Global Operations Research Articles

A New Fast and Efficient MPPT Algorithm for Partially Shaded PV Systems Using a Hyperbolic Slime Mould Algorithm

The design of new efficient maximum power point tracking (MPPT) techniques has become extremely important due to the rapid expansion of photovoltaic (PV) systems. Because under shading conditions the characteristics of PV devices become multimodal having several power peaks, traditional MPPT techniques provide crappy performance. In turn, metaheuristic algorithms have become massively employed as a typical substitute in maximum power point tracking. In this work, a new optimizer, which was named the hyperbolic slime mould algorithm (HSMA), is designed to be employed as an efficient MPPT algorithm. The hyperbolic tangent function is incorporated into the optimizer framework equations to scale down large perturbations in the tracking stage and boost its convergence trend. Moreover, to provide a strong exploration capability, a new mechanism has been developed in such a way the search process is carried out inside the best two power peak regions along the initial iterations. This region inspection mechanism is the prime hallmark of the designed optimizer in avoiding local power peaks and excessive global search operations. The developed algorithm was examined through diverse complicated partial shading conditions to challenge its global and local search abilities. A comparative analysis was carried out against the well-regarded PSO, GWO, and the standard slime mould algorithm. In overall, the designed optimizer defeated its contenders in all aspects offering higher efficiency, superior robustness, faster convergence, and fewer fluctuations to the operating point. An experimental setup that consists of the DSpace microcontroller and a PV emulator was employed to validate the algorithm overall performance. The recorded outcomes outline that the developed optimizer can achieve a tracking time of 0.6 seconds and 0.86 seconds on average, with 99.85% average efficiency under complex partial shading conditions.

Field-Programmable Gate Array-Based Implementation of Zero-Trust Stream Data Encryption for Enabling 6G-Narrowband Internet of Things Massive Device Access

With the advent of 6G Narrowband IoT (NB-IoT) technology, IoT security faces inevitable challenges due to the application requirements of Massive Machine-Type Communications (mMTCs). In response, a 6G base station (gNB) and User Equipment (UE) necessitate increased capacities to handle a larger number of connections while maintaining reasonable performance during operations. To address this developmental trend and overcome associated technological hurdles, this paper proposes a hardware-accelerated and software co-designed mechanism to support streaming data transmissions and secure zero-trust inter-endpoint communications. The proposed implementations aim to offload processing efforts from micro-processors and enhance global system operation performance by hardware and software co-design in endpoint communications. Experimental results demonstrate that the proposed secure mechanism based on the use of non-repeating keys and implemented in FPGA, can save 85.61%, 99.71%, and 95.68% of the micro-processor’s processing time in key block generations, non-repeating checks, and data block transfers, respectively.

Analysis of International Disputes over Digital Services Tax and Exploration of China’s Approach

With the continuous emergence of emerging information technologies, the digital economy is developing rapidly around the world. The global operations of multinational digital enterprises no longer rely on physical presence. The effectiveness of permanent establishment rules based on the traditional real economy settings has been questioned by market countries, which actively participate in Market countries where digital business value is created do not receive corresponding taxation rights. Multinational digital companies take advantage of loopholes in digital platforms and current tax rules to actively carry out tax planning, resulting in digital companies’ global tax burden being much lower than that of traditional companies. Tax sources cannot be fairly and reasonably distributed in the places where value is created and profits are taxed, which harms the tax interests of market countries. Against the background of slow progress in the OECD global unified solution, as a temporary unilateral measure to address the challenges of the digital economy, some countries have taken the lead in imposing digital service taxes on specific digital businesses of digital enterprises. On the basis of discussing the relevant concepts and theories of digital service tax, this article discusses the relevant concepts and theories of digital service tax, and analyzes the international development of digital service tax and the positions of various countries around the world. Further analyze the impact of digital service tax collection from three aspects: national fiscal revenue, tax trade disputes and international tax order coordination, and analyze the impact of digital service tax on digital enterprises. Then the perspective is focused on my country, sorting out and summarizing my country's relevant digital economy tax policies, and analyzing the impact of digital service tax on my country in the near term and long term based on the development of my country's digital economy. Finally, based on the previous comparative analysis and impact analysis, my country's tax system for promoting the development of the digital economy has gradually improved and actively participated in the reshaping of international tax rules, and my country's response strategies under the digital service tax have been proposed. The different levels of digital economic development between China and countries that have levied digital service taxes will inevitably lead to disharmony in the international tax order. The study found that the fiscal revenue brought by digital service tax is limited and has a certain degree of distortion effect. It is necessary to actively participate in international cooperation and make China's voice heard before reaching a consensus on international tax rules. In the long term, we should focus on the connection between the two-pillar plan and the domestic tax system, strengthen tax information exchange, improve the domestic digital tax rule system, and promote the healthy and sustainable development of my country's digital economy.

Practices and strategies for global sourcing and supply chain management: a Pareto analysis and MOORA a mixed method approach

Purpose This study aims to identify and prioritize the key practices and strategies for effective global sourcing and supply chain management (SCM). Design/methodology/approach The study uses a combination of Pareto analysis and multi-objective optimization based on ratio analysis research methodology to analyze and establish the relationships among the identified key practices and strategies. Pareto analysis enables organization to prioritize organizational efforts and resources by focusing on the most critical factors. Findings The study shows that the “eco-friendly sourcing strategy”, “lean manufacturing” and “tool cost analysis” are the top critical practices and strategy variables for global sourcing and SCM, whereas the “risk management”, “procurement strategy” and “leverage digital solutions” are the critical practices and strategy variables. Research limitations/implications The findings of this research can also assist organizations in making informed decisions to optimize their global sourcing and supply chain operations. Originality/value By using these methods, this research paper gives valuable insights into the critical practices and strategies that can enhance efficiency, mitigate risks and drive success in global sourcing and SCM. The subjects and elements this study identified will serve as a framework and suggestions for further theoretical investigation and real-world implementations.

Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set Theory

The widespread integration of smartphones into modern society has profoundly impacted various aspects of our lives, revolutionizing communication, work, entertainment, and access to information. Among the diverse range of smartphones available, those operating on the Android platform dominate the market as the most widely adopted type. With a commanding 70% share in the global mobile operating systems market, the Android OS has played a pivotal role in the surge of malware attacks targeting the Android ecosystem in recent years. This underscores the pressing need for innovative methods to detect Android malware. In this context, our study pioneers the application of rough set theory in Android malware detection. Adopting rough set theory offers distinct advantages, including its ability to effectively select attributes and handle qualitative and quantitative features. We utilize permissions, API calls, system commands, and opcodes in conjunction with rough set theory concepts to facilitate the identification of Android malware. By leveraging a Discernibility Matrix, we assign ranks to these diverse features and subsequently calculate their reducts–streamlined subsets of attributes that enhance overall detection effectiveness while minimizing complexity. Our approach encompasses deploying various Machine Learning (ML) algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbor, Random Forest, and Logistic Regression, for malware detection. The results of our experiments demonstrate an impressive overall accuracy of 97%, surpassing numerous state-of-the-art detection techniques proposed in existing literature.

Global Talent Management: A Critical Review and Research Agenda for the New Organizational Reality

Global talent management (GTM) refers to management activities in a multinational enterprise (MNE) that focus on attracting, motivating, deploying, and retaining high performing and/or high potential employees in strategic roles across a firm's global operations. Despite the critical importance for individual and firm outcomes, scholarly analysis and understanding lack synthesis, and there is limited evidence that MNEs are managing their talent effectively on a global scale. In this article, we review the GTM literature and identify the challenges of implementing GTM in practice. We explore how GTM is aligned with MNE strategy, examine how talent pools are identified, and highlight the role of global mobility. We discuss GTM at the macro level, including the exogenous factors that impact talent management and the outcomes of GTM at various levels. Finally, we identify some emerging challenges and opportunities for the future of GTM.

The cumulative effects of cascade reservoirs control nitrogen and phosphorus flux: Base on biogeochemical processes

The reservoir serves as a water source, a flood control structure, a navigational aid, and also impacts the downstream ecosystem as well as the reservoir zone. However, debate exists about effectiveness of cascade reservoirs in controlling the transportation of nutrients, particularly in the Yangtze River basin, which has been significantly affected by reservoir development. This research develops a new model X-NPSEM (X with Nitrogen and Phosphorus Steady-state Reservoir Model) based on biogeochemical processes of nitrogen and phosphorus reaction for investigating the dynamic storage capacity of cascade reservoirs at both reservoir- and watershed scales. Then the cumulative effects of cascade reservoirs and the related mechanism were investigated in Fujiang watershed, China. Based on the results, cascade reservoirs retained 16.3 % of nitrogen fluxes and 37.6 % of phosphorus fluxes annually. Downstream reservoirs have higher retention rates of phosphorus (0.48/d) compared to upstream reservoirs (0.10/d), mainly due to inflow sediment. Nitrogen retention rates show seasonal variations: wet season (0.21/d) and dry season (0.17/d). These fluctuations in nitrogen retention are primarily influenced by changes in temperature rather than other factors such as operation period, nitrogen and phosphorus concentration, or the nitrogen/phosphorus ratio. In upstream, the concentration of sediment entering the reservoir plays a decisive role in the transformation of P retention from sink to source. The X-NPSRM coupler model could be used for global reservoir operation and watershed management.

Global Robust Newsvendor Operation Strategy for a Two-Market Stochastic Inventory System

Global markets increase sales and profitability opportunities for enterprises, but more environmental uncertainty poses new challenges for operational planning. This paper attempts to introduce the idea of distributionally robust optimization into the global operation problem of a two-market stochastic inventory system, providing theoretical guidance and reference decision-making for enterprises to optimize and configure in a global market with non-overlapping geographic locations and sales seasons. We find that the demand correlation and the lack of demand information will not substantially affect the operation strategy, and the enterprise’s industrial chain and supply chain remain stable. However, reducing inter-market tariffs or logistics costs will lead to a change in strategy, and the existence of the secondary market will lead to more capacity planning in the primary market. In addition, we find that enterprises’ transshipment strategies rely significantly on exchange rate volatility. Numerical experiments were conducted to demonstrate our theoretical results.

Development of a low-harmonic vector hysteresis current controller

The increasing interest in the use of renewable energy sources for the generation of electrical energy makes it necessary to ensure that these type of installations do not jeopardize the global operation of the electrical system to which they are connected. Up until now, this safety has been achieved by imposing very strict limits to the percentage of “non-controllable” renewable energy that can be connected to the grid. In order to increase these limits, new control schemes must be developed, focusing in power quality and, if possible, in trying to help grid stability. The paper proposes two new control methods for the current injected by a variable-speed generation system and compares them to a traditional hysteresis control. The first one obtains a table by using an algorithm that calculates the switching state that minimises switching frequency. The commutation is determined by a single comparator applied to the error vector, achieving a circular error area. The use of this table, along with the selection of a circular error area for the current vector makes that the current error remains always inside the specified error area. The second method is a predictive one, based on the previous one, that decides the optimum switching vector in the precise time when the commutation occurs, taking into account the present state of the system.

A deep learning modeling framework with uncertainty quantification for inflow-outflow predictions for cascade reservoirs

Accurate prediction of reservoir inflows and outflows and their uncertainties is essential for managing water resources and establishing early-warning systems. However, this can be a formidable challenge due to numerous uncertainties, particularly in cascade reservoir systems. To seamlessly quantify aleatoric (data-caused) and epistemic (model network–caused) uncertainties simultaneously in a single framework, we estimated the posterior distribution of parameters in a Bayesian neural network while measuring prediction variance to reflect the noise in data. By randomly discarding certain units within the network, the estimated posterior distribution can be combined with a new loss function. This sophisticated deep learning framework for a long short-term memory network has also included advanced supporting approaches, such as input variable selection, data transformation, and hyperparameter optimization. The model trained in this study was applied to two cascade reservoir systems with four reservoirs in Vietnam, providing uncertainty estimates from two distinct sources. Comparing three global reservoir operation schemes, we found that the proposed model achieved superior performance in all cases, and uncertainty in the forecasts was due primarily to data noise, rather than uncertainty in the model itself. Preprocessing data using a wavelet transform can reduce noise that the model cannot classify on its own, resulting in improved performance, particular with longer lead times. The satisfactory performance of the prediction results confirms that the framework can effectively assess the uncertainty of hydrologic predictions using deep learning.

Decision variable classification based multi-objective multifactorial memetic algorithm for multi-objective multi-task optimization problem

Multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decision variables with different characteristics may harm the effect of knowledge reuse. This paper proposes a novel hybrid multi-objective multifactorial memetic algorithm to address this issue. The proposed variable classification method will classify decision variables into convergence-related and diversity-related decision variables. Only the same type of decision variables in the source and target tasks can transfer information to avoid negative transfer. Different evolutionary operators are adopted according to the characteristics of decision variables during individual recombination. In addition, the proposed algorithm hybridizes the immune algorithm as the global evolutionary operator and the evolutionary gradient search algorithm as the local search operator into the multifactorial framework to enhance the searching ability. Finally, the proposed algorithm is compared with the state-of-the-art multi-objective evolutionary multitasking algorithms. The results of the experiments show that the proposed algorithm can achieve promising performance on the classical and complex multi-task multi-objective benchmark test suites.

Mechanisms Leading to Stabilization and Incomplete Combustion in Lean CH4/H2 Swirling Wall-Impinging Flames

Abstract In gas turbines, confined highly turbulent flames unavoidably propagate in the vicinity of a relatively cool combustor liner, affecting both the local flame structure and global operation of the combustion system. In our recent work, we demonstrated, using simultaneous [OH] × [CH2O] planar laser-induced fluorescence (PLIF) and stereo-particle image velocimetry (stereo-PIV), that lean CH4/H2 flames at a high Karlovitz number can present a highly broken structure near wall, highlighted by a diffuse CH2O cloud, which suggests local quenching and incomplete oxidation. Such high Karlovitz numbers were achieved using an inclined plate, which substantially extended the lean flammability of the low swirl flames. Yet, how a cooled wall acting as a heat sink played a conducive role in stabilizing high Ka flames remains unanswered. In addition, the origin of the CH2O cloud is also unclear. Hence, in this work, we look to better understand the stabilization mechanisms for lean and ultralean flames on the same configuration, and how they may change with a parametric variation of plate incident angle, plate-nozzle distance, and bulk velocity up to the critical values that lead to flame blow off. The results show that the impinging swirling flow creates a low speed region that helps hold the flame, while the wall prevents mixing with ambient cold air. The production of diffuse CH2O, which indicates the occurrence of local quenching, is associated with a mean strain rate K beyond the extinction strain rate (ESR) Ke. For CH4 flames, most of the reaction zones reside within |K|/Ke<1; for 70% H2 flames at ϕ=0.4, the reaction zones are highly broken and scattered in a large area, where |K|/Ke<8, the interspace of which is fully filled by CH2O. In other words, high H2 fraction flames appear to be more robust to persistent strain rate, thus extending their stability envelope. However, these flames can subsist as highly broken flames featuring strong incomplete combustion.

Peran Teknologi Informasi Dalam Meningkatkan Efisiensi Operasional Bisnis Internasional

This research discusses the crucial role of Information Technology (IT) in increasing the operational efficiency of international business through a qualitative approach. Using in-depth interviews and content analysis, this research highlights IT's contribution to simplifying operational processes, strengthening communications across geographic locations, and optimizing resource use. The research findings not only identify the benefits of IT, but also investigate the challenges and opportunities that arise in its implementation in the context of global business operations. The qualitative methodology applied provides a deep understanding of the complex dynamics involved. As a valuable guide, this research provides strategic insights for organizations operating in international markets to optimize the role of IT to achieve operational efficiency and increase their competitiveness at the global level.

Hamburger Plus: A Case Analysis

Due to Hamburger Plus’ global operations, it is planning to spend $1.5 billion over the next 5 years to make its operations digitally mastered through a system called Creative. Rapid decline in sales and numerous customer complaints from one branch to another have prompted this move. Creative is an ambitious multi-million dollar project that would allow Hamburger Plus management to monitor and actively see the physical count of the products produced at any of its stores at any time of the day. Additionally, the management has the ability to verify whether customers are receiving service at their preferred time. It is believed that Creative would bring Hamburger Plus back to what it is known for: the fastest, most consistent service in the industry. Despite its very promising outcomes, there are members of the management team who are not in favor of Creative primarily because of its cost, which would mean a 200% reduction in stockholder’s shares plus closing down at least 300 of its outlets within the next 5 years. These conditions are needed before Creative can do its work and for the company to recoup its investments. The majority of stockholders believe that the upcoming technology is not the cause of customers being sent away during lunch break but because of the sluggish customer service provided by the service crew. A member pointed out that the faltering customer service is due to the general slowdown in cooking burgers. It is also added that customers are so irritated by the long lines and long periods of waiting time before burgers are served. One stockholder said that Hamburger Plus hasn’t introduced another hit product like burger patty strips since 1985. Despite the objections, the CEO is still very optimistic about investing in Creative. He believes that it would give executives the opportunity to monitor operations and ensure product consistency and service that meets customer satisfaction.

Tourist mobility: sociological theory and implementation practices

The article analyzes the connection between the sociology of tourism and the general sociological theory of mobility, which leads to an understanding of modern tourist mobility not as a process of revealing the will of individuals to learn about different cultures, but as a system of social constructions that exist in the form of tourism practices and are focused on maximizing profits for global tour operators. The methodology. The method of the propositional analysis is aimed at identifying logically based semantic units with the aim of selecting and synthesizing reliable knowledge expressed in texts written on issues of sociology of tourism and the general sociological theory of mobility. Scientific novelty is in clarifying the concept of tourist mobility and tourist practices; in revealing that they are largely the result of commercially oriented social construction carried out in the interests of global tour operators; in demonstrating that commercial and non-commercial tourist mobility entail incompatible social consequences. Conclusions. The article is devoted to current problems of modern sociology of tourism in its connection with the general theory of mobility (one of the founders of which was the British sociologist John Urry) that complemented the “old” theory of social mobility by the doctrine of the movement of people, goods, capital, information, etc. in geographical space. Now society has come to be understood as a mobile structure consisting of many mobile practices (including tourism) that can be aimed at either developing essential strength of people or control over their consciousness and behavior.

Analysis of Carbon Footprint Reduction in Supply chains using Blockchains

Integrating carbon footprint reduction into contemporary supply chain networks necessitates strong cybersecurity and data integrity protocols to safeguard communication infrastructures against threats. This study examines the use of blockchain technology as a cybersecurity and traceability framework in supply chain systems, using simulated data that represents logistical information, shipping quantities, carbon emissions, and blockchain transactions. An investigation indicates varied carbon-emitting mechanisms, with air freight exhibiting the greatest emissions and rail transit the lowest. Transportation nodes in Supply varying emission rates, with urban distribution centres generating more emissions than rural logistics hubs, underscoring disparities in carbon footprints across different areas. Moreover, risks such data breaches, illegal access, and cyberattacks underscore the fragility of centralized supply chain systems. Blockchain transactions exemplify secure and transparent exchanges throughout all of the supply chain, highlighting blockchain’s capacity to provide safe and verifiable monitoring of carbon emissions inside the network. This research Supply the potential of blockchain technology to diminish carbon footprints in supply chains, emphasizing its ability to enhance data security, authenticate carbon emission assertions, and augment traceability. The findings highlight the need for customized cybersecurity frameworks for Supply demonstrate blockchain’s capacity to improve the transparency, security, and sustainability of global supply chain operations.

Integration of geosynthetics in reclaiming an oil sands tailings pond

Global mining operations produce significant amounts of soil waste referred to as tailings. In oil sands mining, a portion of the deposited tailings is extremely soft and is primarily in a fluid state with solid contents (by weight) ranging between 30% and 40%. As part of reclaiming its Pond 5 oil sands tailings pond, Suncor constructed an engineered “floating” cover on top of the soft tailings deposits between 2010 and 2017 over an area spanning approximately 200 hectares to provide trafficability for limited construction equipment. This initial cover consisted of two layers of geosynthetics overlain by 2 m of petroleum coke. After construction of the initial cover, Vertical Strip Drains (VSDs) were installed through the majority of the capped area to enhance the consolidation rate of the underlying soft tailings. Additional coke has been placed on top of the initial coke cover to a total thickness of 4 m to 6 m. Previous publications have discussed the cover design, installation details of the geosynthetics, installation and performance of the VSDs, and performance of the cover. This paper presents the geosynthetics design, inspection of the geosynthetics post cover construction, and an update on the performance of the coke cover.

A Gradient-Guided Evolutionary Neural Architecture Search.

Neural architecture search (NAS) is a popular method that can automatically design deep neural network structures. However, designing a neural network using NAS is computationally expensive. This article proposes a gradient-guided evolutionary NAS (GENAS) to design convolutional neural networks (CNNs) for image classification. GENAS is a hybrid algorithm that combines evolutionary global and local search operators to evolve a population of subnets sampled from a supernet. Each candidate architecture is encoded as a table describing which operations are associated with the edges between nodes signifying feature maps. Besides, evolutionary optimization uses novel crossover and mutation operators to manipulate the subnets using the proposed tabular encoding. Every n generations, the candidate architectures undergo a local search inspired by differentiable NAS. GENAS is designed to overcome the limitations of both evolutionary and gradient descent NAS. This algorithmic structure enables the performance assessment of the candidate architecture without retraining, thus limiting the NAS calculation time. Furthermore, subnet individuals are decoupled during evaluation to prevent strong coupling of operations in the supernet. The experimental results indicate that the searched structures achieve test errors of 2.45%, 16.86%, and 23.9% on CIFAR-10/100/ImageNet datasets and it costs only 0.26 GPU days on a graphic card. GENAS can effectively expedite the training and evaluation processes and obtain high-performance network structures.

Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations.

Prior methodologies have disregarded the diversities among distinct degradation types during image reconstruction, employing a uniform network model to handle multiple deteriorations. Nevertheless, we discover that prevalent degradation modalities, including sampling, blurring, and noise, can be roughly categorized into two classes. We classify the first class as spatial-agnostic dominant degradations, less affected by regional changes in image space, such as downsampling and noise degradation. The second class degradation type is intimately associated with the spatial position of the image, such as blurring, and we identify them as spatial-specific dominant degradations. We introduce a dynamic filter network integrating global and local branches to address these two degradation types. This network can greatly alleviate the practical degradation problem. Specifically, the global dynamic filtering layer can perceive the spatial-agnostic dominant degradation in different images by applying weights generated by the attention mechanism to multiple parallel standard convolution kernels, enhancing the network's representation ability. Meanwhile, the local dynamic filtering layer converts feature maps of the image into a spatially specific dynamic filtering operator, which performs spatially specific convolution operations on the image features to handle spatial-specific dominant degradations. By effectively integrating both global and local dynamic filtering operators, our proposed method outperforms state-of-the-art blind super-resolution algorithms in both synthetic and real image datasets.

The program efficiency of environmental and social non-governmental organizations: A comparative study.

Non-governmental organizations (NGOs) are becoming increasingly significant stakeholders in global governance and business operations. However, measuring their efficiency is a challenging task due to their mission-driven nature. While previous research has proposed financial and non-financial indicators to measure NGO efficiency, none of them has compared the differences between environmental and social NGOs. This study aims to investigate the factors influencing the program efficiency of NGOs in China and compare the differences between environmental and social NGOs. 12 indicators are employed and tested using data collected from the Chinese Research Data Services (CNRDS) platform. The study employs multiple regression analysis to examine the influential factors identified in the dataset. The findings demonstrated different influential factors of program efficiency among environmental and social NGOs. The results of the analysis provide valuable insights for NGO operators, policymakers, and researchers in the field of NGOs.