Efficiency Improvement Research Articles

Graph neural network based human detection in videos during occlusion environments

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.

A Reduced Order Schwarz Method for Nonlinear Multiscale Elliptic Equations Based on Two-Layer Neural Networks

Neural networks are powerful tools for approximating high dimensional data that have been used in many contexts, including solution of partial differential equations (PDEs). We describe a solver for multiscale fully nonlinear elliptic equations that makes use of domain decomposition, an accelerated Schwarz framework, and two-layer neural networks to approximate the boundary-to-boundary map for the subdomains, which is the key step in the Schwarz procedure. Conventionally, the boundary-to-boundary map requires solution of boundary-value elliptic problems on each subdomain. By leveraging the compressibility of multiscale problems, our approach trains the neural network offline to serve as a surrogate for the usual implementation of the boundary-to-boundary map. Our method is applied to a multiscale semilinear elliptic equation and a multiscale $p$-Laplace equation. In both cases we demonstrate significant improvement in efficiency as well as good accuracy and generalization performance.

Robot navigation on inclined terrain using social force model

<p><span lang="EN-US">This research introduces an innovative approach to address the limitations of the commonly used social force model-based robot navigation method on flat terrain when applied to sloped terrain. The incline of the terrain becomes a crucial factor in calculating the robot’s steering output when navigating from the initial position to the target position while avoiding obstacles. Therefore, we propose a social forced model-based robot navigation system that can adapt to inclined terrain using inertial measurement unit sensor assistance. The system can detect the surface incline in real time and dynamically adjust friction and gravitational forces, ensuring the robot’s speed and heading direction are maintained. Simulation results conducted using CoppeliaSim show a significant improvement in speed adjustment efficiency. With this new navigation system, the robot can reach its destination in 59.935089 seconds, compared to the conventional social forced model which takes 63.506442 seconds, the robot is also able to reduce slip to reduce wasted movement. This method shows the potential of implementing a faster and more efficient navigation system in the context of inclined terrain.</span></p>

Combining optimization and dynamic movement primitives for planning energy optimal forestry crane motions

Forestry cranes are an important tool for safe and efficient timber harvesting with forestry machines. However, their complex manual control often led to inefficiencies and excessive energy usage, due to the many joysticks and buttons that must be used in a precise sequence to perform efficient movements. To address this, the industry is increasingly turning to partial automation, making manual control more intuitive for the operator and, consequently, achieving improvements in energy efficiency. This article introduces a novel approach to energy-optimal motion planning that can be used along with a feedback control system to automate crane motions, taking over portions of the operator’s work. Our method combines dynamic movement primitives (DMPs) and an energy-optimization algorithm. DMPs is a machine learning technique for motion planning based on human demonstrations, while the optimization algorithm exploits the crane’s redundancy to find energy-optimal trajectories. Simulation results show that DMPs can replicate human-like controlled motions with a 25% reduction in energy consumption. However, our energy optimization algorithm shows improvements of over 40%, providing substantial energy savings and a promising pathway towards environmentally friendly partially automated machines.

Enhancing wireless sensor network security with optimized cluster head selection and hybrid public-key encryption

This paper introduces an integrated methodology that enhances both the efficiency and security of wireless sensor networks (WSNs) against various active attacks. A two-fold strategy is proposed that incorporates an advanced cluster head (CH) selection and a customized, lightweight encryption protocol. The CH selection process is optimized through a multi-phase approach using fuzzy logic, local and global network qualifiers, and a trust index to ensure the election of CHs that are not only energy-efficient but also reliable. To complement the robust CH selection, the study introduces a hybrid yet lightweight encryption scheme customized Rivest-Shamir-Adleman (c-RSA) and customized advanced encryption standard (c-AES) algorithms. This scheme is customized for WSNs with limited computational resources, maintaining strong encryption standards while significantly reducing energy consumption and computational overhead. Experimental results demonstrate that the proposed system substantially enhances network performance, exhibiting a 34.15% improvement in energy efficiency and a 30.95% increase in reliability over existing methods such as LEACH and its modified versions. This comprehensive approach underscores the potential for a synergistic design in WSNs that does not compromise on security while optimizing operational efficiency.

Energy efficiency investment in Swiss industry: Analysis of target agreements

Slowing down the energy efficiency (EE) improvement rate over the past half a decade and relatively low EE-related investments highlight a need for a more targeted approach to encourage EE improvement investment in the Swiss manufacturing sector. The current work is based on an analysis of already implemented energy efficiency measures (EEMs) via target agreements. Four segments of the Swiss manufacturing sector are identified using the K-means clustering based on the energy consumption profiles of the establishments. The EEMs are categorized through text analysis with the help of a machine learning algorithm. The EEMs are ranked using two indicators that can inform different stakeholders: i) EEMs with Internal rates of return well above the reported risk-adjusted hurdle rates are identified for each segment to help industrial decision-makers take ad-hoc, profit-based investment decisions on EE improvement. ii) Levelized costs of saved energy are presented as value ranges for the EEMs that have the largest impact on annual total final energy (TFE) reduction for each segment to help policymakers design a more targeted approach for the promotion of high-impact EEMs. EE system improvements such as insulation of steam systems and process optimization, as well as EE equipment improvements like process equipment insulation, installation of variable frequency drives (VFDs), smart controls, and IE3 motors, were found to be the most profitable options across all clusters. For the electricity cluster, CHP was found to be the most impactful way of EE improvement (high energy savings) but not cost-effective. For fuel-consuming clusters, waste heat recovery measures were found to be the most cost-effective and among the most impactful options for EE improvement, along with fuel substitution measures with varying degrees of cost-effectiveness.

Light Load Efficiency Improvement for Three-Phase Inverter Employing Valley Switching Under Mixed TCM/DCM Operation

Light Load Efficiency Improvement for Three-Phase Inverter Employing Valley Switching Under Mixed TCM/DCM Operation

Explaining the environmental efficiency capability of energy mix innovation among the Nordic countries

Improving the environmental efficiency performance of energy mix is important to the global carbon neutrality goal. Given this motivation, this study delves into providing more explanation about the role of innovation approaches to coal, oil, and natural gas energy resources in the Nordic economies i.e., Denmark, Finland, Norway, and Sweden during the period 2000 to 2019. To achieve this objective and reach an evidence-based insight for policy formulation, the present study leveraged robust panel econometrics estimators that circumvent cross-sectional dependence and heterogeneity issues in the investigated blocs. The empirical evidence shows that economic growth (GDP), coal energy, oil and natural gas contribute to the Nordic economies' environmental degradation in the short- and long-run. Contrarily, innovation via environmental-related patents mitigates environmental degradation, especially in the long run. Furthermore, the interaction models, i.e., applying innovative approaches in the energy mix's consumption, can mitigate carbon emissions across the countries, thus significantly improving environmental sustainability. Specifically, innovative approaches in coal, oil, and natural gas consumption improve environmental sustainability by mitigating carbon emissions, thus delivering respective environmental efficiency improvements of ~94%, ~108%, and ~123%. Additionally, Granger causality evidence further suggests that the examined factors are significant drivers of the environmental indicator. Regarding policy insight, this study affirms that further improvement in energy savings is vital to continuously deploying the conventional energy mix.

The efficiency improvement for traffic operating and modifying delay time in al-Kadessah intersection at AL Ramadi city

The efficiency improvement for traffic operating and modifying delay time in al-Kadessah intersection at AL Ramadi city

Enhancing U.S. workforce productivity through strategic data automation: Key insights and implications

The review explores how strategic data automation can enhance workforce productivity in the U.S., providing key insights and implications for organizations. As businesses increasingly rely on data-driven decision-making, the role of automation in streamlining processes and improving efficiency becomes paramount. This review delves into the potential benefits and challenges of implementing strategic data automation strategies and offers recommendations for organizations looking to enhance their workforce productivity. Strategic data automation has the potential to revolutionize how businesses operate, leading to significant improvements in efficiency and productivity. By automating repetitive tasks and streamlining data processing, organizations can free up valuable time and resources, allowing employees to focus on more strategic and value-added activities. However, the successful implementation of strategic data automation requires careful planning and consideration of various factors, including data security, privacy, and regulatory compliance. Key insights from this review include the importance of aligning data automation initiatives with organizational goals and objectives, as well as the need for ongoing monitoring and evaluation to ensure effectiveness. Additionally, the review explores the implications of strategic data automation for the future of work, highlighting the potential for increased collaboration between humans and machines and the need for new skills and competencies. Overall, this review provides valuable insights into how strategic data automation can enhance workforce productivity in the U.S. It highlights the benefits of automation, such as improved efficiency and reduced costs, while also addressing potential challenges and considerations for organizations. By leveraging the power of strategic data automation, organizations can position themselves for success in an increasingly data-driven world.

Analysis of dual active bridge-based on-board battery charger for electric and hybrid vehicles

This paper presents an in-depth analysis of a Gallium Nitride (GaN) Dual Active Bridge (DAB)-based on-board battery charger for electric and hybrid vehicles. It focuses on the superior efficiency and reliability of GaN-based power electronics over traditional Silicon-based systems. The study explores the implementation of the DAB topology, recognized for its bidirectional power flow and high efficiency, in enhancing vehicle charging infrastructure. Efficiency analysis, switching behavior and losses are thoroughly examined. Simulation results in PSIM software validate the theoretical predictions, showing significant improvements in operational flexibility and energy efficiency.

Technological advancements in drilling: A comparative analysis of onshore and offshore applications

This paper examines the technological advancements in onshore and offshore drilling operations, emphasizing their significance, challenges, and environmental considerations. Technological innovations, including automation, data analytics, and real-time monitoring, have transformed drilling practices, enhancing efficiency, safety, and environmental sustainability. By comparing onshore and offshore applications, the paper evaluates the effectiveness of these advancements in addressing the unique challenges of each environment. Key areas of focus include efficiency improvements, safety enhancements, environmental impacts, and regulatory compliance. Through case studies and analysis, the study aims to provide valuable insights into the current state of onshore and offshore drilling operations and identify opportunities for innovation and improvement. Ultimately, the findings contribute to advancing sustainable practices, mitigating environmental risks, and fostering responsible resource extraction in the oil and gas industry. This comparative study delves into the dynamic landscape of technological advancements within onshore and offshore drilling operations. It explores the evolving role of automation, data analytics, and real-time monitoring in revolutionizing drilling practices, offering insights into their implications for efficiency, safety, and environmental stewardship. By juxtaposing the unique challenges and operational requirements of onshore and offshore environments, the study underscores the importance of a nuanced understanding of technological applications. Through rigorous analysis and case studies, it illuminates the interplay between advancements and environmental considerations, shedding light on opportunities for innovation and sustainable development. The study's comprehensive scope encompasses efficiency gains, safety protocols, environmental impact mitigation, and regulatory compliance, providing a holistic view of the transformative potential of technology in the drilling industry. Ultimately, the study serves as a catalyst for informed decision-making, fostering dialogue, and driving forward-looking strategies for the future of onshore and offshore drilling operations.

Accelerated improvement in tensile superelasticity of electron beam directed energy deposition manufactured NiTi alloys by artificial thermal cycling combined with low temperature aging treatment

ABSTRACT The low-temperature aging treatment at 250°C can significantly improve the tensile superelasticity of NiTi alloys fabricated by electron beam directed energy deposition (EB-DED). However, it requires a very long aging duration (up to 200 h) to achieve excellent tensile superelasticity due to inherent coarse grain size. To accelerate the aging process, the high-density dislocations are introduced by artificial thermal cycling treatment prior to the aging treatment (the original dislocation content in EB-DED processed NiTi alloys is very low), which will promote the subsequent uniform precipitation of nanoscale Ni4Ti3 particles during low-temperature aging treatment. The phase transformation behaviour always maintains a stable two-stage martensitic phase transformation. Under a cyclic tensile test at 6% strain, 24 h aged sample with thermal cycling maintains a recovery rate exceeding 90% even after 10 cycles, comparable to the performance of the sample aged for 200 h without thermal cycling, indicating a substantial improvement in aging efficiency.

A poly(L-lactic Acid)-based flexible piezoelectric energy harvester with micro-zigzag structures

Abstract Piezoelectric energy harvester (PEH) holds great potential for flexible electronics and wearable devices. However, the power conversion efficiency of flexible PEH (fPEH) has often been a limiting factor, especially under variable excitation. Herein, we propose a practical solution: a poly(L-lactic acid)-based fPEH with 3D-printed micro-zigzag structures. This design not only broadens the operational bandwidth and enhances low-frequency response but also offers a tangible improvement in the power conversion efficiency of fPEH. The micro-zigzag structure was designed and fabricated using a digital light processing 3D printing technique with acrylates, a method that is readily accessible to researchers and engineers in the field. Mechanical properties of the 3D-printed acrylic elastomers with different compositions were investigated to obtain the material parameters, and then fPEH with the sandwich structure was fabricated via sputtering and packaging. Subsequently, numerical simulation was conducted on the micro-zigzag structures to determine the structure sizes and oscillation frequencies of fPEH. Finally, four micro-zigzag structures with 3-, 4-, 5- and 6-mm lengths were tested to obtain oscillation frequencies of 51, 37, 22, and 21 Hz consistent with the simulation. The output voltages of fPEH are 11 ~ 30 mV with the load ranges of 60 ~ 100 MΩ. Stability evaluation showed that the fPEH can work under low frequency (<100 Hz) and broadband conditions. The micro-zigzag structure provided new insights for the design of fPEH, paving the way for more efficient and practical energy harvesting solutions in the future.

Desalination setup coated by silicone epoxy nanocomposite: A study on hydrophobic characteristics and condensation mechanisms

The formation of tortuous filmwise condensation in desalination setup alongside with reduction in thermal conductivity is a challenge accompanied by efficiency loss. This issue is directly influenced by hydrophobicity, which is a function of surface roughness, as well as surface chemistry. In this sense, the hydrophobicity of variant silicone epoxy based nanocomposites was evaluated to identify the optimum proportion composite for surface condensation of desalination setup. To this end, three types of fumed silica with distinct surface chemistry were utilized in a matrix at the weight percentage of 0.5, 1, 1.5, and 2. To investigate the hydrophobicity, roughness, and distribution of reinforcement, a series of characterization techniques, including contact angle, AFM, and FESEM were used. It was found that, in comparison to pure matrix, the nanocomposite modified with 5 wt % of hydrophobic additive and 0.5 wt % of hydrophilic fumed silica nanoparticles (SE/5B/0.5R200) has the highest contact angle (about 35%) and roughness (13 times), due to the migration of nanoparticle onto the surface. Also, coating the condensation surface of the desalination setup with optimum thickness, i.e. 60 × 10−6 m, resulted in the efficiency improvement by about 33 % compared to the uncoated setup, even after 168 hrs. Indeed, dropwise condensation, straight path movement of droplets, besides thermal conductivity adjust the efficiency.

Performance improvement of set of worm gears used in soot blower through profile modification

The present design of a set of worm gears used in a soot blower produced by a certain manufacturer has an efficiency of 68.8%. A soot blower is one of the most critical components in industrial applications for removing the large amounts of soot generated by boilers and is required to be operational 24×7. The energy consumption of the soot blower depends on its working efficiency and ultimately the design of its set of worm gears. This paper focuses mainly on the design and analysis of available industrial worm-gear sets used in soot blowers. The theoretical, experimental, and finite-element analysis approaches are validated for the stability of the worm gear set under typical input conditions. This paper also describes an analytical design of experiments (DOE) approach to identify the most significant factor for performance (efficiency) improvement and suggests some design improvements for the worm gear set using the profile modification approach. These ensure the efficiency improvement of the current industrial design of the set of worm gears used in a soot blower. The analytical DOE approach helped identify that the number of worm wheel teeth (Z2) and gear module (m) are the two most significant factors affecting performance. Accordingly, based on the improved design, the final efficiency increased from 68.8% to 74.6% (∼8.5% increment), resulting in lower power consumption during industrial application.

Factors Influencing Adherence to Hazardous Waste Management Policy Frameworks among Health Workers in Private Hospitals in Nairobi County – Kenya

Purpose: This study, therefore, sought to investigate the factors influencing the adherence to hazardous waste management guidelines among healthcare workers in private hospitals in Nairobi County. The specific objectives of the study included; determining the level of adherence to proper HCWM practices among health workers, identifying the individual related factors that influence adherence to proper HCWM practices among health workers, and identifying the health facility-related (institutional) factors that influence adherence to proper HCWM practices in 4 selected hospitals in Nairobi County Kenya. From the population of 900 healthcare workers from 4 levels 4 facilities, random sampling was applied to identify the 90 respondents. Methodology: Data analysis and reporting was done using appropriate quantitative methods. Findings: The findings from this study show that the level of adherence to hazardous waste management policies among healthcare workers in private level four hospitals in Nairobi County is low. Despite the high level of awareness by the health workers on the important measures of collection, segregation, treatment, transportation and disposal of waste, the level of performance on the same is low. This is greatly attributable to negligence and unfavorable attitude towards healthcare waste management practices among healthcare workers. Unique contribution to theory, policy and practice: Based on the study findings, we would recommend the improvement of hazardous waste management efficiency.

Examining the potential of sigma-thermic heat transfer fluid and its nanofluid in a natural circulation loop through CFD studies

This study investigates the impact of hot heat exchanger (HHX) inlet temperature on Sigma-thermic heat transfer fluid (STHF or STF) performance in a natural circulation loop. Various fluids, including STHF and STHF-based nanofluids, with volumetric concentrations ranging from 0.5% to 3%, are employed in the loop. The primary objective is to analyze the thermal performance of the loop under different conditions, focusing on temperature distribution, Nusselt number, friction factor, effectiveness, and mass flow rate. 3D numerical simulations are conducted, and the numerical model is validated against existing literature. The developed model incorporates considerations for viscous dissipation and axial conduction to predict the heat transfer potential of the loop. As the HHX inlet temperature increases, the mass flow rate rises. Notably, STHF/CuO nanofluids exhibit a more substantial enhancement than other nanofluids. At a 1 vol% concentration, the mass flow rate increases by 9.5%, 4%, and 2.7% for STHF/CuO, STHF/Al2O3, and hybrid nanofluids, respectively, compared to pure STHF. The study reveals significant improvements in mass flow rates and heat transfer efficiency with increasing HHX inlet temperatures, especially with STHF/CuO nanofluids. The total entropy generation reduction is notable, with percentages ranging from 2% to 18.5% for various nanofluids compared to pure STHF.

Automating Item Activation Process to Overcome Challenges for Error-free Inventory Operations

This article highlights the need for the successful implementation of a custom engine within Oracle Governance, Risk, and Compliance (GRC) Preventive Controls Governor (PCG) to address challenges encountered during the item activation process in Inventory. The existing manual processes exhibited a high error incident rate, leading to various issues in Bill of Materials (BOM), Costing, and Financial Accounting due to incorrect inventory item attributes. A comprehensive approach was implemented to address these issues. The processes were streamlined and optimized by leveraging controllership principles and implementing multilevel workflow-based approvals. Additionally, user-friendly webpages were developed using the Oracle Application Framework (OAF) to enhance the overall user experience. The implementation of the custom engine within Oracle GRC PCG proved to be a transformative game-changer. It enabled effective control and governance over the item activation process, ensuring data accuracy and integrity. Remarkably, the error incident rate was reduced to zero, resulting in significant improvements in productivity and operational efficiency. This article delves into the strategies employed, the achievements realized, and the profound impact on productivity, highlighting the successful integration of Oracle GRC PCG in optimizing critical business processes.

To Study Opportunities and Challenges of Big Data Analytics in Healthcare

Big data analytics in healthcare is a powerful tool that can bring about significant improvements in patient care, operational efficiency, and medical research. This study explores the various aspects of big data analytics in healthcare, aiming to uncover its many opportunities and challenges. Through a thorough review of existing literature and real-world case studies, this research highlights the potential benefits of big data analytics, such as better disease prediction, personalized treatment approaches, and optimized resource allocation. However, there are also notable challenges to overcome, including concerns about data privacy, problems with sharing information between different systems, and the need for skilled staff and strong infrastructure. By combining insights from different perspectives, this study provides a complete understanding of the current state of big data analytics in healthcare, offering valuable information for policymakers, healthcare professionals, and researchers to navigate this evolving field effectively.