.

Evaluation of the Physico-chemical and Biological Properties of Fermented Nipa (Nypa fruticans) Sap Under Closed and Open Vessel Fermentation System for Bioethanol Production

Background The potential of Nipa (Nypa fruticans) sap in the Cagayan Region, Philippines, for bioethanol production remains largely underexplored despite its vast expanse. Methods This study investigates the key physico-chemical properties of Nipa sap from Aparri, Cagayan, with a focus on fermentation dynamics and storage conditions to optimize ethanol production. Two fermentation treatments closed and open vessel conditions, were compared to determine the optimal conditions for maximum ethanol yield. Over a 7-day fermentation period, the acetic acid production, yeast activity, sugar content, and ethanol concentration of closed vessel fermented sap (CVFS) and open vessel fermented sap (OVFS) were monitored. Results CVFS demonstrated a higher ethanol yield (7.56%v/v at peak day), characterized by minimized acetic acid accumulation and stable yeast activity. In contrast, OVFS exhibited significant fluctuations in acetic acid and ethanol levels, indicating a lower fermentation efficiency. Conclusion The presence of methanol and high acetic acid content in open-vessel fermentation systems highlights the importance of fermentation conditions to prevent impurities. This study provides valuable insights into optimizing ethanol production from fermenting Nipa sap by employing closed vessel fermentation, which is crucial for sustainable bioethanol production in the Cagayan region.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

Research on a Non-Stationary Groundwater Level Prediction Model Based on VMD-iTransformer and Its Application in Sustainable Water Resource Management of Ecological Reserves

The precise forecasting of groundwater levels significantly influences plant growth and the sustainable management of ecosystems. Nonetheless, the non-stationary characteristics of groundwater level data often hinder the current deep learning algorithms from precisely capturing variations in groundwater levels. We used Variational Mode Decomposition (VMD) and an enhanced Transformer model to address this issue. Our objective was to develop a deep learning model called VMD-iTransformer, which aims to forecast variations in the groundwater level. This research used nine groundwater level monitoring stations located in Hangjinqi Ecological Reserve in Kubuqi Desert, China, as case studies to forecast the groundwater level over four months. To enhance the predictive performance of VMD-iTransformer, we introduced a novel approach to model the fluctuations in groundwater levels in the Kubuqi Desert region. This technique aims to achieve precise predictions of the non-stationary groundwater level conditions. Compared with the classic Transformer model, our deep learning model more effectively captured the non-stationarity of groundwater level variations and enhanced the prediction accuracy by 70% in the test set. The novelty of this deep learning model lies in its initial decomposition of multimodal signals using an adaptive approach, followed by the reconfiguration of the conventional Transformer model’s structure (via self-attention and inversion of a feed-forward neural network (FNN)) to effectively address the challenge of multivariate time prediction. Through the evaluation of the prediction results, we determined that the method had a mean absolute error (MAE) of 0.0251, a root mean square error (RMSE) of 0.0262, a mean absolute percentage error (MAPE) of 1.2811%, and a coefficient of determination (R2) of 0.9287. This study validated VMD and the iTransformer deep learning model, offering a novel modeling approach for precisely predicting fluctuations in groundwater levels in a non-stationary context, thereby aiding sustainable water resource management in ecological reserves. The VMD-iTransformer model enhances projections of the water level, facilitating the reasonable distribution of water resources and the long-term preservation of ecosystems, providing technical assistance for ecosystems’ vitality and sustainable regional development.

Emergence of cyclic hypoxia and the impact of PARP inhibitors on tumor progression.

Tumor hypoxia is a dynamic phenomenon marked by fluctuations in oxygen levels across both rapid (seconds to minutes) and slow (hours to days) time scales. While short hypoxia cycles are relatively well understood, the mechanisms behind longer cycles remain largely unclear. In this paper, we present a novel mechanistic mathematical model that explains slow hypoxia cycles through feedback loops involving vascular expansion and regression, oxygen-regulated tumor growth, and toxic cytokine production. Our study reveals that, for the emergence of slow hypoxia cycles, endothelial cells must adapt by decreasing receptor activation as ligand concentration increases. Additionally, the interaction between tumor cells and toxic cytokines influences frequency and intensity of these cycles. By examining the effects of pharmacological interventions, specifically poly (ADP-ribose) polymerase inhibitors, we also demonstrate how targeting cell proliferation can help regulate oxygen levels. Our findings enhance the understanding of hypoxia regulation and suggest PARP proteins as promising therapeutic targets.

Ichnoguild analysis of the Bajocian-Bathonian Yellow Flagstone Member (Goradongar Formation) from the Kachchh Basin, Western India

The Yellow Flagstone is the oldest member of the Goradongar Formation exposed in the Pachchham Island, Kachchh Basin. The members exposed in four sections are mapped for trace fossils, and 17 ichnofossils have been documented. The concept of ichnoguild (IG) is applied to reveal the infaunal tiering structure and the strategies organisms adapt to exploit the resources. Ten IG of similar characteristics and ecological functions have been identified. Five are shallow-tier IGs ( Asterosoma IG, Gyrochorte IG, Nereites IG, Planolites IG and Bolonia IG), three middle-tier IGs ( Rhizocorallium IG, Arenicolites-Diplocraterion IG and Zoophycos-Teichichnus IG) and two deep-tier IGs ( Skolithos Arenicolites IG and Thalassinoides-Ophiomorpha IG). The analysis of three ichnological tiers and their IGs, namely shallow, middle and deep-tiers across the stratigraphy of Yellow Flagstone Member suggests a transition from a lower shoreface environment to offshore transition environments. The transition is supported by an increase in deep-tier IG and the marginal increase in the ichnodiversity and density. Changes in the composition and abundance of IGs over time can indicate shifts in sedimentation patterns, sea level fluctuations and paleoenvironmental conditions within the basin. Thus, the findings suggest an environmental shift from shoreface to offshore transition indicative of a transgression during the deposition of upper Bajocian to lower Bathonian Yellow Flagstone Member in the Kachchh Basin. The study also examines the temporal and spatial changes in the IGs, contributing to a better understanding of ecological changes within the basin. The findings contribute to the broader knowledge of paleoecology and provide valuable data for sedimentological and stratigraphic interpretations in sedimentary basins.

Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.

Control of Instantaneous Abnormal Mold Level Fluctuation in Slab Continuous Casting Mold Based on Bidirectional Long Short‐Term Memory Model

The instantaneous abnormal mold level fluctuation (IAMLF) has significant harmful effects on slab quality. This article proposes a prediction and control method for IAMLF. First, the data processed by difference method is used for the IAMLF prediction and stopper‐rod position prediction. Then, the bidirectional long short‐term memory (BI‐LSTM) is introduced to predict the IAMLF; the corresponding stopper‐rod position is predicted according to the mold level prediction result. BI‐LSTM can predict the IAMLF with the mean absolute error of 1.52 mm and the false alarm rate of 1.8%, and also performs well in predicting the stopper‐rod position with a mean absolute error of only 1.92 mm. Furthermore, the two prediction models are combined to form a closed loop, where the mold level fluctuation is predicted according to the industrial data processed by data difference method, and the stopper‐rod position is adjusted in advance to eliminate IAMLF. Finally, the fuzzy proportional‐integral‐derivative (PID) controller is used to control mold level based on the mold level and stopper‐rod position prediction results. The prediction accuracy of IAMLF reaches 98.4%, and the present proposed fuzzy PID controller can effectively prevent the occurrence of IAMLF with a success rate of 95.6%.

Protective effect of essential oil of Foeniculum vulgare Mill. against bleomycin-induced pulmonary fibrosis and oxidative stress in rat

BackgroundWild fennel (Foeniculum vulgare Mill.) is a very common plant used in traditional medicine to treat several diseases. In recent years, scientific research proved its biological properties. However, the protective effect of fennel against bleomycin-induced pulmonary fibrosis (BLM-IPF) is not yet study. PurposeFennel essential oil (FEO) composition was characterized and its protective effect was assessed. MethodsGC–MS was employed to determine the chemical composition of fennel essential oil. The antioxidant activity was evaluated using TAC, DPPH, RAP and ABTS assays. After inducing fibrosis by bleomycin, several biological assays were used to evaluate the protective effect [proteins content, malondialdehyde MD, thiol group, superoxide dismutase (SOD) and catalase (CAT)]. ResultsFEO was rich in estragole (77.55 %), fenchone (9.23 %), and limonene (9.23 %), and phenolic compounds and showed a significant antioxidant potential. The effects on BLM-IPF were revealed by disruption and alteration of oxidative stress biomarkers in lung, liver and kidney. Treatment of rats with FEO improved abnormal fluctuations in protein and thiol levels, decreased oxidative stress in terms of MDA and also restored the response of the antioxidant system, measured in terms of SOD and CAT, in lung, liver and kidney. The biological activity was recorded in a dose response manner. The potential of FEO in limiting the progress of the histopathologic effects of BLM-IPF was confirmed by microscopic histological observations, with a reduction of the fibrosis score and the inflammatory index in the FEO treated lung tissue. ConclusionThese results, proved that FEO could attenuate BLM-induced PF, thus suggesting that the latter could serve as a potential therapeutic approach for PF.

Comparison of passive and manual chlorination in small piped water networks in rural Ghana: Technical performance, ease-of-use, and cost

Chlorination is the most common water treatment method globally and leads to proven health benefits. Yet, many rural water supplies in low-income settings are unchlorinated, exposing consumers to waterborne diseases. Insufficient technical and financial capacity of water suppliers in low-resource settings are common barriers to more widespread chlorination. We conducted a case study of two approaches to chlorinate small piped water supplies− passive (inline) chlorination and manual chlorination− and compared their technical performance, ease-of-use, and costs in rural Ghana. Based on 685 water quality measurements across two piped networks over three months, both methods provided adequate free chlorine residuals (i.e., 0.2–2.0 mg/L) most of the time (71% for manual chlorination and 86% for passive chlorination). Follow-up measurements five months later revealed a decline in chlorine levels with the manual approach (47% in the target range) and an increase with the passive (inline) approach (100% in the target range). We observed large fluctuations in chlorine levels over time, particularly with inline chlorination, that pH, temperature, conductivity, and turbidity variations did not fully explain. Temporal changes in chlorine demand and/or inconsistently implemented protocols possibly contributed to these fluctuations. Inline chlorination scored higher for ease-of-use (85%) than manual chlorination (70%) but was less financially viable: it represented an 11% increase in operational expenses, compared to 4% for manual chlorination. Initial equipment and installation cost approximately 6,000 USD for inline chlorination and about 260 USD for manual chlorination. Our results highlight the tradeoffs between passive (inline) and manual chlorination. Although less favorable for ease-of-use, manual chlorination is more viable financially and can achieve comparable performance with strict dosing protocol adherence, suggesting this approach deserves similar consideration as passive chlorination when evaluating options for low-resource settings. Both methods are susceptible to changes in operator behaviors and require external oversight plus support for troubleshooting and recalibration.

Formation mechanism and oil-bearing properties of gravity flow sand body of Chang 63 sub-member of Yanchang Formation in Huaqing area, Ordos Basin

Abstract Gravity flow sand body is an important reservoir for deep water deposition. The in-depth study of its formation mechanism and oil enrichment law can provide important guidance for oil and gas exploration in deep water areas. In this article, taking the deep-water gravity flow sand body of the Triassic Chang 63 sub-member in the Huaqing area as an example, the identification characteristics, formation mechanism, and oil-bearing characteristics of the gravity flow sand body are systematically discussed. The results show that sandy debris flow, turbidite flow, and mixed event flow present different superposition combination modes on vertical gravity flow deposition. The lithofacies of the gravity flow sandstone complex are mainly affected by lake level fluctuation, provenance supply, and hydrodynamic conditions. According to the formation conditions, sedimentary types. and distribution characteristics of deep water gravity flow sand bodies, a three-dimensional sedimentary mode of deep water gravity flow sand bodies is established. It is found that the physical and oil-bearing properties of the sandy debris flow sand body are better than those of the turbidity flow sand body. The reason is that the proportion of debris in the sand body of sandy debris flow is high, and the content of debris particles is generally greater than 70% according to the statistics of thin sections, which is conducive to the formation of pores. Second, sandy debris flow belongs to block transport, and the mixing of lithology is not sufficient in the process of block flow transport, so some pores will be preserved. Finally, gravity flows, which are dense at the beginning, become less dense later as detrital sediments unload, allowing them to be transported to distant regions. Therefore, the monolayer thickness of the sandy debris flow sand body is large (generally greater than 0.5 m), and the particle size of the debris ranges from 0.03 to 0.3 mm. Finally, the physical and oil-bearing properties of the sandy clastic flow sand body are better than those of the turbidity flow sand body.

Hypoxic Effects of Heroin and Fentanyl and Their Basic Physiological Mechanisms.

Respiratory depression that diminishes oxygen delivery to the brain is the most dangerous effect of opioid drugs. While plethysmography is a valuable tool to examine drug-induced changes in respiration, the primary cause of brain abnormalities induced by opioids is the global decrease in brain oxygen levels. The primary goal of this review is to provide an overview and discussion on fluctuations in brain oxygen levels induced by opioids, with a focus on heroin and fentanyl. To evaluate fluctuations in brain oxygen levels we used oxygen sensors coupled with high-speed amperometry in awake, freely moving rats. First, we provide an overview of brain oxygen responses induced by natural physiological stimuli and discuss the mechanisms regulating oxygen entry into brain tissue. Then, we present data on brain oxygen responses induced by heroin and fentanyl and review their underlying mechanisms. These data allowed us to compare the effects of these drugs on brain oxygen regarding their latency, potency, time-dependency, and potential lethality at high doses as well as their relationships with peripheral oxygen responses. We also discuss data on the effects of naloxone on brain oxygen responses induced by heroin and fentanyl in the paradigms of both the pre-treatment and treatment, when naloxone is administered at different times after the primary opioid drug. Although most data discussed were obtained in rats, they may have clinical relevance for understanding the mechanisms underlying the physiological effects of opioids and developing rational treatment strategies to decrease acute lethality and long-term health complications of opioid misuse.

Trajectories of procrastination among Swedish University students over one academic year: a cohort study

BackgroundProcrastination is common among university students and associated with adverse outcomes such as physical and mental health problems. According to the Temporal motivation theory procrastination may vary over time depending on the temporal proximity to goals and deadlines.AimsTo determine if mean procrastination levels among university students varies over an academic year, and if trajectories of procrastination are moderated by gender identity, perfectionistic strivings, and/or perfectionistic concerns.SampleSwedish university students (n = 1410).MethodsThe cohort was followed with web-surveys at four time-points over one academic year (Late semester, Mid semester, After semester, and Early semester). Generalized Estimating Equations were used to estimate mean levels of self-rated procrastination at the different time-points.ResultsWe found only small fluctuations in mean procrastination levels over the academic year. Participants with high perfectionistic concerns demonstrated higher mean procrastination levels at all time-points, but neither gender identity, perfectionistic concerns nor perfectionistic strivings affected the slope of the mean procrastination trajectories.ConclusionsIn this cohort of Swedish university students, self-rated procrastination levels were stable over the academic year. Perfectionistic concerns, but not gender identity or perfectionistic strivings, was associated with higher levels of procrastination.

Multi-assessed green sustainable chromatographic resolution of nicotine and caffeine; application to in-vitro release from a new quick mist mouth spray co-formula

The availability of well-established analytical methods is crucial to cope with the fast-ongoing research for the development of new drug delivery formulations. In this work, a rapid highly green chromatographic method was developed for the simultaneous determination of nicotine (NIC) and caffeine (CAF) to be applied for an in-vitro release study from a newly prepared quick mist mouth spray co-formula (QMS), as a complementary synergistic fast-onset relief of cravings during smoking cessation. The chromatographic resolution was accomplished on a cyano column using isocratically delivered (1.0 mL/ min) glycerol: orthophosphoric acid (OPA) (0.2 M) adjusted to pH 3.0 using 0.05 M triethylamine (5:95, v/v) and UV detection at 260 nm. Well resolved peaks of NIC and CAF were eluted at 2.1 and 3.9 min (Rs = 5.64), with linear responses between 0.1 and 20.0 µg/mL and 0.2–40.0 µg/mL, and detection limits of 0.03 and 0.07 µg/mL for NIC and CAF, respectively. The developed method showed good analytical performance (accuracy, precision, robustness, and selectivity) as well as superiority in practicality and ecological profile compared to reported methods applying GAPI, analytical eco-scale, AGREE, BAGI, and whiteness metric tool. The developed method was successfully applied for NIC and CAF determination in their pharmaceutical preparations, and artificial saliva with no significant differences from reported method results (F-test and t-test). Moreover, an in-vitro release study of NIC and CAF from QMS was performed employing the developed method that revealed diffusion-controlled release, compared to mixed diffusion/ polymer chain relaxation for marketed single component formulation, showing the superiority of QMS in reducing drug level fluctuations of NIC and CAF and improving their bioavailability.Graphical

Interval prediction method for dynamic zoning of displacements in super-high arch dam under significant water level fluctuations

Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.