Efficiency Improvement Research Articles

Scalable Solutions for Implementing Satellite-based Sensor Networks

The importance of sensor networks in the field of IoT cannot be overstated, especially with the growing demand for IoT solutions. The new Internet protocols such as IPv6 and 5G mobile provide high-capacity communication networks, demonstrating the relevance and potential applications of IoT in the future. However, with new demands come new challenges. This paper aims to explore the challenges of Satellite-based Sensor Networks, a new topic with various applications. This article explores the challenges and constraints of implementing a scalable satellite-based sensor network. We examined the hardware and software limitations of the Iridium satellite SBD protocol and proposed tailored solutions using design flow algorithms to tackle flexibility, power consumption, bandwidth, and scalability issues. Satellite-based sensor networks face challenges in data transmission and sensor lifespan. In this study, we introduce event-driven and compression algorithms to address these challenges. By implementing clustering, we achieved a significant increase in the number of nodes within each cluster. Our proposed methods were validated using simulated datasets derived from temperature and location data. A unique data transmission method, based on event count, was introduced. Notably, the Differential Huffman compression algorithm resulted in an 81% reduction in data transmission compared to traditional methods. This demonstrates about 62% improvement in efficiency over existing techniques. Our approach not only paves the way for future advancements in this domain but also highlights the untapped potential of satellite-based sensor networks.

Efficiency of Heterojunction Solar Cells Based on Optical Band Gap

Electric energy production by fuel burning produces dangerous pollutants. The photovoltaic solar cell is a suitable alternative. For thin film solar cells, efficiency improvement should be carried out through continued development of their characteristics. The homojunction solar cell has reached its optimum. In the present work, a relationship between optical band gap and solar cell efficiency of heterojunctions has been established. Suitable band gaps of a pair of heterojunctions are required to get maximum efficiency for an appropriate frequency of solar radiation. The efficiency of a couple of extrinsic semiconductors can be calculated theoretically. An optimum band gap of a pair of heterojunctions with the highest efficiency may be found by this process. The efficiency of a few heterojunctions has been calculated using reported data. The results show that efficiency is increased with optical band gaps, and type-I is more efficient than type-II heterojunctions. The work may help to improve the solar cell efficiency.

DISTRIBUTED RESOURCE OPTIMISATION USING THE Q-LEARNING ALGORITHM, IN DEVICE-TO-DEVICE COMMUNICATION: A REINFORCEMENT LEARNING PARADIGM

In the context of wireless systems going forward, particularly in the Beyond 5G (B5G) era, where high data rates and low latency are critical, D2D communication is a pivotal technology that has many advantages. In D2D communication, the allocation of resources plays a critical role in achieving higher throughput while ensuring interference management, improved spectrum and energy efficiency, and system fairness. Conventional resource allocation methodologies encounter challenges in dynamically changing and diverse communication environments. To deal with the dynamic and unpredictable character of channel characteristics, we utilize a distributed iterative resource allocation technique based on the reinforcement learning (RL) approach that empowers the system to learn and adapt to the wireless environment autonomously. In this paper, we formulate a distributed Q learning-based RL method as its real-time learning capabilities, reduced communication overhead, and exploration efficiency make it well-suited for adapting to dynamic D2D environments compared to other RL methods. By applying the Q-learning method, the D2D devices act as learning agents striving to maximize the cumulative rewards. Through interactions with the environment and continuous learning from feedback, these agents adapt to real-time resource allocation decisions over time. Comparing our proposed Q-learning method to the state-of-the-art RL techniques, simulation results show improvements in energy and spectrum efficiency, latency, an increase in Jain's fairness index, and improvements in overall system throughput of about 6% to 8%. The scalability is found to be 1.69 interpreting that Q-learning exhibits a good scalability as the throughput does not abruptly decrease for an increasing number of devices.

Design and simulation of bifacial perovskite solar cell with high efficiency using cubic plasmonic nanoparticles

Bifacial perovskite solar cells (Bi-PSCs) are gaining attention for their ability to exceed the efficiency limits of traditional single-junction cells by absorbing light from both the front and rear surfaces. This study explores a novel approach to boost the efficiency and stability of Bi-PSCs by integrating clusters of cubic metallic nanoparticles (NPs) within the absorber layer. These plasmonic NPs, embedded in the middle of the absorber layer, significantly enhance light absorption, resulting in substantial improvements in power conversion efficiency for both front and rear irradiation. Our three-dimensional simulations demonstrate that incorporating Ag-based NPs can achieve efficiency enhancements of 29.89 % (from 18 % to 23.39 %) for front irradiation and 29.63 % (from 17.22 % to 22.33 %) for rear irradiation. Ag-based NPs exhibit the most notable efficiency improvement, reaching a short-circuit current of 41.30 mA/cm2 and an efficiency of 35.34 % at an albedo of 0.5. To address potential issues such as corrosion and degradation of the perovskite material, the metallic NPs are encapsulated with a thin dielectric nano-shell of SiO₂. This encapsulation strategy effectively prevents degradation, ensuring the long-term stability of the Bi-PSCs. The results indicate that the use of clusters of cubic metallic NPs, combined with dielectric encapsulation, offers a superior approach to optimizing the performance and stability of Bi-PSCs, providing a more efficient alternative to increasing the absorber layer’s thickness.

Effect of Capital Adequacy on Operational Efficiency of Commercial Banks in Kenya

This study investigates the impact of capital adequacy on the operational efficiency of Kenyan banks, highlighting its importance for financial stability and risk management using a two-step analytical approach: first, Stochastic Frontier Analysis was used to determine operational efficiency scores for each bank; second, a panel Generalized Method of Moments (GMM) regression model assessed the relationship between these scores and capital adequacy. The study employed a panel GMM Method, accounting for individual and time-specific effects as well as endogeneity and correlation biases. The study concentrated on data from 2008 to 2022, from verified audited financial statements from the Central Bank of Kenya and the respective banks' websites. The study revealed a positive relationship between capital adequacy and operational efficiency. Specifically, increased capital adequacy was associated with a 10.32% improvement in operational efficiency. The study also found that market structure plays a significant role in this relationship. Based on these findings, policymakers should adopt comprehensive strategies focusing on stringent capital adequacy regulations to enhance bank performance and stability.

Enhancing hydrological model efficiency through satellite image classification

ABSTRACT This paper aims to evaluate the performance of a hydrological model by using satellite image classification (SIM) to extract Land Use (LU) information. Four methods, namely Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF), are assessed for SIM using satellite images. CART demonstrated the highest Overall Accuracy (OA) of 0.996, followed by RF (OA=0.994), SVM (OA=0.797), and NB (OA=0.543). The Soil and Water Assessment Tool (SWAT) model was employed in this study to construct and refine the hydrological model for simulating the streamflow in the basin. For this purpose, the ground-based layer was replaced by LU generated from the classification algorithms. Integrating the four classification approaches yielded a significant improvement in Nash-Sutcliffe efficiency of the SWAT model, increasing it from 0.37 to approximately 0.76. These findings highlight the effectiveness of using satellite image classification in enhancing the efficiency of hydrological models.

Highly efficient modeling and optimization of neural fiber responses to electrical stimulation.

Peripheral neuromodulation has emerged as a powerful modality for controlling physiological functions and treating a variety of medical conditions including chronic pain and organ dysfunction. The underlying complexity of the nonlinear responses to electrical stimulation make it challenging to design precise and effective neuromodulation protocols. Computational models have thus become indispensable in advancing our understanding and control of neural responses to electrical stimulation. However, existing approaches suffer from computational bottlenecks, rendering them unsuitable for real-time applications, large-scale parameter sweeps, or sophisticated optimization. In this work, we introduce an approach for massively parallel estimation and optimization of neural fiber responses to electrical stimulation using machine learning techniques. By leveraging advances in high-performance computing and parallel programming, we present a surrogate fiber model that generates spatiotemporal responses to a wide variety of cuff-based electrical peripheral nerve stimulation protocols. We used our surrogate fiber model to design stimulation parameters for selective stimulation of pig and human vagus nerves. Our approach yields a several-orders-of-magnitude improvement in computational efficiency while retaining generality and high predictive accuracy, demonstrating its robustness and potential to enhance the design and optimization of peripheral neuromodulation therapies.

Can the Pioneering Impact of Artificial Intelligence in Anaesthetic Practice Uphold Good Medical Practice?

The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.

Early Improvement in Interstitial Fluid Flow in Patients With Severe Carotid Stenosis After Angioplasty and Stenting.

This study aimed to investigate early changes in interstitial fluid (ISF) flow in patients with severe carotid stenosis after carotid angioplasty and stenting (CAS). We prospectively recruited participants with carotid stenosis ≥80% undergoing CAS at our institute between October 2019 and March 2023. Magnetic resonance imaging (MRI), including diffusion tensor imaging (DTI), and the Mini-Mental State Examination (MMSE) were performed 3 days before CAS. MRI with DTI and MMSE were conducted within 24 hours and 2 months after CAS, respectively. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated from the DTI data to determine the ISF status. Increments were defined as the ratio of the difference between post- and preprocedural values to preprocedural values. In total, 102 participants (age: 67.1±8.9 years; stenosis: 89.5%±5.7%) with longitudinal data were evaluated. The DTI-ALPS index increased after CAS (0.85±0.15; 0.85 [0.22] vs. 0.86±0.14; 0.86 [0.21]; P=0.022), as did the MMSE score (25.9±3.7; 24.0 [4.0] vs. 26.9±3.4; 26.0 [3.0]; P<0.001). Positive correlations between increments in the DTI-ALPS index and MMSE score were found in all patients (rs=0.468; P<0.001). Conclusion An increased 24-hour post-CAS DTI-ALPS index suggests early improvement in ISF flow efficiency. The positive correlation between the 24-hour DTI-ALPS index and 2-month MMSE score increments suggests that early ISF flow improvement may contribute to long-term cognitive improvement after CAS.

Drying Kinetics of “Jacarezinho” Pumpkin Seeds

ABSTRACT Pumpkin seeds are sources of various nutrients and bioactive compounds and can be used after appropriate treatment. The seeds, when dehydrated, can be used as ingredients in several important foods. Through kinetic analysis it is possible to determine the behavior of the dry material, and describe the process through mathematical models, becoming useful tools for decision-making and contributing to the improvement of the process efficiency. The objective of this study was to construct drying kinetics for “Jacarezinho” pumpkin seeds and adjust the experimental data obtained to mathematical models. The seeds were dried in a convective dryer, varying the drying temperature (30, 40 and 50°C), with an air speed of 1.5 m.s-1. The drying kinetics were well described by the Midilli model (R2 99.90–99.99; MSE > 0.1). The use of the transfer kinetic data presented provides support for the use of pumpkin seeds to produce new foods.

Design and modifications of boiler mounting device in Asmara brewery factory

Energy and environment are closely related and important issues in the present all over world. Present industrial and others organization thought the unique of energy utilization and diverse source reviles such as coal, oil, gas, nuclear, hydropower, solar, wind and biofuel. The Non-renewable energy sources are being limited in this world and energy conservation is one of growing need nowadays. The major issues in the world’s energy consumption are getting used in boilers. Thus, this energy used has to be wisely consumed with great efficiency in industries. Boilers are considered as an important and an expensive property in many industries. In addition to initial cost, boilers have high operational and maintenance cost in order to secure production in safe and acceptable working conditions. In this study concerned with an energy saving, economic and environmental analysis of the fire tube boiler in ASMARA BREWERY. In this project, we concentrated on the improvement of boiler efficiency by recovering a portion of heat content of the flue gas. This is possible due to the most dominant device which is the economizer. The preexisting boiler was designed without an economizer. The present modification involved about the design of economizer in the boiler to reduce fuel consumption and thermal shock. Furthermore, in this type of boiler attain higher efficiency and little enhancement in the efficiency of the boiler will save an outsized amount of fuel and also help to reduce CO2 emission.

Revolutionizing Sourcing with AI: Harnessing Technology for Unprecedented Efficiency and Savings

Artificial Intelligence (AI) is transforming the landscape of sourcing, providing unprecedented efficiency and cost savings. This paper explores how AI technologies such as machine learning, natural language processing, and predictive analytics are revolutionizing sourcing processes. We examine case studies across various industries, highlighting the impacts of AI on supply chain optimization, supplier selection, and procurement processes. The findings indicate significant improvements in operational efficiency, decision-making accuracy, and financial performance. For instance, a leading automotive manufacturer reduced procurement costs by 20% using AI-driven sourcing (Smith et al., 2020), while a global retail giant increased supplier quality by 25% through AI-enhanced evaluation (Johnson et al., 2019). Additionally, a healthcare provider optimized inventory management, resulting in a 10% reduction in inventory costs (Williams and Brown, 2021). Future research directions and practical implications for AI-driven sourcing are also discussed, emphasizing the potential for continued innovation and growth in this field. The challenges and opportunities associated with AI implementation in sourcing are addressed, highlighting the need for quality data, integration with existing systems, and skilled personnel to manage AI technologies.

Rice Promoter Editing: An Efficient Genetic Improvement Strategy.

Gene expression levels in rice (Oryza sativa L.) and other plant species are determined by the promoters, which directly control phenotypic characteristics. As essential components of genes, promoters regulate the intensity, location, and timing of gene expression. They contain numerous regulatory elements and serve as binding sites for proteins that modulate transcription, including transcription factors and RNA polymerases. Genome editing can alter promoter sequences, thereby precisely modifying the expression patterns of specific genes, and ultimately affecting the morphology, quality, and resistance of rice. This paper summarizes research on rice promoter editing conducted in recent years, focusing on improvements in yield, heading date, quality, and disease resistance. It is expected to inform the application of promoter editing and encourage further research and development in crop genetic improvement with promote.

An alumina phase induced composite transition shuttle to stabilize carbon capture cycles.

Limiting global warming to 1.5-2 °C requires a 50-90% reduction in CO2 emissions in 2050, depending on different scenarios, and carbon capture, utilization, and storage is a promising technology that can help reach this objective. Calcium oxide (CaO) carbon capture is an appealing choice because of its affordability, large potential capacity, and ability to withstand the high temperatures of flue gases. However, the structural instability and capacity fading challenge its large-scale industrial applications. Here, we design a reversible reaction shuttle in CaO-based sorbents to improve the structure stability by changing the initial alumina phases. Diverse alumina phases (x-Al2O3) are first synthesized and utilized as the aluminum source for creating CaO@x-Al2O3 composites. As expected, the CaO@δ-Al2O3 composite demonstrates a carbon capture capacity of 0.43 g-CO2/g-sorbent after 50 cycles, with an impressive capacity retention of 82.7%. Combined characterizations and calculations reveal that this stability improvement is attributed to a transition shuttle between Ca3Al2O6 and Ca5Al6O14, which can effectively restrain the complete decompositions of those structure-stabilized intermediate phases. An economic assessment further identifies the significance of heat transfer efficiency improvement upon cycles, and control of capital/operation cost, energy price and carbon tax for a future cost-effective commercialization of current strategy.

Comprehensively Modulated Sub-Attojoule Operated Optoelectronic Synapses for Image Encryption and Inpainting.

High-performance optoelectronic synaptic transistors play a crucial role in developing and emulating artificial visual systems. However, due to the predominant use of single-structure material modulation in optimizing optoelectronic synapses, their energy consumption significantly trails behind that of electronic synapses by several orders of magnitude. Herein, polymer dielectric layers and optimized contact strategies are adopted to realize the ultralow consumption optoelectronic synapses. Integration of polyimide dielectric significantly enhances photogenerated charge carrier dissociation, leading to substantial improvements in photoresponsivity (1.5 × 106 A·W-1), photodetectivity (6.9 × 1012 Jones), and external quantum efficiency (4.0 × 108%). Additionally, optimized contact properties augment their appeal for ultralow energy consumption in optoelectronic synapse applications. Excitatory postsynaptic current is triggered at an incredibly low voltage of 5 μV and boosts an impressively low energy consumption of 0.05 aJ, ranking among the best-reported results in this field. Next, we demonstrate an integrated system combining the MoS2 optoelectronic synapses with a recurrent neural network enabling 100% accurate recognition of optical signals, particularly in scenarios with aJ-leveled energy consumption. Finally, an image encryption system has been developed, in which images are encrypted by photoelectronic conversion of synapse arrays with random voltage settings and decrypted according to the recurrent neural network-based accuracy. More importantly, once partially damaged images are encrypted, through the decryption image inpainting can be realized due to the high accuracy. The proposed innovative approach holds promise for advancing artificial intelligence applications with improved energy efficiency, information security, and computational capabilities.

Design and simulation of an enhanced optical demultiplexer using photonic crystal resonators

The purpose of this paper is to examine the design, simulation, and optimization of a demultiplexer optical system using photonic crystal resonators. Utilizing the distinct properties of photonic crystals, the proposed device achieves high-quality resonances, efficient wavelength separation, and minimal crosstalk. These characteristics are crucial for enhancing the performance of dense wavelength division multiplexing (DWDM) systems, which require precise and efficient channel separation to manage increasing data traffic. Extensive simulations conducted with the RSoft CAD tool explore the impact of varying resonator parameters such as refractive index, geometric dimensions, and material composition on the device's performance. The results indicate substantial improvements in transmission efficiency and quality factor, with average values of 98.675% and 3131.125, respectively. Furthermore, the ability to fine-tune these parameters allows for the customization of the demultiplexer to specific wavelength ranges, making it highly adaptable for various telecommunications applications. This research aims to propel the development of compact, high-performance photonic devices that meet the growing demands of modern communication networks, ultimately contributing to more efficient and reliable optical communication systems.

Review of gliding arc plasma assisted ignition and combustion for gas turbine application

Abstract The potential of gliding arc plasma-assisted ignition and combustion technology to enhance ignition and combustion performance is attracting increasing attention from the scientific community. A multitude of experimental studies have been conducted by scientists and engineers on its application in gas turbine combustors. This paper presents a review of the research conducted on gliding arc plasma-assisted ignition and combustion over the past five years. Gliding arc plasma exerts a multitude of effects on combustion processes. These effects can be broadly categorized as follows: (1) reduction in ignition delay time; (2) expansion of ignition and lean blowout boundaries; (3) enhancement of ultra-lean burning combustion and low-temperature flame stability; (4) improvement in combustion efficiency; (5) reduction in pollutant emissions; (6) augmentation of stability of unstable fuels such as ammonia. Finally, a prospection on the application of gliding arc plasma assisted ignition and combustion technology in gas turbine combustor is presented.

Improving energy-related efficiency towards SDG7 in China: What role does energy poverty play?

The improvement of energy-related efficiency has promoted energy-sustainable development in China; however, it may be inhibited by energy poverty. This paper constructs an index system to measure China's energy poverty. Subsequently, we evaluate energy-related efficiency towards SDG7 comprised of energy production efficiency and energy sustainable utilization efficiency using a dynamic two-stage data envelopment analysis model. Furthermore, we adopt fixed effect models to quantify the impact of energy poverty on improving energy-related efficiency and the underlying mechanism. The findings indicate that: (1) China's energy poverty situation gradually improves from 2011 to 2020. The energy-related system's performance is medium due to the poor performance in its energy production stage in 30 provinces during 2011-2020. Energy-related efficiency is greater in areas with a higher response to SDG7. (2) The impact of energy poverty on improving energy-related efficiency is significantly negative. Addressing energy poverty significantly improves energy-related efficiency in regions characterized by a high energy poverty index and low energy-related efficiency. However, it bears no impact in regions with a low energy poverty index and high energy-related efficiency. (3) The moderating effect of technological innovation effectively improves energy-related efficiency, whereas the marketization level has an inverse effect. Consequently, this paper suggests several policy implications.

Modelling and Characterisation of Orthotropic Damage in Aluminium Alloy 2024

The aim of the work presented in this paper was development of a thermodynamically consistent constitutive model for orthotopic metals and determination of its parameters based on standard characterisation methods used in the aerospace industry. The model was derived with additive decomposition of the strain tensor and consisted of an elastic part, derived from Helmholtz free energy, Hill’s thermodynamic potential, which controls evolution of plastic deformation, and damage orthotopic potential, which controls evolution of damage in material. Damage effects were incorporated using the continuum damage mechanics approach, with the effective stress and energy equivalence principle. Material characterisation and derivation of model parameters was conducted with standard specimens with a uniform cross-section, although a number of tests with non-uniform cross-sections were also conducted here. The tests were designed to assess the extent of damage in material over a range of plastic deformation values, where displacement was measured locally using digital image correlation. The new model was implemented as a user material subroutine in Abaqus and verified and validated against the experimental results for aerospace-grade aluminium alloy 2024-T3. Verification was conducted in a series of single element tests, designed to separately validate elasticity, plasticity and damage-related parts of the model. Validation at this stage of the development was based on comparison of the numerical results with experimental data obtained in the quasistatic characterisation tests, which illustrated the ability of the modelling approach to predict experimentally observed behaviour. A validated user material subroutine allows for efficient simulation-led design improvements of aluminium components, such as stiffened panels and the other thin-wall structures used in the aerospace industry.

Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China

With the acceleration of urbanization and the improvement of people's living standards, the carbon emissions of residential buildings (BCE) in China have been increasing, hindering the achievement of carbon peaking and carbon neutrality goals. It has become increasingly urgent and important to analyze and grasp the dynamic trends of BCE. Given the notable regional variations in carbon emissions, it is necessary to delve into the distribution characteristics of BCE and the underlying factors influencing them. First, based on the emission factor method, the energy consumption and carbon emissions calculation models are established. Next, the main influencing factors are obtained by improving Kaya's constant equation. Meanwhile, the GA-PPC-Kmeans model is established to divide BCE into different regions. Then, the STIRPAT-Ridge model is built to predict the peak carbon emissions. Finally, a combination of static and dynamic methods is used to forecast peaks under three scenarios, which are low-carbon (LC) scenario, baseline (BA) scenario, and high-carbon (HC) scenario. The results show that five pivotal factors significantly impact BCE in China, which are energy carbon emission intensity (ECEI), energy intensity (EI), economic density (ED), residential housing level (RHL) and population size(P). Furthermore, BCE is categorized into five distinct regions: low-carbon demonstration area, energy efficiency improvement area, carbon concentration and innovation area, high-carbon optimization area, and carbon poverty development area. Under BA scenario, the peaks for BCE in these regions converge primarily around 2040 and 2045. This study provides a regional research perspective for China's residential buildings to reach the peak of carbon emissions, and serves as a reference for formulating carbon emission reduction plans in different regions.