Rigorous Comparative Study Research Articles

Features to Involve and Collect in Future Auto Insurance Pricing

The property insurance industry is a highly data-driven sector. In the traditional model, insurance pricing largely depends on the self-reported risk characteristics of clients, such as age, gender, vehicle type, etc. These characteristics help insurance companies categorize clients into different risk levels and set premiums accordingly. With the development of telematics, insurance companies can now collect and analyze more dynamic data that is more directly related to risk. This study presents a comprehensive analysis of three feature selection models for vehicle insurance pricing. Utilizing regression techniques, it evaluated a multitude of factors believed to influence claim frequency and severity. Through a rigorous comparative study, key factors that significantly impact the vehicle are assessed and fit to different regression models. The articles findings indicate that factors such as policy bonus, mileage base data, and car usage are paramount in determining insurance rates. Moreover, the study revealed that incorporating these factors into the pricing model enhances its accuracy and fairness.

A comparative study of advanced evolutionary algorithms for optimizing microgrid performance under dynamic pricing conditions

The integration of microgrids into the existing power system framework enhances the reliability and efficiency of the utility grid. This manuscript presents an innovative mathematical paradigm designed for the optimization of both the structural and operational aspects of a grid-connected microgrid, leveraging the principles of Demand-Side Management (DSM). The focus of this work lies in a comprehensive exploration of the implications brought about by the Renewable Generation-Based Dynamic Pricing Demand Response (RGDP-DR) mechanism, particularly in terms of its influence on the optimal microgrid configuration, considering perspectives from end-users and the utility entity. This inquiry is rooted in a holistic assessment that encompasses technical and economic performance benchmarks. The RGDP-induced DR framework adeptly addresses the needs of the consumer base, showcasing notable efficiency and economic feasibility. To address the intricate nonlinear optimization challenge at hand, we employ an evolutionary algorithm named the "Dandelion Algorithm" (DA). A rigorous comparative study is conducted to evaluate the efficacy of four optimization techniques, affirming the supremacy of the proposed DA. Within this discourse, the complexity of microgrid sizing is cast as a dual-objective optimization task. The twin objectives involve minimizing the aggregate annual outlay and reducing emissions. The results of this endeavor unequivocally endorse the superiority of the DA over its counterparts. The DA demonstrates exceptional proficiency in orchestrating the most cost-effective microgrid and consumer invoice, surpassing the performance of alternative optimization methodologies.

From Fragmentation to Integration: Developing a Coherent Insurance Regulatory Framework for AfCFTA

This study delivers a comprehensive analysis of insurance regulatory harmonization by examining the European Union’s Solvency II framework alongside the regional models of CIMA, EAC, and SADC. Employing a rigorous comparative case study approach, this research dissects the institutional frameworks, regulatory mechanisms, and policy strategies of these models to extract actionable insights for the African Continental Free Trade Area (AfCFTA). Data were meticulously gathered from policy documents, academic literature, and in-depth interviews with key stakeholders, including regulators and industry leaders. The analysis reveals that AfCFTA stands to gain significantly from a regulatory framework that marries central oversight with local adaptability, akin to the EU model but nuanced for the African context. The study underscores the strategic value of integrating digital tools and fortifying regional bodies to enhance cross-border insurance activities. This research provides a critical foundation for developing a robust and cohesive regulatory framework under AfCFTA, addressing the complexities of regulatory fragmentation and advancing the creation of a resilient and integrated African insurance market

Incidence of postoperative neurological deficit with the use of an intraoperative neuromonitoring protocol for lateral lumbar interbody fusion.

Intraoperative neuromonitoring (IONM) has become commonplace in assessing neurological integrity during lateral approaches to lumbar interbody fusion surgeries. Neuromonitoring is designed to aid surgeons in identifying the potential for intraoperative nerve injury and reducing associated postoperative complications. However, standardized protocols for neuromonitoring have not been provided, and outcomes are not well described. The purpose of this study was to provide a standardized protocol for IONM, and to describe clinical outcomes in a cohort of individuals who underwent lateral lumbar interbody fusion (LLIF) surgery. A retrospective review of 169 consecutive patients who underwent LLIF surgery at a single institution from October 2014 to October 2016 was performed. Patient characteristics, intraoperative details, clinical outcomes, and postoperative deficits (PODs) were compared between patients who did and did not trigger IONM alerts, and between patients who did and did not demonstrate a POD. A protocol for IONM decision-making was generated based on these observations. Most patients (91.7%) underwent surgery for a degenerative spine condition. Twenty-three patients (13.6%) triggered neuromonitoring alerts, and 16 patients (9.5%) demonstrated a POD. Leg pain, back pain, and disability improved significantly (p < 0.045), and 2 patients had both motor and sensory deficits at the 12-week postoperative time point. Patients with a POD demonstrated greater operating room time (p = 0.034) and a greater number of interbody fusion levels (p = 0.015) but were less likely to have triggered a neuromonitoring alert (p = 0.04). There was no association between retractor time and POD (p = 0.98). When an IONM protocol was followed, individuals who experienced a POD were less likely to trigger an alert than those who did not experience a POD (p = 0.04). This study provides a protocol algorithm for IONM alert responses in patients undergoing LLIF surgery. PODs are most associated with multilevel fusion, and patients with alerts had a low rate of persistent deficit. Future research is needed to validate these findings using a more rigorous comparative study design.

On optimization formulations for radio resource allocation subject to common transmission rate

We study a radio resource allocation problem in mobile communication systems. As the distinct characteristic of this problem, a common data transmission rate is used on all channels allocated to a user. Because the channels differ in their quality, for each user the achievable rate varies by channel. Thus allocating more channels does not necessarily increase the total rate, as the common rate is constrained to be the lowest one supported by the allocated channels. Radio resource allocation subject to the common-rate constraint is of practical relevance, though little attention has been paid to modeling and solving the problem. We take a mathematical optimization perspective with focus on modeling. We first provide a complexity analysis. Next, several integer linear programming (ILP) formulations for the problem, including compact as well as non-compact models, are derived. The bulk of our analysis consists in a rigorous comparative study of their linear programming (LP) relaxations, to reveal the relationship between the formulations in terms of bounding. Computational experiments are presented to illustrate the numerical performance in bounding and LP-assisted problem solving. Our theoretical analysis and numerical results together serve the aim of setting a ground for the next step of developing model-based and tailored optimization methods.

A study on the effectiveness of non-verbal teaching materials in construction accidents prevention education: through an International comparative experiment

ABSTRACT Safety is a top priority in the construction industry; therefore, safety training is essential to prevent construction casualties as the employment of foreign and young workers on construction sites worldwide increases. More studies are necessary to identify the effective training methods among novices and foreign workers. To address the gap, a rigorous comparative study of verbal and non-verbalized materials from two different countries was conducted. Statistically significant differences were found between nationalities in non-verbal materials due to different safety culture and background, and insignificant differences were found between both safety training materials with the same nationality. By comparing the outcomes of both training materials, the non-verbalized materials work effectively among the construction novices to provide visual aid to relate similar attitudes like technical trainees to the scenarios even without field experiences. The non-verbalized materials provide an alternative to passive learning methods to enhance the other safety training activities in the construction industry. The findings provide an initial stage for creating effective teaching materials that can be generally applied in other countries to capture the issues by a heterogeneous workforce personalized to suit the scenario of each country for better outcomes.

A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.

A comparative study on the most effective machine learning model for blast loading prediction: From GBDT to Transformer

In this paper, we present a rigorous comparative study to assess and identify the most effective machine learning model for blast loading prediction. Blast loads are known to produce catastrophic effects including structural collapse and personnel fatality. Accurate and efficient prediction of these extreme loads using empirical methods and numerical solvers remains a challenging problem. Machine learning provides a promising alternative solution, which has been increasingly used in various engineering applications. However, there is seldom any analysis or justification of the selection of machine learning method that would lead to the best performance for such applications. For example, most existing machine learning-based approaches for blast loading prediction utilise the classic multi-layer perceptron (MLP) network with no justifications of their suitability and efficiency nor attempts of leveraging other state-of-the-art neural network architectures. In this study, four well-known machine learning models, including one ensemble tree method and three neural networks of different types, are investigated to demonstrate the effectiveness of different machine learning methods for blast loading prediction. It is showcased using BLEVE (boiling liquid expanding vapour explosion) pressure prediction that the Transformer model achieves the best performance, reaching a relative error of 3.5% and R2 0.997 that outperforms the existing MLP approach (relative error 6.0%, R2 0.985) with a clear margin. This study shows that the Transformer network is an effective tool for prediction of blast loading from BLEVE as well as other explosion sources.

Holomorphic Embedded Analysis of Unified Power Quality Conditioner Compensated Power Distribution System

Unified power quality conditioner (UPQC) has been used to stabilize the voltage profile with reduced harmonics. Thus, it is important to do the impact study of UPQC over distribution system. But iterative load flow method sometime fails to converge with rise in system complexity. Thus, recursive holomorphic embedding load flow is best suited over here. This article proposes a recursive mathematical modeling of UPQC using holomorphic embedding method. This holomorphic embedded model of UPQC is based on series and shunt converters operation determined by phasor diagram. Different operating modes of UPQC are also modeled and tested. The proposed models have been tested over IEEE 13, 123 bus distribution test systems and a 19 bus practical Indian distribution grid. A rigorous comparative study with existing iterative load flow methods has been done. On analyzing the results, it shows great efficacy and reliability in terms of voltage sag mitigation and total harmonic distortion compared to conventional methods.

Frontier-led swarming: Robust multi-robot coverage of unknown environments

This paper proposes a novel swarm-based control algorithm for exploration and coverage of unknown environments, while maintaining a formation that permits short-range communication. The algorithm combines two elements: swarm rules for maintaining a close-knit formation and frontier search for driving exploration and coverage. Inspired by natural systems in which large numbers of simple agents (e.g., schooling fish, flocking birds, swarming insects) perform complicated collective behaviors for efficiency and safety, the first element uses three simple rules to maintain a swarm formation. The second element provides a means to select promising regions to explore (and cover) by minimising a cost function involving robots’ relative distance to frontier cells and the frontier’s size. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both coverage performance and swarm formation statistics as indicators of the robots’ ability to explore effectively while maintaining a formation conducive to short-range communication. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented map coverage methodologies and conventional swarming methods. • A novel, distributed algorithm leveraging the strengths of frontier-based area coverage and swarm behaviors is proposed for large-scale multi-robot systems. • Each robot can directly exchange the individual coverage and obstacle information with a set of neighbors without passing through a central station. • An improved artificial potential field obstacle avoidance method is designed based on Lidar information. • System robustness and fault tolerance of the proposed strategy is validated and analysed through a series of real experiments. • This paper demonstrates our space coverage controller’s relative merits with respect to the frontierbased map coverage strategy without swarming, and the three different map coverage control laws through a rigorous comparative study. • Algorithms are examined on both homogeneous and heterogeneous robot teams within a shortranged communication. Hence, the proposed system can normally work in unknown and uncertain environments.

Game Theory-Based Authentication Framework to Secure Internet of Vehicles with Blockchain.

The Internet of Vehicles (IoV) is a new paradigm for vehicular networks. Using diverse access methods, IoV enables vehicles to connect with their surroundings. However, without data security, IoV settings might be hazardous. Because of the IoV’s openness and self-organization, they are prone to malevolent attack. To overcome this problem, this paper proposes a revolutionary blockchain-enabled game theory-based authentication mechanism for securing IoVs. Here, a three layer multi-trusted authorization solution is provided in which authentication of vehicles can be performed from initial entry to movement into different trusted authorities’ areas without any delay by the use of Physical Unclonable Functions (PUFs) in the beginning and later through duel gaming, and a dynamic Proof-of-Work (dPoW) consensus mechanism. Formal and informal security analyses justify the framework’s credibility in more depth with mathematical proofs. A rigorous comparative study demonstrates that the suggested framework achieves greater security and functionality characteristics and provides lower transaction and computation overhead than many of the available solutions so far. However, these solutions never considered the prime concerns of physical cloning and side-channel attacks. However, the framework in this paper is capable of handling them along with all the other security attacks the previous work can handle. Finally, the suggested framework has been subjected to a blockchain implementation to demonstrate its efficacy with duel gaming to achieve authentication in addition to its capability of using lower burdened blockchain at the physical layer, which current blockchain-based authentication models for IoVs do not support.

Whale Optimizer-Based Clustering for Breast Histopathology Image Segmentation

Breast histopathology image segmentation is a complex task due to indiscernibly correlated and noisy regions of interest. Breast histopathological images are composed of different types of cells. Some of these cells can be harmful for humans due to the presence of cancer. Under such circumstances, many segmentation techniques for automatic detection of cancer cells have been proposed considering clustering schemes. However, such clustering methodologies are sensitive to initial cluster centers, which promote false-positive solutions. This paper presents the use of the Whale Optimization Algorithm (WOA) for proper clustering segmentation of breast histopathological images to overcome clustering issues. Also, a rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperforms the other utilized clustering methods in terms of precision, robustness, and quality of the segmented outputs.

A deep learning based intelligent approach in detection and classification of transmission line faults

Detection and classification of transmission line (TL) faults are key factors for the fault root cause analysis and rapid restoration of the power network. Deep learning can extract representative features automatically from big data, thus becoming an important breakthrough in fault analysis. Existing deep learning-based fault diagnosis models rely on a large number of data obtained from a variety of fault conditions to be generalized. Indeed, in the TL domain, it is difficult to define and, therefore to collect the fault incepted features of all possible fault scenarios. This paper proposes an unsupervised framework for fault detection and classification (FDC) of TL based on a capsule network (CN). Instead of using the baseline CN, an extension to this with a sparse filtering technique is adopted in this work. The capsule network with sparse filtering (CNSF) voluntarily learns the expensive fault features and significantly improves the model performance without involving a large number of data. The proposed scheme receives 1/2 cycle post-fault three-phase signals and encodes them into a single image that is defined as the input for the proposed CNSF model. The effectiveness of the proposed CNSF model is corroborated by four different TL topology confirming the model’s adaptability to a topology change in response to intended control action or the switching actions due to cascading faults. Further assessment of the model’s performance against noise, high impedance faults (HIF), and line parameter variations are also carried out in order to confirm the high reliability of the proposed model. In addition, a rigorous comparative study is conducted to guarantee the state-of-the-art performance of the proposed model.

Geometrically nonlinear flexural analysis of multilayered composite plate using polynomial and non-polynomial shear deformation theories

In this paper, the geometrically nonlinear bending analysis of multilayered composite plates is carried out through a rigorous comparative study employing Green-Lagrange and von Kármán nonlinearity. The responses are analyzed for various transverse loads and boundary conditions, and emphasized the impending structural condition, which requires the consideration of full geometric nonlinearity. The study is conducted using a C0 finite element plate model using third-order and non-polynomial shear deformation theories (TSDT and NPSDT). The principle of virtual work is utilized to formulate the weak-form of governing equations, and discretized using nine-noded Lagrange elements with seven degrees-of-freedom per node. The performance of the present model has been validated by comparing present results with those available in the literature and obtained ANSYS results. The results reveal that the consideration of Green-Lagrange nonlinearity is essential for plates with all sides subjected to movable simply supported boundary conditions or with one free edge. The present study also provides a clear idea about the utilization of TSDT and NPSDT for the anti-symmetric and symmetric cross-ply plate, respectively, to get more accurate solution. Further, the effect of penalty for various theories and problems is also highlighted, and its prominent impact is asserted for some cases.

Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

Image segmentation process is one of the most interesting and challenging problems in digital image processing tasks. The segmentation process involves finding similar regions within an image. Many segmentation problems are achieved by the incorporation of clustering techniques. One of the most common technique for clustering process is the Fuzzy C-means (FCM) algorithm. However, even when FCM is one of the most popular techniques applied in image segmentation, it presents some issues such as large computational time complexity, noise sensitivity, and initial cluster centers dependency. In order to solve these problems, this paper presents a Histogram Based Fuzzy Clustering (HBFC) technique using an improved version of Firefly Algorithm (FA). In the proposed approach, the FA involves three search strategies: rough set-based population, random attraction and local search mechanism. Also, the clustering process is conducted based on gray level histograms instead of single pixels of an image. Under such circumstances, the occurrence of misclassification of pixels is reduced. A rigorous comparative study is conducted among the proposed approach and several state-of-art Nature-Inspired Optimization Algorithms (NIOAs) and traditional clustering techniques. The numerical results indicate that the proposed approach outperform the well-known NIOA based clustering methods in terms of precision, robustness and quality of the segmented outputs.

Multi-vehicle formation control and obstacle avoidance using negative-imaginary systems theory

This paper presents a compelling combination of the two negative-imaginary (NI) control systems: a consensus-based formation control framework for a hybrid multi-vehicle system and a new dynamic obstacle detection and avoidance algorithm. The incorporation of the two techniques permits robots to self-detect collisions and then self-create a safe path to overcome any unexpected obstacles while traveling to the destination in a given formation. Moreover, owing to the distributed and dynamic architecture, our NI obstacle avoidance method has better adaptability to the change of environment conditions or obstacle/robot dynamics. Also, a rigorous comparative study on the performance of our obstacle avoidance method with respect to that of the artificial potential field function and switching formation-based NI obstacle avoidance algorithms is performed as a benchmark. Finally, simulation and experimental results are then implemented to demonstrate the effectiveness of the proposed theories.

A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem

The appropriate planning of electric power systems has a significant effect on the economic situation of countries. For the protection and reliability of the power system, the optimal reactive power dispatch (ORPD) problem is an essential issue. The ORPD is a non-linear, non-convex, and continuous or non-continuous optimization problem. Therefore, introducing a reliable optimizer is a challenging task to solve this optimization problem. This study proposes a robust and flexible optimization algorithm with the minimum adjustable parameters named Improved Marine Predators Algorithm and Particle Swarm Optimization (IMPAPSO) algorithm, for dealing with the non-linearity of ORPD. The IMPAPSO is evaluated using various test cases, including IEEE 30 bus, IEEE 57 bus, and IEEE 118 bus systems. An effectiveness of the proposed optimization algorithm was verified through a rigorous comparative study with other optimization methods. There was a noticeable enhancement in the electric power networks behavior when using the IMPAPSO method. Moreover, the IMPAPSO high convergence speed was an observed feature in a comparison with its peers.

Performance analysis of real-coded evolutionary algorithms under a computationally expensive optimization scenario: 3D–2D Comparative Radiography

Real-coded evolutionary algorithms have solved numerous real-world optimization problems. In this work, we aim to analyze the behavior and robustness of several real-coded evolutionary algorithms from the state of the art in a challenging real world optimization problem. This optimization problem consists on the superimposition of 3D and 2D images of skeletal structures (i.e. bones and cavities) based on their silhouette. This task is required for the automation of a forensic identification technique known as comparative radiography, via the generation of the best projection of the 3D image with respect to the 2D image. This superimposition problem was tackled in a recent proposal using an evolutionary 3D–2D image registration method based on differential evolution. However, the results obtained were insufficient for its use in real scenarios, due to: (1) the complexity and multi-modality of search space, despite the reduced number of parameters to be optimized (7 in its simple version and 9 in a more complex one, proposed in this work); and (2) the high computational cost of generating and evaluating a superimposition. Particularly, we have performed a rigorous comparative study of six state-of-the-art real-coded evolutionary algorithms (DE, L-SHADE, CMA-ES, BIPOP-CMA-ES, CRO-SL, and MVMO-SH) with synthetic images of three forensic anatomical structures (frontal sinuses, clavicles, and patellae), showing that the best results are always obtained by MVMO-SH in terms of precision, robustness and computational cost. Furthermore, we have validated the quality of the superimpositions obtained by the evolutionary image registration method using MVMO-SH with real images of frontal sinuses. We have performed the comparison of 50 head radiographs and 50 3D images, resulting in 2,500 cross-comparisons (50 positive and 2,450 negatives). The obtained results are promising since the superimpositions obtained allowed us to filter out 88% of the possible candidates with 0 error rate in a fully automatic manner, showing the high quality of the superimposition obtained.

Cauchy with whale optimizer based eagle strategy for multi-level color hematology image segmentation

Pathological color image segmentation is an exigent procedure due to the existence of imperceptibly correlated, and indistinct multiple regions of concern. Multi-level thresholding has been introduced as one of the most significant image segmentation procedures for pathological analysis. However, finding an optimal set of threshold values is an extremely time-consuming task, and crucially depends on the objective function criterion. In order to solve these problems, this paper presents a multi-level hematology color image thresholding approach with the assistance of a two-stage strategy called Eagle Strategy coupled with Whale Optimization Algorithm (ES-WOA), analyzing the performance over five well-known objective functions, namely; Kapur’s entropy, Fuzzy entropy, Tsallis’ entropy, Otsu’s method, and Cross entropy. A rigorous comparative study is performed among classical WOA and existing eagle strategy based optimization algorithms, considering a set of hematology color images, and common performance indexes evaluated by each objective function tested. Experimental results indicate that proposed ES-WOA in combination with Tsallis’ entropy outperforms the rest of tested algorithms in terms of computational effort, image segmentation quality, and robustness. For example, ES-WOA with Tsallis’ produces segmented images with average Peak Signal-to-Noise Ratio (PSNR) values 16.0371, 17.9975, 21.1353, and 23.0759 for threshold values 2, 3, 4, and 5, respectively, which are superior to other tested methods. Additionally, the numerical results are statistically validated using a nonparametric approach to eliminate the random effect in the obtained results.

What explains sub-national variation in maternal mortality rates within developing countries? A political economy explanation.

Available evidence on maternal mortality rates (MMR) reveals stark differences not only between but also sub-nationally within countries. However, the causes of sub-national variation in MMR remain under-researched and under-theorised. This is a serious problem given the widespread reliance on local authorities to deliver health services in developing countries, which means that sub-national efforts to curb MMR are critical. We propose a multi-level political economy analysis framework which, when applied in Uganda, usefully explained the sub-national differences. Drawing on process tracing and rigorous comparative case study analysis of two otherwise similar districts, this approach was able to identify certain political economy factors as being critical to shaping different levels of progress on MMR. The key variables that matter at district level are not necessarily the 'formal' factors identified in the literature, such as levels of democracy and citizens' power. Rather, the character of the local ruling coalition influences how they play out in practice. This analysis of local power relations needs to be located within a similar understanding of the political economy of health provisioning at a national level, which in many developing country contexts is itself profoundly shaped by international actors. Since the early 2000s, political developments have catalyzed a growing sense of vulnerability within Uganda's ruling coalition leading to political capture of the health ministry and undermining efforts to prioritise maternal health at the national level. With development agencies further undermining the emergence of a coherent centralized strategy, performance at the local level has become dependent on whether 'developmental coalitions' of political, bureaucratic and social players emerge to fill the vacuum. The paper concludes that the large variance in capacity and commitment to reduce maternal mortality within subnational levels of government has to be understood in relation to the local political settlement within which health service provision operates.