Current Research Research Articles

Mechanistic Insights and Potential Therapeutic Implications of NRF2 in Diabetic Encephalopathy.

Diabetic encephalopathy (DE) is a complication of diabetes, especially type 2 diabetes (T2D), characterized by damage in the central nervous system and cognitive impairment, which has gained global attention. Despite the extensive research aimed at enhancing our understanding of DE, the underlying mechanism of occurrence and development of DE has not been established. Mounting evidence has demonstrated a close correlation between DE and various factors, such as Alzheimer's disease-like pathological changes, insulin resistance, inflammation, and oxidative stress. Of interest, nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor with antioxidant properties that is crucial in maintaining redox homeostasis and regulating inflammatory responses. The activation and regulatory mechanisms of NRF2 are a relatively complex process. NRF2 is involved in the regulation of multiple metabolic pathways and confers neuroprotective functions. Multiple studies have provided evidence demonstrating the significant involvement of NRF2 as a critical transcription factor in the progression of DE. Additionally, various molecules capable of activating NRF2 expression have shown potential in ameliorating DE. Therefore, it is intriguing to consider NRF2 as a potential target for the treatment of DE. In this review, we aim to shed light on the role and the possible underlying mechanism of NRF2 in DE. Furthermore, we provide an overview of the current research landscape and address the challenges associated with using NRF2 activators as potential treatment options for DE.

A Mechanistic Review on Protective Effects of Mangosteen and its Xanthones Against Hazardous Materials and Toxins.

Due to its pharmacological properties, α-Mangostin, mainly found in Garcinia mangostana (G. mangostana) L. (Mangosteen, queen of fruits), treats wounds, skin infections, and many other disorders. In fact, α-Mangostin and other xanthonoid, including β-Mangostin and γ-Mangostin, are found in G. mangostana, which have various advantages, namely neuroprotective, anti-proliferative, antinociceptive, antioxidant, pro-apoptotic, anti-obesity, anti-inflammatory, and hypoglycemic through multiple signaling mechanisms, for instance, extracellular signal-regulated kinase1/2 (ERK 1/2), mitogenactivated Protein kinase (MAPK), nuclear factor-kappa B (NF-kB), transforming growth factor beta1 (TGF-β1) and AMP-activated protein kinase (AMPK). This review presents comprehensive information on Mangosteen's pharmacological and antitoxic aspects and its xanthones against various natural and chemical toxins. Because of the insufficient clinical study, we hope the current research can benefit from performing clinical and preclinical studies against different toxic agents.

Intelligent femtosecond laser bone drilling via online monitoring and machine learning

In conventional spinal surgeries, mechanical and thermal injuries frequently usually arise due to improper handling, giving rise to a range of complications including infection, poor wound healing and bleeding. Femtosecond laser ablation offers a promising approach owing to high precision and low thermal damage. In this study, an intelligent femtosecond laser drilling method of human spinal bones has been proposed, and a machine learning method has been employed to determine the optimal laser processing window, ensuring high-quality outcomes. A neural network model has been developed to predict drilling quality, achieving an impressive accuracy rate exceeding 98 %, along with precision and recall rates of 100 % and 92.86 %, respectively. To further monitor the process, a fiber spectrometer and a thermal camera has been employed to monitor the focal status and bone temperature during laser processing to make sure the drilling is in a focal position and temperature in safe range. Subsequently, the drilling efficiency has been predicted using another neural network model within high-quality processing window for the maximum ablation processing parameter. The current research has demonstrated a direct, non-destructive and efficient method for intelligent laser spinal drilling.

Gradient Hydrogels Spatially Trapped Optical Cell Profiling for Quantitative Blood Cellular Osmotic Analysis.

The quantitative exploration of cellular osmotic responses and a thorough analysis of osmotic pressure-responsive cellular behaviors are poised to offer novel clinical insights into current research. This underscores a paradigm shift in the long-standing approach of colorimetric measurements triggered by red cell lysis. In this study, we engineered a purpose-driven optofluidic platform to facilitate the goal. Specifically, creating photocurable hydrogel traps surmounts a persistent challenge─optical signal interference from fluid disturbances. This achievement ensures a stable spatial phase of cells and the acquisition of optical signals for accurate osmotic response analysis at the single-cell level. Leveraging a multigradient microfluidic system, we constructed gradient osmotic hydrogel traps and developed an imaging recognition algorithm, empowering comprehensive analysis of cellular behaviors. Notably, this system has successfully and precisely analyzed individual and clustered cellular responses within the osmotic dimension. Prospective clinical testing has further substantiated its feasibility and performance in that it demonstrates an accuracy of 92% in discriminating complete hemolysis values (n = 25) and 100% in identifying initial hemolysis values (n = 25). Foreseeably, this strategy should promise to advance osmotic pressure-related cellular response analysis, benefiting further investigation and diagnosis of related blood diseases, blood quality, drug development, etc.

Beyond Trolleyology: The CNI Model of Moral-Dilemma Responses.

How do people make judgments about actions that violate moral norms yet maximize the greater good (e.g., sacrificing the well-being of a small number of people for the well-being of a larger number of people)? Research on this question has been criticized for relying on highly artificial scenarios and for conflating multiple distinct factors underlying responses in moral dilemmas. The current article reviews research that used a computational modeling approach to disentangle the roles of multiple distinct factors in responses to plausible moral dilemmas based on real-world events. By disentangling sensitivity to consequences, sensitivity to moral norms, and general preference for inaction versus action in responses to realistic dilemmas, the reviewed work provides a more nuanced understanding of how people make judgments about the right course of action in moral dilemmas.

Airflow Triggered Water Film Self-Sculpturing on Femtosecond Laser-Induced Heterogeneously Wetted Micro/Nanostructured Surfaces.

Liquid manipulation is essential for daily life and modern industry, and it is widely used in various fields, including seawater desalination, microfluidic robots, and biomedical engineering. Nevertheless, the current research focuses on the manipulation of individual droplets. There are a few projects for water film management. Here, we proposed a facile method of wind-triggered water film self-sculpturing based on a heterogeneous wettability surface, which is achieved by the femtosecond laser direct writing technology and femtosecond laser deposition. Under the conditions of various airflow velocities and water film thicknesses, three distinct behaviors of the water film were analyzed. As a result, when the water film thickness is lower than 4.9 mm, the self-sculpture process will occur until the whole superhydrophobic surface dewetting. Four potential applications are demonstrated, including encryption, oil containers, reconfigurable patterning, and self-splitting devices. This work provides a new approach for manipulating a water film of fluid control engineering.

Research progress of mechanism of acupuncture for migraine.

The literature regarding the action mechanism of acupuncture for migraine published from 2013 to 2023 was searched in China National Knowledge Infrastructure (CNKI), Wanfang, VIP, Chinese Science and Technology Periodical Database, PubMed, Medline, and Cochrane Library. The pathogenesis of migraine and potential mechanisms of acupuncture treatment were summarized from the aspects of trigeminovascular system (TGVS), metabolic-related mechanisms, genetic factors, and alterations in brain structure and function. The results revealed that current research on the mechanisms of acupuncture for migraine predominantly centered around the TGVS, with studies confirming the specific regulatory effects of acupuncture on trigeminal system-related components, such as cortical spreading depression (CSD), astrocytes, and neurogenic kinin. While metabolic-related mechanisms and genetic factors are gaining attention, further in-depth exploration is needed to fully understand their roles in the mechanism of acupuncture for migraine. The study of brain structure and function in migraine has advanced, albeit with some controversies. Future research should prioritize larger sample sizes and standardized study designs to delve deeper into the mechanisms of acupuncture in treating migraine.

Nephroprotective effect of cranberry (Vaccinium oxycoccos) in streptozocin-induced diabetic nephropathy in mice.

Diabetic nephropathy is a chief reason of mortality particularly in individuals with renal dysfunction. The current research was aimed to assess the nephroprotective portion of Vaccinium oxycoccos toward mice diabetic nephropathy induced by streptozotocin (STZ). V.oxycoccos was purchased and used for hydroalcoholic extraction. Sixty male mice were subjected to STZ-intraperitoneal injection (45 mg/kg). After diabetes induction, mice were divided into five groups of diabetic control (received only STZ), non-diabetic control (received only citrate buffer), two V.oxycoccos treatment (received V.oxycoccos extract (200 and 400 mg/kg) oral daily by gavage), and metformin treatment (received metformin (500 mg/kg) oral daily by gavage). Glucose and weight of mice were checked weekly. After 28days, the effect of V.oxycoccos extract on serum and urine parameters were assessed. STZ caused significant decreased in the mice body weight. Mice treated with the V.oxycoccos (400 mg/kg) harbored the lowest weight loss at day 28 (70.2±1.38 g). STZ caused significant increase in the mice FBS. Mice treated with the V.oxycoccos (400 mg/kg) harbored the lowest FBS at day 28 (189.2±1.20 mg/dL). Treatment of mice with V.oxycoccos (400 mg/kg) caused the lowest increase in the levels of cholesterol, HbA1c and triglycerides compared to the diabetic control mice. Compared to the diabetic control group, mice treated with V.oxycoccos (400 mg/kg) had the highest HDL, insulin, SOD, and GSH (p<0.05). The lowest serum BUN, CR, and UR were found in mice treated with V.oxycoccos (400 mg/kg). Anti-inflammatory effects of V.oxycoccos (400 mg/kg) was shown by the lowest TNF-α, IL-6, and TGF-β1 concentration in mice treated with V.oxycoccos (400 mg/kg). The current study disclosed that treatment with V.oxycoccos resulted in substantial development in the serum and urine parameters and also antioxidant and anti-inflammatory response of STZ-induced diabetic mice.

Average trapping time on horizontally divided 3-dimensional 3-level Sierpinski gasket network

An important characteristic of random wandering is the average trapping time, which is a hot issue in current research. The average trapping time is an important measure of the transmission efficiency of random wandering in a network. In this paper, we construct a 3-dimensional 3-level Sierpinski gasket network divided horizontally by the horizontal division plane P s , that is, the division coefficients s. We study the capture problem on the network and obtain an analytical expression for the average trapping time (ATT). Then, by adjusting the number of iterations and the values of the division coefficients, we obtained the relationship between ATT and them. As can be seen from our numerical simulation plots, ATT is affected by s. The larger s is, the more the self-similar structure of the three-dimensional residual network gradually transforms towards the structure of the two-dimensional complete Sierpinski gasket network. Meanwhile, the shorter ATT is, that is, the more efficient the transmission on the network.

Effective-diode-based analysis of industrial solar photovoltaic panel by utilizing novel three-diode solar cell model against conventional single and double solar cell.

Currently, the majority of the country has moved to renewable energy sources for electricity generation, and power companies are concentrating their efforts on renewable resources. Solar, wind, hydropower, and biomass are examples of renewable resources; of these, due to a lack of non-renewable resources, the solar industry is expanding. All year long, solar electricity is available, and it creates a calm, quiet atmosphere. The majority of large and small companies, as well as individual consumers, have shifted to PV solar cells for electricity generation. A trustworthy and precise simulation design of a photovoltaic system prior to installation is required to predict a photovoltaic system's performance. The current research aims to build models for solar PV systems with one, two, and three diodes and determine which model is most appropriate for each environmental circumstance to forecast performance accurately. By contrasting the experimental data of solar panel with simulated results of single-, double-, and triple-diode models, this study examines the accuracy of each model. These models' comparative performance study has been done using the MATLAB/Simulink, taking into account the influence of changing model parameters and the performance of the models under varying climatic circumstances. These models, despite their simplicity, are quite sensitive and react to even a little change in temperature and irradiance. Under conditions of low solar irradiance or shading conditions, three-diode photovoltaic models are shown to be more accurate. We can forecast the power output of solar photovoltaic systems under changeable input circumstances by understanding the I-V curves with the help of the performance assessment of the models used in this work.

Assessment of Unmanned Aerial System Flight Plans for Data Acquisition from Erosional Terrain

Accurate data mapping and visualization are of crucial importance for the detection and monitoring of slope morphodynamics, including erosion processes and studying small erosional landforms (rills and gullies). The purpose of the current research is to examine how the flight geometry of unmanned aerial systems (UASs) could affect the accuracy of photogrammetric processing products, concerning small erosion landforms that are a result of slope wash and temporary small streams formed by rain. In October 2021, three UAS flights with a different geometry were carried out in a hilly to a low-mountain area with an average altitude of about 650 m where erosion processes are observed. UAS imagery processing was carried out using structure-from-motion (SfM) photogrammetry. High-resolution products such as photogrammetric-based point clouds, digital surface models (DSMs) and orthophotos were generated. The obtained data were compared and evaluated by the root mean square error (RMSE), length measurement, cloud-to-cloud comparison, and 3D spatial GIS analysis of DSMs. The results show small differences between the considered photogrammetric products generated by nadir-viewing and oblique-viewing (45°—single strip and 60°—cross strips) geometry. The complex analysis of the obtained photogrammetric products gives an advantage to the 60°—cross strips imagery, in studying erosional terrains with slow slope morphodynamics.

In Situ Growth of Interfacially Nanoengineered 2D-2D WS2/Ti3C2Tx MXene for the Enhanced Performance of Hydrogen Evolution Reactions.

In line with current research goals involving water splitting for hydrogen production, this work aims to develop a noble-metal-free electrocatalyst for a superior hydrogen evolution reaction (HER). A single-step interfacial activation of Ti3C2Tx MXene layers was employed by uniformly growing embedded WS2 two-dimensional (2D) nanopetal-like sheets through a facile solvothermal method. We exploited the interactions between WS2 nanopetals and Ti3C2Tx nanolayers to enhance HER performance. A much safer method was adopted to synthesize the base material, Ti3C2Tx MXene, by etching its MAX phase through mild in situ HF formation. Consequently, WS2 nanopetals were grown between the MXene layers and on edges in a one-step solvothermal method, resulting in a 2D-2D nanocomposite with enhanced interactions between WS2 and Ti3C2Tx MXene. The resulting 2D-2D nanocomposite was thoroughly characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman, Fourier transform infrared (FTIR), and X-ray photoelectron spectroscopy (XPS) analyses before being utilized as working electrodes for HER application. Among various loadings of WS2 into MXene, the 5% WS2-Ti3C2Tx MXene sample exhibited the best activity toward HER, with a low overpotential value of 66.0 mV at a current density of -10 mA cm-2 in a 1 M KOH electrolyte and a remarkable Tafel slope of 46.7 mV·dec-1. The intercalation of 2D WS2 nanopetals enhances active sites for hydrogen adsorption, promotes charge transfer, and helps attain an electrochemical stability of 50 h, boosting HER reduction potential. Furthermore, theoretical calculations confirmed that 2D-2D interactions between 1T/2H-WS2 and Ti3C2Tx MXene realign the active centers for HER, thereby reducing the overpotential barrier.

Optimizing the White Light Emission of Pr3+ Activated SrCeO3 Perovskite by Controlling the Excitation Wavelength for High CRI wLED Applications.

The current research illustrates excitation energy-triggered photoluminescent characteristics of Pr3+ions in SrCeO3 providing a practical approach for developing high CRI wLED and its applications. SrCeO3: xPr3+ (x = 0, 0.005, 0.01, 0.02, 0.03 wt) perovskites synthesized by fuel excess gel combustion method generate high CRI (~98) for wLED applications. Crystalline phosphors with orthorhombic structures having space group Pnma were confirmed by XRD. The unit cell volume expansion occurred with an increase in Pr3+ concentration was verified through the Rietveld refinement technique. Surface morphology, particle distribution, and size were observed via FE-SEM imaging, and detected a well-defined regular distorted spherical structure with average grain size 0.826μm for Pr3+ doped SrCeO3. Elemental mapping and EDS analysis identified the uniform distribution and elemental purity of SrCeO3: 0.01 Pr3+. Further, the molecular vibrations and modes were analyzed from the Raman spectrum. Moreover, the average particle size assessed via TEM analysis was found to be ~83.2nm, consistent with XRD analysis. UV-visible absorption spectra for optical energy-band gap analysis showed a decrease in band gap energy with an increase in Pr3+ concentration, realizing an effective energy transfer from Ce4+ to Pr3+. PL measurements showed a huge variety of emission transitions, corresponding to excitations 290nm, 321nm, 373nm, and 449nm. The critical dopant concentration instigated by concentration quenching was 1 wt% Pr3+, ascribed to dipole-dipole interaction. The fluorescence lifetime of the optimal sample was 4.835μs. Commission International de I'Eclairage (CIE) diagram exposes the white light emanation of SrCeO3: Pr3+. Among which white light with high CRI (~98) and comparably low CCT (~6311K) was obtained for SrCeO3: 0.01 Pr3+ at 373nm excitation. The obtained results recommend that SrCeO3: Pr3+ perovskite as an efficient white phosphor for fabricating high-performance wLEDs.

Reconnoitering the impact of corporate governance on carbon emission disclosure in an emerging setting

Purpose This study aims to determine the influence of corporate governance characteristics on carbon emission disclosure in an emerging economy. Design/methodology/approach The study is based on S&amp;P BSE 500 Indian firms for the period of 6 years from 2016–2017 to 2021–2022. The panel data regression models are used to gauge the association between corporate governance and carbon emission disclosure. Findings The empirical findings of the study support the positive and significant association between board activity intensity, environment committee and carbon emission disclosure. This evinced that the board activity intensity and presence of the environment committee have a critical role in carbon emission disclosure. On the contrary, findings reveal a significant and negative relationship between board size and carbon emission disclosure. Practical implications The present study provides treasured insights to regulators, policymakers, investors and corporate managers, as the study corroborates that various corporate governance characteristics exert significant influence on carbon emission disclosure. Originality/value The current research work provides novel insights into corporate governance and climate change literature that good corporate governance significantly boosts the carbon emission disclosure of firms. Previous studies examining the impact of corporate governance on carbon emission disclosure ignored emerging economies. Thus, the current work explores the role of governance mechanisms on carbon emission disclosure in an emerging context. Further, to the best of the author’s knowledge, the current study is the first of its kind to investigate the role of corporate governance on carbon emission disclosure in the Indian context.

Sustainable adsorption of methylene blue (MB) dye using individual and combined ratios of Trigonella foenum-graecum (F) seed galactomannan and Linum usitatissimum (L) seed

Abstract This study explores the adsorption of methylene blue (MB) dye from aqueous solution using different forms of fenugreek galactomannan and linseed (both individually and in combination) as an adsorbent. Characterization study of adsorbents reveal the involvement of carboxyl, hydroxyl and amine groups in MB adsorption taking place on the amorphous and porous surface of the adsorbent. BET surface area of the adsorbent (F+L) was found to be 394 m2/g. Different parameters including pH, adsorbent dose, adsorbate concentration, adsorption time and temperature were studied. Results of the study shows 86.62, 86.94, 88.04, 88.24, 88.56 and 89.09% of MB dye adsorption with F, L, F + L (1:1), F + L (1:2), F + L (2:1) and F + L (2:2) under optimized conditions. Maximum adsorption take place at pH 12, i.e., highly basic medium. Isothermal study shows the fitness of the experimental data to Langmuir isotherm model. Kinetic modeling shows that both physical and chemical interactions are involved in dye adsorption. Further, thermodynamic study confirms the spontaneity, endothermicity and feasibility of the process. Overall, current research involves the use of low-cost raw material for MB dye removal that aids in waste management and in promoting sustainability.

Water-Stable Pillared Three-Dimensional Zn-V Bimetal-Organic Framework for Promoted Electrocatalytic Urea Oxidation.

Urea oxidation reaction (UOR) is one of the potential routes in which urea-rich wastewater is used as a source of energy for hydrogen production. Metal-organic frameworks (MOFs) have promising applications in electrocatalytic processes, although there are still challenges in identifying the MOFs' molecular regulation and obtaining practical catalytic systems. The current study sought to synthesize [Zn6(IDC)4(OH)2(Hprz)2]n (Zn-MOF) with three symmetrically independent Zn(II) cations connected via linear N-donor piperazine (Hprz), rigid planar imidazole-4,5-dicarboxylate (IDC3-), and -OH ligands, revealing the 3,4T1 topology. The optimized noble-metal-free Zn0.33V0.66-MOF/NF electrocatalysts show higher robustness and performance compared to those of the parent Zn monometallic MOF/NF electrode and other bimetallic MOFs with different Zn-V molar ratios. The low potential of 1.42 V (vs RHE) at 50 mA cm-2 in 1.0 M KOH with 0.33 M urea required by the developed Zn0.33V0.66-MOF electrode makes its application in the UOR more feasible. The availability of more exposed active sites, ion diffusion path, and higher conductivity result from the distinctive configuration of the synthesized electrocatalyst, which is highly stable and capable of synergistic effects, consequently enhancing the desired reaction. The current research contributes to introducing a practical, cost-effective, and sustainable solution to decompose urea-rich wastewater and produce hydrogen.

A decade analysis of employees’ well-being and performance while working from home: a bibliometric approach

Purpose Work-from-home (WFH) has gained popularity over the past years. This study aims to conduct a bibliometric analysis to systematically review and synthesize scholarly literature on the complex interplay between WFH, employee well-being and performance. Design/methodology/approach The study incorporates analysis of the bibliometric including performance analysis, content analysis and scientific mapping that is applied to 497 Scopus papers. VOSviewer software was used to evaluate the data. Findings This study posits an imbalance between the count of documents and the citations earned by each author. International Journal of Environmental Research and Public Health was regarded as a leading journal with maximum citations and publications. The highest count of publications came from most Asian countries such as India, China, Indonesia and Japan. The investigation indicated that the writers with the maximum citations were predominantly the authors of the majorly cited papers. Further, the text mining through co-occurrence of keyword analysis generated five clusters and cocited references revealed three themes. Practical implications The current research might benefit both research groups as well as human resource professionals since it also reveals the research necessity and gaps in the WFH domain. Originality/value This research delves into unexplored facets of WFH beyond traditional studies over the past decade by examining remote work arrangements in today’s economy, revealing previously unnoticed dynamics affecting employee well-being and performance. This innovative viewpoint enhances the literature and provides an empirical foundation for strategic organizational decision-making and future study.

Nutrient consumption patterns of Lactobacillus acidophilus.

One of the greatest challenges in using Lactobacillus acidophilus as a probiotic is acid stress. The current research aimed to identify substances that help L. acidophilus resist acid stress; this was achieved through assessing its nutrient consumption patterns under various pH conditions. The consumption rates of alanine, uracil, adenine, guanine, niacin, and manganese were consistently higher than 60% for L. acidophilus LA-5 cultured at pH 5.8, 4.9, and 4.4. The consumption rates of glutamic acid + glutamine and thiamine increased with decreasing pH and were higher than 60% at pH 4.9 and 4.4. The viable counts of L. acidophilus LA-5 were significantly increased under the corresponding acidic stress conditions (pH 4.9 and 4.4) through the appropriate addition of either alanine (3.37 and 2.81 mmol L-1 ), glutamic acid + glutamine (4.77 mmol L-1 ), guanine (0.13 and 0.17 mmol L-1 ), niacin (0.02 mmol L-1 ), thiamine (0.009 mmol L-1 ), or manganese (0.73 and 0.64 mmol L-1 ) (P < 0.05). The viable counts of L. acidophilus LA-5 cultured in a medium supplemented with combined nutritional factors was 1.02-1.03-fold of the counts observed in control medium under all acid conditions (P < 0.05). Alanine, glutamic acid + glutamine, guanine, niacin, thiamine, and manganese can improve the growth of L. acidophilus LA-5 in an acidic environment in the present study. The results will contribute to optimizing strategies to enhance the acid resistance of L. acidophilus and expand its application in the fermentation industry. © 2024 Society of Chemical Industry.

Exploring the nutritional landscape and emerging trends in kidney stone research: visualization and bibliometric analysis

BackgroundKidney stones, or renal nephrolithiasis, are common and dangerous, increasing the risk of chronic kidney disease. Dietary manipulation is essential for the prevention and recurrence of kidney stones. Research into nutrition and kidney stones lacks library analysis, making it difficult to identify new trends. This bibliometric study was conducted to explore the current landscape of research on nutrition and kidney stones worldwide. This paper also emphasizes significant research trends in this area over the past two decades, aiming to assist researchers in understanding the current research status and identifying potential future directions.MethodsThe scholarly literature pertaining to diet and kidney stones was systematically explored utilizing SciVerse Scopus to identify pertinent research articles released from 2003 to 2022. The examination encompassed an assessment of publication patterns, key contributors, focal areas of research, prevalent themes, influential articles, and emerging research avenues. Following data extraction to Excel, analyses, including frequencies, percentages, and linear regression, were conducted. Visual exploration was facilitated through the use of the VOSviewer program version 1.6.19.ResultsBetween 2003 and 2022, 697 publications on nutrition and kidney stones were identified. There were 478 original articles (68.58%), 120 reviews (17.22%) and 99 other types of publications (14.20%). The United States is the most productive country, with significant growth in research in the fields of nutrition and kidney stones. The United States has demonstrated the strongest partnerships between researchers’ networks from various countries. Cluster analysis revealed three major research themes: sex differences in kidney stone disease, the correlation between increased dietary acid consumption and calcium oxide kidney stones, and nephrolithiasis associated with a ketogenic diet.ConclusionsThis study offers a thorough examination of nutrition and kidney stone research, encompassing key research domains, collaborative networks, and emerging patterns. The findings can aid researchers in gaining insight into the present landscape of the discipline and determining future research directions.

Short-Term Intensity Prediction of Tropical Cyclones Based on Multi-Source Data Fusion with Adaptive Weight Learning

Tropical cyclones (TCs) can cause significant economic damage and loss of life in coastal areas. Therefore, TC prediction has become a crucial topic in current research. In recent years, TC track prediction has progressed considerably, and intensity prediction remains a challenge due to the complex mechanism of TC structure. In this study, we propose a model for short-term intensity prediction based on adaptive weight learning (AWL-Net) for the evolution of the TC’s structure as well as intensity changes, exploring the multidimensional fusion of features including TC morphology, structure, and scale. Furthermore, in addition to using satellite imageries, we construct a dataset that can more comprehensively explore the degree of TC cloud organization and structure evolution. Considering the information difference between multi-source data, a multi-branch structure is constructed and adaptive weight learning (AWL) is designed. In addition, according to the three-dimensional dynamic features of TC, 3D Convolutional Gated Recurrent (3D ConvGRU) is used to achieve feature enhancement, and then 3D Convolutional Neural Network (CNN) is used to capture and learn TC temporal and spatial features. Experiments on a sample of northwest Pacific TCs and official agency TC intensity prediction records are used to validate the effectiveness of our proposed model, and the results show that our model is able to focus well on the spatial and temporal features associated with TC intensity changes, with a root mean square error (RMSE) of 10.62 kt for the TC 24 h intensity forecast.