Efficiency Improvement Research Articles

The Self-Improving Customer Service System: How AI Learns from Every Interaction

This article explores the transformative impact of self-improving AI systems in customer service, examining the parallels between AI and human learning processes, the mechanics of AI self-improvement, and the advantages of AI's learning at scale. It delves into real-world applications and impacts across various industries, highlighting improvements in personalization, efficiency, accuracy, customer satisfaction, and cost reduction. The article also addresses ethical considerations such as data privacy, transparency, and bias mitigation, while offering a future outlook on advanced AI capabilities in emotional intelligence, predictive service, and seamless human-AI collaboration. Through a comprehensive analysis of current technologies and future possibilities, this study provides insights into how self-improving AI is revolutionizing customer service, promising more efficient, personalized, and satisfying experiences for customers.

Monte Carlo Modeling of a High‐Efficiency Tandem Luminescent Solar Concentrator Containing a Polarization Volume Grating Layer

This article develops a Monte Carlo model to optimize a newly introduced tandem luminescent solar concentrator. This innovative structure comprises two parallel transparent polymeric waveguides separated by an air gap. The first waveguide, which is exposed to sunlight, contains fluorophores and performs as a traditional luminescent solar concentrator. In contrast, the second waveguide is equipped with an inner polarization volume grating layer, strategically placed to couple the emitted photons within the escape cone, directing them into the second waveguide and preventing reabsorption. The finite difference time domain method is employed to optimize the performance of this grating. The results show a significant improvement in external photon efficiency compared to the conventional luminescent solar concentrator.

Cycle analysis and environmental assessments of cascade organic rankine cycle on diesel engine ships

This study investigates the application of organic Rankine cycle (ORC) on ships to reduce fuel oil consumption and assess improvements in the Energy Efficiency Existing Ship Index (EEXI), and carbon intensity indicator (CII). A cascade ORC(C-ORC) configuration is designed to utilize the exhaust gas from a diesel engine.Thermodynamic simulation identified a combination of toluene and R1233zd as the optimal working fluids for the C-ORC. Exergy analysis was conducted to evaluate the performance difference between the C-ORC system and a version with an internal heat exchanger (C-IHX ORC). The results showed a maximum exergy efficiency of 56.78 % for the C-IHX ORC, compared to 49.33 % for the C-ORC. Further evaluations of the C-IHX ORC, including fuel savings, energy generation, EEXI, and CII, demonstrated that average vessel fuel consumption decreased by 1.02 %, and average attained EEXI improved by 0.93 %. Additionally, the attained CII value was enhanced by 0.98 % on average, depending on operating loads and traveled distances. Although the improvements of EEXI, and CII offered were modest, they present the potential for further enhancement through the utilization of additional waste heat sources, such as economizer steam, and jacket cooling water.

The effects of digital economy on manufacturing resource allocation efficiency across Chinese cities: a spatial economic perspective

ABSTRACT The development of digital economy provides significant impacts on spatial distribution of industries. Using microdata from Chinese manufacturing enterprises and adopting a spatial economic perspective, this study reveals that the digital economy exerts non-linear impacts on resource allocation. As the level of urban digital economy improves, the efficiency of manufacturing resource allocation shows an inverted U-shaped trend, and high hierarchy cities exhibit higher critical values. The study finds a positive spatial correlation in manufacturing resource allocation efficiency across cities; however, the growth of digital economy in neighbouring cities may lead to backwash effects and impede local improvements in allocation efficiency. The mechanism analysis indicates that the digital economy primarily affects spatial resource allocation through urban agglomeration economies and diseconomies. Consequently, economic agents and policy makers in cities should consider the dual effects of ‘location optimization’ and ‘transforming the economy from substantial to fictitious’ during digital transformation processes to select optimal development paths and improve allocation efficiency.

Visualizing the Structure and Dynamics of Transition Metal-Based Electrocatalysts Using Synchrotron X-Ray Absorption Spectroscopy.

As the global energy structure evolves and clean energy technologies advance, electrocatalysis has become a focal point as a critical conversion pathway in the new energy sector. Transitional metal electrocatalysts (TMEs) with their distinctive electronic structures and redox properties show great potential in electrocatalytic reactions. However, complex reaction mechanisms and kinetic limitations hinder the improvement of energy conversion efficiency, highlighting the necessity for comprehensive studies on structure and performance of electrocatalysts. X-ray Absorption Fine Structure (XAFS) spectra stand out as a robust tool for examining the electrocatalyst's structures and performance due to its atomic selectivity and sensitivity to local environments. This review delves into the application of XAFS technology in characterizing TMEs, providing in-depth analyses of X-ray Absorption Near-Edge Structure (XANES) spectra, and Extended XAFS (EXAFS) spectra in both R-space and k-space. These analyses reveal intrinsic structural information, electronic interactions, catalyst stability, and aggregation morphology. Furthermore, the paper examines advancements in in-situ XAFS techniques for real-time monitoring of active site changes, capturing critical intermediate and transitional states, and elucidating the evolution of active species during electrocatalytic reactions. These insights deepen our understanding on structure-activity relationship of electrocatalysts and offer valuable guidance for designing and developing highly active and stable electrocatalysts.

Optimum Planning of Carbon Capture and Storage Network Using Goal Programming

Carbon capture and storage (CCS) is a critical technology used for mitigating climate change by capturing carbon dioxide emissions from industrial sources and storing them underground to prevent their release into the atmosphere. Despite its potential, optimizing CCS systems for cost-effectiveness and efficiency improvement remains a significant challenge. In this paper, the optimization of CCS systems through the development and application of two mathematical optimization techniques is introduced. The first technique is based on using a superstructure optimization model, while the second technique relies on applying a goal programming optimization model. These models were solved using LINGO software version API 14.0.5099.166 to enhance the efficiency and cost-effectiveness of CCS systems. The first model, seeking to maximize the exchange of CO2 flowrate from sources to sinks, achieved a CO2 capture rate of 93.36% with an annual total cost of USD 1.175 billion. The second model introduced a novel mixed-integer non-linear programming (MINLP) approach for multi-objective optimization, targeting the minimization of total system cost, alternative storage, and unutilized storage while maximizing CO2 load exchange. The application of the second model, when prioritized to maximize CO2 flowrate exchange using the goal programming technique, resulted in a cost reduction of 36.46% and a CO2 capture rate of 75.87%. In contrast, when the second model prioritized minimizing the total annual cost, a 48% cost reduction was achieved, and the CO2 capture rate was decreased by 68.37%. A comparison of the two models’ results is presented. The results showed that the second model, with the priority of maximizing CO2 capture, provides the best economic–environmental objective balance, which offers notable cost reductions while keeping an efficient CO2 capture rate. This study highlights the potential of advanced mathematical modeling in increasing the feasibility of CCS as one of the very important strategies of mitigating climate change and reducing global warming.

Emergency Care Efficiency vs. Quality: Uncovering Hidden Consequences of Fast-Track Routing Decisions

Problem definition: This work aims to examine the role of emergency department (ED) operational status related to congestion in fast-track (FT) routing decisions and the subsequent effects on patient outcomes. Methodology/results: In this paper, we utilize a two-year data set from two hospital EDs in Alberta, Canada, and adopt an instrumental variable approach to examine the effects of FT routing decisions on patient outcomes. Based on the empirical findings, we utilize a data-calibrated simulation to compare the performance of different routing policies. First, our study reveals that FT routing decisions are not purely clinically driven, and ED operational status is also associated with FT routing decisions. Second, being routed to FT can improve ED efficiency by reducing the average length of stay and left without being seen rates. However, this efficiency improvement comes at the cost of potential quality decline. In particular, being routed to the FT leads to an 8.2% increase in the 48-hour revisit rate for the high-complexity group and a 2.3% increase for the medium-complexity group. Third, we delve into the mechanisms behind observed patient outcomes and find that physicians in the FT area may prioritize expediting patient flow by simplifying patient diagnosis and treatment procedures. Consequently, the quality of care may be compromised for high- and medium-complexity patients. Finally, our simulation findings highlight the importance of selecting the “right” patients to be routed to the FT unit. To this end, the complexity-based classification method and dynamic routing policies emerge as promising avenues. Managerial implications: Our findings call for immediate attention from healthcare practitioners to carefully balance the trade-off between emergency care efficiency and quality, emphasizing the necessity of selecting the right patients for routing. Funding: This study is partially supported by the Hong Kong Research Grants Council [Grants GRF 11508921 and CRF C7162-20G]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0440 .

Improvement of Productivity, Quality and Nutrient Use Efficiency of Indian Spinach (Basella Alba L.) by Organic and Inorganic Nutrition

Improvement of Productivity, Quality and Nutrient Use Efficiency of Indian Spinach (Basella Alba L.) by Organic and Inorganic Nutrition

Transformative Impact of AI and Cloud Technologies: A Comparative Analysis across Healthcare, Retail, and Mobile Financial Services

This article examines the transformative impact of artificial intelligence (AI) and cloud technologies across healthcare, retail, and mobile financial services sectors. Through a comprehensive analysis of industry-specific applications, we explore how these technologies are revolutionizing operational efficiency, customer experiences, and innovation in each domain. In healthcare, we investigate the role of CI/CD automation and scalable cloud infrastructure in improving system reliability and patient care. The retail sector analysis focuses on AI-driven customer insights and supply chain optimization. For mobile financial services, we examine AI applications in fraud detection and real-time analytics. The findings reveal significant improvements in operational efficiency, decision-making processes, and customer satisfaction across all three industries. However, we also identify unique challenges and opportunities specific to each sector. This article contributes to the growing body of literature on digital transformation and provides valuable insights for industry professionals and policymakers navigating the integration of AI and cloud technologies in their respective fields.

A Python GPU-accelerated solver for the Gross-Pitaevskii equation and applications to many-body cavity QED

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

Are Climate Change Strategies Effective in Managing Urban Water Resources? The Case of Portugal

This study examines the relationship between climate mitigation, adaptation strategies, and water management practices in Portugal from 2015 to 2021. Utilizing climate and water resource data from 2015 to 2021, including meteorological data (emperature, rainfall), wastewater treatment volumes, and energy efficiency metrics, the data are sourced from national agencies such as IPMA, ERSAR, APA, and Eurostat. The methodology employs correlation analysis to assess the relationships between climate variables (e.g., temperature, rainfall) and water resource indicators (e.g., reclaimed wastewater, energy efficiency). Despite notable reductions in greenhouse gas emissions and improvements in wastewater treatment efficiency, water resource stability remains a complex issue, particularly with regional disparities such as severe droughts in the Algarve. Additionally, the study evaluates the effectiveness of rainwater harvesting systems, reclaimed wastewater, and infiltration facilities, revealing a decline in reclaimed wastewater efficiency despite increased wastewater treatment. Rainwater harvesting systems (RWHSs) offer resilience, but their broader adoption is hindered by high costs and public perception challenges. Key recommendations include the development of resilient infrastructure, enhanced support for reclaimed water use, and increased investment in research to address water management challenges amid climate variability.

Codebase release 1.0 for TorchGPE

TorchGPE is a general-purpose Python package developed for solving the Gross-Pitaevskii equation (GPE). This solver is designed to integrate wave functions across a spectrum of linear and non-linear potentials. A distinctive aspect of TorchGPE is its modular approach, which allows the incorporation of arbitrary self-consistent and time-dependent potentials, e.g., those relevant in many-body cavity QED models. The package employs a symmetric split-step Fourier propagation method, effective in both real and imaginary time. In our work, we demonstrate a significant improvement in computational efficiency by leveraging GPU computing capabilities. With the integration of the latter technology, TorchGPE achieves a substantial speed-up with respect to conventional CPU-based methods, greatly expanding the scope and potential of research in this field.

Simulation and Optimization of a Hybrid Photovoltaic/Li-Ion Battery System

The coupling of solar cells and Li-ion batteries is an efficient method of energy storage, but solar power suffers from the disadvantages of randomness, intermittency and fluctuation, which cause the low conversion efficiency from solar energy into electric energy. In this paper, a circuit model for the coupling system with PV cells and a charge controller for a Li-ion battery is presented in the MATLAB/Simulink environment. A new three-stage charging strategy is proposed to explore the changing performance of the Li-ion battery, comprising constant-current charging, maximum power point tracker (MPPT) charging and constant-voltage charging stages, among which the MPPT charging stage can achieve the fastest maximum power point (MPP) capture and, therefore, improve battery charging efficiency. Furthermore, the charge controller can improve the lifetime of the battery through the constant-current and constant-voltage charging scheme. The simulation results indicate that the three-stage charging strategy can achieve an improvement in the maximum power tracking efficiency of 99.9%, and the average charge controller efficiency can reach 96.25%, which is higher than that of commercial chargers. This work efficiently matches PV cells and Li-ion batteries to enhance solar energy storages, and provides a new optimization idea for hybrid PV/Li-ion systems.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Embedding Quantum Random Phase Encoding Arnold Transform for Advanced Image Security

Purpose: This research proposes an improvised version of the image encryption technique by incorporating Quantum Random Phase Encoding with the Arnold Transform to help enhance the strength and non-predictability of the encryption process. In this research work, some ideas gained from quantum-based methods have been brought to use with conventional approaches in image encryption techniques for enhancing their security. Methods: This model represents the basic methodology that underlies the Arnold Transform for scrambling the arrangement of image pixels to mask recognizable structures within quantum random phase encoding to introduce complexity through quantum-generated random phases. Result: The experimental results show much improvement in encryption efficiency. For example, in the case of "Cameraman" and "Lena", MSE parameters are 98.134 and 104.76, respectively; these now go up to 832.01 and 888.78. This implies that the higher decrement of these values 21.17 dB and 23.98 dB to 13.41 dB and 13.33 dB translates into higher distortion with higher security. Meanwhile, UACI and NPCR are also very steady and the mean value is about 0.3356 to 0.3358 and 99.60 to 99.61, which proves that this method has been effective in changing the pixel's value, and sensitive input changes. Novelty: This work is novel due to the introduction of quantum technologies in the classical methodology of image encryption. While classical techniques make use of conventional transforms for scrambling, like the Arnold Transform, this work embeds quantum randomness and intricacy in the process as a means of encoding namely, Quantum Random Phase Encoding.

CMMI & Lean-Based Metrics Governance Model to Ensure Data Accuracy & Consistency for Ai/Ml Applications for Sustainable Operations in Mining Industry

In today’s competitive business environment, even small improvements in productivity and efficiency can have a huge impact on company’s profitability. Whether it is through reduced lead time/waste, minimized downtime or improved quality, operation managers are looking for any advantage at every stage. Lean management is a long-term operational discipline that methodically seeks to enhance efficiency and quality by eliminating wastage. The concept is being used in varied industries and helped by improved productivity, safety & better management. Lean in being used by few mining companies also at a small scale, which has helped them in limited way. AI/ML is another important technology used by many organizations. Many global mining companies have also used it for improving operational efficiency, which have helped miners in a limited ways in improving the productivity and safety. Mining companies have to deal with tons of data set generated from heavy machinery and plants, which have to be stored, processed, and stored. There are many challenges in the data set generated/used by AI/ML-application for mining operations. One of the key challenges in AI/ML is accessibility and availability of accurate & consistent data. To ensure consistency of data there is a need for a governance structure which will guarantee the availability of required accurate data set for AI/ML system. One possible solution could be, CMMI’s metrics processes used by IT companies and have helped in improving quality. CMMI’s Metrics related processes are being used extensively by the organizations for better monitoring & control of key measures. This paper explores the possibility of usage of CMMI’s metrics practices with lean implementation in mining for ensuring availability of accurate data sets for AI/ML applications. The proposed framework will put a governance model in place to ensure accuracy & consistency of data to be used for AI/ML-applications in Mining.

High‐Throughput Absorbance‐Activated Droplet Sorting for Engineering Aldehyde Dehydrogenases

AbstractRecent decades have seen a dramatic increase in the commercial use of biocatalysts, transitioning from energy‐intensive traditional chemistries to more sustainable methods. Current enzyme engineering techniques, such as directed evolution, require the generation and testing of large mutant libraries to identify optimized variants. Unfortunately, conventional screening methods are unable to screen such large libraries in a robust and timely manner. Droplet‐based microfluidic systems have emerged as a powerful high‐throughput tool for library screening at kilohertz rates. Unfortunately, almost all reported systems are based on fluorescence detection, restricting their use to a limited number of enzyme types that naturally convert fluorogenic substrates or require the use of surrogate substrates. To expand the range of enzymes amenable to evolution using droplet‐based microfluidic systems, we present an absorbance‐activated droplet sorter that allows droplet sorting at kilohertz rates without the need for optical monitoring of the microfluidic system. To demonstrate the utility of the sorter, we rapidly screen a 105‐member aldehyde dehydrogenase library towards D‐glyceraldehyde using a NADH mediated coupled assay that generates WST‐1 formazan as the colorimetric product. We successfully identify a variant with a 51 % improvement in catalytic efficiency and a significant increase in overall activity across a broad substrate spectrum.

Improvement Schemes for Bacteria in MICP: A Review

Biomineralization is a common phenomenon in nature, and the use of microbial-induced calcium carbonate precipitation (MICP) technology for engineering construction is a successful attempt to utilize natural biological phenomena, which has become a hot topic of current research. There are many factors affecting MICP, such as bacterial properties and external environmental factors. Many scholars have carried out a lot of research on these factors, but even under appropriate conditions, the MICP process still has the problem of low efficiency. According to different engineering, the tolerance and effect of bacteria in different environments are also different. At the same time, the cultivation and preservation of bacteria will also consume a large amount of raw materials, which is far more significant than the cost of engineering construction. The efficiency and cost limit the large-scale application of this technology in practical engineering. In response to these problems, researchers are exploring new ways to improve the efficiency of MICP technology. Based on the bacteria used in MICP, this paper explores the mechanism of bacteria in the process of MICP and reviews the improvement of bacteria from the perspective of efficiency improvement and economy.

AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

This article explores the multifaceted applications of Artificial Intelligence (AI) technologies in precision agriculture, focusing on their potential to significantly enhance crop yields while optimizing resource utilization. The article examines five key areas where AI is making substantial impacts: predictive analytics for crop management, intelligent irrigation systems, automated pest and disease detection, precision fertilizer application, and robotic harvesting. By integrating data from various sources and employing advanced machine learning algorithms, these AI-driven systems demonstrate remarkable improvements in efficiency, accuracy, and sustainability. The article highlights significant advancements, such as a 15% improvement in yield prediction accuracy, up to 30% reduction in water usage, and a 20% decrease in fertilizer use without compromising crop yields. While acknowledging challenges such as data privacy concerns and initial investment costs, the article underscores the long-term benefits of AI adoption in agriculture, including increased profitability, environmental sustainability, and improved food security. This comprehensive analysis provides insights into how AI-driven precision agriculture is reshaping modern farming practices and its potential to address global food production challenges while reducing agriculture's environmental footprint.

Intelligent Optimization and Impact Analysis of Energy Efficiency and Carbon Reduction in the High-Temperature Sintered Ore Production Process

The coordinated optimization of energy conservation, efficiency improvement, and pollution reduction in the sintering production process is vital for the efficient and sustainable development of the sintering department. However, previous studies have shown shortcomings in the multi-objective collaborative optimization of sintering systems and the quantification of pollutant impacts. To address these, this paper proposes a multi-objective optimization method integrated with the NSGA-III algorithm and establishes an integrated system optimization model for sintered ore production and high-temperature waste heat recovery. The results demonstrate significant improvements: energy utilization efficiency increased by 0.67%, energy consumption decreased by 17.3 MJ/t, production costs were reduced by 11.45 CNY/t, and the emissions of CO2, SO2, and NOx were reduced by 0.464 kg/t, 0.034 kg/t, and 0.008 kg/t, respectively. Additionally, the study identified optimal configuration parameters and analyzed the quantitative impact of several key factors on multiple indicators. The results also show that reducing the water content of the mixture, decreasing the middling coal content in the fuel, and increasing the thickness of the material layer are effective strategies to reduce energy consumption and pollutant emissions in the sintering process. Overall, implementing these optimizations can bring significant economic and environmental benefits to the steel industry.