Abstract: This paper presents a theoretical framework aimed at examining the Riemann Hypothesis (RH) through the lens of a differential interaction operator Φ(s,δ) acting on the Hilbert space l²(N). By mapping the Dirichlet η-function to a trace-class operator, we analyze the resulting phase torque J(δ,t), which is governed by a hyperbolic sine bias. We propose a product criterion wherein the operator trace vanishes if and only if a zero exists at mirrored coordinates across the critical line. Furthermore, we explore how the Diophantine independence of prime logarithms, when amplified by the hyperbolic lever, may mathematically restrict the trace from vanishing off the critical line Re(s) = 1/2. Within the constraints of this oper ator construct, the analysis suggests a geometric mechanism consistent with the confinement of non-trivial zeros to the critical line.

Abstract: Agrivoltaics (Agri-PV) represents a promising solution to improve land-use efficiency by simultaneously allowing crop growth and photovoltaic (PV) energy generation, with additional benefits for crop production if properly engineered. However, when crystalline silicon (c-Si) PV modules are used for Agri-PV, even in semi-transparent configurations, shading occurs over crops, potentially reducing agricultural yields. Enhancing light diffusion is a key strategy to partially compensate for this effect, as diffuse light is more efficiently utilized by most plants. This study aims at engineering the transparent section of a semi-transparent c-Si PV module, assessing its optical, light-scattering, and efficiency-related properties for Agri-PV applications. The experimental work involved fabricating and testing various transparent stack configurations and mini-module prototypes to evaluate their suitability for Agri-PV integration. Optical characterization using a spectrophotometer revealed that certain stack configurations significantly enhance light diffusion, while maintaining good transmittance values for crops growth. To further analyze angular light scattering, a custom-built setup to measure the Bidirectional Transmittance Distribution Function (BTDF) was developed. The results showed that primarily anti-glare films (AG) and secondarily specific encapsulants (TPO) and flexible layers can effectively improve light distribution, helping to mitigate shading effects. Following AG application, Haze values exceeded 89%, indicating enhanced light diffusion capabilities. The impact of different stacks on module efficiency was also assessed through mini-modules testing. Findings indicate that enhanced light diffusion can be achieved with minimal efficiency losses. Specifically, the application of the AG resulted in a reduction of the Cell-To-Module efficiency ratio (CTM_η) of less than 1%. These results confirm that semi-transparent PV modules can be optimized for Agri-PV applications without significantly compromising energy output.

Abstract: Reliable Aboveground Biomass (AGB) estimates for woody crops are essential for carbon accounting and for Measurement, Reporting and Verification (MRV) frameworks. However, it remains unclear how LiDAR modality and sampling geometry influence plot-scale and tree-scale AGB predictions in intensively managed Mediterranean orchards. In this study, we benchmarked four LiDAR modalities, namely open national airborne laser scanning from the Spanish National Aerial Orthophotography Plan (PNOA/ALS), a dedicated Riegl airborne laser scanner (ALS), unmanned laser scanning (ULS) and mobile laser scanning (MLS), across three woody-crop sites in Córdoba (southern Spain): IFAPA, Doña María, and Villaseca. Plot-level LiDAR metrics (mean height, 95th height percentile, maximum height, and canopy cover proxies) were extracted from normalised point clouds and related to field AGB using Random Forest and XGBoost regression models, together with an ensemble predictor, under an 80/20 train–test split. In parallel, TreeQSM-based Quantitative Structure Models (QSMs) were evaluated as an independent tree-level three-dimensional reconstruction approach. XGBoost achieved the lowest errors at IFAPA (RMSE = 0.400 Mg ha⁻¹; R² = 0.994) and Villaseca (RMSE = 0.872 Mg ha⁻¹; R² = 0.995), whereas PNOA/ALS was competitive at Doña María (RMSE = 0.725 Mg ha⁻¹; R² = 0.994). TreeQSM closely matched field inventory at the low-biomass IFAPA site but tended to overestimate biomass at Doña María and Villaseca, and only 28% of scanned trees yielded usable reconstructions. The results support the use of cross-platform LiDAR for orchard AGB and carbon mapping and identify the conditions under which open national LiDAR can enable scalable MRV of Mediterranean woody crops.

Abstract: Docker is widely used to deploy applications in containerised environments. This study investigates whether the physical memory utilisation of databases deployed in Docker differs from that of equivalent non-Docker deployments during a Structured Query Language injection (SQLi) attack. A quantitative approach was adopted, using Glances to collect system data, JASP 0.18 for descriptive statistics and paired-samples t tests, and StatKey to examine mean differences. Two application stacks were evaluated: DVWA (PHP/MariaDB) and Acunetix (MySQL). Within the conditions examined in this study, the Docker-based deployments did not demonstrate improved memory performance when compared with the non-Docker deployments during SQLi. Instead, the results suggest that the Docker-based configurations were associated with higher memory use.

Abstract: Ozone (O₃) is a potent disinfectant, yet its efficacy in environmentally complex waters can be inconsistent. This study investigated the effectiveness of ozonation across 14 water samples (wells, rainwater, ponds) and identified key success factors. Ozone exposure (1-20 min) significantly reduced colony-forming units (CFU) in most samples, achieving complete elimination in four. However, two high-organic-load samples showed minimal change. Physicochemical analyses revealed a strong correlation between increased Oxidation-Reduction Potential (ORP) and disinfection success. We observed that the efficient generation of ozone microbubbles was the critical factor in raising the ORP to lethal levels (> ~450 mV). A paradigmatic case was sample 2, where the elimination of total coliforms (98.7%) only occurred in replicates where microbubbles formed (ORP >520 mV), failing in the replicate without microbubbles (ORP 122 mV). Mass spectrometry (MALDI-TOF) identified genera such as Pseudomonas and Bacillus, indicating the presence of microbial diversity. We conclude that ozo-nation is highly effective, but its success depends on optimizing mass transfer via microbubbles, with ORP as a fundamental real-time indicator to ensure process reliability. This study offers a practical guideline for implementing more robust and safer ozone systems, overcoming limitations observed in waters with complex contamination.

Abstract: In the wake of the Fifth Industrial Revolution, artificial intelligence (AI) has become a disruptive force in architectural design processes. One AI technique is text-to-image, which generates visual representations from textual descriptions. This research questions how architects and students organise the text-to-image prompts. Unfortunately, AI images have neglected the basic principles of architectural theories. The problem explored here is whether AI-generated images truly reflect architectural theory or replicate styles without deep understanding. This research, therefore, aims to propose a chart of semantic textual models, including keywords of theories of architecture, to organise the text-to-image prompts. To achieve this aim, the article followed scientific methodology, began with a literature review, and then analysed previous readings that highlighted this gap and proposed solutions. Through three AI platforms, the research followed an experimental method, injecting five architectural theories into AI prompts to compare images before and after. As a result, the images (after) became more realistic, expressing more clearly the trend's characteristics, and conveying symbolic meanings. The conclusion is that AI architectural images must have a maestro to organise prompts. This maestro is the 'Theory of Architecture', which is expected to bridge the gap between AI's ultimate imagination and the authentic principles of design trends.

Abstract: Background: In this study, we radiologically assessed potential increases in microvascularity, the extracellular matrix, collagen deposition, and tissue viscoelasticity following carboxytherapy for periorbital hyperpigmentation (POH). We also analyzed the correlation between radiological changes and clinical outcomes and explored implications for future outpatient selection, as well as the potential to predict treatment success based on radiological–clinical correlations. Materials and Methods: The present study included 78 patients (76 women and 2 men) aged over 18 years with Fitzpatrick skin types I-V and moderate-to-severe infraorbital dark circles who applied for treatment at the Dermatology Department in the Cosmetology Unit of Medical Faculty Hospital. Each patient was given manual, pressure-controlled injections of sterile CO2 into the upper and lower eyelids for 7 weeks, with one round of treatment per week. We conducted dermatoclinical and radiological evaluations, including measurements of epidermis–dermis thickness and SWE elastography, musculus orbicularis oculi pars pretarsalis thickness, and cSMI vascular index percentage, as well as SOOF tissue SWE elastography (measured in Kpa). These analyses were performed on both lower eyelids before treatment and at 1 month and 6 months after treatment. Results: After treatment, VAS scores improved significantly. Grayscale ultrasonography showed significant increases in epidermis–dermis and orbicularis oculi thickness at 1 and 6 months (p<0.05). SMI presented a significant increase in vascular index at both follow-ups (p<0.05). SOOF SWE values increased significantly at 1 and 6 months, whereas epidermis–dermis SWE did not. Procedural pain was common, and 25 participants withdrew during the 7-week period due to discomfort. Conclusions: Radiological findings indicated collagen accumulation, increased microvascularity, myocyte proliferation, and enhanced viscoelasticity resulting from carboxytherapy treatment. The continuity of radiological and clinical improvements from the first to sixth months following treatment suggests the enduring benefits of this therapy.

Abstract: The novel biology-guided radiotherapy system (BgRT), the RefleXion X1 SCINTIX™ system uses annihilation photons produced from a PET-avid tumor to plan and then subsequently guide the delivery of beamlets of radiation to the tumor, tracking the dose delivered in real time. The current BgRT system is cleared by the U.S. Food and Drug Administration (FDA) to treat tumors in the lung or bone characterized by periodic or non-periodic motion respectively. In this work, we assessed the robustness and adaptability of the dose tracking capability under tumor motion perturbation. We designed a phantom study based on a lung case planned under periodic tracking for four pass (4-Pass) BgRT and then introduced transient translational shifts to the phantom motion continuously under treatment. By comparing the dose delivered under a normal periodic sinusoidal tumor motion to the dose delivered under a perturbed sinusoidal delivery, with a simple perturbation introduced at the end of the second of a 4-Pass delivery and a more complex situation with perturbation introduced at each of the four passes of the 4-Pass delivery, we assessed the robustness and adaptability of the RefleXion X1 SCINTIX™ system to deliver the correct dose under such circumstances.

Abstract: Background: Respiratory syncytial virus (RSV) is a serious disease in older adults with comorbidities; however, comparative data across epidemic waves, both clinically and in terms of inflammatory profiles and their diagnostic and prognostic utility, remain limited. Methods: We conducted a retrospective study of adults hospitalized with RSV infection across two epidemic waves (2022–2023 and 2024–2025). Clinical characteristics, comorbidities, severity scores, and outcomes were collected. Serum interleukin-6 (IL-6), C-reactive protein (CRP), and hematological parameters were analyzed and compared with healthy controls. Results: A total of 152 patients were included (81 in wave 1 and 71 in wave 2). Patients in wave 2 were older and had a higher burden of comorbidities, although ICU admission and in-hospital mortality were similar across waves. RSV induced a consistent systemic inflammatory response in both waves, characterized by elevated IL-6 and CRP levels, neutrophilia, lymphopenia, and increased neutrophil-to-lymphocyte ratio, without relevant inter-wave differences. All biomarkers demonstrated good diagnostic performance. The neutrophil-to-lymphocyte ratio, showed the highest accuracy, while IL-6 exhibited excellent rule-in capacity due to perfect specificity. However, none of the evaluated biomarkers were associated with disease severity (McCabe index) or in-hospital mortality. Conclusion: RSV infection in older adults is associated with a stable inflammatory signature across epidemic waves. Although biomarkers showed strong diagnostic utility, they lacked clinical prognostic value. We suggest that disease severity is mainly driven by host-related factors, particularly comorbidities, rather than differences in inflammatory response, highlighting the need for improved preventive and risk stratification strategies in this population.

Abstract: The purpose of this investigation was to analyze the effect of financial structure and fruit-fly control on the development of Small and Medium Enterprises (SMEs) of citrus in the Central Jungle of Peru. Using a quantitative design and a balanced sample of 54 observations, the analysis estimates complementary linear models with interaction terms and restricted cubic spline specifications to capture direct, synergistic, and nonlinear effects. The baseline results show that both financing structure and fruit-fly control exert positive and statistically significant effects on business growth. The interaction term is also positive and significant, indicating that the returns to improved financing rise when phytosanitary management is stronger, and that effective pest control becomes more productive when firms operate with more stable and diversified financial resources. Flexible spline estimates further reveal that these relationships are not constant across the explanatory range, but vary according to firms’ positions within the financial and technological space. Overall, the findings suggest that sustainable growth in citrus SMEs depends on the simultaneous strengthening of rural finance and phytosanitary capabilities under conditions of production risk and market constraints. The study contributes to the agricultural development literature by linking crop protection, farm-level managerial capacity, and enterprise performance in a single empirical framework.

Abstract: Neospora caninum is a master of reproductive disruption, causing devastating abortion storms in cattle and inflicting annual billion-dollar losses on the global livestock industry. Yet, in water buffaloes (Bubalus bubalis)—a phylogenetically close relative often raised in the same environments—the same parasite often takes on a different role: a silently persisting infection with significantly lower rates of clinical abortion. This review inverts the traditional narrative. Instead of focusing solely on the susceptible host (cattle), we argue that the key to unlocking next-generation control strategies lies in understanding the resistant host (buffalo). By dissecting this “Neospora paradox,” we explore the cut-ting-edge molecular and immunological crosstalk that dictates pregnancy outcomes. We journey from the parasite’s sophisticated arsenal of invasion proteins, revealed by CRISPR-Cas9 screens, to the maternal–fetal interface, where the battle between immune tolerance and parasite control is won or lost. We further examine the intriguing rela-tionship between N. caninum and its similar Toxoplasma gondii, revealing how differential host immune recognition determines infection outcomes. Ultimately, we propose that deciphering the buffalo’s successful equilibrium with N. caninum could illuminate novel pathways for vaccines and immunotherapeutic strategies, transforming the management of neosporosis worldwide.

Abstract: Bacterial infections impose a substantial global health burden, with antimicrobial resistance (AMR) further compounding the urgency of accurate and timely etiological diagnosis. Conventional culture-based methods, limited by extended turnaround times of 48–96 hours, reduced sensitivity in the presence of prior antibiotic exposure, and an inability to characterize resistomes at the molecular level, are progressively insufficient in the face of contemporary clinical demands. Molecular technologies have transformed bacteriological diagnostics by enabling rapid, sensitive, and highly specific pathogen identification directly from clinical specimens. Despite a growing body of primary evidence, no current review synthesizes these platforms under a unified comparative analytical framework that simultaneously addresses three critical dimensions: (1) the quantitative performance benchmarking of principal molecular platforms, including polymerase chain reaction (PCR) and its variants, isothermal amplification technologies, next-generation sequencing (NGS), clustered regularly interspaced short palindromic repeats (CRISPR) based diagnostics, and digital PCR (dPCR), across standardized parameters of sensitivity, specificity, limit of detection (LOD), time-to-result, and multiplexing capacity; (2) the integration of artificial intelligence and machine learning (AI/ML) algorithms into molecular diagnostic workflows for AMR prediction and clinical decision support; and (3) the translational trajectory of these technologies toward point-of-care (POC) deployment in decentralized and resource-limited settings. This review addresses this gap by providing a structured, evidence-based comparative analysis of molecular platforms applicable to bacterial infection diagnostics, critically evaluating their clinical validation status, AMR genotyping capabilities, AI augmentation potential, and readiness for POC implementation. We further delineate regulatory, health-economic, and implementation considerations, and identify key research priorities for the next generation of culture-independent precision bacteriological diagnostics.

Abstract: Customer segmentation is a core application of data mining in the retail industry. Traditional K-means clustering is widely adopted here for its simple principle and high computational efficiency, yet it has notable drawbacks: random initial clustering centers easily lead to local optimal solutions, it is highly sensitive to abnormal data, and the cluster number K relies on manual experience, resulting in unstable clustering performance. This paper designs an improved K-means algorithm, which filters outliers through a two-layer mechanism combining Local Outlier Factor and distance threshold. It also constructs a multi-index system with Silhouette Coefficient, Calinski-Harabasz and Davies-Bouldin indices to automatically determine the optimal K-value, optimizes initial centers via density peak clustering, and introduces weighted Euclidean distance to enhance clustering compactness. Experiments on the Mall Customer Segmentation dataset compare the proposed algorithm with traditional K-means, K-medoids and DBSCAN. Results show it achieves a Silhouette Coefficient of 0.5821, a CH index of 1025.36 and a DB index of 0.5107, outperforming all comparison algorithms in all indicators with more reasonable and stable clustering results. Applied to mall customer segmentation, this algorithm divides customers into 5 groups with distinct characteristics, providing solid data support for malls to formulate scientific and differentiated marketing strategies.

Abstract: Urban construction activities are recognized as significant contributors to particulate matter (PM) emissions; however, the accurate real-time monitoring of size-resolved PM fractions presents a formidable challenge. Traditional low-cost PM sensors predominantly report cumulative concentrations, which obscures the distinct health and regulatory sig-nificance of PM1, PM2.5, and PM10. This study systematically evaluates the performance of two low-cost sensors—PMS5003 and Sniffer4D, utilizing non-cumulative measure-ments obtained under controlled laboratory conditions designed to simulate construction PM generated from concrete slab drilling. Sensor performance was rigorously analyzed using Pearson correlation coefficients, standard deviation, and mean percentage differ-ences. Six correction models—Linear Regression, Polynomial Regression, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Kalman Filter—were independently developed for each PM size fraction to enhance measurement precision. Findings reveal that RF and ANN consistently provided the most accurate corrections, particularly for PM1 and PM2.5, with RF achieving a coefficient of determination (R²) > 0.89 for PM1 and R² > 0.87 for PM2.5 at the 50-second duration. This investigation introduces a size-resolved correction framework specifically designed for construction environments, thereby advancing the capability of low-cost sensors to enable accurate particle-specific exposure assessments.

Abstract: Behavioral ransomware detection often achieves high accuracy in standard evaluations; however, these results frequently fail to generalize under distribution shifts or when encountering previously unseen families. This study evaluates detection performance on the MLRan dataset (4,880 samples across 64 families) using four rigorous evaluation protocols: stratified, temporal, family-disjoint, and open-set. To ensure a strict separation of learned features, the family-disjoint and open-set splits were executed at the family level. We propose the Hierarchical Sparse Neural Network (HSNN), a taxonomy-aligned model with group-level and branch-level gating for structured interpretability. Unlike flat architecture, HSNN introduces a hierarchical gating mechanism aligned with a predefined behavioral taxonomy, enabling structured interpretability and modality-level analysis. The baseline FlatMLP had a slightly higher average macro-F1 score (0.9860 vs. HSNN's 0.9839), but the HSNN was better calibrated and more parameter efficient. The HSNN reduced calibration error by 34.1% (absolute reduction of 0.0056 in ECE) and model complexity by 42% in terms of parameter count. HSNN showed slightly lower variability than FlatMLP and broadly stable gate patterns across seeds. The proposed HSNN achieved one of the highest performances under the paper’s open-set family protocol (0.9930 vs. 0.9913) using a maximum-softmax novelty baseline. Our feature analysis shows that string-based artifacts remain strong predictors, but the HSNN’s hierarchical structure encourages a more balanced weighting across behavioral modalities, reducing reliance on any single feature type. These results indicate that structured, sparse architecture presents a competitive and well-calibrated alternative to conventional dense models under the evaluated settings.

Abstract: In this paper, the general moment form of Green’s Theorem curve integral, for the calculation of the area and moments values of the planar region enclosed by arbitrary curve, is derived from the numerical version of its belonging moment integrals that are obtained from discrete vector product and differential vector product properties. Then, all six area and moments integrals are derived in a new, uniform integral form, for calculation of: area itself, its static moments, area centroids and moments of inertia of observed enclosed area region below arbitrary curve, based on the initial Green’s theorem curve integral for the calculation of the area enclosed by general curve, and the properties of discrete and differential vector products.

Abstract: We study the degree-d Jensen polynomials Jd,n(X) built from the moment sequence Mn=∫0∞Φ1(u)u2ndu of the Riemann Ξ-function, which coincides with the classical Pólya–Jensen family. Using bridge coordinates, the staircase law, and Plancherel–Rotach asymptotics, we prove that Jd,nγ is hyperbolic for all n≥C0∞d4 (C0∞≈0.020, proved); combined with the GORZ theorem for d≤8, this covers the entire asymptotic regime. We identify a phase-transition law n*(d)=C0∞d4+αd3+β(−1)dd2+O(d) (Conjecture 3.5): the leading constant C0∞≈0.0195 is proved analytically; the formula for α is derived; its numerical value ≈−0.2 to −0.3 is numerical evidence; the parity structure β(−1)dd2 is proved. For the finite strip 0≤n&lt;C0∞d4 with d≥9, the sole remaining gap, whose closure is equivalent to the Riemann Hypothesis under standard transversality, we establish four structural obstructions: ratio-barrier saturation (no usable margin, certified and numerical); frozen zero count (parity blocks any ladder, certified for d≤21); interlacing-lift vacuity (proved); and a discriminant equivalence (proved under transversality), showing that all known local and inductive mechanisms fail simultaneously in this region. The problem reduces to: Disc(Jd,nγ)&gt;0 for all d≥9 and 0≤n&lt;C0∞d4; this requires moment data Mk for k≥130, currently inaccessible.

Abstract: The paper explores how Green digital technologies (GDTs) - ERP systems, cloud, IoT, artificial intelligence, and big data analytics can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). On the basis of institutional isomorphism theory, we examine the relationship between the coercive, mimetic and normative institutional pressures and adoption of green technology interaction on the sustainability reporting practices. On the basis of panel data of 30 ASE-listed industrial companies during the period of 20202024 (N = 146 firm-year observations), we use pooled OLS and random-effects frameworks characterized by strong clustering of standard errors. Findings show that Green Digital Technology Index has a positive and significant agreement with the ESG disclosure scores (0.019; 0.024; 2.486, p value 0.019; 2.507, p value 0.024), with adopting firms having an average score of 1.73 higher. Its impact has been the most significant to the environmental aspect ( = 3.460, 0.074) = 0.074. Although institutional pressures fail to modulate the GDT-disclosure relationship, mediation analysis shows that institutional pressure is also a powerful predictor of GDT adoption (0.098, p 0.100), indicating that institutional forces play the role through technology adoption. The quality of disclosure has a negative relationship with CEO duality ( -4.863, p < 0.001). The results validate the assumption that the green digital technologies are a transmission mechanism where institutional pressures are converted to an enhancement of sustainability disclosure in the emerging markets.

Abstract: Vanilla planifolia, a high-value tropical orchid, is significantly impacted by viral pathogens that threaten its cultivation and productivity. This study employs metagenomic technique to detect and characterize the viral communities associated with V. planifolia in south Florida. Using high-throughput RNA sequencing, the Cymbidium mosaic virus (CymMV) and Vanilla latent virus (VLV) were prevalent in the plant system, with CymMV being the dominant viral species. Phylogenetic analysis of the CymMV coat protein gene revealed notable genetic divergence in the Homestead isolate, forming a distinct clade from global reference strains, suggesting local adaptation or host-specific evolution. Viral distribution across plant system revealed higher viral loads in stem tissue, consistent with their role in systemic transport, whereas leaves exhibited greater viral diversity, likely due to in-creased environmental exposure. The low abundance of other viral species, including Garlic viruses and Senna severe yellow mosaic virus, highlights the complex viral ecology associated with V. planifolia. This study underscores the value of metagenomic approaches for uncovering both well-characterized and novel viruses in plant systems and highlight the need for continuous viral surveillance to guide disease management strategies in economically important crops such as vanilla.

Abstract: Background: Clinical trials showed mRNA-1345 (mRESVIA) was highly efficacious against RSV-associated lower respiratory tract disease in adults; real-world studies are critical to confirm vaccine impact on RSV-related healthcare usage.Methods: A retrospective matched test-negative case-control study used Veterans Health Administration electronic health records from the 2025–2026 respiratory season. Veterans aged ≥50 years with acute respiratory illness (ARI) who underwent RSV testing were included. Vaccine effectiveness (VE) of mRNA-1345 was estimated against RSV-positive ARI-associated hospitalization, urgent care/emergency department, and/or outpatient visits using multivariable conditional logistic regression. Results: The matched analysis included 2,500 RSV cases and 9,907 controls with balanced baseline characteristics. Among RSV-positive ARI-associated hospitalizations, <3 of 448 cases and 59 of 1,770 controls were vaccinated (VE of 87% (95%CI: 47%-97%)). Protection was consistent across settings.Conclusions: mRNA-1345 is associated with high protection against RSV-positive ARI-associated hospitalization that was generally consistent across medically-attended outcomes in the US Veteran population.