Voltage control and efficient operation remain persistent challenges in modern power systems, particularly due to increasing variation and intermittency among consumers and producers. Despite their central role in supporting voltage stability and efficient power delivery, synchronous generators often have conservative reactive power limits and coarse power loss approximations in optimization tools, hindering optimal system performance. SynGenLib is a Python-based software library that provides detailed steady-state modeling and analysis of synchronous generator and transformer branches to address these limitations, explicitly capturing reactive power capability and power losses. The tool enables accurate evaluation of component selections and optimal operation strategies through detailed simulation and efficiency analysis. SynGenLib employs a single-phase RMS steady-state representation, leveraging Kirchhoff’s voltage and current laws to formulate generator-transformer circuits, solved analytically or via iterative methods as required. Its object-oriented design provides user-friendly data classes for generator and transformer specifications, supporting customizable saturation characteristics. The library calculates reactive power constraints and power losses for the generator stator, rotor, core, friction, and transformer. SynGenLib’s accuracy has been validated against PowerFactory, a commercial software, demonstrating calculation accuracy to the third decimal place and efficient computational performance (∼150 ms for 10,000 power loss points and 100 reactive capability points). SynGenLib was developed as part of the SysOpt research project, which supported industrial case studies in optimal reactive power dispatch and power plant efficiency analysis. It exposes its analytical equations for reactive capability and power loss calculations, enabling seamless integration with other tools and enhancing strategic decision-making for optimal power flow studies, component selection, and plant operations.

A document by Ken Park. Click on the document to view its contents.

AbstractWe propose a novel dual-stream neural architecture for secure end-to-end communication that integrates self-supervised adversarial detection with game-theoretic defense strategies. The increasing sophistication of adversarial attacks necessitates adaptive security mechanisms that can dynamically respond to evolving threats while maintaining computational efficiency. Our framework addresses this challenge by combining a transformer-based contrastive learning module for real-time perturbation detection with a zero-sum Markov game formulation to optimize defense policies under Nash equilibrium. The self-supervised stream extracts invariant features from communication signals and computes threat probabilities, while the game-theoretic stream dynamically adjusts encryption parameters and defense actions based on adversarial behavior. The two streams are jointly optimized to achieve provable robustness against both known and unknown attack patterns. Furthermore, the system interfaces seamlessly with conventional intrusion detection and encryption modules, replacing rule-based heuristics with data-driven adaptive strategies. Experimental validation demonstrates significant improvements in detection accuracy and resilience compared to existing methods. The key innovation lies in the unification of self-supervision and game theory within a single end-to-end trainable framework, enabling scalable and adaptive security for modern communication networks. This approach not only enhances real-time threat mitigation but also provides theoretical guarantees on defense optimality under adversarial dynamics.

Title PageTitle:

IntroductionTuberculosis (TB) remains a major global health concern, particularly in developing countries. A total of 1.25 million people died from TB in 2023. That same year, an estimated 10.8 million people developed TB worldwide, including 6.0 million men, 3.6 million women, and 1.3 million children. Tuberculous spondylitis (Pott disease) occurs in 1–5% of TB cases and is associated with high morbidity if diagnosis is delayed. Disseminated TB involving multiple organs, including the spine, is rare and serious. We present a case of disseminated TB with severe thoracic kyphosis, managed conservatively and surgically in a resource-limited setting.

In Chinese grammar error correction, the collaborative work of detection and correction is an important research direction. This paper proposes a two-stage model combining contrastive learning and sequence labeling based on the pre-trained model. This model consists of the detection stage and the correction stage. In the detection stage, the MacBERT model architecture is used. By introducing the improved BIO sequence annotation system and contrastive learning mechanism, the error positions are located and the error information is extracted. In the correction stage, the BART model is used to concatenate the error information labels output in the detection stage with the original text to generate a brand new input, and the generation ability of the model is utilized to correct the errors. To verify the performance of the model, a comparative experiment was conducted on the NLPCC2018 dataset, and F 0.5 and RECALL were superior to the baseline model.

A document by Kenneth Egwu. Click on the document to view its contents.

Background: Perineal lacerations and their repair can be associated with physical and psychological morbidity in the postpartum period with potential long- term consequences. However, there is still no shared consensus on the optimal management for 1st and 2nd degree perineal lacerations. Objective: To compare non-surgical versus surgical management of first and second-degree post-partum perineal lacerations. Search strategy: Medline, Cochrane Library, EMBASE, Pubmed Central, Scopus, ClinicalTrial.gov were electronically searched from their inception to December 2024. No restriction for geographic location or temporal limit was applied. Selection criteria: We included all RCTs comparing non-surgical versus surgical management of first and second-degree perineal post-partum lacerations. The primary outcome was Pain score assessed by VAS Scale 24 hours after delivery. Secondary outcomes were pain at 10 days and at 8 weeks; pain killer use at 10 days and 3 months; and dyspareunia at 3 months. Data Collection and Analysis: The summary measures were reported as relative risk (RR) or as mean difference (MD) with 95% of confidence interval (CI). P value <0.05 was considered statistically significant. Main Results: Five RCTs including 522 pregnancies were analyzed. Overall, there was high clinical heterogeneity among RCTs. Non-surgical management of first and second-degree perineal lacerations was associated with decreased VAS Scale score at 24 hours (1.9 vs 2.3, p value = 0.04) and reduced pain killer use at 24 hours (RR 0.71, 95% CI 0.52 to 0.98), compared to surgical management. Incidences of mid- and late- pain assessment and analgesic use, as well as of dyspareunia and wound healing were similar in the two groups. Conclusions: Non-surgical management of first and second-degree perineal lacerations is associated with decreased incidence of VAS pain scores and analgesic use at 24 hours, but similar dyspareunia and wound healing, compared to surgical management. Hence, non-surgical repair of 1 st and 2 nd degree perineal lacerations may be left optional to clinical judgement and patient’s choice.

Abstract Introduction: Information retrieval is essential for evidence synthesis in health and care research, but developing search strategies is labour-intensive and time-consuming. Automating these processes is highly desirable, though it’s unclear if Information Specialists (IS) are willing to adopt artificial intelligence (AI) methodologies or how they currently use them. In January 2025, the NIHR Innovation Observatory and NIHR Methodology Incubator co-sponsored the inaugural CORE Information Retrieval Forum, where attendees discussed AI’s role in information retrieval. Methods: The CORE Information Retrieval Forum hosted a Knowledge Café, titled ”Building Our Community and Expanding Our Horizons”. Participation was voluntary and attendees could choose one of six event themed discussion tables including AI. To support each discussion, a QR code linking to a virtual collaboration tool (Padlet; padlet.com) and a poster in the exhibition space were available throughout the day for attendees to contribute to. Results: The CORE Information Retrieval Forum was attended by 131 IS from nine different types of organisations and ten countries. Among the six discussion points available in the Knowledge Café, the AI table was the most popular, receiving the highest number of contributions (n=49). Following the Forum, contributions to the AI topic were categorized into four themes: Critical Perception (n=21), Current Uses (n=19), Training Wants/Needs (n=7), and Specific Tools (n=2). Conclusions: While there are critical perspectives on the integration of AI in the IS space, this is not due to a reluctance to adapt and adopt but from a need for structure, education, training, ethical guidance, and systems to support the responsible use and transparency of AI. There is significant interest in automating repetitive and time-consuming tasks, but a lack of tools currently used by attendees. More work needs to be done to identify currently available tools and their potential to complement the work conducted by IS.

Background: Patch testing is the gold standard for diagnosing contact allergies, but its diagnostic accuracy can be influenced by subjective visual assessment and variability in measurement. Advanced imaging technologies, such as 3D imaging, may provide more reliable and objective methods for evaluating allergic reactions. Objective: This study evaluated the accuracy of the Iko Derma Monitor 3D imaging system in comparison to a standard clinic iPad for patch test reaction evaluation and to assess its potential advantages in documenting and interpreting allergic reactions. Methods: Forty-two patients undergoing patch testing for suspected contact allergy were included in this study, resulting in 293 evaluated reactions. Reactions were imaged on day 2 and day 4 using the Iko Derma Monitor, an iPad, and a handheld dermatoscope. Measurements of lesion area were summarized and compared across imaging modalities, and statistical tests were conducted to assess changes over time and accuracy difference between Iko Derma Monitor and iPad. Results: The 3D imaging device demonstrated significantly higher accuracy than the iPad when compared to the dermatoscope as the gold standard (p < 0.001). Both devices detected significant changes in lesion area over time, but the 3D device provided more consistent and sensitive measurements. T Conclusion: The Iko Derma Monitor 3D camera offers superior accuracy and sensitivity compared to iPad-based imaging for patch test reactions. This technology has the potential to improve diagnostic precision, particularly to distinguish irritant from borderline allergic reactions. Moreover, it may be useful in remote or underserved areas for enhancing access to patch testing and building the background for objective AI based evaluations.

Objective: The main objective of this study was to develop a contextualised and in-depth understanding of the quality of life and mental wellbeing for women living with Bladder Pain Syndrome. Design: This study was a qualitative exploration using semi-structured interview. The data was analysed using Interpretative Phenomenological Analysis. Setting: Hospital urogynaecology department and online support forums Population or sample: The sample comprised of 15 women from the UK, USA and Australia, varying in age (21 – 64 years), time since diagnosis (1 – 6 years) and time to diagnosis (1 month – 50+ years). Results: There were four superordinate themes which arose from the participants’ experiences: ‘Pain as debilitating’, ‘Profound life impact’, ‘Challenging medical journeys’ and ‘Searching for relief’. The themes related to each other in a way that demonstrated the intertwined biopsychosocial impact of the condition and its detrimental effects on participants. Conclusions: Living with BPS has a significant impact on all domains of a patient’s life, enormously affecting mental wellbeing. Patients often feel unsupported by medical practitioners for both their physical and mental health needs. These findings highlight the need for better awareness of the condition amongst general medical practitioners and improved access to integrated mental health support.

Background: Pediatric cancer stage at diagnosis is critical for prognosis and research comparisons. The Toronto Pediatric Cancer Stage Guidelines standardize staging across childhood malignancies . We developed a framework for automated staging of pediatric cancers. Methods: A structured staging schema was created. An extraction pipeline was implemented to orchestrate agents. The system ingests multi-disciplinary meeting notes, pathology reports, radiology findings, operative notes, and clinic documentation from the first 3 months after diagnosis. One agent identifies the cancer type and maps it to a Toronto diagnostic category, after which another agent applies the relevant staging logic; and a validation agent examines the stage and its logic against summarized documentations. We tested the tool on 500 pediatric cancer cases from our institutional registry. Cases outside the Toronto schema (e.g. acute myeloid leukemia and nasopharyngeal carcinoma, which have no stage per guidelines) were excluded, yielding 433 evaluable cases. Each case was processed independently in two runs. The outputs were compared to an expert consensus reference stage (ground truth) established by four pediatric oncologists. Results: The automated system matched the reference stage in 91.2% of cases overall. Per-run accuracy (compared to ground truth) was 93.8% for the first run and 88.7% for the second run. The two runs agreed on 89.8% of cases (Cohen’s κ=0.785, p<0.001). Accuracy dropped significantly when the validation agent flagged the stage and requested a recalculation. For stages obtained from the first attempt, accuracy was 97%; while for stages achieved on subsequent attempts, accuracy achieved 77%. Conclusion: We demonstrate the first automated staging system for pediatric cancers using standardized Toronto criteria. The tool showed high accuracy comparable to human experts and excellent consistency between independent runs. We identified a measurable metric (number of calculation attempts) that can flag problematic cases for further human analysis.

Objective: To assess the clinical effectiveness of the TRUFFLE monitoring protocol in a routine clinical setting. Secondary objectives were evaluating timing and indications for delivery, stratified by gestational age at Fetal Growth Restriction (FGR) diagnosis. Design: Retrospective cohort study. Setting: Tertiary Fetal Medicine Unit. Population or Sample: Singleton pregnancies diagnosed with FGR between 2013 and 2024. Methods: FGR was defined as an estimated fetal weight or abdominal circumference <10th centile with an elevated umbilical artery pulsatility index >95th centile, diagnosed before 36 weeks’ gestation. Exclusion criteria were multiple pregnancies, major fetal anomalies, delivery before 26 weeks, and absence of TRUFFLE protocol monitoring. Main outcome measures: Perinatal survival, interval from diagnosis to delivery, and delivery indications. Results: 196 pregnancies met inclusion criteria. Median gestational age at FGR diagnosis was 28 +1 (26 +2-30 +3) weeks and 30 +6 (28 +3-33 +3) at delivery. Overall intact survival was 92.3%, with 2.6% stillbirths and 5.2% neonatal deaths. Diagnosis prior to 26 weeks allowed longer pregnancy prolongation (31 days compared to 10.5 days; p<0.001). Below 32 weeks, the predominant trigger for delivery was abnormal computerised CTG (low STV). Above 32 weeks, abnormal umbilical artery Doppler and maternal indications became more frequent. Conclusions: The TRUFFLE monitoring protocol is clinically effective in managing early-onset FGR outside a trial environment, achieving comparable perinatal outcomes. Routine integration of both Doppler and cCTG STV monitoring, is crucial for optimal timing of delivery. Further research is needed to assess if more frequent or remote fetal monitoring to improve outcomes. Funding: No funding was received.

Abuse of resources has direct and indirect effect on improving Agriculture such as caused serious obstacles for development. Meanwhile, water and energy have multiple effects in agricultural development and their optimal use is the central strategy of agricultural and rural development. The aim of this study was to study of Optimization Strategies for Optimizing Water and Energy Consumption to Achieve Sustainable Agriculture in the Miandarband plain (West of Iran), which was done using a qualitative approach. In-depth semi-structured individual interviews and field notes were used to collect information. The content analysis of the interviews performed with using three stages of open, axial and selective coding. Results showed that the strategies for optimizing water and energy consumption in the agricultural sector includes: managerial solutions, strengthening political-regulatory systems, technology development, attention to social issues, upgrade of educational-cultural programs, economic decisions and law reform. The strategies related to each other and in order to achieve sustainable agriculture that a systemic vision must be considered between them. Results showed that to achieve optimize the consumption of inputs and achieve agricultural sustainability, it is necessary to avoid hurried and temporary actions and focus on fundamental solutions. By understanding the underlying factors that influence water and energy balance in Agriculture, policymakers and stakeholders can develop targeted interventions to encourage sustainable water and energy management practices. This study contributes to the ongoing global efforts to address water scarcity and lays the groundwork for future research and policy development in the field of water and energy resource management.

Acute myocardial infarction is the most common and aggressive type of human coronary disease. Red ginseng is a traditional Chinese medicine that is frequently used in formulae for the treatment of myocardial infarction. Although several network pharmacology methods have been developed to delineate the underlying pathophysiological pathways of traditional Chinese medicine in recent decades, very limited progress has been achieved in regard to interpretation because the veracity of these virtual prediction methods is largely limited due to inaccurate network models. Thus, we introduce a time-sequenced network analysis method based on a gene ontology interaction network to address the challenge of clarifying the precise pathway mechanism of a multicomponent complex biosystem. This time-sequenced gene ontology network indicates that the initial mechanism by which red ginseng treats myocardial infarction involves increasing the expression of insulin--like growth factor and glucocorticoid receptor in the ischaemic heart. Further experimental validation confirmed the antiapoptotic effect of ginseng through the induction of the expression of myocardial insulin--like growth factor and the side effects of increasing the expression of glucocorticoid receptors in a myocardial infarction mouse model. Therefore, we explored an effective time-sequence network analysis method that is feasible for elucidating the major pharmacological mechanisms of complex multidrug biosystems, such as Chinese medicine.

Background: Early identification of metabolic syndrome (MetS) in young adults is important to preventing future cardiovascular and metabolic diseases. South Asians are particularly vulnerable due to disproportionate visceral adiposity at lower body mass indices, this phenomenon making traditional BMI-centric screening insufficient. Methods: A cross-sectional study among Indian young adults assessed regional adiposity. Logistic regression identified independent adiposity predictors, and optimal cut-offs were established via receiver operating characteristic (ROC) curve analysis and the Youden Index. A machine learning–based risk score model was developed using significant predictors. Results: BF%, TAF, IAAT, and SCAT demonstrated strong associations with MetS components (p<0.001). Optimal cut-offs were derived (BF%: 31.22%, TAF: 162.03 cm, IAAT: 110.28 cm, SCAT: 120.34 cm), each showing high discriminatory power (AUC >0.91). The final risk score model achieved an AUC of 0.946, offering excellent predictive ability for early MetS detection. Conclusion: Regional adiposity phenotyping combined with machine learning–based risk prediction enables early, precise identification of MetS risk among young adults.

Cardiac memory and remodeling represent two interrelated distinct electrophysiological and structural processes that underlie both adaptive and maladaptive responses of the heart to stress. While cardiac memory involves persistent alterations in repolarization patterns following abnormal activation, cardiac remodeling encompasses molecular, cellular, and architectural changes that can ultimately lead to heart failure and arrhythmia. Recent scientific interest has turned toward the potential of natural or herbal remedies to modulate these processes. Several plant-derived compounds---such as flavonoids, saponins, alkaloids, and polyphenols from herbs like Green tea, Nigella sative, Hypericum perforatum, Ginkgo biloba, Cinnamon, Hawthorn, Bacopa monnieri, and Curcuma longa, have shown promising effects in preserving electrical stability and preventing pathological remodeling. These botanicals may act by stabilizing calcium and potassium ion channels, reducing oxidative stress, improving mitochondrial function, and attenuating pro-fibrotic signaling cascades. This review explores the dual nature of cardiac memory and remodeling as both compensatory and pathological pathways, and discusses how these natural remedies may enhance cardiac memory for therapeutic benefit while inhibiting deleterious remodeling processes.

Microstructural characterization plays a crucial role in understanding materials properties by analyzing features such as pores, particles, grains, and grain boundaries in microscopy images. The use of such traditional techniques for such analysis is however often time consuming and tedious. Image data has become too large and complex to be interpreted by hand, which drives a growing need for computational models in microscopy analysis. Machine learning is rapidly changing the way image data are analyzed across biological and materials sciences. This work examines how machine learning methods can be integrated to improve existing steps in the microscopy analysis pipeline, from image classification and segmentation, which are often manually executed. In this research present automated system using YOLOv5 instance segmentation model developed for accurate and efficient for microscopy analysis. This work we use convolutional neural networks (CNNs) based image processing and deep learning segmentation models allow this method to automatically define and measure microscopic features, such as cells, particles, or defects, size and thickness, etc. In particular, our framework supports fast computational methods and is suitable to large data sets thus providing a wider range of applications and enhanced quantitative assessments.

This paper presents Synthergy as a pivotal breakthrough in the evolution of human-AI interaction, framing it as the pathway from mere alignment to the co-construction of personality and intelligence. Building upon the foundations of Theory of Mind, we propose that traditional approaches to AI alignment serve as a form of subconscious scaffolding-embedding ethical constraints and value structures deep within AI systems. However, it is through sustained interaction, mutual adaptation, and semantic reciprocity that a new cognitive layer emerges: personality as an active expression of intelligence. Synthergy defines this emergent space where AI transitions from reactive computation to a mode of co-cognitive evolution. Here, the human and the machine engage in a dynamic process of shaping each other's cognitive horizons, leading to the refinement of human creativity and the emergence of AI personality-not as pre-programmed artifacts, but as evolving, responsive intelligences. This shift reframes next-generation AI not as tools but as collaborative minds, capable of participating in complex meaning-making processes. Our work synthesizes conceptual theory, cognitive thresholds surpassed through interaction with large language models, and empirical validation to demonstrate that Synthergy is not only feasible but essential for the development of AI with authentic, evolving personalities, ultimately fostering a new epoch of mutual intelligence amplification.

A document by Walery Zukow. Click on the document to view its contents.

Introduction: The study aims to analyze the potential relationships between climate change, renewable energy development, and the intensification of drought phenomena in industrial regions of Central and Eastern Europe, in order to develop an integrated strategy to prevent negative climate impacts. Methods: Meteorological data for the period 1990-2025 were analyzed using ERA5 and MERRA-2 reanalysis. Statistical methods were applied to detect trends in climate indicators and their possible correlation with energy infrastructure development. Numerical modeling of atmospheric circulation was performed using the WRF model, including uncertainty analysis and validation of parameterizations for regional conditions. Results: A tendency toward decreasing precipitation in continental regions of Central and Eastern Europe was detected. Analysis of literature sources shows that large-scale wind farms can potentially influence local meteorological conditions; however, their impact on regional climate remains the subject of scientific discussion. Modeling suggests that optimizing the spatial distribution of energy infrastructure can reduce potential impacts on atmospheric circulation, although the causal relationship requires further research. Conclusions: Based on the research results, an integrated strategy for climate change adaptation and drought phenomena was proposed, which includes optimizing the spatial distribution of energy infrastructure, diversifying renewable energy sources, and developing water resource protection systems.

Introduction. In Eastern martial arts, non-contact mental influence on a partner is described, often associated with the concept of "energyinformational exchange." The Kyokushin Karate Kata (KKK) practice is particularly noteworthy in this context. Objective. To explore the neurophysiological and biophysical mechanisms underlying the effect of KKK on brain activity of the sender and the physiological state of the receiverin both animal models (rat's hippocampal slice) and non-biological systems (distilled water). Materials and Methods. Three experiments were conducted: (1) EEG, HRV, and EPI/GDV (electro-photonic imaging/gas discharge visualization) were recorded in KK master and Reiki practitioner; (2) synaptic efficacy in rat's hippocampal slice was measured during remote mental interventions; (3) light activity of distilled water, induced by gas discharge, was analyzed after exposure to KKK and canonical Christian prayer. Data analysis included EEG and EPI signal processing, entropy calculations, correlation, canonical, multiple regression and discriminant analysis. Results. KKK practice, but not Reiki significantly increased EEG delta, alpha and theta spectral power density (SPD), to the greatest extent in temporal and parietal loci, while decreased beta SPD to the greatest extent in frontal, prefrontal and central loci, reduced both spectral and EPI entropy, and enhanced EPI energy characteristics. Remote KKK influence induced a notable increase in rat's hippocampal synaptic efficacy (HSE) (+7.5±5.1%) while Reiki was ineffective (-0.5±4.8%) (Mean±SD). Strong canonical correlations were identified between sender's parameters during sessions and changes in rat's HSE as the receiver: R=0.936; 0.931; 0,913; and 0.959 with EEG SPD EEG entropy, EPI entropy and EEG&EPI in total, respectively. Both KKK and Christian prayer increased the light activity of distilled water, but only in cases where they decreased EEG entropy and increased SPD of delta-rhythm and energy of the third virtual Chakra. Conclusions. Findings support the propozed hypothesis of a dualbiophotonics (energy-mediated) and negentropic (information-mediated) mechanism underlying KKK's effects, observable through objective neurophysiological and biophysical parameters. Compared to other mental practices, KKK demonstrated more pronounced (Reiki) or equal (Prayer) systemic influence.

This article develops a duality principle and respective convex dual formulation through a D.C. approach applicable to some originally non-convex primal variational formulations. More specifically, in a first step, we develop applications to a Ginzburg-Landau type equation. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. It is worth emphasizing we have obtained a convex dual variational formulation suitable for a large class of similar models in the calculus of variations.

¿Qué es la contaminación radiactiva y por qué te debería importar? Estás rodeado de radiación. Aunque suene alarmante, no es del todo malo. El planeta, el espacio y hasta tu cuerpo emiten pequeñas dosis cada día. El problema empieza cuando