Specific Information Research Articles

A Simple Approach for Module Temperature and Power Prediction

In this work a mathematical approach to calculate solar panel temperature based on measured irradiance, temperature and wind speed is applied. With the calculated module temperature, the electrical solar module characteristics is determined. A program developed in MatLab App Designer allows to import measurement data from a weather station and calculates the module temperature based on the mathematical NOCT and stationary approach with a time step between the measurements of 5 minutes. Three commercially available solar panels with different cell and interconnection technologies are used for the verification of the established models. The results show a strong correlation between the measured and by the stationary model predicted module temperature with a coefficient of determination R2 close to 1 and a root mean square deviation (RMSE) of ≤ 2.5 K for a time period of three months. Based on the predicted temperature, measured irradiance in module plane and specific module information the program models the electrical data as time series in 5-minute steps. Predicted to measured power for a time period of three months shows a linear correlation with an R2 of 0.99 and a mean absolute error (MAE) of 3.5, 2.7 and 4.8 for module ID 1, 2 and 3. The calculated energy (exemplarily for module ID 2) based on the measured, calculated by the NOCT and stationary model for this time period is 118.4 kWh, resp. 116.7 kWh and 117.8 kWh. This is equivalent to an uncertainty of 1.4% for the NOCT and 0.5% for the stationary model.

Translating meaning representations to behavioural interface specifications

Higher-order logic can be used for meaning representation in natural language processing to encode the semantic relationships in text. Alternatively, using a formal specification language for meaning representation is more precise for specifying programs and widely supported by automatic theorem provers, while deductive verification based on higher order logic is less common for mainstream programming languages. This paper addresses the research question of translating higher-order logic meaning representations generated from method-level code comments into a formal specification language that extends first-order logic. Doing so requires resolving possible ambiguities in determining the appropriate semantics for predicates. This is an open challenge in the path toward using natural language processing with formal methods. To address this, the paper proposes an approach and constructs a compiler for translating meaning representations, generated from Java programs with method-level comments, into Java Modelling Language. We evaluate the compiler on a set of representative benchmarks, including programs and specifications from the Java API, by generating Java Modelling Language specifications and statically checking them with a theorem prover. Results show that in 94% of the cases Java Modelling Language is accurately generated and in 97% of those cases it can be automatically checked with a state-of-the-art theorem prover.

Correlation between Oxidative Stress Markers and Periodontal Disease in Dogs.

In dogs, periodontal disease (PD) is a highly common condition whose prevalence rises significantly with age. Studies on rats with experimentally induced PD and human clinical trials have found an association between oxidative stress (OS) and PD, as has been observed in many other inflammatory disorders. The objective of this research was to assess the main indicators of oxidative stress detected in the saliva of dogs and to compare them to the degree of inflammation and tartar amount on their teeth and gums. By identifying these correlations, we intended to identify early-stage PD markers that would allow us to diagnose the condition in dogs without requiring for invasive or traumatic procedures. The antioxidant enzyme superoxide dismutase (SOD), total antioxidant capacity (TAC), malondialdehyde (MDA) and 8-hydroxyguanosine (8OHG), matrix metalloproteinase-8 (MMP8), and the quantity of total proteins are the markers that have been investigated in order to accomplish these objectives. The appearance of specific forms of periodontal disease has been confirmed by an increase in the saliva concentration of MMP8, a marker commonly used in the diagnosis of these pathologies in humans. The study was carried out on 22 dogs divided in a control group (C) and study groups (S), the second one being divided into three subgroups according to the severity of the gum inflammation and the presence or absence of tartar: S1-small accumulation of tartar, lack of infection-related signs; S2-moderate accumulation of tartar, gums swollen, red, and bled easily; S3-significant accumulation of tartar; gums bright red and bleeding; marked pain and halitosis. A correlation between the concentration of the tested parameters and the severity of the local appearance of the gum and teeth was noted. In comparison to the results of individuals from the control group, MMP8 reported increases of 1.2, 2, and 3.7 times, respectively, in the case of individuals from subgroups S1, S2, and S3. Oxidative stress is caused by inflammatory processes in the oral cavity. The presence of oxidative stress was demonstrated by the evaluation of different indicators in the saliva: an increase in the general antioxidant status, which was associated with an increase in SOD activity; intensification of the lipid peroxidation processes, as demonstrated by the accumulation of the MDA peroxidation product; and an increase in the DNA oxidation processes, as demonstrated by the accumulation of the oxidation product 8OHdG. In consequence, it was observed that there were up to 2-fold increases in protein levels, total antioxidant activity, SOD, and MDA, and up to 8.6-fold increases in the case of 8-OHdG when comparing dogs from the S3 group (significant tartar accumulation; bright red and bleeding gums; marked pain and halitosis) to those in the control group.

Coping with personhood limbo: Personhood anchoring work among undocumented workers in Italy

Prevailing socio-legal structures create a state of personhood limbo for undocumented workers, where broader society undermines various aspects of their personhood in a way that prevents them from fully representing and embracing all dimensions of their selves in and around the workplace. But how do undocumented workers cope with personhood limbo? Drawing on interviews with undocumented workers and civil society workers in Italy, we identify specific forms of what we call “personhood anchoring work” that undocumented workers engage in to claim aspects of personhood that are meaningful to them. Our theorization suggests that workers’ experiences of personhood are influenced not only by socio-legal structures, but also by their own agentic acts in response to external conditions, as well as their aspirations, past experiences, and future plans. A key finding of our study is that these practices do not aim to create or disrupt social orders, even in subtle or hidden forms of resistance. Instead, they enable undocumented workers to temporarily position themselves within the social order. In doing so, we also introduce a new way of conceptualizing the integration of undocumented workers that can account for the possibilities and limits of retaining rather than redefining personhood in the face of prevailing constraints.

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

Investigating the damage to masonry buildings during shield tunneling: A case study in Hohhot Metro

After prolonged service, masonry buildings constructed in the 1970s and 1980s often experienced significant deterioration in strength and stiffness. Excessive ground deformation caused by shield tunneling frequently leads to cracking in adjacent masonry buildings. Most studies have focused on the settlement and tilting caused by shield tunneling on masonry structures, failing to reflect the specific damages caused by shield tunneling. This paper employs a jointed masonry model in numerical simulations to simulate masonry buildings, considering the actual measurement data from the Hohhot Metro Line 1 project. It discusses a thorough investigation into the potential damages and cracking on masonry buildings that may occur during shield tunneling in masonry structures. It provides a detailed study of the specific forms of cracking in masonry buildings. Then, a comprehensive and in-depth analysis is conducted on the impact of bricklaying methods, mortar adhesive strength, and synchronous grouting pressure on buildings in the context of shield tunneling through structures. The results show that using the jointed masonry model can effectively reflect the cracking and damage situation of masonry structures during shield tunneling construction, providing valuable guidance for actual shield tunneling operations. The area of cracking damage caused by shield tunneling construction gradually increases from the top to the bottom of the building. It is concentrated above the doors and windows of the structure. Potential cracks around door and window openings extend from the upper-middle part of the openings towards both sides, forming a regular figure-eight distribution. With changes in the masonry construction method, grouting pressure, and mortar bond strength, the settlement and damage levels of the building also undergo alterations. Specifically, as the mortar bond strength increases from 30 kPa to 50 kPa, the maximum tensile strain of the masonry construction decreases to 0.11 %. Simultaneously, when the construction method of the building bricks transitions from A to B, the damage at the door and window openings on the west wall of the building gradually intensifies, resulting in a rise in the maximum tensile strain to 0.109 %. Furthermore, as the grouting pressure increases from 50 kPa to 300 kPa, the maximum settlement of the building walls decreases by 51 %, and the maximum tilt reduces by 48 %. However, this study did not account for the lintels and sills in the masonry building, which may lead to an overestimation of simulated strain data.

The relationship between psychopathy facets and types of criminal offences

AbstractThe Psychopathy Checklist—Revised (PCL‐R) is a widely used measurement of psychopathy comprising interpersonal, affective, lifestyle, and antisocial facets representing narrow‐band characteristics associated with the construct. However, there is little research on whether the distinct facets of psychopathy show specialised relationships with different types of criminal offences. The PCL‐R was administered to 138 adults incarcerated at a county jail. Arrests were coded into broad categories (i.e., crimes against persons, property or society), as well as for specific offence types (e.g. burglary, assault) using US crime definitions. Zero‐order correlations and zero‐inflated regression models showed significant relationships between: the lifestyle facet and property and societal crimes, mainly drug offences; the affective facet and crimes against persons; and the antisocial facet with all three major crime categories, as well as burglary, motor vehicle theft, robbery and assault. Overall, these results indicate that the PCL‐R facets offer unique information about specific forms of criminal behaviour.

Where Two Are Fighting, the Third One Does What? Digitalisation and Its Effects on Social Entrepreneurship’s Social-Economic Hybridity

Simultaneously pursuing social and economic value, social entrepreneurship (SE) builds on hybrid organisations that need to align competing institutional logics. But what influences this alignment? Despite digitalisation’s growing importance, there is no systematic analysis of how digitalisation influences SE’s hybridity, that is, its social and economic logic and their compatibility. To address this gap, we first operationalise SE’s digital orientation as a matter of digital depth and digital breadth. We then briefly illustrate the positive and negative effects of digitalisation on social and economic value creation. Our findings then identify nine specific forms of how digitisation can affect SE’s logic compatibility in positive, negative, and neutral ways. For future research, our discussion presents initial suggestions for measuring SE’s digital orientation, provides a Weberian ideal type definition of digital social entrepreneurship, and invites investigating the institutional implications of digitising SE.

A narrative review of research linking non-sexual social touch to sleep quality.

This narrative review describes the current state of the literature that has examined associations between non-sexual social touch (i.e., affectionate touch, touch therapies, touch with animals and inanimate objects that mimic social touch) and sleep quality. It also highlights areas for future research to clarify the links and to identify underlying mechanisms. Most existing studies have focussed on and shown positive effects of touch therapies (e.g., massage, therapeutic touch) on sleep quality in clinical populations. Although there are fewer studies examining how other forms of social touch are linked with sleep quality, the existing research provides preliminary evidence supporting affectionate touch (e.g., hugging, skin-to-skin contact) and tactile contact with animals (e.g., dogs) and objects that mimic social touch (e.g., robots, weighted blankets) as predictors of better sleep quality, while touch deprivation and touch aversion are associated with worse sleep quality. Informed by the existing literature, we additionally reviewed potential relational-cognitive (e.g., felt-security) and neurobiological (e.g., oxytocin) mechanisms likely to underlie associations between social touch and sleep quality. Overall, current research supports associations between non-sexual social touch and sleep quality. However, future research is needed to establish these links for specific forms of social touch (and in various populations), to test explanatory mechanisms, and to identify boundary conditions. Understanding associations between non-sexual social touch and sleep quality can inform the development of touch-based interventions to improve sleep quality and health.

An advanced gas leakage traceability & dispersion prediction methodology using unmanned aerial vehicle

Rapid prediction of gas leakage traceability & dispersion (GLTD) is critical for the emergency management of chemical industrial parks (CIPs). Existing GLTD prediction methods are limited by the spatial coverage and number of fixed gas sensors. It is also not possible to provide any specific information about the leak scene. Inspired by emerging drone technologies, this work proposed an advanced UAV-driven GLTD prediction methodology. The dispersion concentration field of the hazardous chemical leakage is simulated based on an improved Gaussian plume model, and then high precision and efficiency prediction is achieved by an improved sparrow search algorithm (ISSA), which can periodically update the flight strategy of UAVs. The proposed methodology is validated by a case study of a CIP in China. The results indicate that, compared with the traditional fixed sensors-driven strategy based on PSO, GA and SSA, the involvement of UAVs based on ISSA can reduce the prediction errors by 86.7%, 88.2% and 53.4%. Further analysis is conducted on the impact of algorithm parameters (UAV locations, number of UAVs, sampling frequency and step size) on search efficiency. The optimal algorithm parameters are discussed from the perspectives of cost effectiveness and computational efficiency. This work has the potentials to assist in constructing an intelligent emergency decision support system for CIPs.

Designing a structure-function alphabet of helix based on reduced amino acid clusters

Several simple secondary structures could form complex and diverse functional proteins, meaning that secondary structures may contain a lot of hidden information and are arranged according to certain principles, to carry enough information of functional specificity and diversity. However, these inner information and principles have not been understood systematically. In our study, we designed a structure-function alphabet of helix based on reduced amino acid clusters to describe the typical features of helices and delve into the information. Firstly, we selected 480 typical helices from membrane proteins, zymoproteins, transcription factors, and other proteins to define and calculate the interval range, and the helices are classified in terms of hydrophilicity, charge and length: (1) hydrophobic helix (≤43%), amphiphilic helix (43%∼71%), and hydrophilic helix (≥71%). (2) positive helix, negative helix, electrically neutral helix and uncharged helix. (3) short helix (≤8 aa), medium-length helix (9–28 aa), and long helix (≥29 aa). Then, we designed an alphabet containing 36 triplet codes according to the above classification, so that the main features of each helix can be represented by only three letters. This alphabet not only preliminarily defined the helix characteristics, but also greatly reduced the informational dimension of protein structure. Finally, we present an application example to demonstrate the value of the structure-function alphabet in protein functional determination and differentiation.

Amplification‐Free, Sequencing‐Free, Detection of Viral RNAs with Variant Specification by Discrete Nanocounting

AbstractThis study describes an amplification‐free, sequencing‐free platform (NanoPick‐array) for fast analysis of viral RNAs. The platform combines selective short‐cut of viral RNAs, cherry‐picking isolation of target genes, and micro‐arrayed discrete nanoimaging to enable the detection of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) at a concentration of 60 copies µL−1, a detection limit that is hardly achieved by Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)‐based methods without amplification, regardless of electrochemical or colorimetric approaches. Notably, the NanoPick‐array provides specific virus variant information with differentiation to single‐nucleotide genetic mutation. The avoidance of the amplification procedure gives direct quantification of viral copy number and reduces false positive results caused by amplicon contamination; the sequencing‐free viral variant specification significantly reduces the turn‐over time for the acquisition of a complete diagnostic viral picture within just 2 h. In a demonstration using clinical samples with a wide range of viral loads of cycle threshold (Ct) value ranging from 18 to 36, the technique achieves an overall accuracy of 89.7% viral detection and 100% accuracy for identifying all Delta variants. The viral detection accuracy is further tested to be 100% for the clinical samples with Ct values around or less than 28.

Contexts and Characteristics of Imaged-Based Sexual Exploitation and Abuse of Children: Incident Dynamics in a National Sample.

This paper describes incident characteristics and dynamics associated with six specific forms of image-based sexual exploitation and abuse of children (IBSEAC). Data were collected on a national sample of 2639 individuals aged 18-to-28 from a probability-based online panel. Respondents completed a self-administered survey questionnaire, providing detailed follow-up information on their experiences of image-based sexual victimization before the age of 18. A total of 607 incidents of IBSEAC were included in the analyses. Findings show substantial diversity in incident characteristics within and across the six forms of IBSEAC (nonconsensual sharing of sexual images, nonconsensual taking or making of images, forced image recruitment, threatened sharing, voluntary image sharing with an older adult, and commercial sexual exploitation involving images). Some notable patterns include frequent involvement of perpetrators who are other youth or young adults, who are known in-person to the victim, and who are intimate partners. The diversity and complexity of dynamics revealed in this study underscores the need for careful design and evaluation of prevention programs and the core messages directed at youth.

Understanding key factors in automated vehicle collisions: Automating data extraction and analyzing key insights using explainable AI

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions.

Application of Hydrogen Fuel Cell in Marine Power

The current development of hydrogen fuel cells as a ship power source is reviewed, and the characteristics of hydrogen fuel cell powered ships are summarized, as well as the development and research status of relevant technologies, including standardized specification of hydrogen fuel cell power, the safety of hydrogen system and production and storage of hydrogen. Based on the current research in various countries, the challenges faced by the development of hydrogen fuel cell-powered ships are put forward. According to the research survey, the number of hydrogen fuel-powered ships in the world is limited, and most of them are small passenger ships in inland rivers and lakes. The formulation of relevant standards and specifications for hydrogen fuel cell-powered ships is still in development. Hydrogen fuel cell is the safest and most environmentally friendly way to apply hydrogen fuel to ship power. Hydrogen fuel storage can be optimally installed on a ship using a combination of different methods.

MSIE-Net: Associative Entity-Based Multi-Stage Network for Structured Information Extraction from Reports

Efficient document recognition and sharing remain challenges in the healthcare, insurance, and finance sectors. One solution to this problem has been the use of deep learning techniques to automatically extract structured information from paper documents. Specifically, the structured extraction of a medical examination report (MER) can enhance medical efficiency, data analysis, and scientific research. While current methods focus on reconstructing table bodies, they often overlook table headers, leading to incomplete information extraction. This paper proposes MSIE-Net (multi-stage-structured information extraction network), a novel structured information extraction method, leveraging refined attention transformers and associated entity detection to enhance comprehensive MER information retrieval. MSIE-Net includes three stages. First, the RVI-LayoutXLM (refined visual-feature independent LayoutXLM) targets key information extraction. In this stage, the refined attention accentuates the interaction between different modalities by adjusting the attention score at the current position using previous position information. This design enables the RVI-LayoutXLM to learn more specific contextual information to improve extraction performance. Next, the associated entity detection module, RIFD-Net (relevant intra-layer fine-tuned detection network), identifies each test item’s location within the MER table body. Significantly, the backbone of RIFD-Net incorporates the intra-layer feature adjustment module (IFAM) to extract global features while homing in on local areas, proving especially sensitive for inspection tasks with dense and long bins. Finally, structured post-processing based on coordinate aggregation links the outputs from the prior stages. For the evaluation, we constructed the Chinese medical examination report dataset (CMERD), based on real medical scenarios. MSIE-Net demonstrated competitive performance in tasks involving key information extraction and associated entity detection. Experimental results validate MSIE-Net’s capability to successfully detect key entities in MER and table images with various complex layouts, perform entity relation extraction, and generate structured labels, laying the groundwork for intelligent medical documentation.

Case Report: Comprehensive management of Bulbar Motor Neuron disease in an elderly male: A Multidisciplinary case report approaching the treatment

Abstract* Bulbar Motor Neuron Disease (MND), a specific form of amyotrophic lateral sclerosis (ALS) primarily affecting the upper and lower motor neurons in the brainstem’s bulbar region, presents a significant challenge in clinical management. This case report delves into the clinical presentation and management of an 85-year-old male patient with typical symptoms, such as left-sided weakness, dysphagia, and dysarthria, along with a history of aphasia and hypertension, underlining the complexities associated with diagnosing and managing this condition. Methods: The patient underwent a thorough clinical examination, diagnostic investigations, and imaging, leading to a confirmed diagnosis of bulbar MND. Multidisciplinary interventions were employed to addressed the diverse symptoms, including compensation therapy, swallowing function training, physiotherapy, bolus modification, behavioral adjustments, oromotor exercises, postural corrections, and sensory and neurophysiologic stimulation to enhance swallowing safety and comfort. Additionally, interventions for dysarthria and left-sided weakness encompassed speech rate control, strength training, and the use of augmentative communication aids. Results: Over a 30-day follow-up period, the patient showed improvements in dysphagia, dysarthria, and activities of daily living. The treatment strategies effectively addressed the specific challenges associated with bulbar MND, focusing on enhancing the patient’s quality of life and functional abilities. Conclusions: This case report underscores the need for a comprehensive, multidisciplinary approach to manage bulbar MND. It highlights the complexities of diagnosing and addressing the diverse symptoms and difficulties that patients with this condition encounter, emphasizing the importance of a holistic and individualized therapeutic strategy.

Ritual networks and the structure of moral communities in Classic Maya society

Ritual plays an important integrative function in the creation, maintenance, and transformation of human society. The shared experience of ritual establishes strong bonds between individuals that defines their membership in certain social groups. However, rituals are not timeless traditions, nor do they simply restore social equilibrium. Rather, rituals are active and ongoing social processes that unite and divide across multiple social categories. This paper applies archaeological network methods to analyze the multiscalar structure of ritual traditions across Classic Maya (ca. 300–900 CE) society using hieroglyphic inscriptions from dated and provenienced monuments cataloged in the Maya Hieroglyphic Database (Looper and Macri 1991–2024). For the Classic Maya, public ritual and performance were highly charged political events where meaning and power could be negotiated, creating opportunities for identity formation and community integration. Such contexts helped establish strong moral bonds in Classic Maya society. However, we know relatively little about the specific forms and content of these ritual practices. In this study we construct ritual similarity networks from hieroglyphic inscriptions to analyze the structure and organization of these moral communities as well as the ritual relations that bound them together.

Waiting as sociality – relational waithood in Norwegian activation

ABSTRACT In research on waiting and unemployment, waiting tends to be described as an asymmetrical power relation between those who wait and those who impose waiting within institutional contexts. This paper applies the concepts ‘waiting for’ and ‘waiting on’ to unravel waiting as a specific form of sociality and presence in an employment support context in Norway. Drawing on qualitative interviews with frontline staff and young participants in in-house activity centres, we find that waiting takes different forms; the counsellors not only wait for the participants to comply with the activity requirements, they also wait on them, to interact. Similarly, the young participants wait for ‘something to happen’, like a job opportunity, but they also wait on staff in the centres. Hence, both actors experience waiting in relation to rules, employment etc., but also as subjects with agency. We argue that activation can involve waiting as a form of sociality and presence, a dimension that receives scant attention in the literature on waiting and power.

Preparation and characterisation of baicalin magnesium and its protective effect in ulcerative colitis via gut microbiota-bile acid axis modulation

BackgroundScutellaria baicalensis Georgi is a well-known herb in traditional Chinese medicine that is frequently prescribed for various gastrointestinal conditions, including ulcerative colitis (UC). Its primary active constituent, baicalin, has poorly water solubility that reduces its efficacy. PurposeTo enhance the aqueous solubility of baicalin by optimising its extraction process. We compared the modulatory effects of isolated water-soluble baicalin and water-insoluble baicalin on UC, and delved deeper into the potential mechanisms of water-soluble baicalin. MethodsWe successfully extracted a more hydrophilic baicalin directly from an aqueous S. baicalensis Georgi extract through the process of recrystallisation following alcoholic precipitation of the aqueous extract obtained from S. baicalensis Georgi, eliminating the need for acid additives. This specific form of baicalin was conclusively identified by UV, IR, atomic absorption spectroscopy, elemental analysis, 1H NMR, 13C NMR, and ESI-HRMS. We subsequently compared the regulatory effects of baicalin on UC before and after optimisation, employing 16S rDNA sequencing, bile acid-targeted metabolomics, and transcriptome analysis to elucidate the potential mechanism of water-soluble baicalin; and the key genes and proteins implicated in this mechanism were verified through RT-PCR and western blotting. ResultsA new form of baicalin present in the aqueous solution of S. baicalensis Georgi was isolated, and its structural characterisation showed that it was bound to magnesium ions (baicalin magnesium) and exhibited favorable water solubility. Baicalin magnesium offers enhanced therapeutic benefits over baicalin for UC treatment, which alleviated the inflammatory response and oxidative stress levels while improving intestinal mucosal damage. Further investigation of the mechanism revealed that baicalin magnesium could effectively regulate bile acid metabolism and maintain intestinal microecological balance in UC mice, and suppress the activation of the nuclear factor-kappa B and peroxisome proliferator-activated receptor α signalling pathways, thereby playing a therapeutic role. ConclusionsBaicalin magnesium has good water solubility, which solves the bottleneck problem of water insolubility in the practical applications of baicalin. Moreover, baicalin magnesium exhibits therapeutic potential for UC significantly better than baicalin.