Abstract: Unmanned Aerial Vehicles (UAVs) have been widely used in recent years in various applications thanks to advances in communication, Internet of Things and electronics. However, despite the advantages they offer, reports of cybersecurity attacks represent a serious threat to their operation. Classic cryptographic-based solutions and traditional intrusion detection approaches generally struggle to deal with these attacks due to their adaptive and stealthy nature. In this context, Artificial Intelligence (AI) models emerged as potential solutions that hold great promise in addressing this type of attacks. However, most related surveys presented fragmented picture of the state-of-the-art failing to cover all sub-types of AI models, and sometimes not following structured taxonomies or describing popular datasets that were used in the literature. In this paper, we bridge this gap by proposing a novel and comprehensive survey that classifies UAV security research according to the type of AI model, the cyber attacks it thwarts and the related security properties it enforces. This taxonomy does not stop at describing Machine Learning (ML) and Deep Learning (DL) approaches, but it also dives into emerging approaches such as Federated Learning (FL), Reinforcement Learning (RL), Graph Neural Network (GNN) and Generative AI (GAI). We also classify the threat vector according to the layer in the UAV functional stack where the attack takes place. In addition, we describe the datasets, tools and evaluation metrics that were mostly used in the literature. We conclude the survey by summarizing the key insights, discussing the open challenges and enumerating future research directions. We aim that this survey serves as a reference for cyber security researchers and practitioners who tackle UAV security using AI.

Abstract: To address the issue of 2D laser-guided automated guided vehicles (AGVs) in industrial intelligent material handling scenarios being susceptible to interference from changes in lighting and complex obstacles, leading to abnormal positioning and mapping and frequent false stops, this paper designs a lightweight, multi-dimensional perception and anti-false-stop YOLOv8 anomaly recognition network, achieving accurate identification of various interferences in complex environments. An adaptive decision-making fault-tolerant control algorithm is proposed, introducing a temporal logic verification and dynamic threshold adjustment mechanism to achieve real-time dynamic switching of obstacle avoidance levels, ensuring efficient coordination between perception decision-making and control execution. An AGV anomaly detection sample set suitable for complex industrial scenarios is constructed, providing reliable data support for model optimization and accuracy evaluation. Finally, real-world deployment verification in a real electronics factory environment shows that this method reduces the vehicle false-stop rate and improves task handling efficiency. This research effectively solves the robust perception problem of AGVs in complex industrial environments and has significant engineering application value.

Abstract: Detecting rare structural damage without labeled fault data remains a critical unsolved challenge in structural health monitoring (SHM). This paper introduces BcDCGAN, a Bayesian conditional deep convolutional generative adversarial network designed for unsupervised anomaly detection in multivariate vibration time series from prestressed concrete catenary poles. The architecture integrates variational Bayesian inference over generator and critic weights with temporal convolutional networks, enabling epistemic uncertainty alongside reconstruction and critic objectives. Trained exclusively on healthy acceleration signals with wind speed conditioning, the model produces a log-space Bayesian anomaly score that jointly combines normalized reconstruction error, critic evaluation, and epistemic uncertainty estimates into a single weighted decision function. An adaptive threshold is calibrated from the validation data for deployment-ready performance. Evaluation on a real 2017 catenary pole dataset (1606 signals, 70/10/20 split) with injected anomalies achieves 99.2% recall while revealing clear latent space separation and appropriate uncertainty signaling for out-of-distribution samples. Progressive posterior uncertainty reduction during training confirms robust learning of healthy structural dynamics, supporting interpretable, risk-aware decisions in safety-critical railway infrastructure.

Abstract: The deployment of agentic artificial intelligence systems in clinical environments is accelerating rapidly, with autonomous agents increasingly applied across radiology, clinical decision support, intensive care monitoring, drug discovery, and patient facing care. Unlike conventional single turn AI tools, agentic systems autonomously plan multistep tasks, invoke external tools, retain memory across interactions, and pursue clinical goals with minimal human intervention, introducing a qualitatively distinct and poorly characterised safety profile that existing literature has not comprehensively addressed. This paper addresses that gap through a Systematic Literature Review conducted in accordance with PRISMA 2020 guidelines, synthesising evidence from 113 peer reviewed publications published between January 2019 and December 2025 across PubMed, IEEE Xplore, Scopus, ACM Digital Library, arXiv, and Web of Science. The review makes four original contributions: it develops the first structured failure mode taxonomy specific to agentic health AI, classifying seven distinct categories spanning reasoning failures, hallucination failures, tool misuse failures, memory failures, automation bias failures, adversarial and distributional failures, and equity and bias failures; it maps a clinical hallucination typology across factual, contextual, citation, and numerical types with associated risk profiles; it systematically evaluates existing safety frameworks and mitigation strategies including Retrieval Augmented Generation, Human in the Loop design, Constitutional AI, and red teaming against the identified failure mode taxonomy; and it proposes an integrated safety evaluation framework combining Failure Mode and Effects Analysis, the Swiss Cheese Model, and Human Factors theory as a practical governance tool for clinical deployment. The findings confirm that agentic health AI presents compounding safety risks driven by autonomy, multistep reasoning, tool access, and confidence presentation, that current mitigation strategies remain predominantly reactive and incomplete, and that critical gaps persist in standardised benchmarking, longitudinal deployment evidence, and equity focused evaluation, underscoring the urgent need for aligned engineering, clinical governance, and regulatory frameworks.

Abstract: Background/Objectives: The increasing prevalence of nutrition-related diseases and the limited availability of convenient, metabolically safe, high-protein foods represent a pressing public health challenge. This study aimed to evaluate the effects of four composite animal-derived high-protein ingredients based on collagen enzymatic hydrolysates on physical endurance, feeding behaviour, carbohydrate metabolism, renal function, and behavioural parameters in rats. Methods: Four lyophilised collagen hydrolysate-based ingredients were developed using enzymatic biotransformation of bovine and porcine raw materials, combined with whey protein concentrate, bovine meat trim hydrolysate, blood plasma proteins, and an api-component (Samples 1–4; protein content 87–89%). Ninety male Wistar rats were randomised into one control group and four experimental groups (n = 20 per experimental group, n = 10 controls) and received test samples by intragastric gavage at 3000 mg/kg/day for 40 days. Physical endurance was assessed via a weighted forced swimming test (days 0, 30, and 40); behavioural status by open field, adhesive removal, and marble burying tests; and biochemical parameters (blood glucose, serum urea, creatinine, urinary protein, and GFR) at days 0 and 40. Results: All experimental groups demonstrated a significant reduction in standard chow consumption (19–24%, p < 0.01) without affecting body weight gain. Physical endurance improved significantly in all groups relative to baseline, with the most pronounced effect in the Sample 3 group (+39% at day 40, p < 0.05). Blood glucose levels were significantly reduced across all groups (9–16%, p < 0.05). No adverse behavioural effects were observed. Biochemical markers indicated an adaptive rather than pathological renal response, with elevated GFR in three of four experimental groups (p < 0.05) and reduced proteinuria in the Sample 1 and Sample 3 groups. Conclusions: Forty-day administration of collagen hydrolysate-based protein complexes improved physical endurance and glucose metabolism, reduced food intake without compromising body weight, and did not impair renal function or behavioural status in healthy adult rats. These findings support the potential of such ingredients as functional food components, pending confirmation of long-term safety in extended studies.

Abstract: This study examines the impact of improved maize seed varieties (IMVs) on farm yield among smallholder Benue state, Nigeria and identifies key determinants of adoption. Benue State is often referred to as “Food Basket”, but has an average yield of less than 2 tons per hectare, compared to 8-10 tons per hectare that can be achieved under improved technologies. While previous nationally representative studies disguise local heterogeneity, this study focuses specifically on Benue State using primary cross-sectional data from 205 maize farmers. However, minimizing selection bias was carried out by matching adopters and non-adopters with similar observable characteristics and this method was introduced by using Propensity Score Matching (PSM) to estimate the causal impact of improved maize seed varieties (IMVs) adoption on maize yield. Nearest Neighbour Matching is used to compute the Average Treatment Effect on the Treated (ATET), with robustness checks using Radius and Kernel Matching. The results indicated that IMV adoption is significantly determined by gender (heads of male household), formal education, use of fertilizer, irrigation access, members of cooperative, and extension contact, emphasizing the significant roles of human capital, complementary inputs, as well as institutional support. Afterwards, the control of observable differences through matching led adopters to achieving a yield gain of 0.399 log-units which is relative to non-adopters that were not matched, and this is equivalent to 49% increase in output per hectare. The robustness across alternative matching algorithms is effective, compared with national-level evidence reporting a 38.7% yield increase [11]. Our finding suggests that the productivity of premium for IMVs may be greater in regions like Benue. The reliability of this treatment effect is confirmed using alternative matching algorithms in Robustness checks. Conclusively, the study of IMVs full potential is limited by inadequate access to quality seeds, complimentary inputs, funds, and gender-specific interventions.

Abstract: The growing consumption of plant-based meat alternatives (PBMAs) has increased attention to their microbiological safety, particularly under refrigerated storage conditions. Although the PBMA market has expanded rapidly, data on the microbiological status of industrially produced, heat-treated products remain limited. The present study aimed to evaluate, within a descriptive framework, the microbiological safety of industrially produced, heat-treated PBMAs during refrigerated storage. A total of 100 PBMA formulations, including salami-type and frankfurter-type ready-to-eat products, were manufactured under standardized industrial conditions and subjected to validated thermal processing (core temperature ≥ 92 °C for varying durations). Microbiological analyses were conducted at four predefined storage intervals (day 0, day 15, day 35, and day 60 at 0˗4 °C) to assess the presence of selected foodborne pathogens (Salmonella spp. and Listeria monocytogenes) and hygiene indicator microorganisms (generic Escherichia coli, Enterobacteriaceae, coagulase-positive Staphylococcus aureus, and Bacillus cereus). Intrinsic physicochemical parameters relevant to microbial survival and growth, including pH, water activity, moisture content were also determined. Salmonella spp. and Listeria monocytogenes were not detected (absence in 10 g) in any sample at any storage time point. Hygiene indicator microorganisms were not detected during early storage (day 0-15), while limited occurrence was observed at extended storage (day 60), including Escherichia coli (3%), coagulase-positive Staphylococcus aureus (20%), and Bacillus cereus (15%). Detected Staphylococcus aureus levels ranged between 103 and 105 CFU/g. These findings indicate strong microbiological stability during early refrigerated storage, with limited microbial occurrence at extended storage intervals (day 60). Overall, the evaluated products demonstrated a favorable microbiological safety profile under the applied processing and storage conditions. Given formulation heterogeneity and the absence of biological replication, findings are interpreted descriptively and provide an industrially relevant safety overview rather than inferential conclusions.

Abstract: The renin–angiotensin–aldosterone system (RAAS) is a central regulator of blood pressure and fluid homeostasis. However, its dysregulation contributes to the development of cardiovascular and chronic kidney diseases, including hypertension, diabetes, and metabolic disorders. The identification of the prorenin receptor (PRR) has expanded the understanding of RAAS, revealing functions beyond its classical role in angiotensin II (Ang II) generation. In this review, we provide an updated and integrative overview of PRR biology, emphasizing its multifunctional roles in both Ang II–dependent and independent signaling. PRR also functions as an accessory component of the vacuolar H⁺-ATPase and participates in key intracellular pathways, including ERK1/2-MAPK, PI3K/Akt, and Wnt/β-catenin. Through these mechanisms, PRR contributes to cardiovascular remodeling, renal inflammation and fibrosis, metabolic dysregulation, and angiogenesis. Emerging evidence further identifies the soluble form of PRR (sPRR) as a biologically active circulating factor with endocrine-like properties. Clinical and experimental studies suggest that sPRR serves as both a biomarker and a mediator linking tissue RAAS activation to systemic cardiorenal and metabolic disease progression. Collectively, this review highlights PRR as a central molecular hub that integrates extracellular hormonal signals with intracellular metabolic and inflammatory pathways, underscoring its relevance in the pathophysiology of cardiovascular, renal, and metabolic diseases.

Abstract: The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and forest stand characteristics. In the Republic of Korea (hereafter ROK), most wildfires are caused by anthropogenic factors rather than by natural factors. However, the current forest fire forecasting system being operated in ROK does not account for anthropogenic factors. To analyze the impact of human factors, along with physical factors, on wildfire occurrence, a binary logistic regression model was constructed with data for the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, artificial forests could boost forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending the rotation age of forests and the conversion of coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access to prevent wildfires.

Abstract: Standard quantum information is formulated over complex Hilbert spaces, where the pure state space of a single qubit is geometrically encoded by the first Hopf fibration S³→S². Beyond this familiar setting, the normed division algebras C, H and O provide a hierarchy of increasingly rich algebraic and topological structures that has motivated several extensions of quantum theory and quantum computation. This review synthesizes the literature connecting quaternionic and octonionic frameworks, Clifford algebras, spinors, projective spaces and Hopf fibrations. We emphasize a central conceptual point that is often blurred in the literature: the second and third Hopf fibrations play two distinct roles, namely, (i) as kinematical descriptions of hypercomplex single-particle state spaces such as the quaternionic projective line HP¹≃S⁴, and (ii) as entanglement-sensitive descriptions of multi-qubit complex systems, especially two- and three-qubit Hilbert spaces. On the algebraic side, Clifford and geometric algebras provide a natural language for rotations, spinors, and gate synthesis, while quaternionic Hilbert modules furnish a mathematically consistent extension of standard qubit kinematics and dynamics. By contrast, octonionic models face major obstructions due to non-associativity, which affects inner products, tensor products, spectral theory and circuit composition. We therefore distinguish carefully between robust results, partial constructions and speculative directions. The outcome is a unified geometric review of hypercomplex quantum information, together with a map of open problems at the interface of topology, noncommutative algebra, and quantum computation.

Abstract: Background/Objectives: Astragalus root, a traditional Chinese herbal remedy, has shown potential benefits against diabetic nephropathy (DN). However, the mechanisms driving its effects remain poorly understood. This study explored the molecular pathways through which Astragalus root improves DN. Methods: To identify possible targets and mechanisms of Astragalus root in DN treatment, we applied network pharmacology, molecular docking, molecular dynamics simulation, and in vitro assays. Results: Network pharmacology screening uncovered 46 overlapping targets between Astragalus root and DN. Protein-protein interaction (PPI) network analysis identified five core candidate targets: CASP3, VEGFA, CTNNB1, MYC, and PRKCB. KEGG pathway analysis indicated that the AGE-RAGE signaling pathway was the most significantly enriched. Molecular docking revealed that quercetin, β-carotene, daidzein, capsaicin, and kaempferol—major bioactive components of Astragalus root—bound strongly to each of the five core targets. Molecular dynamics simulations further confirmed the conformational stability of kaempferol when complexed with these target proteins. In vitro experiments showed that kaempferol markedly reduced protein levels of α-SMA, Col I, and Col IV; lowered secretion of TNF-α, IL-6, and IL-1β; and decreased ROS and MDA content. Additionally, kaempferol's therapeutic effects were mediated through suppression of the AGE-RAGE-PKC-TGF-β signaling axis. Conclusions: This work identified kaempferol, a bioactive ingredient of Astragalus root, as a potential therapeutic agent against DN, along with its target pathways. These findings provide a scientific foundation for its clinical translation.

Abstract: The existence of supermassive black holes (SMBHs) within the first 800 million years after the Big Bang remains difficult to explain and is still under active debate. At the same time, a dynamical vacuum energy density has been proposed as a possible solution to the cosmological coincidence problem. It is therefore natural to explore its implications for black hole evolution. In this work, we study the rapid growth of SMBHs in a decaying-vacuum cosmology with a time-dependent cosmological constant. In this framework, black holes can grow at rates far exceeding the Eddington limit, which can be phenomenologically described as an effective conversion of vacuum energy into black hole mass. This mechanism may offer a new perspective on the formation and early growth of SMBHs.

Abstract: Interventions implemented in geographic space (geo-interventions), have had success in reducing preventable deaths across the world. However, many studies supporting geo-interventions have focused on where to implement them rather than what they are. In this paper, we answer how to model and generate geo-interventions using spatial data, providing what these geo-interventions are and where to apply them. We defined geo-intervention modelling as a problem of optimizing actions and their locations, given the objective of maximizing predicted outcomes. To solve this, we produced a framework for transforming spatial data to model potential actions for generating geo-interventions. Finally, we conducted a case study of reducing traffic collisions in Toronto, Canada, to demonstrate the framework, which produced a machine learning model that discovered geo-interventions modifying red light camera, transit shelter, and wayfinding infrastructure predicted to reduce collisions by 5.7%. We highlight the importance of the framework for bridging research and practice through unified understanding, actionable outputs, human guidance, and iterative refinement. With recent advances in big data and artificial intelligence, we envision an acceleration in the discovery of geo-interventions, and emergence of interdisciplinary work towards predicting accurate and precise future real-world outcomes at scale.

Abstract: Real-time video object detection on unmanned aerial vehicles (UAVs) is essential for urban inspection and autonomous perception, yet its deployment on edge devices is severely constrained by the high computational cost of accurate detectors, the quantization sensitivity of hybrid convolution-attention networks, and the system-level latency of full video processing pipelines. To address these challenges, we present DUST-YOLO, a deployment-oriented algorithm-hardware co-design framework for lightweight and efficient UAV small-object detection on edge platforms. First, we introduce a multi-dimensional structured pruning strategy that applies asymmetric channel pruning to convolutional and feature-fusion modules while compressing the Swin Transformer prediction heads and bottleneck stacks, thereby reducing parameters and computation with limited impact on multi-scale representation capability. Second, we develop a hardware-aware mixed-precision quantization-aware training (QAT) scheme that maps computation-intensive backbone layers to INT8 while preserving the Transformer-related modules in FP16, improving inference efficiency while mitigating the accuracy loss caused by uniform low-bit quantization. Third, we compile the optimized network with TensorRT and integrate the resulting inference engine into a DeepStream-based asynchronous video pipeline on the edge platform, enabling end-to-end acceleration by reducing decoding, preprocessing, and memory-transfer overheads. Experimental results on the VisDrone2019-DET dataset and the NVIDIA Jetson Orin NX demonstrate that DUST-YOLO achieves 43.7% mAP@0.5 acuracy with an end-to-end latency of 36.3 ms and a throughput of 27.5 FPS. Compared with the state-of-the-art detector, DUST-YOLO reduces end-to-end latency by 56.9% and improves end-to-end video throughput by ×2.31, while lowering total energy consumption by 68.5%.

Abstract: Recognizing emotions from written text is a very important part of Natural Language Processing (NLP) and is commonly used for feeling or sentiment analysis or keeping track of someone’s mental health status. This study uses a readable emotion-detecting framework with a RoBERTa-base model that has been modified and trained specifically for the Emotions for NLP dataset and provides an accuracy of 0.924% and f1 score of 0.925%. The main contributions of this study are the use of four different techniques that will help understand how the model works: SHAP (SHapley Additive exPlanations) provides global token credit attribution; LIME (Linear Interpretable Model-Agnostic Explanation) provides instance-level explanations; multi-head Attention Visualization provides structural interpretability; and Integrated Gradients via Captum provides gradient-based attribution using integration. The combination of these four techniques works together to improve transparency, help identify bias in the models, and support the responsible use of this model. Finally, the developers of this model performed many experiments that demonstrated the consistency with which the model could identify important emotional tokens (words or phrases) as predictive indicators of emotion.

Abstract: Lentinula edodes (L. edodes) is a significant edible and medicinal mushroom with essential nutrient elements for its growth, including Fe²⁺, K⁺, and Mn²⁺. However, the molecular mechanisms by which these metal ions regulate the mycelial growth of L. edodes have been poorly elucidated at the transcriptomic level. In this study, plate culture was performed using concentration gradients to screen for optimal concentrations. Transcriptome sequencing (RNA‑seq) and qRT‑PCR validation were performed to elucidate the regulatory effects and molecular mechanisms of the three metal ions on the mycelial growth of L. edodes. The results showed that Fe²⁺ at concentrations above 20 µg/mL significantly inhibited mycelial growth; K⁺ at 1200 µg/mL and Mn²⁺ at 50 µg/mL significantly promoted mycelial growth, with increases of 21.22% and 10.77%, respectively. Transcriptomic analysis revealed that Fe²⁺ primarily induced abnormal protein folding and suppressed material and energy metabolism, thus inhibiting mycelial growth. Mycelial growth is promoted by K⁺ by enhancing detoxification and secondary metabolism and by activating mitochondrial function and the oxidative phosphorylation pathway. The proliferation and growth of mycelial cells are regulated by Mn²⁺ through mechanisms that govern DNA repair and recombination, cell cycle progression, and detoxification. This study elucidates the differential regulatory mechanisms of the three metal ions on the mycelial growth of L. edodes at the transcriptomic level, offering a rationale for enhancing mineral nutrition and high‑yield cultivation of L. edodes.

Abstract: Institutional Review Boards (IRBs) exercise veto power on most empirical research in the social and behavioural sciences. Although widely regarded as essential safeguards in behavioural research, their overall impact on knowledge production has been seldom scrutinized, much less systematically examined. Rather than evaluating IRBs in terms of their stated aims, this article considers them as institutions based on process characteristics: that is, as decision making units facing bureaucratic incentives to impose costs on others. From this political economic perspective, ethics review functions not as a neutral guardrail, but as an active agent influencing the selection pressures within the scientific ecosystems they regulate. This article examines the following key mechanisms through which IRBs affect knowledge production: (1) cost inflation and quality dilution that reduces both the supply of and demand for the knowledge produced by research; (2) selection effects operating on researcher characteristics and on the bureaucratization of decision-making processes in a direction detrimental to the quality and integrity of research production; and (3) non-random distortions of methods, topics, and rates of independent replication are all expected to contribute to a reduction in the practical significance and societal benefit of affected academic institutions. These impacts escalate because of asymmetric accountability and motivated mission expansion in a system where overreach more often self-reinforces than becomes restrained by corrective feedback. This points to empirical predictions and highlights the need to quantify the real costs of unchecked IRB expansion.

Abstract: General Relativity predicts the formation of cosmological and gravitational singularities and, being fundamentally time-reversal invariant, lacks an intrinsic mechanism for the emergence of an arrow of time. In this work, we construct a covariant effective field theory (EFT) extension of gravity based on curvature invariants that implements a dual regularization mechanism. The framework combines (i) a bounded-curvature kernel (sinR-type operator) that dynamically saturates high-curvature growth, and (ii) a geometric memory contribution (“slip”) that correlates the expansion rate with its temporal variation, thereby regulating curvature flow. Within a controlled regime below an explicit curvature cutoff, the resulting field equations remain second order and admit a nonempty, algebraically characterized perturbatively stable parameter domain. A canonical Hamiltonian (ADM) analysis fixes the degree-of-freedom counting and supports the absence of additional pathological propagating modes within the EFT regime. In homogeneous cosmology, the dual mechanism yields nonsingular bouncing solutions with finite curvature invariants and ultraviolet damping driven by geometric memory. The bounce can be interpreted as a transition between contracting and expanding phases governed by curvature regulation rather than singular dynamics. Perturbative analysis indicates stability of both tensor and scalar sectors throughout the EFT-consistent domain. Geometric memory introduces an effective temporal ordering of cosmological solutions: a relational time variable can be defined that evolves monotonically along dynamical trajectories, while the underlying action remains local, covariant, and CPT invariant. This suggests a dynamical origin for the arrow of time without explicit symmetry breaking. The framework predicts characteristic observational signatures, particularly in gravitational-wave physics. These include curvature-dependent damping of tensor modes, potential deviations in the primordial stochastic gravitational-wave spectrum, and imprints associated with nonsingular bounce dynamics, providing concrete avenues for observational tests. Rather than an ultraviolet completion, the theory is a structurally consistent curvature-based EFT with explicit stability control and a well-defined domain of validity, offering a controlled setting to explore singularity resolution, emergent temporal structure, and testable deviations from standard cosmology.

Abstract: Whole-slide imaging has transformed histopathology into a data-intensive domain, with current approaches dominated by end-to-end deep learning that encode morphology implicitly within latent representations. This limits interpretability, reproducibility, and cross-dataset generalization. This review positions histomics as an intermediate phenotype representation layer that maps histological images to structured, multi-scale descriptors of tissue morphology, spatial organization, and architectural context. A unified taxonomy of histomic features across biological scales is presented, along with an analysis of artificial intelligence frameworks spanning classical machine learning, deep learning, weakly supervised learning, and multimodal integration. The review presents core failure modes in histomic pipelines, including segmentation dependence, feature instability, and domain shift, and examines their impact on robustness and generalization. Emerging trends in representation learning and multimodal modeling are analyzed in the context of phenotype-centric inference. Overall, this work reframes histomics as a representation-driven paradigm and outlines directions for developing stable, interpretable, and generalizable computational pathology systems.

Abstract: The purpose of the study is to analyse and to identify structural characteristics reffering to melanocytic nevi, in youth patients. Using both optical and electronic microscope, could be possible a better describtion related specificity in melanocytic nevi characteristics. Epiderm is composed by specific layers. From a curently research pespective we can mention that in utero, specific stem cells from the neuroectoderm play a signifiant role such as migration to the skin as melanoblasts. Future trends, are important key points in management, including preventive and prophylactic methods.