Salient Feature Research Articles

Constructing prediction intervals to explore uncertainty based on deep neural networks

The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of ‘high coverage and narrow width’. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of ‘high coverage, narrow width, and small bias’ in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths.

The Heterotic-Ricci Flow and Its Three-Dimensional Solitons

We introduce a novel curvature flow, the Heterotic-Ricci flow, as the two-loop renormalization group flow of the Heterotic string common sector and study its three-dimensional compact solitons. The Heterotic-Ricci flow is a coupled curvature evolution flow, depending on a non-negative real parameter κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa $$\\end{document}, for a complete Riemannian metric and a three-form H on a manifold M. Its most salient feature is that it involves several terms quadratic in the curvature tensor of a metric connection with skew-symmetric torsion H. When κ=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa = 0$$\\end{document} the Heterotic-Ricci flow reduces to the generalized Ricci flow and hence it can be understood as a modification of the latter via the second-order correction prescribed by Heterotic string theory, whereas when H=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$H=0$$\\end{document} and κ>0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa >0$$\\end{document} the Heterotic-Ricci flow reduces to a constrained version of the RG-2 flow and hence it can be understood as a generalization of the latter via the introduction of the three-form H. Solutions of Heterotic supergravity with trivial gauge bundle, which we call Heterotic solitons, define a particular class of three-dimensional solitons for the Heterotic-Ricci flow and constitute our main object of study. We prove a number of structural results for three-dimensional Heterotic solitons, obtaining, in particular, the complete classification of compact three-dimensional strong Heterotic solitons as hyperbolic three-manifolds or quotients of the Heisenberg group equipped with a left-invariant metric. Furthermore, we prove that all Einstein three-dimensional Heterotic solitons have constant dilaton and leave as open the construction of a Heterotic soliton with non-constant dilaton. In this direction, we prove that Einstein Heterotic solitons with constant dilaton are rigid and therefore cannot be deformed into a solution with non-constant dilaton. This is, to the best of our knowledge, the first rigidity result for compact supergravity solutions in the literature.

Advances Image-Based Automated Security System

As security is a serious concern nowadays, it becomes important to develop a product that deals with security issues without any human intervention. Hence, an automatic security system is a proposed device that ensures the security of the premises. Using both emerging technologies and specialized hardware, we can achieve safety goals and be able to develop the proposed device. It is an IoT-based approach that includes cloud computing, OpenCV, and web application for developing a security-based automatic system. Using raspberry pi and software, the authors design an automated security system where all the used electrical items are controlled. This system deals with the protection of possessions, minimizing break-ins, and avoiding any dangerous situations. The additional salient feature is that it also deals with the COVID-19 alerts, which are generated from the temperature sensor. Therefore, it protects the premises not only from any unauthorized access but also protects the premises from any infected person.

Multiportfolio Optimization: A Fairness-Aware Target-Oriented Model

Problem definition: We consider a multiportfolio optimization problem in which nonlinear market impact costs result in a strong dependency of one account’s performance on the trading activities of the other accounts. Methodology/results: We develop a novel target-oriented model that jointly optimizes the rebalancing trades and the split of market impact costs. The key advantages of our proposed model include the consideration of clients’ targets on investment returns and the incorporation of distributional uncertainty. The former helps fund managers to circumvent the difficulty in identifying clients’ utility functions or risk parameters, whereas the latter addresses a practical challenge that the probability distribution of risky asset returns cannot be fully observed. Specifically, to evaluate the quality of multiple portfolios’ investment payoffs in achieving targets, we propose a new class of performance measures, called fairness-aware multiparticipant satisficing (FMS) criteria. These criteria can be extended to encompass distributional uncertainty and have the salient feature of addressing the fairness issue with the collective satisficing level as determined by the least satisfied participant. We find that, structurally, the FMS criteria have a dual connection with a set of risk measures. For multiportfolio optimization, we consider the FMS criterion with conditional value-at-risk being the underlying risk measure to further account for the magnitude of shortfalls against targets. The resulting problem, although nonconvex, can be solved efficiently by solving an equivalent converging sequence of tractable subproblems. Managerial implications: For the multiportfolio optimization problem, the numerical study shows that our approach outperforms utility-based models in achieving targets and in out-of-sample performance. More generally, the proposed FMS criteria provide a new decision framework for operational problems in which the decision makers are target-oriented rather than being utility maximizers and issues of fairness and ambiguity should be considered. Funding: This work was supported by the Hong Kong Research Grants Council [Grants 14210821, 16204521], Leading Talent Program of Guangdong Province [Grant 2016LJ06D703], and the National Natural Science Foundation of China [Grants 71971187, 72171156, 72231002, 72331009]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0363 .

Ongoing evolution of the Mycobacterium tuberculosis lactate dehydrogenase reveals the pleiotropic effects of bacterial adaption to host pressure.

The bacterial determinants that facilitate Mycobacterium tuberculosis (Mtb) adaptation to the human host environment are poorly characterized. We have sought to decipher the pressures facing the bacterium in vivo by assessing Mtb genes that are under positive selection in clinical isolates. One of the strongest targets of selection in the Mtb genome is lldD2, which encodes a quinone-dependent L-lactate dehydrogenase (LldD2) that catalyzes the oxidation of lactate to pyruvate. Lactate accumulation is a salient feature of the intracellular environment during infection and lldD2 is essential for Mtb growth in macrophages. We determined the extent of lldD2 variation across a set of global clinical isolates and defined how prevalent mutations modulate Mtb fitness. We show the stepwise nature of lldD2 evolution that occurs as a result of ongoing lldD2 selection in the background of ancestral lineage-defining mutations and demonstrate that the genetic evolution of lldD2 additively augments Mtb growth in lactate. Using quinone-dependent antibiotic susceptibility as a functional reporter, we also find that the evolved lldD2 mutations functionally increase the quinone-dependent activity of LldD2. Using 13C-lactate metabolic flux tracing, we find that lldD2 is necessary for robust incorporation of lactate into central carbon metabolism. In the absence of lldD2, label preferentially accumulates in dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate (G3P) and is associated with a discernible growth defect, providing experimental evidence for accrued lactate toxicity via the deleterious buildup of sugar phosphates. The evolved lldD2 variants increase lactate incorporation to pyruvate while altering triose phosphate flux, suggesting both an anaplerotic and detoxification benefit to lldD2 evolution. We further show that the mycobacterial cell is transcriptionally sensitive to the changes associated with altered lldD2 activity which affect the expression of genes involved in cell wall lipid metabolism and the ESX- 1 virulence system. Together, these data illustrate a multifunctional role of LldD2 that provides context for the selective advantage of lldD2 mutations in adapting to host stress.

Chinese social value change and its relevant factors: an age-period-cohort effect analysis

Using data from the World Values Survey (1990–2012), this article attempts to describe and analyze the changing trends in public values during social transformations in China. It is found that public attitudes toward authority tend to be increasingly favorable over time, values in the private sphere tend to be more open, while postmaterialist values show a trend of first rising and then falling. The most salient feature of the cohort effect is that the generations born after the reform and opening-up in 1978 identify more closely with the value of respect for authority, while their recognition of postmaterialist values tends to decline. The change in values is related to China's modernization process, global cultural diffusion, and important historical events and is also deeply influenced by the two-way interaction between tradition and modernity.

EPANet-KD: Efficient progressive attention network for fine-grained provincial village classification via knowledge distillation.

Fine-grained classification of historical traditional villages plays a crucial role in guiding the future development and construction of urban and rural areas. This study aims to propose a new dataset for fine-grained classification of traditional villages and to propose an efficient progressive attention network for the problem of low accuracy and efficiency of fine-grained traditional historical village classification. Firstly, in order to further study the long-standing problem of fine-grained classification of traditional villages, a new fine-grained classification dataset of traditional villages containing 4,400 images, referred to as PVCD, is proposed by crawling and hand-arranging. Secondly, a new Progressive Attention Module, abbreviated as PAM, is also proposed. PAM engages in attentional modeling of prominent spatial features within the spatial dimension, subsequently applying attentional modeling to channel features beneath the identified salient spatial features. This process involves salient spatial feature attention modeling of prominent channel features within the dimension to extract discriminative information for fine-grained classification, thereby enhancing the performance of classifying traditional villages with precision. Finally, a new knowledge distillation strategy of softened alignment distillation, or SAD for short, is proposed, which simply and efficiently transfers the knowledge of softened category probability distributions through. Notably, based on the above proposed PAM, the lightweight EPANet-Student and the heavyweight EPANet-Teacher are proposed. In addition, the heavyweight EPANet-Teacher transfers the knowledge of fine-grained categorization of traditional villages to the lightweight EPANet-Student through the proposed SAD, abbreviated as EPANet-KD. The experimental results show that the proposed EPANet-Teacher achieves state-of-the-art performance with an accuracy of 67.27%, and the proposed EPANet-KD achieves comparable performance to the proposed EPANet-Teacher with 3.32M parameters and 0.42G computation. The proposed EPANet-KD maintains a good balance of accuracy and efficiency in the fine-grained classification of traditional villages, considerably promoting the research on the fine-grained classification of traditional villages. In addition, it facilitates the digital preservation and development of traditional villages. All datasets, codes and benchmarking results are publicly available for the promotion of this research area. https://github.com/Jack13026212687/EPANet-KD.

Correlations between Trunk Control and Balance in Children with Bilateral Spastic Cerebral Palsy.

Impairments of postural responses are a salient feature of children with cerebral palsy (CP). While the systems approach describes balance in seven components, the relationship between trunk control and balance in children with CP has not been previously examined with all seven of these components. In this study, we aimed to identify correlations between trunk control and all seven systems approach balance components in children with bilateral spastic CP. Our participants were 30 children (M age = 11.83, SD = 2.32 years) with CP having a Gross Motor Function Classification System level ranging from I to III. We assessed trunk control with the Trunk Control Measurement Scale, including static and dynamic balance (selective voluntary control and reaching). Balance in standing was assessed using Kids-Mini-BESTest involving four domains: anticipatory, reactive, sensory orientation and stability in gait. We used Spearman's rank correlations to correlate trunk control and balance, and we obtained a moderate correlation between the trunk control measurement scale and the Kids-Mini-BESTest in children with both bilateral spastic CP (rs = .618, p < .001) and spastic diplegic CP (rs = .52, p = .02). Analysis of the correlations between separate domains of the Kids-Mini-BESTest and the trunk control measurement scale subscales revealed moderate correlations between the static sitting balance subscale and all four domains of the Kids-Mini-BESTest. The dynamic selective motor control subscale of the trunk control measurement scale moderately correlated with the anticipatory domain of the Kids-Mini-BESTest. The dynamic reaching subscale also correlated moderately with anticipatory and stability in gait domains. This correlation was statistically significant in the 13 to 17-year-old age group and was strong among females, whereas the correlation was moderate in males. Trunk control was moderately associated with balance considering all the systems theory components of balance in children with bilateral spastic cerebral palsy.

Generalize Deep Neural Networks With Adaptive Regularization for Classifying

Regularization is a crucial technology to improve the generalization of deep neural networks. However, traditional regularization method approaches are all scenario-specific, because they are generally with ingeniously designed feature representations from input layer, hidden layer, and output layer, which increase the difficulty of model development and interpretation. To this end, a novel practical and flexible regularization method is presented to obtain higher generalization and interpretability. Specifically, the feature maps are decoupled by global suppression and partial suppression from various scales and locate the salient feature with strong low-resolution semantic information. Moreover, the guided discarding specification for feature decoupling by measuring the feature contributions to network decisions, leads to the logics with better interpretability. Subsequently, the max values of the feature map are suppressed by discarding the corresponding salient features. Comprehensive experiments demonstrate that the proposed adaptive regularization outperforms the state-of-the-art performance in image classification accuracy, generalization, and interpretability on several widely used datasets. And adaptive regularization helps the network to mine the connection between salient features, nonsalient features, and ground truth, encouraging the network to construct multiple layers of feature associations.

Hand Movement Recognition and Salient Tremor Feature Extraction With Wearable Devices in Parkinson’s Patients

Tremor is one of the earliest signs of Parkinson’s disease (PD), which seriously disrupts patients’ daily lives. It is important to study upper limb tremors quantitatively to control PD progression. In this study, surface electromyography (sEMG) signals from wearable devices are used to recognize rest, posture, and kinetic tremor action from 6 upper-limb clinical actions and to quantify features of tremors. A multivariable time series classification model (MTSCM) based on fully convolutional networks and a long short-term memory network is proposed to recognize tremor actions. MTSCM achieves a high degree of accuracy both on the left-hand and right-hand data sets for tremor actions. An improved Hilbert-Huang transform (HHT) method is proposed to decompose the inertial signals of tremor actions to obtain tremor components. Using the improved HHT, tremor and motion components can be decomposed effectively. In addition, 53 features are extracted from inertial and sEMG signals, and a canonical correlation analysis is used to determine the correlation between features and MDS-UPDRS scores. Several of the relevant characteristics are related to MDS-UPDRS scores, notably the dominant frequency and amplitude of the tremor component are significantly correlated (p < 0.01) with tremor scores. Detecting upper-limb clinical tremors in PD patients using wearable sensors is feasible according to our findings.

Salient Feature Pyramid Network for Ship Detection in SAR Images

Salient Feature Pyramid Network for Ship Detection in SAR Images

On the dynamics of the turbulent flow past a three-element wing

A comprehensive analysis of the unsteady flow dynamics past the 30P30N three-element high lift wing is performed by means of large eddy simulations at different angles of attack (α = 5°, 9°, and 23°) and at a Reynolds number of Rec=750 000 (based on the nested chord). Results are compared with experimental and numerical investigations, showing a quantitatively good agreement and, thus, proving the reliability and accuracy of the present simulations. Within the slat and main coves, large recirculation bubbles are bounded by shear layers, where the onset of turbulence is triggered by Kelvin–Helmholtz instabilities. In the energy spectrum of the velocity fluctuations, the footprint of these instabilities is detected as a broadband peak; its frequency being moved toward lower values as the angle of attack increases. Kelvin–Helmholtz vortices roll-up and break down into small scales that eventually impinge into the slat and main coves lower surfaces. The slat impingement shows to be more prominent, and hence, larger velocity and pressure fluctuations are observed. The impingement strength diminishes with the angle of attack in both coves, while higher fluctuations are originated on the slat and main respective suction sides, leading to larger boundary layers. This is associated with the displacement of the stagnation point with the angle of attack. Another salient feature observed is the laminar-to-turbulent flow transition in the main and flap leading edges although the average location of this transition seems to not be affected by the angle of attack. Tollmien–Schlichting instabilities precede this transition, with the disturbances amplified by the inviscid mode at low angles of attack, while at α=23°, the local Reynolds number on the main suction side is incremented and the viscous mode becomes important. The analysis shows that the turbulent wake formed at the trailing edge of all elements dominates the dynamics downstream. This is especially true at the higher angle of attack, where a large region of velocity deficit above the flap is observed, thus indicating the onset of stall conditions.

Distributed State Estimation Under Jointly Connected Switching Networks: Continuous-Time Linear Systems and Discrete-Time Linear Systems

In this paper, we address the distributed state estimation problem for both continuous-time linear time-invariant (LTI) systems and discrete-time LTI systems under switching networks. The observed system is jointly observable, i.e., each agent can only access a part of the measurement output of the observed system and cannot recover the full state by itself. The full state estimation has to be achieved by network communication of neighboring agents. In contrast to existing works, the salient feature of this work is that the developed approach can deal with the jointly connected switching network and thus is more resilient to unreliable communication. First, we propose a new observability decomposition method for linear systems in modal canonical form. Then, we design distributed observers for both the continuous-time and the discrete-time system. Based on the common Lyapunov function approach, we show that the switched estimation error system is asymptotically stable and thus, the full state estimation can be achieved under jointly connected switching networks.

A Specified-Time Convergent Multiagent System for Distributed Optimization With a Time-Varying Objective Function

This technical note presents a specified-time convergent multi-agent system for distributed optimization with a time-varying objective function subject to equality constraints. Different from the static optimal solutions to most existing distributed optimization problems, the optimal solutions are time-varying due to the time-varying objective function in this problem. A distributed protocol law is designed to ensure all the agents' convergence to feasible and suboptimal solutions within a specified settling time and keep tracking the time-dependent optimal solutions. The specified-time convergence of the system and the asymptotic optimality of the solution generated by the system are proved based on the Lyapunov theory. A salient feature of the multi-agent system is that its upper bound of settling time can be specified in advance. Two examples are presented to illustrate the theoretical results.

Advanced Driver-Assistance Systems: Features Journey for Tomorrow

Intelligent associated vehicles (ICVs) are accepted to completely change people sooner rather than later by making the transportation more secure, cleaner and more agreeable. Albeit numerous models of ICVs have been created to demonstrate the idea of independent driving and the plausibility of further developing traffic effectiveness, there actually exists a critical hole prior to accomplishing large scale manufacturing of undeniable level ICVs. The goal of this study is to introduce an outline of both the cutting edge and future viewpoints of key necessary advances for future ICVs. It is a moving undertaking to survey every connected work and foresee their future viewpoints, particularly for such a perplexing and interdisciplinary area of examination. Advanced driver-assistance systems (ADASs) have become a salient feature for safety in modern vehicles. They are also a key underlying technology in emerging autonomous vehicles. State-of-the-art ADASs are primarily vision based, various type of features for ex. Lane Departure Warning (LDW), Blind Spot Monitoring (BSM), Forward Collision Warning (FCW), Automatic Emergency Braking (AEB), Traffic Sign Recognition, High Beam Assist, Rear Cross Traffic Alert, Driver Drowsiness Detection, Obstacle Aware Acceleration, Auto-steer etc. The paper aims at giving a complete picture focusing on the ADAS features for the user-friendly design of human-machine interfaces between driver and assistance system.

Numerical solution of nonlinear diffusion advection Fisher equation by fourth‐order cubic B‐spline collocation method

AbstractThis article investigates the effect of diffusion, advection and Fisher terms when nonlinear diffusion occurs in a porous medium. The main advantage of this article is the derivation of a fourth‐order cubic B‐spline collocation method to solve the nonlinear advection‐diffusion Fisher equation, which represents many important natural phenomena. The Crank‐Nicholson method has been used to discretize space and time. The salient feature of the article is the demonstration of the unconditional stability of the proposed method using the Fourier method. While applying on existing problem having an exact solution, it is shown through error analysis that our proposed scheme is very effective. The important feature of the article is the graphical showcasing of the solution profiles for different particular cases.

Other-Repetition to Convey and Conceal the Stance of Institutional Participants in Chinese Criminal Trials

Based on the examination of 49 Chinese criminal trials transcribed from the audio-visual recordings on the ‘China Court Trial Online’ website (https://tingshen.court.gov.cn/), the institutional participants–prosecutors, defence lawyers, and judges–are found to frequently repeat defendants’ responses (‘other-repetition’), after a question–answer adjacency pair. Other-repetition has been described as a resource for showing participation and familiarity (Tannen 2007), initiating repair and registering receipt (Schegloff 1997), and displaying understanding and emotional stance (Svennevig 2004). However, other-repetition in trial discourse has not been thoroughly researched. Therefore, this article aims to further examine this salient institutional linguistic feature in order to explore stance construction as a third turn. It is common to associate certain prosodic features of a repetition with the stance of the speaker. However, prosody may not be a reliable cue for stance interpretation in Chinese courtrooms, as institutional participants might conceal their stance during a repetition. Though they are more explicit in displaying their stance with turn-initial repeats, the distinction between the prosody of repeats for different functions is not categorical. It is found that defendants are sensitive to the lexicogrammar and multimodal cues for stance interpretation.

Feasibility of using geopolymer solution in the conservation of architectural monuments

the ordinary cement mortar. The authors present the argument for the expediency of using a Preservation and maintenance of architectural monuments have always been the number one task for civil engineers. In this context, the engineer encounters a “wagon load” of problems. On the one hand, there are strict requirements for the composition of materials used for restoration. On the other hand, it is the almost insurmountable challenge of selecting the right materials with the essential properties for the proper restoration of an architectural monument. Fixing of cracks and cavities is crucial for the durability of reinforced concrete and stone structures. These need to be treated scientifically to ensure usability.&#x0D; In this setting, the use of Epoxy resins has become common. While Epoxy resins perform well under certain circumstances, they have many disadvantages such as heat instability, high costs, high resource demand, and serious health and environmental hazards. Furthermore, Epoxy resins are proscribed in monument preservation. To overcome the potential disadvantages of Epoxy resins, Geopolymers offer a promising alternative. Geopolymers are an environmentally friendly and durable substitute with low viscosity, supported by high-temperature stability. Using inorganic additives, geopolymer can expand and create a preload during solidification; this is a salient favorable feature since any decrease in volume will lead to the creation of a new cavity or crack of a smaller size. According to previous studies [1], the implementation of this low-CO2 geopolymer cement in construction and civil engineering would reduce CO2 emissions caused by the cement and concrete industry by 80%&#x0D; This article presents the main advantages and disadvantages of the proposed geopolymer solution, in comparison to geopolymer solution in the reconstruction of architectural monuments.

Multilingual Speaker Recognition using Mel-frequency Cepstral Coefficients and Gaussian Mixture Model

Introduction: People can recognize a speaker with the help of their voice via mobile or digital devices. Method: To obtain this congenital human being ability, authentication techniques based on speaker biometrics like automated speaker recognition (ASR) have been proposed. An ASR identifies speakers by speech signals analysis and salient feature extraction from their voices. Result: This will become an important part of recent research in the voice biometrics field. This paper proposes multilingual speaker recognition with the help of MFCC as feature extraction and GMM as classification techniques using various available datasets such as TIMIT, librespeech, etc. Conclusion: The results achieved from the given datasets enhance the recognition rate of 70.98% with MFCC.

Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks

In the context of Modular High-Rise Residential Buildings (MHRBs), designing floor plans involves intricate complexities due to the need for adherence to numerous domain-specific design rules. To address this issue, our research introduces a novel framework based on a Graph-Constrained Generative Adversarial Network (GC-GAN) specialized for generating MHRB floor plans. This enhanced GC-GAN incorporates knowledge graphs that encapsulate domain-specific constraints and guidelines, thereby generating floor plans that exhibit realism, diversity, and conformity to established design principles. Additionally, the framework integrates a sophisticated image-to-vector conversion algorithm that enables seamless alignment with a predefined flat-design standardization library. A salient feature of this framework is the automated generation of Building Information Modeling (BIM) models, which rigorously conform to the modularity specifications essential for efficient modular construction. The efficacy and practical applicability of our approach have been validated through an exhaustive analysis covering fifteen cases across five diverse scenarios.