cost reductions Research Articles (Page 1)

A low-cost, LED-based SPRi platform with adaptive spectral analysis and localized enrichment for ultrasensitive detection of Tau.

This paper focuses on the synthesis of ethylene from various biomass feedstocks, with ethanol as a transitional chemical platform. Four different production routes are investigated: two first-generation pathways using sugarcane and cereal crops (e.g., corn), and 2 s-generation alternatives based on lignocellulosic materials (e.g., corn stover, wheat straw). An analysis orientated to technological performance and economic feasibility comparison is established in depth. The reference facility is modelled with an annual output of approximately 117,000t of ethylene, which would be suitable for industrial-scale deployment. The project results include material and energy balances, along with a financial evaluation using metrics such as the Net Present Value (NPV) and the Minimum Ethylene Selling Price (MESP). Among all scenarios addressed, no route initially emerges as an economically viable option under current assumptions. However, it can be stated that the sugar-based ethylene production pathway reaches the best economic results, producing an NPV of -€173M€ at current ethylene prices and an MESP of €1265/t of ethylene. The remaining pathways require cost reductions (i.e., reducing biomass purchasing cost, maintenance and overhead, and consumptions) to achieve profitability. Sensitivity analyses state the significant influence of biomass procurement costs and market price fluctuations on economic outcomes. Overall, the findings of this research underscore the promise of bioethylene as a value-added chemical, supporting broader efforts toward decarbonization. • An economic analysis of ethylene synthesis from various feedstocks is presented. • Sugar-based ethylene production path has a net present value of -€173M€. • For this path, the minimum ethylene selling price needed is €1265/t of ethylene. • The cost of biomass is the highest one in all he paths 36 %–59 % of the share.

Enhancing pallet load stability: A MILP model for the Manufacturer’s Pallet Loading Problem with interlocking constraints

ABSTRACT The utilization of concrete structural materials demonstrates significant potential for cost reduction in floating offshore wind turbine platforms owing to their inherent cost-effectiveness. This study proposes a novel steel-concrete hybrid (SCH) semi-submersible platform with a three-column configuration for floating offshore wind turbines. A detailed numerical model integrating aero, hydro, servo, elastic, and mooring dynamics was created to thoroughly examine the platform's dynamic responses in intermediate water depths (65 m). A comparative analysis was performed to evaluate the hydrodynamic performance between the steel-concrete hybrid platform and a geometrically equivalent all-steel semi-submersible platform. Results reveal that the proposed SCH platform exhibits enhanced hydrodynamic characteristics. Specifically, the SCH platform demonstrates superior performance in surge, mooring line tension, and nacelle acceleration when compared to its steel counterpart.

Abstract In today's competitive business landscape, organizations increasingly adopt automation to enhance operational efficiency, ensure financial accuracy, and reduce costs. This study investigates the comparative effectiveness of automated versus manual invoice processing within Vignesh Polymers India Pvt Ltd., a prominent manufacturer of plastic and polymer components. A structured questionnaire was administered to 201 employees drawn from a population of 420 using purposive sampling. To analyze the collected data, statistical tools including ANOVA, Pearson Correlation, Cronbach's Alpha Reliability Testing, and Binary Logistic Regression were applied via SPSS. Results indicate that automated invoicing significantly improves financial accuracy, reduces processing time, and leads to measurable cost savings relative to manual methods. Correlation analysis reveals strong relationships between automation adoption and reductions in data entry errors and human error. Reliability testing confirms the consistency of the instrument (α = 0.838). Binary logistic regression further demonstrates departmental variation in cost-saving success rates. Employee perceptions broadly support automation as a financially beneficial transformation for the organization. The study contributes empirical evidence to the literature on financial process automation within the Indian manufacturing context and offers actionable recommendations for practitioners. Keywords: invoice automation, manual processing, financial accuracy, cost reduction, accounts payable, manufacturing, India

• Bio-methanol hits fossil parity in 2030 s, staying 3-times cheaper than e-methanol. • 10% IRR requires e- and bio-methanol selling prices of >1713 €/t and 955 €/t. • FuelEU and EU ETS enable premiums for bio-methanol; e-methanol needs support. • Reliability of European renewable methanol supply remains policy-dependent. Renewable methanol is a key option for decarbonizing European industrial sectors, particularly the chemical industry and maritime transport. Despite growing project announcements, large-scale deployment remains limited due to high production costs and investment risks. This study assesses the techno-economic feasibility of renewable methanol production in Europe by comparatively evaluating e-methanol, bio-methanol and import pathways within a harmonized modelling framework. Levelized cost of methanol, net present value and internal rate of return calculations are applied across multiple regional scenarios for Germany, Sweden and Portugal, explicitly accounting for policy boundary conditions under the European Emissions Trading System and FuelEU Maritime. Across all scenarios, bio-methanol approaches cost parity with conventional methanol by around 2030 and consistently outperforms e-methanol in terms of cost levels and economic robustness. E-methanol remains substantially more expensive under current conditions, while imports become competitive only after 2040. In the German Baseline Scenario, selling prices of 1079 €/t for bio-methanol and 2169 €/t for e-methanol are required to achieve a target internal rate of return of 10 %. Although regulatory frameworks increase willingness to pay for renewable methanol in shipping, they are insufficient to enable low-risk e-methanol investments without further cost reductions. Overall, the results indicate that renewable methanol deployment in Europe remains structurally conditioned by cost competitiveness and input cost dynamics. Bio-methanol represents the most viable near-term pathway, whereas e-methanol depends on substantial hydrogen cost reductions and sustained policy support to achieve economic viability.

Robotic-assisted surgery is increasingly used in acute care surgery, but the impact of individual surgeon robotic case volume on outcomes for both elective and emergency general surgery procedures remains uncertain. To evaluate the association between annual surgeon robotic case volume and patient outcomes following robotic-assisted elective and emergency general surgery. This retrospective cohort study used data from the Premier Healthcare Database (PHD), a large US all-payer hospital database, from January 2021 to December 2023. The PHD aggregates data from nonprofit, community, and teaching hospitals across rural and urban areas, representing 25% of all US inpatient admissions. Adult patients (aged ≥18 years) undergoing robotic-assisted cholecystectomy, colectomy, appendectomy, small bowel resection, or ventral hernia repair were included. Annual surgeon-level robotic case volume, categorized as low (≤25), intermediate (26-75), high (76-150), or very high (≥151). The primary outcome was conversion to open surgery; secondary outcomes included postoperative complications, intensive care unit (ICU) admission, 30-day readmission, operative time, hospital length of stay, total hospital cost, and in-hospital mortality. Multivariable logistic and linear regression models, respectively, were used to estimate adjusted odds ratios (AORs) and mean ratios with 95% CIs. Models adjusted for patient demographics, hospital characteristics, and surgeon specialty. Among 185 924 patients undergoing robotic procedures (137 879 elective and 48 045 emergency), most (58.2%) were female (57.1% of elective and 61.5% of emergency cases). Mean (SD) patient age was 54.9 (16.6) years overall (55.6 [15.8] years for elective and 53.0 [18.5] years for emergency procedures). In elective procedures, increasing annual surgeon volume was associated with stepwise improvements across most outcomes; compared with low volume surgeons, very high volume surgeons had lower odds of conversion to open surgery (AOR, 0.45; 95% CI, 0.36-0.56), complications (AOR, 0.87; 95% CI, 0.79-0.96), readmission (AOR, 0.79; 95% CI, 0.68-0.91), and ICU admission (AOR, 0.61; 95% CI, 0.46-0.82). Operative time (mean ratio, 0.77; 95% CI, 0.75-0.79), hospital length of stay (mean ratio, 0.89; 95% CI, 0.88-0.91), and costs (mean ratio, 0.83; 95% CI, 0.82-0.84) were also significantly lower. In emergency procedures, very high vs low surgeon volume was associated with lower odds of conversion to open surgery (AOR, 0.73; 95% CI, 0.54-1.00) and modest reductions in operative time (mean ratio, 0.88; 95% CI, 0.85-0.91) and cost (mean ratio, 0.92; 95% CI, 0.89-0.94). No association was observed between surgeon volume and in-hospital mortality in either cohort. In this cohort study, greater annual surgeon robotic case volume was associated with better patient outcomes in elective general surgery and, to a lesser degree, in emergency procedures. These findings highlight the importance of surgeon-specific experience in robotic surgery and may inform training, credentialing, and strategies for safe expansion of robotic capabilities in acute care surgery.

Adaptive optimization of combined steam and CO2 reforming for hydrogen production from variable biogas feed.

• Following DRG implementation, the cost structure for hospitalized children with Kawasaki disease underwent moderate adjustments, with stable medical insurance reimbursement rates. • The positive correlation between length of stay and total costs slightly weakened, accompanied by a minor decrease in average hospitalization days. • DRG demonstrated more pronounced cost changes for male patients and older children aged 36 months and above. Kawasaki disease (KD), prevalent among children, represents a significant public health concern globally. Epidemiological evidence suggests that the incidence of KD has increased over the past few decades, imposing considerable economic burdens on families attributable to hospitalization costs. This study utilized descriptive statistics, non-parametric tests, Spearman correlation analysis and multivariate generalized linear models on discharge data (2020-2022) from KD inpatients to explore the association between the Diagnosis-Related Group (DRG) system in China and hospitalization costs (in CNY), specifically comparing the total costs and cost composition before and after DRG implementation and examine the differences between subgroups. After DRG implementation, the median total hospitalization cost showed an upward trend, and the medical insurance reimbursement amount also increased simultaneously. Significant cost reductions occurred in material costs, while Western medicine costs increased. The cost structure changed modestly. The correlation between length of stay and total hospitalization cost remained significant but weakened slightly. Reimbursement rates remained stable and cost changes were more pronounced in males and older children (> 36 months), although these interaction effects were not statistically significant in the model. DRG effectively standardizes specific costs and alters cost structure. Under the premise of maintaining a stable medical insurance reimbursement ratio, the direct burden on patients had not increased. Its impact varies by age and gender, suggesting that refined DRG grouping criteria could improve policy precision for pediatric KD patients.

A smart building HVAC control competition crowdsourced and compared algorithms on fair and equal ground using the standardized BOPTEST framework. The competition attracted 138 participants, but only 9% submitted valid solutions for the final stage, highlighting the complexity of advanced HVAC control design. The winning solutions showed significant potential to reduce energy use and cost by shifting demand, without compromising occupant comfort. Across scenarios, thermal energy cost reductions of 36–76% relative to a baseline, were achieved. In peak heat periods, the cost reduction leveraged limited energy use reduction (0–15%), but more significant energy price reduction (34–62%). This shows smart controls' ability to avoid as much as possible consumption during the morning peak hours, when spot prices are tendentially the highest. Hosting the competition has highlighted challenges in creating competitions that both are fair and promotes solutions that are transferable to real life implementation.

Industrial decarbonisation requires the large-scale integration of renewable energy into energy-intensive processes traditionally characterised by limited flexibility, high heat demands, and strong dependence on fossil fuels. In this context, energy storage, encompassing thermal and electrical storage as well as hydrogen as an energy carrier, emerges as a key enabling solution to reconcile variable renewable supply with industrial process demands. This paper proposes a dynamic techno-economic framework linking sectoral energy profiles to storage sizing and economic performance in industrial renewable integration. Storage technologies are assessed with hydrogen emerging as a long-duration buffer and a solution for decarbonising high-temperature heat. A representative industrial plant with 5 GWh/y energy demand and an 80%/20% thermal-to-electric load split is analysed under increasing solar-to-load ratios (20–60%), with storage technologies evaluated both individually and in hybrid configurations. Results demonstrate that hybrid battery–hydrogen configurations systematically outperform single-technology solutions. Yearly energy cost reductions reach 16.6–33.8% at 20% renewable penetration, 30.0–49.6% at 40%, and 43.4–62.8% at 60%, with advantages over the best standalone option increasing on average from 13.5% to 28.0% as renewable availability rises. Overall, the study identifies scale-dependent feasibility thresholds and highlights small and medium-sized industrial plants as the most actionable deployment context under current technological and market conditions.

The work uses a comprehensive approach based on the information and communication concept of construction management templates to minimize information asymmetry between construction stakeholders when implementing innovative technologies. An analysis of the regulatory framework and patent research of existing analogs of wall structures was conducted. It was theoretically substantiated that the use of removable reusable formwork for monolithic walls made of expanded polystyrene concrete allows significant reduction in cost and logistics costs. A technology for erecting heat-insulating walls made of expanded polystyrene concrete (EPC) has been developed, which involves preliminary preparation of the insulation with the application of a protective reinforced layer. This allows avoiding performing labor-intensive and dangerous operations at height. A design of a noise-proof wall with sound-absorbing hollow-forming elements has been proposed, improving acoustic characteristics while saving materials. Thermophysical tests of fragments of walls made of expanded polystyrene concrete with a density of D250 (thickness of 260 mm) confirmed the need for additional insulation for heat transfer resistance for regulatory compliance. Acoustic studies have proven the effectiveness of using hollow-forming elements to increase the airborne noise insulation index and to reduce material consumption. All this helped to develop and patent the polystyrene concrete wall technology. For the first time, the concept of implementing the technological process of expanded polystyrene concreting of monolithic walls into construction management and production using construction management templates was proposed. This allowed the transformation of technological operations into a flow of objective data to minimize information asymmetry between project participants. It was theoretically proven that the objectification of production indicators through construction management templates is a base for measuring the commercial value and investment attractiveness of the technology being implemented.

This article proposes a simple and reliable Dynamic Economic Emission Dispatch (DEED) model for a power system integrating Renewable Energy Sources (RES), including photovoltaic (PV) systems and wind energy. The thermal units in the system under concern are operating under limited fuel constraints. The proposed DEED effectively manages limited fuel conditions through Dynamic Generation Capacity (DGC). The Walrus Optimization Algorithm (WaOA) and Marine Predators Algorithm (MPA) are competing for the implementation of the proposed DEED with DGC and RES. The performance of the competing optimizers is compared against a mature meta-heuristic optimizer, Particle Swarm Optimization (PSO). The MPA is the most promising candidate, significantly outperforming the contenders, WaOA and PSO. For the 1000MW load scenario, MPA and WaOA achieved a reduction in generation cost compared to PSO. Moreover, MPA substantially reduced the variability of results, demonstrating superior consistency compared to the competing optimizers. The proposed energy management strategies are tested on a system with 10 units under various operating scenarios. The results showed that the RES for power systems with limited/shortage fuel scenarios were insufficient to meet the required load, as fuel shortages tend to occur suddenly and unpredictably, thereby reducing system security. The DEED approach, incorporating both DGC and RES, achieved a notable reduction in fuel costs and emissions compared to the full fuel scenario and the fuel shortage scenario without RES. The results highlight the potential of integrating both DGC and RES into the DEED framework, demonstrating effectiveness in reducing operational costs and emissions while simultaneously enhancing system security and contributing to the development of a sustainable, intelligent future power grid.

ABSTRACT Lean manufacturing has emerged as a transformative operational philosophy aimed at maximizing value while minimizing waste across production systems. Rooted in the principles of continuous improvement, waste elimination, and respect for people, lean practices have significantly influenced operational efficiency in modern industries. This study explores the impact of lean manufacturing techniques such as Just-In-Time (JIT), Kaizen, Six Sigma integration, value stream mapping, and standardized work processes on improving productivity, reducing lead time, enhancing quality, and optimizing resource utilization. The research highlights how lean practices contribute to cost reduction, process stability, and improved customer satisfaction. By analyzing operational performance metrics, the study demonstrates that organizations implementing lean principles experience enhanced efficiency, reduced variability, and better adaptability to dynamic market demands. Furthermore, the study emphasizes the importance of organizational culture, employee involvement, and leadership commitment in ensuring successful lean implementation. The findings provide valuable insights for manufacturing firms aiming to improve competitiveness through operational excellence. Keywords: Lean Manufacturing, Operational Efficiency, Waste Reduction, Continuous Improvement, Just-In-Time, Kaizen, Productivity, Process Optimization

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a 1.9% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises.

This study critically evaluates conventional, reduced, and zero tillage systems with a focus on soil and water conservation, residue management, and sustainability in global agriculture and the Indo-Gangetic Plains. A systematic narrative review approach was used, synthesizing peer-reviewed literature (1990-2025) from major scientific databases. Results indicate that zero tillage systems, particularly Happy Seeder-based practices, can enhance soil aggregation, increase infiltration, and improve moisture retention through residue mulching. Quantitative evidence suggests yield gains of 6.8-17%, cost reductions of up to 29%, irrigation savings of 20-30%, and greenhouse gas emission reductions exceeding 50% compared to conventional systems. However, outcomes vary across soil types, climatic conditions, and management practices. Key limitations include herbicide dependence, weed resistance, and socio-economic constraints affecting adoption. The study concludes that conservation tillage offers significant potential for climate-smart agriculture, but its effectiveness depends on region-specific adaptation, integrated nutrient and weed management, and supportive policy frameworks.

The adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMBs) has emerged as a key strategy for improving quality management. This article discusses the main benefits and challenges of implementing AI in this context. A systematic review of the academic literature and relevant business reports was carried out, in order to collect and analyze empirical evidence on the application of AI in the quality management of SMEs, obtaining as results that the main benefits of the implementation of AI in the quality management of SMEs include: Improved data-driven decision-making, process automation and error reduction, early detection of quality issues, customization of products and services, and resource optimization and cost reduction. On the other hand, the main challenges were: Lack of knowledge and skills in AI, high implementation costs, concerns about data security and privacy, resistance to cultural change, and integration of AI with existing systems and processes, concluding that the implementation of AI in SMB quality management offers significant benefits, but it also entails challenges that must be addressed strategically. SMEs that manage to overcome these challenges will be able to take advantage of the competitive advantages offered by AI, improving their efficiency, quality and adaptability in an increasingly dynamic business environment.

Background and Objective: Farm mechanization plays a critical role in enhancing agricultural productivity. However, limited empirical evidence exists on how farmers’ operational experience influences their perceptions of machinery efficiency and advantages. This study aimed to evaluate farmers’ perceived efficiency and advantages of farm machinery and to examine the relationship between operational experience and these perceptions among paddy rice farmers in Nong Saeng district, Saraburi province, Thailand.Methodology: A cross-sectional survey was conducted with 93 paddy rice farmers using a structured questionnaire. Perceived efficiency and advantages across six dimensions were assessed using a 5-point Likert scale. Data were analyzed using descriptive statistics and Spearman’s rank correlation coefficient to determine relationships between years of operational experience and perception variables.Main Results: Farmers generally rated farm machinery efficiency as high, with tractors receiving the highest scores, while trucks showed comparatively lower evaluations. Tractors were the only machinery consistently rated “very good” across all advantage dimensions. “Increased working speed” was the most positively perceived benefit across all machinery types. Correlation analysis revealed a significant negative relationship between operational experience and perceived efficiency for tractors (rs = -0.361, P < 0.05). Additionally, longer experience with tractors was negatively associated with perceived advantages in multiple dimensions, including labor reduction, productivity, output quality, and cost reduction. Similar negative correlations were observed in selected dimensions for other machinery types.Conclusions: The findings indicate a perception gap between less experienced and more experienced users, with recent adopters exhibiting more favorable perceptions. This suggests the need for targeted extension strategies that consider farmers’ experience levels to enhance perception accuracy and support sustained adoption of mechanization.

Non-surgical casting versus surgical reduction for children with severely displaced distal radial fractures (the CRAFFT Study): a multicentre, randomised, controlled non-inferiority trial and economic evaluation.

Developing a scientific decision optimisation framework is essential for improving resource efficiency in bridge maintenance. This study proposes a multi-level group maintenance optimisation framework for bridge systems based on a genetic algorithm with agglomerative mutation (GA-A). The framework integrates component-level deterioration simulation using a continuous-time Markov chain, system-level cost savings considering dual economic-structural correlations, and optimisation of dynamic group scheduling. By incorporating an agglomerative mutation operator, the GA-A algorithm enables adaptive regrouping, effectively addressing the non-deterministic polynomial (NP)-hard problem of dynamic maintenance scheduling. A case study on a bridge system demonstrates that the GA-A-based strategy significantly outperforms independent maintenance approaches, achieving higher cost savings and lower system downtime, particularly under strong structural correlations. When the structural correlation coefficient increases from 0 to 1.0, the optimal group maintenance strategy for the bridge system achieves cost reductions ranging from 6.68% to 19.35% compared with the independent maintenance strategy. The proposed framework effectively addresses current gaps in cost-savings assessment, resource allocation and dynamic scheduling for bridge system maintenance.