Analytical Technique Research Articles

The Role of English Language Skills in the Tourism Industry in the 4.0 Era in Kuningan District, West Java Province

Through communication media, English plays a significant role in the sustainable tourism sector with the goal of promoting foreign travel, service, effective communication between foreign visitors and tourism actors, and the surrounding community, all of which are ultimately related to the growth of local tourism in the industrial era's sustainable tourism sector. 4.0 Tourist actors need to be fluent in English, especially in the vernacular, which includes basic discussion, linguistic proficiency, and basic conversation. The purpose of this study is to investigate the advantages of English- speaking culture in West Java Province's sustainable tourism sector during the Industrial Era 4.0. Descriptive qualitative analysis is the analytical technique employed. This study demonstrates the necessity of English-speaking culture in Kunuingan District, West Java Province's sustainable tourism industry in the industrial era 4.0 in order to promote the province's local tourist destinations overseas, increase the number of foreign visitors, and facilitate English-language communication. The ability to speak effectively in English is crucial for both those who work in the tourism business and those who live near popular tourist sites.

Determining Marine Biodegradation Kinetics of Chemicals Discharged from Offshore Oil Platforms─Whole Mixture Testing at High Dilutions Increases Environmental Relevance.

Offshore oil platforms discharge enormous volumes of produced water that contain mixtures of petrochemicals and production chemicals. It is crucial to avoid the discharge of particularly those chemicals that are persistent in the marine environment. This study aims to (1) develop a biodegradation testing approach for discharged chemicals by native marine microorganism, (2) determine how dilution affects biodegradation, and (3) determine biodegradation kinetics for many discharged chemicals at low and noninhibitory concentrations. Produced water from an offshore oil platform was diluted in the ratio of 1:20, 1:60, and 1:200 in seawater from the same location and incubated for 60 days at 10 °C. Automated solid-phase microextraction GC-MS was used as a sensitive analytical technique, and chemical-specific primary degradation was determined based on peak area ratios between biotic test systems and abiotic controls. Biodegradation was inhibited at lower dilutions, consistent with ecotoxicity tests. Biodegradation kinetics were determined at the highest dilution for 139 chemicals (43 tentatively identified), and 6 chemicals were found persistent (half-life >60 days). Nontargeted analysis by liquid chromatography-high-resolution MS was demonstrated as a proof-of-principle for a comprehensive assessment. Biodegradation testing of chemicals in discharges provides the possibility to assess hundreds of chemicals at once and find the persistent ones.

Processing the Narrative: Innovative Graph Models and Queries for Textual Content Knowledge Extraction

The internet contains vast amounts of text-based information across various domains, such as commercial documents, medical records, scientific research, engineering tests, and events affecting urban and natural environments. Extracting knowledge from these texts requires a deep understanding of natural language nuances and accurately representing content while preserving essential information. This process enables effective knowledge extraction, inference, and discovery. This paper proposes a critical study of state-of-the-art contributions exploring the complexities and emerging trends in representing, querying, and analysing content extracted from textual data. This study’s hypothesis states that graph-based representations can be particularly effective when annotated with sophisticated querying and analytics techniques. This hypothesis is discussed through the lenses of contributions in linguistics, natural language processing, graph theory, databases, and artificial intelligence.

Optimal Control of Underdamped Systems: An Analytic Approach

Optimal control theory deals with finding protocols to steer a system between assigned initial and final states, such that a trajectory-dependent cost function is minimized. The application of optimal control to stochastic systems is an open and challenging research frontier, with a spectrum of applications ranging from stochastic thermodynamics to biophysics and data science. Among these, the design of nanoscale electronic components motivates the study of underdamped dynamics, leading to practical and conceptual difficulties. In this work, we develop analytic techniques to determine protocols steering finite time transitions at a minimum thermodynamic cost for stochastic underdamped dynamics. As cost functions, we consider two paradigmatic thermodynamic indicators. The first is the Kullback–Leibler divergence between the probability measure of the controlled process and that of a reference process. The corresponding optimization problem is the underdamped version of the Schrödinger diffusion problem that has been widely studied in the overdamped regime. The second is the mean entropy production during the transition, corresponding to the second law of modern stochastic thermodynamics. For transitions between Gaussian states, we show that optimal protocols satisfy a Lyapunov equation, a central tool in stability analysis of dynamical systems. For transitions between states described by general Maxwell-Boltzmann distributions, we introduce an infinite-dimensional version of the Poincaré-Lindstedt multiscale perturbation theory around the overdamped limit. This technique fundamentally improves the standard multiscale expansion. Indeed, it enables the explicit computation of momentum cumulants, whose variation in time is a distinctive trait of underdamped dynamics and is directly accessible to experimental observation. Our results allow us to numerically study cost asymmetries in expansion and compression processes and make predictions for inertial corrections to optimal protocols in the Landauer erasure problem at the nanoscale.

Study of a detector prototype for detecting special nuclear materials based on differential die-away technology

Differential die-away Analysis (DDAA) technology is a powerful analytical technique to detect fissile materials such as 235U and 239Pu. It can reveal the presence of nuclear materials even when shielded or embedded in non-fissile matrices by inducing fission through externally applied pulsed neutrons. An innovative modular system that is convenient for transportation and suitable for various application scenarios is proposed. The detection sensitivity of the system, with different structures, is studied. The data acquisition hardware, along with real-time online measurement and alerting software, have been developed. The results indicate that a detection system with a bilaterally arranged array of detectors offers enhanced sensitivity, enabling rapid detection of a single nuclear fuel rod (200.4g) under heavy shielding and at standard distances.

Geospatial analysis of elephant habitat suitability and movement for mapping the elephant corridor in Dalma Wildlife Sanctuary (India).

Wildlife and natural resources constitute an integral part of the ecosystem, whereas human interventions dismantled the living conditions of the wildlife. This is testified in the Dalma Wildlife Sanctuary (DWS) where the habitats of Asian elephants have changed due to human intervention and deforestation over the decades. The present study aimed to assess the elephant habitat suitability in the DWS of Jharkhand state (India) using the geospatial parameters such as forest density, degree of slope, proximity to water bodies, land use land cover, proximity to agricultural land, built-up density, and road density. The analytical hierarchical process technique was utilized to determine habitat preference and selection of relevant factors to categorize criteria. The study revealed that about 6.7% (26.74 km2) of the area is very highly suitable for elephant habitat, while 52.26% (208.49 km2) of the forest area was found highly suitable. The most suitable habitat was identified in the core parts of the forest, while the least suitable areas were found in the southern part, where the presence of roads, built-up, and agricultural land was prominent. It was also observed that most human-elephant conflicts were exhibited in the low and very low suitable areas, while 90% of the elephant movement was witnessed in the high and very high suitable areas. Among the four identified corridors, three are inactive, and their location corresponds with low to very low suitable habitats. The study identified the migratory corridor routes inside the sanctuary where effective management is required for the conservation of elephant habitats and minimizing conflicts.

Evaluation of protein-polysaccharide interactions through ζ-potential and particle size measurements to assess their functionality in wine.

Protein-polysaccharide-tannin interactions are important in every aspect of red wine production from physical stability to color, astringency, and body. For this model study, bovine serum albumin (BSA) was selected as the protein, while carboxymethyl cellulose (CMC), mannoproteins, and pectin were the model polysaccharides. Each protein-polysaccharide combination was analyzed for zeta (ζ) potential and particle size at neutral pH and within the wine-like solution. Mixtures were assessed regarding their protective, affinitive, and aggregative behaviors. Based on their individual ζ-potentials, pectin and mannoprotein were most stable at lower concentrations. At higher concentrations, they reduced the suspension's stability and increased the aggregate sizes. CMC consistently increased the stability of any solution under neutral pH conditions. However, with increasing concentrations, these large aggregates are expected to precipitate. Fruit pectin (FP) and BSA interactions seemed to be the main factors in the formation of visible precipitates at neutral pH. FP and the mannoprotein decreased stability enough to cause precipitation without haze formation. The mannoprotein decreased particle sizes, in both the suspension and precipitation, which may indicate greater selectivity toward proteins. FP also decreased the suspended particle sizes under wine conditions. These findings demonstrate the use of ζ-potential and particle size values to characterize macromolecular interactions in model systems and can also be used to indicate effective fining agents. PRACTICAL APPLICATION: This work demonstrates the capabilities of ζ-potential analysis paired with size particle measurements to predict and characterize the interactions between macromolecules in complex systems. The interactions between model wine macromolecules can be evaluated with this technology at a level that cannot be reached with any other analytical technique.

Validation of a rapid GC–MS method for forensic seized drug screening applications

With the lack of standardized validation protocols across the forensic chemistry community, validation of instrumentation can be a challenging and time-consuming task. However, this process is crucial to understanding the associated capabilities and limitations, especially for nascent technologies. Rapid GC–MS is one such emerging analytical technique being increasingly implemented in forensic laboratories due to its fast and informative screening capabilities. However, a full validation for forensic samples has yet to be published since its debut. This work presents the results of a comprehensive validation of a rapid GC–MS system for seized drug screening through the assessment of nine components: selectivity, matrix effects, precision, accuracy, range, carryover/contamination, robustness, ruggedness, and stability. Single- and/or multi-compound test solutions of commonly encountered seized drug compounds were used to assess method and system performance. Results met the designated acceptance criteria for a majority of components. For example, retention time and mass spectral search score % RSDs were ≤10 % for precision and robustness studies. Limitations were identified for components that did not meet the acceptance criteria (e.g., inability to differentiate some isomers). The study design is part of a larger validation package developed for rapid GC–MS that includes a validation plan and automated workbook. The template, available for adoption by laboratories, ultimately aims to reduce the barrier of implementation for rapid GC–MS technology.

Real-time solar PV generation in a building using LSTM-based time series forecasting

The increasing demand for renewable energy has forced the researchers to lay importance on studying the behavior of these sources by monitoring the operational data and performing data analysis. The application of data analytics to renewable energy is one of the most powerful methods to maintain the system's reliability and stability. As the power system becomes more complex the necessity to perform data analytics also increases. When data analytic techniques are applied to solar energy generations through Photovoltaic (PV) dataset, the possible behavior of PV generation performance which is affected by changes in environmental conditions can be predicted and further analytical approaches allow us to detect possible PV panel and inverter failures. This paper is an attempt towards applying the intelligent data analytics approaches to solar PV generation of a real-time photovoltaic plant. The main purpose of the data analytics platform is to analyze the energy yield, assess the PV system performance, and forecast solar PV generation. In this article, Long Short-Term Memory (LSTM) machine learning model is developed to assess and interpret the available information from the gathered data of the PV plant. The proposed model is a sequence-to-sequence regression-based forecasting model which performs time series forecasting in a real-time 100 kW PV plant in the University building of SRM Institute of Science and Technology, Kattankulathur, Tamilnadu.

Robust 1D selective correlation NMR spectroscopy for rapid chemical and biological applications

Selective homonuclear proton correlation NMR spectroscopy (COSY) provides a useful detection tool for elucidating molecular structures and identifying chemical compositions in 1D spectroscopic patterns. However, conventional 1D selective COSY experiments highly rely on the performance of selective excitation on targeted signals and their applications generally suffer from spectral congestion in complex chemical and biological samples. Herein, based on the concept of targeted excitation on coupled proton pairs and spectroscopic separation on their respective COSY responses, we propose a 1D selective NMR approach that is capable of individually recording direct coupling correlation information of targeted proton groups for analyses on complex samples, free of spectral congestion. The performance of the proposed approach is demonstrated on a medicine sample, a biological molecule, and a real metabonomics sample of human serum. This approach shows a promising analytical technique for structural studies and component analyses in chemical and biological applications. Keywords: NMR spectroscopy, Correlation spectroscopy, Targeted signal excitation, Spectral congestion, Molecular structure analysis.

Exploring the Field of Virtual Avatar: A Bibliometric and Content Analysis

The virtual avatar (VA) concept has gained significant attention since the introduction of virtual products. This study aims to investigate the present level of advancement and forthcoming research directions in the domain of VA while also constructing a conceptual framework for VA. A thorough examination of the VA field was carried out by integrating bibliometric and content analytic techniques. Eight hundred ninety-nine journal articles underwent descriptive bibliometric analysis using CiteSpace and VOSviewer software to construct and visualize synthetic networks. Two hundred forty-eight documents were selected for content analysis. VA is mostly focused on a few specific disciplines and journals prioritizing innovation. Conducting empirical investigations in many settings can enhance the research areas of Virtual Reality (VR), Augmented Reality (AR), and Extended Reality (XR), including improving algorithms, exploring accessibility usage, and examining public service applications. Alternative theoretical frameworks can provide novel insights and additional directions for the development of the VA field.

Fading model due to scintillation and pointing error on an optical wireless MISO channel

Optical wireless communication (OWC) yields high data rates and a license-free spectrum. However, OWC is drastically affected by scintillation owing to atmospheric turbulence and pointing errors because of misalignment. To alleviate the ramifications of scintillation and pointing error, multiple input single output (MISO) optical wireless channels are studied by several researchers. In this paper, it is proposed to study the performance of a MISO system in terms of the probability of outage and normalized threshold. Gamma–Gamma distribution is used to model scintillation because it is suitable for modeling both weak and strong turbulence conditions. The analytical technique is used to derive the closed-form expressions for the probability of outage of a 2×1 MISO channel. Moreover, under numerous scintillation conditions, the Mellin transform is applied to derive the closed-form expressions for the probability of outage of an optical wireless MISO channel. The closed-form results are validated through simulations.

Determination of free formaldehyde in cosmetics products through derivatization with 2,4-DNPH using an optimized and validated HPLC-PDA method

AbstractFormaldehyde plays a significant role in the global economy due to its unique chemical properties and widespread use in both the medical field and various industrial sectors. However, formaldehyde (HCHO) is classified as a persistent organic pollutant and is known to be harmful. Exposure to formaldehyde, both direct and prolonged, can cause severe health issues and potentially lead to death. It is recognized as a human carcinogen by numerous organizations and can negatively impact the endocrine system, as well as having mutagenic or teratogenic effects. Formaldehyde has been detected in various matrices at levels exceeding those permitted by international health regulatory bodies. Chromatography, particularly high-performance liquid chromatography (HPLC) coupled with detection methods, is an analytical technique frequently used to determine volatile organic molecules, including formaldehyde. This investigation aimed to develop and validate an assay method to identify and quantify formaldehyde in cosmetic products using HPLC-PDA. The chosen isocratic system consisted of a ZORBAX RX-C8 column (250 mm × 4.6 mm; 5 μm) and a mobile phase mixture of acetonitrile and water in a 55:45 (v/v) ratio, with a flow rate of 1.5 mL min−1. Formaldehyde was detected at 353 nm after derivatization with a 0.1N 2,4-dinitrophenylhydrazine solution, with a retention time of approximately 5.0 min and a total run time of 7.0 min for formaldehyde. The method was validated according to ICH Q2 (R1) guidelines. The validation parameters demonstrated high selectivity, precision (RSD <2%), accuracy (mean recovery of 101.3%), linearity (r > 0.995 in the 1,000–2,400 ppm range), and a limit of quantification (LOQ) of 0.1 ppm. The validated analytical method was applied to 35 cosmetic products. None of these products were labeled as containing formaldehyde, although 6 imported products were labeled as containing formaldehyde-releasing preservatives. The results indicated that 88.6% of the samples tested positive for formaldehyde, with free formaldehyde levels ranging from 20 to 981 ppm. All samples contained formaldehyde levels below the 2000 ppm threshold.

Development and testing of the capacity of organisations for system practices scale.

Systems change approaches are increasingly adopted in public health to address complex problems. It is important that measures of systems change be developed so that the effects of systems change on health outcomes can be evaluated. Organisational practices are potential levers for systems change. However, robust measures of organisational capacity to engage in these practices are lacking. Informed by the Theory of Systems Change, we developed and tested the Capacity of Organisations for System Practices (COSP) scale. The COSP scale comprises four inter-related system practices within organisations-adaptation, alignment, collaboration and evidence-driven action and learning. We applied a three-stage process: (1) Item generation; (2) Scale pre-testing; and (3) Structural analyses. Item response theory tests and semantic review, together with factor analytic techniques, were applied to refine the item set and determine the scale structure. An initial pool of 97 items was generated and pre-tested with six content experts and four target audience representatives. Modifications resulted in 60 items. In total, 126 participants provided data for the structural analysis. A second-order hierarchical four-factor model fit the data better than the more basic correlated factor model (Δχ2 = 1.758, p = .415). The fit indices for the final 31-item model were acceptable (RMSEA = .084, TLI = .819). The COSP scale is ready for further testing to ensure construct validity, stability and utility. SO WHAT?: Once validated, the Capacity of Organisations for System Practices (COSP) scale has the potential to advance the theory and practice of systems change approaches.

Lateral Electronic Junction of a Single Ultrathin Silicon Induced by Interfacial Dipole of Self-Assembled Monolayer.

Interface engineering is pivotal for enhancing the performance and stability of devices with layered structures, including solar cells, electronic devices, and electrochemical systems. Incorporating the interfacial dipole between the bulk layers effectively modulates the energy level difference at the interface and does not significantly influence adjacent layers overall. However, interfaces can drastically affect adjoining layers in ultrathin devices, which are essential for next-generation electronics with high integrity, excellent performance, and low power consumption. In particular, the interfacial effect is pronounced in ultrathin semiconductors, which have a weak electric field screening effect. Herein, the substantial interfacial impact on the ultrathin silicon is shown, the p- to n-type inversion of the semiconductor solely through the deposition of a self-assembled monolayer (SAM) without external bias. The effects of SAMs with different interfacial dipoles are investigated by using Hall measurement and surface analytic techniques, such as UPS, XPS, and KPFM. Furthermore, the lateral electronic junction of the ultrathin silicon is engineered by the regioselective deposition of SAMs with opposite dipoles, and the device exhibits rectification behavior. When the interfacial dipole of SAM is manipulated, the rectification ratio changes sensitively, and thus the fabricated diode shows potential to be developed as a sensing platform.

Construction of Cu2Y2O5/g-C3N4 Novel Composite for the Sensitive and Selective Trace-Level Electrochemical Detection of Sulfamethazine in Food and Water Samples.

The most frequently used sulfonamide is sulfamethazine (SMZ) because it is often found in foods made from livestock, which is hazardous for individuals. Here, we have developed an easy, quick, selective, and sensitive analytical technique to efficiently detect SMZ. Recently, transition metal oxides have attracted many researchers for their excellent performance as a promising sensor for SMZ analysis because of their superior redox activity, electrocatalytic activity, electroactive sites, and electron transfer properties. Further, Cu-based oxides have a resilient electrical conductivity; however, to boost it to an extreme extent, a composite including two-dimensional (2D) graphitic carbon nitride (g-C3N4) nanosheets needs to be constructed and ready as a composite (denoted as g-C3N4/Cu2Y2O5). Moreover, several techniques, including X-ray diffraction analysis, scanning electron microscopy analysis, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, and Raman spectroscopy were employed to analyze the composites. The electrochemical measurements have revealed that the constructed g-C3N4/Cu2Y2O5 composites exhibit great electrochemical activity. Nevertheless, the sensor achieved outstanding repeatability and reproducibility alongside a low limit of detection (LOD) of 0.23 µM, a long linear range of 2 to 276 µM, and an electrode sensitivity of 8.86 µA µM-1 cm-2. Finally, the proposed GCE/g-C3N4/Cu2Y2O5 electrode proved highly effective for detection of SMZ in food samples, with acceptable recoveries. The GCE/g-C3N4/Cu2Y2O5 electrode has been successfully applied to SMZ detection in food and water samples.

Transforming Data Analytics with AI for Informed Decision-Making

This study delves into how advanced data analytics and artificial intelligence (AI) can work together to enhance decision-making processes. As we navigate today’s data-driven environment, discovering the synergy between these fields is crucial, given the growing complexity of datasets. Advanced analytical tools are essential, and AI offers exceptional capabilities in pattern recognition and automation. This research investigates how cosmbining data analytics techniques—such as Predictive Modeling, Clustering, and Trend Analysis—with AI approaches like Machine Learning and Deep Learning can improve decision-making. A key focus of the study is on making AI models more interpretable and transparent. It emphasizes the importance of ensuring that AI-driven decisions are clear and understandable. Additionally, the research addresses ethical considerations and the need for human-centered design, aiming to balance AI’s power with openness. It also strives for responsible AI use by tackling issues such as bias and promoting ethical practices in the application of advanced data analytics and AI. The study demonstrates practical applications in areas like healthcare and finance, showing how these technologies can transform personalized medicine, disease prediction, risk assessment, fraud detection, and market trend analysis. Overall, this research highlights the valuable interaction between advanced data analytics and AI, offering a guide for organizations to enhance their decision-making while adhering to ethical standards and responsible AI use.

Machine-learning techniques for enhancing electricity theft detection considering transformer reliability and supply interruptions

Machine-learning techniques are extensively used in fraud detection and have a huge potential application in electricity theft detection. Electricity theft occurs when consumers manipulate their electricity consumption to be recorded as lower than the actual, resulting in reduced revenue for the electricity supplier. This study explores the application of advanced machine-learning techniques to detect electricity theft, focusing on the impact on supply of electricity to electricity usage pattern. We used a dataset that contains supply interruptions influenced by transformer reliability to construct a robust detection model. This model integrates data analytic techniques from decision trees, support vector machines, k-nearest neighbors, and logistic regression classifiers. After selecting the optimal classifier, we used aggregated parameters to improve theft-detection accuracy. A comprehensive sensitivity analysis was used to evaluate the effects of variations in transformer reliability factors on interruption, interruption costs, electricity revenue, and theft-detection performance. Our findings indicate that using optimal aggregated parameters significantly enhances detection accuracy 10.709 percent, 11.435 percent, 4.909 percent, and 2.097 percent from original parameters depend on the number of aggregation in 2-hour, 4-hour, 6-hour, and 12-hour, respectively. The lower transformer reliability leads to increased loss with percentage 66.87 percent, 46.98 percent, 17.22 percent and reduced theft-detection efficiency by 27.32 percent, 19.23 percent, 6.20 percent. depend on reliability factor20 percent, 50 percent, and 80 percent, respectively.

Closing PFAS analytical gaps: Inter-method evaluation of total organofluorine techniques for AFFF-impacted water

Multiple poly- and perfluoroalkyl substances (PFASs) are present in aqueous film-forming foams (AFFF) used for firefighting activities. Currently, no single analytical technique provides a complete accounting of total PFASs or total organofluorine content in AFFF-contaminated samples. To provide insight into the performance of existing methods, we compared ten previously described PFAS measurement techniques. In AFFF-amended tap water, US EPA Methods 533 and 1633, adsorbable organic fluorine with particle induced gamma emission spectroscopy (AOF-PIGE) and fluorine-19 nuclear magnetic resonance (19F NMR) provided similar estimates of total fluorine. The total oxidizable precursor (TOP) assay, suspect screening, and adsorbable organic fluorine with combustion ion chromatography (AOF-CIC) yielded estimates of total organic fluorine that were about two to three times higher than the other techniques. Proximate to AFFF sources, suspect screening and modified EPA Method 1633 yielded higher results, while the TOP assay results were between the other two sets of analyses. Further from sources, suspect screening, modified EPA Method 1633, and the TOP assay yielded similar results that were 4-fold higher than results from targeted quantification methods, such as EPA Method 1633. These results are consistent with expectations about PFAS behavior and inform the selection of analytical techniques used for PFAS contamination characterization efforts.

Huang-Lian-Jie-Du Decoction alleviates diabetic encephalopathy by regulating inflammation and pyroptosis via suppression of AGEs/RAGE/NF-κB pathways

Ethnopharmacological relevanceCognitive dysfunction associated with diabetes, known as diabetic encephalopathy (DE), is a grave neurodegenerative condition triggered by diabetes, and persistent inflammation plays a vital role in its development. The renowned traditional Chinese medicine Huang-Lian-Jie-Du Decoction (HLJDD) is clinically proven to manage diabetes mellitus and Alzheimer's disease and is famous for its heat-clearing and detoxifying effects. However, the underlying mechanisms through which HLJDD affects DE remain to be elucidated. Aim of the studyTo explore the beneficial effects of HLJDD on improving cognitive dysfunction in DE mice. Study design and methodsA diabetic mouse was established through a high-fat diet and subsequent administration of streptozotocin over five consecutive days. After the animals were confirmed to have diabetes, they were treated with HLJDD. After oral administration of HLJDD or metformin for 14 weeks, behavioral tests were used to assess their cognitive capacity. Biochemical analyses were then performed to detect levels of glucose metabolism, followed by histological analyses to assess pathological damage. Furthermore, AGEs/RAGE/NF-κB axis related proteins were detected by Western blot or immunofluorescence techniques. An advanced UPLC-Q-Orbitrap HRMS/MS analytical technique utilizing a chemical derivatization strategy was employed for comprehensive metabolic profiling of carbonyl compounds in the plasma of DE mice. ResultsPharmacological assessment revealed that HLJDD effectively mitigated cognitive dysfunction, normalized glucose metabolic imbalances, and repaired neuronal damage in DE mice. It reduced neuroinflammation by attenuating carbonyl stress, deactivating astrocytes and microglia, and preserving dopaminergic neurons. Additionally, metabolomics analysis revealed 18 carbonyl compounds with marked disparities between DE and control mice, with 12 metabolites approaching normal levels post-HLJDD intervention. Further investigations showed that HLJDD regulated inflammation and pyroptosis through suppressing AGEs/RAGE/NF-κB pathways. ConclusionOur study indicated that HLJDD could ameliorate carbonyl stress via the regulation of carbonyl compound metabolism profiling, and inhibiting the AGEs/RAGE/NF-κB pathway, thereby alleviating inflammation and pyroptosis to exert beneficial effects on DE.