Abstract: The merging of the Internet of Things (IoT) and Artificial Intelligence (AI) advances has intensified challenges related to data authenticity and security. These advancements necessitate a multi-layered security approach in ensuring security, reliability and integrity of critical infrastructure and intelligent surveillance systems. This paper proposes a two-layered security approach combining a discrete cosine transform least significant bit 2 (DCT-LSB-2) – with artificial neural networks (ANN) for data forensic validation and mitigating deepfakes. The proposed model encodes validation codes within the LSBs of cover images captured by an IoT camera on the sender side, leveraging the DCT approach to enhance the resilience against steganalysis. On the receiver side, a reverse DCT-LSB-2 process decodes the embedded validation code, which is subjected to authenticity verification by a pre-trained ANN model. The ANN validates the integrity of the decoded code, and ensures that only device-originated, untampered images are accepted. The proposed framework achieved an average SSIM of 0.9927 across the entirely investigated embedding capacity of between 0 to 1.988 bpp. DCT-LSB-2 showed a stable Peak Signal-to-Noise Ratio (average 42.44 dB) under different evaluated payloads of between 0 to 100 kB. The proposed model achieved a resilient and robust multi-layered data forensic validation system.

Abstract: Proteoglycans are macromolecules consisting of a core protein and one or more glycosaminoglycan side chains. Proteoglycans synthesized by vascular endothelial cells modulate various functions, such as anticoagulant activity and vascular permeability. We previously reported that some heavy metals interfere with proteoglycan expression and that organic–inorganic hybrid molecules, such as metal complexes and organometallic compounds, serve as useful tools to analyze proteoglycan synthesis mechanisms. However, the effects of metal compounds lacking electrophilicity on proteoglycan synthesis remain unclear. Au25(SG)18, a nanoscale gold cluster consisting of a metal core protected by gold–glutathione complexes, exhibits extremely low intramolecular polarity. In this study, we investigated the effect of Au25(SG)18 on proteoglycan synthesis in vascular endothelial cells. Au25(SG)18 accumulated significantly in vascular endothelial cells at low cell density and suppressed the expression of perlecan, a major heparan sulfate proteoglycan in cells, by inactivating ADP-ribosylation factor 6 (Arf6). Additionally, Au25(SG)18 reduced the expression of biglycan, a small dermatan sulfate proteoglycan, in vascular endothelial cells at low cell density; however, the underlying mechanisms remain unclear. Overall, our findings suggest that organic–inorganic hybrid molecules regulate the activity of Arf6-mediated protein transport to the extracellular space and that perlecan is regulated through this mechanism, highlighting the importance of Arf6-mediated extracellular transport for maintaining vascular homeostasis.

Abstract: Ensuring the required microclimate parameters is the most critical task in hot climates. In pig farms, air cooling is provided by means of steam-compression chillers or water-evaporative cooling, which is the simplest way to cool the air. The implementation of water-evaporative cooling depends largely on the interaction of the media involved in this process. The paper considers the process of interaction of cooling water with the surface of a cellular polycarbonate heat exchanger. A mathematical model describing the process of wetting the sprayed surface of the heat exchanger is obtained. The dependence of theoretical water flow rate to provide air cooling in a given operation mode is determined. Production tests of recuperative heat recovery unit with heat exchanger made of cellular polycarbonate equipped with water evaporative cooling system were carried out. The efficiency of water-evaporative cooling of air has been determined, which was reflected in the temperature reduction by 6.3 °C. Characteristic operating modes of the unit, depending on the cooling water flow rate, providing effective air cooling are revealed.

Abstract: Antibiotic resistance poses a growing global health crisis, demanding innovative therapeutic strategies beyond traditional drug classes. This review hypothesizes that selective targeting of bacterial metalloprotein pathways—particularly siderophore-mediated iron acquisition and manganese-dependent oxidative defense—offers the most promising route to narrow-spectrum antibacterial agents with reduced host toxicity. Metals and metal-containing compounds are increasingly recognized for their potent antimicrobial properties and underexplored biochemical roles. By synthesizing current evidence, this article critically evaluates translational strategies including siderophore–antibiotic conjugates, metal trafficking inhibitors, and catalytic metallodrugs. It argues that receptor-mediated uptake and mechanism-informed designs supported by genomic context should be prioritized for clinical development. The review also highlights unresolved challenges in selectivity, toxicity, and resistance mechanisms, offering a roadmap for future research. This manuscript is prepared as a narrative review with systematic elements, integrating evidence from multiple databases to provide a comprehensive framework for targeting bacterial metalloproteins.

Abstract: Ensuring consistency, fairness, and transparency in cross-regulatory compliance has become a critical national priority as enterprises increasingly file interdependent reports to agencies such as the IRS, SEC, and DOL. However, fragmented regulatory ecosystems often lead to inconsistent filings, elevated fraud risk, and inefficient allocation of audit resources. To address these challenges, this paper proposes a unified audit-intelligence framework that integrates neuro-symbolic reasoning, blockchain-based trust management, and deep reinforcement learning within a multi-agent system. First, a neuro-symbolic consistency engine combines graph neural networks with first-order logical rules to detect subtle, cross-form discrepancies that may indicate misreporting or fraud. Second, all generated evidence trails and verification outcomes are anchored on a permissioned blockchain to ensure tamper-proof traceability and transparent regulatory collaboration. Third, a multi-agent deep reinforcement learning module dynamically allocates IRS audit resources by jointly optimizing long-term tax recovery and fairness objectives, mitigating disproportionate enforcement on disadvantaged groups or small businesses. Experimental simulations using synthetic and semi-real regulatory datasets demonstrate that the proposed system significantly improves cross-agency report consistency detection (+21.7%), enhances audit transparency, and reduces allocation bias by up to 34%. This research provides a technologically grounded pathway for modernizing regulatory intelligence, safeguarding tax bases, and supporting equitable and sustainable compliance enforcement in the United States.

Abstract: In this study, we establish novel hypergeometric representations for the two classical sequences that are the Pell and Jacobsthal sequences. Building on Dilcher’s hypergeometric formulation of the Fibonacci sequence, we extend similar results and derive analogous structures for these two classical sequences. The results unify several known identities, provide new explicit representations, and offer a broader perspective on hypergeometric interpretations of linear second order recurrence sequences.

Abstract:

Mushrooms have long been valued not only as food but also for their medicinal properties, especially in Eastern European traditional medicine. Species such as Inonotus obliquus, Fomitopsis officinalis, Piptoporus betulinus and Fomes fomentarius have been used to treat gastrointestinal problems, cancers, respiratory ailments and more. Modern research confirms their diverse pharmacological effects, including antitumor, immunomodulatory, antioxidant, antiviral, antibacterial and antidiabetic activities. In addition, mushrooms are widely incorporated into functional foods and nutraceuticals that promote health. Their sustainable cultivation, efficient use of agricultural residues, rapid growth cycles and resilience to environmental stressors make them an environmentally friendly source of food and pharmaceuticals. This review focuses on the potential of fungi to inhibit advanced glycation end products (AGEs)—harmful compounds formed through the non-enzymatic binding of sugars to proteins, lipids or nucleic acids. AGEs are strongly associated with the progression of chronic diseases such as diabetes, cardiovascular disorders, neurodegeneration and aging. Natural AGE inhibitors from mushrooms represent a promising therapeutic alternative to synthetic agents, as they may offer broader mechanisms of action with fewer adverse effects.

Abstract:

California is currently navigating the confluence of two acute systemic challenges: a chronic housing affordability deficit and increasing grid instability driven by climate-induced volatility and the aggressive transition to variable renewable energy. This review posits that the strategic integration of Accessory Dwelling Units (ADUs) with residential Battery Energy Storage Systems (BESS) constitutes a synergistic, decentralized intervention capable of mitigating these dual crises simultaneously. Adopting the “Photovoltaic-Energy Storage-Direct Current-Flexibility” (PEDF) architectural framework, this study evaluates the transition of the residential dwelling unit from a passive consumption endpoint to an active “prosumer” node capable of providing critical grid services. We employ a stochastic financial simulation using the RShiny framework to assess the economic viability of prefabrication-based deployment strategies under Senate Bill 9 (SB 9) provisions for three investment scenarios: Acquisition-to-Rent, Acquisition–Development-Resale, and Long-Term-Asset-Retention. Our results indicate that modular prefabrication reduces project timelines by 30–50% and embodied carbon by up to 47%, while financial modeling confirms that “Acquisition-Development-Resale” and “Long-Term-Asset-Retention” strategies yield robust returns on investment, validating the economic competitiveness of sustainable densification. Despite identifying implementation barriers—specifically the “split-incentive” dilemma in rental markets and emerging data sovereignty constraints—this review concludes that the BESS-powered ADU represents the fundamental atomic unit of a resilient, low-carbon urban dwelling infrastructure, necessitating aligned policy support to achieve scalable deployment.

Abstract: Digital product passports outline information about a product’s lifecycle, circularity, and sustainability related data. Sustainability data contains claims about carbon footprint, recycled material composition, ethical sourcing of production materials, etc. Also, upcoming regulatory directives require companies to disclose this type of information. However, current sustainability reporting practices face challenges, such as greenwashing, where companies make incorrect claims that are difficult to verify. There is also a challenge of disclosing sensitive production information when other stakeholders, such as consumers or other economic operators, wish to independently verify sustainability claims. Zero-knowledge proofs (ZKPs) provide a cryptographic system for verifying statements without revealing sensitive information. The goal of this research paper is to explore ZKP cryptography, trust models, and implementation concepts for extending DPP capability in privacy-aware reporting and verification of sustainability claims in products. To achieve this goal, first, formal representations of sustainability claims are provided. Then, a data matrix and trust model for the proof generation are developed. An interaction sequence is provided to show different components for various proof generation and verification scenarios for sustainability claims. Lastly, the paper provides a circuit template for the proof generation of an example claim and a credential structure for their input data validation.

Abstract:

The remote monitoring and quantification of industrial gas emissions, such as sulfur dioxide (SO\( _2 \)), are critical for environmental protection. This research demonstrates the application of passive Fourier Transform Infrared (FTIR) spectroscopy for the remote detection and quantitative analysis of SO\( _2 \) emissions from a metallurgical plant chimney. Infrared spectra were acquired at a stand-off distance of 570 m within the 7–14 \( \mu m \) spectral range at a resolution of 4 cm\( ^{-1} \). Path-integrated SO\( _2 \) concentrations were determined through cross-sectional scanning of the gas plume. To translate these optical measurements into an emission rate, the atmospheric dispersion of the plume was modeled using the Pasquill–Briggs approach, incorporating source parameters and meteorological data. Over two experimental series, the calculated average SO\( _2 \) emission rates were 15 kg/s and 22 kg/s, with coefficients of variation of 45.2\% and 32.8\%, respectively. This work highlights the value of FTIR spectroscopy as a powerful analytical tool for the remote, molecular-specific monitoring of atmospheric pollutants, providing a methodology applicable to the environmental chemistry of industrial emissions.

Abstract:

Marine scientific research missions often face challenges such as heterogeneous multi-source data, unstable links, and high packet loss rates. Traditional approaches decouple integrity verification from encryption, rely on full-packet processing, and depend on synchronous sessions, making them inefficient and insecure under fragmented and out-of-order transmissions. The HMR+EMR mechanism proposed in this study integrates “block-level verification” with “hybrid encryption collaboration” into a unified workflow: HMR employs entropy-aware adaptive partitioning and chain-based indexing to enable incremental verification and breakpoint recovery, while EMR decouples key distribution from parallelized encryption, allowing encryption and verification to proceed concurrently under unstable links and reducing redundant retransmissions or session blocking. Experimental results show that the scheme not only reduces hashing latency by 45%–55% but also maintains a 94.1% successful transmission rate under 20% packet loss, demonstrating strong adaptability in high-loss, asynchronous, and heterogeneous network environments. Overall, HMR+EMR provides a transferable design concept for addressing integrity and security issues in marine data transmission, achieving a practical balance between performance and robustness.

Abstract: We investigated the diffusive dynamics of a L\'evy walk subject to stochastic resetting through combined numerical and theoretical approaches. Under exponential resetting, the process mean squared displacement (MSD) undergoes a sharp transition from free superdiffusive behavior with exponent \( \gamma_0 \) to a steady-state saturation regime. In contrast, power-law resetting with exponent \( \beta \) exhibits three asymptotic MSD regimes: free superdiffusion for \( \beta < 1 \), superdiffusive scaling with linearly \( \beta \)-decreasing exponent for \( 1 < \beta < \gamma_0 + 1 \), and localization characterized by finite steady-state plateaus for \( \beta > \gamma_0 + 1 \). MSD scaling laws derived via renewal theory-based analysis demonstrate excellent agreement with numerical simulations. These findings offer new insights for optimizing search strategies and controlling transport processes in non-equilibrium environments.

Abstract: Background Generative AI (genAI) has the capacity to create realistic and convincing animal videos, however, it must simplify and reduce behavioural variation to do so, possibly leading to misinformation. Methods We categorised 29 videos in the press release for a specific video genAI engine. Twelve featured animals. We mapped each video to the Five Domains and categorised behaviour and welfare within. Results Negative welfare was rarely seen, ranging from 8% (n = 1) for Nutrition, to 42% (n =5) for Behavioural Interactions. By contrast, Mental State, Environment, and Behavioural Interactions appeared positive in >42% (n = 5) of the videos featured. However, videos were often misleading or did not represent accurate animal behaviour. Limitations This work was limited to a press-release of data and does not explore user experience. Conclusions GenAI videos pose a new route for client confusion and veterinarians need to incorporate genAI misinformation combatting in their practice.

Abstract:

Nuclear protein in testis (NUT) carcinoma is a rare, aggressive, and poorly differentiated epithelial malignancy characterized by the rearrangement of NUTM1 (NUT middle carcinoma family member 1) on 15q14. It primarily originates along the midline structures, including the head, neck, thorax, and mediastinum. Although NUT carcinoma of the pelvic gynecological organs is exceedingly rare, reported cases have been limited to primary or metastatic ovarian tumors. Here, we present the first documented case of primary uterine NUT carcinoma. A 53-year-old postmenopausal woman presented with abnormal uterine bleeding and a uterine mass. She underwent a total abdominal hysterectomy with bilateral salpingo-oophorectomy. The initial postoperative histopathological evaluation suggested undifferentiated endometrial sarcoma; however, subsequent immunohistochemical (IHC) analysis and fluorescence in situ hybridization revealed NUTM1 rearrangement, confirming the diagnosis of NUT carcinoma. The patient experienced tumor recurrence six months postoperatively and succumbed to the disease nine months later. The pathological diagnosis was challenging; the presence of abrupt squamous differentiation prompted further IHC analysis, leading to the definitive diagnosis. Primary uterine NUT carcinoma may be misdiagnosed as other undifferentiated uterine tumors due to its rarity and histological overlap. Given the diagnostic challenges, NUT IHC staining and molecular testing for NUTM1 rearrangement should be considered in undifferentiated uterine tumors with ambiguous histopathological features.

Abstract:

The main objective of this work is assessing the potential negative impact of organic farming on the thyroid gland and comparing it with the negative impact of conventional farming on this organ. Conventional farming practices deploy synthetic agrochemicals to maximize yields, many of which have endocrine-disrupting properties, like pesticides, while organic farming practices use natural alternative substances, favoring environmental sustainability and health protection. Studies suggest that organic farming yield can be contaminated with pesticide residues. Thyroid disruption underlies some of the most common endocrine pathologies worldwide. Previous studies have linked exposure to conventional farming with thyroid disruption; relatively less is known about effects of exposure to organic farming on the thyroid. Wild mice were selected as bioindicators, captured in a conventional farm (CF); an organic farm (OF), and two reference areas (RF’) without agriculture. Histomorphometric and histomorphological measurements of the thyroid were performed. Hypothyroidism signs were observed in mice exposed to either farming system, being less pronounced in organic farming-exposed mice: epithelium thickness, and the area and volume of epithelial cells were lower than in non-exposed mice [epithelium thickness (µm): 4.1617 ± 0.50860 (CF); 6.2825 ± 0.19308 (OF); 7.4605 ± 0.25412 (RF’)]. Histomorphologic alterations included lower follicular sphericity, irregularly-delimited epithelium, increased exfoliation into the colloid, and increased inflammation of thyroid tissue. Results suggest that, while organic farming might be a better alternative to conventional farming, it is not completely free of health hazards. Exposure to organic farming can cause thyroid disruption, with less pronounced effects. Although there are risks to be considered, results support the benefit of transitioning from conventional farming systems towards organic farming systems.

Abstract: This paper presents a general algorithm for rapidly generating all N×N Latin squares, along with its precise counting framework and isomorphic (quasi-group) polynomial algorithms. It also introduces efficient algorithms for solving Latin square-filling problems. Numerous combinatorial isomorphism problems, including Steiner triple systems, Mendelsohn triple systems, 1-factorization, networks, affine planes, and projective planes, can be reduced to Latin square isomorphism. Since groups are true subsets of quasigroups and group isomorphism is a subproblem of quasi-group isomorphism, this makes group isomorphism an automatically P-problem. A Latin square of order N is an N×N matrix where each row and column contain exactly N distinct symbols, with each symbol appearing only once. A matrix derived from such a multiplication table forms an N-order Latin square. In contrast, a binary operation derived from an N-order Latin square as a multiplication table constitutes a pseudogroup over the Q set. I discovered four new algebraic structures that remain invariant under permutation of rows and columns, known as quadrilateral squares. All N×N Latin squares can be constructed using three or all four of these quadrilateral squares. Leveraging the algebraic properties of quadrilateral squares that remain unchanged by permutation, we designed an algorithm to generate all N×N Latin squares without repetition when permuted, resulting in the first universal and nonrepetitive algorithm for Latin square generation. Building on this, we established a precise counting framework for Latin squares. The generation algorithm further reveals deeper structural aspects of Latin squares (pseudogroups). Through studying these structures, we derived a crucial theorem: two Latin squares are isomorphic if their subline modularity structures are identical. Based on this important and key theorem, and combined with other structural connections discussed in this paper, a polynomial-time algorithm for Latin square isomorphism has been successfully designed. This algorithm can also be directly applied to solving quasigroup isomorphism, with a time complexity of 5/16(n⁵−2n⁴−n³+2n²)+2n³ Furthermore, more symmetrical properties of Latin squares (pseudogroups) were uncovered. The problem of filling a Latin grid is a classic NP-complete problem. Solving a fillable Latin grid can be viewed as generating grids that satisfy constraints. By leveraging the connections between parametric group algebra structures revealed in this paper, we have designed a fast and accurate algorithm for solving fillable Latin grids. I believe the ultimate solution to NP- complete problems lies within these connections between parametric group algebra structures, as they directly affect both the speed of solving fillable Latin grids and the derivation of precise counting formulas for Latin grids.

Abstract: This work presents a hybrid chaotic–cryptographic image encryption method that integrates a physical two-dimensional delta-kicked oscillator with a PBKDF2-HMAC-SHA256 key derivation function. The key consists in a 12-symbol human key and four user-defined salt words into 256-bit high-entropy material, later converted for the KDF into 96 balanced decimal digits that seed the chaotic functions. The encryption occurs on the real domain, using a partition–permutation mechanism followed by modular diffusion, both governed by chaos. Experimental results confirm the perfect reversibility of the process, high randomness (entropy = 7.9981), and zero adjacent-pixel correlation. Known and chosen plaintext attacks revealed no statistical dependence between cipher and plain images, while NPCR≈99.6% and UACI≈33.9% demonstrate complete diffusion. The PBKDF2-based key derivation expands the key space to 2256 combinations, effectively eliminating weak-key conditions and enhancing reproducibility. The proposed approach bridges deterministic chaos and modern cryptography, offering a secure and verifiable method for protecting sensitive images.

Abstract: Background: This study analyzed the epidemiological profile of patients with non-melanoma skin cancer, female breast cancer, prostate cancer, colon and rectum cancer, lung cancer, and stomach cancer in southeastern Brazil (Espírito Santo, São Paulo, Minas Gerais, and Rio de Janeiro), covering the pre-pandemic period (2017 to 2019) and the pandemic period (2020 to 2022). Methods: The DATASUS database was used to assess possible impacts of percentage differences between these periods through point regression analysis and comparisons between the pre-pandemic and pandemic periods, as well as between the sexes using the Student's t-test. Results: The results suggest that sex-specific characteristics impose varying impacts and incidences on each gender. In Espírito Santo, there was a growing trend in stomach and non-melanoma skin cancers, while in São Paulo, only stomach cancer showed a statistically significant upward trend. Regarding staging, it was observed that during the pandemic period, the highest incidence was in stage IV, whereas in the pre-pandemic period, the highest incidences were in stages 0 and I, with medium and small effect sizes. Furthermore, the loss of pre-pandemic data was 11.46% (54,080 cases), while during the pandemic, the loss was 27.52% (129,869 cases), both statistically significant values of considerable magnitude. Conclusion: It can be concluded that there was a significant increase in the temporal trend only for stomach cancer in the states of São Paulo and Espírito Santo; for the other cancers and states, the trends were stationary. The high rate of missing data during the pandemic suggests a considerable impact on the results, highlighting the importance of additional prospective studies to better understand the effects of COVID-19 on oncology.

Abstract: Programmable organoids are emerging as a powerful new class of engineered developmental systems in which genetic circuits, epigenetic memory architectures, synthetic organizers, and closed-loop control frameworks converge to enable precise regulation of morphogenesis. Traditional organoids rely on spontaneous self-organization, but this intrinsic variability limits reproducibility, causal inference, and translational relevance. Recent advances in CRISPR-based transcriptional and epigenetic engineering, optogenetic and chemogenetic patterning technologies, reaction–diffusion design, and real-time biosensing now allow developmental trajectories to be scripted with increasing precision. This review synthesizes these developments into a unified framework spanning genetic circuit construction, epigenetic programming, synthetic morphogenesis, multi-scale sensing, adaptive regulation, and AI-guided design. Applications across human developmental biology, disease modeling, and regenerative medicine are highlighted, alongside the technical, biosafety, and ethical considerations associated with building increasingly autonomous, self-regulating developmental systems. Collectively, these advances establish programmable organoids as a foundation for developmental synthetic biology and outline a roadmap toward fully engineered human developmental architectures.

Abstract: We will prove the generalized stability of an additive-quadratic-cubic functional equation in the sprit of Găvruţa.