Efficiency Improvement Research Articles

Growth of Life and Non-life Insurance Market in India

In the midst of high cost of health care both at the macro and micro levels, health insurance becomes a viable alternative for financing health care in India. Health insurance is fast emerging as an important mechanism to finance health care needs of the people. The insurance industry in India has witnessed an impressive growth rate over the last two decades driven by the greater private sector participation and an improvement in distribution capabilities, along with substantial improvements in operational efficiencies. The paper presents the growth of life and non-life health insurance in India

Hybrid Energy-Efficient Task Offloading Algorithm (HEETA): A Framework for Optimizing Edge Computing Offloading Decisions

In the evolving realm of Mobile Edge Computing (MEC), efficient task offloading remains pivotal. This paper introduces the Hybrid Energy-Efficient Task Offloading Algorithm (HEETA) to address the deficiencies of the current Joint Optimization Task Offloading Strategy based on Particle Swarm Optimization (JOBPSO). Drawing from a broad dataset encompassing diverse MEC operational variables, HEETA exhibits exemplary performance metrics, with a notable mean fitness of approximately 0.99984 and a minimal standard deviation of 0.000116. Such metrics not only reflect HEETA's robustness but also its adaptability across multifaceted MEC parameters. Furthermore, its dynamic nature facilitates adaptability to variables including task numbers, computational capacity, and latency constraints, resulting in marked improvements in energy efficiency. Quantitative evaluations, as evidenced by a performance matrix, position HEETA's global best fitness values between approximately 0.9998 and 0.9999. While HEETA signifies a monumental step in enhancing energy efficiency, prolonging device longevity, and optimizing overall MEC system performance, the research acknowledges potential limitations, emphasizing the imperatives of accurate modeling and subsequent validations within distinct MEC environments.

Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence.

The pursuit of artificial general intelligence (AGI) continuously demands higher computing performance. Despite the superior processing speed and efficiency of integrated photonic circuits, their capacity and scalability are restricted by unavoidable errors, such that only simple tasks and shallow models are realized. To support modern AGIs, we designed Taichi-large-scale photonic chiplets based on an integrated diffractive-interference hybrid design and a general distributed computing architecture that has millions-of-neurons capability with 160-tera-operations per second per watt (TOPS/W) energy efficiency. Taichi experimentally achieved on-chip 1000-category-level classification (testing at 91.89% accuracy in the 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content with up to two orders of magnitude of improvement in efficiency. Taichi paves the way for large-scale photonic computing and advanced tasks, further exploiting the flexibility and potential of photonics for modern AGI.

Efficiency Improvements Must be Holistic

Efficiency Improvements Must be Holistic

Complementarities between product and process innovation and their effects on employment: a firm-level analysis of manufacturing firms in Colombia

ABSTRACT The introduction or adoption of innovations at the firm level has consequences for job creation that may differ across low-middle and high-income countries. Also, the type of innovation that firms introduce, such as process or product innovations, can affect employment through different channels. This paper aims to study the effects of innovation on employment growth at the firm level using a framework that considers the nature of innovation and the relative efficiency of the firms. The study uses a rich panel dataset that combines information from two different surveys in Colombia: the Annual Manufacturing Survey and the Survey on Development and Technological Innovation in the Manufacturing Sector. The article provides empirical evidence supporting the idea that the nature of innovation in the country involves complementarities between process and product innovations. The paper discusses how this result is related to the patterns of innovation in middle income countries, which need not only new technologies but also imitation of processes and products. Another novelty of this analysis is the study of displacement effects of process innovation through improvements in the relative efficiency of the firms. Findings show that some firms reduce employment from process innovations, reflecting high heterogeneity in efficiency among firms.

Enhancing nitrogen removal performance using immobilized aerobic denitrifying bacteria by modified polyvinyl alcohol/sodium alginate (PVA/SA)

Aerobic denitrification has emerged as a promising and efficient method for nitrogen removal from wastewater. However, the direct application of aerobic denitrifying bacteria has faced challenges such as low nitrogen removal efficiency, bacterial loss, and poor stability. To address these issues, this study developed a novel microbial particle carrier using NaHCO3-modified polyvinyl alcohol (PVA)/sodium alginate (SA) gel (NaHCO3-PVA/SA). This carrier exhibits several advantageous properties, including excellent mass transfer efficiency, favorable biocompatibility, convenient film formation, abundant biomass, and exceptional pollutant treatment capacity. The carrier was modified with 0.3% NaHCO3, 8.0% PVA, and 1.0% SA, resulting in a remarkable 3.4-fold increase in the average pore diameter and a 12.8% improvement in mass transfer efficiency. This carrier was utilized to immobilize the aerobic denitrifying bacterium Stutzerimonas stutzeri W-2 to enhance nitrogen removal (NaHCO3-PVA/SA@W-2), resulting in a NO3−-N removal efficiency of 99.06%, which was 21.39% higher than that without modification. Compared with the non-immobilized W-2, the degradation efficiency was improved by 43.70%. After five reuses, the NO3−-N and TN removal rates remained at 99% and 93.01%, respectively. These results provide a solid foundation for the industrial application of the modified carrier as an effective tool for nitrogen removal in large-scale wastewater treatment processes.

Design of a tunable bacterial gene expression system using engineered σ factors.

The synthesis of organic compounds involves the action of multiple enzymes in a biosynthetic pathway. Incorporating such biosynthetic pathways into microbes often leads to substantial cellular and metabolic stress resulting in low titers of the target compound. This limitation can be offset, in part, by optimizing enzyme efficiency and cellular enzyme concentration. The former involves significant efforts to achieve improvements in catalytic efficiency with the caveat that the metabolic load on a microbial cell imposed by the overexpression of the exogenous enzyme could result in reduced cell fitness. Here, we demonstrate the feasibility of engineered σ factors to modulate gene expression levels without significant genetic engineering. We note that changing the sequence of two flexible polypeptide loops without any changes to the structural scaffold of the transcription initiation factor σE could modulate the expression levels of the target genes. This ability provides a route to improve the efficiency of a biosynthetic pathway without altering the overall genomic makeup. The σE chimera library thus provides an avenue for pre-determined conditional gene expression of specific genes in Escherichia coli.

A new synergistic hydrocolloid with superior rheology: Locust bean /xanthan gum binary solution powdered by different drying methods

In the current study, a novel gum powder containing locust bean and xanthan gums was prepared by three different drying methods of their synergistic aqueous solutions: direct cabinet drying (CD), drying after alcohol precipitation (PD), and direct freeze drying (FD). The purpose of this process is to develop ready-to-use synergistic gum powders with improved rheological properties by modifying intermolecular interactions. Significant improvements in viscosity, consistency efficiency and pseudoplastic character were observed for PD, followed by CD and FD, as well as the higher values of G′ and G″ values of PD, indicating a stronger entanglement network development between the macromolecules for PD. PD exhibited better elasticity and shear resistance compared to FD and CD in the strain sweep test. In Lissajous curves, straight line shapes were found for synergistic solutions at low strain, which continued for increasing strain values for PD and CD. However, the pronounced viscous behavior was found for FD at 500% strain. Compared to CD and FD, PD exhibited a more desirable thixotropic character. A lower zeta potential was observed in the production of synergistic gums using the PD process. Gel strength was found to be higher for PD. Therefore, PD was a more effective method to produce a ready-to-use synergistic powder with better viscoelastic properties and a more stable solution.

Expectations and Outcomes when Quantifying Energy Improvements Achieved by Building Envelope Retrofitting

This paper assesses the energy efficiency of two buildings constructed in the 1960s in Madrid. One of the buildings is refurbished including passive energy efficiency improvements, while the other remains in its original state. The area is one of a series of low-income residential inefficient developments built by the state on the capital’s outskirts in the 1950s. Their buildings require huge amounts of energy to meet occupants’ basic energy needs. This paper quantifies the energy savings and improved comfort achieved by building envelope energy retrofitting. For this purpose, it proposes a comprehensive methodology spanning data monitoring in homes in buildings, occupant surveys and energy simulation models—a standard approach to estimating improvement potential. Our aim is to compare the expected energy savings predicted by energy certificates with monitored data. The paper concludes that the comfort level in the retrofitted building improved tangibly but that the differing behaviours of the building’s occupants make the energy saving difficult to quantify with any precision. The calibrated model targets energy consumption savings after renovation of approximately 25% in heating and 50% in cooling for a typical household of four people with basic comfort needs reasonably met. Regarding heating consumption, the results of the calibrated model are lower than expected savings using the official certificate input data. However, cooling consumption savings were found to be greater than expected.

Unleashing Deep Learning: Transforming E-commerce Profit Prediction with CNNs

This research examines the potential of Convolutional Neural Networks (CNNs), including VGG16, ResNet50, and InceptionV3, in predicting ecommerce profits. Emphasizing the importance of high-quality datasets, the study showcases the superior performance of CNN models over traditional algorithms, particularly noting a notable accuracy rate of 92.55% with CNN (VGG16). These results highlight deep learning's capability to extract actionable insights from complex ecommerce data, offering significant opportunities for revenue optimization and operational efficiency improvement. The conclusion underscores the need for investment in infrastructure and expertise for successful CNN integration, alongside ethical and privacy considerations. This research contributes valuable insights to the discourse on deep learning in ecommerce, offering guidance to businesses navigating the competitive global market landscape.

Design and Techno-economic Analysis of an Improved Multipurpose Cooker Stove

In response to the growing concerns surrounding the adverse health and environmental impacts of inefficient and hazardous traditional cooking fuels and stoves, this research aims to design and assess the performance of an improved multipurpose cooker stove. The study focuses on Zambia, a country deeply affected by air pollution and deforestation due to charcoal and firewood production using inefficient cooking stoves called Mbaula. The primary objective is to create an economically viable and environmentally friendly cooking stove that addresses the sustainability challenges of traditional cooking practices. The research is guided by key objectives. The study outlines the design process of the improved multipurpose cooker stove, emphasizing the incorporation of sustainable design principles. The improved multipurpose cooker stove demonstrated a thermal efficiency of 87.49%, significantly higher than the 11.88% thermal efficiency of traditional stoves. This improvement in efficiency is crucial for reducing fuel consumption and maximizing heat transfer during cooking processes. In addition, the research findings showed that the improved multipurpose cooker stove consumed 0.3643 kg/L less fuel compared to traditional stoves. This fuel savings not only reduces the cost of raw materials but also contributes to environmental sustainability by lowering deforestation rates and air pollution. Despite the initial investment cost of the improved cooker stove, the long-term operation costs are lower due to reduced fuel consumption and maintenance expenses. The study's findings and recommendations hold promise for addressing critical challenges related to clean cooking solutions, environmental preservation, and the well-being of households in Zambia and similar contexts.

Smart Cities and Smart Supply Chain: Integration for Sustainable Urban Development.

As urbanization accelerates globally, the concept of smart cities emerges as a promising solution to address the multifaceted challenges of sustainable urban development. Smart cities leverage advanced technologies to enhance efficiency, sustainability, and quality of life for their residents. Concurrently, the evolution of supply chain management towards smart supply chains has gained traction, driven by the imperative of optimizing logistics, minimizing waste, and enhancing resilience. This paper explores the integration of smart cities and smart supply chains as a holistic approach to foster sustainable urban development. The integration of smart cities and smart supply chains presents a symbiotic relationship wherein advancements in one domain catalyze improvements in the other. Smart cities rely on seamless and efficient supply chain operations to ensure the smooth flow of goods and services, essential for meeting the needs of urban inhabitants. [1],[2] Conversely, smart supply chains benefit from the data-rich environment and infrastructure of smart cities to optimize routing, inventory management, and last-mile delivery, thereby reducing environmental impact and enhancing overall efficiency. Key components of this integration include the utilization of Internet of Things (IoT) devices, big data analytics, artificial intelligence (AI), and blockchain technology to enable real-time monitoring, predictive analytics, and transparent, secure transactions throughout the urban supply chain. By harnessing these technologies, cities can achieve significant improvements in resource utilization, energy efficiency, and carbon footprint reduction. Furthermore, the paper examines the role of public-private partnerships (PPPs) and regulatory frameworks in facilitating the integration of smart cities and smart supply chains. Collaborative efforts between government entities, industry stakeholders, and technology providers are essential to overcome barriers such as data privacy concerns, interoperability challenges, and investment constraints. In conclusion, the integration of smart cities and smart supply chains represents a promising pathway towards achieving the goals of sustainability, resilience, and prosperity in urban environments. By embracing innovative technologies and fostering collaboration, cities can pave the way for a more efficient, equitable, and environmentally conscious future.

In Situ Utilization of Electron-Enhanced Degradation of Azo Dyes in a Constructed Wetland–Microbial Fuel Cell Coupling System

In this study, a constructed wetland was coupled with a microbial fuel cell to establish a coupled system known as the constructed wetland–microbial fuel cell (CW–MFC), utilized for the treatment of X-3B azo dye wastewater at varying concentrations. Experimental results indicated that the anodic region made the primary contributions to the discoloration of azo dyes and COD removal, with a contribution rate of 60.9–75.8% for COD removal and 57.8–83.0% for the effectiveness of discoloration. Additionally, the role of plants in the constructed wetland area could achieve the removal of small molecular substances and further discoloration. In comparison to open-circuit conditions, under closed-circuit conditions the CW–MFC effectively degraded X-3B azo dye wastewater. Under an external resistance of 2000 Ω, a maximum COD removal rate of 60.0% and a maximum discoloration rate of 85.8% were achieved for X-3B azo dye at a concentration of 100 mg/L. Improvements in the treatment efficiency of X-3B dye wastewater were achieved by altering the external resistance. Under an external resistance of 100 Ω and an influent concentration of X-3B of 800 mg/L, the COD removal rate reached 78.6%, and the decolorization rate reached 85.2%. At this point, the CW–MFC exhibited a maximum power density of 0.024 W/m3 and an internal resistance of 99.5 Ω. Spectral analysis and GC–MS results demonstrated the effective degradation of azo dyes within the system, indicating azo bond cleavage and the generation of numerous small molecular substances. Microbial analysis revealed the enrichment of electrogenic microorganisms under low external resistance conditions, where Geobacter and Trichococcus were dominant bacterial genera under an external resistance of 100 Ω, playing crucial roles in power generation and azo dye degradation within the system.

FDI in India: Analyzing Financial Flows and Economic Impacts

Foreign Direct Investment (FDI) plays a crucial role in shaping the economic landscape of India, attracting significant attention from investors, policymakers, and economists alike. As one of the fastest-growing economies in the world, India has been successful in improving its attractiveness to foreign investors through various policy reforms and liberalization measures. The study finds important patterns and connections in the dynamics of FDI inflow by applying quantitative analytical techniques such a regression analysis and correlation analysis. The results of regression analysis highlight the impact of the GDP on FDI inflow, showing a robust positive connection between GDP and FDI inflow. In recent years, there has been a substantial increase in FDI inflows into India across different sectors, such as telecommunications, manufacturing, and services. This surge in FDI can be attributed to the country's large and growing market, skilled workforce, improving infrastructure, and stable political environment. Analyzing the financial flows of FDI into India reveals that a significant portion of inflows comes from countries such as the United States, Singapore, and Mauritius. This diversification of funding sources indicates the global interest in investing in India and leveraging its potential as a key market in the region. The economic impacts of FDI in India are multifaceted and go beyond simply bringing capital into the country. FDI inflows contribute to job creation, technology transfer, skill development, and overall economic growth. Foreign investors bring in expertise, best practices, and access to new markets, which can benefit local industries and enhance competitiveness. Moreover, FDI inflows can also lead to improvements in productivity, efficiency, and innovation within domestic firms through spillover effects. However, it is essential to recognize the challenges associated with FDI in India, such as regulatory complexity, infrastructure bottlenecks, bureaucratic inefficiencies, and socio-political factors. Addressing these challenges requires continuous efforts from the government to further streamline regulations, improve infrastructure, enhance the ease of doing business, and foster a more investor-friendly environment. Keywords: Foreign Direct Investment (FDI), Regression, Correlation, GDP, Telecommunications.

Multifunctional Biomolecules Bridging a Buried Interface for Efficient Perovskite Solar Cells.

The inevitably positively and negatively charged defects on the SnO2/perovskite buried interface often lead to nonradiative recombination of carriers and unfavorable alignment of energy levels in perovskite solar cells (PSCs). Interface engineering is a reliable strategy to manage charged defects. Herein, the nicotinamide adenine dinucleotide (NAD) molecules with multiple active groups of ─P=O, ─P-O, and ─NH2 are introduced to bridge the SnO2/perovskite buried interface for achieving simultaneous elimination of positively and negatively charged defects. We demonstrate that the ─P=O and ─P-O groups in NAD not only fix the uncoordinated Pb2+ but also fill the oxygen vacancies (VO) on the SnO2 layer to eliminate positively charged defects. Meanwhile, ─NH2 groups form hydrogen bonds with PbI2 to reduce the number of negatively charged defects. In addition, the NAD biomolecules as a bridge induce high perovskite crystallization and accelerated electronic transfer along with favorable energy band alignment between SnO2 and perovskite. Finally, the PSCs with the ITO/SnO2/NAD/Cs0.15FA0.75MA0.1PbI3/Spiro-OMeTAD/Ag structure deliver an improvement in the power conversion efficiency from 20.49 to 23.18% with an excellent open-circuit voltage (Voc) of 1.175 V. This work demonstrates that interface engineering through multifunctional molecular bridges with various functional groups is an effective approach to improve the performance of PSCs by eliminating charged defects and simultaneously regulating energy level alignment.

Improvement of thermostability and catalytic efficiency of xylanase from Myceliophthora thermophilar by N-terminal and C-terminal truncation.

Extracting xylanase from thermophilic filamentous fungi is a feasible way to obtain xylanase with good thermal stability. The transcriptomic data of Myceliophthora thermophilic destructive ATCC42464 were differentially expressed and enriched. By comparing the sequences of Mtxylan2 and more than 10 xylanases, the N-terminal and C-terminal of Mtxylan2 were truncated, and three mutants 28N, 28C and 28NC were constructed. GH11 xylan Mtxylan2 was identified by transcriptomic analysis, the specific enzyme activity of Mtxylan2 was 104.67 U/mg, and the optimal temperature was 65°C. Molecular modification of Mtxylan2 showed that the catalytic activity of the mutants was enhanced. Among them, the catalytic activity of 28C was increased by 9.3 times, the optimal temperature was increased by 5°C, and the residual enzyme activity remained above 80% after 30 min at 50-65°C, indicating that redundant C-terminal truncation can improve the thermal stability and catalytic performance of GH11 xylanase.

Optimal operating scenario and performance comparison of biomass-fueled externally-fired microturbine

This paper focuses on the determination of the optimal operating scenario of an Externally-Fired Micro-Gas turbine utilizing syngas produced by biomass gasification. Biomass exploitation can contribute to the achievement of the net-zero emissions target in 2050. This can be boosted by the advancement of externally-fired technology. In this study, a dual-objective optimization process, including a validated thermodynamic model, is combined with preliminary cost insights to assist the decision-making process. The resulting performance is compared with the actual data-based performance of a reference natural-gas-fired Micro-Gas Turbine. The performance comparison is performed through an exergetic analysis, identifying the main losses contributors. The optimization results in an externally-fired performance of 77.9 kWel electrical power and 20.3% efficiency, improved by 31.5% compared to the average current externally-fired demonstrated efficiencies. Yet, there is still room for improvement to reach natural-gas-fired Micro-Gas Turbine efficiency. The study highlights an unavoidable efficiency decrease of at least 4.0 percentage points due to the use of syngas itself instead of natural gas, which conversely increases heat recovery potential. However, compared to the state-of-the-art demonstrations, the proposed analysis is a significant step towards efficiency improvement, which can be exploited for future biomass demonstrations, attractive for electricity generation as well.

The Second-Round Effects of the Agriculture Value Chain on Farmers’ Land Transfer-In: Evidence from the 2019 Land Economy Survey Data of Eleven Provinces in China

In the context of the separation of three rights of land and agricultural modernization, this paper is based on the land economic survey data from eleven provinces in China in 2019, covering the eastern, middle, and western regions of China. Based on the value chain theory and its “second-round effect”, which pertains to the multi-round effects of value chain distribution theory, various research methods such as Probit, Tobit, the two-part model, SFA, PSM, and the intermediary effect model are employed to analyze the direct impact of the agriculture value chain (AVC) on farmers’ land factor inputs and the income effects caused by them, which are the “second-round effect” of the AVC on land factor inputs. The research results show the following: Firstly, the AVC has a significant positive impact on the behavior and area of farmers’ land transferring-in, which helps guide farmers towards large-scale land operation. Secondly, the AVC significantly improves farmers’ production efficiency and promotes land transfer through differences in production efficiency, representing the “second-round effect” mechanism of the AVC on land factor inputs. Moreover, the AVC will increase farmers’ net land production income by 48.74%, which is the “second-round effect” of the AVC on farmers’ agricultural income and also the motivation for farmers’ land factor inputs. Finally, the expansion of land area and the improvement of production efficiency jointly increase farmers’ agricultural income, among which production efficiency plays a partial intermediary effect in increasing agricultural income if farmers join the AVC. This paper believes that we should further promote the market-oriented reform of land factors, support the innovation of the benefit linkage mechanism of the AVC, and promote appropriate areas of land operation by farmers, thereby achieving common prosperity and promoting agricultural modernization in China.

Prediction and Elucidation of Physical Properties of Polycrystalline Materials Using Multichannel Machine Learning of Electron Backscattering Diffraction

AbstractThe application of machine learning in materials science has yielded several benefits, including the prediction of physical properties and the improvement of experimental efficiency. However, with complex models, such as convolutional neural networks (CNN), learning has become a black box, from which no universal physical knowledge can be obtained. In this study, a highly accurate prediction of the electrical properties of polycrystalline semiconductor thin films is achieved by learning multichannel CNN models from electron backscattering diffraction (EBSD) data, that is band contrasts, grain boundaries, and inverse pole figures. In addition, it examines how the CNN model learned the correlation between the crystallinity, grain boundaries, crystallographic orientation, and carrier mobility by polarizing certain EBSD data and checking the predicted changes in carrier mobility. Physical parameters affecting carrier mobility can be extracted, which is challenging via human image recognition. The methods proposed in this study will not only enable the prediction of electrical properties from EBSD data for all materials but also will contribute to the discovery of complex physical phenomena beyond the limits of human analysis.

3D-CFD Modeling of Hollow-Fiber Membrane Contactor for CO2 Absorption Using MEA Solution.

Membrane technology is considered an innovative and promising approach due to its flexibility and low energy consumption. In this work, a comprehensive 3D-CFD model of the Hollow-Fiber Membrane Contactor (HFMC) system for CO2 capture into aqueous MEA solution, considering a counter-current fluid flow, was developed and validated with experimental data. Two different flow arrangements were considered for the gas mixture and liquid solution inside the HFMC module. The simulation results showed that the CO2 absorption efficiency was considerably higher when the gas mixture was channeled through the membranes and the liquid phase flowed externally between the membranes, across a wide range of gas and liquid flow rates. Sensitivity studies were performed in order to determine the optimal CO2 capture process parameters under different operating conditions (flow rates/flow velocities and concentrations) and HFMC geometrical characteristics (e.g., porosity, diameter, and thickness of membranes). It was found that increasing the membrane radius, while maintaining a constant thickness, positively influenced the efficiency of CO2 absorption due to the higher mass transfer area and residence time. Conversely, higher membrane thickness resulted in higher mass transfer resistance. The optimal membrane thickness was also investigated for various inner fiber diameters, resulting in a thickness of 0.2 mm as optimal for a fiber inner radius of 0.225 mm. Additionally, a significant improvement in CO2 capture efficiency was observed when increasing membrane porosity to values below 0.2, at which point the increase dampened considerably. The best HFMC configuration involved a combination of low porosity, moderate thickness, and large fiber inner diameter, with gas flow occurring within the fiber membranes.