Search Results (158)

Negative Ion Source Neutral beam Injection (NNBI), as a critical auxiliary heating system for magnetic confinement fusion devices, directly affects the plasma heating efficiency of tokamak devices through the reliability of its beam source system. The single-shot experiment constitutes a significant experimental program for NNBI. This study addresses the frequent equipment failures encountered by the NNBI beam source system during a cycle of experiments, employing fault tree analysis (FTA) to conduct a systematic reliability assessment. Utilizing the AutoFTA 3.9 software platform, a fault tree model of the beam source system was established. Minimal cut set analysis was performed to identify the system’s weak points. The research employed AutoFTA 3.9 for both qualitative analysis and quantitative calculations, obtaining the failure probabilities of critical components. Furthermore, the F-V importance measure and mean time between failures (MTBF) were applied to analyze the system. This provides a theoretical basis and practical engineering guidance for enhancing the operational reliability of the NNBI system. The evaluation methodology developed in this study can be extended and applied to the reliability analysis of other high-power particle acceleration systems. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

Identifying and eliminating critical links in multi-hop networks is essential for enhancing overall network resilience. In this study, we propose a novel algorithm to detect links that significantly impact the pairwise connectivity of multi-hop networks. We formulate the critical link detection problem as minimizing pairwise connectivity subject to a total edge weight constraint c. The proposed method first computes the maximum flow between neighboring nodes to evaluate strong connections, and then progressively contracts these nodes to expose weaker connections. Throughout this iterative process, the algorithm records previously identified flows to minimize redundant flow computations. At each step, it also keeps track of the cut sets that reduce the network’s pairwise connectivity. Ultimately, it selects the subset of these cut sets whose removal minimizes pairwise connectivity while satisfying the total weight constraint c. This approach consistently identifies fewer yet more impactful critical edges than traditional Min-Cut or Greedy strategies. We evaluate the performance of our method against existing algorithms across various network sizes and node degrees. Experimental results show that the proposed method consistently discovers more influential edges and achieves a 34–38% reduction in pairwise connectivity, outperforming Greedy (22–24%), Min-Cut (24–32%), and Degree-based (12–19%) methods. Full article

Despite the well-documented health risks of noise pollution, its impact remains overlooked mainly in life cycle assessment (LCA). This study introduces a methodological innovation by integrating both traffic and construction noise into the LCA framework for concrete construction, providing a more holistic and realistic evaluation of environmental and health impacts. By combining building information modeling (BIM) with LCA, the method automates material quantification and assesses both environmental and noise-related health burdens. A key advancement is the inclusion of health-based indicators, such as annoyance and sleep disturbance, quantified through disability-adjusted life years (DALYs). Two scenarios are examined: (1) a comparative analysis of concrete versus timber flooring and (2) end-of-life options (reuse vs. landfill). The results reveal that concrete has up to 7.4 times greater environmental impact than timber, except in land use. When noise is included, its contribution ranges from 7–33% in low-density regions (Darwin) and 62–92% in high-density areas (NSW), underscoring the critical role of local context. Traffic noise emerged as the dominant source, while equipment-related noise was minimal (0.3–1.5% of total DALYs). Timber slightly reduced annoyance but showed similar sleep disturbance levels. Material reuse reduced midpoint environmental impacts by 67–99.78%. Sensitivity analysis confirmed that mitigation measures like double glazing can cut noise-related impacts by 2–10% in low-density settings and 31–45% in high-density settings, validating the robustness of this framework. Overall, this study establishes a foundation for integrating noise into LCA, supporting sustainable material choices, environmentally responsible construction, and health-centered policymaking, particularly in noise-sensitive urban development. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

Background/Objective: Laser therapy has gained attention in dental practice to minimize bleeding and enhance blood clot formation. This study aimed to explore the utilization and to compare the clinical efficacy of laser-mediated hemostasis for older patients receiving routine dental treatment. Methods: A prospective observational study was conducted across research networks between October 2023 and August 2024, involving 60 patients aged 50 years and older (average = 63.35 years) at risk of postoperative bleeding following dental treatments. Additionally, laser therapy for hemostasis was selected and provided among calibrated operators. A single researcher performed data collection. Before statistical analysis, data verification and clinical assessment were conducted by the operators and researcher. A clinical cut-off for hemostasis was set at 5 min. Two diode laser machines were used namely, an 810 nm and dual wavelengths of 635 nm and 980 nm. Results: There were 94 extraction sockets, 28 procedures of scaling and root planing and 18 procedures of minor oral surgery. Combining laser ablating sulcular fiber and photobiomodulation initiating blood clot formation was a preferable hemostatic technique for extraction socket, while photobiomodulation alone was a preferred technique for soft tissue hemostasis (p < 0.001). All operators confirmed that 97.86 percent of bleeding events achieved more rapid hemostasis. 61.43 percent of bleeding events clinically achieved hemostasis within 5 min by using laser-mediated hemostasis alone (p = 0.092). Full recovery of the extraction socket was significantly observed during the 2- to 4-week follow-up period (p = 0.005). No clinical complications were reported. Conclusions: Laser-mediated hemostasis effectively reduced hemostatic duration, prevented postoperative bleeding and promoted wound healing in older patients undergoing routine dental treatment. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

The quantitative assessment of Parkinson’s disease (PD) is critical for guiding diagnosis, treatment, and rehabilitation. Conventional clinical evaluations—heavily dependent on manual rating scales such as the Unified Parkinson’s Disease Rating Scale (UPDRS)—are time-consuming and prone to inter-rater variability. In this study, we propose a fully automated UPDRS gait-scoring framework. Our method combines (a) surface electromyography (EMG) signals and (b) inertial measurement unit (IMU) data into a single deep learning model. Our end-to-end network comprises three specialized branches—a diagnosis head, an evaluation head, and a balance head—whose outputs are integrated via a customized fusion-detection module to emulate the multidimensional assessments performed by clinicians. We validated our system on 21 PD patients and healthy controls performing a simple walking task while wearing a four-channel EMG array on the lower limbs and 2 shank-mounted IMUs. It achieved a mean classification accuracy of 92.8% across UPDRS levels 0–2. This approach requires minimal subject effort and sensor setup, significantly cutting clinician workload associated with traditional UPDRS evaluations while improving objectivity. The results demonstrate the potential of wearable sensor-driven deep learning methods to deliver rapid, reliable PD gait assessment in both clinical and home settings. Full article

This study evaluated the effects of a plant pathogenic bacterium Pectobacterium carotovorum strain BD163 inoculation and nutrient solution (CaCO₃ (2 mM), NaCl (1 mM) and K₂Cr₂O₇ (0.001 mM)) on the growth, photosynthesis, nutrient uptake and metabolomics of tomato seedlings. The experiment had four experimental treatments (1. solution + BD163 inoculation, 2. solution alone, 3. BD163 inoculation, 4. control). Plant growth and photosynthesis responses were minimal, and differences in nutrient assimilation and metabolite profiles were clear-cut. Of the photosynthesis parameters, only water use efficiency was impacted; it was higher in the bacterium-only treatment and unchanged in the other treatments. The quantities of boron, bismuth and nickel were affected, accumulating mostly in the “solution + BD163 inoculation” experimental set. Principal component analysis of metabolomics data separated the treatments into three groupings; group 1 was the double treatment, group 2 was the nutrient solution treatment and, finally, group 3 was the P. carotovorum and control treatments. Correlation analysis of the data showed an assumed interdependence of several plant factors. The authors concluded that the interaction between the bacterium, the plant and the nutrient solution is complex and more pronounced at the chemical and metabolite level than at the growth and photosynthesis level. Full article

The residual stress induced during the processing of titanium alloy materials can significantly influence the deformation control of precision-machined workpieces, especially for workpieces characterized by low stiffness and high-precision requirements. In this study, TC18 titanium alloy forgings with a dense structure were manufactured via forging. By conducting turning and heat treatment experiments on the workpiece, the distribution and evolution of residual stress and the deformation characteristics of TC18 titanium alloy on slender shafts were systematically investigated under different turning and heat treatment conditions. Based on the experimental results, the effects of the turning parameters, including feed rate, cutting speed, cutting depth, and axial thrust force of machine tool center, on workpiece deformation were quantitatively analyzed, and an optimal heat treatment strategy was proposed. The findings indicate that between-centers turning is recommended to control workpiece deformation. Optimal turning parameters include a cutting speed of 640–800 r/min, a feed rate of 0.05–0.1 mm/r, a cutting depth of 0.1 mm, and a thrust force of the center set to 10% of the rated value, resulting in minimal deformation and superior surface quality. In addition, during the heat treatment annealing of slender shaft titanium alloys, residual stress is effectively eliminated at temperatures ranging from 640 to 680 °C with a holding time of 1–3 h. Furthermore, the vertically fixed placement method during heat treatment reduced deformation by approximately 50% compared to free placement. These results provide valuable insights for optimizing machining and heat treatment processes to enhance the dimensional stability of titanium alloy components. Full article

Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article

Investigating failure mechanisms in cutting tools used in advanced industries like biomedical and aerospace, which operate under extreme mechanical and chemical conditions, is essential to prevent failures, optimize performance, and minimize financial losses. The diamond-turning process, operating at micrometer-length scales, forms a tightly bonded built-up edge (BUE). The tribochemical interactions between a single-crystal diamond and its deformed chip induce inter-diffusion and contact, rapidly degrading the cutting edge upon BUE fracture. These effects intensify at higher deformation speeds, contributing to the observed rapid wear of diamond tools during d-shell-rich metal machining in industrial settings. In this study, these interactions were studied with niobium (Nb) as the transition metal. Tribochemical effects were observed at low deformation speeds (quasistatic; <1 mm/s), where thermal effects were negligible under in situ conditions inside the FEI /SEM vacuum chamber room. The configuration of the interface region of diamond and transition metals was characterized and analyzed using focused ion beam (FIB) milling and subsequently characterized through transmission electron microscopy (TEM). The corresponding inter-diffusion was examined by elucidating the phase evolution, element concentration profiles, and microstructure evolution via high-resolution TEM/Images equipped with an TEM/EDS system for elemental characterization. Full article

Concrete demolition waste represents a critical bottleneck in achieving a circular economy for the construction sector. This study develops a system-dynamics model that couples material flows with economic and logistical feedback to quantify how cost structures affect concrete recycling in the Sydney (Australia) metropolitan area. The model is calibrated with (i) official New South Wales 2020–2021 construction-and-demolition waste statistics, (ii) concrete consumption data scaled from state infrastructure reports, and (iii) parameters elicited from structured interviews with recycling contractors and plant operators. Scenario analysis systematically varies recycling-plant fees, landfill levies, and transport costs to trace their nonlinear impacts on three core performance metrics: recycling rate, cumulative landfill mass, and virgin gravel extraction. Results reveal distinct cost tipping points: a 10% rise in landfill-logistics costs or a 25% drop in recycling logistics costs shifts more than 95% of concrete waste into the recycling stream, cutting landfill volumes by up to 47% and reducing virgin aggregate demand by 5%. Conversely, easing landfill costs by 25% reverses these gains, driving landfill dependency above 99% and increasing gravel extraction by 39%. These findings demonstrate that carefully calibrated economic levers can override logistical inefficiencies and accelerate circular construction outcomes. The system-dynamics framework offers policymakers and industry stakeholders a decision-support tool for setting landfill levies, recycling subsidies, and infrastructure investments that jointly minimize waste and conserve natural resources. Full article

Background: The advent of tandem mass spectrometry (MS/MS) had an essential role in the expansion of newborn screening (NBS) for different inborn errors of metabolism (IEMs). Nowadays, almost 50 IEMs are screened in Italy. The use of second-tier tests (2-TTs) in NBS minimizes the false positive rate; nevertheless, the metabolic profile is influenced not only by the genome but also by environmental factors and clinical variables. We reviewed the MS/MS NBS data from over 37,000 newborns (of which 8% required 2-TTs) screened in the Italian Abruzzo region to evaluate the impact of neonatal and maternal variables on propionate-related primary biomarker levels. Methods: Expanded NBS and 2-TT analyses were performed using MS/MS and liquid chromatography–MS/MS methods. We set up layered cut-offs dividing all 37,000 newborns into categories. Statistical analysis was used to create alarm thresholds for NBS-positive samples. Statistically significant differences were found in both neonatal and maternal conditions based on the 2-TTs carried out. According to the stratified cut-offs, only 1.47% of the newborns would have required a 2-TT while still retaining the ability to recognize the true-positive case of methylmalonic acidemia with homocystinuria, which has been identified by NBS. To further support the clinical applicability, we performed an external evaluation considering nine positive cases from an extra-regional neonatal population, confirming the potential of our model. Interestingly, the setting of alarm thresholds and their application would allow for establishing the degree of priority/urgency for 2-TTs. Conclusions: Tailoring NBS by customized cut-offs may enhance the application of precision medicine, focusing on true-positive cases and also reducing analysis costs and times. Full article

The contribution of this study is in the novel application of the bin packing algorithm that is used to optimize the robotic bricklaying process with the aim of minimizing the wearing of a robotic saw used for splitting brick blocks so as to minimize brick consumption. To optimize the cutting of masonry blocks with a robotic saw, a new bin packing algorithm has been developed to enhance the design of a digital cutting plan. The algorithm is based on the principle of random search for all combinations of cutting execution with respect to the maximum number of objects (cuts) found in one container (masonry block). The new bin packing algorithm (NBPA) minimizes the number of total masonry blocks (containers) and the number of cuts made with a robotic saw, thus reducing the cutting length. The algorithm can converge to a solution rather quickly and reliably to identify optimal variants of a digital plan designed for a robotic saw to be used in different object assemblies. This article describes the optimization algorithm, including step-by-step calculations, and provides a practical example and a comparison of the results with earlier algorithms. The concept of the robotic saw is also presented in detail, including a description of a prototype. The simulation of the performance on 20 different sets of elements showed that NBPA has a similar use of space compared to the First-Fit Decreasing algorithm (FFD). Multicriteria analysis demonstrated that when the weighting criterion for saw wear was 40% of all the criteria, the use of NBPA was approximately 3.5 times more effective than FFD. The application of the new methodology to a robotic bricklaying process has the potential to reduce the wear of robotic saw, to increase the speed of the construction process and to reduce the generation of construction and demolition waste (CDW). Full article