Aspects Of Optimization Research Articles

Optimizing Early-Stage Design for Energy-Efficient Schools in Bandiagara, Mali

In Bandiagara, Mali, extreme heat, humidity, lack of modern amenities, and low literacy rates present significant challenges in building design. Designing a school aims to provide access to quality education, foster community development, and promote sustainable building practices to empower the local community through job creation and the use of local resources. To address these concerns, this study focuses on an early-stage school model to determine the optimal window-to-wall ratio (WWR), rotation, and aspect ratio of the building. The analysis process includes three steps: (1) determining the input values based on ASHRAE 90.1 2019; (2) analyzing models with various aspect ratios (1:1, 1.3:1, 1.5:1, 2:1, 2.7:1, 3.2:1, 4.2:1) to find the optimum energy use along with five different WWRs (0.1, 0.3, 0.5, 0.7, 0.9); (3) rotating a selected model to find the optimal orientation for the lowest energy consumption (30, 60, 90, 120, 150 and 180). The optimal aspect ratio, WWR, and rotation were chosen based on the optimum total energy consumption (cooling, heating, equipment and lighting) over a year. Energy analysis was executed through Climate Studio 2.0 in Rhino 8.14, following ASHRAE 90.1 2019 standards. The findings indicate that aspect ratios of1.3:1 and 1.5:1with WWRs 0.1 and 0.3 with no rotation (0-180o: west-east stretched) have the optimum total energy consumption [kWh/m2] for an educational building in this climate and Bandiagara. These choices could be a framework for designing energy-efficient educational buildings in the early stage without relying on extensive mechanical systems in this region or any other region in similar climates.

Influence of aspect ratios on water disinfection efficiency in a serpentine flow channel using ultraviolet C light-emitting diode

ABSTRACT The flow channel configuration affects microbial inactivation efficiency in water because it considerably influences hydrodynamics and ultraviolet (UV) light distribution. The ratio of channel length to breadth (α) and the ratio of channel width to depth (β) of a serpentine flow channel on Escherichia coli inactivation efficiency were investigated. Simulations revealed that when α was 1.0, the flow channel had a uniform velocity distribution and performed the longest microbial residence time. Furthermore, biodosimetry tests indicated that when β was 1.0, the reactor resulted in a maximum inactivation efficiency of 3.7 log when the distance between the UV LEDs and the water was 11.5 mm. The optimal aspect ratio β led to the ideal balance between microbial residence time and UV irradiance, resulting in the highest UV fluence (10.6 mJ/cm2) for the bacteria. The inactivation efficiency observed at the aforementioned aspect ratio was approximately 48% higher than that observed at an aspect ratio of 2.56. Additionally, decreasing the distance between the UV LEDs and the water from 11.5 to 3.5 mm enhanced the inactivation efficiency at all aspect ratios because of the considerable increase in UV irradiance in the water.

Efficiently Processing Joins and Grouped Aggregations on GPUs

There is a growing interest in leveraging GPUs for tasks beyond ML, especially in database systems. Despite the existing extensive work on GPU-based database operators, several questions are still open. For instance, the performance of almost all operators suffers from random accesses, which can account for up to 75% of the runtime. In addition, the group-by operator which is widely used in combination with joins, has not been fully explored for GPU acceleration. Furthermore, existing work often uses limited and unrepresentative workloads for evaluation and does not explore the query optimization aspect, i.e., how to choose the most efficient implementation based on the workload. In this paper, we revisit the state-of-the-art GPU-based join and group-by implementations. We identify their inefficiencies and propose several optimizations. We introduce GFTR, a novel technique to reduce random accesses, leading to speedups of up to 2.3x. We further optimize existing hash-based and sort-based group-by implementations, achieving significant speedups (19.4x and 1.7x, respectively). We also present a new partition-based group-by algorithm ideal for high group cardinalities. We analyze the optimizations with cost models, allowing us to predict the speedup. Finally, we conduct a performance evaluation to analyze each implementation. We conclude by providing practical heuristics to guide query optimizers in selecting the most efficient implementation for a given workload.

Efficient Deployment of Peanut Leaf Disease Detection Models on Edge AI Devices

The intelligent transformation of crop leaf disease detection has driven the use of deep neural network algorithms to develop more accurate disease detection models. In resource-constrained environments, the deployment of crop leaf disease detection models on the cloud introduces challenges such as communication latency and privacy concerns. Edge AI devices offer lower communication latency and enhanced scalability. To achieve the efficient deployment of crop leaf disease detection models on edge AI devices, a dataset of 700 images depicting peanut leaf spot, scorch spot, and rust diseases was collected. The YOLOX-Tiny network was utilized to conduct deployment experiments with the peanut leaf disease detection model on the Jetson Nano B01. The experiments initially focused on three aspects of efficient deployment optimization: the fusion of rectified linear unit (ReLU) and convolution operations, the integration of Efficient Non-Maximum Suppression for TensorRT (EfficientNMS_TRT) to accelerate post-processing within the TensorRT model, and the conversion of model formats from number of samples, channels, height, width (NCHW) to number of samples, height, width, and channels (NHWC) in the TensorFlow Lite model. Additionally, experiments were conducted to compare the memory usage, power consumption, and inference latency between the two inference frameworks, as well as to evaluate the real-time video detection performance using DeepStream. The results demonstrate that the fusion of ReLU activation functions with convolution operations reduced the inference latency by 55.5% compared to the use of the Sigmoid linear unit (SiLU) activation alone. In the TensorRT model, the integration of the EfficientNMS_TRT module accelerated post-processing, leading to a reduction in the inference latency of 19.6% and an increase in the frames per second (FPS) of 20.4%. In the TensorFlow Lite model, conversion to the NHWC format decreased the model conversion time by 88.7% and reduced the inference latency by 32.3%. These three efficient deployment optimization methods effectively decreased the inference latency and enhanced the inference efficiency. Moreover, a comparison between the two frameworks revealed that TensorFlow Lite exhibited memory usage reductions of 15% to 20% and power consumption decreases of 15% to 25% compared to TensorRT. Additionally, TensorRT achieved inference latency reductions of 53.2% to 55.2% relative to TensorFlow Lite. Consequently, TensorRT is deemed suitable for tasks requiring strong real-time performance and low latency, whereas TensorFlow Lite is more appropriate for scenarios with constrained memory and power resources. Additionally, the integration of DeepStream and EfficientNMS_TRT was found to optimize memory and power utilization, thereby enhancing the speed of real-time video detection. A detection rate of 28.7 FPS was achieved at a resolution of 1280 × 720. These experiments validate the feasibility and advantages of deploying crop leaf disease detection models on edge AI devices.

A HOLISTIC APPROACH TO HPLC TECHNIQUEFOR ADDRESSING PROCESS IMPURITIES: REVIEW ONMETHOD DEVELOPMENT, OPTIMIZATION, AND VALIDATION

High-Performance Liquid Chromatography (HPLC) has become an indispensable analytical tool in pharmaceutical, environmental, and food sciences due to its precision, sensitivity, and versatility. A holistic approach to HPLC method development, optimization, and validation ensures reliable and reproducible results while meeting regulatory requirements. This review outlines the critical steps in HPLC method development, emphasizing the importance of understanding analyte properties, selecting appropriate chromatographic conditions, and fine-tuning parameters for optimal performance. Key aspects of method optimization, such as mobile phase composition, column selection, flow rate, and pH, are discussed to enhance resolution and reduce analysis time. Additionally, the article delves into the rigorous validation process, which includes assessing accuracy, precision, specificity, linearity, and robustness in compliance with International Council for Harmonisation (ICH) guidelines. By integrating systematic method development, experimental optimization, and thorough validation, this holistic framework ensures efficient and high-quality analytical methods, driving advancements in research and quality assurance across various industries.

Global Length and Overhang Control for Level Set and Density Approaches via Perimeter Minimization

ABSTRACTTopology optimization is probably one of the most efficient techniques for structural design. However, running topology optimization without geometry control provides complex designs, which often are manufactured with additive manufacturing methods. Consequently, a fundamental aspect in topology optimization is to consider the following additive manufacturing constraints: minimal length scale and overhang. The aim of this paper is to propose a new numerical method to control globally such additive manufacturing constraints. The idea relies on penalizing a regularized version of the perimeter: an isotropic version for global length and an anisotropic version for overhang. Besides, we show that the method may be used with density and level set approaches. Several numerical examples, including compliant mechanisms and material design, show that some bars have been removed, decreasing the complexity of the shape, and vertical tendency orientation of boundaries is generally obtained.

Synthesis, crystal structure, hirshfeld study, DFT analysis, molecular docking study, antimicrobial activity of β-enaminonitrile bearing 1H-pyran

3-Amino-1-(3,4-dimethoxyphenyl)-8-methoxy-1H-benzo[f]chromene-2-carbonitrile (3-ABC, 4) was prepared and affirmed through the spectral data and single crystal X-rays diffraction. The molecule spatial attributes were revealed by the three-dimensional Hirshfeld surface, which is validated by comprehensive statistical evaluations. The key interactions that contribute to the crystal's stability and properties, such as π–π stacking and H⋯X contacts, were highlighted. The dominant forces in the crystal were explained by energy framework calculations and density functional theory analyses, which also show the geometric optimization aspects and molecular reactivity indicators. The focus is on the investigation of frontier molecular orbitals, aromaticity, and π–π stacking abilities. The research concluded by identifying electron density distributions, aromatic features, and possible reactivity sites, offering a complete picture of the compound's structural and electronic landscape. Compound 4 was screened for its antimicrobial capability against three Gram-positive, three Gram-negative bacteria, and three fungi, utilizing the reference antibiotic drug Ampicillin, Gentamycin, and Ketoconazole. Compound 4 exhibited favorable antimicrobial activities that were lower/resembled the reference antimicrobial agents with an IZ range of 15–31 mm. In addition, MIC, MBC, and MFC were assessed and screened for the target molecule 4, unveiling its revealing antimicrobial activities. Lastly, the molecular docking evaluation was implemented for the molecules interaction with the DHFR protein. The compounds exhibit significant interactions with the EGFR protein's active site.

Understanding the Unique Reactivity of Cu for Electrochemical CO2 Reduction with a 3-Site Model.

Cu-based catalysts for the electrochemical reduction of CO2 and CO exhibit a perplexingly unique reactivity toward multicarbon based products compared to other studied electrocatalysts. Here we use insights gained from a recent phenomenological 3-site microkinetic model and grand-canonical density functional theory calculations to clarify the importance of an underemphasized aspect critical to Cu's unique reactivity: a population of so-called "reservoir" sites. Using model Cu surface motifs, we discuss how these types can be represented by undercoordinated structural defects like step edges and grain boundaries which form a network of highly anisotropic migration channels. These pathways are found to be amenable for feeding *CO over time to reactive sites like Cu adatoms more active toward C-C coupling. These results highlight an often overlooked aspect of catalyst optimization: reservoir site engineering, which exploits surface mobility and presents an equally important avenue for electrocatalyst engineering oversimply maximizing active site densities.

Artificial Intelligence in Project Management: Enhancing Decision-Making, Efficiency and Risk Management

Artificial Intelligence (AI) has emerged as a transformative force in project management, redefining traditional workflows and enhancing overall project outcomes. This study reviews and synthesizes findings from over 50 research papers to assess the impact of AI on various aspects of project management, including decision-making, resource optimization, risk management, and workflow automation. AI-powered tools such as predictive analytics, task automation platforms, and Natural Language Processing technologies have demonstrated significant potential in improving project efficiency, accuracy, and collaboration. By automating routine tasks, providing actionable insights through data-driven analytics, and enabling real-time communication, AI empowers project managers to make informed strategic decisions and address risks proactively. The findings also reveal critical challenges, including high implementation costs, integration complexities, ethical concerns, and the difficulty in processing unstructured data. Despite these limitations, the study highlights the substantial advantages of AI in reducing human error, enhancing resource allocation, and ensuring timely project completion. By synthesizing extensive literature, this research provides a comprehensive understanding of AI's transformative role in project management while offering practical insights into its adoption. It concludes that addressing the challenges of AI integration through ethical frameworks and thoughtful implementation strategies is

The status of MATLAB in the Software Development Market

With the continuous development of technology, it is necessary to analyze the applicability of MATLAB in the field of software development according to the mainstream trend of the market. This article aims to outline the design purpose of MATLAB and its applicable user groups, and emphasizes the advantages of MATLAB in scientific computing and engineering applications. By comparing it with other programming languages such as Python, this article further highlights the unique advantages of MATLAB in computing performance and functions, thereby clarifying its importance and application value in related fields. This article concludes that as a programming language specializing in computing, MATLAB is not only loved by professionals in engineering, finance, physics, and other fields but also has a high status in education. Through the experiment of developing small games and comparing the rich functions of Python in software development, it points out the immaturity of MATLAB in UI design, code optimization, data transmission, and other aspects. The subsequent research provides some improvements that MATLAB can make in code performance optimization and loop performance. The significance of this study is to let people who are new to software development choose a more suitable programming language.

Unlocking Insights with Natural Language: The Role of Natural Language Interfaces in Modern Data Exploration

Natural Language Interfaces (NLIs) for data exploration represent a transformative advancement in data analytics, combining sophisticated natural language processing with modern artificial intelligence techniques to democratize data access and analysis. This article presents a comprehensive examination of NLI architectures, exploring their core components including semantic parsing, intent recognition, and entity extraction systems, while detailing their integration with large language models and transformer-based architectures. This article analyzes the technical challenges and solutions in implementing NLIs within enterprise environments, particularly focusing on their integration with major data warehouses like BigQuery and Snowflake, and discusses crucial aspects of scalability, performance optimization, and security considerations. This article also addresses the critical role of business intelligence integration, exploring how NLIs enhance data visualization and real-time analytics capabilities. Examination of emerging technologies and industry applications demonstrates how NLIs are evolving to meet complex enterprise requirements while simultaneously lowering the technical barriers to sophisticated data analysis, ultimately driving innovation in decision-making processes across various sectors.

Proteinoid-polyaniline neuromorphic composites for audio recognition

Abstract We present an innovative neuromorphic system using a proteinoid-polyaniline (PANI) composite for recognition of audio inputs of the English alphabet. Neuromorphic devices, which draw inspiration from the neural networks of the brain, have emerged as very promising potential solutions for efficient signal processing. The proteinoid-PANI composite was synthesized through a template-free method, resulting in a unique nanostructure consisting of both nanorods and nanospheres. Principal component analysis (PCA), spectrogram analysis, and temporal spiking response analysis were among the signal processing methods used to examine the composite's audio response to English alphabet stimuli. The system showed a moderate positive correlation between input and output signals, unique time-frequency response patterns, and convoluted spiking behaviour. In addition, the output amplitude showed less variation compared to the input, while maintaining the same temporal characteristics.
Microscopic analysis provided detailed information about the morphology of the composite. The nanorods displayed an optimal aspect ratio and had diameters of around 
100~nm, while the nanospheres varied in size, ranging from 200 to 500~nm in diameter. The nanostructure, morphological characteristics, and signal processing properties of the proteinoid-PANI composite demonstrate its potential for advanced applications in neuromorphic computing and signal processing, particularly in speech recognition and human-machine interaction.

Optimal Economic and Environmental Aspects in Different Types of Loads via Modified Capuchin Algorithm for Standalone Hybrid Renewable Generation Systems

Greenhouse gas emissions have become a significant concern for many countries due to their effect on the global economy and environment. This work discusses a standalone hybrid renewable generation system (HRGS) for use in isolated areas with different load demand profiles. Three load profiles were studied in this work: educational, residential, and demand-side management (DSM)-based residential load profiles. To investigate the economic and environmental aspects, a proposed modified capuchin search algorithm (MCapSA) was implemented, and the obtained results were compared with those of different conventional optimal procedures, such as the genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. The Levy flight distribution method, which is based on random movement, enhances the capuchin algorithm’s search capabilities. The cost of energy (CoE), electric source deficit (ESD), greenhouse gas (GHG) emissions, and renewable factor (RF) indicators were all optimized and estimated to emphasize the robustness of the proposed optimization technique. The results reveal that the shift in the residential load profile based on individual-household DSM-scale techniques leads to significant sharing of renewable sources and a reduction in the utilization of diesel generators, consequently diminishing GHG emissions. The proposed MCapSA achieved optimal values of economic and environmental aspects that are equal to or less than those achieved through PSO. From the overall results of the three scenarios, the modified algorithm gives the best solution in terms of GHG, COE, and ESD compared to other existing algorithms. The usage of MCapSA resulted in decreases in COE and GHG in three types of loads. The robustness and effectiveness of MCapSA are demonstrated by the fact that the DSM-based optimal configuration of the renewable energy sources produces the lowest CoE and GHG emissions of 0.106 USD/kWh and 137.2 kg, respectively.

Evaluasi Nilai Diagnostic Reference Level (DRL) pada Pemeriksaan CT Scan Abdomen 3 Fase di Rumah Sakit Umum Pusat Persahabatan

Background: Diagnostic Reference Levels (DRL) are stated in CTDI and DLP hence due to an accurate parameter selection, good control practices and the use of appropriate DRL, they will significantly reduce the radiation dose to the patient by more than 50%. In addition to DRL, the value of estimated effective dose and estimated cancer risk is as additional information to assist in several aspects of justification and optimization as well as to become an educational device that increases the awareness of the risk of cancer. This research also aims to obtain DRL value for a 3-phase abdominal CT scan because Indonesia does not yet have a DRL for a 3-phase abdominal CT scan. Methods: This research uses an observational approach with descriptive quantitative methods using secondary data. As for the data analyzed herein, they involve the patient information and the radiation dose received during a 3-phase abdominal CT scan obtained from the dose report on the CT Scan machine workstation. Meanwhile, the research population included all the patients who underwent a 3-phase abdominal CT scan undertaken at Radiology Installation of Persahabatan Central General Hospital starting from January up to March 2024 with a total population of 40 patients. The number of samples used was 30 samples. The collected data are processed and analyzed using a descriptive frequency test and then compared with DRLs from the other countries and Abdominal I-DRL, AbdoPelvis and Whole Abdomen. Furthermore, it is then calculated to obtain the estimated effective dose and cancer risk which is presented in tabular form. Results The results of this research suggests that DRL value at CTDI Persahabatan Central General Hospital is lower than that in the other countries and below Abdominal I-DRL, AbdoPelvis and Whole Abdomen, and DLP value is much higher than that in the other countries and above Abdominal I-DRL DLP, AbdoPelvis and Whole Abdomen.

Scaling Cloud-Based Transaction Systems: How Modern Architectures Handle Growing Demand

This comprehensive article examines how modern cloud architectures handle the scaling challenges of transaction-based systems in today's digital economy. It explores the evolution from traditional monolithic architectures to sophisticated cloud-based solutions, analyzing various scaling approaches, including vertical scaling, horizontal scaling, and autoscaling mechanisms. The article investigates key components such as load balancing, distributed databases, and implementation strategies that enable organizations to maintain high performance and reliability under growing demands. Through a detailed examination of real-world implementations, the article demonstrates how advanced cloud architectures leverage intelligent automation, predictive analytics, and distributed processing to overcome traditional scaling limitations. Special attention is given to the financial technology and e-commerce sectors, where system performance directly impacts business success. The article also addresses critical aspects of fault tolerance, disaster recovery, and cost optimization in scaled cloud environments.

The innovation of engineering project management in the context of big data

ABSTRACT With the development of China’s economy and society, the society continues to move forward. Related to this, the scale of China’s engineering construction is also increasing. With the development of the times and the continuous growth of demand, the complexity and specialisation of engineering projects continue to improve. This has put forward more stringent requirements for the construction of engineering projects. As the traditional engineering project construction management in China, the innovation is mainly through the practice of construction projects and the research of the engineering problems that arise. So, in today’s environment of big data background, the innovation of construction project management is to combine with the actual situation of construction projects, from the construction characteristics of the project, using a variety of advanced scientific technology, for the project data collection, calculation and storage applications and other aspects of optimisation and innovation, so that more convenient to achieve The modern construction engineering project management innovation work of diversified needs, for the final completion of the entire construction project can be expected to provide a strong guarantee and support the comprehensive benefits of the goal. The results show that our innovative scheme outperforms other comparative methods and achieves a 23% improvement in project management efficiency.

Inexpensive advanced diatomite evaporator with optimal structure and density for efficient seawater desalination

Inexpensive advanced diatomite evaporator with optimal structure and density for efficient seawater desalination

Mechanics of elliptical interlocking sutures in biological interfaces

Mechanics of elliptical interlocking sutures in biological interfaces

A convenient one-pot –three component syntheses of a new diaminophosphonate via heterogeneous organo-catalyst, and its application for titanium recovery: design, optimization and kinetic aspects

A convenient one-pot –three component syntheses of a new diaminophosphonate via heterogeneous organo-catalyst, and its application for titanium recovery: design, optimization and kinetic aspects

Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model

Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model.