Search Results (6,367)

Composting chicken manure is a source of significant ammonia (NH₃) emissions, which, because of propagation, contributes to the eutrophication of the environment and decreases in air quality. Therefore, it is reasonable to use methods to limit its emission into the atmosphere. Biofiltration, using the metabolic activity of nitrifying and heterotrophic microorganisms capable of oxidizing ammonia, is an effective method to reduce ammonia emissions. In addition, the performance of the biofiltration process depends on operational parameters such as the humidity of the medium, the temperature, the contact time of the gas with the biofiltering medium, and the chemical composition and structure of the filter material. The aim of the study was to evaluate the effectiveness of biofilter fillings in reducing ammonia emissions from composting chicken manure along with the identification of factors allowing us to determine the proposed design solution as the most advantageous in terms of efficiency. Experiments on reducing odour emissions with biofiltration were carried out in two compact composting reactors, in which a compost mixture with a C:N ratio of 10:1 was used. The mixture was prepared in a ratio of 5:1 of chicken manure to the structuring material, with wheat straw used as the structuring material. Based on the results of the research on the course of the composting process, high values of ammonia concentration were recorded. Ammonia concentrations of 886 ppm (composter 1) and 811 ppm (composter 2) were recorded, which confirms the intensive nature of this gas emissions during the process of stabilizing the chicken manure. As part of the conducted research, the effectiveness of biofiltration in reducing ammonia emissions was evaluated by analysing the influence of the aeration intensity of the biofilter (20 dm³/h and 50 dm³/h), directly determining the time of contact of the gas with the bed (EBCT—Empty Bed Contact Time). Coconut-activated carbon was used as a filter bed, which was an effective carrier for the development of microorganisms responsible for the biological removal of ammonia from waste gases generated during composting. In addition, this material showed the ability to physically adsorb ammonia, thus supporting the process of its elimination. Each of the test stations has been equipped with a biofiltration installation. To determine the effectiveness of biological removal of ammonia and to assess the legitimacy of the use of selected strains of microorganisms in the process of biological removal of ammonia, the bed of one of the biofilters (biofilter 2) was inoculated with a strain of nitrifying bacteria. During the study, the high efficiency of ammonia removal because of biofiltration was noted in each of the configurations. In the case of an aeration intensity of 20 dm³/h, a reduction in emissions of 99% was achieved; with a higher aeration value, i.e., 50 dm³/h, the efficiency was 89%. These results indicate that the intensity of aeration has a significant impact on the efficiency of the biofiltration process. The analysis of a biofilter enriched with a strain of nitrifying bacteria requires long-term testing. This is important to reliably determine the effect of inoculation on the efficiency of the biological removal of ammonia in biofilters. It has been shown that optimizing these factors allows us to achieve a reduction in ammonia emissions of up to 90%, while minimizing the formation of unpleasant odours. The use of biofiltration in composting systems for organic waste of animal origin is an effective, sustainable solution that fits into the idea of sustainable development, combining the efficiency of air purification technology with environmental protection and the responsible management of resources. This study demonstrates that biofiltration using coconut-shell-activated carbon is an effective and economical method for reducing ammonia and odour emissions from composting chicken manure. The results provide valuable theoretical and practical information on emissions management in organic waste composting processes. Data from this study could be useful in developing strategies to minimize odour emissions, including from the agricultural sector. Full article

Magnetite-rich iron ores present analytical challenges due to mineralogical complexity, including titanium–vanadium (Ti-V) substitution within magnetite and variable silicate gangue contributions. Reliable iron (Fe) quantification in such systems is essential for accurate resource evaluation and beneficiation planning, particularly in layered intrusion-hosted deposits. This study compares fusion-based inductively coupled plasma optical emission spectroscopy (ICP-OES) and fused-bead X-ray fluorescence (XRF) methods for the determination of Fe and associated major elements in magnetite-bearing Fe ores from the Bushveld Igneous Complex, South Africa. Four representative ore samples and certified reference materials were analysed using both techniques. Comparative statistical parameters like the t-test and F-test exhibit no significant differences in either precision and mean concentration between fused-based ICP-OES and fused-based XRF methods for the determination of Fe and other elements. The results indicate that, despite the existence of titanomagnetite and lithologies that are rich in silicates, both fusion-based methods provide consistent and reliable bulk chemical analysis datasets. While both approaches show suitability for routine chemical analysis, fusion-based ICP-OES offers a practical advantage in terms of throughput and operational efficiency. This work emphasises the importance of matching analytical methods with mineral ore characterisation in order to ensure reliable Fe grade determination in complicated oxide deposits. Full article

This research aims to establish design guidelines for a cantilever triple-layer piezoelectric harvester (CTLPH) with tip mass and tip excitation, operating under resonance conditions. The guideline is derived by combining constitutive equations with Euler–Bernoulli beam theory to identify the effective parameters of the CTLPH and, subsequently, the storage voltage after rectification using a germanium diode bridge. The analysis shows that excitation frequency, piezoelectric coefficients, geometrical dimensions, and the mechanical properties of the layers all significantly influence CTLPH performance. The effects of storage capacitance and excitation frequency were experimentally validated through the design, fabrication, and testing of a prototype. Furthermore, the LTC3588 energy storage module was employed to store the generated charge from resonance motion. An advanced non-contact optical method was employed to determine the bending stiffness of the CTLPH. The output power after the energy storage module was measured across a range of resistive loads at frequencies near the resonance condition (f = 65 Hz). Results demonstrate that both excitation frequency and external resistance affect the maximum harvested power. The developed CTLPH achieved an optimum output power of 46.18 ± 0.98 μW at an external resistance of 3 kΩ, which is sufficient to supply micropower sensors. Full article

This study addresses the problem of traffic congestion in large cities caused by long-term repairs of underground utility networks. An innovative mobile overpass is considered, which combines the functions of a vehicle and a temporary bridge, allowing passenger cars up to 3.5 t to pass directly over repair trenches without detours. The research focuses on optimizing the placement of overpass supports relative to the trench edge to reduce soil deformation and prevent trench wall instability. A numerical methodology is developed in ANSYS Workbench that integrates finite element analysis of the soil-support system with parametric optimization using the nonlinear Drucker–Prager elastoplastic model. The soil parameters are obtained from oedometer compression tests (KPr-1M) and direct shear tests (PSG-2M) on clayey soils and then used to calibrate the numerical model. The optimization results show that the optimal distance from the trench wall to the overpass support is L_min = 2.78 m, which is 13.5% greater than the initial design value. This modification reduces the maximum horizontal displacement of the trench wall by more than a factor of two and ensures compliance with the displacement criteria. Comparison between experimental and numerical compression curves yields an average deviation of 37.55%, with errors below 5% at higher stress levels, confirming that the Drucker–Prager model is suitable for engineering optimization of mobile overpass support placement on similar soils. The proposed methodology can be applied to the design and verification of temporary bridge systems operating above utility trenches in urban environments. Full article

Introduction: Rapid and reliable neurovascular imaging is critical for time-sensitive diagnosis in acute cerebrovascular disorders, yet conventional magnetic resonance imaging (MRI) workflows remain constrained by acquisition speed, motion sensitivity, and limited integration of physiological context. We introduce the Intelligent Neurovascular Imaging Engine (INIE), a sensor-informed, topology-aware framework that jointly optimizes accelerated data acquisition, physics-grounded reconstruction, and cross-scale physiological consistency. Methods: INIE combines adaptive sampling, structured low-rank (Hankel) priors, and topology-preserving objectives with multimodal physiological sensors and scanner telemetry, enabling phase-consistent gating and confidence-weighted reconstruction under realistic operating conditions. The framework was evaluated using synthetic phantoms, a translational porcine stroke recanalization model with repeated measures, and retrospective human datasets. Across $N_{\mathrm{runs}}=120$ acquisition–reconstruction runs derived from $N_{\mathrm{animals}}=18$ pigs with animal-level train/validation/test separation, performance was assessed using image quality, topological fidelity, and cross-modal consistency metrics. Multiple-comparison control was performed using Bonferroni/Holm–Bonferroni procedures. Results: INIE achieved acquisition acceleration exceeding 70% while maintaining high reconstruction fidelity (PSNR $\approx 35$–36 dB, SSIM $\approx 0.90$–0.92). Topology-aware analysis showed an approximately twofold reduction in Betti number deviation relative to baseline accelerated methods. Cross-modal validation in a PET subset demonstrated strong agreement between MRI-derived perfusion parameters and metabolic markers (Pearson $r\approx 0.9$). INIE improved large-vessel occlusion detection accuracy to approximately 93% and reduced automated time-to-decision to under three minutes. Conclusions: These results indicate that sensor-informed, topology-aware, closed-loop imaging improves the reliability and physiological consistency of accelerated neurovascular MRI and supports faster, more robust decision-making in acute cerebrovascular imaging workflows. Full article

This study examined the effects of environmental conditions, behavioral history, management practices, and personality traits on the operational performance of search and rescue (SAR) dogs and dogs admitted to SAR certification testing. Thirty-two handlers completed a questionnaire collecting demographic data, as well as information on their dogs’ behavioral history, management practices, and personality descriptors. Each dog–handler unit also undertook a search trial consisting of locating a hidden person in a wooded area, which was evaluated both by professional instructors and the handlers through ratings of critical behavioral indicators. Objective measurements were obtained through a weather station and GPS devices. Handlers described their dogs mainly in terms of work-relevant traits, such as socio-cognitive engagement, assertiveness, and arousal. The performance evaluation form was practical and efficient, though the Distraction parameter may require refinement, and handler ratings suggested a self-reporting bias. Temperature and wind speed were negatively associated with performance, whereas higher humidity was positively associated with it. Performance was also associated with litter size, age at adoption, dog experience, and management-related factors. Finally, speed, ground coverage, and a canine profile characterized by high arousal and reactivity were strong determinants of good search performance (|ρ| ≥ 0.3; p < 0.05). Although these findings require confirmation in larger samples, search performance appears to be a multifactorial construct shaped by the interplay of extrinsic and intrinsic factors. Defining the contribution of each factor could help optimize performance and dogs’ welfare. Full article

The temperature regulation of nonlinear continuous stirred tank reactor (CSTR) processes remains a challenging control problem due to strong nonlinearities, time-delay effects, and sensitivity to disturbances and parameter variations. Conventional proportional–integral–derivative (PID)-based control strategies often fail to provide the robustness and precision required under such conditions, motivating the use of more flexible controller structures and advanced optimization techniques. In this study, an enhanced joint-opposition artificial lemming algorithm (JOS-ALA) is proposed for the optimal tuning of a fractional-order PID (FOPID) controller applied to CSTR temperature control. The proposed JOS-ALA incorporates a joint opposite selection mechanism into the original ALA to improve population diversity, convergence stability, and resistance to local optima stagnation. A nonlinear CSTR model is linearized around a stable operating point, and the resulting model is employed for controller design and optimization. The FOPID controller parameters are tuned by minimizing a composite cost function that simultaneously accounts for tracking accuracy, overshoot suppression, and instantaneous error behavior. The effectiveness of the proposed approach is assessed through extensive simulation studies and benchmarked against state-of-the-art and high-performance metaheuristic optimizers, including ALA, electric eel foraging optimization (EEFO), linear population size reduction success-history based adaptive differential evolution (L-SHADE), and the improved artificial electric field algorithm (iAEFA). The benchmarking set is further extended with the success rate-based adaptive differential evolution variant (L-SRTDE) to broaden the comparative evaluation. Simulation results demonstrate that the JOS-ALA-based FOPID controller consistently achieves superior performance across multiple criteria. Specifically, it attains the lowest mean cost function value of 0.1959, eliminates overshoot, and yields a normalized steady-state error of 4.7290 × 10⁻⁴. In addition, faster transient response and improved robustness under external disturbances and measurement noise are observed when compared with competing methods. Statistical reliability of the observed performance differences is additionally examined using a Wilcoxon signed-rank test conducted over 25 independent runs. The resulting p-values confirm that the improvements achieved by the proposed approach are statistically significant at the 5% level across all pairwise algorithm comparisons. These findings indicate that the proposed JOS-ALA provides an effective and reliable optimization framework for high-precision temperature control in nonlinear CSTR systems and offers strong potential for broader application in complex process control problems. Full article

Background: Gemcitabine–cisplatin (GC) combined with a programmed death-ligand 1 (PD-L1) inhibitor has become an important first-line regimen for advanced intrahepatic cholangiocarcinoma (ICC). However, overall efficacy remains modest, and inter-patient heterogeneity in outcomes is substantial, highlighting the need for simple biomarkers for pretreatment risk stratification. The systemic immune-inflammation index (SII), derived from peripheral neutrophil, lymphocyte, and platelet counts, has been associated with prognosis in various malignancies, but its clinical relevance in advanced ICC treated with first-line GC plus PD-L1 inhibitor remains unclear. Aims: To evaluate the association of baseline SII with objective response and survival outcomes in patients with advanced ICC receiving first-line GC plus PD-L1 inhibitor. Methods: We retrospectively analyzed 193 consecutive patients with advanced ICC who received first-line GC plus a PD-L1 inhibitor at our center. Baseline clinicopathologic characteristics and laboratory parameters were collected, and SII was calculated as platelet count (×10⁹/L) × neutrophil count (×10⁹/L)/lymphocyte count (×10⁹/L). Receiver operating characteristic (ROC) analysis was performed to assess the discriminative ability of baseline SII for objective response and to determine an internally derived cut-off value. Patients were categorized into low- and high-SII groups accordingly. Logistic regression was used to identify factors associated with objective response rate (ORR). Progression-free survival (PFS) and overall survival (OS) were estimated by the Kaplan–Meier method and compared using the log-rank test. Multivariable Cox proportional hazards models were constructed to evaluate the independent prognostic significance of SII for PFS and OS. Results: Among the 193 patients included, 55 achieved complete or partial response and 138 had stable or progressive disease, yielding an ORR of 28.5%. Baseline SII showed good discrimination for objective response (AUC = 0.91), and the optimal cut-off value was 495.75. Patients in the low-SII group had a significantly higher ORR than those in the high-SII group (p < 0.001). Kaplan–Meier analysis demonstrated that both PFS and OS were longer in the low-SII group than in the high-SII group (median OS: 13.0 vs. 8.0 months, log-rank p < 0.001; median PFS: 8.5 vs. 6.0 months, p = 0.025). In multivariable Cox models adjusting for differentiation, CA19-9, tumor multiplicity, and distant metastasis, SII grouping remained independently associated with PFS and OS, and distant metastasis was consistently associated with increased risks of progression and death. Conclusions: Baseline SII is a readily available prognostic biomarker associated with objective response and survival in patients with advanced ICC treated with first-line GC plus PD-L1 inhibitor. Given the retrospective single-center design, the absence of a non-immunotherapy comparator cohort, and internal cut-off derivation, these findings should be interpreted as hypothesis-generating and warrant external validation. Full article

Seawater flotation is increasingly adopted to reduce freshwater demand; however, its complex ionic environment often deteriorates sulfide mineral floatability and necessitates effective regulation strategies. In this work, seawater magnetization and collector magnetization were evaluated as two independent treatment routes affecting chalcopyrite flotation, and their impacts on flotation performance and interfacial properties were quantified. Pure-mineral flotation tests were conducted at pH 8 using butyl xanthate as the collector and pine oil as the frother, with magnetic field strength and magnetization duration varied in a controlled manner. Both flotation recovery and interfacial responses exhibited a distinct parameter-window behavior, rather than a monotonic enhancement. Under magnetized seawater conditions, chalcopyrite recovery increased from 80.45% to 92.7% at 200 mT and 8 min, while magnetized collector treatment under identical conditions produced a stronger enhancement, yielding a maximum recovery of 96.5%. Contact-angle measurements demonstrated an increase in chalcopyrite surface hydrophobicity within the effective magnetization range, whereas zeta-potential measurements revealed a positive shift toward less negative values, indicating weakened electrostatic repulsion in the seawater system. The consistent trends among flotation recovery, surface wettability, and surface electrical properties suggest that magnetization influences chalcopyrite floatability by modifying the balance between hydrophobic surface stabilization and electrostatic interactions, thereby highlighting an effective operating window for seawater flotation systems. Full article

This paper investigates the feasibility of manufacturing hydraulic fittings using additive manufacturing (AM) technologies, specifically Fused Deposition Modeling (FDM) and Stereolithography (SLA). The study addresses the environmental challenge of material waste in conventional fitting production by exploring 3D printing as an alternative manufacturing method. Hydraulic fittings were designed using CAD software :SolidWorks 2022 and fabricated using FDM with PETG (Polyethene Terephthalate Glycol) material and SLA with UV-sensitive photopolymer resin. In present studies, on-destructive leak testing was conducted in accordance with PN-EN 1254-4 and PN-EN 1254, at pressures ranging from 0.1 to 1.0 bar. Dimensional accuracy analysis revealed shrinkage of approximately 1% for SLA-printed parts and 2% for FDM-printed parts. Microscopic examination at 50× and 80× magnification showed superior thread quality in SLA samples compared to FDM, which exhibited visible layer separation and material porosity. Leak testing demonstrated that while the brass reference fitting maintained complete seal integrity, both 3D-printed variants failed to achieve leak tightness under operational pressures, with structural failure occurring at 1.0 bar during tightening. The study showed that FDM with PETG material and SLA with UV-sensitive photopolymer resin, despite achieving acceptable dimensional tolerances (±1–2%), do not meet hydraulic leak tightness requirements at pressures exceeding 0.5 bar in their raw state after printing. The results suggest that alternative material formulations (e.g., carbon fiber-reinforced PEEK for FDM or epoxy engineering resins for SLA) warrant further investigation. Potential avenues for improvement include advanced surface treatment, optimization of printing parameters, and modifications to thread geometry to reduce interthread gaps. Full article

TMR-based magnetic encoders provide sensitive SIN/COS signals, but practical accuracy is degraded by channel mismatch and decoder dynamics. This study evaluates an end-to-end embedded implementation on a PMSM (Permanent Magnet Synchronous Motor) bench. We consider amplitude mismatch, quadrature non-orthogonality, and harmonic/noise disturbances in the measured differential channels. We implement a lightweight compensation chain, including fixed-window moving-average filtering, min–max amplitude normalization, and correlation-based quadrature identification with sample-shift correction. We then compare four demodulation configurations under identical sampling and reference alignment to a 24-bit encoder: (A0) conventional second-order PLL (phase locked loop), (A1) compensation + open-loop atan2, (A2) compensation + fixed-ω_n third-order PLL, and (A3) compensation + adaptive-ω_n third-order PLL. Experiments with a TMR3081 sensor and an STM32 controller show clear differences among A0–A3. In steady operation, A3 removes the DC bias observed with A0 and keeps the angle error within approximately ±0.3° in the evaluated steady window. During commutation and ramp-like segments, PLL-based tracking (A0/A2/A3) is more robust than open-loop atan2 (A1), and bandwidth adaptation in A3 improves the acquisition–noise trade-off within the preset ω_n bounds. These results are reported for this prototype and the tested parameter settings. Full article

Accurate identification of the rotational moment of inertia of a top drive system is essential for dynamic modeling, control design, and performance optimization in drilling operations. However, the strong coupling between the drive motor, transmission components, and drill string makes direct inertia measurement challenging under field conditions. To address this issue, this study proposes a moment of inertia identification method based on dynamic response analysis of the top drive system. A simplified torsional dynamic model is established by representing the top drive and drill string assembly as an equivalent lumped inertia system. By applying controlled torque excitation under no-load conditions, the system’s angular velocity response is measured and analyzed in both time and frequency domains. The relationship between applied torque and angular acceleration is utilized to identify the equivalent rotational inertia through parameter estimation. Experimental results indicate that low-frequency excitation provides more favorable conditions for reliable and accurate inertia identification, yielding improved stability and reduced estimation error compared with higher-frequency inputs. In addition, frequency response characteristics are investigated to validate the consistency and robustness of the identified inertia across different excitation frequencies. Experimental results obtained from a top drive test rig demonstrate that the proposed method can reliably estimate the equivalent moment of inertia with good repeatability under controlled experimental conditions. The identified inertia shows good agreement with theoretical calculations and exhibits stable behavior over a wide frequency range. The proposed approach avoids the need for additional sensors or structural modifications and is well suited for practical engineering applications. This study provides an effective and experimentally validated method for inertia identification of top drive systems, offering valuable support for dynamic modeling, control parameter tuning, and vibration analysis in drilling engineering. Full article

Electrochemical Impedance Spectroscopy (EIS) is a widely used technique for characterizing the electrode–electrolyte interface. EIS analysis can be very complex and tedious. In this work, a fitting algorithm written in C is implemented on OriginPro software to avoid the data import/export operation and speed up the analysis. An automated fitting procedure that assigns the initial parameters is implemented for the simplest equivalent circuit. In addition, the possibility of using a custom error function is explored, with results comparable to that of reference software. The developed algorithm is tested on two different case studies. Full article

Reservoir water-level fluctuations periodically alter the physical and mechanical properties of accumulation deposits in the bank slope zone, potentially triggering geological hazards such as collapses and landslides. This study developed an original laboratory mechanical testing system to systematically investigate the evolution of deformation and shear strength parameters in these accumulation deposits throughout the reservoir operation period. Tests conducted on the accumulation deposits in the Baijiabao bank slope demonstrate that under the coupled effects of anisotropic stress and cyclic wet–dry conditions, the compression modulus, cohesion, and internal friction angle decrease significantly, by 10.6%, 11.4%, and 13.2%, respectively. As the number of wet–dry cycles increases, the rate of reduction in these parameters gradually diminishes. Between the second and fourth cycles, the decreases in compression modulus, cohesion, and internal friction angle were 9.7%, 8.6%, and 6.9%, respectively. Beyond the eighth cycle, the values of these parameters stabilize with minimal further change. Full article

Agile and flexible resource modelling is essential for informed decision-making in early-stage mineral project assessment, and in more advanced stages, particularly when compared with conventional deterministic geological modelling and single-estimate resource evaluations. This study presents a case of rapid scenario generation to view, interpret and test the impact of alternative geological and modelling assumptions, including the definition of geological domains, geological interpretation, grade estimation within domains, and the associated uncertainty. The workflows are implemented in Annapurna™ Resource, a cloud-native geostatistical platform designed to support agile, advanced, and multivariate modelling workflows. Focusing on the multi-commodity San Antonio–Potrerillos district, we demonstrate how rapid model construction enables the systematic evaluation of geological and statistical assumptions, contrasting deterministic estimates with probabilistic outcomes and testing their impact on estimated grades and tonnage under multiple scenarios for five elements: copper (Cu), molybdenum (Mo), gold (Au), silver (Ag), and arsenic (As). The approach provides quantitative measures of model reliability, identifies areas of high uncertainty, and supports the prioritization of new drilling to improve geological knowledge, exploration targeting, and resource classification. This case study highlights the value of fast-turnaround, probabilistic modelling not as a replacement for traditional resource reporting, but as a decision-support framework that enhances understanding of the geology, tests the sensitivity of assumptions, and accelerates learning throughout exploration and into operations. The main results suggest that additional drilling can be strategically placed to reduce the geological uncertainty derived from comparing the current interpretation with the probabilistic model built with indicator kriging. Furthermore, this has relevance in reducing the risk in the assessment of the metal content in each area of the deposit. Sensitivity analysis performed over key parameters of the estimation suggests that outliers’ treatment is the most impactful step during estimation. With current technological tools, it is possible to maintain a live resource model, which can be continuously updated to assess the impact of new data and decisions in near real time. Full article