Two-stage anaerobic digestion (TSAD) is an alternative to single-stage anaerobic digestion, separating hydrolytic–acidogenic and methanogenic phases to improve stability, organic matter degradation, and methane production. This review examines TSAD for co-digestion of sewage sludge (SS) and the organic fraction of municipal solid waste (OFMSW), emphasizing performance, scale-up challenges, digestate intensification, and nutrient recovery. TSAD can increase methane production by 25–50% compared with single-stage systems, while volatile solids removal can reach 87–93% depending on substrate type, temperature regime, hydraulic retention time, and organic loading rate. However, improvement remains variable and depends on substrate biodegradability, reactor configuration, and process control. Beyond methane recovery, the review highlights valorizing digestate as a secondary resource. Digestate post-treatment technologies, including thermal hydrolysis and steam explosion, report methane improvements from 26% to more than 300%, although energy demand and economic feasibility remain constraints. Nitrogen recovery technologies, including ammonia stripping and membrane contactors, can achieve efficiencies above 80–95% under optimized conditions, while phosphorus may be recovered through struvite precipitation, calcium phosphate recovery, or biochar-based pathways. Future TSAD development should integrate biological conversion, digestate recirculation, nutrient recovery, techno-economic assessment, and life-cycle evaluation to support circular, resource-efficient organic waste treatment systems.
Precise prediction of pressure drop and drag-reduction performance is essential for improving the hydraulic efficiency and reducing the energy demand of crude-oil pipeline transportations. Therefore, this study aims to develop an integrated computational fluid dynamics (CFD)–machine learning (ML) framework for predicting pressure drop (∆p), drag reduction (DR), pumping power reduction (PPR), energy savings (ES), and flow-rate enhancement (Q) in turbulent crude-oil pipeline flow containing drag-reducing agents (DRAs). The investigated system considers the effect of pipeline length (L), diameter (D), surface roughness (ε), operating temperature (T), and DRA concentration (25–200 ppm). The Reynolds-average Navier–Stokes equations (RANS) were solved using the shear stress transport (SST) k-ω turbulence model approaching near-wall resolution of y+ ≈ 1 for DRA3 at 20 ppm. The CFD modelling was first used to validate an experimental benchmark and subsequently used to expand the available dataset over the investigated operating conditions. The combined experimental–CFD dataset was then employed to develop a multi-output Kolmogorov–Arnold network (KAN) surrogate model. The proposed framework predicted DR up to 44.2%, PPR of approximately 55 W, ES of 30%, and flow-rate enhancement up to 5–10(Lday). The KAN model effectively captured the nonlinear relationships among DRA characteristics, pipeline geometry, and operating conditions, achieving R2 = 0.9318 for PPR prediction. The novelty of the proposed work lies in integrating a validated, near-wall-resolved SST k-ω CFD model with a multi-output KAN surrogate model, combining physics-based flow analysis with rapid data-driven prediction of hydraulic and energy-performance indicators.
Direct comparisons of microplastic (MP) contamination between semi-enclosed and open Mediterranean coasts remain scarce. We quantified MP concentration and morphotype composition at ten sandy beaches in Thessaly, Greece (five in the semi-enclosed Pagasitikos Gulf and five on the open NW Aegean coast), sampling supralittoral and active-swash sediments (n = 100): (~800 g composite per 1 m2 quadrat). Particles were extracted by NaCl density separation and screened using Nile Red fluorescence. We recorded 10,506 Nile Red-positive putative MPs (1–1297 items m−2; mean 105.1 ± 175.1; median 53.5 items m−2). Pellets/beads dominated (51.7%), followed by fragments (37.2%) and fibres (10.6%). Mean concentration was higher in the gulf than on the open coast (147.6 ± 226.0 vs. 62.5 ± 73.6 items m−2), but the area effect was non-significant (GLMM, p = 0.110), while among-beach heterogeneity was high (CV = 91.4%). A localized pellet hotspot occurred in the Kala Nera active swash (525.8 pellets m−2), producing a significant pellet-specific Area × Zone interaction (p = 0.033). Pellets contributed 42.9% of between-area dissimilarity (SIMPER, p = 0.013). These results indicate that localized, source-specific inputs may outweigh broad hydrodynamic differences in shaping regional contamination patterns.
Wind direction time series exhibit angular periodic discontinuity, multi-scale non-stationary fluctuations and abrupt wind shifts, which hinder the precision of short-term forecasting for wind turbine yaw control. In this paper, a dual-branch forecasting framework based on Variational Mode Decomposition (VMD) and Transformer is developed to address the above drawbacks, with a hysteresis gating and zoning residual compensation module embedded for targeted error correction. First, sine–cosine encoding is adopted to eliminate the numerical discontinuity between 0° and 360° for wind direction angular data, and valid meteorological input features are screened to discard redundant covariates. Second, the sine–cosine-encoded wind direction sequence is decomposed into multiple band-limited intrinsic mode functions (IMFs) via VMD, extracting frequency-specific features that reduce non-stationarity and facilitate subsequent Transformer modeling. The standard Transformer encoder serves as the normal branch to capture long-range temporal dependencies across the whole time series, while a lightweight multilayer perceptron (MLP) constitutes the compensation branch to learn prediction deviations between baseline predictions and ground-truth values. The hysteresis gating unit activates residual compensation based on historical prediction errors and angular variation, without requiring access to the current ground-truth value, and compensation intensity is adaptively adjusted via the zoning strategy; relevant coefficients are optimized by random search. Verified on a real wind farm dataset consisting of 10,421 15 min sampling points, the proposed model achieves the lowest MAE of 9.64° among six benchmark models. For the improved genuine mutation samples (angle change > 70°), the model achieves a mean improvement of 6.02°. Ablation experiments verify that VMD preprocessing, the MLP compensation branch, and the hysteresis gating mechanism play indispensable roles in forecasting performance. The proposed framework can support accurate yaw control of wind turbines, and the decomposition–compensation workflow can also be generalized to other periodic non-stationary forecasting tasks.
This study compares the elastic modal characteristics of reinforced-concrete cantilever beams containing four and six 12 mm longitudinal reinforcing bars using three-dimensional finite element analysis (FEA) and an exploratory machine learning (ML) exercise. Beam geometry, material properties, bond assumptions, and boundary conditions were held constant. Mesh refinement from a 50 mm to a 25 mm nominal element size changed the fundamental natural frequency by 0.18%, satisfying the adopted 1% convergence criterion. The Fine mesh FEA fundamental frequency of 36.048 Hz differed by 1.49% from the Euler–Bernoulli transformed-section estimate of 35.52 Hz. Block Lanczos extraction provided the first six natural frequencies and corresponding normalised mode shapes. Increasing the longitudinal steel area from 452.39 to 678.58 mm2 changed the calculated frequencies by −0.02% to +0.19%, while the two layouts exhibited similar mode-shape topology under the assumed intact, linear-elastic, perfectly bonded conditions. Four regression algorithms were fitted to the 12 correlated mode–configuration records using only mode number and reinforcement count as inputs and natural frequency as the output. The reported R2, MAE, and RMSE values describe complete dataset fitting or interpolation and do not establish generalisation to unseen configurations. No physical modal displacement, strain, or stress amplitude is reported because eigenvector scaling is arbitrary and no forced-response analysis with defined excitation and damping was performed.
Multi-stakeholder supply chains require decision mechanisms capable of simultaneously interpreting dynamic system states, coordinating conflicting stakeholder interests, and managing uncertainty across interconnected operational decisions. This study proposes an integrated Human–AI Collaborative Neuromorphic Digital Twin framework in which synchronized supply-chain data are transformed into cognitive representations, enriched through human–AI collaborative intelligence, strategically coordinated through adaptive game-theoretic interactions, and subsequently mapped into a unified decision-knowledge representation for fuzzy multi-objective optimization. The principal innovation lies in this closed and interconnected decision architecture, where the outputs of cognitive, collaborative, and strategic intelligence layers are explicitly fused and transferred to the optimization space rather than being applied as independent analytical modules. The framework jointly optimizes economic, environmental, service, resilience, energy, and operational-risk objectives under fuzzy uncertainty. Evaluation was conducted using combined real and statistically consistent simulated data across six operational scenarios ranging from baseline conditions to a critical scenario involving simultaneous demand growth, capacity restrictions, cost escalation, uncertainty, and stakeholder conflicts. Results demonstrate progressive improvements in decision quality under increasingly complex conditions; in the critical scenario, Human–AI collaboration achieved a 19.6% cost improvement, while the service level reached 99.4%. The findings demonstrate that integrating cognitive representation, collaborative intelligence, strategic adaptation, and fuzzy optimization provides a unified mechanism for adaptive multi-stakeholder supply-chain decision-making.
The industrial sector faces one of the biggest challenges in decarbonization, mainly due to the high costs associated with the development and implementation of low-carbon, energy-efficient technologies and solutions. The long lifespan of industrial assets and infrequent replacement contribute to maintaining high levels of energy consumption and emissions. As electric motors represent a significant portion of energy consumption in industries, improving their efficiency generates substantial reductions in consumption, energy demand, and emissions, thus optimizing overall energy performance. This article proposes an integrated guideline for the application of multi-criteria decision-making (MCDM) methods, computational thinking (CT), and technical standards in industrial energy management problems. To validate this proposal, the guidelines were applied to a real-world case of electric motor selection in an industrial complex. In this context, the structured analysis of the problem, when based on computational thinking, MCDM methods, and technical standards, provides transparency and traceability to decisions. The motor-selection case study, which incorporated computational thinking and MCDM tools (AHP/TOPSIS) aligned with technical standards, demonstrated that these integrated guidelines can substantially improve decision-making in industrial contexts by structuring selection problems and aligning them with the strategic objectives of organizations.
This experimental work aims at the study by non-destructive and destructive testing of the mechanical and acoustical properties of cold-setting epoxy resins plasticized with amounts of plasticizer and of PMMA (Plexiglas), both belonging to the two basic categories (thermosetting and thermoplastics respectively) of polymeric materials, which usually can be modified because of polymerization rate and curing, change in temperature and frequency, by the addition of plasticizers and/or inclusions as well as due to discontinuities (defects, voids and porosity) where stress concentration exists. On the other hand, ultrasound is a mechanical, elastic wave of very high frequency, and can be used for material testing. Using ultrasounds, defects, discontinuities, and damage can be detected, and moduli can be evaluated accurately. It should be noted that the moduli determined in this way are the dynamic moduli and differ from the static ones for any material. Here, the authors focus their study on plasticized epoxy resins and PMMA and apply this NDT method to estimate mechanical properties and correlate the results with those from destructive tests. Finally, the glass-transition temperature of plasticized epoxies was also evaluated from thermal experiments to determine the effect of the plasticizer.
The decarbonization of industrial steam production, representing up to 57% of energy use in the food industry, is critical for achieving EU climate neutrality goals. This study developed an integrated digital framework for the research project Hy4GreenSteam to optimize green-hydrogen integration through advanced predictive modeling. The employed LightGBM gradient-boosting algorithms were trained on 68,697 PV power measurements and 57,000 meteorological observations from 2020 to 2022. A “Production-Split” methodology was introduced for 24 h ahead forecasting, segmenting training into high (>2 kW) and low (≤2 kW) production regimes to manage solar heteroscedasticity. Results show the 15 min model achieved an R2 of 0.868 and the 1 h model an R2 of 0.832, while the day-ahead model—trained exclusively on information available at forecast issue time—achieved an R2 of 0.701, a 70% relative improvement over same-time-yesterday persistence. A complementary regime analysis shows that the production regime is predictable with 90.7% accuracy and quantifies the accuracy headroom of regime-specialized models (oracle R2 0.794). These methods were integrated into a real-time React-based platform that calculates optimal H2/CH4 blending; for the reference pilot configuration, driven by measured on-site PV generation, the computed CO2 emission reduction reaches 34% relative to natural-gas-only operation during high-solar operating intervals. Predictive modeling combined with a Digital Twin interface provides a TRL 6 decision-support solution, demonstrated in a relevant industrial environment, for managing renewable sources in industrial hydrogen applications.
Overpressure prediction is critical for safe and efficient drilling, yet remains challenging in complex basins with multiple genetic mechanisms. This study systematically investigates the overpressure origins in the Xihu Sag, East China Sea, a prolific hydrocarbon-bearing sag with widespread overpressure and complex pressure regimes. By integrating well logging data and direct pore pressure measurements from nine wells across three major structural units, the Western Slope Belt, the Western Sub-sag and the Central Inversion Belt, a multi-method diagnostic framework is employed. This combines Bowers’ effective stress analysis with sonic-density cross-plots to discriminate between loading and unloading mechanisms. Results show obvious vertical zoning of pore pressure—normal-pressure zone, overpressure zone, and pressure reversal zone—with distinct horizontal heterogeneity. Results reveal a distinct spatial differentiation in dominant overpressure mechanisms. In the Western Slope Belt, overpressure in the deep Pinghu Formation primarily results from a composite of undercompaction (creating initial pressure seals) and subsequent hydrocarbon generation-induced fluid expansion. In contrast, in the Central Inversion Belt and Western Sub-sag, overpressure is predominantly driven by hydrocarbon charging along faults coupled with tectonic compression, with minimal undercompaction signatures. Previous studies on overpressure genesis in the Xihu Sag have largely focused on the Western Slope Belt. This study expands the analytical scope to the Western Sub-sag and Central Inversion Belt, and conducts a systematic comparative analysis of overpressure genesis across multiple tectonic units. The value of this work lies in the systematic application of classical diagnostic methods to fill the regional research gap regarding the overpressure characteristics of the Huagang Formation and the composite nature of overpressure. With accurately constrained genetic mechanisms, the findings can provide support for optimized drilling fluid design and wellbore stability management, and effectively mitigate deep hydrocarbon exploration risks in this sag and analogous overpressured basins.
This study presents a numerical model of electromagnetic and thermal processes characteristic of a submerged arc furnace. Because direct modeling of a full-scale industrial furnace is complex and difficult to validate experimentally, a laboratory system without an electric arc is considered at this stage. The system reproduces the main features of current supply and energy distribution in the conductive region of the furnace bath. The model is implemented in ANSYS Fluent 2020 R1 using user-defined scalar equations for the electric potential, the components of the magnetic vector potential, and their time derivatives. The implementation was assessed in terms of mesh independence, time-step sensitivity, current and energy balances. The calculations yielded consistent distributions of electric potential, current density, magnetic flux density, Joule heat generation, and temperature. Heating was described using a two-stage scheme: the transient electromagnetic problem is first solved to obtain period-averaged Joule heat generation, which is then used as a source term in the energy equation. The model represents the first stage of a computational framework for submerged arc furnace modeling: at this stage, it is developed and assessed using a simplified laboratory configuration without an electric arc, while in future work it can be supplemented with an arc-channel description and extended to industrial furnace conditions.
The increasing complexity of residential energy systems and the growing penetration of distributed resources require practical energy-management solutions that extend beyond conventional metering. This paper presents the design and implementation of a real-time Internet of Things (IoT)-based energy-management system for monitoring and controlling household energy consumption under different operating conditions. The proposed system adopts a dual-processor architecture, in which a primary microcontroller performs time-critical electrical measurements and low-level load switching, while a secondary processor operates as a local IoT gateway for data handling, rule-based control decisions, local visualization, and message queuing telemetry transport (MQTT)-based cloud communication through a 4G link. The contribution of this work is not associated with the individual use of dual processing, cellular communication, cloud monitoring, load shedding, or backup power, as these technologies have been previously reported in smart-metering and home energy-management systems. Instead, the study focuses on their coordinated integration within a residential-scale prototype that combines calibrated per-load monitoring, priority-based load control, outage-resilient reporting, and credit-aware load restriction. The system measures voltage, current, active and apparent power, power factor, and energy consumption for individual loads and supports centralized visualization through a cloud-based dashboard. The prototype was experimentally evaluated under three representative scenarios: overload, main power outage, and low-credit operation. In the overload scenario, automatic priority-based load shedding reduced the total load by up to 75%. During power outages, a battery-supported subsystem maintained monitoring and communication for real-time outage reporting. In the low-credit scenario, non-essential loads were disconnected when the user balance fell below a predefined threshold, while essential loads remained energized. The results demonstrate that the implemented prototype can provide integrated monitoring, local rule-based control, cloud reporting, and backup-supported operation within a unified residential energy-management platform.
Small- and medium-sized enterprises (SMEs) face significant challenges in adopting robotic solutions due to limited financial resources, insufficient technical expertise, and uncertainty regarding operational and economic outcomes. Existing automation approaches are often technologydriven and provide limited support for systematic decisionmaking. This study proposes the Agile Robotics Implementation Model (ARIM), an iterative framework integrating Lean Manufacturing, Lean Robotics, and Lean Startup principles. ARIM combines process assessment, key performance indicator (KPI)-based evaluation, and iterative experimentation within the Robotic Startup Cycle, supported by a decision-support software tool. The framework was developed using a Design Science Research (DSR) approach and validated through an industrial case study. Results demonstrate strong agreement between predicted and realized KPI values. The implemented solution achieved a 24.5% return on investment (ROI), with a payback period of approximately 2.1 years, reduced labor demand by 3900 h, and improved productivity, ergonomics, and quality. The findings indicate that ARIM supports reliable and data-driven robotics implementation in the studied SMEs; broader transferability requires validation across multiple cases.
This article presents the results of a computational parametric study, a global sensitivity analysis, multi-objective optimization, and a technical and economic evaluation of the parameters of phase-change materials (PCMs) incorporated into the building envelope of an office building in a sharply continental climate (using Astana, Kazakhstan, as an example). The study was conducted using simulation modeling, incorporating dynamic thermal calculations in the EnergyPlus software package and the NSGA-II genetic algorithm. The CondFD algorithm was used, for which results of independent verification and experimental validation conducted by other researchers have previously been published. This study used this validated implementation without conducting additional experimental verification of the structure under consideration. Based on the results of a parametric analysis (1232 calculations) and an optimization run (≈25,000 calculations), the range of quasi-optimal phase transition temperatures for the PCM was determined to be 23–25 °C. For further analysis and a technical–economic evaluation, a value of 24 °C was selected as the recommended compromise solution, with a PCM layer thickness of 16 mm and a distance of 15 mm from the inner surface of the wall. This compromise solution reduces annual specific energy consumption for heating and cooling by 22% and hours of thermal discomfort by 42% compared to a reference concrete wall without PCM. A technical and economic assessment, based on post-processing of the simulation results using current electricity rates and market data on the cost of PCM, shows a simple payback period ranging from 3.8 to 38 years, depending on the assumed cost of the encapsulated PCM layer. The results are limited to the specific case considered (south-facing orientation, standalone office module, and continuous ventilation) and are intended for subsequent experimental verification. The information in this article can be used by architects and engineers in the early stages of designing energy-efficient office buildings in regions with a sharply continental climate.
Cold-climate dwellings can face coincident electricity and domestic hot-water shortfalls in winter, when solar availability is at its lowest. This study evaluates an integrated residential system for Aomori, Japan, combining photovoltaics, evacuated-tube solar water heating, and battery storage with electrolysis, compressed-hydrogen storage, and a PEM fuel cell operated in combined-heat-and-power mode. Building on a screening-level annual-balance analysis, a coupled annual TRNSYS simulation with a 0.125 h time step resolved battery dispatch, electrolyzer part-load operation, hydrogen compression and finite storage, seasonal fuel-cell operation, and heat recovery. The results show that the principal value of seasonal hydrogen lies in improving winter supply adequacy, dispatchability, and heat recovery rather than annual conversion efficiency. Fuel-cell heat recovery increased the number of days satisfying the hot-water screening indicator—a daily mean tank temperature of at least 43 °C—from 221 to 332. A reserve-aware criterion identified a 225 W electrolyzer operating-power cap as the positive-reserve case; 205 W was near-cyclic with a negligible margin, whereas the original 475 W cap was substantially oversized. The hydrogen pathway remained markedly less efficient than direct photovoltaic and solar-thermal use, and the estimated storage hardware’s lower bound substantially exceeded the break-even capital ceiling supported by the annual operating value. Seasonal hydrogen can therefore strengthen winter energy adequacy and heat recovery but is not yet cost-effective at the single-dwelling scale under the investigated conditions.
The aim of this study was to investigate the effect of reduction roasting parameters on the phase transformations of the Ushkatyn-III ferruginous manganese ore and the efficiency of subsequent magnetic separation. The experimental procedure included preliminary high-intensity magnetic separation, reduction roasting at 650 °C for 3–5 h using 20–30 wt.% coal as the reducing agent, and low-intensity dry magnetic separation at magnetic field intensities of 0.1–0.6 T. Chemical composition was determined by standard analytical methods, while phase composition was analyzed by X-ray diffraction (XRD). Preliminary magnetic separation increased the manganese content in the magnetic pre-concentrate to 30.71–35.09 wt.%. The optimum results were obtained after roasting for 5 h with 30 wt.% coal, followed by magnetic separation at 0.2 T, producing a low-iron concentrate containing 33.26 wt.% Mn and 0.67 wt.% Fe, with a manganese recovery of 87.79% and an Mn/Fe ratio of 49.6. XRD analysis confirmed the partial reduction of hematite to magnetite (Fe3O4), providing the basis for efficient magnetic separation. The proposed process offers an effective approach for upgrading low-grade ferruginous manganese ores for manganese ferroalloy production.
The article is aimed at studying the features of the hot rolling mill CWBRM-1700 of JSC “Qarmet”, which negatively affect the operation of the distribution network of the workshop. Such factors are frequent shock loads of technological mechanisms with high installed capacity of the equipment. Experimental studies of the distribution network of the rolling production on the buses of the 10 kV substation showed that shock loads of synchronous electric drives of roughing stands lead to periodic voltage drops of up to 13% lasting 5–6 s. Mathematical modeling in the MATLAB/Simscape/Electrical environment, the results of which coincide with the data of the experimental study, showed that the most significant factor affecting the quality of electricity are abrupt changes in the reactive power of the synchronous motor from −0.5 to +0.5 MVAR. To solve the problem, it is proposed to use a controlled filter-compensating device. Variants of circuit solutions for such devices are considered. The choice was made in favor of a three-phase adjustable LLC filter with diode–transistor keys. The article develops a method for calculating the electromagnetic parameters of such a filter and establishes that in order to reduce the level of harmonic distortion of voltage, it is necessary to use a triangle connection of the controlled reactive compensator and select the PWM frequency of the transistors, a multiple of the tripled frequency of the power grid. Two options for creating a closed-loop control system for energy modes are studied: a reactive power stabilization system and a voltage stabilization system in a distribution network node, which reduce the duration of transient processes to 0.5 s and reduce the voltage drop in the network node to −4 to + 1% in the first case and to −4 to + 3% in the second, also reducing reactive power consumption to 0.02 MVAR and 0.25 MVAR, respectively. The advantage of a closed-loop control system with voltage stabilization is the ability to use a technically less complex voltage sensor.
Super duplex stainless steels are widely used in seawater desalination plants due to their high mechanical strength and excellent corrosion resistance in chloride-rich environments. However, during reverse osmosis processes, the salinity of the reject stream increases progressively, generating concentrated brines with concentrations close to 7 wt.% NaCl, which represent a chloride-rich service environment that may affect passive film stability and promote localized corrosion. This study investigates the effect of novel La Geria-inspired microstructures (LGMs) generated by laser surface texturing on the microstructure, microhardness, and electrochemical behavior of UNS S32750 super duplex stainless steel in 3.5 wt.% and 7.0 wt.% NaCl solutions, simulating seawater and concentrated desalination brine. Electrochemical results show that textured surfaces exhibit improved corrosion resistance, with more stable corrosion potentials, lower corrosion current densities, and higher impedance values. Microhardness measurements revealed a homogeneous mechanical response, confirming that laser texturing does not alter the mechanical integrity of the material. Microstructural observations showed reduced surface degradation and improved preservation of the duplex ferrite–austenite structure in textured samples after exposure to chloride solutions. These findings demonstrate that biomimetic laser surface texturing enhances corrosion resistance by modifying interfacial conditions and stabilizing the passive film, providing experimental evidence of the beneficial effect of LGMs in aggressive desalination environments.
Background: Traditional osteopathic manipulative medicine (OMM) instruction relies on laboratory sessions constrained by scheduling, faculty availability, and practice partner variability. Virtual reality (VR) may address these limitations by providing on-demand access to standardized training scenarios. This pilot feasibility and acceptability study evaluated first-year osteopathic medical students’ motivational responses and perceptions of a novel VR training module focused on thoracic spine assessment techniques. Methods: This pilot single-arm observational study enrolled 45 first-year students at the New York Institute of Technology College of Osteopathic Medicine to assess the feasibility and acceptability of VR-based OMM instruction. A VR training module developed using Unity 3D and deployed on Oculus Quest headsets included 40 interactive assessment items covering thoracic diagnostic techniques. Participants received one-week access to the program then completed the Reduced Instructional Materials Motivation Survey (RIMMS) based on the Attention, Relevance, Confidence, and Satisfaction (ARCS) model and a custom 10-item feedback survey. Session duration and assessment scores were automatically recorded. Descriptive statistics and Pearson correlation analysis were performed. Results: The Relevance domain achieved the highest RIMMS composite mean (3.98), while Attention demonstrated the greatest opportunity for enhancement (3.49). The overall RIMMS composite score was 3.81, indicating favorable motivational reception. Feedback survey results showed that 89.2 percent of participants endorsed the VR experience as educationally positive, and 81.1 percent supported expansion to additional OMM procedures. Performance analysis revealed minimal correlation between session duration and assessment scores (R2 = 0.0169). Conclusions: First-year osteopathic medical students demonstrated positive motivational responses and favorable perceptions toward VR-based OMM training. Based on student perception and feasibility, these findings suggest VR is well-received and perceived by students as a potentially viable supplement to traditional OMM instruction, though attention-capturing elements and interface usability warrant refinement. This pilot study did not measure learning outcomes or skill acquisition.
Autonomous acoustic sensing systems are increasingly investigated for unmanned aerial vehicle (UAV)-based surveillance and environmental monitoring applications due to their passive operation and relatively low computational requirements. However, the integration of acoustic classification and direction-of-arrival estimation on UAV-mounted microphone arrays remains challenging, particularly because realistic flight conditions introduce propulsion noise, aerodynamic flow, vibration, and complex acoustic interference. This paper presents a static ground validation of an AI-assisted acoustic target detection and azimuth estimation framework integrated on a flying-wing vertical take-off and landing (VTOL) UAV equipped with a distributed microphone array. The proposed system combines MFCC-based chainsaw sound classification using a Random Forest model with amplitude-based and SRP-PHAT-based azimuth estimation. Four HiFiBerry measurement microphones were mounted on a 4 m wingspan flying-wing VTOL UAV and connected to a Raspberry Pi 5 processing unit. Experimental validation was conducted under controlled indoor laboratory conditions using loudspeaker playback, with the UAV propulsion system inactive and only the acoustic acquisition and processing subsystem powered. The tests included single-source angular measurements, simultaneous multi-source acoustic scenarios, and source height variation. The SRP-PHAT method achieved a mean angular error of 3.55° in the single-source tests and 4.81° in the multiple-source tests, outperforming the amplitude-based baseline. The results support the feasibility of the proposed acoustic-processing framework under static ground conditions. However, because propulsion noise and in-flight aerodynamic effects were not included in the present validation, future work must address simulated propulsion noise injection, propulsion-on static testing, outdoor validation with real chainsaw sources, and eventual in-flight experiments. Because propulsion noise, aerodynamic flow, and in-flight vibration were not included in the present experimental campaign, the results should be interpreted as baseline static ground validation results rather than evidence of in-flight robustness.
