The use of digital medical imaging has increased significantly in healthcare. Hospitals produce large amount of medical images. These medical images include imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and X-ray. Also. these medical images are stored in hospital databases, transmitted across healthcare net works, and shared through telemedicine platforms. Although, they provide accessibility and efficiency, they still raise the con cern of security and privacy. Because medical images frequently contain sensitive patient data, they are susceptible to malicious manipulation, unauthorized distribution, and privacy violations. Medical images are frequently protected during transmission and storage using traditional security techniques like encryption. Encryption can prevent unauthorized access because the data remains encrypted. However, once the image is decrypted for clinical analysis, images may be modified and used illegally. This limitation makes use of encryption alone insufficient for long term protection of medical images. Digital watermarking offers an additional layer of protection by embedding hidden infor mation directly into the image itself. The embedded watermark remains within the image even after decryption. Which provides authentication, ownership verification, and integrity checking. Over the last two decades, many watermarking techniques have been proposed for medical images. Although these techniques have shown promising results, several challenges remain. They are limited in robustness, lack of adaptability, and sensitivity to image processing operations. This paper reviews existing medical image watermarking techniques and discusses their strengths and limitations. The survey focuses on spatial-domain and transform domain approaches, more recent developments in watermarking methods. Also, it describes evaluation metrics, and open research challenges. The goal is to provide a clear overview of the current state of research and highlight possible future directions for secure medical image management.

The rapid expansion of rehabilitation needs in China has intensified pressure on a workforce that remains unevenly distributed. Digital health technologies (DHTs) offer potential to increase service reach and efficiency. However, little is known about how rehabilitation professionals currently gather and document clinical information, nor about their readiness to integrate digital tools into routine practice within China's rapidly digitalizing health system. This study aimed to describe how rehabilitation professionals in China collect subjective and objective clinical information, document patient data in routine practice, and assess their willingness to use DHTs in clinical settings. We conducted a multi-province observational cross-sectional survey using a culturally adapted questionnaire based on the World Health Organization Digital Health Interventions framework. The instrument assessed participant characteristics, information collection methods, documentation practices, and willingness to adopt digital functions across rehabilitation activities. Descriptive analyses and subgroup comparisons were performed on 324 complete responses from certified rehabilitation professionals. The multi-province cross-sectional online survey was conducted among licensed rehabilitation professionals in China with internet access. Participants were recruited through professional networks and social media platforms. Respondents were drawn from 20 provincial-level administrative regions across China, including Fujian (n=72), Guangdong (n=77), and Shanxi (n=45), among others, with 82.7% (268/324) employed in public sector rehabilitation services. Traditional methods dominated clinical work. Face-to-face communication was used frequently for subjective assessment by 96.3% (312/324) of respondents, whereas digital channels such as email (22/324, 6.8%) and telephone (47/324, 14.5%) saw limited use. For objective information, visual observation (271/324, 83.7%) and manual measurement tools (195/324, 60.2%) remained the primary approaches, while motion capture technology (45/324, 13.8%) and wearable sensors (13/324, 4%) were rarely used. Documentation practices also relied heavily on analogue formats, with 82.1% (266/324) using handwritten notes and 60.2% (195/324) using paper templates. In contrast, willingness to adopt DHTs was consistently high, with 80.6% (261/324) of respondents indicating readiness to use digital systems for identity verification, 79.0% (256/324) for progress tracking, and 78.1% (253/324) for outcome measurement. Subgroup analyses revealed that educational level significantly influenced the adoption of advanced technologies, with master's or doctoral degree holders reporting higher use of sensor-based assessment, motion capture, and wearable devices. In contrast, professional title and clinical specialty showed limited influence, with no significant differences observed for most digital health functions. Rehabilitation professionals in China demonstrate strong readiness to use DHTs, yet their routine practice remains largely paper-based and analogue. These findings provide evidence to inform implementation strategies, workforce training, and system-level planning aimed at accelerating digital transformation in rehabilitation services.

With the escalating need to derive and evaluate subjective information from social media platforms such as Twitter, where millions of Twitter users update their tweets in the form of textual data every day, the application of Sentiment Analysis becomes even more relevant. This paper proposes an integrated system by integrating the applications of NLP and ML techniques to classify and derive the sentiment and psychological indicators of users instantaneously and with higher accuracy and explainability. For the instant derivation of sentiment polarity and psychological indicators with better accuracy, the proposed system uses TF-IDF feature extraction with Logistic Regression classification. In addition, it uses VADER sentiment analyzer that gives an instant rule-based classification of emotional tone of given text. The proposed system includes a number of preprocessing stages of data such as microtext normalization, tokenization, removal of stopwords and lemmatization. The proposed system is implemented and the results indicate that the proposed lightweight and interpretable approach obtains performance close to the state-of-the-art while maintaining the lowest computational cost. The proposed system is showcased via the development of a Streamlit-based web application to make the system user-friendly for researchers, practitioners as well as for the public. The derived system can instantaneously classify any text to positive, negative, neutral sentiments and also derive the psychological indicators of users related to mental health such as anxiety, sadness and also depression. Hence, the proposed system bridges the existing gap between research prototypes and practical applications with respect to social media sentiment analysis.

Vegetation plays a vital role in regulating air quality and mitigating climate change by converting carbon dioxide into oxygen. However, ongoing human activity continues to degrade vegetation ecosystems, necessitating scalable and accurate monitoring methods. Traditional field-based statistical approaches are often costly and inefficient. This study proposes a deep learning model, dilated residual U-Net, for semantic segmentation of vegetation from drone-acquired aerial imagery. The model incorporates residual connections to reduce infor mation loss and dilated convolutions to enhance receptive field coverage with out increasing computational cost. Experiments conducted on the DroneDeploy Segmentation dataset demonstrate that the proposed model achieves a Dice co efficient of 0.4451 with an inference speed of 0.0675 seconds per image, outper forming baseline U-Net and Residual U-Net models. These results highlight the potential of lightweight, CNN-based architectures for environmental monitoring in resource-constrained settings.

Most of the existing evaluation systems for hydrocarbon-bearing play are using various evaluation indicators and fixed weights, which are not sensitive to the subjective/objective cognition or the exploration stages. We construct a multi-level and multi-type play evaluation criteria system with unified standards, the subjective uncertainty of which is formulated by the fuzziness of the indicators. Then, a full-stage dynamic fuzzy multi-criteria decision-making (MCDM) method is presented for play evaluation, in which a dynamic fuzzy-game model is built to combine the objective Criteria Importance Through Intercriteria Correlation (CRITIC) weights improved by the Theil index and the subjective Analytic Hierarchy Process (AHP) weights. This approach can simulate hesitation and strategic trade-offs in the human mind to balance the subjective and objective information. Thereafter, a stage-aware model is developed for play assessment by using dynamic fuzzy comprehensive evaluation, covering the regional exploration, pre-exploration, and evaluation stages. Using the data from plays at different exploration stages in the Tarim Basin, empirical application shows that the evaluation results are consistent with actual exploration judgment. Sensitivity analysis and comparative experiments verify the rationality of parameter setting and the effectiveness and reliability of the presented method. This study offers a practical MCDM for optimizing plays and guiding exploration decisions, which overcomes the limitations of traditional methods, including the lack of a unified evaluation framework, insufficient utilization and integration of multi-source information, inadequate characterization of phased priorities, and limited representation of fuzziness in evaluation indicators.

Doppler ultrasound measurements have been recorded since the 1970s across the world and provide a valuable data resource for learning, analysis, and potential training of deep learning algorithms to recognise and grade venous gas emboli (VGE) allowing assessment of decompression sickness (DCS) risk. We collected a 'big database' of Doppler recordings and associated metadata. Audio tapes with recorded Doppler data were converted to digital files, then cut into individual recordings and matched with their metadata, including subject and pressure profile information. The audio signals and their Doppler grades were then processed further for suitability to train an algorithm to identify VGE. A total of 10,099 Doppler ultrasound recordings were compiled. Divers (n = ≤ 311; 170 identified, ≤ 141 unidentified) were male, with a median age of 31.5 years among the 170 identified divers. The maximum depth of the dives included ranged from 24 m (80 feet) to 91.4 m (300 feet). The timing of the Doppler measurements ranged from two minutes post-dive to 594 min post-dive, with a median time of 52 min. Breathing gases included air, nitrox, and heliox. DCS was noted in only 12 individuals. The dataset centred around lower VGE loads (Spencer Grades 0, I, and II). This database represents a landmark in DCS investigation as the audio dataset and metadata collected have been released under a public domain license for further use. The large number of data points has also allowed the development of a deep learning algorithm that can grade bubble loads without a human operator.

Post-treatment has been regarded as an important strategy in thin-film fabrication, which surmounts the limitations in directly deposited films through manipulating the chemical, electrical, morphological, and defect properties. In terms of the emerging photovoltaic material antimony selenosulfide (Sb2(S,Se)3), conventional hydrothermal synthesis of Sb2(S,Se)3 thin film has achieved great improvement toward 10% efficiency bottleneck in solar cell applications. However, this fabrication method fails to achieve desirable carrier transport and defective properties. In this study, we develop an InCl3-based post-treatment method to enhance the carrier transport and passivate the deep-level defects, including S and Se vacancies and anti-site defects (SbS(e)3). We find that the indium ions generated by post-treatment preferentially diffuse into the interstitial sites of (Sb4S(e)6)n ribbons, leading to the formation of In-S(e) chemical bonds. These atomic interactions facilitate efficient carrier transport across the entire film. Furthermore, the synergistic effects of energy level alignment optimization, deep-level defect passivation, and interfacial trap inhibition effectively suppress non-radiative carrier recombination and improve photovoltaic performance. Consequently, this post-treatment enables the Sb2(S,Se)3 solar cell to achieve a remarkable power conversion efficiency of 10.55%. This study provides a novel post-treatment method for defect passivation, electronic structure regulation, and carrier transport management for high-performance antimony selenosulfide solar cells.

This paper explores emerging trends in the teaching of the subject Information Society, highlighting how rapid technological developments and societal shifts are reshaping educational approaches. The study examines key pedagogical innovations, including the integration of artificial intelligence (AI), blended and hybrid learning environments, mobile and immersive technologies, and an increased emphasis on digital, media, and information literacy. It also addresses the growing importance of teaching ethical, civic, and critical perspectives on digital life, data use, and online communication. These trends reflect a broader shift from traditional, skills-based instruction toward learner-centered, interdisciplinary models that prepare students for active participation in a digitally connected world. The paper concludes by identifying the challenges educators face—such as digital equity, teacher training, and curriculum relevance—and offers recommendations for adapting teaching practices to meet the needs of the 21st-century information society. When enrolling in a course, it is essential to address learners’ initial motivation, the evaluation and classification of individual modules, and the process of familiarization with the curriculum. For teachers to guide students in using the internet as part of the learning process, they must first develop strong ICT and digital literacy competencies. This includes acquiring the ability to understand and explain fundamental concepts related to ICT and digital technologies, use computers and digital devices effectively, work with datasets and textual materials, create and interpret tables, charts, and figures, manage databases, develop presentations, and obtain, share, and communicate information. Furthermore, teachers should be able to use the internet proficiently, manage email communication, and create digital content such as web pages, blogs, and vlogs.

ObjectiveThis study aimed to explore the risk of potential root and nerve damage in planning the insertion of paramedian miniscrews without radiological information.Study designThe study included CBCT and intra-oral scan (IOS) records of 60 subjects (28 males, 32 females) featuring normal transverse palate dimension (Group A = 30 subjects, mean age 20.5 ± 4.7) and skeletal maxillary constriction (Group B = 30 subjects, mean age 21.9 ± 5.1). Two miniscrews (Spider Screws Regular Plus Konic - HDC Srl, Vicenza, Italy), featuring 2 mm in diameter and 9 mm in length were virtually inserted using only the.stl IOS file following established clinical guidelines (T-Zone). Specific linear measurements were registered to calculate the distance between the miniscrews and both incisors’ roots and the naso-palatine duct after integrating CBCT images; same measurements were performed after adjusting the position with the aid of CBCT. All data were analyzed for comparison between both procedures and groups.ResultsSafe distances were recorded between miniscrews and incisors’ roots in both groups (p > 0.05). The distances from the nasopalatine duct were significantly closer (and in few cases risky) in group B and required significant adjustment using CBCT (p < 0.05).ConclusionThe present findings would discourage planning or inserting miniscrews in the paramedian region without radiological information (CBCT) in subjects with skeletal maxillary constricted.

ABSTRACT We examine the effect of managerial overoptimism on discretionary disclosure of subjective information, such as earnings forecasts. The market applies a discount upon disclosure to capture the possibility that the revealed subjective expectation is too optimistic. While this discount is correct on average, it is too high (low) for a truly objective (overoptimistic) manager. Consequently, overoptimistic managers disclose more frequently, and their firms are overvalued. We show that higher levels of overoptimism or a greater fraction of overoptimistic managers amplify the market discount, which ultimately reduces overall disclosure in equilibrium.

This study conducts a comprehensive comparative analysis of past tense verb morphology between Arabic, a primary representative of the Semitic inflectional (fusional) language family, and Vietnamese, a quintessential isolating (analytic) language of the Austroasiatic family. By examining the complete paradigm of twelve grammatical persons in the past tense (Al Fil Al Maadi), the paper elucidates the complex mechanism of integrating subject information (person, number, and gender) into verbal suffixes in Arabic. This is contrasted with the Vietnamese analytic mechanism, which relies heavily on independent function words and a sophisticated system of personal pronouns and honorifics. The results indicate that while Arabic achieves high communicative efficiency and"morphological economy"through inflection,Vietnamese prioritizes "lexical transparency" and socio pragmatic flexibility. This research provides a systematic framework to assist learners and educators in effectively transitioning their cognitive approach from lexico syntactic structures to morpho-grammatical constructions.

The purpose of this study is to report on the development of an artificial intelligence (AI) model designed to improve compliance in periodontal maintenance patients. Fifty adult patients (32 females and 18 males; aged 25 to 78 years) with diagnosis and treatment of Generalized or Localized Periodontitis stages I, II, or III; grades A-C who were following a 3-month periodontal maintenance interval were provided an AI-based smartphone application (app) that provided personalized educational, motivational, and risk-awareness information every week between maintenance visits. Recession (REC), probing pocket depth (PPD), clinical attachment level (CAL), plaque index (PI), bleeding on probing (BOP), and gingival index (GI) captured at each maintenance visit out to 6 months were compared with baseline. In-office interviews were conducted to assess subjective data points, and cognitive interviews were conducted to validate patient-reported outcomes (PRO). REC, PPD, and CAL remained stable through all timepoints. PI, BOP, and GI demonstrated significant improvement compared with baseline (p<0.01). PRO were validated and revealed that most subjects found the app to be easy to use, desired more interaction, perceived it as educational by raising their awareness of their oral hygiene, and would recommend the use of the app to a friend. An AI-based smartphone application was trained on a private practice maintenance population of three periodontists. The subjects demonstrated improvements in PI, BOP, and GI with no changes in REC, PPD, or CAL. Patients found the app to be educational and beneficial by improving their understanding of their periodontal disease risk. An artificial intelligence model (AI) was developed to improve periodontal maintenance compliance. Compliance with periodontal maintenance in patients decreases dramatically after 1 year. This is a proof-of-concept study using an AI-based smartphone application (app) that communicates individualized periodontal disease risk awareness messages that were educational and motivational to effect a behavioral change in hopes of improving compliance. Measurements of disease stability/instability, such as gum inflammation, bleeding, bone loss, and assessment of brushing effectiveness, were captured at baseline and at subsequent office visits every 3 months for a total of 6 months. Subjective patient information was captured via in-office interviews to supplement clinical data and provide the AI model with multimodal data points. Patient-reported outcome surveys were developed via cognitive interviews. Results showed improvements in brushing effectiveness, which translated to less gum bleeding and inflammation. Other clinical measurements of disease activity showed no change, which could translate to a lack of disease progression. Patient-reported outcome development revealed that most of the subjects found the interaction with the app to be positive and beneficial. This proof-of-concept study demonstrates a novel approach using an AI-based smartphone app to be worthy of further investigation on efficacy and possibly defines an objective measure of patient compliance.

This research aimed: (1) to study the current and the desirable states of teachers’ competencies in classroom management in schools under the Pathum Thani Primary Educational Service Area Office, and (2) to explore strategies for developing teachers’ competencies in classroom management in these schools. The sample consisted of 354 teachers in schools under the Pathum Thani Primary Educational Service Area Office in the academic year 2022, selected using multistage random sampling. The research instrument was a questionnaire with an overall reliability coefficient of 0.995. The data were analyzed using frequency, percentage, mean, standard deviation, and the Priority Needs Index (PNI). The results revealed that: (1) the current state of teachers’ classroom management competencies was at a high level, while the desirable state was at the highest level. The analysis of priority needs indicated that the highest need was for creating a learning atmosphere that promotes students’ happiness, safety, and engagement, followed by maintaining classroom and subject information and documents. The lowest level of need was found in promoting positive teacher–student behavior. (2) The strategies for developing teachers’ classroom management competencies comprised four main strategies and eleven sub-strategies.

ObjectiveTo explore the incidence density and risk of hypertension among employees in different workshops of a coking plant.MethodThe research subjects were employees of a coking plant in western Inner Mongolia. Based on inclusion and exclusion criteria, we studied 448 employees hired between 2011 and 2023. Of these, 285 were front-line workers exposed to occupational hazards, while 163 were second-line workers with lesser or no exposure. We collected data on the general demographic characteristics, systolic blood pressure, diastolic blood pressure, and other information of the research subjects, calculated the incidence density of hypertension, and used the Cox proportional hazards regression model to analyze the association between exposure to occupational risk factors and the risk of hypertension.ResultsDuring the observation period, we identified 229 cases of hypertension, with an incidence density of 11,187.10 per 100,000 person-years. The average age of the patients was 42.33 ± 8.24 years, and the average working duration was 3.57 ± 2.84 years. The number of new cases in logistics, coal preparation, chemical production, and coking workshops were 53, 45, 47, and 84, respectively, with incidence densities of 7,019.83, 14,469.45, 12,737.13, and 13,725.49 per 100,000 person-years, respectively. The risk of hypertension in coal preparation, chemical production, and coking workshops was 4.061, 3.364, and 2.427 times higher than that in logistics workers, respectively. Cox regression analysis showed that, after controlling for gender, age, smoking, drinking, and other factors, the risk ranking of hypertension in each workshop was as follows: coking workshop (1.822) > chemical production workshop (1.752) > coal preparation workshop (1.622). The log-rank test revealed that the differences in disease-free survival distribution among workers in different workshops were statistically significant (p < 0.05).ConclusionThe study shows that the incidence density of hypertension among employees in the chemical industry, coking, and coal preparation workshops after joining the company is significantly higher than that in the logistics group, and the risk of incidence is relatively higher (HR = 1.622–1.822 after controlling for confounding factors). Long-term exposure of coking plant workers to related environments increases the risk of hypertension.

STARD10 regulates human pancreatic β cell differentiation and triglyceride metabolism.

Longitudinal epidemiological studies often face challenges with incomplete follow-up and missing data, which can bias results and reduce statistical power. Conventional imputation methods may not adequately capture the complex patterns and dependencies in such multivariate time series data. While more recently developed generative machine learning models offer improved solutions, few methods are available which can handle inconsistently spaced intervals between measurements across long time periods and completely missing time steps, characteristics which are common in real-world studies evaluating long-term health outcomes. This paper introduces a variational autoencoder-based generative neural network designed for imputing partially and fully missing information in irregular time series with extensive missingness. Our approach exploits both correlations between features at a single time step and trends of the same feature over time to reconstruct missing values. Experiments on synthetic data designed to resemble the characteristics of longitudinal epidemiological studies and a case study on a real-world dataset demonstrate the effectiveness of our approach. We show superior performance and parameter stability across varying degrees of missingness and missingness patterns compared to prior work.

AI or human: How the type of information to be disclosed alters customer service agent preferences

In the context of a low-carbon economy, automotive parts supply chains face multifaceted risks, making an effective supply chain risk assessment model a crucial means of ensuring supply chain stability. Traditional evaluation methods struggle to comprehensively and accurately identify all influencing factors and their interrelationships in automotive parts supply chains. This article constructs an evaluation model based on the principle of symmetry. The “structural symmetry” is determined by the ratio of the completeness of risk dimension coverage in the indicator system to the precision of indicators, while “fusion symmetry” refers to the degree of equilibrium in information contribution during the fusion of subjective and objective weights. First, Fault Tree Analysis (FTA) and the Delphi method are adopted to establish a risk evaluation index system, whereby structural symmetry is ensured by the equilibrium between the completeness of risk dimension coverage and the accuracy of indicators in the index system. Second, drawing on the symmetric fusion principle, this study proposes a hybrid evaluation approach integrating hesitant fuzzy DEMATEL with entropy weight-coefficient of variation (HDEC), and the fusion symmetry is guaranteed by the balanced integration of subjective and objective weight information. Finally, a case study of an automotive parts supply chain enterprise quantitatively assesses and ranks risk factors, with corresponding countermeasures proposed. The symmetry-guided HDEC method achieves high accuracy, identifying indicator–causal relationships. Compared with the traditional entropy-weighted AHP algorithm, the Pearson correlation coefficient is 0.8566, and Spearman’s rank correlation coefficient is 0.88, indicating strong weight correlation and robust stability. The integration of mathematical symmetry enhances the model’s theoretical rigor, which aligns with symmetry-oriented optimization research.

Precision farming is an application that uses infor mation to maximize crop production and resource use. The paper states that we have JK soil analysis data and it has been trained on a machine learning model to give the prediction of the crops. Environmental factors (Rainfall, Temperature) were evaluated and soil properties (pH, N, P, K, Zn, Fe, Mn). In order to achieve a high data quality and reliability, sophisticated preprocessing methods were used. Z-score-based filtering was used to eliminate outliers and the Min-Max scaling was used to standardize the input space by normalizing numerical features. The Chi-Squared test was used to reduce the most significant 10 features to further do the prediction. The preprocessing pipeline was thorough and the noise and redundancy were reduced to provide a solid dataset to train the model. The neural network was created using two hidden layers (64 and 32 neurons) and attained 98.45 percent test accuracy. Other important contributions are strong outlier management, environmental data incorporation and scalability in newer datasets. The method provides a basis of real-time and scalable accurate farming frameworks, and possible utilizations of pest and fertilizer management.

Understanding the impact of sleep on cardiovascular risk estimation: comparison of LS7 and LE8 performances in a European population