Abstract: The rapid growth of Internet of Things (IoT) ecosystems has significantly increased cybersecurity threats due to device heterogeneity, resource limitations, and exposure to distributed attacks. Although Federated Learning (FL) has emerged as a promising privacy-preserving machine learning paradigm for decentralized intrusion detection, existing FL approaches often suffer from non-independent and identically distributed (non-IID) data, communication inefficiency, adversarial attacks, and unstable convergence in heterogeneous IoT environments. This study proposes a Privacy-Enhanced Federated Learning (PEFL) framework for adaptive and secure intrusion detection in large-scale IoT networks. The framework integrated differential privacy, secure aggregation, adaptive client selection, trust-aware federated optimization, and edge-assisted hierarchical coordination to improve robustness, scalability, and communication efficiency. The framework was evaluated using benchmark cybersecurity datasets, including CICIDS2017, UNSW-NB15, TON_IoT, and Bot-IoT under heterogeneous and adversarial conditions. Experimental results established that the proposed PEFL framework achieved improved intrusion detection accuracy, faster convergence stability, enhanced resilience against poisoning attacks, and reduced communication overhead compared with conventional FL approaches such as FedAvg and FedProx. The findings further indicated that adaptive client selection and trust-aware aggregation significantly improve model reliability and robustness in resource-constrained IoT environments. This framework will contribute toward the development of scalable, privacy-preserving, and deployable federated intrusion detection systems for next-generation intelligent IoT infrastructures.

Abstract: The long-term relationship between climate change, vegetation change and soil development, is a highly complex process. Findings of multiproxy (sedimentological, MS, geochemical (AAS, XRD), micromorphological, anthracological, phytolith and malacological) studies from a loess/paleosol sequence in northeastern Hungary highlighted the transformation of a reddish-brown fossil soil layer (cambisol) to a podzolic soil with signs of iterative wildfires during the terminal part of MIS3. According to our findings, a Scots pine (Pinus sylvestris) dominated open parkland emerged on the northern slopes during the second phase of MIS3 hosted by a special reddish-brown soil. Then the last phase of MIS3 was marked by the development of spruce (Picea) dominated open parkland. Results further suggest that vegetation change passed a critical threshold leading to an unusually rapid expansion of spruce (within ca. 100 yr). This rapid expansion of spruce, changing the geochemistry of the litter to a more acidic state likely caused the initiation of podzolization and the transformation of the original soil. The opening of MIS2 marked not only intensive dust accumulation but a steady decline of arboreal elements as well leading to the emergence of a cold tundra on top of the podosol with charcoal remains.

Abstract: Brasenia schreberi is a nationally protected aquatic macrophyte of substantial ecological value and economic significance, yet its wild populations have declined drastically due to habitat degradation and anthropogenic disturbances. This review systematically synthesizes research progress on the effects of water pH and depth on the growth, ecophysiology, mucilage quality, and community structure of B. schreberi, integrating findings from field surveys and controlled greenhouse experiments to elucidate critical ecological thresholds under combined environmental stressors. Our analysis reveals that natural B. schreberi populations are predominantly distributed in lentic habitats with stable water depths of 0.5-1.5 m (optimally 1.2-1.5 m) and circumneutral to weakly acidic conditions (pH 6.0-7.5). Deviations from these parameters substantially impair plant performance: when water depth exceeds 1.5 m or pH falls below 5.5, photosynthetic efficiency declines, root-to-shoot ratios increase aberrantly, and mucilage thickness decreases significantly. The synergistic critical threshold for population decline was identified at 1.1 m depth × pH 6.3. For artificial propagation, optimal cultivation strategies diverge from wild habitat preferences: maintaining pH at 7.0-7.5 (weakly alkaline) enhances mucilage polysaccharide accumulation and commercial quality, whereas a phenological stage-specific dynamic water-depth management regime (“shallow-deep-shallow-deep”) maximizes vegetative propagation success and yield. This review provides a theoretical framework and parameterized technical guidance for wild population restoration, standardized cultivation, and hydrological regulation in plateau wetland ecosystems. Future research priorities should focus on elucidating the molecular mechanisms underlying pH- and depth-mediated mucilage synthesis, developing precision water quality management systems, and strengthening ex situ germplasm conservation.

Abstract: Smart warehouses rely on fleets of autonomous mobile robots that must continually assign tasks, plan paths, avoid collisions, and maintain battery energy. Existing lifelong multi-agent path finding studies often emphasize travel cost or makespan, while practical deployments also involve charging, payload-dependent energy use, turning and waiting costs, and congestion. This paper presents an energy-constrained hybrid repair framework for lifelong multi-agent path finding in warehouses. The method combines a risk-aware graph representation, search-based safety repair, and learning-compatible policy modules, and it is evaluated in a reproducible Python simulator. We compare independent and prioritized A* planning, windowed cooperative planning, large-neighborhood repair, lazy configuration search, bounded conflict-based search, the proposed repair variant, and a graph neural learning baseline. The virtual evaluation reports raw task completion separately from energy-feasible completion, together with collision, charger-conflict, energy, scalability, ablation, sensitivity, and case-study measures. On 40×40 warehouse maps with 20 robots, large-neighborhood repair improves raw success from 0.345 to 0.468 relative to windowed cooperative planning and reduces energy per completed task from 369.34 to 276.83. The proposed repair variant reduces several conflict measures, but throughput remains problem-dependent. The results support energy-aware repair as a practical direction for warehouse robot coordination.

Abstract: This paper presents a theoretically informed critical review of climate change discourse in Sub-Saharan Africa. Drawing on peer-reviewed scholarship and authoritative policy documents, it examines how climate knowledge is framed, communicated, authorized, and translated into public and policy use. Guided by decolonial theory and the Multiple Evidence Base approach, the review assesses climate discourse through four linked dimensions: epistemic authority, communicative accessibility, representational framing, and policy relevance. The review finds that recent scholarship and policy increasingly recognize Indigenous and local knowledge, public participation, climate education, and context-specific communication. However, significant gaps remain between formal recognition and operational integration. Climate literacy continues to vary across and within African countries; climate services become useful only when institutions align them with user needs and local decision-making contexts; and educational and policy discourse can still reproduce epistemic hierarchy even when it invokes inclusion. The paper contributes to sustainability scholarship by showing that demystification and decolonization are complementary requirements for inclusive climate governance and sustainable development. Demystification improves the intelligibility and usability of climate knowledge, whereas decolonization strengthens legitimacy by challenging hierarchies that privilege some knowledge systems while marginalizing others. A stronger climate discourse for Sub-Saharan Africa, therefore, requires institutional changes in how actors authorize knowledge, translate uncertainty, frame vulnerability and agency, and design climate communication, education, and services for public and policy use.

Abstract: Unmannedaerial vehicle (UAV)-assisted millimeter-wave (mmWave) and terahertz (THz) communications are promising enablers of ultra-reliable and low-latency communication in next-generation wireless networks. However, the initial access and beam alignment process remains challenging because highly directional beams must be rapidly aligned in a three-dimensional environment. In this paper, we investigate a risk-aware beam alignment framework for UAV-assisted mmWave/THz systems, where user equipment scans a 3D spherical region to detect UAV base stations. The objective is to jointly minimize the expected cell-search latency and its variance while satisfying detection-failure and link-quality constraints. To solve this non-convex optimization problem efficiently, we employ the Lévy Self-Renewable Flow Direction Algorithm (LSRFDA), which combines Lévy-flight exploration with self-renewal to improve convergence robustness. A unified propagation model is adopted to cover both mmWave and THz regimes by incorporating free-space spreading loss and frequency-dependent molecular absorption. Extensive Monte Carlo simulations compare the proposed approach with Particle Swarm Optimization, Random Search, Reinforcement Learning, and PPO-Lagrangian methods. The results show that LSRFDA achieves lower latency, lower latency variation, more reliable detection, and lower energy consumption across a wide range of UAV densities and coverage radii. These outcomes highlight the effectiveness of risk-aware geometric optimization for fast and dependable initial access in UAV-assisted 5G mmWave and 6G THz networks.

Abstract: This work presents a comprehensive theoretical analysis and numerical calculation of the fundamental physical parameters surrounding a non-rotating, spherically symmetric Schwarzschild black hole. Quantitative Analysis of Schwarzschild Black Hole Spacetime Radius, Light Deflection, Redshift, and Tidal Phenomena. Utilizing General Relativity, to compute the Schwarzschild radius as the defining event horizon. The gravitational time dilation, showing the dramatic slowing of time as the event horizon is approached, and the gravitational redshift of signals emitted from near the horizon. Additionally, this study calculates the relativistic deflection angle of light in the weak-field limit using the geodesic equation. To analyse the structural integrity of objects near the black hole, I have calculated the tidal acceleration and resultant tidal force, demonstrating that tidal stresses approach infinite values at the singularity, causing powerful tidal disruptions and “spaghettification”. Planetary mass black holes have a tiny size and an intense gravitational field to tear apart objects passing nearby their external surface, in contrast to supermassive black holes. These calculations provide a unified model for validating relativistic effects, offering precise quantitative measurements for astrophysical observation. Gravitational time dilation near a black hole is a profound prediction of Einstein’s general relativity, where intense gravity causes time to pass significantly slower for objects closer to the event horizon compared to distant observers. This effect means that an observer near the horizon experiences time as almost frozen from an external viewpoint.

Abstract: This study examines whether a rubric-guided large language model (LLM) can approximate local human grading practice for text-based responses in three university courses. A total of 930 student responses from Prompt Engineering, Photoshop Design, and AI Video Production were scored by two human instructors and by ChatGPT using the same five-criterion analytic rubric (Accuracy, Logical Flow, Specificity, Quality, and Originality; 0.0–3.0 each; Total 0–15). Human consensus (HC) was defined as the mean of the two human scores and was treated as a pragmatic reference rather than ground truth. Pairwise agreement among H1, H2, AI, and HC was evaluated using ICC(3,1), Pearson correlations, mean absolute error (MAE), and Bland–Altman bias and limits of agreement (LoA); a course-specific held-out calibration analysis was additionally conducted. On the Total score, human–human agreement was strong (ICC = 0.819 [0.797, 0.839]). AI–H1 and AI–H2 Total-score agreement were ICC = 0.700 [0.666, 0.732] and 0.767 [0.739, 0.792], respectively, while AI–HC agreement was ICC = 0.763 [0.735, 0.789], with MAE = 1.603 and LoA = [−4.246, 4.045]. At the trait level, AI–HC ICCs exceeded H1–H2 ICCs for all five rubric dimensions, although Quality remained weakly defined in the human baseline. On a 70/30 held-out test split, a course-specific linear calibration modestly improved Total-score ICC from 0.774 to 0.782 and reduced MAE from 1.624 to 1.215, narrowing the LoA from [−4.290, 4.188] to [−3.157, 3.329]. However, threshold-adjacent agreement remained imperfect after calibration. The findings concern written responses only and support a conservative conclusion: rubric-guided LLM scoring can assist human grading under fixed local rubrics, but the current evidence supports calibrated human–AI co-grading rather than unsupervised replacement.

Abstract: Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, newly established or emerging-market WWTPs often lack historical aeration logs, making it difficult to construct high-precision surrogate models. Conventional cross-plant model deployments face severe data distribution shifts, and standard multi-objective optimization algorithms are prone to generating non-physical extrapolation errors, such as achieving compliance with "zero aeration" under low-concentration conditions. To break through inter-plant data barriers, this study proposes an intelligent aeration decision-making framework that integrates cross-domain transfer learning with physics-informed constraints. First, this study designs an adversarial network based on air-to-water ratio and removal rate features. By employing a gradient reversal layer (GRL) to extract domain-invariant representations, this network achieves cross-plant knowledge transfer. Second, this study proposes a physics-informed multi-objective particle swarm optimization (PI-MOPSO) algorithm, which embeds the theoretical oxygen demand as a physical penalty into the fitness function, ensuring the physical reliability of the optimization decisions. Experiments demonstrate that the surrogate model restricts the prediction errors for effluent chemical oxygen demand (COD) and total nitrogen (TN) removal rates to within 1%. Validated by statistical tests, the improved algorithm effectively circumvents non-physical prediction biases. Its Pareto front achieves a spacing metric of 0.0027, outperforming baseline algorithms in hypervolume stability. This framework provides optimal aeration scheduling strategies conforming to biochemical dynamics for target WWTPs lacking aeration action labels, demonstrating substantial practical engineering value.

Abstract: Avian viruses formed the foundation of early retrovirology. The historical line extends from the discovery of the first sarcoma virus by Peyton Rous to the quantitative determination of oncogenic activity in cell culture by the focus assay. As a viral group, avian retroviruses offered exclusive advantages that allowed the assembly of a unique and powerful tool chest for the analysis of viral activity. Among the fundamental discoveries facilitated by these tools were viral and cellular oncogenes, cell surface receptors, virus-specific detection of inapparent infection, high-frequency genetic recombination between retroviruses, and the genetic maps of simple retroviruses. The work with avian viruses was soon complemented by research on mammalian retroviruses, and several oncogenes that became the basis of successful targeted therapies were defined. The field of cancer genes is at a point of transition. Future developments will be driven by new technologies and interpretations. They will also require a more comprehensive approach.

Abstract: Background/Objectives: Previous studies have demonstrated the safety of pre-seasonal treatment with the mannan-conjugated birch pollen allergoid EP-088-T502. However, the safety of a combined pre- and co-seasonal treatment regi-men has not yet been investigated. This study aimed to compare the safety and tolera-bility of pre-seasonal versus pre- and co-seasonal treatment with EP-088-T502. Meth-ods: In this prospective, open-label, phase III trial (T502-SIT-059) (EudraCT No.:2022-004082-20), patients (N=109) who had participated in a preceding pivotal phase III study were offered continuation treatment with active EP-088-T502 (10.000 mTU/mL) across five treatment visits. For the subgroup analysis, all patients who completed their last treatment visit before 9 April 2023 (and thus before the start of the birch pollen season in Germany) were assigned to the pre-seasonal group (N=20). Those who performed the last treatment visit thereafter were assigned to the pre/co-seasonal group (N=83). Both groups were compared in terms of local and sys-temic immediate and late phase reactions and other EP-088-T502-related adverse events (AEs). Results: No deaths nor serious adverse events (SAEs) were reported during the study. No epinephrine administration was required. Systemic adverse drug reactions (SADRs, N=3) occurred in two patients who had previously received placebo. No grade III or IV systemic reactions according to the German AWMF classification were observed. Patients receiving pre- and co-seasonal treatment developed smaller wheals (mean diameter) compared with the pre-seasonal group (immediate reactions: 0.6 vs. 0.7 cm, late phase reactions: 0.3 vs. 0.4 cm at the last treatment visit). This was also reflected in the medians (immediate reactions: 0.2 cm vs. 0.4 cm, late-phase reac-tions: 0.2 vs. 0 cm at the last treatment visit). Of all AEs that were (possibly) related to EP-088-T502 (N=89), 74 (83%) occurred at the first three treatment visits (before the birch pollen season). The frequency of AEs was comparable between groups for the last two treatment visits. Patients who had received placebo in the previous trial experienced more treatment related side effects compared to patients who had already received EP-088-T502 in the previous year. Conclusions: These data suggest that EP-088-T502 is safe and well tolerated even when administered during the birch pollen season, regardless of prior exposure to EP-088-T502.

Abstract: Background/Objectives: Primary care clinics increasingly receive urgent and semi-urgent requests from patients who may otherwise attend emergency departments or urgent care centres when same-day physician or nurse practitioner appointments are unavailable. A meaningful proportion of emergency department visits involve conditions that could potentially be managed in primary care [1,2], and the Canadian Institute for Health Information reported that 15% of Canadian emergency department visits between April 2023 and March 2024 involved conditions that could potentially have been managed in primary care [3]. This article describes the Registered Nurse Prescriber-led Triage-Treatment-Continuity model developed at Cranston Ridge Medical Clinic in Calgary, Alberta, Canada. Methods: The manuscript is reported as a clinic-based practice innovation and service evaluation using aggregate, non-identifying operational service data. The model includes medical office assistant emergency recognition, RN prescriber-led structured triage, a traffic-light urgency classification system, a booking algorithm, clinical support tools, diagnostic test ordering and prescribing within authorized scope, and communication with the patient's primary care provider through the electronic medical record. No patient-identifiable information, patient-level chart review, interviews, surveys, biological samples, or experimental interventions were used. Under TCPS 2 Article 2.5, quality improvement and program evaluation activities conducted exclusively for assessment, management, or improvement purposes do not constitute research for that policy and do not fall within Research Ethics Board review [4]. Results: During a 12-month service evaluation period from April 2025 to April 2026, 5032 patient calls or encounters were managed through the RN prescriber-led pathway. These encounters are interpreted as internal urgent and semi-urgent primary care capacity and potential diversion, not as confirmed emergency department avoidance. Conclusions: The model reframes triage as an integrated primary care intervention rather than a passive sorting process. Further ethics-approved research is required to evaluate patient-level outcomes, safety events, comparative effectiveness, confirmed health-system utilization effects, and cost-effectiveness.

Abstract: Understanding the mechanical behavior of bimetallic nanoparticles under compressive stress is relevant for the use of these nanostructures in catalysis and nanomechanics. In this work, we present molecular dynamics (MD) simulations of compressive deformation in Pt-Ni nanoparticles—and bulk systems for comparison—with varying compositions (PtxNi1−x) and local distributions, performed using LAMMPS. The simulations show that the mechanical response is governed by local strain fields, which influence both elastic and plastic regimes. Post-processing analysis was made using OVITO, simulated STEM imaging, and Geometric Phase Analysis (GPA), which allowed the obtention of high-resolution strain maps. Analysis of von Mises stress distribution allowed us to correlate composition and atomic ordering with the formation and evolution of dislocations in the nanoparticles. The intermetallic compound with x=0.5 exhibits superior mechanical performance under uniaxial compression, with enhanced elastic energy storage is in bulk. In polycrystalline nanoparticles, energy dissipation increased with decreasing average grain size, which is attributed to elevated plastic activity induced by the presence of multiple crystallographic orientations. GPA results show that it is possible to discriminate between compositions differing by as little as Δx = 0.1 based on local strain distributions, and the comparison with GPA performed on real STEM micrographs gives a fair agreement. GPA and atomistic stress maps reveal how strain fields evolve during compression and how they correlate with the development of plasticity. These findings highlight the critical role of local structural heterogeneities in dictating the mechanical behavior of nanoscale Pt-Ni systems, and give strong evidence on the capability of GPA to correlate local strain and composition in real high-resolution micrographs.

Abstract: We study finite orbit-sum termination for the two-sided exponential iteration generated by \( f(x)=2^x-1 \) and its inverse on the unit interval. For \( w\in(0,1] \), put \( u_k(w)=f^k(w) \) for \( k\in\mathbb Z \). A binary digit sequence \( a=(a_k)_{k\in\mathbb Z} \) with $a_0=1$ is normalized by \( \sum_{k\in\mathbb Z}a_k u_k(w)=1. \) Thus the expansion scale is generated by the point being expanded, rather than by an external base or partition, and finite termination is governed by finite orbit-sum equations. We prove existence and uniqueness of normalization roots for admissible digit sequences, construct the associated greedy code, and characterize finite termination by finite orbit-sum hitting equations compatible with the greedy order. The finite terminal set is countable and has Lebesgue measure zero. On the arithmetic side, the first positive and first negative boundary roots are transcendental, and the first positive second-order boundary root is irrational. Assuming Schanuel's conjecture, we exclude all non-trivial rational finite terminal points and prove transcendence for all purely positive finite roots, for all two-term boundary roots paired by the mirror identity, and for all roots with one first negative layer and arbitrary finite positive support.

Abstract: This paper presents the Micro Video Guidance Sensor (UVGS-2), an advanced evolution of the Smartphone Video Guidance Sensor (SVGS), a vision-based 6-DOF pose estimation system for proximity operations in spacecraft, UAVs, and mobile robot platforms. The system is based on the photogrammetric estimation of a beacon’s relative position and attitude in a camera coordinate system, by processing images of illuminated 4-points in the beacon with known non-coplanar geometry. Estimation of the beacon’s 6-DOF position and attitude (XYZ, RPY) in a coordinate system attached to the camera is achieved with higher accuracy compared to standard localization methods based on mapping or inertial navigation. Image acquisition, feature extraction, and state estimation are executed using hardware resources (camera and CPU) of the host robot, resulting in a low-mass, low-power, computationally efficient sensing architecture suitable for embedded and resource-constrained systems. As case study, SVGS and UVGS-2 have been deployed as real-time guidance, navigation, and motion control sensor through integration with NASA’s Astrobee free-flying robot aboard the International Space Station (ISS), supporting autonomous proximity operations and formation flight. Sensor fusion based on the use of existing localization pipelines improves robustness against line-of-sight interruptions and illumination disturbances, and improves accuracy compared to the native Astrobee localization system (AstroLoc). Other case studies have demonstrated high-precision positioning performance in autonomous UAV landing experiments, with reliable target tracking over extended ranges and low angular estimation error, outperforming existing infrared-based landing approaches.

Abstract: Endophytic fungi are microorganisms that reside asymptomatically within healthy plant tissues and establish complex symbiotic relationships with their host plants. In recent years, these microorganisms have gained increasing attention due to their remarkable ability to produce diverse secondary metabolites with significant biological and pharmacological activities. Numerous studies have demonstrated that endophytic fungi isolated from medicinal plants can synthesize bioactive compounds exhibiting antioxidant, antimicrobial, antiviral, anti-inflammatory, antidiabetic, and anticancer properties. Interestingly, some endophytes are capable of producing metabolites structurally similar or identical to those found in their host plants, including taxol, camptothecin, and various phenolic compounds. Compared with conventional plant extraction, endophytic fungi offer several advantages such as rapid growth, sustainable production, reduced environmental impact, and the potential for large-scale fermentation. These characteristics make endophytes promising alternative sources of natural products for pharmaceutical, cosmetic, agricultural, and industrial applications. This review summarizes the biological characteristics of endophytic fungi, methods for isolation and identification, and the major classes of bioactive compounds derived from fungal endophytes, with emphasis on their pharmacological significance and future biotechnological potential.

Abstract: To address the issues of large temperature fluctuations, poor spatial perception, and low control robustness in traditional residential air conditioners, this paper proposes an adaptive temperature control algorithm based on millimeter-wave radar and a Light Gradient Boosting Machine (LGBM). Given that the bed is the primary obstacle and heat source in a bedroom, we develop a bed localization method using point cloud clustering. This method accurately identifies the bed position through time - window filtering, outlier removal, and density clustering. An LGBM weak teacher model, trained on massive cloud data, takes the bed position, indoor temperature, and compressor parameters as inputs to optimize air direction and fan speed, thereby effectively suppressing steady-state fluctuations in the return air temperature. Experiments on 719 real - world devices demonstrate that the bed positioning accuracy reaches 83.6% under an error tolerance of 0.5 m, the average absolute temperature fluctuation is reduced to 0.21◦C, and the control accuracy of the air guide mechanism exceeds 0.98. The proposed method requires no hardware modification, offers strong generalizability and low deployment cost, significantly improves temperature stability and thermal comfort in bedroom environments, and provides a feasible technical solution for intelligent residential air conditioning control.

Abstract: Large language model (LLM) agents are increasingly expected to solve long-horizon tasks through repeated interaction with external environments, tools, users, and other agents. In this setting, agent skills have emerged as a central mechanism for transforming fragmented experience into reusable procedural knowledge. Unlike raw memory which preserves past traces, or abstract rules which often lack executable detail, skills compress recurring patterns of successful behavior into operational artifacts that can guide future action. However, existing studies on agent skills remain scattered across diverse areas, making it difficult to form a unified understanding of what skills are, how they are represented, and how they should be governed. To bridge this gap, this paper presents a comprehensive survey of agent skills. We propose a six-layer taxonomy covering ontology, representation and packaging, lifecycle, runtime integration, governance, and applications. We first define agent skills as reusable procedural abstractions that connect memory, human expertise, and execution. We then review major skill representations, including natural-language guidance, executable code snippets, decision graphs, filesystem-based packages, and structured skill records. Next, we analyze the skill lifecycle, from acquisition, storage, retrieval, usage, and maintenance to selective internalization into model behavior. We further examine how skills integrate with terminal interfaces, tool interfaces, multi-agent systems, and agent harnesses. In addition, we discuss the emerging skill ecosystem, its security and governance risks, and mechanisms for trusted deployment. Finally, we survey applications in robotics, games, web agents, GUI/mobile/OS agents, and software engineering, and identify open challenges in evaluation, robustness, standardization, safety, and infrastructure. Related resources, as well as the latest developments in this field, are accessible on https://github.com/DataArcTech/Awesome-Agent-Skill-Papers.

Abstract: Background/Objectives: This study evaluates the adaptation of a novel filling material (Odne®Fill, Switzerland) to the walls of the shaped root canal using micro-computed tomography. Methods: Fourteen extracted human molars (6 maxillary and 8 mandibular), comprising a total of 32 root canals, were included. After canal preparation, all canals were filled with Odne®Fill according to the manufacturer’s instructions. Periapical radiographs and micro-computed tomography were used to assess the extent of canal wall coverage by the filling material. Surface coverage was quantified by image segmentation and statistically analyzed. Results: The percentage of canal wall coverage ranged from 93.31% to 99.98%. The mean coverage rate was 97.63% (SD 1.70%) and the median was 98.23%, indicating a high degree of adaptation of the filling material to the ex vivo prepared canal walls. Conclusions: Under the conditions of this ex vivo study Odne®Fill demonstrated high canal wall adaptation values. These findings should be interpreted with caution and further com-parative and long-term studies are required before clinical relevance can be established.

Abstract: This article advances a systematic theological diagnosis of the contemporary crisis in Christian thought, contending that dominant modes of theologizing have become epistemically, politically, and spiritually toxic. Beginning with a critical analysis of the habitus of theologization, it demonstrates how inherited theological dispositions reproduce and normalize forms of violence embedded within colonial, cisheteropatriarchal, and necropolitical regimes. The study then interrogates the proliferation of theological narratives of terror and the corrosive effects of decent, docile, and obedient theologies that legitimize exclusion, dehumanization, and imperial projects—most starkly exemplified in the deployment of theological discourses to rationalize the ongoing genocide in Palestine and the systematic marginalization of queer/cuir/maricas, trans, and gender-nonconforming bodies. Against this backdrop, the article proposes two liberative antidotes. The first is Palestinian Liberation Theologies (PLT), which reclaim theological imagination through situated resistance, political commitment, and forms of spiritual endurance. The second emerges from Latin American Liberation Theology (LLT), as reconfigured through queer/cuir/marica dissident experiences, whose embodied, indecent, and decolonial imaginaries disrupt regimes of theological purity and open pathways for insurgent, life-affirming practices. Taken together, these antitoxic interventions articulate a decolonial and emancipatory horizon for theology—one grounded in relationality, insurgent imagination, and activist commitment. In this sense, theological detoxification is not merely a critical task but an indispensable condition for envisioning alternative worlds amid ongoing civilizational collapse.