Processing Cost Research Articles (Page 1)

Abstract Research funding is a key determinant of scientific progress. However, current allocation procedures for third-party funding are criticized due to high costs and biases in the selection. Here, we present data from a large German funding organization on an implementation of a lottery-first approach followed by peer review to allocate funding. We examine the changes in submissions and funded projects of female applicants after implementation, estimate the costs of the overall allocation process, and report on the attitudes and satisfaction of researchers and reviewers. The data show an increase of 10% in submissions and a 23% increase in funded projects from female applicants with the lottery-first approach compared to a previously used procedure. Additionally, the lottery-first approach was estimated to have 68% lower economic costs compared to a conventional single-stage peer review approach. Satisfaction with this funding approach was high and around half of applicants preferred an initial lottery followed by peer review over a conventional approach. Thus, the lottery-first approach is a promising addition to allocation procedures.

Excavation is a common requirement in engineering construction within rock masses. While excavation volumes are generally limited in road slope projects, they may become substantial in large-scale operations such as deep open pit mines. The interaction between time and cost in excavation processes is strongly controlled by rock mass excavatability, which has been recognized as a key factor in project budgets. Since the 1970s, excavatability assessment has therefore attracted considerable research interest in rock mechanics. In this study, the excavatability cases previously plotted on the Geological Strength Index (GSI) versus Uniaxial Compressive Strength of the Rock Mass (σc_rm) diagram in the literature were improved by employing an Artificial Neural Network (ANN). The ANN approach was used to investigate the boundaries between digger, ripper, and hammer+blasting excavation classes within the available case zones defined by GSI–σc_rm data pairs. The prediction performance of the developed rock mass excavatability chart is highly acceptable, with correct classification rates of 91.1% for blasting+hammer and ripper classes, and 87.2% for the ripper class. Considering GSI and σc_rm as the main input parameters, the proposed ANN-oriented excavatability chart is highly acceptable for preliminary equipment selection during the design stage of surface rock mass excavations, including slope cases.

The growing global demand for clean energy and sustainability has increased interest in lignocellulosic biomass as a viable alternative to conventional fossil fuels. Among the various biomass resources, sugarcane bagasse, an abundant agro-industrial by-product, has emerged as a promising feedstock to produce renewable fuels and value-added chemicals. Its high carbohydrate content offers significant potential for bioconversion. However, its complex and recalcitrant lignocellulosic matrix presents significant challenges that necessitate advanced pretreatment techniques to improve enzymatic digestibility and fermentation efficiency. This review consolidates recent developments in the valorization of sugarcane bagasse focusing on innovative pretreatment and fermentation strategies for sustainable bioethanol production. It emphasizes the synergistic benefits of integrating various pretreatment and fermentation methods to improve bioethanol yields, reduce processing costs and enhance overall process sustainability. This review further explores recent technological advancements, the impact of fermentation inhibitor, and emerging strategies to overcome these challenges through microbial strains and innovative fermentation methods. Additionally, it highlights the multi-faceted advantages of bagasse valorization, including waste minimization, renewable energy production and the promotion of sustainable agricultural practices. By evaluating the current state of research and outlining future perspectives, this paper serves as a comprehensive guide to advancing the valorization of sugarcane bagasse in the transition towards a low-carbon economy. The novelty of this review lies in its holistic integration of technological, economic, and policy perspectives, uniquely addressing the scalability of integrated pretreatment and fermentation processes for sugarcane bagasse, and outlining practical pathways for their translation from laboratory to sustainable industrial biorefineries within the circular bioeconomy framework.

In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, which includes both interruption cost and processing cost. To address this problem, we propose ASM-DRL, a deep reinforcement learning approach based on the soft Actor-Critic framework. ASM-DRL introduces an adaptive entropy adjustment mechanism to dynamically balance exploration and exploitation, and adopts a dual-Critic architecture with soft target updates to enhance training stability and reduce Q-value overestimation. Extensive simulations show that ASM-DRL significantly outperforms baseline approaches in reducing service cost.

In research on Spanish-English bilingual code-switching (CS), bilingual compound verbs (BCVs) have been shown to occur at different frequencies in distinct bilingual communities. This paper proposes production frequencies play an important role in the online processing of Spanish-English CS. Sixteen early Spanish-English bilinguals participated in a reading-while-eye-tracking study where they read sentences containing BCVs that are attested in their bilingual community and those that are syntactically plausible, yet unattested in the same community. Participants were expected to show a processing cost for the light verb (LV) condition with the switch occurring at the lexical infinitive, as this switch is unattested in this participant group’s speech community. Participants were also expected to display this sensitivity during earlier stage processing when compared to age-matched learners of Spanish. The results showed a trend of longer reading time for the LV at lexical infinitive switch that became most robust in the spillover region. A z-scored analysis of an offline acceptability assessment of the presented stimuli revealed a strong dispreference for the unattested switch. These results indicate that prior exposure to language had an important impact in online and offline processing of Spanish-English CS.

In recent years, earnings call slide decks (hereafter, earnings decks) have become increasingly common in the earnings announcements of public firms. In this paper, I examine the characteristics of earnings decks and the determinants of firms’ use of them. More importantly, I study their capital market roles. While I fail to find evidence that earnings decks on average offer information incremental to traditional disclosures, such as press releases and conference calls, they significantly reduce users’ information processing time. My analyses reveal three mechanisms that contribute to this acceleration: inclusion of visuals (e.g., charts and tables), summarization of key information, and release prior to earnings calls. Overall, this study documents a novel aspect of earnings announcements and highlights the role of multimedia elements in reducing information processing costs of corporate disclosures. This paper was accepted by Suraj Srinivasan, accounting. Funding: This work was supported by National Natural Science Foundation of China [Grants 72402110, 72442014]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.04956 .

The high electricity and water consumption in industrial textile dyeing processes represents an environmental and economic challenge, requiring optimization strategies to reduce costs and impacts toward cleaner production. This work proposes an optimization model to minimize costs associated with water and electricity consumption in industrial textile dyeing processes. The model has a Mixed Integer Linear Programming (MILP) formulation. The objective function to be minimized is the total process costs. The constraints consider production capacity, daily production limits, and specific costs per material. A case study was conducted in a real industrial process for three types of tissue: cotton, polyester, and polyamide. The model was coded in GAMS and the CPLEX solver was used to solve the problem. The results showed that water consumption accounted for 78.2% of the total cost in the optimal solution. Using the same model, an alternative simulation was performed, replacing four smaller-capacity machines with a single larger-capacity machine, resulting in a marginal reduction in total costs. Simulations were also performed to replace the current machines with highly efficient automated HT (High Temperature) machines, indicating a potential 71.39% reduction in water consumption costs. The conclusion is that the proposed model is effective for optimizing textile dyeing processes, balancing operational efficiency and sustainability, and is applicable in complex industrial scenarios.

To address the demand for lightweight, high-precision, real-time, and low-computation detection of targets with limited samples—such as laboratory instruments in portable AR devices—this paper proposes a small dataset object detection algorithm based on a hierarchically deployed attention mechanism. The algorithm adopts Rep-YOLOv8 as its backbone. First, an ECA channel attention mechanism is incorporated into the backbone network to extract image features and adaptively adjust channel weights, improving performance with only a minor increase in parameters. Second, a CBAM-spatial module is integrated to enhance region-specific features for small dataset objects, highlighting target characteristics and suppressing irrelevant background noise. Then, in the neck network, the SE attention module is replaced with an eSE attention module to prevent channel information loss caused by dimensional changes. Experiments conducted on both open-source and self-constructed small datasets show that the proposed hierarchical Rep-YOLOv8 model effectively meets the requirements of lightweight design, real-time processing, high accuracy, and low computational cost. On the self-built small dataset, the model achieves a mAP@0.5 of 0.971 across 17 categories, outperforming the baseline Rep-YOLOv8 (0.871) by 11.5%, demonstrating effective recognition and segmentation capability for small dataset objects.

Background: The burden of documentation has led to the adoption of Medical Scribes to ease Clinician workloads. However, Scribe duties have evolved beyond note taking; Pre-charting, care-coordination, and other administrative tasks are increasingly completed by Scribes. Moreover, training is time-intensive, and high turnover limits Scribe efficiency and efficacy. The development of Agentic AI, individual AI units capable of processing data and communicating with each other autonomously, presents opportunities to optimize workflows while reducing costs and maintaining proficiency. Methods: Our AI ScribeBot was developed from a modular system of AI agents, each designed with a complex prompt to perform a task, such as extracting clinical information or interpreting visit transcriptions, with outputs passed to subsequent agents in the chain. All data is run through a secure instance of google cloud with a BAA connected to a private instance of Open AI. We compared an Experienced Scribe, a New Scribe, and our AI ScribeBot during 12 outpatient cardiology visits. Scribes, contracted at $27/hr, were timed while Pre-charting and writing notes. Visits were transcribed from ambient audio recording. AI performance was then measured in cost per execution and completion time. A Scribe spent 1 hour reviewing the AI notes and made any necessary corrections. Results: AI documentation demonstrated similar speed and lower cost compared to Scribes. The average time spent per patient was 00:12:34 for the AI, compared to 00:14:30 for a well-trained scribe and 00:24:00 for a newly trained scribe. Additionally, while the Experienced Scribe cost $78.30 per 12-patient day and New Scribe cost $129.60, the AI processing cost was $3.87. Even with the reviewer's hourly rate, the total cost was $30.87. Discussion: Our findings demonstrate that Agentic AI offers a scalable, cost-effective alternative to traditional Scribes. Even with limited human oversight, AI generated notes were completed at lower cost, a 60–76% reduction compared to Human Scribes. Given AI operates concurrently, total completion time was not an accurate measurement of AI performance. However, this asynchronous processing had the added benefit of reducing delays associated with sequential note-writing allowing for improved clinic flexibility and speed. While larger-scale studies are needed to evaluate long-term accuracy and impact, our pilot demonstrates that Agentic AI can reduce documentation costs and enhance efficiency.

The article studies the effect of electrolytic plasma hardening (EPH) on the properties of 20GL steel. The work is aimed at solving an important problem for mechanical engineering and construction industries - increasing the service life of parts made of 20GL cast steel through the use of a highly efficient and relatively new method of electrolytic plasma hardening. The processing modes that increase the microhardness and corrosion resistance of the material are experimentally determined. It is found that EPH promotes the formation of a fine-grained structure and oxide film, improving the surface characteristics. Comparative data on the structure and properties of samples under different EPH modes are presented. The optimal hardening mode (voltage ~300 V, holding time 8-10 s) is a rational choice for cast parts made of 20GL steel, providing a balance between achieving high hardness parameters and a relatively small increase in corrosion activity. The EPH method has shown high applicability in industry as an alternative to traditional heat treatment. The results of the study will improve the methods of heat treatment of 20GL steel, which will contribute to the improvement of the performance characteristics of materials used in mechanical engineering, transport equipment and related industries. The use of electrolytic plasma hardening can reduce the time and energy costs of steel processing and increase the efficiency of the process itself.

ABSTRACT Soft materials, with high elasticity and low glass transition temperatures ( T g s), present significant challenges in fabricating finely structured components via 3D printing due to their inherent softness and slow curing kinetics. Current direct ink writing (DIW) methods for soft polymers typically rely on external stimuli (e.g., light and heat) or precious metal catalysts to ensure structural stability during printing, increasing process complexity and cost. Here, a simple DIW 3D printing strategy for rubber was developed by introducing modified lignin. By virtue of its rigid benzene ring structure and abundant reactive groups, the modified lignin forms covalently bonded crosslinked networks and intermolecular hydrogen bonds with rubber to enhance the viscoelasticity, and thixotropy of the ink. The addition of 30–50 wt% modified lignin increased the modulus of the ink by five orders of magnitude, which resulted in stable self‐supported printing during the printing process. Water‐collecting materials with a bionic cactus spine structure were fabricated utilizing 3D printing, which demonstrated superior capabilities for efficient fog capturing and photothermal evaporation, respectively. By combining these two water‐harvesting methods, a daily cycle can ideally deliver an overall water yield approximately 22 L m −2 , which will providing a high‐performance solution for all‐weather fresh water access.

Is destabilized coupled flat plate membrane technology valuable for high-salinity mine water treatment?

The return of in-person plastic surgery interviews and its effect on applicant preferences.

Strategies for enhancing microalgal carbon sequestration: a review on strain development, culture system optimization, parameter control, and metabolic engineering.

A critical review of the contradictory roles of algal organic matter in microalgae coagulation-flocculation: effects of composition, properties, and mechanisms.

Large language models accurately extract aortic information from abdominal imaging reports in a large, real-world database.

The cement industry is under constant pressure to reduce its environmental footprint while ensuring economic competitiveness and technological reliability. One of the most effective strategies to achieve this goal is the substitution of traditional raw materials with alternative ones derived from industrial (by)products, waste, or secondary resources. This paper presents a structured methodology for the selection and evaluation of potential raw materials for clinker production. The proposed approach integrates four key criteria: physical compatibility, which determines whether the raw material can be handled by existing processing equipment; chemical compatibility, which ensures compliance with clinker quality requirements; environmental compliance, which assesses adherence to local and international environmental regulations; and economic viability, including the costs of material acquisition, processing, equipment adaptation, and CO2 emissions associated with the raw mix. The research procedure involves initial communication with suppliers, visual inspection of the material, laboratory analysis (chemical and environmental), raw mix modelling, and full economic evaluation. If at any stage the material fails to meet the required criteria, feedback is provided to the supplier, avoiding unnecessary costs and efforts. Results indicate that this integrated methodology offers a systematic and transparent making of decision framework that can accelerate the acceptance of alternative raw materials, improve resource efficiency, and contribute to sustainable cement production.

Introduction: The batik industry is a vital part of the creative economy, contributing to increased incomes. However, its production activities also have the potential to cause environmental pollution due to suboptimal waste management. Objective: This study aims to analyze the influencing factors and formulate a strategy for sustainable waste management for batik MSMEs in Jambi City. Methods : using the Analytical Hierarchy Process (AHP) involving 25 respondents consisting of batik craftsmen, government, and community leaders. Results and Discussion: Social factors had the greatest influence (0.362), followed by economic factors (0.268), environmental factors (0.240), and institutional factors (0.130). The main priority sub-criteria were supervision and monitoring (0.616), knowledge (0.510), and waste processing costs (0.481). The most prioritized alternative strategy was the application of natural and environmentally friendly dye technology (0.549), followed by community-based communal wastewater treatment plants (0.228), and multi-party collaborative partnerships (Pentahelix) (0.224). Conclusion: The success of sustainable batik waste management requires synergy between the government, the community, and business actors through a green technology approach and participatory governance

Solid waste biomass utilization is progressively contemplated a practical strategy for sustainable energy supply and long-term environmental care as well as climate action around the world. Consumption & Processing of fruits generates a vast amount of waste, which accounts to 25–30% of the total product. Tamarind seed starch is regarded as a non-edible and low-cost component with several industrial applications. Starch-based waste has been utilized for the production of various value-added products viz. bioethanol, biohydrogen, along with culture development. Lignocellulosic based biomass in terms of seed coat is also helpful in the production of biochar. Present investigation focuses on the extraction of starch from the waste tamarind seed as well as the utilization of its seed coat for biochar synthesis under the holistic utilization approach. It has been observed that HCl treated starch was found quite effective as compared to H 2 SO 4 treatment for the sugar recovery. 2% HCl along with thermal (60 min) starch treatment recovered sugars effectively. Recovered sugars were further utilized for the culture development of Saccharomyces cerevisiae to make the process cost effective. Highest growth (1.574) of S. cerevisiae was achieved at 12 hr. under recovered glucose based on 2% HCl along with thermal treated waste seed starch. Alternatively, Tamarind seed coat biochar was also found quite effective for the decolourization of toxic dyes viz. methyl red and methylene blue. Maximum decolourization percentage (88.74%) was observed with tamarind seed coat biochar (0.5g) of at 3 hr of treatment.

The global reliance on fossil fuels has caused severe environmental challenges, emphasizing the urgent need for sustainable and renewable energy sources. Bioethanol production from lignocellulosic biomass has emerged as a promising alternative due to its abundance, renewability, and carbon-neutral footprint. However, its economic feasibility remains a major obstacle owing to high production costs, particularly those associated with low ethanol titers and the energy-intensive distillation process costs for low titers. High-solid loading processes (≥15% w/w or w/v) have demonstrated potential to overcome these limitations by minimizing water and solvent consumption, enhancing sugar concentrations, increasing ethanol titers, and lowering downstream processing cost. Nevertheless, high-solid loading also introduces operational bottlenecks, such as elevated viscosity, poor mixing, and limited mass and heat transfer, which hinder enzymatic hydrolysis efficiency. This review critically examines emerging pretreatment and enzymatic hydrolysis strategies tailored for high-solid loading conditions. It also explores techniques that improve sugar yields and conversion efficiency while addressing key technical barriers, including enzyme engineering, process integration, and optimization. By evaluating these challenges and potential mitigation strategies, this review provides actionable insights to intensify lignocellulosic ethanol production and advance the development of scalable, cost-effective biorefinery platforms.