During the electric arc furnace (EAF) steelmaking, the coherent lance is the core oxygen supply device for improving oxygen utilisation rate and the mixing efficiency of the molten bath. To further prolong the jet core length of the coherent lance installed on the furnace wall, a detachable coherent lance with a bunch-type nozzle structure is designed, and an attempt is made to use H 2 as the shrouded gas to reduce dependence on natural gas. For this purpose, a three-dimensional model is established in this paper to analyse the influences of different nozzle tip structures and shrouding gas conditions on the coherent jet characteristics. The simulation results show that, for different nozzle tip structures, the bunch-type design is more helpful in prolonging the jet core length. Increasing the hydrogen–oxygen input ratio helps the main oxygen jet maintain a large axial velocity and jet core length. However, when this ratio exceeds 20%, the increase in the input ratio has little effect on the jet velocity and jet core length. Compared with pure H 2 fuel, adding an appropriate amount of CH 4 to H 2 can rapidly increase both the axial velocity and the core length of the main oxygen jet. However, when the CH 4 in the mixture exceeds 40%, increasing the CH 4 ratio has a limited effect on the jet axial velocity and the jet core length. Compared with the 280-CH 4 supplement method, the axial velocity and jet core length of the primary oxygen jet are shortened by the 280-H 2 supplement method at the same volume flow rate, but prolonged by the 840-H 2 supplement method at the same mass calorific value.

Abstract The application of organic materials has a profound impact on CH₄ emissions from paddy fields. Biochar has been reported to mitigate CH₄ emissions, but this conclusion has recently been challenged and requires further investigation. This study aimed to determine the effect of biochar on paddy CH₄ emissions by integrating organic amendment emission data through network meta-analysis (NMA), and to identify the key moderators using multiple meta-regression (MR) approaches. Field experiments were conducted to verify the conclusions of MR. Based on 146 entries from 51 studies, a mixed-effects meta-analysis was conducted to evaluate the effects of organic material applications on soil CH₄ emissions. We focused on the biochar mitigation potential in rice systems and validated the conclusions through a field experiment. Biochar demonstrated the lowest methane emissions among all treatments. Carbon to nitrogen ratio of biochar (MC:N) and mineral nitrogen input (ICN) were identified as key moderators influencing the methane mitigation potential of biochar in rice cultivation. ICN was the most influential factor. When ICN exceeded 291.18 kg ha −1 , biochar tended to increase methane emissions, whereas at lower ICN levels, it contributed to emission reductions. Field experiments confirmed that at high mineral N levels (310 kg ha −1 ), biochar significantly increased CH₄ flux and emission potential. Overall, this study highlights the potential of biochar to reduce methane emissions in rice systems and underscores the importance of regulating mineral nitrogen inputs to maximize its mitigation effectiveness. Graphical Abstract

ABSTRACT This article utilises customs data from 2010 to 2016 to measure export values at the ‘city‐industry’ level, focusing on the impact of industrial robot adoption in developed market‐oriented countries on China's export performance. A theoretical model is constructed to explore how the use of industrial robots abroad affects China's exports. Additionally, a penetration index of foreign industrial robot usage and an instrumental variable are developed. The main findings are as follows: First, the use of robots in developed market‐oriented countries has a negative impact on China's exports, while the impact of robot adoption in competitive countries with similar endowments to China is not significant. Second, the negative effects from robot usage in developed countries are intensified in industries with a high cross‐industry input ratio, low international trade costs, and a high degree of robotisation. Third, robot adoption in developed countries also poses certain obstacles to China's efforts in exploring new export markets. This article has significant implications for formulating policies aimed at ‘stabilizing foreign trade’ and provides insights into the new dynamics of global industrial chain restructuring.

Cobalt-based superalloy Co06A exhibits excellent high-temperature performance and is widely used in the repair and additive manufacturing of critical hot-end components via direct laser deposition (DLD). However, improper energy–mass input during direct laser deposition often leads to defects such as porosity, cracks, and poor surface quality, which seriously affect the performance of formed parts. In this study, a systematic experimental investigation based on an orthogonal design was carried out to examine the effects of laser power, scan speed, and powder feed rate on the dilution rate, surface roughness, and powder capture efficiency of a one-layer single Co06A track. Range analysis and multiple linear regression were employed to quantify the influence of each parameter. The results showed that the powder feed rate was the dominant factor affecting both η and Sa, while the laser power had the most significant impact on PE. Through multi-objective optimization, a balanced parameter set (u = 6.66 mm/s, f = 20.81 g/min, P = 2543 W) was recommended, which achieved a dilution rate of about 11.95%, a surface roughness of 4.64 um, and a powder capture efficiency of 79.6%. Through testing, it was found that the energy/mass input ratio was approximately 8. This work demonstrated that matching energy–mass input and adopting a constrained optimization strategy could effectively improve the forming quality and manufacturing efficiency of Co06A in the first-layer manufacturing process, providing a promising prospect in guidance for engineering applications.

Ship motion exhibits strong nonlinearity due to the combined effects of wind, waves, and currents, which brings difficulties to ship motion forecasting. To overcome the above challenges, this paper attempts to propose a novel nonlinear forecasting approach for ship motion. Firstly, focusing on the nonlinear characteristics of ship motion, an instantaneous input data encoder is designed, and an Instantaneous-Informer (I-Informer) is established based on the Informer network. Then, targeting the hyper-parameter optimisation problem of the I-Informer and focusing on the inherent defects of the Pelican Optimisation Algorithm (POA), a new quantum POA (QPOA) is proposed to construct a hyper-parameter optimisation method for the I-Informer. Finally, based on the established I-Informer and the proposed QPOA, a novel ship motion forecasting approach is proposed, namely I-Informer_QPOA. Subsequently, rolling data of a C11 container ship in irregular waves is used to test the performance of the new approach. Through example analysis, this paper discusses the recommended value ranges of output length, input and output ratio, and hyper-parameter for the I-Informer, and conducts comparative tests within the recommended value ranges. The experimental results demonstrate that the established I-Informer can obtain more accurate forecasting results than the model selected in this paper. In addition, the proposed QPOA can provide a better and more stable hyper-parameter combination for the I-Informer compared with the algorithm selected in this paper.

Subject. The article deals with ensuring the financial inclusion as one of priorities of the socio-economic policy. Objectives. The study aims to perform a comprehensive assessment of the effectiveness of using the financial infrastructure of Russian regions in ensuring the financial inclusion and identifying determinants of interregional differences. Methods. We applied DEA analysis, which enabled to assess the relative efficiency of resource use, based on the ratio of input and output indicators. The empirical base includes data for Russia’s regions for 2019–2023. To analyze the use of infrastructure, we calculated input utilization coefficients, reflecting the contribution of individual elements (banking network, digital channels, insurance organizations, etc.) to overall efficiency. Results. During the study period, there was a positive dynamics of efficiency, accompanied by a reduction in inter-regional differences. The highest values are typical for the Far Eastern, Ural, and North Caucasian Federal Districts. The leading factors in increasing the efficiency are the development of banking and insurance infrastructure, digitalization of financial services, and growing involvement of the population in non-cash payments. Furthermore, the study unveiled structural limitations in certain regions of Central and Southern Russia related to low density of financial institutions and limited access to digital services. Conclusions. Sustainable development of financial inclusion requires an integrated approach, including the development of digital channels, institutional strengthening of the financial sector, and increasing financial literacy. The findings can be used to create regional programs for financial accessibility development and the digital economy.

Over the last century, agricultural intensification and urban expansion have significantly disrupted nitrogen (N) and phosphorus (P) cycles, with detrimental effects on the integrity of aquatic ecosystems. Nonetheless, current understanding of riverine nutrient dynamics -naturally influenced by geo-hydrological and climatic features - is still incomplete. This study aims to understand how the composition of the anthropogenic nitrogen and phosphorus load and geo-hydrological watershed characteristics influence riverine loads. To investigate these aspects, a trophic classification was applied to distinguish autotrophic (production-driven) watersheds typically associated with fertilizer use from heterotrophic (consumption-driven) systems, which rely on imported feed and food. Our analysis focused on the Po River Hydrographic District, one of the regions most impacted by N and P contamination in Europe. Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI and NAPI), riverine N and P exports, and geo-hydrological characteristics were assessed to understand export variability between trophic groups. In autotrophic systems, nitrogen export was more influenced by hydrology than by NANI levels. Conversely, nitrogen loads were closely associated with manure and feed imports in heterotrophic watersheds, with runoff and leaching acting as primary pathways. Phosphorus export was largely governed by erosion and soil retention, showing a weak correlation with NAPI. Although N:P input ratios differed between groups, output ratios tended to converge, suggesting that different processes regulate nutrient stoichiometry.

Abstract The paper proposes a methodological framework for performance efficiency assessment that integrates entropy weighting with data envelopment analysis (DEA) and assurance region constraints. Entropy-derived lower and upper bounds for input and output weight ratios are constructed, providing a more objective, data-driven way to limit weight flexibility without relying on additional information or expert judgment. The proposed model addresses key limitations of classical DEA - particularly its tendency to assign extreme or zero weights that can artificially overestimate the efficiency of low-performing units. By incorporating a scalable parameter, the model enables a controlled narrowing of the admissible weight space. The methodology is applied to the assessment of healthcare systems in European countries. Four aggregated input variables cover the staff providing healthcare, the scope and availability of healthcare services and the population behaviour. The survival to 65 years of age and the expected healthy life years at age 65 as significant health outcomes covering the whole human life are considered outputs. Efficient healthcare systems with satisfactory outcomes are identified, and the healthcare systems are ranked based on adjusted efficiency scores. The results reveal that, under stricter assurance region constraints, the DEA frontier becomes more robust, and efficiency scores better reflect acceptable performance. The approach provides a flexible tool for analysts and policymakers seeking to evaluate efficiency with greater realism and policy alignment.

Proximity graph-based methods have emerged as a leading paradigm for approximate nearest neighbor (ANN) search in the system community. This paper presents fresh insights into the theoretical foundation of these methods. We describe an algorithm to build a proximity graph for (1+ε)-ANN search that has O((1/ε) λ • n log Δ) edges and guarantees (1/ε) λ • polylog Δ query time. Here, n and Δ are the size and aspect ratio of the data input, respectively, and λ = O(1) is the doubling dimension of the underlying metric space. Our construction time is near-linear to n , improving the Ω(n 2 ) bounds of all previous constructions. We complement our algorithm with lower bounds revealing an inherent limitation of proximity graphs: the number of edges needs to be at least Ω((1/ε) λ • n + n log Δ) in the worst case, up to a subpolynomial factor. The hard inputs used in our lower-bound arguments are non-geometric, thus prompting the question of whether improvement is possible in the Euclidean space (a key subclass of metric spaces). We provide an affirmative answer by using geometry to reduce the graph size to O((1/ε) λ • n) while preserving nearly the same query and construction time.

Mucorales quantitative PCR (qPCR) was first developed and evaluated in 2013, and since then, substantial evidence has accumulated supporting its clinical utility. This review highlights the major advances this technique has brought to the management of mucormycosis. Different in-house and commercial assays are now available, targeting a broad range of clinically relevant Mucorales species. Performance depends strongly on preanalytical parameters such as volume of plasma or serum used for DNA extraction, and ratio of DNA input in the PCR reaction mix. Multiplex assays have expanded diagnostic scope to include Aspergillus and other molds, supporting differential diagnosis of invasive mold infections. Serial qPCR monitoring provides prognostic information, with DNA clearance after treatment associated with improved survival. Routine screening in high-risk populations, including hematology and severely burnedpatients, shortens diagnostic delays and improves outcomes. Compared with conventional methods, Mucorales qPCR provides a rapid, noninvasive, and sensitive diagnostic tool. Large studies have shown that it enables earlier detection, facilitates timely antifungal treatment, and is associated with improved survival.

IntroductionYield improvement of Tartary buckwheat is primarily hindered by the lack of effective cultivation practices. Understanding the effects of improved cultivation practices (ICPs) on the yield and economic benefits is of great importance for high-yield cultivation and resources efficient utilization of Tartary buckwheat.MethodsA two-season field experiment was conducted on Tartary buckwheat variety Jinqiao 2 using six cultivation practices, including no nitrogen application (0 N), local farmers’ practice (LFP, CK), and four ICPs consisting of improved practice of increased planting density with reduced nitrogen application (ICP1), the same practices as ICP1 but with moderate tillage depth (ICP2), the same practices as ICP1 but with deep tillage depth (ICP3), and the same practices as ICP3 but with rice straw returning (ICP4).Results and discussionICP4 treatment remarkably increased the chlorophyll content, photosynthetic rate, photosynthetic nitrogen utilization efficiency, non-structural carbohydrate accumulation, transportation, contribution rate to grain yield, physiological utilization rate of nitrogen fertilizer, agronomic utilization rate of nitrogen fertilizer, and partial productivity of nitrogen fertilizer. Compared with LFP treatment, ICP1, ICP2, ICP3, and ICP4 treatments increased the yield of Tartary buckwheat by 15.63%, 32.03%, 46.09%, and 79.69%, respectively. ICP4 treatment was the best among the cultivation practices, but considering the cost (Compared with LFP, ICP3 increased the production value, economic output–input ratio, and cost–output rate by 45.99%, 47.97%, and 64.45%, respectively), the use of ICP3 was favorable in the production of Tartary buckwheat. This study was helpful in establishing integrated agronomic practices for high-yield cultivation and resources efficient utilization for the Tartary buckwheat production.

Evaluating the effectiveness of education management requires the integration of multi-source data and information. Based on data modeling technology, combined with data enhancement and transfer learning methods, this paper analyzes the differences in the allocation of education management resources in six universities in different semesters, and systematically explores the actual effectiveness of university education management. By combining data enhancement technology, we expanded the training data, simulated various real-life scenarios, and ensured that the model is more robust to various data changes. This study mainly used two models: simulation-verification model and BP (back propagation) neural network model, and analyzed their management efficiency, prediction accuracy, stability and time cycle. This study proposed two models: simulation-verification model (evaluating the effect by simulating the consistency of management conditions and verification results) and BP neural network model (prediction model based on data enhancement and transfer learning). Experiments show that the BP neural network model is superior to the simulation model in management efficiency (ratio of resource input to actual effect) and stability (volatility of model prediction results), with an average management efficiency of 85.9%, prediction accuracy of 93.1%, and stability of 72.3%. The BP neural network model is superior to the simulation verification model in terms of management efficiency, prediction accuracy, and stability, demonstrating the potential of integrating advanced data processing technologies such as data enhancement and transfer learning into the education management system.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-13728-3.

Gas porosity affects part quality in additive manufacturing and is influenced by multiple physical factors that are not well-understood. Here, we calculated several mechanistic variables representing the physical factors related to porosity and evaluated their hierarchical influence using machine learning. We found that the mechanistic variables representing the dynamics of the gas bubbles inside the pool, time to rise, Marangoni number, time to solidify, and Stokes’ velocity are more important than the variables indicating the pore nucleation and stability such as heat input ratio, internal pressure of gas bubbles, and activity of gas dissolution. Laser power and scanning speed were the two most important process variables. Porosity prediction was better using mechanistic variables than using process variables and alloy properties.

Heat pump use for heating and cooling in buildings is increasing significantly due to renewable energy integration. While cascade optimization has been extensively studied for cooling applications achieving up to 17% energy savings, limited research addresses this approach for heat pump heating systems despite similar performance characteristics. This work presents an analogous optimization strategy using heuristic mathematical optimization approach is used to calculate optimal threshold values for heat pump cascade. A simulation of three heat pumps with the optimized cascade reduces electricity consumption by 13.80% compared to a baseline configuration. Economic assessment is conducted at the design phase to evaluate the capital expenditure and operational expenditure of different heat pump capacity configurations meeting the same nominal demand. While single high-capacity heat pumps minimize investment costs, a multi-unit configuration offers superior long-term savings over five years. The proposed optimization further accelerates cost recovery. Abbreviations: AI: Artificial intelligence; BEM: Building energy model; BIM: Building information model; CAPEX: Capital expenditure; CAPFT: Capacity modifier curve with temperature; CO2: Carbon dioxide; COP: Coefficients of performance; DMS: Demand side management; EER: Energy efficiency ratio; EIR: Energy input ratio; EIRFPLR: Energy input ratio with part load ratio; EIRFT: Energy input ratio with temperature; HVAC: Heating ventilation and air conditioning; OPEX: Operational expenditure; PLR: Part load ratio; RES: Renewable energy sources; ROI: Return of investment; VRF: Variable refrigerant flow;

Thin films of all-proportional PZT system, i.e., Pb(ZryTi1.00−y)O3 with y = 0–1.00, were deposited by hydrothermal deposition with a low reaction temperature of 150 °C. The PZT films were grown epitaxially on the surface of (100)SrTiO3:La substrates via a microwave-assisted hydrothermal reaction using various input ratios of precursor chemicals x = [ZrOCl2]/([ZrOCl2] + [TiO2]), which included more Zr ions (or less Ti ions) with a higher y value than that of the input ratio x. Dielectric and ferroelectric parameters for the resulting films, i.e., the relative dielectric constant (εr) and remanent polarization (Pr), exhibited maxima at the certain Zr/Ti ratio y = ∼0.60 though these parameters were relatively lower than those of the conventional films.

Productivity cycle analysis is a necessity for every company, it is intended to determine the level of performance that has been set and as an evaluation for performance improvement in the future. The problem that occurs is that the level of productivity fluctuates and there is a decrease in the productivity index, where the value of input goes up and output goes down. The purpose of this study is to determine the measurement of productivity and productivity index and how to increase the productivity. The method proposed is David J. Sumanth. The data used are input and output data. The results obtained are the productivity level of total output and input for the first period of 2.90%, the second period of 2.79%, the third period of 2.88%, the fourth period of 3.33% and the fifth period of 3.06%. The figures obtained from the productivity calculation show that each month the ratio of output and input is still not balanced. The highest index occurred in 2020 at 1.16% and the lowest index occurred in 2018 at 0.97%. This is because the partial input input also fluctuates.

This study presents an energy-aware Condition-Based Maintenance (CBM) framework for an SKF 6205 bearing in a motor-driven system, integrating Industry 4.0 technologies. Real-time data from ESP32-based IoT sensors enabled degradation modeling using a Gamma process and evaluation of energy efficiency. The degradation index (Xt), derived from tri-axial RMS vibration, identified a failure threshold of 41.5 g·min, with a CBM trigger set at 75% (31.13 g·min). An Energy Efficiency Indicator (EEI), defined as the ratio of power input to incremental degradation, highlighted performance drops near failure, validating the thresholds. Remaining Useful Life (RUL) was estimated using Maximum Likelihood Estimation on Gamma distribution parameters (α = 16.61, β = 0.000286). The proposed approach links energy efficiency with wear progression, enabling accurate, sustainable maintenance decisions in smart manufacturing environments.

This research discusses micro workforce planning in Indonesia in the context of budget efficiency and its impact on the national economy. MSMEs, which have a large role as labor absorbers in Indonesia, have to face budget cuts, as planned by the government in 2025, potentially hampering the process of workforce planning that is adaptive, efficient, and based on local needs. This descriptive qualitative approach based on literature study aims to understand and describe the phenomenon in depth, contextually, and holistically. The results show that budget efficiency in micro workforce planning can be achieved through output to input ratio, budget absorption rate, cost per unit of output, program completion time, and output quality. Effective strategies identified include community-based training, utilization of digital technology, and multi-stakeholder collaboration, especially for resource-constrained areas such as in eastern Indonesia. The findings are expected to provide theoretical and practical contributions to the development of human resource management in the micro sector as well as input for the government in formulating HR development policies and inclusive economic growth.

This study aimed to investigate if mashed ripe bananas (bananagram) could be used to assess the integrity of the gut before transverse colostomy closure in patients with Hirschsprung disease (HSCR) in a low- and middle-income country tertiary-level hospital. This is a prospective, single-center, hospital-based cohort study. A feeding tube tip was inserted about 5-8 cm through the distal loop, and 15 mL of mashed banana was introduced. The amount introduced and the spontaneous expulsion of mashed banana were measured. We also measured colonic transit time (CTT), input and expulsion ratio, color of the expulsed banana stream, size of the banana stream passed through the anus and clinical signs of bowel obstruction or perforation after the procedure. From January 2018 to June 2023, 266 HSCR patients with a transverse colostomy were included. The mean±standard deviation (SD) age was 26.45±6.41 months. The median CTT of mashed banana was 8 min (interquartile range (IQR): 4-13 min). The mean amount of mashed banana spontaneously expelled through the anus was 13.25±0.95 mL. More than 90% of patients expelled unchanged mashed bananas, and 9.4% expelled stool-banana mixtures. Banana stream was 0.3-1.5 cm in 257 patients and <0.3 cm in nine patients. These nine patients needed revision pull-through. Bananagram can be performed at the bedside. It is easy to perform, cost-effective, available throughout the year and does not pose radiation hazards.

Abstract A novel approach to investigate the processability of polyamide 12/dimethylacetamide (PA12/DMAc) microspheres for laser sintering through molecular interactions of the functional groups and the effects of DMAc on the morphology, particle size distribution, and thermal properties of the polymer composites was addressed in this study. The increase in DMAc concentrations enhanced polymer dissolution, minimized agglomeration, and improved the binding adhesive forces within the polymer matrix. It also enhanced the stretching vibrations in the primary amide (C=N), amine bond (C–N), and hydrogen bond (N–H) of the PA12. The melting enthalpy of the polymer powder increased with greater molecular interactions with the solvent. All the PA12 powder samples exhibited a consistent average crystallization point of 147.5°C, similar to that of virgin PA12 (149.3°C) due to identical exothermic properties. The SEM and particle size distribution analysis of PA12/DMAc samples indicated variability in molecular interspaces between powder particles due to differences in the interfacial mobility, which expanded with an increase in the solvent input ratios. However, the particle size distribution span reduced with an increase in the volume of DMAc. The optimal mean particle size distribution was 64.87 μm, accompanied by a bulk density of 0.38 g/cm3, which could facilitate the sinterability of the PA12 powder.Highlights Compatibility of PA12/DMAc microspheres Molecular interactions during TIPS Effects of DMAc on PA12 dissolution and precipitation time Dissolution and precipitation rate analysis Melting and crystallization behavior.