Operational Limitations Research Articles

Implementation and Mathematical Modelling of Data Sharing and Privacy Preserving Technique in a Blockchain Network by Using Machine Learning Algorithms

Blockchain technology has emerged as a reliable and secure decentralized network with multifaceted applications in banking, finance, insurance, healthcare, and business domains. Recent trends within blockchain communities indicate a growing interest in deploying machine learning models to extract valuable insights from extensive, geographically dispersed datasets owned by individual participants. To enable learning models without centralized data repositories, extensive research has focused on developing machine learning algorithms tailored for blockchain networks. However, despite numerous proposals, privacy and security concerns remain inadequately addressed, revealing vulnerabilities in architecture and operational efficiency limitations. The proposed ensemble machine learning model presents a pioneering solution, aiming to systematically resolve privacy, security, and performance issues within blockchain systems. The novelty of this approach lies in its targeted resolution of critical challenges at the intersection of blockchain technology and machine learning. While prior research has delved into integrating machine learning into blockchain networks, this model stands out by introducing a privacy-centric methodology that systematically addresses the core issues of privacy, security, and performance. Moreover, the proposed model promises enhanced resilience against adversarial attacks compared to other aggregation rules within a differentially private scenario. The innovative ensemble machine learning model for blockchain exhibits a significant 20% improvement in data privacy, a 15% boost in security, and a notable 25% enhancement in operational performance.

Breathing Clean Air: Navigating Indoor Air Purification Techniques and Finding the Ideal Solution.

The prevalence of airborne pathogens in indoor environments presents significant health risks due to prolonged human occupancy. This review addresses diverse air purification systems to combat airborne pathogens and the factors influencing their efficacy. Indoor aerosols, including bioaerosols, harbor biological contaminants from respiratory emissions, highlighting the need for efficient air disinfection strategies. The COVID-19 pandemic has emphasized the dangers of airborne transmission, highlighting the importance of comprehending how pathogens spread indoors. Various pathogens, from viruses like SARS-CoV-2 to bacteria like Mycobacterium (My) tuberculosis, exploit unique respiratory microenvironments for transmission, necessitating targeted air purification solutions. Air disinfection methods encompass strategies to reduce aerosol concentration and inactivate viable bioaerosols. Techniques like ultraviolet germicidal irradiation (UVGI), photocatalytic oxidation (PCO), filters, and unipolar ion emission are explored for their specific roles in mitigating airborne pathogens. This review examines air purification systems, detailing their operational principles, advantages, and limitations. Moreover, it elucidates key factors influencing system performance. In conclusion, this review aims to provide practical knowledge to professionals involved in indoor air quality management, enabling informed decisions for deploying efficient air purification strategies to safeguard public health in indoor environments.

ANALYSIS OF INVENTORY MANAGEMENT IN A BAKERY INDUSTRY

This case study analyzes the wheat stock management, a crucial input in the bakery industry, in a company located in the Greater Vitória region, Espírito Santo, Brazil. Using the case study methodology, the objective is to identify ways to save and reduce costs, considering the company's logistics and production needs. The Economic Order Quantity (EOQ) system through the Continuous Review Model is employed in the analysis of Inventory Management. Prioritizing the French bread, the best-selling product, the application of the ABC method highlights wheat flour as essential. The calculation of lead time, safety stock, and economic order quantity proved promising; however, delivery logistics pose a challenge. The 600-bag limit per truck and the location in a residential area hinder larger orders. The proposed safety stock does not even cover a day's production, requiring adjustments to prevent interruptions. Although the continuous review model presents advantages, logistical and operational limitations must be considered. A hybrid approach may be more suitable to ensure operational efficiency and continuity of operations.

Beneficiary appointment and delivery planning in a conflict setting

AbstractIn this study, we explore the challenges faced by humanitarian organizations engaged in relief efforts for internally displaced individuals during armed conflicts. Based on our semistructured interviews with three local nongovernmental organizations (LNGOs) in Syria, we introduce a new appointment scheduling problem to improve decision‐making for aid delivery planning in conflict settings. Operating in a highly resource‐constrained environment, these LNGOs face complexities that necessitate effective decision support tools to streamline supply delivery at relief facilities, where a large number of registered beneficiaries are served. Our proposed appointment scheduling problem aims to optimize the allocation of delivery times for various supplies, taking into account the urgency of needs and operational limitations. We present a heuristic that addresses the complexities of the proposed scheduling problem in a flexible way. The heuristic can accommodate simple rules derived from LNGOs' operational policies on the ground, such as imposing a single visit per beneficiary, delivering a single supply type per day, and preallocating time slots to conflict groups. We present a case study based on the Latakia district of Syria to assess the performance of our heuristic and the effectiveness of simplified delivery strategies. Our results not only showcase the efficiency of the heuristic, but also provide valuable managerial insights. We find that cross‐training of staff is more beneficial when supplies are relatively abundant. Furthermore, the simplified delivery policies are effective in certain conditions contingent upon various factors, including supply scarcity, difficulty of travel, and the level of conflict in the population.

Combined economic emission load dispatch solution using Cuckoo Search Algorithm

The Cuckoo Search Algorithm (CSA) has been selected to find the best optimum solution for Combined Economic and Emission Load Dispatch Network (CEELD) problems. The objective, in the (CEELD) Analysis, that operating a generator schedule is necessary with both minimum fuel costs and emission levels, together, while satisfying the load demand and operational limitations. In this paper, the objective of research is oriented to minimize the fuel cost and emission for generation sets. The numerical results captured from the suggested method are compared with other techniques such as the Gravitational Search Method, strength Pareto evolutionary algorithm and nondominated sorting genetic algorithm-II to illustrate the efficiency of suggested method.

Climate-Adaptive Nature-Based Regenerative Urban Green Streetscapes: Design Exploration from the City of Matera

Over the past two decades, global cities have been addressing climate challenges by transforming their gray infrastructural spaces through climate-adaptive and nature-based regeneration processes. These efforts also aim to tackle local ecological, social, and economic disparities. Despite the prevailing focus on technical and performance-based approaches, research on climate-adaptive, nature-based regeneration of streetscapes remains limited and has yet to significantly influence design-driven approaches to urban regeneration. This paper seeks to address these gaps by emphasizing the importance of spatial dimensions in overcoming current theoretical and operational limitations. It introduces a research experience that aims to leverage the potential of design to promote climate-adaptive, nature-based green regeneration of streetscapes and to integrate this approach into mainstream practices through a “research-by-design” methodology. To achieve this, an operational framework has been developed and is presented here, offering both conceptual and practical insights for creating climate-adaptive, nature-based streetscapes. Following the proposed methodology, two pilot design cases are introduced and discussed, both located in the Italian city of Matera: Piccianello and La Martella. This iterative process aims to establish a framework for sustainable, long-term urban resilience, making cities greener, more adaptive, and more equitable.

A colorimetric biosensor with infrared sterilization based on CuSe nanoparticles for the detection of E. coli O157:H7 in food samples.

Escherichia coli O157:H7 is a major threat to public health. At present, the detection methods for E. coli O157:H7 mainly include traditional bacterial culture, immunology (enzyme-linked immune-sorbent assay) and molecular biology techniques (polymerase chain reaction). These methods have the limitations of professional operation, waste of time and energy, and high cost. Therefore, we have developed a simple, fast, bactericidal colorimetric biosensor to detect E. coli. O157:H7. The entire process was completed in 80 minutes. The method has been successfully applied to milk and mineral water samples with satisfactory results, proving that the method is an effective method for real-time detection and inactivation of bacteria.

An improved rule-based control of battery energy storage for hourly power dispatching of photovoltaic sources

This paper presents an improved rule-based control scheme for battery energy storage (BES) system with the goal of minimising the fluctuation output from photovoltaic (PV) sources while ensuring the operational constraints of BES are regulated at the specified ranges for safety purposes. The control scheme is formulated in accordance with the intended operational limitations of the BES, including charge/discharge current limits and state-of-charge (SOC). The simulation studies were carried out using MATLAB/Simulink to evaluate the effectiveness of an improved rule-based control scheme on the 1.2 MW PV system data acquired from a location in Malaysia. Furthermore, a comparative study on the effectiveness of an improved rule-based control scheme compared with the conventional rule-based control scheme has been carried out. The simulation results show that an improved rule-based control scheme can effectively reduce the fluctuations in the output power of the PV sources and dispatch the output to the utility grid on an hourly basis with an efficiency of up to 94.47%. Finally, the comparison results on the effects of the BESS capacity also illustrate that an improved rule-based control scheme is more effective compared to the conventional rule-based control scheme in the previous study.

A Critical Analysis of the Standard Urban Model in the Context of Greater Manchester

This study critically examines the applicability of the Standard Urban Model (SUM) in the context of Greater Manchester, utilizing empirical data and spatial analysis to explore its operational limitations and potentials. Originally conceptualized by William Alonso, the SUM provides a theoretical framework to understand intra-urban economic dynamics predicated on a monocentric city structure. Through a comprehensive dataset encompassing census demographics, housing prices, and GIS-mapped land-use patterns from 2015, this paper investigates the models ability to predict and describe urban land rent gradients and residential distribution relative to the citys Central Business District (CBD). The findings indicate that while the SUM may provide a generalized view of urban structure, it significantly underestimates the socio-economic diversities, technological advancements, and cultural influences that are inherent in urban development. Particularly, the analysis reveals that rental price declines with distance from the CBD are not as pronounced as predicted by the SUM, with a low coefficient of determination (R=0.2551). Furthermore, the study highlights how urban development in Manchester deviates from the monocentric model due to historical shifts, regeneration efforts, and the emergence of multi-centric hubs. This research underscores the necessity for adapting urban economic models to reflect the complexities and dynamic nature of modern cities, suggesting a shift towards more nuanced and flexible frameworks that accommodate the multifaceted influences on urban landscapes.

Evolution of Industrial Robots from the Perspective of the Metaverse: Integration of Virtual and Physical Realities and Human–Robot Collaboration

During the transition from Industry 4.0 to Industry 5.0, industrial robotics technology faces the need for intelligent and highly integrated development. Metaverse technology creates immersive and interactive virtual environments, allowing technicians to perform simulations and experiments in the virtual world, and overcoming the limitations of traditional industrial operations. This paper explores the application and evolution of metaverse technology in the field of industrial robotics, focusing on the realization of virtual–real integration and human–machine collaboration. It proposes a design framework for a virtual–real interaction system based on the ROS and WEB technologies, supporting robot connectivity, posture display, coordinate axis conversion, and cross-platform multi-robot loading. This paper emphasizes the study of two key technologies for the system: virtual–real model communication and virtual–real model transformation. A general communication mechanism is designed and implemented based on the ROS, using the ROS topic subscription to achieve connection and real-time data communication between physical robots and virtual models, and utilizing URDF model transformation technology for model invocation and display. Compared with traditional simulation software, i.e., KUKA Sim PRO (version 1.1) and RobotStudio (version 6.08), the system improves model loading by 45.58% and 24.72%, and the drive response by 41.50% and 28.75%. This system not only supports virtual simulation and training but also enables the operation of physical industrial robots, provides persistent data storage, and supports action reproduction and offline data analysis and decision making.

In situ Production of Hydroxyl Radicals via Three‐Electron Oxygen Reduction: Opportunities for Water Treatment

The electro‐Fenton (EF) process is an advanced oxidation technology with significant potential; however, it is limited by two steps: generation and activation of H2O2. In contrast to the production of H2O2 via the electrochemical two‐electron oxygen reduction reaction (ORR), the electrochemical three‐electron (3e‐) ORR can directly activate molecular oxygen to yield the hydroxyl radical (·OH), thus breaking through the conceptual and operational limitations of the traditional EF reaction. Therefore, the 3e‐ ORR is a vital process for efficiently producing ·OH in situ, thus charting a new path toward the development of green water‐treatment technologies. This review summarizes the characteristics and mechanisms of the 3e‐ ORR, focusing on the basic principles and latest progress in the in situ generation and efficient utilization of ·OH through the modulation of the reaction pathway, shedding light on the rational design of 3e‐ ORR catalysts, mechanistic exploration, and practical applications for water treatment. Finally, the future developments and challenges of efficient, stable, and large‐scale utilization of ·OH are discussed based on achieving optimal 3e‐ ORR regulation and the potential to combine it with other technologies.

In Situ Production of Hydroxyl Radicals via Three-Electron Oxygen Reduction: Opportunities for Water Treatment.

The electro-Fenton (EF) process is an advanced oxidation technology with significant potential; however, it is limited by two steps: generation and activation of H2O2. In contrast to the production of H2O2 via the electrochemical two-electron oxygen reduction reaction (ORR), the electrochemical three-electron (3e-) ORR can directly activate molecular oxygen to yield the hydroxyl radical (⋅OH), thus breaking through the conceptual and operational limitations of the traditional EF reaction. Therefore, the 3e- ORR is a vital process for efficiently producing ⋅OH in situ, thus charting a new path toward the development of green water-treatment technologies. This review summarizes the characteristics and mechanisms of the 3e- ORR, focusing on the basic principles and latest progress in the in situ generation and efficient utilization of ⋅OH through the modulation of the reaction pathway, shedding light on the rational design of 3e- ORR catalysts, mechanistic exploration, and practical applications for water treatment. Finally, the future developments and challenges of efficient, stable, and large-scale utilization of ⋅OH are discussed based on achieving optimal 3e- ORR regulation and the potential to combine it with other technologies.

Noise considerations for the distributed modular-based approach to cryptocurrency data centers

While the establishment of cryptocurrency data centers becomes increasingly prevalent, the significant noise impact of these facilities on the surrounding community and environment has also become of interest. One of the most common designs for cryptocurrency mining facilities is to utilize a set of modular data centers, commonly constructed out of modified shipping containers, which are distributed over a large space. Each individual data center module can contain a large number of cryptocurrency mining computers, with each of these equipped with its own cooling fan system. The individual miner fans, coupled with the large ventilation fans required to cool the data center modules themselves, can lead to a complex, widespread acoustic environment that can be difficult to mitigate. This work presents commonly implemented noise mitigation strategies for cryptocurrency mining operations and the common issues that come along with those strategies, as well as operational limitations that must be considered when designing the proper noise control solution. Applications to regulatory constraints on noise, or lack thereof, are discussed.

Control, Operating Limitations, and Stability Analysis for Single-Phase Quasi-Z Source Inverters With Reduced Capacitance

Control, Operating Limitations, and Stability Analysis for Single-Phase Quasi-Z Source Inverters With Reduced Capacitance

African Lovegrass Segmentation with Artificial Intelligence Using UAS-Based Multispectral and Hyperspectral Imagery

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.

Design and Development of an Automated PLC Electropneumatic Conveyor System Apparatus: An Industrial Process Simulator for Teaching Electrical/Electronics Technology Courses

The study was focused on the design and development of An Industrial Process Simulator for Teaching Electrical/ElectronicTechnology Courses . This project development research tried to achieve the following objectives: (1. To design and construct a valuable, innovative and inexpensive prototype for electrical and electronics instructional equipment; 2. Test the performance of the developed prototype in conformity to its design parameters and operational limitations; and 3. To determine the acceptability of the developed prototypes in terms of its functionality, aesthetics, economy, workability, durability, safety and instructional applicability). The five-point Likert’s scale was used to determine the descriptive meaning of the indicators of the variables used. Furthermore, the Weighted Average Mean (WAM) was used to interpret the equivalent meaning of the data gathered. Evaluation result shows that the project obtained an overall mean of 4.72 which means that the completed project is “HIGHLY ACCEPTABLE” to the evaluators based on the criteria of functionality, aesthetics, durability,workability economy, safety and instructional applicability

Current Status and Development Trends of Morphing Wing of Aircraft

Abstract:: Aircraft morphing wings, also known as adaptive wings or shape-variable wings, represent a revolutionary development in the field of aerospace engineering. Inspired by the adaptability observed in birds and insects during flight, researchers have been exploring potential applications of morphing wing technology to enhance the performance and efficiency of aircraft. This innovative technology holds the promise of improving aerodynamic efficiency, reducing fuel consumption, and enhancing overall flight maneuverability. This manuscript aims to explore and analyze the potential applications and effects of morphing wing technology for aircraft. Furthermore, this paper aims to gain an in-depth understanding of the principles and implications of morphing wing technology for aircraft and to assess its potential advantages in terms of flight performance, handling characteristics, and energy efficiency. Based on different driving mechanisms, patents related to aircraft morphing wings were systematically categorized and summarized, highlighting the most typical intra-wing and extra-wing deformations. Through an examination of patents related to aircraft morphing wings, various morphing wing technologies' unique characteristics were summarized. A comparative analysis of different deformation methods revealed their respective strengths and limitations. The paper concludes with a summary of current technical challenges facing morphing wing technology and a forward-looking exploration of its future trends and directions. Categorizing aircraft morphing wings into intra-wing and extra-wing deformations based on the method of deformation, this paper elaborates on the operational principles, advantages, and limitations of these two categories. Compared to traditional fixed wings, morphing wings exhibit superior flight performance, and it is anticipated that more patents will be developed in the future.

An empirical study of excitation and aggregation design adaptions in CLIP4Clip for video–text retrieval

CLIP4Clip model transferred from the CLIP has been the de-factor standard to solve the video clip retrieval task from frame-level input, triggering the surge of CLIP4Clip-based models in the video–text retrieval domain. In this work, we rethink the inherent limitation of widely-used mean pooling operation in the frame features aggregation and investigate the adaptions of excitation and aggregation design for discriminative video representation generation. We present a novel excitation-and-aggregation design, including (1) The excitation module is available for capturing non-mutually-exclusive relationships among frame features and achieving frame-wise features recalibration, and (2) The aggregation module is applied to learn exclusiveness used for frame representations aggregation. Similarly, we employ the cascade of sequential module and aggregation design to generate discriminative video representation in the sequential type. Besides, we adopt the excitation design in the tight type to obtain representative frame features for multi-modal interaction. The proposed modules are evaluated on three benchmark datasets of MSR-VTT, ActivityNet and DiDeMo, achieving MSR-VTT (43.9 R@1), ActivityNet (44.1 R@1) and DiDeMo (31.0 R@1). They outperform the CLIP4Clip results by +1.2% (+0.5%), +4.5% (+1.9%) and +9.5% (+2.7%) relative (absolute) improvements, demonstrating the superiority of our proposed excitation and aggregation designs. We hope our work will serve as an alternative for frame representations aggregation and facilitate future research.

A numerical scale-up study of annular fluidized bed reactor with high centrifugal acceleration

A rotating fluidized bed imposes a strong centrifugal force to form the dense annular bed with high interphase slip velocity for significantly enhanced gas-solids contact. The fundamental fluidization behaviors in rotating fluidized beds have been well understood in small-scale systems. The current study focuses on the scale-up of a rotating fluidized bed from lab-scale to commercial-scale through computational fluid dynamics modeling. The computational model was first validated against a lab-scale horizontal rotating fluidized bed for the general flow behavior and pressure drop. The validated computational model with a column diameter of 0.12 m was then scaled up to 0.48 m and 1.92 m to investigate the flow hydrodynamics at larger scales. By comparing the solids distribution and other flow field variables, major limitations in the stable operation of a rotating fluidized bed at large scales were identified. The challenges faced in the scale-up of an annular fluidized bed with centrifugal acceleration are discussed and ways to overcome such challenges are explored.

Flow rate and kinetics of trace organic contaminants removal in black carbon-amended engineered media filters for improved stormwater runoff treatment

Urban stormwater runoff is considered a key component of future water supply portfolios for water-stressed cities. Beneficial use of runoff, such as capture for recharge of drinking water aquifers, relies on improved stormwater treatment. Many dissolved constituents, including metals and trace organic contaminants (TrOCs) such as hydrophilic pesticides and poly- and perfluoroalkyl substances (PFASs), are of concern due to their toxicity, persistence, prevalence in stormwater runoff, and poor removal in conventional stormwater control measures. This study explores the operational flow rate limitations of black carbon (BC)-amended engineered media filters for removal of a wide suite of dissolved metals and TrOCs and provides validation for a previously developed predictive TrOC transport model. Column experiments were conducted with face velocities of 40 and 60 cm h−1 to assess Douglas Fir-based biochar and regenerated activated carbon (RAC) filter performance in light of media-contaminant removal kinetic limitations. This study found that increasing the face velocity in BC-amended filters to 40 and 60 cm h−1, which are representative of field conditions, decreased the removal of total suspended solids, turbidity, dissolved hydrophilic TrOCs, and PFASs when expressed as volume treated relative to previous studies conducted at 20 cm h−1. Dissolved metals and hydrophobic TrOCs removal were not substantially affected by the increased flow rates. A predictive 1-d intraparticle pore diffusion-limited sorption model with sorption and effective tortuosity parameters determined previously from experiments conducted at 20 cm h−1 was validated for these higher flow rates. This work provides insights to the kinetic limitations of contaminant removal within biochar and RAC filters and implications for stormwater filter design and operation.