Abstract: Objective: To review multimodal artificial intelligence (AI) models for predicting adverse outcomes in pneumonia, with emphasis on prognosis, validation, benchmarking, explainability, and clinical implementation readiness. Materials and Methods: We conducted a PROSPERO-registered systematic review. PubMed, Scopus, IEEE Xplore, and supplementary register/citation-search sources were searched between 01 January 2000 and 27 February 2026. Eligible studies integrated at least two heterogeneous clinical data modalities within one predictive framework for pneumonia-related deterioration, mortality, escalation of care, ventilation, or related adverse outcomes. We extracted modality composition, fusion architecture, outcome alignment, validation, performance metrics, clinical benchmarking, explainability, predictive uncertainty, workflow integration, governance reporting, and risk of bias. Results: Of 10,460 identified records, 49 studies were included. The evidence base was dominated by COVID-era studies. Structured electronic health record data, laboratories, and vital signs were more common than imaging or clinical notes. Early fusion was the predominant strategy, while attention-based and graph-based fusion were rare. External validation was reported in 19/49 studies, calibration in 9/49, temporal validation in 5/49, decision curve analysis in 3/49, and prospective evaluation in 3/49. Incremental AUROC over the best unimodal baseline was computable in 19 studies and was usually modest. Discussion: The literature emphasised retrospective discrimination more than clinical utility, with limited reporting of added value, workflow, predictive uncertainty, and governance. Conclusion: Multimodal pneumonia prognosis research is active but not yet implementation-ready. Future studies should prioritise externally validated, calibrated, explainable, clinician-aligned, and workflow-aware models.

Abstract: Education in the Philippines continues to face the challenge of integrating cultural heritage into classrooms in ways that are meaningful, policy-aligned, and sustainable. Mindanao’s oral epics, folktales, and musical traditions embody resilience, identity, and collective memory, yet remain at the margins of formal curricula. This study addresses that gap by introducing the Technographic Framework for Multilingual Literature and Musical Orality in Education (TFMLMOE), which connects heritage with curriculum design through artificial intelligence as an interpretive lens. Guided by a qualitative interpretive design that employed corpus construction, sentiment analysis, hybridity mapping, and audio pattern recognition, the research examined cultural materials including the Ulahingan, Tudbulul, Darangen, folktales in Cebuano, Maranao, Chavacano, and English, as well as ritual chants and gong ensembles. The analysis was anchored in Orality-Literacy Theory, Multilingual Hybridity, Ethnomusicology, Digital Humanities and Technography, and Peace Studies with Values Integration, providing a multidimensional lens for interpretation. A total of sixty-five scholarly sources spanning 1975 to 2025 were reviewed; following Creswell and Poth’s (2018) methodological guidance, this breadth ensured saturation of themes and strengthened validity through multiple perspectives. Findings revealed ten themes like oral epics as vehicles of language and values, modern texts as sites of hybridity and identity negotiation, musical orality as collective memory, and artificial intelligence as a means of uncovering hidden cultural patterns. From these findings, the framework is articulated through four components: corpus analysis, cultural preservation, values integration, and curriculum application. It aligns with CHED mandates, MTB-MLE and OBE compliance, UNESCO principles, and the United Nations Sustainable Development Goals. While the study is limited to selected Mindanao communities, it advances scholarship and policy by revitalizing cultural heritage and positioning it as a dynamic resource for pedagogy, resilience, and cultural revitalization.

Abstract: To address the large volume of reservoir dam seepage safety assessment reports, inconsistent textual descriptions, low efficiency of manual statistics, and difficulty in maintaining consistent classification criteria, this study proposes a human-computer interactive agent collaboration framework for dam seepage safety assessment. Using a large language model as the core for semantic understanding, the framework decomposes the report review process into subtasks including data preprocessing, knowledge retrieval, semantic classification, result feedback, and manual review. Structured collaboration among multiple agents is achieved through an "entity location-safety hazard" knowledge primary key, semantic rule constraints, and the Model Context Protocol (MCP). While preserving expert control, the system transforms original texts into aggregable and traceable risk statistics, forming a closed-loop mechanism of "machine-enabled efficiency, human oversight, and knowledge accumulation". Application validation was conducted using more than 2,000 reservoir safety assessment reports, from which 2,804 severe-risk records were extracted. The results show that severe risks were mainly concentrated in key locations including the dam body, culverts, spillway, dam foundation, downstream dam, drainage facilities, and abutments. The main risk types were leakage, abnormal seepage behavior, construction quality problems, noncompliant seepage control, structural damage, and noncompliant drainage facilities. The findings indicate that the proposed framework can improve the efficiency of seepage safety assessment text processing and the consistency of risk statistics, thereby providing intelligent support for reservoir dam hazard screening and scientific decision-making.

Abstract: For a graph G, let \( \nu_{\triangle}(G) \) be the largest number of pairwise edge-disjoint triangles and let \( \tau_{\triangle}(G) \) be the smallest number of edges meeting every triangle. Tuza's conjectured that \( \tau_{\triangle}(G)\leq 2\nu_{\triangle}(G) \) for every graph. We prove the conjecture for every split graph with a specified split partition \( V(G)=C\mathbin{\dot\cup}I \) such that \( |C|=8 \), for which the triangle-active vertices of I have at most two distinct neighborhoods in C. The result allows arbitrary multiplicities of the two types. The infinite statement is reduced to 44,702 canonical cases by an exact multiplicity-truncation lemma and symmetry. Each case carries an explicit triangle cover and an explicit edge-disjoint triangle packing, checked by an independent standard-library verifier. We also give exact cover and packing reductions for arbitrary split graphs and a maximum-cut criterion that settles, at \( |C|=8 \), every instance whose independent-side neighborhood 2-shadow has at most seven edges. The unrestricted split-graph and interval-graph cases remain open.

Abstract: In this study, a new pressure stabilization (PS) system composed of a gas stable vessel (SV), PID control system and a reaction vessel (RV), etc. was used to study the dynamic characteristics of methane separation from low concentration coalbed methane com-pared with PV system. A porous medium system was constructed by sodium lignosul-fonate (SL) or calcium lignosulfonate (CL) solution which could will reduce gas-liquid interfacial tension, and enhance methane dissolution and mass transfer. At the same time, a thermodynamic accelerator of cyclopentane (CP), which effectively reduced hydration reaction conditions and improved methane storage rate, was added to the hydration reaction solution. The experiments were carried at 275.15 K and 3.0 MPa, the mass concentration of lignin was 500 ppm, the volume ratio of CP to deionized water was 1:10. The results shown that higher CH4 recovery (66.7%) and higher gas uptake (0.1139 mol) were obtained in PS + SL + CP system, but shorter t90 (154 min) and higher CH4 concentration (69.5%) in the hydrate phase were appeared under PV + SL + CP system, however shorter induction time (9 min) was found in PS + CL + CP system.

Abstract: Software-defined wireless sensor networks (SDWSNs) are deployed in mission-critical applications such as environmental monitoring, smart cities, and healthcare. However, existing protocol architectures such as SDN-WISE lack built-in mechanisms for the detection or mitigate of Denial-of-Service (DoS). Existing trust-based security solutions are rarely fully integrated into the protocol stack; nearly all of them rely on manually configured thresholds which severely undermine their effectiveness. This study integrates the Adaptive, Threshold-Free, and Automatically Weighted Trust Model (ATAW-TM) into the SDN-WISE protocol stack; the ATAW-TM model was adjusted to the resource-constraint sensor node environment by embedding three lightweight adaptations: an inverse-quadratic approximation of Gaussian-like cooperation probability that eliminates exponential function calls and mathematical libraries dependences, a quadratic approximation of second-order Rényi (replacing Shannon entropy) for entropy weight calculation that avoids logarithmic operations, and a lightweight sigmoid approximation for the aging factor that preserves monotonicity and smoothness without the use of exponential functions. To evaluate the integrated model, we developed a comprehensive evaluation framework encompassing 14 distinct DoS attack types and one data tampering attack against SDWSNs. The integrated model was evaluated through extensive Cooja-based simulations. The experimental results indicated that compared to the standard SDN-WISE, the proposed security solution successfully detects a comprehensive range of DoS attacks and one type of data tampering attack, with a negligible performance overhead (less than 5.3% additional hop delay, and 8.1% additional forwarding delay). Furthermore, simulation results show that the proposed method can adapt to variations in network traffic conditions without requiring manual threshold tuning. Overall, the proposed lightweight trust-based attack recognition model offers a practically achievable security solution for resource-constrained SDWSNs, without sacrificing protocol efficiency.

Abstract: The therapeutic landscape of cholangiocarcinoma (CCA) has undergone a paradigm shift towards precision oncology following recognition of its molecularly heterogenous landscape and rapid development of novel targeted therapeutics. Although outcomes remain poor, particularly in locally advanced or metastatic disease, comprehensive genomic profiling has identified multiple clinically actionable genomic alterations, leading to the successful implementation of biomarker-directed therapies. Targeted therapies are being rapidly developed; both to exploit additional molecular alterations that are not currently targeted in CCA and to overcome mechanisms of acquired treatment resistance. This review covers the current standard-of-care systemic treatments for localised, locally advanced and metastatic CCA with a particular emphasis on molecularly targeted therapies and immunotherapy. We discuss established precision oncology strategies targeting FGFR2 fusions and rearrangements, IDH1 mutations, ERBB2 amplification / HER2 overexpression and BRAFV600E mutations, together with the current evidence supporting immune checkpoint inhibition. Emerging therapeutic targets with the potential to further expand precision oncology in CCA are also reviewed, including homologous recombination deficiency (BRCA1/2 and PALB2), MDM2 amplification, NTRK fusions and MTAP loss. Finally, we discuss the current challenges to implementing precision oncology in CCA, including limitations in tumour tissue acquisition for comprehensive genomic profiling and difficulties performing adequately powered molecularly stratified clinical trials in this rare malignancy. We highlight how advances in molecular diagnostics, next-generation targeted therapies and innovative basket and umbrella trial designs are expected to further expand precision oncology strategies with the aim of further improving clinical outcomes for patients with CCA.

Abstract: For nearly a century, flotation kinetics has relied on deterministic first-order rate equations treating the cell as a homogeneous reactor, a paradigm that inherently fails for heterogeneous ores, especially coal, whose organic macerals, porosity, and oxidation susceptibility defy a single rate constant. Breaking from chronological cataloguing, this review proposes a three-dimensional taxonomy based on physical scale, inherent material heterogeneity, and epistemic certainty. We demonstrate that critical industrial prediction failures arise from structural mismatches between model physics and particle surface chemistry, notably time-dependent oxidation deactivation and selective maceral recovery. Six fundamental failure modes are identified, from neglected time-dependence of rate constants to the absence of a thermodynamic deactivation term, corroborated by experimental evidence from coal and base-metal flotation. Advanced microfluidic, automated mineralogical, surface-sensitive spectromicroscopic, CFD-DEM, and physics-informed machine learning tools are dismantling the black box of the flotation rate constant “k”. We introduce the Distributed Reactive Surface Kinetics (DRSK) framework, which embeds particle-scale heterogeneity into a population balance via an adaptive surface-sensitive selection function and treats kinetic uncertainty through stochastic differential equations. A comprehensive comparison table facilitates the transition from conventional models to the DRSK paradigm. We conclude with a roadmap for flotation kinetics 4.0, where digital twins, real-time froth analytics, and self-calibrating hybrid models transform this empirical discipline into a truly predictive engineering science. The framework is elaborated for coal and conventional minerals, underscoring why coal demands its own dedicated kinetic theory and how these lessons can revolutionize the processing of increasingly complex, low-grade ores and secondary resources.

Abstract: This work presents the ZPIF-USAC (Zero Pairs Interaction Functional – Unified Spectral Alignment and Coherence) framework, a comprehensive mathematical theory that introduces fractional self-fragmentation of zeros as a fundamental property of the Riemann zeta function. Unlike classical approaches that treat zeros as passive, independent entities, ZPIF-USAC reveals that each zero is an active spectral entity capable of splitting into multiple fractional components and later reuniting through natural alignment dynamics. The framework introduces three revolutionary concepts: (1) the Fractional Alignment Parameter \(\mathcal{A}_{nm}^{(\alpha)}\), which generalizes spectral alignment to arbitrary fractional orders \(\alpha\) and reveals that optimal coherence occurs at \(\alpha = 1/2\); (2) the Self-Fragmentation Coefficient \(F_n\), which describes the intrinsic ability of each zero to fragment into multiple components; and (3) the Reunification Coefficient \(R_{nm}\), which governs the tendency of fragments to reunite into coherent structures. These concepts are unified through the Fractional ZPIF-USAC Functional: \( \operatorname{ZPIF}_{SF}^{(\alpha)}(x) = \sum\limits_n \gamma_n^{2\alpha} |c_n|^2 + \sum\limits_n F_n \sum\limits_{k=1}^{M_n} \gamma_{n,k}^{2\alpha} |c_{n,k}|^2 + \sum\limits_{n \neq m} R_{nm} \, c_n c_m \) This functional represents the first unified framework that combines fractional interactions, self-fragmentation, reunification dynamics, and active spectral properties into a single mathematical structure. The framework is rigorously applied to four fundamental domains. For prime gaps, ZPIF-USAC predicts the statistical distribution through fractional alignment dynamics. For dark energy, the fractional coherence term \(\mathcal{C}^{(\alpha)}\) provides a natural model for cosmic acceleration. For neural dynamics, it describes synchronization patterns in the brain through alignment dynamics. For spacetime geometry, it induces a fractional spectral metric that bridges quantum mechanics and general relativity. Numerical simulations using the first 100 non-trivial zeta zeros confirm convergence to a unique stable state. The results demonstrate that fractional interactions of order \(\alpha = 1/2\) are the most efficient in generating spectral coherence, and that self-fragmentation provides a natural mechanism for the emergence of complex structures in the universe. This work establishes ZPIF-USAC as a new paradigm in spectral theory, number theory, and mathematical physics, revealing that fractional self-fragmentation and natural alignment are fundamental principles governing the behavior of spectral systems and their applications to prime gaps, dark energy, neural dynamics, and spacetime geometry.

Abstract: For \(q \geq 1\) and \(\gcd(a,q)=1\), consider the restricted weighted Goldbach sum \[ R_{a,q}(N):=\sum_{\substack{p_1+p_2=N\\p_1\equiv a\pmod q}}(\log p_1)(\log p_2). \] We give a fully rigorous, self-contained treatment of the restricted binary, ternary and quaternary Goldbach problems in this setting. We first prove an elementary but decisive local obstruction: if an odd prime \(\ell\) divides \(q\), then \(R_{a,q}(N)\) collapses to \(O_q(\log N)\) on a positive-density set of even \(N\), so no main term of size \(\asymp N/\varphi(q)\) can hold uniformly; for \(q=2^k\) this obstruction is absent, and we identify the correct local main term \[ M_{a,q}(N)=\frac{C_2}{\varphi(q)}S(N)N, \] Within this scope we prove a qualitative almost-all theorem, with a complete major/minor-arc derivation, showing the exceptional set has density zero. We record a conditional impossibility theorem showing that no \(X\)-independent threshold can upgrade this to an effective almost-all statement once a matching second-moment lower bound is granted, and we isolate, as honestly labelled structural cautions rather than theorems, four classical routes that fail to upgrade the almost-all theorem to unconditional finiteness. We add a fully unconditional restricted Chen-type theorem obtained from the Selberg--Chen sieve and the classical Bombieri--Vinogradov theorem, a ternary prime-anchoring transfer, and positivity of the restricted quaternary singular series through explicit local densities. As a companion study, we then prove that the variable factor \[ S_0(n):=\prod_{\ell\nmid n}\frac{\ell-1}{\ell-2} \] of the Hardy--Littlewood singular series, evaluated along shifted primes \(n=p+h\) for fixed \(h\neq 0\), converges in distribution to an explicit random Euler product \[ Y_h=\prod_{\ell>2,\ell\nmid h}\left(\frac{\ell-1}{\ell-2}\right)B_\ell \] with independent Bernoulli local factors \(P(B_\ell=1)=1/(\ell-1)\); we identify its Mellin transform as an entire function of order one, prove convergence of every integral moment, and establish that the law is non-atomic, has unbounded support, and superpolynomially decaying tails, strictly amplified relative to generic integers. We then connect the two studies: since \(S(N)=S_0(N)\) is exactly the amplitude of the restricted Goldbach main term \(M_{a,q}(N)\), the limit law furnishes a rigorous probabilistic description of how that amplitude fluctuates as \(N\) ranges over the shifted-prime sequence \(N=p+h\), a connection neither source study states. Throughout, every asserted theorem is unconditional and every numerical constant is independently certified via partial Euler products with explicit tail bounds; statements retracted at an earlier stage of this programme after failing independent verification are recorded only as open problems.

Abstract: Counter-arguing against the proposition that homology between the coleoptile and the leaf sheath is impossible because a sheath cannot be located above the lamina of the same leaf (Mavrodiev 2025), while defending the bipartite interpretation of the grass cotyledon (BIGC), Scanlon et al. (2026) advance five principal objections: (1) during embryo ontogeny, the coleoptile initiates proximal to the initiation point of the distal scutellum; (2) the coleoptile must be part of the cotyledon because of embryo distichy; (3) tissue continuity between the scutellum and coleoptile supports the interpretation that these two structures together constitute a single composite organ, the cotyledon; (4) BOP1a marker expression in coleoptile supports coleoptile-sheath homology; and (5) ligule and leaf-sheath homologies of the coleoptile are compatible because the ligule represents an extension of the sheath margin. In this response, I show that all five objections are incorrect. (1) Along the embryo's morphological axis, rather than the proximodistal axis of the cotyledon, the coleoptile occupies a position distal relative to the scutellum, thus lying above the latter. (2) The disruption of embryonic distichous phyllotaxy by the placement of the coleoptile does not necessarily constitute a morphological argument, and the interpretation of the epiblast as a reduced leaf preserves embryo distichy. (3) Tissue continuity does not imply organ identity. (4) BOP1a expression in coleoptile provides evidence against, rather than in support of the BIGC. (5) Coleoptile-leaf sheath homology does not constitute evidence for coleoptile-ligule homology (or vice versa). Moreover, the claim that the ligule originates from the sheath margin remains open to question because it is formulated without consideration of the basipetal pattern of maize leaf maturation. The reply to Objection 1 affects the very foundation of the argument of Scanlon et al. (2026). Therefore, further discussion on the topic is optional (sublato fundamento, cadit tota propositio).

Abstract:

Let \(C=ap_k^\#\) and consider symmetric offsets \(\{C-d,C+d\}\) under the sieve of Eratosthenes. Small primes generate a primorial wheel, while each later prime forbids one or two lift residues. The full Chinese remainder theorem (CRT) pattern has positive density, but a fixed center supplies only a translated fragment. We characterize terminal survivors as the disjoint union of a conservative avoiding set and explicit endpoint-prime exceptions, obtaining an exact unequal-prime-pair count and \(R_G(2C)\geq\max\{0,|U(C)|-1\}\). We derive arbitrary-order finite-phase CRT intersection formulas and odd Bonferroni lower bounds. The third-order bound gives \(|U(86)|\geq3\) and \(|U(128)|\geq2\), whereas first-moment, spanning-tree, and complete-block bounds are nonpositive; the latter certificate guarantees a prime-pair representation. We also express the survivor count as a shift-aware cyclic Fourier correlation, factor the pattern transform locally, and obtain computable spectral bounds. For a fixed wheel, \(\log P\sim\sqrt{2ap_k^\#}\), so the complete-block condition is asymptotically too restrictive. Exact-integer computations reproduce all finite cases. The results isolate the finite-window obstruction but prove neither an unconditional Goldbach theorem nor a new infinite prime-pair family.

Abstract:

A complete observation may change because the prepared-object condition changes or because the physical process forming its record changes. We ask when the latter dependence becomes an irreducible state responsibility. The primitive input is an operationally role-faithful closed-world table μ0 : P × A → Y_⊥. Here the object and record-forming roles are certified by experimental protocol or antecedent physics, rather than inferred from the recorded values, and ⊥ denotes certified nonimplementability. If two operations give different entries at one fixed object label, no exact encoding that preserves the certified first-role map can identify the witnessed pair. Quotienting complete row and column behaviour removes precisely duplicate labels and yields reduced behavioural objects X and S, a descended law eF : X × S → Y⊥, and an admissible joint domain B. The second quotient is canonically bijective with the implemented family H_F ⊆ Map(X , Y⊥), thereby acquiring a behaviour-minimal observer-state identity. Record-forming non-triviality makes S non-singleton, while injectivity of the exponential transpose forbids any further factorwise lossless quotient. The reduced realization is terminal among active, product-separable, pointwise-faithful realizations and is invariant under admissible role-preserving empirical covers, including redundant refinements and relabellings. These results do not assert that every arbitrary joint encoding admits a global S-decoder; such a decoder is an additional compatibility condition. For a fixed reduced interface, whether the observer state is fixed, recorded, hidden or actively controlled changes later inference, not the complete conditional law. We state the resulting interface retrospectively as the Dual-Axis Axiom of Observation. Within the operationally role-faithful domain, it expresses a foundational structural law: witnessed same-first-role dependence cannot be merged or made to factor through the first role alone without changing the complete observational content or abandoning the certified first-role semantics.

Abstract: The performance of coatings, corrosion barriers, photocatalysts, and tribological materials is greatly influenced by in situ surface properties, requiring highly sensitive and reproducible operando surface characterization methods. We previously developed a temperature-programmed photoelectron emission (TPPE) method to clarify electron transfer behavior on light-irradiated metal surfaces. TPPE is sensitive to surface temperature and prior chemical exposure, which affect the total photoemitted electron count (N_T), the photothreshold, and the activation energy derived from Arrhenius plots of N_T obtained during heating–cooling cycles. This study examines the reproducibility of TPPE data and the TPPE mechanisms for Cu₂O/Cu surfaces subjected to mechanical abrasion, cleaning, plasma treatment, and subsequent immersion in organic liquids. The resulting Arrhenius plots reveal both positive and negative activation energies, distinctly depending on the treatment conditions. Negative activation energies during cooling are associated with photoredox-mediated emission. TPPE is attributed to oxygen vacancies within the Cu₂O surface layer interfaced with metallic Cu, serving as a direct probe of these vacancy-related states. The TPPE characteristics (N_T intensity and activation energy) following exposure to various polar and nonpolar organic molecules (e.g., acetone, toluene, hexane, ethanol) correlate with the electronic properties of these vacancies, consistent with previous observations for ambient air, alcohol, and water vapor exposure. Under illumination, Cu₂O vacancy states enhance photocarrier extraction (electrons and holes) and accelerate surface redox reactions within adsorbed thin films, thereby improving photocatalytic performance. Notably, the reciprocal dielectric constant of the solvents significantly influences TPPE, indicative of electrostatic surface–solvent interactions. Finally, the TPPE mechanism is discussed in the context of antiviral inactivation at the metallic copper–environment interface.

Abstract: Three novel compounds with potential antitubercular activity—N-(2-isonicotinoylhydrazine-1-carbonothioyl)benzamide (3), N-(2-nicotinoylhydrazine-1-carbonothioyl)benzamide (4), and N-(2-picolinoylhydrazine-1-carbonothioyl)benzamide (5) were synthesized in good yields via the reaction of benzoyl isothiocyanate with isonicotinic hydrazide, nicotinic hydrazide, and picolinic hydrazide in acetonitrile, respectively. The crystal structures of compounds (3) and (5) were determined by single-crystal X-ray diffraction analysis; both structures exhibit intramolecular C=O···H-N hydrogen bonds, as well as C=S···H-N interactions. Computational pharmacological studies, ADME profiling, and molecular docking were performed for the three synthesized compounds, indicating that they represent promising candidates as alternatives to current tuberculosis treatment regimens, owing to their predicted favorable pharmacokinetic profile and binding affinity for the Mycobacterium tuberculosis InhA enzyme.

Abstract: There are always repeatable functions like building similar CRUD workflows, implementing authentication layers, role based access control and admin interfaces for various projects on an enterprise web application, and different con-ventions can be used by different developers or code-bases. AI-powered coding tools recently emerged that promise to make this possible, but existing studies have shown that automated LLM-based code generation, vibe coding and free-running multi-agent pipelines have difficulty maintaining consistency between relational schemas, backend APIs, and frontend interfaces as applications grow in size, and become a maintenance night-mare. The main idea is that structural code which can be programmed from an application data model does not need to be probabilistic (only truly ambiguous decisions are language-model decisions such as interpreting the relational semantics, or resolving ambiguous requirements). We build this insight into a system called CodeCraft, where a Prisma schema and its Data Model Meta Format (DMMF) representation are the only source of truth for deterministic generators to generate backend APIs, frontend manifests, RBAC structures and database seeders, while a LangGraph six-agent multi-agent orchestration pipeline (from requirements engineering, schema design, orchestration, backend generation, frontend generation, and containerization) can only be used to clarify requirements, validate schema, and make decisions that cannot be made structurally. Generated systems have a consistent schema representation, as opposed to the prompt-driven tools which are used for each individual file. In all the applications it is demonstrated that schema validation slashes the number of automated iterations required to correct schemas to 2-4 and the end-to-end generation generates deploy-able, containerised applications after requirements and schema approval (no manual effort required). Comparative engineering indicates that the development effort is reduced by 70–80%, compared to manual implementation, but this has not yet been substantiated with a controlled external study.

Abstract: Accurate land-cover mapping of urban botanical gardens is essential for biodiversity assessment, ecosystem monitoring, and sustainable landscape management. However, their high structural complexity creates significant challenges for remote sensing-based classification. This study evaluates the influence of spatial resolution on classification performance by comparing 3 m PlanetScope satellite imagery and centimeter-scale unmanned aerial vehicle (UAV) data within a 28 ha heterogeneous area of the Yerevan Botanical Garden (YBG) in Yerevan, Armenia. Three pixel-based machine learning (ML) algorithms, Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB), were compared with the spatial-context-based U-Net deep learning (DL) semantic segmentation model. The results revealed a strong relationship between sensor resolution and classifier performance. For satellite imagery, U-Net achieved the highest statistical accuracy, with an Overall Accuracy (OA) of 78.56% and a Kappa coefficient (κ) of 0.726, demonstrating the benefit of contextual feature extraction for reducing pixel-level noise. However, spatial evaluation indicated that RF generated land-cover patterns with better preservation of local landscape structures and class boundaries. In contrast, UAV-based classification showed superior performance for pixel-based ML methods, with GB achieving the highest accuracy (OA = 85.9%, κ = 0.839), followed by SVM (OA = 84.7%, κ = 0.825) and RF (OA = 84.3%, κ = 0.821). Area comparison revealed that medium-resolution satellite imagery overestimated continuous tree canopy coverage (30.17% vs. 16.53% from UAV mapping) and underestimated fragmented vegetation classes due to mixed-pixel effects. The findings highlight that classifier selection should be adapted to sensor characteristics and landscape complexity. For satellite imagery, U-Net achieved the highest overall accuracy while RF preserved landscape structure and class boundaries more effectively; for UAV imagery, GB achieved the best overall performance, confirming that spectral-based pixel classifiers perform strongly at fine spatial resolutions. This is probably due to reduced mixed-pixel effects rather than enriched spatial information, as the latter was not leveraged in the classification process used.

Abstract: Local topological singularities on digital or voxel grids motivated well-composedness conditions excluding them; P-well-composedness lifts this regularity into an intrinsic, order-theoretic setting on posets. We study whether P-well-composedness is inherited by the strict neighborhood of a face in a finite embedded cubical complex. Let X = F(K) be the face poset of a finite nonempty set of grid n-cubes, with ambient rank n ≤ 3, and let N_h = θ′_X (h) carry the induced order. We prove N_h is P-well-composed whenever X is, provided h is a vertex or has no strict coface in the border ΔX, via a coherence argument for vertices and a finite, computer-assisted enumeration otherwise. The remaining rank-three case, an edge with a strict coface in the border, is posed as a No-Shrinkage Conjecture, with computational evidence reported separately.

Abstract: Precise monitoring of ammonia (NH₃) in humid agricultural environments is essential for livestock management and environmental protection. However, conventional metal oxide semiconductor sensors often suffer from signal attenuation and baseline instability because of competitive water adsorption under room-temperature, high-humidity conditions. Here, a non-annealed Sn/Ce-containing polyacrylonitrile (PAN) nanofiber membrane was fabricated by electrospinning for room-temperature NH₃ sensing. The resulting Sn/Ce/PAN membrane exhibits an amorphous hybrid structure formed through interactions between the metal species and the PAN matrix. The Sn/Ce/PAN membrane-based sensor delivers a response of 90% toward 100 ppm NH₃ at 80% RH, with a response time of 16 s and a recovery time of 37 s. The sensing response increases with relative humidity and reaches its maximum at 80% RH, demonstrating excellent sensing performance under high-humidity conditions. Combined experimental and theoretical investigations reveal that the outstanding sensing performance originates from humidity-mediated proton conduction enabled by enhanced water adsorption and the formation of a continuous hydrogen-bond network, whereas excessive water accumulation suppresses charge transport under excessively humid conditions. This work provides mechanistic insights into room-temperature NH₃ sensing under high humidity and offers a promising strategy for developing high-performance gas sensors for practical humid environments.

Abstract: Acinetobacter baumannii, a multidrug-resistant member of the ESKAPE pathogen group, poses a major global public health threat due to its persistence in clinical environments, rapid acquisition of antibiotic resistance, and limited treatment options. Although mammalian and non-mammalian infection models have advanced our understanding of A. baumannii pathogenesis, many are constrained by cost, scalability, or experimental tractability. While Drosophila melanogaster S2 cell lines have proven valuable for investigating A. baumannii–host interactions, development of a whole-organism D. melanogaster infection model has remained limited. To our knowledge, the only previous in vivo study reports no significant infection-associated mortality relative to controls. Here, we demonstrate that A. baumannii can cause lethal infection in adult D. melanogaster. Across a series of experiments, inoculation with two A. baumannii strains, A118 and AMA_NO, produced significantly increased mortality relative to PBS-inoculated controls. Moreover, the magnitude of mortality differed between bacterial strains under some experimental conditions, with the multidrug-resistant clinical isolate AMA_NO producing greater mortality than A118, consistent with strain-specific differences in pathogenic potential. Infection outcomes depended on the experimental context, indicating that further refinement will be important for improving the reproducibility and sensitivity of the system. Together, these findings establish that D. melanogaster can support lethal A. baumannii infection and detect biologically meaningful differences among bacterial strains, providing a foundation for its continued development as an accessible and genetically tractable whole-organism model for investigating A. baumannii virulence and host–pathogen interactions.