Abstract: In a sentinel health event investigation of a back disorder claim, the vibration exposure and ergonomic function of a modified suspension seat were assessed. (1) Background: In a forensic occupational injury investigation an after-market altered operator seat in a railroad rail-track tamper machine was evaluated. (2) Methods: Detailed whole-body vibration (WBV) exposure measurements were conducted according to current applicable technical standards and guidelines (i.e., ISO 2631-1; 1997) on a 09-16 DYNACAT Continuous Action Tamper with Stabilizer during routine track repair services. The modified Grammer Mfg. suspension operator seat was evaluated for performance and ergonomic features (i.e., adjustability, posture, and suspension quality); (3) Results: The tested seat appeared to underperform and was overloaded with the aftermarket control devices, attachments and modifications. The suspension system's end-stopper was damaged. The seat system had excessive play and wobbles; it was not firmly braced and attached. The vector sum (av) results ranged from 0.26 m/s² (no tamping) to maximal 0.55 m/s² (tamping). The seat transfer (SEAT) analysis showed magnification of vibration input and variable performance of the suspension depending on operational tasks. (4) Conclusions: The modified suspension seat underperformed and seemed to magnify and worsen the vibration, jolts and shock exposures of the seated operator. The heavy and bulky seat modifications likely limited the suspension function. The malfunctioning seat was more likely than not a contributing factor in the pathogenesis of the spinal disorders for the injured machine operator.

Abstract: This systematic review on intelligent HVAC systems for residential buildings focuses on advanced control techniques and AI applications. Model Predictive Control (MPC) is the most common method (≈40% of studies), achieving 15–20% energy savings and 10–30% peak demand reduction. Deep reinforcement learning (DRL) offers a model-free alter-native, reducing energy costs by 15% and comfort violations by up to 98%. Neural networks (LSTM, CNN-BiLSTM, attention mechanisms) significantly improve load pre-diction and thermal comfort modelling, with fusion models boosting accuracy by 66–85%. Comprehensive AI-based systems deliver 22–44% energy savings and 22–86% comfort improvements. Performance varies by climate, building type, and baseline; field trials show lower but more reliable savings than simulations. Hybrid MPC–ML approaches are emerging as best practice. Barriers include model complexity, computational demands, limited training data, and integration with legacy systems. Occupancy-aware strategies save 19–45% energy, while intelligent thermal storage can raise solar fractions from 11% to 61%. Overall, intelligent HVAC control is technically feasible and economically advantageous, but success depends on accurate modelling, tailored control strategies, and robust han-dling of occupancy uncertainty.

Abstract: Accurate distance measurement in outdoor environments remains a challenging problem for mobile augmented reality (AR) systems due to sensor noise, environmental variability, and the limitations of single-modality approaches. Existing consumer AR solutions often prioritize usability over metric robustness, leading to performance degradation in large-scale or heterogeneous outdoor scenarios. This work presents EfMAR, an adaptive framework for outdoor mobile AR-based geospatial measurements that integrates multiple sensing modalities through a structured sensor fusion architecture. EfMAR combines visual SLAM, inertial sensing, depth information, and global positioning cues to improve robustness and consistency in distance estimation across diverse outdoor conditions. Beyond implementation, the framework formalizes a reusable architectural model for adaptive multi-sensor fusion, supporting reproducibility and future comparative research. A dedicated dataset is described, comprising a combination of real-world field measurements collected in representative outdoor scenarios and synthetic data informed by existing literature, enabling structured performance analysis while maintaining methodological transparency. Performance evaluation focuses on analyzing relative behavior, stability, and variability across sensing approaches rather than establishing absolute accuracy benchmarks. Comparative results across multiple distance ranges and environments indicate that hybrid sensor fusion strategies exhibit more stable and consistent performance trends compared to single-modality solutions, particularly in challenging urban contexts. Dispersion analysis further highlights the influence of environmental factors such as lighting conditions and spatial scale on measurement variability. Overall, the results position EfMAR as a flexible and adaptive framework designed to enhance robustness in outdoor AR-based geospatial measurement tasks. By emphasizing consistency, transparency, and architectural generalization, this work contributes a practical foundation for future research and development in mobile AR sensing for real-world outdoor applications.

Abstract: Newton published his mechanics in the form of an axiomatic system just as the Euclidean geometry. The Newton’s three laws are three axioms, from which, together with the necessary definitions of physical concepts and propositions, all contents of classical mechanics can be derived. Non-Euclidean geometries tell us that one of the axioms in an axiomatic system may take different forms. Modified axioms can lead to new axiomatic systems that are logically rigorous and self-consistent. The fifth axiom in the Euclidian geometry was modified to be two other different form, and consequently, two non-Euclidean geometries were developed. We think that Newton’s second law can be modified. The second law can be simply stated as: force is the cause of acceleration. It can be modified as: force is the cause of deceleration. This results in a new axiomatic system called new classical mechanics. This paper presents the fundamental formulas of the new classical mechanics. The most distinctive feature of the new mechanics is that the direction of momentum is opposite to that of velocity, and the kinetic energy is negative, i.e., a negative sign is attached to usual positive kinetic energy (PKE). Therefore, the new classical mechanics can be called negative kinetic energy (NKE) one, while the existing classical mechanics can be called PKE one. These two parts can be collectively referred to as a whole classical mechanics, which includes both PKE and NKE parts. The formulas of these two parts have symmetry with respect to positive and negative kinetic energy. The PKE classical mechanics describes the motion of macroscopic matter that we can observe, while the NKE classical mechanics describes the motion of macroscopic matter that we cannot observe, i.e., the motion of dark matter. Our universe has symmetry with respect to PKE and NKE, which is also the symmetry with respect to matter and dark matter. The basic equations of quantum mechanics describing the motion of micro-particles also have symmetry with respect to PKW and NKE, which has been elaborated in the author’s previous work. The theory presented in this paper describe the motion of macroscopic NKE matter.

Abstract: It has long been recognized that the oxygen reduction reaction occurs more readily on Pt(111) surfaces that include steps, both (111) and (100), than on near-perfect Pt(111). Theoretical models were developed involving the water structure in the electric double layer and its interactions with adsorbed OH, with the actual O₂ reduction occurring on the (111) terraces adjacent to the steps. However, the present density functional theory (DFT) calculations confirms that O₂ adsorbs strongly at the steps and can undergo dissociation aided by adjacent water molecules to produce adsorbed OH. OH produced at the steps can move to the (111) terraces, where it can be more readily reduced to H₂O and desorbed. This model avoids the scaling relation, which predicts that all oxygen-containing reactants and intermediates are proportional to each other on any given surface. Efforts to develop new O₂ reduction catalysts have been hampered by this assumption, which supposes that the reaction rate can be increased by decreasing OH adsorption strength, even though decreased OH adsorption strength is accompanied by decreased O₂ adsorption strength. This proposed model can explain the experimental results on stepped surfaces and may also be important for the development of Pt nanoparticle catalysts.

Abstract: Based on the dilemma of analyzing the resonance stability of AC power grids using im-pedance models, it is demonstrated that the "negative resistance" mechanism of wide-band oscillation is untenable. A general method for describing power electronic devices using wideband voltage source converter models and wideband current source convert-er models is proposed, thereby representing the nonlinear characteristics of power elec-tronic devices with harmonic voltage sources and harmonic current sources. This allows the new energy power system to still be described by a linear system, and interprets the mechanism of wideband oscillation as a "harmonic amplification" phenomenon caused by network resonance, thus establishing a new framework for explaining the mechanism of wideband oscillation in new energy power systems. Through the analysis of two basic resonant circuits, the relationship between the damping ratio of resonant modes and the harmonic amplification factor is derived, laying a theoretical foundation for the analysis and suppression of wideband oscillation based on the s-domain nodal admittance matrix method and C-type damping filters. Based on the maximum damping criterion, a design method for C-type damping filters is proposed. The designed C-type damping filters ex-hibit strong broadband damping effects.

Abstract: This tutorial presents a coherent overview of reinforcement learning (RL), tracing its evolution from theoretical foundations to advanced deep learning algorithms. We begin with the mathematical formalization of sequential decision-making via Markov Decision Processes (MDPs). Central to RL theory is the Bellman equation for policy evaluation and its extension, the Bellman optimality equation, which provides the fundamental condition for optimal behavior. The journey from these equations to practical algorithms is explored, starting with model-based dynamic programming and progressing to model-free temporal-difference learning. We highlight Q-learning as a pivotal model-free algorithm that directly implements the Bellman optimality equation through sampling. To handle high-dimensional state spaces, the paradigm shifts to function approximation and deep reinforcement learning, exemplified by Deep Q-Networks (DQN). A significant challenge arises in continuous action spaces, addressed by actor-critic methods. We examine the Deep Deterministic Policy Gradient (DDPG) algorithm in detail, explaining how it adapts the principles of optimality to continuous control by maintaining separate actor and critic networks. The tutorial concludes with a unified perspective, framing RL's development as a logical progression from defining optimality conditions to developing scalable solution algorithms, and briefly surveys subsequent improvements and future directions, all underpinned by the enduring framework of the Bellman equations.

Abstract: Despite the Minamata Convention’s targeted reductions in mercury consumption, global trade data exhibits a ‘Compliance Paradox’ where reported flows vanish while artisanal gold mining output remains stable. This research proposes a ‘Mineral Intelligence’ pipeline utilizing unsupervised machine learning to detect illicit mercury trafficking disguised as Electronic Waste (HS 8549). By applying Gaussian Mixture Models (GMM) and Isolation Forest algorithms to UN Comtrade data (2020–2024), we identify a systemic ‘Balloon Effect’: as elemental mercury bans took effect in 2022, illicit volumes were structurally displaced into ‘fake waste’ classifications. Forensic analysis reveals a statistically significant ‘Smuggler’s Signature’ within these flows, characterized by a price anomaly of $24–$80/kg (mirroring liquid mercury markets) and a Net to-Gross weight ratio exceeding 90%, physically corresponding to standard 34.5 kg steel mercury flasks. Furthermore, Node2Vec and spectral embedding analysis exposes a ‘Decoupling Chasm’ (Manifold Distance: 2.06) that topologically separates financial gold hubs from mercury-intensive mining zones. Finally, Recursive LSTM forecasts predict a ‘burnout’ of the current HS 8549 smuggling vector (-618M kg/yr), warning of an imminent regime shift toward chemically masked commodities.

Abstract: The aim of this study was the validation of the published method for the estimation of the personal maximum manual handling weight limit (PMHWLmax) [1] which is based on the personal body weight, as a reference to general population percentiles anthropometrics. For this purpose, an experiment was designed, were a repetitive process of transferring progressively increasing loads, was performed, with the aim of identifying through EMG recording of the erector spinae muscle, the load that brings about the first distinct quantity change in muscle activation and fatigue, compared to the preceding trials and afterwards the matching of the specific load value with the body weight of the participant

Abstract: Exporting food products from the European Union (EU) to the United States of America (USA) involves navigating complex regulations and procedural barriers that hinder market access. Italian food businesses (FBs), particularly small and medium-sized enter-prises (SMEs), often face difficulties accessing clear guidance, as national procedures are scattered across multiple sources. This paper proposes a structured four-step analytical framework to support EU FBs: product-specific analysis, identification of relevant EU and USA legislation, comparative legislative analysis via concordance tables, and identifica-tion of procedures to integrate into the Food Safety Management System. The framework was applied to an Italian medium-sized FB exporting pork-based pasta sauce to the USA. Beyond the specific case study, the proposed framework was designed to be replicable and adaptable to different food products and third-country destinations. As such, it can support both FBs and Competent Authorities in conducting risk-based assessments of regulatory equivalence and export compliance. Results indicated the need for Sanitation Standard Operating Procedures, thermal process validation, direct verification activities, and pre-shipment review. Findings emphasize that operational and procedural barriers disproportionately affect SMEs, highlighting the importance of targeted support to facil-itate market access and strengthen certification systems.

Abstract: As an emerging threat to global food security, wheat blast necessitates the development of a rapid and field-deployable detection system to facilitate early diagnosis, enable effective management, and prevent its further spread to new regions. In this study, we aimed to validate and improve an Recombinase Polymerase Amplification coupled with PCRD lateral flow detection (RPA-PCRD strip assay) kit for the rapid and specific identification of Magnaporthe oryzae pathotype Triticum (MoT) in field samples. The assay demonstrated exceptional sensitivity, detecting as low as 10 pg/µL of target DNA, and exhibited no cross-reactivity with M. oryzae Oryzae (MoO) isolates and other major fungal phytopathogens under the genera of Fusarium, Bipolaris, Colletotrichum and Botrydiplodia. The method successfully detected MoT in wheat leaves as early as 4 days post-infection (DPI) (asymptomatic plants), as well as in infected spikes, seeds, and alternate hosts. Furthermore, by combining a simplified polyethylene glycol-NaOH method for extracting DNA from plant samples, the entire RPA-PCRD strip assay enabled the detection of MoT within 30 min with no specialized equipment and high technical skills at ambient temperature (37-39 °C). When applied to field samples, it successfully detected MoT in naturally infected diseased wheat plants from seven different fields in wheat blast hotspot district, Meherpur in Bangladesh. This method offers a practical, low-cost, and portable point-of-care diagnostic tool suitable for on-site surveillance, integrated management, seed health testing, and quarantine screening of wheat blast in resource-limited settings. Furthermore, the RPA-PCRD platform serves as a modular diagnostic template that can be readily adapted to detect a wide array of phytopathogens by integrating target-specific genomic primers.

Abstract: We present a virtual reality (VR) framework for controlling dual-arm robotic manipulators through immersive interfaces, integrating both simulated and real-world platforms. The system combines the Webots robotics simulator with Unreal Engine 5.6.1 to provide real-time visualization and interaction, enabling users to manipulate each arm’s tool point via VR controllers with natural depth perception and motion tracking. The same control interface is seamlessly extended to a physical dual-arm robot, allowing for teleoperation using the same VR environment. Our architecture supports real-time bidirectional communication between the VR layer and both the simulator and hardware, enabling responsive control and feedback. We describe the system design, and performance evaluation in both domains, demonstrating the viability of immersive VR as a unified interface for simulation and physical robot control.

Abstract: With the increasing adoption of EMV-based digital payment systems, ensuring compliance with privacy regulations (GDPR, PSD2, PCI DSS) has become essential. A critical challenge in regulatory-compliant payment transactions is the risk of transaction linkability, which can expose sensitive user data and violate privacy mandates. In this paper, I analyze the privacy vulnerabilities of EMV 2nd Gen payment protocols and propose an improved key agreement mechanism to enhance unlinkability and transaction security. The approach builds on the Blinded Diffie-Hellman (BDH) key establishment protocol, integrating cryptographic enhancements to mitigate active and passive tracking threats. I introduce a stronger unlinkability definition, accommodating active attackers and ensuring compliance with EMVCo security requirements. The proposed scheme uses anonymous credential techniques to prevent transaction tracing while preserving authentication integrity. Experimental results show that the method significantly improves transaction unlinkability, reducing privacy leakage risks and aligning with regulatory standards in secure payment processing. This research highlights the role of privacy-preserving cryptographic techniques in ensuring regulatory compliance for modern digital payment ecosystems.

Abstract: The vaginal microbiome plays a crucial role in protecting its host from bacterial, viral, and fungal pathogens and serves as a first line of immune system defense for the lower reproductive tract. The composition of the microbiome is relatively understudied despite its integral role in women’s reproductive health. Maintenance of the correct abundances of bacteria can help prevent many different infections. Additionally, the microbiota also serve to initiate and support the immune system of a newborn in the case of vaginal birth. The introduction of these organisms can mediate many interactions in the developing brain and nervous system of the infant (Günther, V. et al. 2022). Similarly, research on biological causes of sexual orientation is a new field that severely lacks extensive research. There are a few theories related to the development of homosexuality that have been backed by research. Synthesizing what we know from previous research on these two fields, we aim to bridge the gap between these two areas of study by postulating a relationship between the composition of the female vaginal microbiome during pregnancy and the fraternal birth order effect leading to male homosexuality. We propose that during pregnancy, changes in the maternal vaginal microbiome result in a change in the vaginal microbiome that makes them more susceptible to the development of anti-NLGN4Y antibodies responsible for the immune attack on the Y-linked NLGN4Y protein responsible for male brain development (Bogaert, A. et al. 2017), specifically in the anterior hypothalamus. We propose that mothers who have already given birth to males are especially susceptible to this antibody development, providing a possible explanation for the Fraternal Birth Order effect.

Abstract: This study introduces a pioneering digital twin platform that embeds graph neural networks (GNNs) to replicate power system topologies, where buses function as nodes and transmission lines serves as edges, facilitating precise real-time state estimation and predictive analytics. Transfer learning enables efficient adaptation of pre-trained GNN models from extensive synthetic datasets, such as modified IEEE benchmarks, to operational environments with scarce data, slashing training requirements by approximately 60% while upholding robustness across varied grid configurations from local distribution to national wholesale markets. Quantum-safe protocols, drawing on NIST-approved post-quantum algorithms like Kyber for key encapsulation and Dilithium for digital signatures, secure the continuous data synchronization between physical infrastructure and virtual models, defending against quantum-enabled threats such as harvest-now-decrypt-later scenarios without introducing latency penalties.The hybrid edge-cloud deployment supports advanced resilient control mechanisms, encompassing model predictive control for stabilizing grid operations amid faults and optimization routines for electricity market clearing that enhance social welfare under stochastic conditions involving renewables and demand response. Validation occurs through rigorous simulations on IEEE 118-bus, 300-bus, and large-scale 10,000-bus networks subjected to false data injection attacks, renewable intermittency, and topological shifts. Results reveal 25% reductions in fault recovery durations, 18% gains in locational marginal price precision, and near-perfect (99.9%) resilience to encrypted interceptions. Ablation analyses affirm the additive value of GNN topology awareness, which alone reduces state prediction errors by 32% compared to recurrent baselines, alongside transfer learning's convergence acceleration and quantum cryptography's seamless integration.This platform bridges critical gaps in cyber-physical security and operational agility, positioning it as a deployable solution for smart grid evolution, including distributed energy integration and peer-to-peer transactions. Prospects for enhancement involve hybrid quantum-classical computing to further optimize control horizons, ensuring long-term viability in decarbonized energy ecosystems.

Abstract: Background/Objectives: Angiosarcoma of the thorax is a very rare and malignant disease. We studied the incidence and survival of thoracic angiosarcomas with special focus on primary and secondary angiosarcoma. Methods: We analyzed data from the population-based cancer registry North Rhine-Westphalia (NRW), Germany, of the years 2008–2023. We included primary and secondary angiosarcoma of the thorax (ICD-O-3: morphology 9120/3, topography C34, C38, C44.51, C49.3, C50) and report age-standardized (Old European Standard population) incidence rates and survival (Kaplan-Meier Curves). Results: We analyzed 421 cases of thoracic angiosarcoma. 90.0% were female (n = 379). The age-standardized incidence rates of angiosarcoma of the thorax were 0.25 per million and year for male patients (SE 0.0) and 1.5 per million and year for female patients (SE 0.1). All male patients had primary angiosarcoma (n = 42). The majority of thoracic angiosarcoma among females were second primary tumors (n=262, 69.1%). The 5-year overall survival (OS) was 38.5% (SE 2.6). OS for women was 41.4% (SE 2.8) and for men was 12.0% (SE 5.4). OS for female patients was 40.9% (SE 4.1) and 41.8% (SE 3.8) for primary and second primary angiosarcoma respectively. The worst OS had patients with angiosarcoma of the lung (men 20.0% (SE 12.7) and mediastinum, heart and pleura (men 4.7% (SE 4.5). The OS for women was 0%, all females died within 2.2 years after diagnosis of angiosarcoma with these topographies. Conclusions: Angiosarcoma of the thorax is a rare condition with poor prognosis. Irrespective of the classification into primary and second primary, women with angiosarcoma have a better prognosis than men. Topography seems to be the most determining prognostic factor in this disease.

Abstract:

We develop an operational certification framework for micro–macro modeling in local quantum systems. Audited closure is defined as a uniform discrepancy bound between microscopic and surrogate predictions over a declared observable suite on a declared domain of states. A receiver-region copy time τ_copy(η;A→B) is defined as the earliest time at which a finite receiver region B can distinguish—at trace-distance threshold η—a localized perturbation supported on A. Under explicit Lieb–Robinson hypotheses for Hamiltonian or Lindbladian generators, we derive an exponential receiver distinguishability envelope and an explicit time-to-threshold lower bound, including the regime η≥Γ where the threshold is provably never reached and τ_copy=+∞. As an achieved-closure demonstration, we analyze a split-step quantum walk lifted to quasi-free lattice fermions and provide computable dispersion and two-point density audit bounds on a band-limited quasi-free domain. As a signature contribution beyond a repackaging of locality, we prove a locality–mixing separation theorem for primitive Lindbladians with a log-Sobolev (or mixing) rate λ: the receiver distinguishability is bounded by the minimum of a ballistic LR tail and an intrinsic mixing contraction, yielding a measurable crossover time and an operational separation between unitary and dissipative dynamics.

Abstract: Background: Chronological age does not always accurately reflect biological matura-tion in children, particularly in the presence of systemic diseases. Dental age assess-ment is widely used as a biological maturity indicator; however, the effect of juvenile diabetes mellitus on dental maturation remains insufficiently clarified, with incon-sistent findings reported across populations. Objective: This study aimed to compare dental age estimated using the Demirjian method with chronological age in children with juvenile diabetes and in age- and sex-matched healthy controls. Materials and Methods: This observational comparative study included panoramic radiographs from 30 children aged 8–15 years: 15 patients diagnosed with juvenile diabetes mellitus and 15 systemically healthy controls, all presenting Angle Class I malocclusion. Dental age was assessed using the Demirjian method and compared with chronological age. Ap-propriate parametric or non-parametric statistical tests were applied based on data distribution. Results: Children with juvenile diabetes exhibited a statistically signifi-cant advancement in dental age relative to chronological age, with a mean DA–CA difference of 1.36 years (p = 0.0066). No statistically significant differences between dental and chronological age were observed in the control group. Sexual dimorphism was evident from crown completion stages onward, with females demonstrating earli-er dental maturation. Conclusions: Juvenile diabetes mellitus is associated with accel-erated dental maturation. These findings have clinical implications for orthodontic treatment timing and growth assessment and indicate a potential risk of age overesti-mation in forensic contexts. Dental age should therefore be interpreted alongside skel-etal and chronological indicators, particularly in pediatric patients with systemic metabolic conditions.

Abstract: High-quality RGB–thermal infrared (RGB-T) semantic segmentation datasets are crucial for search-and-rescue (SAR) applications, yet their development is hindered by the scarcity of annotated ground truth and by the challenges of thermal-camera calibration, which typically depends on heated targets with limited geometric definition. Recent approaches, such as MATT, focus on transferring SAM-based RGB masks to multi-spectral data, but they do not fully address the need for robust cross-modal alignment, quality control, or human-in-the-loop reliability assessment in RGB-T segmentation. To fill this gap, we propose a general annotation methodology that performs geometric alignment of RGB-T pairs, combines model-based proposals with interactive refinement, and incorporates annotation cost and systematic quality checks using inter-annotator agreement. In this methodology, multimodal alignment is ensured through feature-based matching and homography estimation. Annotation integrates automatic proposals and guided refinement, and final masks undergo quantitative cost and quality control before being used in downstream model training. The proposed methodology was evaluated on a SAR-oriented RGB-T dataset comprising 306 image pairs. Consistent cross-modal alignment was achieved via SuperGlue-based matching and homography estimation, enabling the implementation of a SAM2-based semi-automatic annotation pipeline in Label Studio. Results across two annotators show that the proposed approach reduces annotation time by 21% while achieving a high annotation quality mean IoU = 74.9%) and a high inter-annotator agreement (mean pixel accuracy = 88.4%, Cohen's kappa = 83%). The curated labels were then used to benchmark two representative RGB-T segmentation models. These findings demonstrate the practical value of the proposed methodology and establish a reproducible framework for generating reliable RGB-T semantic segmentation datasets, complementing and extending recent multispectral auto-labeling approaches.

Abstract: Mediterranean greenhouses are characterized by the use of passive climate control techniques, thus avoiding energy inputs that would make crop production more expensive. This study was carried out in Almería (Spain), in a greenhouse divided in two sectors. (West sector: with double roof with a pink spectrum converter film combined with an increased ventilation surface, ratio of vent surface/greenhouse surface SV/SC = 26.0%; East sector: acted as a control with only standard ventilation surface, SV/SC = 16.6%). This study analysed the effect of a double roof and an increased ventilation surface on the main fungal diseases in different crops (Solanum lycopersicum L., Capsicum annuum L., and Cucumis sativus L.). Different diseases were found that develop naturally, powdery mildew (Leveillula taurica) in both the tomato and the pepper crop, and early blight (Alternaria linariae) only in the tomato crop. In the case of cucumber crop, three diseases that developed naturally were found, (i) downy mildew (Pseudoperonospora cubensis), (ii) powdery mildew (Podosphaera xanthii) and (iii) gummy stem blight (Stagonosporopsis spp). The sector that combined the double roof and the increased ventilation surface had lower disease levels compared to the control sector, with statistically significant differences.