Search Results (18,726)

In response to the pressing issues of unclear adhesion mechanisms during the soil-removal process in peanut harvesting, poor soil fragmentation quality, and difficulties in separating the pods from the soil. Based on TRIZ theory, this study has innovatively designed a separation device that relies on external forces, such as kneading and squeezing. A mechanical model of soil fragmentation and separation was developed. The key factors affecting the device’s operational performance were identified. Through theoretical analysis and discrete element simulation, this study elucidates the working principle by which the device crushes and separates soil particles using kneading and squeezing forces. Through analysis of one-factor and orthogonal experiments, the optimal operating parameter combination for the device was determined to be: a drum installation clearance of 104.7 mm, a rotational speed difference of 75.2 rpm, and a pattern roughness of Grade III (reticulated). The system’s performance metrics are a soil removal rate of 96.59% and a pod damage rate of 2.48%. Field tests have confirmed that the deviation from simulation results is minimal. The device’s performance meets the requirements of actual production. Full article

This research investigates the optimization of surface integrity in powder-mixed electrical discharge machining (PMEDM) through the innovative use of Jatropha biodielectric fluid enhanced with titanium dioxide (TiO₂) nanoparticles. A comprehensive experimental framework was developed using design expert software (DOE) with Response Surface Methodology (RSM) to systematically analyze the machining of AISI D2 tool steel using copper electrodes. The study examined five critical process parameters, gap current (Ip), pulse-on duration (Ton), pulse-off time (Toff), gap voltage (V), and powder concentration, evaluating their combined effects on surface roughness (SR), surface crack density (SCD), and residual stress characteristics. Advanced characterization techniques including scanning electron microscopy (SEM) were employed to analyze surface topography and subsurface microstructural changes. The optimization process successfully identified optimal machining conditions of current = 9 A, Ton = 100 µs, Toff = 10 µs, and gap voltage = 65 V, achieving exceptional surface quality with a minimum surface roughness of 3.22 µm. Remarkably, these optimized parameters resulted in crack-free surfaces with zero surface crack density and minimal residual stress values across the 2θ range of 90° to 180°. To enhance predictive capabilities, supervised machine learning algorithms were implemented to model surface roughness behavior. Comparative analysis of classification algorithms demonstrated that Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), and Gaussian Naïve Bayes achieved superior performance with F1-scores of 0.88 and prediction accuracies of 90%. The integration of sustainable Jatropha biodielectric with TiO₂ nanoparticles represents a significant advancement in environmentally conscious precision machining, while the machine learning approach establishes a robust framework for intelligent process optimization and quality prediction in advanced manufacturing applications. Full article

Shared autonomous vehicle (SAV) services are gaining attention as an innovative urban transportation paradigm due to their potential to lower travel costs and improve operational efficiency. Unlike manually operated vehicles, SAVs exhibit unique behavioral dynamics, including safe passenger pick-up and drop-off processes, as well as strategic repositioning and autonomous parking to anticipate future travel demands. Consequently, effective and dynamic route planning is paramount to optimizing SAV safety and operational efficiency. This study proposes a novel traffic information, termed Autonomous Driving Stress (ADS), designed to enhance the safety and efficiency of SAV route planning by quantitatively capturing the level of driving challenge encountered during autonomous operation. To predict ADS, a machine learning framework was developed, utilizing microscopic traffic simulation data that incorporates a comprehensive set of 22 input features describing SAV driving behavior, roadway characteristics, and prevailing traffic conditions. Among five machine learning algorithms evaluated, Random Forest exhibited superior predictive performance, achieving an accuracy of 80.9%. The proposed framework enables real-time ADS level prediction by continuously integrating streaming traffic data into the trained model. The dissemination of this real-time ADS information to SAVs supports proactive, informed, and dynamic route planning decisions, thereby enhancing operational safety, traffic flow, and the sustainability of SAV operations within urban mobility systems. Full article

Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign—faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma—remains a critical, unquantified gap. This study aimed to perform a rigorous failure analysis to define the specific conditions under which commercial AI/CAD systems fail in a low-dose CT (LDCT) screening setting. Methods: In this retrospective diagnostic accuracy study, a primary cohort of 100 patients and an external validation cohort of 50 patients with moderate/low-risk nodules on LDCT were included. An expert reference standard was established by a consensus panel of three thoracic radiologists. Two independent, commercially deployed AI/CAD systems from different vendors (Vendor A & Vendor B) processed all cases. Nodules confirmed by experts but missed by AI were analyzed. Their morphology was categorized, and their mean CT attenuation (HU) was measured via manual region-of-interest placement. Results: The AI systems demonstrated significant and comparable false negative rates in the combined cohort: 12.7% for Vendor A and 14.7% for Vendor B. The vast majority of missed nodules were GGOs (92.3% and 78.6%, respectively, in the primary cohort). Crucially, quantitative analysis revealed a consistent density threshold for AI failure: the mean CT value of missed GGOs was −737 ± 51.50 HU for Vendor A and −727 ± 70.07 HU for Vendor B. This algorithmic blind spot was fully corroborated by the external validation cohort (−741 ± 48.2 HU and −733 ± 62.5 HU, respectively). Anatomical complexity (juxta-pleural/endobronchial location) was a secondary failure factor. Conclusions: This study identifies a quantifiable “−730 HU blind spot” as a common limitation of current commercial AI/CAD systems in diagnosing early lung adenocarcinoma. This finding represents a pivotal advancement in understanding AI’s role in diagnostics: it is not infallible. To innovate and safeguard screening efficacy, radiologists must adopt a human–AI collaborative model with mandated manual verification targeting low-attenuation opacities, ensuring this diagnostic innovation fulfills its promise while mitigating the risks of overdiagnosis. Full article

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Although the estimated tuberculosis (TB) incidence in Gabon is declining, there have been challenges with treatment coverage, HIV status and treatment outcome documentation. Thus, the National TB Program (NTP) conducted an innovative data review at the Nkembo Health Treatment Center in Libreville, which manages more than 70% of Gabonese TB patients. Since our hypothesis was that the Nkembo treatment center was struggling with data mismanagement due to the workload, the objective was to perform a TB data quality review and triangulation exercise at the Nkembo health facility in Libreville, from January to August 2023, and propose recommendations for data improvement. Methods: The study used the data reconciliation method. This is a process that involves comparing and aligning data from multiple sources to ensure consistency, accuracy, and integrity. The primary purpose of data reconciliation is to identify and resolve discrepancies or differences between datasets and make them consistent. Using the “TB onion model”, analysis identified data mismanagement as a key contributor to underreporting. A data review compared TB records to TB registry data and patient folders from January to August 2023 for notification and to the 2022 cohort for treatment results. The study focused on notified TB cases, HIV status and TB treatment outcome documentation. Discrepancies were reconciled, and treatment outcomes re-evaluated. Results: After review, statistically significant increases were observed: +22% for total TB cases (p = 0.0003), +141% for the number of TB cases with known HIV status (p = 0.0017) and +104% for the number of TB cases successfully treated (p = 0.0001), as compared with the previous data. Discussion: This data reconciliation showed the usefulness of triangulation across data sources to improve the completeness of data. Also, current reported data underestimate the number of reported cases, documentation of HIV status, and treatment success. Conclusions: The study shows that data reconciliation can improve TB programmatic data completeness to better reflect program performance. Full article

The escalating demand for sustainable, nutrient-dense feeds underscores the need to valorize the agro-industrial byproducts utilizing innovative bioconversion strategies. In this context, we have studied the feasibility of incorporating tomato (Solanum lycopersicum) cultivation residues into Black Soldier Fly (BSF) larvae diets to produce high-protein insect meals. These residues are known to contain the naturally occurring toxic steroidal alkaloids tomatidine and α-tomatine, prohibiting their incorporation into human and animal diets. Herein, the tomato cultivation biomass was dried and mill-ground, and its varying volumes were incorporated into standard poultry feed (seven diet levels with 0–100% biomass inclusion) and tested in BSF-larvae-rearing trials to produce insect meals. The optimal results with respect to larvae growth, protein accumulation (highest value = 30.61%), lipid–fiber content, and antioxidant capacity were determined for insect meals obtained from BSF larvae reared with a ration composed of 40% tomato plant biomass. In addition, the toxicity of this insect meal was substantially low, as a consequence of the observed groundbreaking reduction in the contained toxic steroidal alkaloids α-tomatine and its aglycone tomatidine. The results herein reveal the efficacy of the BSF-larvae-rearing process in acting as a biological filter for the bioconversion of the toxic tomato cultivation waste into a functional, safe, and protein-rich livestock feed, supporting the principles of a circular economy. Full article

Each of the stages of the New Product Development (NPD) process is vulnerable to different forms of risk. The existing categorizations of these risks are partial, ill-defined, or lack depth. Deploying a systematic literature review methodology, we identify 65 empirical studies that identify sources of risk in NPD. Synthesizing this information, we develop a broad, meaningful, and recognizable taxonomy with five main categories of risk, each with a number of sub-categories. It also takes into consideration the increasingly significant role of sustainability-oriented innovation, including how environmental, technological, and operational risks interact in influencing sustainable performance and resilience in NPD systems. This taxonomy is then used to identify the existing risks inherent in each stage of the NPD process, revealing areas for future research and the applicability of the taxonomy for resolving current issues in NPD risk management theory and practice. Linking the issues of NPD risks with sustainability challenges, this study contributes to innovation management theory and the sustainable development of new products. Full article

Roasted coffee silverskin (RCSS) is a by-product of coffee production characterized by its content of phenolic compounds, including those contributing to bitterness. The aim of this study was to evaluate RCSS as an analog for hops in the production of non-alcoholic beer. Beers were developed using hops, RCSS, or a combination of both. Their sensory and physicochemical properties were evaluated, including bitterness, total phenolic content, and antioxidant capacity. Compared to hopped beer, the RCSS beer exhibited a significantly higher original gravity (7.11°P vs. 6.70°P), apparent extract (6.52°P vs. 6.20°P), and darker color (18.02 vs. 4.65 EBC). The total phenolic content was also significantly higher in the RCSS beer, reaching 0.51 ± 0.03 mg CGA/mL, which represents a 34% increase compared to the hopped variant. Importantly, the addition of RCSS had no negative effect on fermentation process. Moreover, the RCSS beer was characterized by improved overall sensory quality. These results indicate that RCSS is an innovative, sustainable alternative to hops, enhancing both sensory and functional properties while supporting zero-waste brewing strategies. Full article

At present, most plateau-constrained cities worldwide—plateau cities whose spatial form is strictly constrained by topography—have entered the late stage of urbanization. The relationship between urban form and the surrounding geographic spatial pattern has consequently exhibited distinctive new characteristics. However, planning and policy often continue to adopt green-space allocation schemes developed in the mid-stage of urbanization and based on the experience of plain cities, resulting in difficulties in plan implementation, intensified human–land conflicts, and imbalances in both the supply–demand relationship and equity of green public services with severe challenges to urban sustainable development, calling for urgent correction and reconstruction. Through a literature review and comparative case analysis, this study clarifies global trends in the paradigm shift in plateau-city planning and develops an evaluation system comprising “adaptability analysis of originally planned spaces within the built-up area + assessment of the potential for converting ecological value in green spaces outside the built-up area + integrated spatial optimization.” Building on Analytic Hierarchy Process (AHP) weighting and spatial analysis, the study establishes a comprehensive assessment framework and applies it empirically to Kunming as a typical case, with the aim of proposing a correction-and-reconstruction paradigm for green-space allocation tailored to plateau-constrained cities to achieve sustainable development goals. The results indicate a widespread paradigm shift in many cities from “pattern optimization during incremental expansion” and “passive adaptation to ecological patterns” toward “enhancing governance effectiveness during stock-based renewal” and “proactive innovation in governance instruments.” The Kunming case shows that, during the mid-stage of urbanization, numerous parks and green spaces were planned within the built-up area (flat land), yet many of these proposals proved infeasible due to excessive costs and trade-offs. Meanwhile, the adjacent mountainous ecological spaces with substantial scenic and recreational potential were long excluded from the urban public service system. In response, this study proposes a three-dimensional allocation model that combines “optimized adaptation” within the built-up area and “potential conversion” in adjacent peri-urban areas together with differentiated policy instruments and an implementation/transfer assurance mechanism. This approach not only offers practical planning guidance for Kunming but also provides a broadly applicable set of theoretical and practical tools for improving land-use efficiency and promoting green equity in similar cities worldwide. Full article

Flotation circuits typically incorporate grinding stages, yet mathematical models for these processes often operate on different principles, leading to misalignment in circuit design. Building on a previously established grinding model for flotation performance, this research introduces significant advances to develop a more comprehensive and industrially relevant framework. The primary innovation is the integration of mechanical entrainment and gangue recovery into the kinetic model, distinguishing between species captured by true flotation and those carried to the surface despite being non-hydrophobic. We developed a robust set of grinding-mill equations based on first-order kinetics to describe the mass-fraction transformation of both true-flotation and entrainment species. To ensure practical applicability, a systematic experimental and modeling methodology for parameter adjustment is introduced, providing a clear sequence for identifying breakage rate constants and flotation kinetic parameters. The proposed strategy was validated using two distinct case studies: an expanded analysis of a copper sulfide ore (ore A) and a new case involving significant gangue entrainment (ore B). The results demonstrate that the model accurately predicts species kinetics, providing a high-fidelity, cost-effective tool to optimize mineral recovery and prevent economic losses from overgrinding in industrial processing plants. Full article

The concept of the 15 minutes city is increasingly present in the structure of spatial planning for large urban centers, with the main goal of improving quality of life by facilitating access to basic necessities for the population. This study aims to provide an integrated assessment of spatial accessibility for two urban centers that differ in structure and organization, with the main goal of identifying best practices that can be borrowed from one urban center to another in order to streamline sustainable spatial planning based on the strategic concept of the 15 minutes city. The entire research process is based on the development of a completely new and innovative GIS spatial analysis model that will add value to the specialized literature both through the geoinformational approach to the analysis, integration and through the exclusive use the freely available GIS databases (using the OpenStreetMap database), functionally integrated through network analysis and equations weighing the importance of accessibility needs for the population. For the analysis of pedestrian accessibility, in minutes, a total of 4826 locations were used for Cluj–Napoca and 5050 for Pecs, which were structured into 12 subclasses and five main classes (Recreational and Cultural, Public Services and Safety, Education and Health, Commercial, and Public Transport) established in accordance with the main requirements of the 15 minutes city development methodology. The integration of subclasses and accessibility classes was achieved by weighting their importance according to the responses obtained after the implementation of questionnaires to identify the working population’s perception of accessibility in their daily routine. The comparative analysis of the intermediate and final results of the proposed model leads to the establishment of directions and decision-making in the territorial planning process through the transfer of knowledge, solutions, and techniques between the two urban centers to eliminate or reduce negative hotspots and develop a more sustainable urban center in terms of accessibility and as close as possible to a 15 minutes city. Full article

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy–Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin’s minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton–Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity. Full article

The paper investigates the applicability of time series models for processing data obtained from a customized crack-propagation sensor. Because the sensor records a variable and noise-affected waveform, the study focuses on models capable of forecasting signals composed of both trend and stochastic components. Adaptive, analytical, and autoregressive approaches were examined, with particular attention to their suitability for short, non-stationary sequences typical of fatigue-related measurements. Based on the statistical characteristics of the sensor output during crack growth, the ARIMA model was selected for further analysis and algorithm development. The forecasting performance of ARIMA was evaluated for different parameter configurations by comparing the range and variability of the base and predicted data. Initial tests using first-order parameters produced unsatisfactory results, with high variance observed in both raw and modeled signals. Therefore, model parameters were optimized using the aicbic function, and the analyses were repeated. For the selected datasets, variance reduction by 3–4 orders of magnitude was achieved, demonstrating a substantial improvement in prediction stability. The presented results confirm that the proposed methodology is effective for processing complex sensor signals and highlight the broader significance of applying statistically grounded time series models in structural health monitoring. The study introduces an innovative framework for evaluating fatigue-related sensor data and establishes a reliable baseline for future predictive methods. Full article