Abstract: The removal of nitrogen and phosphorus from wastewater with low organic carbon content requires the addition of an external carbon source. The objective of this study was to assess the influence of hydraulic retention time (HRT) on the efficiency of external carbon source utilization and on nitrogen and phosphorus removal in a Rotating Electro-Biological Disc Contactor (REBDC). The energy demand was evaluated based on energy consumption (E) and current efficiency (CE). Hydroponic tomato wastewater was treated in the REBDC at a constant current density of 2.5 A/m². Sodium acetate was used as the carbon source. Two C/N ratios were tested: 2.0 and 3.0, under HRT conditions of 24 h and 48 h. For both C/N ratios, extending the HRT resulted in decreased nitrogen removal efficiency. At HRT = 48 h and C/N = 3.0, the nitrogen concentration in the effluent was more than three times lower compared with C/N = 2.0. The highest phosphorus removal efficiency was achieved at C/N = 3.0 and HRT= 48h (98.8%). Increasing the HRT led to reduced TOC utilization for both C/N ratios. As a consequence of extended HRT, lower CE values and higher E values were observed, indicating increased energy demand for nutrient removal.

Abstract: Trajectory planning algorithms are essential in human-robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a digital twin of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post-hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant’s left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in CHR environments.

Abstract: Micro-electro-mechanical systems (MEMS) sensors offer the benefits of compact size, lightweight design, and low cost, making them widely applicable in consumer electronics, vehicles, health, defense, and communications. With enhanced performance, MEMS sensors have also found applications in oil exploration and geophysical studies. For years, pressure and temperature sensing during hydraulic fracturing has been employed to enhance the down-hole conductivity of oil and gas extraction. Nevertheless, the pursuit of developing high-precision MEMS sensors for oil exploration continues to be an active area of research. This paper details the design, fabrication, packaging, and characterization of a silicon-on-insulator (SOI) MEMS piezoresistive pressure sensor integrated with a temperature sensor. It also describes the design of a chamber that simulates real conditions at the bottom of oil exploration wells. The sensors were successfully designed and fabricated based on physics-based simulations, deep reactive ion etching and anodic bonding techniques. The pressure sensors, along with the signal conditioning system, demonstrated a linear response, with a maximum linear error of 4.95% at 7 MPa and a reduced linear error of less than 3.5% at 24 MPa. Additionally, a quadratic approximation for the temperature sensors was achieved, showing a maximum resistance change of 8.5% at 140 ºC.

Abstract: An alternative MEMS navigation method for determining latitude, longitude, and altitude is proposed, based on Velocity-Aided Navigation, when global navigation satellite system (GNSS) or Starlink signals are unavailable for various reasons. Currently, GNSS receivers are the primary navigation systems that meet consumer demand for location accuracy. However, GNSS receivers are not autonomous and are susceptible to jamming and spoofing. Strapdown inertial navigation systems (SINS), unlike GNSS, are autonomous. Their operating principle is based on double integration of accelerometer output signals. However, they have a significant drawback: SINS errors increase significantly over time. Two approaches are used to improve accuracy. The first involves using expensive, high-precision gyroscopes and accelerometers. The other involves correcting the SINS by integrating it with navigation systems built on physical principles different from those of the SINS. An alternative method based on MEMS IMU for determining navigation parameters is proposed that does not require double integration of accelerometer output signals. Analytical expressions for the errors of the new method are derived. Calculations showed that the errors of the new method are significantly smaller than those of the SINS. Experimental testing confirmed the calculation results and demonstrated that the errors of the new method are comparable to those of the SINS integrated with GNSS and a Kalman filter. The proposed alternative NEMS navigation method for determining latitude, longitude, and altitude can be used independently, as an alternative to GNSS for integration with the SINS, and can also serve as a backup navigation system.

Abstract: Developing a solar hybrid banana dryer designed explicitly for South Pacific is a significant step forward in addressing both environmental sustainability and economic efficiency in the region. This project encompasses the design and implementation of a sophisticated drying system tailored to meet local conditions, featuring an array of components such as solar panels, batteries, a solar collector, an air filter, a heat exchanger, a drying chamber, electric heaters, forced ventilation, and a control unit. This paper details the various phases of the project, including design objectives, options, literature review findings, design selection processes, detailed design calculations, and forward-looking recommendations. Project, bringing diverse expertise to ensure the dryer met South Pacific economic and environmental needs. The primary aim was to create a low-cost, sustainable, portable dryer that is simple to construct and maintain and capable of upholding hygiene standards despite variable weather conditions. When designing the dryer banana was used as the model material to produce banana powder but it can be used for crops like breadfruit, pineapple, tomatoes and cassava and whatever else can benefit. By adopting this innovative solution, south pacific can benefit from an environmentally friendly, economically viable method of banana drying, supporting local agriculture and contributing to sustainable development in the region.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO₂ emissions, yet their real-world performance often deviates substantially from type-approval expectations. This study examines whether traction battery capacity provides an independent explanatory signal for the test-to-reality CO₂ gap (gap%), or whether it primarily acts as a proxy for market segmentation and usage patterns. Using European on-board fuel and energy consumption monitoring (OBFCM) records for 457,555 PHEV observations (2021–2023) from 14 manufacturers, we estimate nested fixed-effects models and introduce engineered usage proxies describing charge-depleting operation (EUR), hybrid utilization intensity (HI), energy-into-battery intensity (EDE), and a real-world to type-approval fuel-consumption ratio proxy (ELP). Battery capacity alone explains limited variation in gap% (R² = 0.075), while adding segment/year/manufacturer fixed effects increases R² to 0.203 and adding usage proxies increases it to 0.826, with the battery coefficient attenuating from 19.6 to 8.9 percentage points per kWh. Allowing non-linear battery terms via cubic B-splines yields only a modest additional improvement (R² ≈ 0.829), although the conditional shape is non-monotonic. Importantly, the battery–gap association is strongly segment-dependent, ranging from −22.1 pp/kWh in medium vans to +10.5 pp/kWh in large cars. Robustness checks using model-identifier fixed effects (MS_Cn) with standard errors clustered by MS_Cn further attenuate the battery effect (p ≈ 0.085), whereas ELP remains strongly associated with gap%. Overall, battery capacity is informative for compliance analytics mainly as a proxy variable capturing segmentation and real-world usage, rather than a universal lever of PHEV CO₂ performance.

Abstract: Deep metamorphic rock reservoir permeability prediction faced great challenge due to strong heterogeneity. Fractures were widely developed, but the traditional models did not involve into the contribution of fractures. To establish an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19-6 Region - a typical deep metamorphic rock reservoir and located in southwestern Bohai Bay Basin - as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and relationships between the shape of porosity frequency spectrum and rock pore structure were analyzed. Two parameters, which were defined as the logarithmic mean (φgm) and standard deviation between two golden section points (φgsr), were used to reflect the main position and wide of porosity frequency spectrum, and a novel model to predict permeability was established. After the target formations were classified into two types based on the difference of pore type and pore-fracture configuration relationship, the involved model coefficients were calibrated. Consecutive permeability curves were derived from our raised model in the intervals with which porosity frequency spectra were acquired. Comparisons of predicting permeabilities with core-derived results illustrated that this proposed model was much reasonable, and the resolution of predicted permeability curve was high. Except for the deep metamorphic rock reservoirs, the estimated model could be widely used in other types of formations (e.g., volcanic rock reservoirs, carbonate rock reservoirs) where secondary pores were developed.

Abstract: This research proposes a robust multi-objective NSGA-II heuristic optimization framework for CFRP (Carbon Fiber Reinforced Polymer) wrapping to promote seismic-resilient and low-carbon high-rise infrastructure. The method integrates nonlinear time-history analysis with a multi-objective genetic algorithm to determine optimal CFRP application conditions, including whether floors require CFRP wrapping, mortar jacketing, steel jacketing, or combinations thereof. It further optimizes CFRP thickness, orientation, design pattern (unilinear, bidirectional, hybrid), coverage, and anchorage details. Optimization simultaneously minimizes overall cost, torsional irregularity index, Park–Ang damage index, and seismic sensitivity while maintaining structural reliability under seismic loading. Simulation results indicate that proposed CFRP framework reduces torsional impacts by approximately 35%, enhancing seismic resilience. Hybrid CFRP configurations combined with 20–40 MPa mortar and optional 10–40 mm steel jacketing showed improved structural performance. Anchorage of 0–2 per end per face reduced torsional drift to ≤0.5–1.0%. For a 10-storey building, lower floors benefit from CFRP with mortar/steel jacketing up to ±45°, mid-level floors from hybrid (0/±45°) configurations, and upper floors from predominantly ±45° CFRP with occasional 90° bands. CFRP thickness of 0–3 mm (0–6 plies) achieved improved seismic resilience, cost efficiency, and structural reliability, supporting its potential for seismic-resilient infrastructure policy and design.

Abstract: Adaptive Traffic Signal Control (ATSC) remains a critical challenge for urban mobility. In this direction, Deep Reinforcement Learning (DRL) has been widely investigated for ATSC, showing promising improvements in simulated environments. However, a noticeable gap remains between simulation-based results and practical implementations, due to reward formulations that do not address phase instability. Stochastic variations may trigger premature phase changes ("flickers”), affecting signal behavior and potentially limiting deployment in real scenarios. Although several works have examined delay, queues, and decentralized coordination, stability-focused variables remain comparatively less explored, particularly in single yet complex intersections. This study proposes a decentralized DRL model for ATSC with Noise Injection (ATSC-DRLNI) applied to a single intersection, introducing a stability-oriented reward function that integrates flickers, queue length, and Advantage Actor-Critic (A2C) learning feedback. The model is evaluated in Simulation of Urban MObility (SUMO) platform and compared against seven baseline methods, using real traffic data from a Mexican city for calibration and validation. Results suggest that penalizing flickers may contribute to more stable phase transitions, while reductions of up to 40\% in queue length were observed in heavy-traffic scenarios. These findings indicate that incorporating stability-related variables into reward functions may help bridge the gap between DRL-based ATSC studies.

Abstract: We present a high-fidelity aerodynamic shape optimization framework for the NREL S809 airfoil using Free-Form Deformation (FFD) parameterization coupled with a RANS CFD solver and a discrete adjoint for gradient computation. The design objective is to maximize aerodynamic efficiency (lift-to-drag ratio, CL/CD) under single- and multi-point operating conditions relevant to horizontal-axis wind turbines. Geometry changes are controlled by 40 FFD surface control points, and the Sequential Least Squares Programming (SLSQP) algorithm enforces lift, thickness and volume constraints. We validate our solver against NREL Phase VI wind-tunnel data and perform mesh-convergence and gradient-verification studies. Single-point optimization yields a CL/CD increase of ≈22.5% relative to the baseline at Re = 3.48×10⁶, while multi-point optimizations (2–4 points) produce robust improvements across the operating envelope. We discuss sensitivity to turbulence/transition modeling, show gradient check results (finite-difference vs adjoint), and provide mesh-independence evidence. The optimized shapes reduce maximum thickness and move camber forward, improving lift while reducing drag; however, structural implications of thickness reduction are discussed. The framework and validation illustrate a reproducible path for high-fidelity airfoil optimization targeted at wind-energy applications.

Abstract: Alkaline electrolysers are a proven and cost-efficient technology for large-scale hydrogen production. While established, improving their performance remains an active research area. A significant but less recognized factor is the internal geometry of electrode supports, which influences gas removal and mass transport, thereby affecting efficiency. This study details the design, fabrication, and experimental validation of three unique alkaline electrolysers, each featuring a modified internal electrode-support geometry. Their performance was comprehensively assessed through polarization curve analysis in individual, partial, and global configurations. The development followed the Advanced Product Quality Planning (APQP) methodology, employing pure nickel electrodes for stability. The research experimentally demonstrates how specific geometric alterations directly influence electrochemical performance and overall efficiency. By operating three electrolysers simultaneously, the system achieved an overall efficiency of 42% and a maximum oxyhydrogen production rate of 10 L min-1 with minimal electrolyte carryover This systematic work establishes essential design guidelines aimed at advancing the technology from Technology Readiness Level (TRL) 5 to TRL 6, facilitating the development of a reliable 5 kW hydrogen production system.

Abstract: Fractured reservoir characterization is a complex and challenging task due to its depositional nature and high uncertainty in the spatial distribution of fractures, typically when well data is limited and interpolation algorithms are employed. This paper introduces an alternative workflow designed to enhance fracture modeling between well locations by incorporating seismic attributes, using publicly released data from the Teapot Dome field. The paper's objective is to create a fracture model for the Tensleep reservoir in the Teapot Dome Anticline by employing seismic attributes sensitive to fault and fracture features, while also demonstrating the limitations of interpolation-based models such as Gaussian simulation. The approach uses artificial neural networks to predict fracture intensity by analyzing seismic data and well logs, training supervised probabilistic artificial networks to identify the seismic attributes that most closely correlate with the fracture intensity property derived from well log data. The validated network successfully transformed the 3D seismic data into 3D fracture intensity data, achieving a high correlation coefficient between the selected seismic attributes and the training wells. The research findings are extremely valuable because they help address the lack of information on fractures, improve reservoir management, and optimize well placement.

Abstract: The article presents a case study of an event that occurred in Gdańsk, Poland, in 1994. During a rock concert at the old Gdańsk Shipyard Arena, a fire started. The audience was evacuated in a state of emotional agitation. This event resulted in the death of seven people and left many people injured, some of which had permanent injuries. This article presents the technical characteristics of the building where the event occurred and the event course. Based on the analysis of many source materials, the fire course as well as rescue and firefighting operations are described. The fire course was accurately recreated using computer simulations. Analyses conducted immediately after the event 30 years ago primarily concluded that all emergency exits being open would have been sufficient for the evacuation of the crowd in the building. A total of nine evacuation scenarios were simulated, the first of which was the recreation of the real event. In the other scenarios, the conditions were modified to investigate the impacts of the number of people and availability of emergency exits on the outcomes. As a result of the conducted research, the hypothesis regarding the recognition of blocked emergency doors as the main cause of death and injuries of the participants of the analysed event was questioned. The second issue that should be considered innovative was the description of the blocking pile phenomenon. An attempt was made to identify similar situations reported in the literature and elucidate this phenomenon.

Abstract: Thermochemical energy storage (TCES) using metal oxides offers a promising path for applications requiring high-temperature heat and long duration storage. Among numerous mixed metal oxides that have been studied for TCES, magnesium manganese oxide (MgMnO) has emerged as a promising candidate. A significant challenge of using this material in shaped form is crack formation during manufacturing and redox cycling. To develop mitigation strategies, we have first used a novel additive manufacturing process for manufacturing rods and plates of MgMnO that allows to rapid material mixing and produce a variety of shapes with relative ease. Graphite powder was utilized as pore-forming material to prevent the material from cracking and also to increase the porosity, thereby improving the thermal storage. After cycling, samples demonstrated high energy density that remained consistent. The introduction of graphite powder appeared to significantly reduce cracks, and yielded rods with over 17.24% higher energy density, and improved cyclability.

Abstract: Years of empirical research and technical effort have brought the world into an era defined by modern engineering amenities. The Brushless Direct Current or BLDC motor for electric propulsion systems stands as a significant innovation within this modern period. These motor drives are currently utilized across various sectors including automation systems and electric vehicles along with robotics and industrial applications. While popular control methods like fuzzy logic or PWM offer distinctive functionalities they often struggle with the nonlinear behavior and load variations inherent in BLDC motors. High speed configurations and parametric varieties further complicate stability. To enhance performance a rugged and quickly adaptable controller is required to minimize ripples and improve response times. This study proposes an adaptive PID controller that combines the strengths of a PID autotuner with a standard PID framework. The autotuner provides self adjusting parameters to handle nonlinearities and speed variations through a frequency response estimation process while the fast responsive PID controller compensates for the slow performance of the autotuning phase. These elements work in tandem to automatically readjust parameters for superior accuracy. Using the MATLAB simulation platform a benchmark motor system was developed to verify these results. The proposed design was compared against traditional PID and FPA speed controllers. Results demonstrate that the adaptive PID controller achieves less ripple and an overshoot of less than one percent while maintaining excellent load performance. This research contributes a reliable and highly adaptable control solution for modern BLDC motor systems. Benchmark results in MATLAB Simulink confirm that the proposed controller maintains an overshoot threshold of less than 1% and consistently yields lower torque ripple than existing PID and FPA models.

Abstract: The chemical agents, the injection modes and displacement characteristics of chemical compound flooding, consisting of plugging agent, oil displacement agent, and viscosity reducer, were investigated by laboratory experiments for the target heavy oil reservoirs after multiple cycles of huff and puff. The performance of oil displacement agent, viscosity reducer and plugging agent were evaluated and the formulation and concentration were optimized. The oil displacement effects and displacement characteristics of different injection modes were studied by two-pipe models. The experiment results showed that the alternating injection of oil displacement agent and viscosity reducer yielded better results than their mixed injection, and small segments alternating injection achieved the highest recovery, which playing a role in gradual adjustment of the profile and its seepage resistance was greater. The dosage of the plugging agent should be no less than 0.5 pore volume (0.5 PV). There was a balance between the viscosity increase of polymer and the reduction of interfacial tension of viscosity reducer. The larger the dosage of the oil displacement agent, the higher the capacity to expand the swept volume and to adjust the profile enhanced, the larger the maximum liquid production ratio between high and low permeability layer, but the shorter of the liquid production reverse duration. The larger the dosage of the viscosity reducer, the greater the water cut decrease, but the smaller of maximum liquid production ratio. For chemical compound flooding in the Zhong'er block in Gudao oilfield, the recommended injection mode was 0.1 PV plugging agent + 2000mg/L oil displacement agent + 0.5wt% viscosity reducer, with small segments of oil displacement agent followed by viscosity reducer at an injection slug ratio of 6:4, which providing an efficient and economical chemical compound flooding technology solution for field application.

Abstract: Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to support reliable quality assessment and scalable reverse‑engineering workflows. This study evaluates whether mechanical response curves can be used to infer critical manufacturing parameters—specifically build direction, layer thickness, and infill density. Force–displacement and stress–strain data obtained from tensile tests were converted into image‑based representations and classified using individual and ensemble machine learning models. The influence of applying a moving‑average filter to smooth the curve‑derived images was also examined. Ensemble approaches, particularly AdaBoost, achieved higher accuracy and robustness across the evaluated variables, with the best results obtained from unfiltered stress–strain images. Under limited‑data conditions, ensemble models generally outperformed individual classifiers, while Multilayer Perceptron and Support Vector Machine models showed more stable but less accurate behavior. Overall, the findings demonstrate the feasibility of predicting FDM printing parameters directly from mechanical‑curve‑derived images, enabling a non‑destructive approach suitable for scalable reverse‑engineering and improved traceability within additive manufacturing processes.

Abstract: Denoising-based CT reconstruction methods can suppress high-frequency textures that are relevant for subtle lesion visibility. Motivated by hybrid convolution–attention designs such as CTLformer, this paper proposes a frequency-constrained denoising framework that preserves diagnostically relevant textures while reducing noise. The method introduces a dual-domain loss combining spatial fidelity with frequency-band constraints computed using discrete cosine transform representations. Evaluations on 52,000 paired slices from two low-dose CT datasets show that, relative to CNN-only and attention-only baselines, the proposed approach increases PSNR by 0.7–1.1 dB while maintaining higher high-frequency energy consistency. Reader-oriented texture metrics also improve by 8%–14% in regions with fine structural patterns.

Abstract: The modern prosthetic foot market is characterized by a pronounced polarization between affordable but low-function devices and high-performance yet costly composite prosthe-ses. The aim of this study was to develop and comprehensively evaluate cost-effective, functional prosthetic feet manufactured by fused deposition modeling (FDM). An iterative design methodology was employed, combining finite element analysis to optimize the biomechanical response of the device, incorporation of user-specific requirements and ex-perimental validation. Two TPU 95A-based 3D-printed prosthetic foot designs were de-signed and developed, and their strength and functional characteristics were assessed numerically under the ISO 22675:2024 normative loading cycle. Bench-top mechanical tests were conducted on the fabricated prototypes. Functional performance was evaluated by a transtibial amputee using an inertial motion capture system to analyze gait kinemat-ics. The results demonstrated that both designs operate predominantly within the elastic range with an adequate safety margin. The pilot gait assessment indicated biomechani-cally acceptable walking kinematics for both prototypes, with a subjective preference for the smoother rollover provided by Model 2.

Abstract: Water pollution due to insufficient wastewater treatment is a global concern. In this paper coagulation and flocculation as a tertiary unit process was investigated to find the solution for a non-complying wastewater treatment facility. The Palapye Pond Enhanced Treatment and Operation (PETRO) system has not been compliant for a long time with effluent characterised by high turbidity, Biological Oxygen Demand/Chemical Oxygen Demand (BOD/COD), Total Suspended Solids (TSS), Nitrates (NO3), and Phosphates (PO4.) The effluent from the plant is released into the stream that drains into the nearby Lotsane dam, posing a lot of danger to the water quality of the dam. The main objective of the project was to investigate the effect of coagulation and flocculation processes at the secondary stage of the wastewater treatment. Response Surface Methodology (RSM), Central Composite Design (CCD) and Multi Response Surface (MRS) were used to optimize the coagulation process and generate regression models to predict the coagulation and flocculation. The performance was evaluated using turbidity, Colour, COD and TSS as response variables. Response surface analysis indicated that the experimental data could be adequately Fitted to quadratic polynomial models. Under optimum conditions the removal efficiency for Al2(SO4)3·18H2O: 91.1% (turbidity), 88.2% (colour), 58.9% (COD), 83.0% (TSS); for FeCl3·6H2O: 93.2%, 88.7%, 63.8%, 91.3%; for Moringa: 91.8%, 85.4%, 56.6%, 83.7%. The optimal removals based on MRS for Al2(SO4)3.18H2O, FeCl3.6H2O and Moringa were 90.7%, 89.7%, 59.9% and 88.5%; 94.7%, 90.8%, 58.1% and 93.8%; 94.0%, 87.2%, 60.1% and 82.1% for turbidity, colour, COD and TSS respectively. This research has demonstrated that the coagulation/flocculation process can be incorporated into the secondary stage of the wastewater treatment facility and the treatment process optimized using RSM, CCD and MRS. The study introduces comparative evaluation of three coagulants within a single RSM-CCD optimization framework, employing desirability functions for multi-response optimization.