I introduce φHash, a novel one-way hash function defined entirely in the categorical framework of Alpay Algebra. In this setting, each input (finite or infinite sequence) is encoded as an object in a small cartesian-closed category A with a distinguished initial state I, and a transfinite evolution functor φ : A → A. Repeated application of φ with inputdependent updates yields a unique fixed-point object φ ∞ (I) that encapsulates the entire input data. The hash digest is then obtained by a universal φ-algebra "fold" (projection) onto an n-bit object in A. I formalize this construction axiomatically, prove determinism and one-wayness, and show that φHash resists both classical and quantum brute-force attacks. As illustrations, I symbolically compute φHash-256, φHash-512, and φHash-1024 of the word "alpay" within Alpay Algebra, verifying repeatability and irreversibility. Finally, I explain how φHash extends to arbitrary output lengths (including φHash-∞) via universal fold operators. Throughout, I rely solely on Faruk Alpay's foundational axioms and the SHA-256 standard for contrast.

A document by Bhaskar Mangal. Click on the document to view its contents.

The application of engineered live biotherapeutic products (LBPs) to secrete small molecules, peptides, or proteins to benefit a human or animal host, relies on heterologous protein expression. Key challenges in this area include expressing protein in a targeted location, the use of antibiotic-free platforms, and expressing recombinant proteins at titers capable of the desired therapeutic effect. In this study, we sought to engineer the promising candidate probiotic chassis Escherichia coli Nissle 1917 (EcN) as an in-situ drug delivery platform. Despite its long history of safe human use and general probiotic characteristics, wild-type EcN is not optimal for routine protein expression. In this work, we present several approaches to improve protein production in this host. First, we enable stable antibiotic-free protein expression system via native cryptic plasmids. Next, we integrate the T7 RNA polymerase for high-level protein expression. Finally, we knock out OmpT protease activity, enabling expression levels comparable to the industry standard E. coli BL21 (DE3). To demonstrate its application, the above system was adapted to express antimicrobial peptide microcin L (MccL) from EcN, which can potentially reduce gut related pathogens and enhance fitness of the probiotic in the competitive niche of the gut. Overall, this study establishes an antibiotic free and high-level protein expression platform in EcN, expandable for in situ delivery of therapeutic proteins.

Background: Eating disorders (EDs) are increasingly prevalent among young adults in Lebanon, a country experiencing rapid sociocultural change. While parenting styles and social appearance anxiety have each been linked to disordered eating, no study in the MENA region has examined their interaction. Objective: This study investigated whether social appearance anxiety moderates the relationship between perceived parenting styles and eating disorder symptoms in a sample of Lebanese emerging adults. Methods: A cross-sectional survey was completed by 296 participants aged 18 to 25. The study used the Eating Attitudes Test (EAT-26), the Social Appearance Anxiety Scale (SAAS), and the short-form EMBU to assess disordered eating, social appearance anxiety, and perceived parenting styles. Moderation analyses were conducted using the PROCESS macro in SPSS. Each model included one parenting style as the independent variable, with the remaining parenting styles entered as covariates. Separate models were conducted for maternal and paternal parenting. Results: Maternal overprotection significantly predicted higher eating disorder symptoms only at high levels of social appearance anxiety (B = 0.012, p = .034). In the same model, paternal emotional warmth was independently associated with fewer eating disorder symptoms (B = –0.45, p = .006), suggesting a protective factor. No other parenting dimensions showed significant interaction effects. Conclusions: This is the first study in Lebanon, and possibly the Arab region, to statistically test the moderation effect of social appearance anxiety on the relationship between perceived parenting styles and eating disorder symptoms. The findings highlight the need for culturally adapted interventions that address both parenting dynamics and appearance-based anxiety in emerging adults.

A document by Shivali Rao. Click on the document to view its contents.

IntroductionPrimary breast melanoma (PBM) constitutes < 5 % of cutaneous melanomas and < 0.5 % of malignant breast tumors (1). It arises from ectopic melanocytes in the breast skin and must be distinguished from cutaneous melanoma metastatic to the breast. Because PBM is exceptionally uncommon, it is frequently mistaken for more typical breast lesions, delaying diagnosis and complicating treatment (2–4). Confirmation relies on histopathology and immunohistochemistry—especially strong reactivity for S-100, HMB-45, and SOX-10 (2,5–8). In the absence of specific guidelines, every case adds valuable evidence for improving multidisciplinary management (9).

This paper presents a comprehensive framework for understanding observer effects in self-referential mathematical systems. I develop theoretical foundations for observer-coupled collapse phenomena and demonstrate how system identity undergoes temporal drift through repeated interactions. The work includes practical implementation guidelines for fixed-point computation architectures and advanced resilience engineering approaches for distributed deployments. I provide continuous validation mechanisms and synchronization safeguards essential for maintaining system integrity in dynamic environments.

This paper introduces and explains "The Framework of Family Functions and Dysfunctions from the Perspective of the Socialization Process." This analytical tool, developed through a comprehensive review of existing literature and research studies, holds significant potential. It enables the identification of all existing types of crimes and their causes and consequences within specific families and households. In this context, it allows for assessing whether existing policy measures to address these issues are in place, and if so, evaluates their comprehensiveness and effectiveness. If no measures currently exist, it facilitates the design of tailored interventions specifically targeting both perpetrators and their victims. Furthermore, if new variables emerge that are not yet included in the framework, they can be easily integrated. We illustrate its potential by using examples to assess the complexity of strategic goals and measures aimed at addressing different types of crime in selected regions of Czechia.

The Internet of Things (IoT) has revolutionized industries by creating a vast, interconnected ecosystem. Still, the rapid deployment of IoT devices has introduced severe security risks, including DDoS, DoS GoldenEye, DoS Hulk attacks, and Port scanning. Traditional Machine Learning (ML)based Intrusion Detection Systems (IDS) often operate passively, detecting threats without taking action, and are rarely evaluated under real-time attacks. This limits our understanding of their performance within the resource constraints typical of IoT systems-an essential factor for stable, resilient systems. This paper proposes a Security Edge with Deep Reinforcement Learning (SecuEdge-DRL) specifically designed for the IoT edge, aiming to enhance security while maintaining energy efficiency, contributing to sustainable IoT operations. Our IDS integrates DRL with the MAPE-K (Monitor, Analyze, Plan, Execute, Knowledge) control loop, enabling real-time detection and adaptive response without relying on predefined data models. DRL allows continuous learning, while MAPE-K provides structured self-adaptation, ensuring the system remains effective against evolving threats. We also implemented four targeted security policies tailored to a specific attack type to enhance the IDS's threat mitigation capabilities. Experimental findings indicate that the proposed SecuEdge-DRL achieves an average detection accuracy of 92% across diverse real-world cyber threats (e.g., DoS Hulk, DoS GoldenEyes, DDoS, and Port scanning). Statistical analysis further validates that these security policies enhance IoT systems' defense without compromising performance, establishing our approach as a resilient, resource-efficient security solution for the IoT ecosystem.

The Internet of Things (IoT) has significantly expanded the digital landscape, interconnecting an unprecedented array of devices, from home appliances to industrial equipment. This growth enhances functionality, e.g., automation, remote monitoring, and control, and introduces substantial security challenges, especially in defending these devices against cyber threats. Intrusion Detection Systems (IDS) are crucial for securing IoT; however, traditional IDS often struggle to adapt to IoT networks' dynamic and evolving nature and threat patterns. A potential solution is using Deep Reinforcement Learning (DRL) to enhance IDS adaptability, enabling them to learn from and react to their operational environment dynamically. This systematic review examines the application of DRL to enhance IDS in IoT settings, covering research from the past ten years. This review underscores the state-of-the-art DRL techniques employed to improve adaptive threat detection and real-time security across IoT domains by analyzing various studies. Our findings demonstrate that DRL significantly enhances IDS capabilities by enabling systems to learn and adapt from their operational environment. This adaptability allows IDS to improve threat detection accuracy and minimize false positives, making them more effective in identifying genuine threats while reducing unnecessary alerts. Additionally, this systematic review identifies critical research gaps and future research directions, emphasizing the necessity for more diverse datasets, enhanced reproducibility, and improved integration with emerging IoT technologies. This review aims to foster the development of dynamic and adaptive IDS solutions essential for protecting IoT networks against sophisticated cyber threats.

A document by Saeid Jamshidi. Click on the document to view its contents.

The rapid expansion of the Internet of Things (IoT) has brought transformative benefits across various domains and introduced significant security challenges, especially in resourceconstrained edge gateways. This paper proposes an innovative Intrusion Detection System (IDS) powered by Deep Reinforcement Learning (DRL) to dynamically detect and mitigate network threats by selecting IoT security patterns. Leveraging adaptive IoT security patterns, the system addresses diverse attack scenarios (e.g., Distributed Denial of Service (DDoS), DoS GoldenEye, DoS Hulk, and Port Scanning) with significant efficiency. The system achieves an average detection accuracy of 97% and demonstrates reduced response times and efficient resource utilization, making it well-suited for edge gateways. The experimental evaluations validate the proposed model's ability to enhance security while optimizing CPU and memory usage, reducing energy consumption, and lowering carbon emissions. Furthermore, its adaptability to evolving cyber threats and alignment with green computing principles highlight its potential to support secure and sustainable IoT networks.

The rapid growth of the Internet of Things (IoT) has transformed industries, resulting in unprecedented opportunities alongside significant cybersecurity challenges. Malware, for example, Mirai and Gafgyt, exploits IoT vulnerabilities, leading to large-scale attacks. Traditional Intrusion Detection Systems (IDS) struggle to detect these evolving threats due to their reliance on static rule-based or classic Machine Learning (ML) models, which lack adaptability to zero-day attacks and dynamic traffic patterns. This paper presents EdgeShield-DRL, a novel Deep Reinforcement Learning (DRL)-based IDS designed for IoT edge gateways. EdgeShield-DRL dynamically detects and mitigates evolving threats in real-time while ensuring efficient operation on resource-constrained edge devices. We evaluated EdgeShield-DRL on the N-BaIoT dataset, achieving a high detection accuracy of 97% during training phases and 96% in real-time detection scenarios. Moreover, the system demonstrates robust resource efficiency, maintaining minimal energy consumption and carbon emissions even under attack conditions. Experiments on a realworld testbed further validate EdgeShield-DRL's effectiveness, showcasing resilience against diverse attack scenarios, including large-scale botnet activity. Furthermore, EdgeShield-DRL effectively balances robust security with resource constraints, making it particularly suitable for critical IoT systems, e.g., smart cities, healthcare, and industrial automation.

This paper extends the Alpay Algebra framework to address a fundamental problem in recursive systems: how to maintain stability when internal observers monitor and verify the system's own evolution. I develop a mathematical framework that incorporates observer-coupled dynamics and temporal drift within categorical fixed-point architectures. Key contributions include the introduction of observer and temporal functors acting on algebraic objects, proof of existence for distributed verification limits that prevent collapse under recursive observation, derivation of entropy accumulation bounds ensuring system convergence, characterization of phase dynamics and interference patterns in verification processes, analysis of observer cascade phenomena and their stability conditions, and bifurcation theory for identity drift under strong observation coupling. Building on cartesian-closed category theory and set-theoretic foundations, I prove that φ^∞ fixed-point architectures can remain stable even under interleaved observation. The work introduces novel concepts including phase-locked verification, entropy-based Lyapunov analysis, and cascade operators for multiple observers. This research addresses the paradox of self-observation in recursive systems where the act of internal verification can destabilize the very structures being observed. The results have implications for categorical AI, temporal logic systems, and any computational architecture requiring self-consistent internal monitoring. The framework provides mathematical guarantees for observer-aware systems while maintaining the structural elegance of the original Alpay Algebra formulation.

Background Pelvic organ prolapse (POP) represents a major global health challenge for women. This study systematically analyzed the global disease burden of POP and associated risk factors from 1990 to 2021, including projections of future trends. Methods The data were derived from the GBD 2021 database and genome-wide association studies (GWAS). Additionally, we combined the age-period-cohort (APC) model with the autoregressive integrated moving average (ARIMA) model to analyze the driving factors and project future disease burden trends. Furthermore, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationships between risk factors and POP. Results From 1990 to 2021, the global crude incidence, prevalence, and disability-adjusted life years (DALYs) of POP increased by 66.2%, 68.7%, and 67.5%, respectively. However, in 2021, the global age-standardized disability rate (ASDR) of POP was 18.4% lower than in 1990. Notably, regions with a low sociodemographic index (SDI) exhibited a statistically significant elevation in disease burden. Furthermore, obese populations had a higher risk of developing POP, while those with higher education levels had a lower risk. According to the ARIMA model, global POP cases are projected to reach 148 million by 2036, while the age-standardized prevalence rate (ASPR) and ASDR are expected to decline further by 10.8% and 18.4%, respectively. Conclusion Aging population and socioeconomic inequality will further aggravate the disease burden of POP. To reduce the disease impact, future efforts should focus on strengthening global monitoring, optimizing medical resource allocation, and implementing targeted prevention and control strategies for high-risk populations.

Background: Syphilis serum fixation (SF), defined as persistent low-titer antibodies after treatment, poses a diagnostic challenge because of the overlap with serologic features of active infection. Exosomal miRNAs are stable in body fluids and have potential as diagnostic markers. objectives: This study aimed to identify plasma exosomal miR-1246 as a diagnostic biomarker for SF and elucidate its role in NLRP3 inflammasome suppression, providing mechanistic insights into SF pathogenesis. Methods: Using microarray analysis and reverse transcription quantitative polymerase chain reaction (RT-qPCR), differential miRNA expression was measured in the plasma samples of SF patients. The plasma levels of NLRP3 and related cytokines were quantified using ELISA, and the regulatory effect of miR-1246 on NLRP3 was measured in vitro. Diagnostic performance was assessed based on receiver operating characteristic (ROC) curve analysis for miR-1246 alone and in combination with the rapid plasma reagin (RPR) test. Results: Plasma exosomal miR-1246 was significantly upregulated in SF patients (P < 0.001), whereas NLRP3 and its associated factors were downregulated (P < 0.05). In vitro experiments confirmed that miR-1246 negatively regulated NLRP3 inflammasome activity. ROC analysis showed that miR-1246 alone yielded an area under the curve (AUC) of 0.760 (sensitivity 77.4%, specificity 62.9%). When combined with RPR, the AUC increased to 0.824 (sensitivity 83.3%, specificity 65.7%). Conclusions: Exosomal miR-1246 is elevated in SF and may contribute to its pathogenesis by inhibiting NLRP3 inflammasome. It demonstrates potential as a diagnostic biomarker, particularly when combined with RPR.

We present the first documented case of jackdaws (Coloeus monedula) collecting and placing Iberian lynx (Lynx pardinus) scat in their nests in the Montes de Toledo, Spain. This behaviour may significantly compromise conservation efforts for species whose monitoring relies on non-invasive genetic sampling—such as the lynx—especially in areas with dense populations of jackdaws or other species exhibiting similar behaviour, where such removal may substantially reduce sample availability. Using artificial nest boxes equipped with camera traps, we confirmed that jackdaws actively transport lynx scat to their nests. In a controlled experiment simulating a lynx latrine, all scat was removed in just over an hour. Simulations using spatial capture-recapture (SCR) models showed that this behaviour can introduce bias and reduce the accuracy of population estimates based on genetic sampling, a widely used method in wildlife monitoring. These findings highlight the importance of considering interspecific interactions when designing monitoring protocols for threatened species. More broadly, this case illustrates how overlooked ecological behaviours can compromise conservation tools and underscores the need for adaptive monitoring strategies in dynamic ecosystems.

Metabolome is gaining consideration as a viable approach to disease detection and even shows promising results in COVID-19 diagnosis. This work extends the study of the relationship between solid-phase microextraction (SPME) extractable sweat compounds (SPME-ECs) and COVID-19 positive patients. Sweat samples were collected from 426 patients (126 positives and 300 negatives) recruited at Merida and Progreso (Yucatán, México) health centers. The composition of sweat was analyzed with a solid phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) method. The statistical analysis revealed significant differences between infected and non-infected patients’ SPME-EC profiles. Then, we evaluated different classification models to discriminate between positive and negative patients. The best model was the PLS-DA model, with percentages of specificity and sensitivity above 80%. Six relevant chromatographic peaks for the classification model were annotated (hexadecanoic acid, tetracosanoic acid, 6-hexadecenoic acid, oleic acid, squalene, and undecanal) based on the mass spectrum recognition using the NIST library (> 90% identity match). Palmitic and oleic acid signal was elevated in COVID-positive patients. While sapienic acid, lignoceric acid, squalene, and undecanal were detected in lower amount in COVID-positive patients. The present list of chemical opens the possibility to be used as a reliable serologic biomarker.

Molnupiravir (MOV), an oral antiviral, is prescribed to treat adult patients with mild-to-moderate COVID-19 at risk of progressing to severe disease. This systematic literature review assessed the real-world effectiveness of MOV for reducing the progression to severe COVID-19 outcomes in clinical settings, including high-risk or special populations (type 2 diabetes, chronic respiratory diseases, immunocompromised conditions, older adults, and nursing home residents). Studies comparing MOV-treated with untreated groups of non-hospitalized adults at risk of progression to severe COVID-19 outcomes (hospitalization, death, and the composite of hospitalization/death) were identified from EMBASE and PubMed (January 1, 2021‒May 24, 2024). Twenty-one general and special population studies were included. General population studies (n=16) showed that MOV reduced the risk of death, hospitalization, and hospitalization/death. Special population studies (n=10; five additional and five general population articles with subgroups of interest) also showed that MOV reduced the risk of the same outcomes, with a more pronounced effect in older adults (≥60 years). The wide range of risk reduction observed might be attributed to variability in COVID-19 hospitalization guidelines and vaccination coverage. Findings support the effectiveness of MOV in reducing the risk of hospitalization, death, and hospitalization/death compared with untreated groups, including high-risk adults with underlying comorbidities.

Transparency, Consent, and Control (TCC) is a critical macOS security framework intended to enforce user consent over app access to sensitive data. I report a collapse of TCC's access isolation guarantees discovered in macOS 15.5 (codename Sequoia, build 24F74) on both Intel and Apple Silicon devices. In this "access collapse" scenario, a malicious application could trigger a consent dialog that masquerades as a request from a different application and then hijack the user's approval for itself, thereby bypassing intended privacy controls. This paper analyzes the underlying vulnerabilities-particularly a TCC daemon validation flaw (CVE-2025-31250)-and introduces a solution framework grounded in formal methods. I present Alpay Algebra, a symbolic framework, to model and redesign TCC's permission system with provable isolation guarantees. The proposed behavioral-structural redesign augments TCC with cryptographic Ξ-tokens and formal policy invariants (ψ) to enforce that consent cannot be misdirected. I outline how the redesigned TCC processes maintain security invariants under all execution traces (τ), preventing unauthorized data access. Theoretical guarantees are provided, and implementation considerations are discussed. This solution-focused approach demonstrates how a rigorous symbolic redesign can preempt entire classes of TCC vulnerabilities, strengthening macOS privacy for future iterations.

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved, additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated; the Random Forest classifier, with 100 estimators, achieves a high accuracy of 97%, outperforming Logistic Regression (58%), Support Victor Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. The results reveal that demographic factors, such as weight and height, contribute more to the model’s predictions than physiological signals. With a computation time of 0.06 seconds, Random Forest is efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios.

In this work we study a strongly coupled system between the equation of plates with fractional rotational inertial force κ ( - Δ ) β u tt where the parameter 0 ≤1 and the equation of an electrical network containing a fractional dissipation term δ ( - Δ ) θ v t where the parameter 0≤ θ≤1, the strong coupling terms are given by the Laplacian of the displacement speed γ Δ u t and the Laplacian electric potential field γ Δ v t . When β=1 we have the Kirchoff-Love plate and when β=0, we have the Euler-Bernoulli plate recently studied in Suárez-Mendes (2022)[ Suarez]. The contributions of this research are: We prove the semigroup S( t) associated with the system is not analytic in ( θ,β)∈[0 ,1]×(0 ,1]-{(1 ,1/2)}. We also determine two Gevrey classes: s 1 > 3 - β 1 + β and s 2 > 2 ( 2 + θ - β ) θ when the parameters θ and β lies in the interval (0 ,1) and we finish by proving that at the point ( θ,β)=(1 ,1/2) the semigroup S( t) is analytic and with a note about the asymptotic behavior of S( t). We apply semigroup theory, the frequency domain method together with multipliers and the proper decomposition of the system components and Lions interpolation inequality.

Orf virus infection in human ecthyma contagiosum: a case report in the Northestern of IranRunning title: Orf Virus Infection: A Case of Ecthyma Contagiosum in IranMahnaz Arian1, Nazanin Moradi Sani2, Mohammad Saleh Safavi Shamlou3, Kiana Ketabi1*Department of Infectious Diseases and Tropical Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranStudent of Professional Doctorates in Medicine, Imam Reza Medical Research and Training Hospital, Mashhad University of Medical Sciences, Mashhad, IranStudent research committee, Mashhad University of Medical Sciences, Mashhad, Iran.Corresponding Author:Kiana KetabiPhD in Medical Virology, Department of Infectious Diseases and Tropical Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, IranEmail: Ketabi.kiana@yahoo.comTel: +989151217794ORCID: 0000-0001-5189-3046

Background: Advanced pancreatic adenocarcinoma (PA) remains a formidable challenge with limited therapeutic options. Gemcitabine-based regimens, often combined with novel targeted therapies, are widely employed, yet their comparative efficacy is poorly understood. This network meta-analysis (NMA) aims to systematically evaluate the efficacy of various targeted drug combinations in previously treated patients with advanced or metastatic PA. Methods: We conducted a systematic search of phase III randomized controlled trials (RCTs) published between 2004 and 2024, sourced from PubMed, EMBASE, Cochrane Library, and ClinicalTrials.gov. Studies comparing targeted drug combinations with chemotherapy or chemotherapy alone in advanced/metastatic PA were included. Primary outcomes were overall survival (OS) and progression-free survival (PFS), assessed via hazard ratios (HRs) with 95% confidence intervals (CIs) using a Bayesian fixed-effects NMA. Surface under the cumulative ranking (SUCRA) scores were calculated to rank treatment efficacy. Results: Thirteen RCTs, encompassing 5,759 patients and evaluating 13 targeted drug combinations, were included. No statistically significant improvements in OS or PFS were observed for any targeted drug combination compared to gemcitabine monotherapy. Similarly, indirect comparisons among targeted therapies revealed no significant differences. SUCRA rankings identified Gemcitabine plus Sorafenib (Gem-Soraf, 77%) as having the highest probability of ranking first for OS, followed by Gemcitabine plus Rigosertib (Gem-Rigos, 76%), Gemcitabine plus Cetuximab (Gem-Cetux, 62%), and Gemcitabine plus Nimotuzumab (Gem-Nimot, 17%). For PFS, Gem-Soraf (87%) ranked highest, followed by Gem-Cetux (65%), Gemcitabine plus Tipifarnib (Gem-Tipi, 65%), and Gemcitabine plus Erlotinib-Bevacizumab (Gem-Erlot-Beva, 14%). Conclusions: This NMA suggests that Gem-Soraf offers the highest probability of superior OS and PFS among targeted therapies for advanced PA. However, no targeted combination significantly outperformed gemcitabine monotherapy, highlighting the need for further research into personalized treatment strategies.