Output Quantities Research Articles

Leveraging Municipal Solid Waste Management with Plasma Pyrolysis and IoT: Strategies for Energy Byproducts and Resource Recovery

The escalating challenges of municipal solid waste (MSW) management, exacerbated by the classification of MSW as hazardous waste due to the presence of heavy metals (HMs) and toxic compounds, necessitate innovative treatment strategies. Plasma pyrolysis has emerged as a promising technology for converting MSW into valuable energy byproducts, such as syngas, bio-oil, and slag, while significantly reducing waste volume. However, maintaining optimal operational parameters during the plasma pyrolysis process remains a complex challenge that can adversely affect both the efficiency and the quality and quantity of outputs. To address this issue, the integration of the Internet of Things (IoT) presents a transformative approach. By leveraging IoT technologies, real-time monitoring and advanced data analytics can be employed to optimize the operational conditions of plasma pyrolysis systems, ensuring consistent performance and maximizing resource recovery. This review explores the synergistic integration of plasma pyrolysis and IoT as a novel strategy for MSW management. The slag from plasma treatment can be efficiently channeled into anaerobic digestion (AD) systems, promoting resource recovery through biogas production and the generation of nutrient-rich digestate. This synergy not only mitigates the environmental impacts associated with traditional MSW disposal methods but also paves the way for sustainable energy recovery and resource management. Ultimately, this review presents a comprehensive framework for exploiting plasma pyrolysis and IoT in addressing the pressing issues of hazardous MSW, thereby fostering a circular economy through innovative waste-to-energy solutions.

Improved Tribological Properties of Blanking Punches for Copper Alloys Utilizing Deterministic Surface Texturing by Machine Hammer Peening

Constant efforts to increase resource efficiency, productivity, and output quantities, as well as growing product requirements, result in high tribological loads on forming tools. The manipulation of the tribological properties of blanking punches by deterministic texturing using machine hammer peening on the lateral surface is a solution to ensure an extended service life. For this purpose, rotationally symmetrical blanking punches were textured using a specially developed texturing center. The machine hammer peening center allows surface texturing with positioning accuracies of less than 2% for individual indentations by controlling a rotary and feed axis in combination with frequency control of the machine hammer peening setup. A modified hammer head with a micro-tip was used as the texturing tool. Different coverage ratios with the same aspect ratio were applied to the surface. These punches were then tested on an industrial high-speed press. To evaluate the effectiveness, the force curves were analyzed and the evolution of the textured topography was continuously evaluated. The experiments showed that the withdrawal force could be reduced by 38% due to microtexturing with a coverage ratio of 18%. Other coverage ratios resulted in an increase. By texturing the lateral surfaces of blanking punches using machine hammer peening, the service life was also significantly improved.

Uncertainty quantification and global sensitivity analysis of turbulence model closure coefficients for sheet cavity flow around a hydrofoil

This paper presents the uncertainty quantification and sensitivity analysis of the standard k-ɛ turbulence model applied to the numerical prediction of the non-cavitating and cavitating flow around a 2D NACA66MOD hydrofoil. The turbulence model parameters are treated as epistemic uncertain variables, and the forward propagation of uncertainty is evaluated using the non-intrusive polynomial chaos approach. The required simulations are performed using a commercial CFD solver. The Sobol indices are used to rank the relative contribution of closure coefficients to the total uncertainty in the output quantities. For the considered case, the ranking of the model parameters is not influenced by the presence of the sheet cavity flow, while it varies with respect to the considered output quantity.

Health insurance expansion and academic production: The case of Medicare Part D legislation.

Understanding how market demand influences academic research is crucial for comprehending the innovation process and informing policy decisions. This study examines the impact of the 2003 Medicare Part D legislation on biomedical journal article production across diseases. Using a difference-in-differences framework, together with a sample of over 2 million original research articles published between 1987 and 2015 sourced from PubMed database, I find a relatively 18.5% increase in academic publications on diseases more common among Medicare beneficiaries, indicating responsiveness to market demand. Further analysis shows that non-government funding significantly contributed to this increase. However, these additional publications tend to be of lower quality, as measured by citations, patent citations, and journal impact factors. This suggests a trade-off between the quantity and quality of research outputs. Overall, this paper highlights how market demand influences academic production, demonstrating that healthcare policy changes, such as Medicare Part D, can spur academic research output and reshape research focus. Policymakers should consider these spillover effects to balance research quantity and quality while ensuring equitable benefits.

Analysis of the Validity of P2D Models for Solid-State Batteries in a Large Parameter Range

Simulation models are nowadays indispensable to efficiently assess or optimize novel battery cell concepts during the development process. Electro-chemo-mechano models are widely used to investigate solid-state batteries during cycling and allow the prediction of the dependence of design parameters like material properties, geometric properties, or operating conditions on output quantities like the state of charge. One possibility of classification of these physics-based models is their level of geometric resolution, including three-dimensionally resolved models and geometrically homogenized models, known as Doyle-Fuller-Newman or pseudo two-dimensional models. Within this study, the advantages and drawbacks of these two types of models are identified within a wide range of the design parameter values. Therefore, the sensitivity of an output quantity of the models on one or a combination of parameters is compared. In particular, the global sensitivity, i.e. the sensitivity in a wide range of parameter values, is computed by using the Sobol indexes as a measure. Furthermore, the local sensitivity of the difference in the output quantities of both models is evaluated to identify regions of parameter values in which they contain significant deviations. Finally, remarks on the potential interplay between both models to obtain fast and reliable results are given.

Modeling of vibrational nonequilibrium effects on pressure Hessian tensor using physics-assisted deep neural networks

This study focuses on modeling the effect of vibrational nonequilibrium on the pressure Hessian tensor. The pressure Hessian tensor is one of the unclosed processes involved in the velocity gradient evolution equation. Accessing the velocity gradient tensor following a fluid particle is needed to understand the physics of various nonlinear turbulent processes. Our modeling strategy employs a combination of two different deep neural networks (DNNs) to model the magnitude and the directional aspects of the vibrational nonequilibrium tensor separately. Both the DNNs are optimized using two appropriate physics-assisted custom loss functions, comparing the desired features of the DNN predictions against the exact behavior observed in the direct numerical simulation (DNS) database. A detailed investigation of four different DNS databases of vibrationally excited decaying compressible turbulence with different initial Reynolds numbers, Mach numbers, and vibrational Damköhler numbers is performed to identify the appropriate normalized forms of input and output quantities for the two neural networks. The training of both the DNNs is done using the DNS data of merely one simulation. The trained models are then subjected to extensive evaluation against different DNS databases. Indeed, the new model captures many DNS features quite well. Such an extensive evaluation of the new model proves the generalizability of the model, at least in the range of parameters involved in our study.

Analysis of the Relationship between Digital Literacy Program Development and Lecturer Productivity in Higher Education

The rapid development of digitalization and technology forces the awareness of digital literacy to be in a strategic and vital position as a tool to enhance the academic productivity of lecturers. This study analyzes the relationship between digital literacy and academic productivity among lecturers at Sekolah Tinggi Agama Islam Tangerang Raya. Digital literacy encompasses access, processing, and utilization of information technology to support academic tasks. This research employs biometric analysis methods to gather data on lecturers’ digital behaviour, including the frequency of digital device usage and access to online academic resources. The findings reveal a significant positive correlation, where lecturers who frequently use digital devices experience a 25% increase in academic productivity in scientific publications. Additionally, the study highlights that active participation in webinars and online courses contributes to improvements in the quality and quantity of research output. Recommendations for institutions include the importance of providing comprehensive training programs focused on data analysis software, reference management, and online academic writing to enhance research efficiency. Furthermore, improving access to online academic resources and providing adequate digital devices will strengthen academic performance. Increasing digital literacy focused on technological capabilities not only significantly enhances the academic productivity of lecturers but also serves as an effective solution for improving other competencies, such as research productivity and scientific collaboration, to achieve more optimal institutional outcomes.

Comparing modelling approaches for a generic nuclear waste repository in salt

This paper contains a comparison of five modelling approaches for a simplified nuclear waste repository in a domal salt formation. It is the result of a four-year collaboration between five international teams on Task F of the DECOVALEX-2023 project on performance assessment modelling. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. This work demonstrates how these objectives are accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Participating teams made a wide range of model assumptions, ranging from compartmentalized networks to full 3D models of the salt formation and repository. Despite differences in the modelling strategies, all models indicate that salt compaction and diffusion of radionuclides in brine are key processes in the repository. For the isothermal spent nuclear fuel and vitrified waste scenario with multiple early failures considered, all models indicate little of the disposed radionuclides will migrate beyond the repository seal over the 100,000-year simulations. In general, the model output quantities have the largest differences over the short term and near the waste. Disparities between the models are believed to be due to differing simplifications from the conceptual model.

Estimating production functions using costs when outputs are restricted

Conventional proxy-variable approaches to production-function identification assume that output quantities are flexibly adjustable, with firms endogenously choosing them in pursuit of profits. But in not so few industries outputs are exogenously restricted, constraining input allocations by producers. Model assumptions of most proxy estimators become untenable in such settings. We provide a new structural methodology based on a dual formulation of firm production, indirectly identifying technology and productivity from cost data while appropriately accommodating exogenous output restrictions. We showcase our methodology on a panel of U.S. natural gas-fired power-generation plants in 2005–2017 facing price-inelastic demand that restricts their outputs in the short run. We find that power plants were scale-efficient, with (short-run) marginal costs of net electricity generation declining over time. With little evidence of productivity growth in the industry during our sample period, we conclude that the reduction in marginal costs was fueled mainly by diminishing input prices and, potentially, convergence to the optimal long-run input allocation.

Sinergi Akuntansi dan Agroteknologi: Pemberdayaan UMKM Ibu Rumah Tangga

Housewives managing Micro, Small, and Medium Enterprises (MSMEs) in the agricultural sector and agricultural processed products often face challenges in running their businesses, particularly in financial record-keeping and productivity enhancement. Mastery of appropriate accounting technology can help them manage business finances efficiently, while the application of agrotechnology can improve the quality and quantity of production output (Supriyadi, 2020). This community service program aims to integrate accounting technology and agrotechnology for MSMEs managed by housewives in Bintaro Village, South Ampenan District, Mataram City. The methods used in this program include training in financial record-keeping through a simple accounting application and the application of easy-to-adopt agricultural technology suited to local potential. The results of this program show an increase in participants' understanding of managing business finances, as well as improvements in efficiency and productivity in agricultural outputs and processed products. The integration of accounting and agrotechnology is expected to help housewives develop their businesses more independently and sustainably, thereby making a significant contribution to the well-being of their families and surrounding communities.

Analisis Penambahan Jumlah Tenaga Kerja dalam Meningkatkan Pendapatan pada Mie Ayam Baso H. Lili Karawang

Revenue is the revenue earned through the results of economic activities related to the sales activities of production factors owned by the company. As known in the theory of production factors, the quantity of output associated with income directly depends on the amount of labor owned. In increasing production supported by efficient resources and running according to the system, appropriate income can be obtained from the level of production. Theoretically, a positive contribution of labor to the increase of business income can occur, especially by involving a large number of workers. This study was conducted to analyze the additional number of workers in increasing revenue at Baso H. Lili Chicken Noodle in Karawang. The type used in this research is descriptive quantitative. The results of this study indicate that Mie Ayam Baso H. Lili Karawang has experienced an increase in labor since the beginning of the business until now, initially only having 3 workers per branch as of 2005, but currently in 2023 it has approximately 7 to 15 workers in each branch. After the additional workforce, H. Lili Karawang's Baso Chicken Noodle business experienced an increase in income generated with an average of IDR Rp 588.858.818 or the same as around 408%.

Evaluation of 10‐m Wind Speed From ISD Meteorological Stations and the MERRA‐2 Reanalysis: Impacts on Dust Emission in the Arabian Peninsula

AbstractMineral dust is one of the most important aerosols when studying the radiative balance and climate of the planet. There are different dust emission schemes utilized by the atmospheric modeling communities, many of which disagree on basic output quantities such as mass of dust emitted and distribution of mass among size bins. In this work, we examined mineral dust emission from a leading model scheme, the Goddard Chemistry Aerosol Radiation and Transport (GOCART), as utilized in the Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2) Reanalysis and compared it to dust emissions calculated using wind measurements from ground based weather stations located in the Arabian Peninsula that are included in the National Oceanic and Atmospheric Administration’s (NOAA) integrated surface database (ISD). An intercomparison of 10‐m wind speed is shown for the Arabian Peninsula region, differences of the observed and modeled wind field are quantified, and impacts of differences on dust emissions are calculated. This analysis shows 10‐m winds in the ISD were generally lower than MERRA‐2 winds, which propagated to dust emissions errors. Our estimate of one of the most significant mass impacts in dust emission is 0.178 Tg/year/grid box with a percent change of over 200% to the recalculated dust emissions from MERRA‐2. These differences in wind speed propagated to a difference in dust mass emitted by the use of a static source function which aids in scaling the mass emitted by the availability of dust in each grid. Additionally, the magnitude of these differences varies seasonally.

Compound effects of sea level and flow on river-induced flooding in coastal areas of southern Sweden

Study regionRönne River, Säve River, and Höje River, Sweden. Study focusRiver-induced flooding in coastal areas results from a multitude of drivers interacting in complex ways. The primary drivers are sea level (SL) and river flow (Q) that often exhibit coherent behavior to be considered in flood risk management. To describe and quantify the compound effects of SL and Q on flooding, a methodology was developed involving hydraulic simulations with long time series of data yielding statistical properties of output quantities such as river water level and flooded areas. Dominance analysis was conducted to quantify the relative influence of SL and Q on river water level along reaches. Also, simplified, empirically based equations were derived to predict the river water level at any location based on SL and Q. New hydrological insights for the regionThe long-term simulations revealed that the relative influence of SL and Q on the river water level changes significantly from the coast to upstream. For example, at the Rönne River, influence of SL decreases from 90 % to 20 % between 1 km and 11 km from the coast. Meanwhile, influence of Q increases from 10 % to 80 % over the same distance. The simplified equations derived to predict the water level can be used by stakeholders to forecast flood events or in risk assessment where many alternatives need to be considered.

Methane Desorption-Diffusion Behaviors in Micropores of Coal under Different Water Displacement Pressures.

Hydraulic fracturing not only enhances the flow conductivity of coal seams but also transforms the CH4 adsorbed in coal seams into free CH4 and then outputs it via the desorption-diffusion process, thus improving the output quantity and efficiency of CH4. In this paper, taking the coal from Changping mine in Shanxi as the research object, the 3D macromolecule model of coal was established by using analytical test experiments, and the molecular dynamics simulation method of desorption and diffusion was applied to study the characteristics of methane desorption rate and diffusion behaviors under different driving water pressures through the parameters of gas-water concentration distribution, desorption and diffusion rates, and the interaction energies between the coal and the gas-liquid. The results show that the water drive can increase the desorption rate of methane. With the increase of methane adsorption pressure, the desorption effect of water drive becomes stronger and then weaker. The desorption effect of water drive was best when the methane adsorption pressure was 5 MPa and the pressure difference was 2 MPa. Generally, the water drive promoted the methane diffusion, but if the water drive time was too long, it formed a water plug, and the water drive methane inhibited the diffusion. In shallow reservoirs, water drive can obviously promote the desorption-diffusion of methane. The binding energy between CH4 molecules is mainly composed of the repulsive force, while that between water molecules is mainly the adsorption force. In the binding energy between CH4 molecules and coal structures, the van der Waals (VDW) force accounts for 99% of the effect; in the binding energy between water molecules and coal structures, the electrostatic force contributes 84% to ∼88% thereto. The VDW force is significantly influenced by the pressure, while the electrostatic force is slightly affected. As the water displacement differential pressure is increased, the binding energy between CH4 molecules and coal structures decreases.

2D mechanical representation of superconducting magnets: A comparative study of plane stress and plane strain

When approaching the mechanical design of a superconducting magnet, whenever possible the starting model is a 2D approximation. If rotational symmetry (solenoid-like winding) is present, the 2D representation is unique and contains no approximations. If, on the other hand, a non-axisymmetric system is opted for, the 2D representation is not unique and there are main options available, as plane stress, plane stress with thickness, plane strain and generalized plane strain. Considering z as the direction normal to the 2D plane, the plane stress option defines a stress state in which no normal or shear stresses perpendicular to the xy plane can occur (σz=σxz=σyz=0). In this option, deformation can occur in the thickness direction of the element, which will become thinner when stretched and thicker when compressed; it is generally used for objects with limited depth (thin objects).In contrast, plane strain refers to the fact that deformation can only occur in plane, which means that no out-of-plane deformation will occur (ϵz=ϵxz=ϵyz=0). The plane strain option is generally appropriate for structures of nearly infinite length, relative to their cross section, that exhibit negligible length changes under load. The “generalized plane strain” option imposes the axial strain equal to a constant value; this condition does not reproduce the real operating condition of the magnet and is therefore excluded. The “plane stress with thickness” uses the same equations as the plane stress option but, output quantities are given per unit length defined with thickness; therefore, it is again excluded from the comparison. Superconducting magnets, such as dipoles, do not fit neatly into any of the above options: they are far from thin but deform longitudinally under load. This work reports a comparative study of plane stress and plane strain in the specific case study of a dipole magnet.

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

AbstractWe present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

Using a Multivariate Virtual Experiment for Uncertainty Evaluation with Unknown Variance

Virtual experiments are a digital representation of a real measurement and play a crucial role in modern measurement sciences and metrology. Beyond their common usage as a modeling and validation tool, a virtual experiment may also be employed to perform a parameter sensitivity analysis or to carry out a measurement uncertainty evaluation. For the latter to be compliant with statistical principles and metrological guidelines, the procedure to obtain an estimate and a corresponding measurement uncertainty requires careful consideration. We employ a Monte Carlo sampling procedure using a virtual experiment that allows one to perform a measurement uncertainty evaluation according to the Monte Carlo approach of JCGM-101 and JCGM-102, two widely applied guidelines for uncertainty evaluation in metrology. We extend and formalize a previously published approach for simple additive models to account for a large class of non-linear virtual experiments and measurement models for multidimensionality of the data and output quantities, and for the case of unknown variance of repeated measurements. With the algorithm developed here, a simple procedure for the evaluation of measurement uncertainty is provided that may be applied in various applications that admit a certain structure for their virtual experiment. Moreover, the measurement model commonly employed for uncertainty evaluation according to JCGM-101 and JCGM-102 is not required for this algorithm, and only evaluations of the virtual experiment are performed to obtain an estimate and an associated uncertainty of the measurand. We demonstrate the efficacy of the developed approach and the effect of the underlying assumptions for a generic polynomial regression example and an example of a simplified coordinate measuring machine and its virtual representation. The results of this work highlight that considerable effort, diligence, and statistical considerations need to be invested to make use of a virtual experiment for uncertainty evaluation in a way that ensures equivalence with the accepted guidelines.

Numerical and experimental investigation of extrusion processes from coil

The use of coil material as semi-finished product in sheet-bulk metal forming (SBMF), offers the possibility to use high-speed presses and thus to achieve high output quantities. However, compared to industrially established forming of pre-cut blanks, the reduced geometric accuracy of the parts due to the asymmetrical material flow represents a challenge. Against this background, the aim of this study is to gain an in-depth knowledge on the influence of the process setup on the material flow direction and on the forming of geared functional parts. A combined numerical-experimental approach of each a lateral and a backward extrusion process of a DC04 sheet metal ( t0 = 2 mm) is applied for a fundamental process analysis. For this purpose, process-dependent causes for the asymmetric part forming are analyzed by means of material flow simulations. Subsequently, the numerical results are verified experimentally in terms of the part geometry, the process force and grain structural changes. Based on the numerical and experimental results, cause-effect relations between material flow, part accuracy and tool load are derived. In addition, the transferability of the causes of the asymmetric material flow to processes with different primary material flow directions and part geometries is verified. The findings facilitate the future counteraction of asymmetrical material flow in SBMF from coil, which in turn leads to enhanced component quality.

Macroeconomic determinants of labour costs in the EU: a comprehensive panel and cluster analysis

Abstract Labour costs are a fundamental component of production expenses, significantly impacting both the quantity and quality of output. This study explores the determinants of labour costs within EU member states that have implemented minimum wage policies over the past two decades. The research technique includes a comprehensive panel analysis of EU member states to identify significant variables influencing labour costs, as well as cluster analysis to discover underlying patterns across the nations under examination. Our findings reveal that higher minimum wage levels, higher employment rates, increased labour productivity, and greater trade openness are positively correlated with higher labour costs. Specifically, increases in these variables lead to higher wages and a broader tax base, while greater trade openness results in elevated labour costs due to expanded market opportunities. Conversely, gross fixed capital formation negatively affects labour costs, as investments in production assets tend to reduce labour requirements or hours worked. The cluster analysis led to the identification of three distinct groups. The first cluster consists of well-developed economies with modest labour cost increases and average minimum wages. The second cluster includes countries with substantial labour cost increases, low minimum wages, and significant productivity gains. The third cluster features nations with high minimum wages and high employment rates. This paper contributes to the field by highlighting the complex interplay between labour costs and economic factors, offering insights for decision-makers to tailor macroeconomic and company-level strategies to specific local conditions. The findings emphasise the importance of balancing wage policies with sustainable economic development to enhance competitiveness while ensuring fair labour conditions.

The orthopedics and sports medicine research in India from 2013 to 2022: Comparison with South Asia, European Union, China, and USA

Objectives: In the present study, we explored the research progress in Orthopedics and Sports Medicine within India and South Asia from 2013 to 2022. Materials and Methods: In November 2023, the data were retrieved from Scopus databases. Key indicators such as the number of publications, citations, citations per paper, field-weighted citation impact (FWCI), and distribution across journal quartiles were analyzed. Results: While India demonstrated a substantial contribution to the field, comparative analysis revealed it lags behind China, Europe, and the USA in both quantity and quality of research output. Specifically, India (6,498) and South Asia (6,983) exhibit lower scholarly outputs as compared with China (30,350), European Union (EU) (105,228), and the USA (100,041). Citations per publication reveal gaps, with the USA leading (17), followed by the EU (15.5), China (7.7), and India (7.6). FWCI placed the USA at 1.22, EU at 1.2, and lower values were noted for South Asia (0.74), India (0.70), and China (0.69). Conclusion: The distribution across quartiles indicated that India and South Asia’s comparatively lower emphasis on Q1 (21.30% and 21.72%, respectively), while the EU, China, and the USA demonstrated higher proportions (49.29%, 27.07%, and 53.05%). Conversely, in Q4, India and South Asia (15.04% and 14.87%) have higher concentrations, signaling a potential area for improving research quality. Recommendations include increased funding, interdisciplinary collaboration, investment in advanced technologies, and a focus on high-impact journals.