Razor Principle Research Articles

Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning.

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam's razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Evolving Transparent Credit Risk Models: A Symbolic Regression Approach Using Genetic Programming

Credit scoring is a cornerstone of financial risk management, enabling financial institutions to assess the likelihood of loan default. However, widely recognized contemporary credit risk metrics, like FICO (Fair Isaac Corporation) or Vantage scores, remain proprietary and inaccessible to the public. This study aims to devise an alternative credit scoring metric that mirrors the FICO score, using an extensive dataset from Lending Club. The challenge lies in the limited available insights into both the precise analytical formula and the comprehensive suite of credit-specific attributes integral to the FICO score’s calculation. Our proposed metric leverages basic information provided by potential borrowers, eliminating the need for extensive historical credit data. We aim to articulate this credit risk metric in a closed analytical form with variable complexity. To achieve this, we employ a symbolic regression method anchored in genetic programming (GP). Here, the Occam’s razor principle guides evolutionary bias toward simpler, more interpretable models. To ascertain our method’s efficacy, we juxtapose the approximation capabilities of GP-based symbolic regression with established machine learning regression models, such as Gaussian Support Vector Machines (GSVMs), Multilayer Perceptrons (MLPs), Regression Trees, and Radial Basis Function Networks (RBFNs). Our experiments indicate that GP-based symbolic regression offers accuracy comparable to these benchmark methodologies. Moreover, the resultant analytical model offers invaluable insights into credit risk evaluation mechanisms, enabling stakeholders to make informed credit risk assessments. This study contributes to the growing demand for transparent machine learning models by demonstrating the value of interpretable, data-driven credit scoring models.

An EGF technique to infer the source parameters of a circular crack growing at a variable rupture velocity

Circular crack models with a constant rupture velocity struggle to effectively model both the amplitude and duration of first P-wave pulses generated by small magnitude seismic events. Assuming a constant rupture velocity is unphysical, necessitating a deceleration phase in the rupture velocity to uphold the causality of the healing process. Moreover, a comprehensive failure model might encompass an initial nucleation phase, typically characterized by an increase of the initial rupture velocity. Studies have demonstrated that quasi-dynamic circular crack models featuring variable rupture velocities can accurately model the shape of the observed first P-wave pulse. Based on these principles, an Empirical Green’s function (EGF) approach was previously formulated to estimate the source parameters of small magnitude earthquakes, called MAIN. In addition to determine the source radius and stress drop, this method also enables the inference of the temporal evolution of rupture velocity. However, this method encounters difficulties when the noise-to-signal ratio in the recordings of smaller earthquakes used as EGF exceeds 5%, a common situation when employing regional-scale recordings of small-magnitude earthquakes as EGF. Through synthetic tests, we demonstrated that, in such instances, the problem of this technique is that the alignment between the onset of P waves of EGF and MAIN is not rightly recovered after the initial inversion step. Consequently, a novel inversion method has been developed to address this issue, enabling the identification of the optimal alignment of P-wave arrivals in EGF and MAIN across all stations. A Bayesian statistical approach is proposed to meticulously investigate the solutions of model parameters and their correlations. Using the new technique on a small magnitude earthquake (ML = 3.3) occurred in Central Italy enabled us to identify the most likely rupture models and examine the issue of correlation among model parameters. Application of Occam’s Razor Principle suggests that, for the investigated event, a circular crack model should be favored over a heterogeneous rupture model.

Reliability-based design optimization for a vertical-type breakwater with multiple limit-state equations under Korean marine environments varying from sea to sea

In this study, as part of basic research aimed at enhancing the accuracy of load and resistance coefficients to ensure their suitability for practical design and promoting the application of underutilized reliability-based design in Korea, the author conducts optimal design based on the reliability analysis of a vertical-type breakwater in the seas off of Haeundae, Yeosu, Mokpo, Gunsan, and Incheon—representative ports in Korea. In doing so, the author utilized the double-loop approach, which simultaneously addresses a reliability problem nested within an optimization process, employing the Polak–He optimization algorithm. To mitigate the substantial numerical effort required by the double-loop approach based on the Polak–He optimization algorithm, which necessitates the gradients of both cost and constraint functions, the subset simulation method was employed. In this process, the author deliberately refrained from using design waves of a specific return period and linear probabilistic models such as the Gaussian distribution, especially concerning wave and lift forces, often viewed as barriers to the widespread application of reliability-based design in Korea. Instead, the author focused on characterizing the uncertainties associated with the wave force, lift force, and overturning moment—variables that significantly impact the integrity of vertical-type breakwaters—by developing probabilistic models for these random variables directly from long-term in situ wave data. These models capture the varied characteristics of the Korean marine environment from sea to sea. In this way, the need for additional assumptions concerning the interrelationship between significant wave and maximum wave heights, along with the wave period, can be eliminated. Following Occam's razor principle, which suggests that explanations constructed with the smallest possible set of assumptions are superior, the reliability-based design optimization of a vertical-type breakwater presented in this study demonstrates promise in terms of simplicity and practicality. The limit state of the vertical-type breakwater was defined to encompass sliding, overturning, and collapse failures, and the strong interrelations between the wave force, lift force, and overturning moment were described using the Nataf joint distribution. As anticipated, simulation results show that solely considering sliding failure, as in the current reliability-based design platform in Korea, leads to an underestimated failure probability. Furthermore, ensuring a consistent failure probability for vertical-type breakwaters using design waves with a specific return period, as in past studies, is not feasible. In contrast, this study demonstrated that breakwaters optimally designed to meet the reliability index requirement of β = 3.5–4 consistently maintain a target failure probability in all sea areas.

Enhancements in BlenderBot 3: Expanding Beyond a Singular Model Governance and Boosting Generational Performance

This paper provides a pioneering examination and enhancement of generative chat models, with a specific focus on the BlenderBot 3 model. Through meticulous interaction with a diverse set of human participants, we dissected the fundamental components of these models, unveiling several deficiencies, including long-term memory and entity recognition. Leveraging these insights, we engineered refined, streamlined iterations, culminating in a chatbot that transcends the capabilities of all existing models. Our work follows Occam’s razor principle and proves that, for tasks with relatively low complexity, using large overparameterized models instead of smaller ones does not bring significant benefits but increases latency, which may result in a lowered overall user experience. In upholding our commitment to transparency and the progression of shared knowledge, we have made our improved model universally accessible through open-source distribution.

Second Flavor of Heavy Ions (SFHI) instead of WIMP?

Among two dozen theories of dark matter, only three of them do not go beyond the Standard Model and do not invent new physical laws. Therefore, these three theories are favored by Occam's razor principle. The theory based on the experimentally confirmed existence of the second flavor of hydrogen atoms is one of these three and it explains more observed manifestations of dark matter than any other theory. However, none of the two dozen theories explained by itself each and every observed manifestation of dark matter. It is possible that dark matter is a multi-faceted phenomenon having a variety of observed manifestations – analogously, e.g., to electrons manifesting as particles in some experiments or as waves in other experiments. Therefore, theories beyond the above three could be also entertained. One of dark matter hypotheses going beyond the Standard Model is Weakly Interactive Massive Particles (WIMP) that are assumed to be much more massive than baryons. However, despite various experimental attempts, WIMP were never observed. We could suggest that if there are massive dark matter particles, then more realistically they could be the Second Flavor of Heavy Ions (SFHI) described in the present paper. The SFHI could have a doubly-magic spherical nucleus, e.g., 40Ca20, or 48Ca20, or 48Ni28, or 56Ni28, or 78Ni28, or 100Sn50, or 132Sn50. The SFHI are really dark, as explained in the present paper. While there is no experimental confirmation of the existence of WIMP, we refer to the first experimental indication of the existence of the SFHI having 40Ca20 or 48Ca20 as the nucleus.

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

A simplest seesaw model of the TM1 and μ-τ reflection symmetries for the neutrino masses and leptogenesis

In this paper, following the Occam’s razor principle, we have put forward a very simple form of the Dirac neutrino mass matrix in the minimal seesaw model with the right-handed neutrino mass matrix being diagonal ; it has one texture zero and only contains three real parameters, whose values can be determined from the neutrino oscillation experimental results. Such a model leads to a neutrino mass matrix that obeys the TM1 and μ-τ reflection symmetries simultaneously. In this way all the lepton flavor mixing parameters except for are predicted; the value of is predicted by the TM1 symmetry, while those of , δ, ρ and σ by the μ-τ reflection symmetry. And the neutrino masses are predicted to be of the NO case with , for which all three light neutrino masses will be pinned down with the help of the experimental results for the neutrino mass squared differences. For these results, the effective Majorana neutrino mass that controls the rate of the neutrinoless double beta decay is predicted to be 1.6 or 3.8 meV in the case of σ = 0 or π/2. We have also studied the implications of the model for leptogenesis. It turns out that only in the two-flavor leptogenesis regime (which holds in the temperature range 109–1012 GeV) can leptogenesis have a chance to be successful. And a successful leptogenesis can be achieved at GeV in the case of σ = π/2, but not in the case of σ = 0.

The Proton and Occam’s Razor

Otto Stern’s 1933 measurement of the unexpectedly large proton magnetic moment indicated to most physicists that the proton is not a point particle. At that time, many physicists modeled elementary particles as point particles, and therefore Stern’s discovery initiated the speculation that the proton might be a composite particle. In this work, we show that despite being an elementary particle, the proton is an extended particle. Our work is motivated by the experimental data, which we review in section 1.By applying Occam’s Razor principle, we identify a simple proton structure that explains the origin of its principal parameters. Our model uses only relativistic and electromagnetic concepts, highlighting the primary role of the electromagnetic potentials and of the magnetic flux quantum Φ M = h/e. Unlike prior proton models, our methodology does not violate Maxwell’s equation, Noether’s theorem, or the Pauli exclusion principle.Considering that the proton has an anapole (toroidal) magnetic moment, we propose that the proton is a spherical shaped charge that moves at the speed of light along a path that encloses a toroidal volume. A magnetic flux quantum Φ M = h/e stabilizes the proton’s charge trajectory. The two curvatures of the toroidal and poloidal current loops are determined by the magnetic forces associated with Φ M . We compare our calculations against experimental data.

What can Occam’s razor principle tell us about theories of dark matter?

What can Occam’s razor principle tell us about theories of dark matter?

The PoliTO–UniRoma1 database of cyclic and dynamic laboratory tests: assessment of empirical predictive models

The soil nonlinear hysteretic behaviour is usually described, in the moderate strain range, through the shear modulus reduction and material damping ratio (MRD) curves. In common practice, in absence of specific laboratory tests, the curves are estimated by employing empirical regression models. Such predictive models, typically calibrated on large experimental datasets, correlate the soil response to its physical properties. This research fits within this context, presenting a comprehensive database of cyclic and dynamic laboratory tests conducted on natural Italian soils. The database, publicly available as supplementary data of the paper, contains the results of the tests conducted by the geotechnical laboratories of the Politecnico di Torino (Turin, Italy) and the Sapienza Università di Roma (Rome, Italy) over the past 30 years. The experimental data are employed to assess the performance of some widely used empirical models in predicting the MRD curves of natural uncemented fine-grained soils, emphasizing the importance of using an independent dataset for conducting a reliable statistical analysis. The results show that the use of many soil parameters as proxies for predicting the soil response does not necessarily lead to an improvement in the performance of the model. Therefore, according to Occam’s razor principle, simple models are to be preferred.

Reanalysis of ProteomicsDB Using an Accurate, Sensitive, and Scalable False Discovery Rate Estimation Approach for Protein Groups

Estimating false discovery rates (FDRs) of protein identification continues to be an important topic in mass spectrometry–based proteomics, particularly when analyzing very large datasets. One performant method for this purpose is the Picked Protein FDR approach which is based on a target-decoy competition strategy on the protein level that ensures that FDRs scale to large datasets. Here, we present an extension to this method that can also deal with protein groups, that is, proteins that share common peptides such as protein isoforms of the same gene. To obtain well-calibrated FDR estimates that preserve protein identification sensitivity, we introduce two novel ideas. First, the picked group target-decoy and second, the rescued subset grouping strategies. Using entrapment searches and simulated data for validation, we demonstrate that the new Picked Protein Group FDR method produces accurate protein group-level FDR estimates regardless of the size of the data set. The validation analysis also uncovered that applying the commonly used Occam’s razor principle leads to anticonservative FDR estimates for large datasets. This is not the case for the Picked Protein Group FDR method. Reanalysis of deep proteomes of 29 human tissues showed that the new method identified up to 4% more protein groups than MaxQuant. Applying the method to the reanalysis of the entire human section of ProteomicsDB led to the identification of 18,000 protein groups at 1% protein group-level FDR. The analysis also showed that about 1250 genes were represented by ≥2 identified protein groups. To make the method accessible to the proteomics community, we provide a software tool including a graphical user interface that enables merging results from multiple MaxQuant searches into a single list of identified and quantified protein groups.

Hierarchical identification of nonlinear hybrid systems in a Bayesian framework

This paper presents a hierarchical framework for the identification of nonlinear hybrid systems in the form of Switched Nonlinear AutoRegressive models with eXogenous variables (SNARX). The identification is done via three levels of inference, using Bayes' rule. In the first level, model parameters are computed via a Maximum a Posteriori (MAP) estimator. The posterior distribution therein involved depends on hyper-parameters that are tuned in the second level of inference. Such terms determine model complexity, and the Bayesian framework is key in returning values that trade off complexity with accuracy by automatically embodying the Occam's razor principle. Lastly, the third level compares different model structures by means of a quality measure that encompasses data fitness, model complexity, and data classification. The proposed framework is compared with existing relevant methods and is tested on different numerical models, showing promising performance.

Self-replicating probes are imminent – implications for SETI

AbstractIn the early 1980s, the Sagan-Tipler debate raged regarding the interpretation of the Fermi paradox but no clear winner emerged. Sagan favoured the existence of ETI on the basis of the Copernican principle and Tipler favoured the non-existence of ETI on the basis of the Occam's razor principle. Tipler's stance was an expansion of the similar but earlier Hart declaration. However, crucial to the Tipler argument was the role played by self-replicating interstellar robot probes. Any technologically capable species will develop self-replication technology as the most economical means of exploring space and the Galaxy as a whole with minimal investment. There is no evidence of such probes in our solar system including the asteroid belt, ergo, ETI do not exist. This is a powerful and cogent argument. Counter-arguments have been weak including Sagan's sociological explanations. We present a Copernican argument that ETI do not exist – humans are developing self-replication technology today. We are developing the ability to 3D print entire robotic machines from extraterrestrial resources including electric motors and electronics as part of a general in-situ resource utilization (ISRU) capability. We have 3D-printed electric motors which can be potentially leveraged from extraterrestrial material that should be available in every star system. From a similar range of materials, we have identified a means to 3D print neural network circuitry. From our industrial ecology, self-replicating machines and indeed universal constructors are feasible. We describe in some detail how a self-replicating interstellar spacecraft may be constricted from asteroidal resources. We describe technological signatures of the processing of asteroidal material (which is expected to be common to most star systems), and the excess production of certain types of clay and other detritus materials. Self-replication technology is under development and imminent – if humans are pursuing self-replication technology, then by the Copernican principle, so would any technologically savvy species elsewhere. There is no evidence that they have.

Ovarian cancer detection using optimized machine learning models with adaptive differential evolution

Ovarian cancer is one of the leading causes of mortality among women. The most common detection approach is by tracking related biomarkers to see whether patient has ovarian cancer. Despite emphasis on biomarkers, feature subsets may include physiological features. In this current research, we propose an optimal simultaneous feature weighting/parameter optimization for detecting ovarian cancer occurrence. The weights are optimized using adaptive differential evolution (ADE) with cross validation error and least absolute shrinkage and selection operator (LASSO) regularization as the fitness function. Furthermore, to be in line with the Occam’s razor principle, we apply LASSO regularization in the form of weighted ℓ1 norm to reduce features being selected. The regularization constant, λ, is integral to balance the feature weights and the cross validation error. Using ADE method and, in particular LASSO regularization, we obtain the results showing clear reduction and elimination of features from feature subset. Using K-nearest neighbors (KNN) with λ=0.01, 97.24% accuracy (mean) is obtained. Meanwhile, when using support vector machines (SVM) with λ=0.01, an accuracy of 96.48% (mean) is achieved. From the viewpoint of machine learning, this, at least, suggests that the proposed approach may be a good solution for optimizing feature weights/parameters at the same time. From the perspective of cancer research, it demonstrates that a plethora of features may be valuable in building a model and should not be discarded for having weak discriminative capability. As a result, the combination of feature subset and non linear mapping plays a significant role.

Evaluation of the NANDA International, Inc. diagnostic classification in Spain: Development and validation of the EVALUAN-I tool.

To develop and validate a tool to evaluate the NANDA International, Inc. diagnostic classification. The tool, EVALUAN-I, was validated in a non-probabilistic sample (N = 460) on Spanish Registered Nurses (September-December 2019) in two phases. First, design and construct the instrument in three steps: (1) literature review to define the construct focusing on the orientation toward nursing concepts and theoretical foundations, the level of scientific evidence, the structural configuration, the applicability, the nurses' clinical reasoning skills, and the attitudes toward nursing diagnosis, (2) substantiation of the questionnaire items and design according to the criteria for a diagnostic classification, (3) expert test to establish the face validity and content validity. The second phase revolved around (4) conducting a pilot test and measuring the temporal stability (test-retest) and Cohen's kappa coefficient; assessing psychometric properties by measuring (5) reliability (internal consistency using Cronbach alpha and interfactor correlation) and (6) construct validity (exploratory and confirmatory factor analysis). The manuscript follows the STROBE checklist. The study was approved by the Research Ethics Committee with registration number 2019-190-1. EVALUAN-I displayed moderate test-retest stability, adequate construct validity, and excellent reliability. The confirmatory factor analysis provided evidence about the configuration of EVALUAN-I in relation to nine analytical dimensions: clinical competence, nurses' reasoning skills, attitudes towards nursing diagnosis, discipline's central concepts, classification's contents, physiopathological attributes, level of scientific evidence, diagnostic precision, and conceptual correspondence between terminologies. EVALUAN-I is a valid and reliable instrument, which can be used to improve the epistemological, normative, and intuitive configuration of NANDA International, Inc. in a structured, systematic manner. Comprehensive evaluation of NANDA International, Inc. in different clinical settings around the world using a validated instrument, like EVALUAN-I, would allow strengths and weaknesses to be identified and contribute to the classification's development and practical application.

Do deep learning models make a difference in the identification of antimicrobial peptides?

In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first generation of these predictors was filled with what is now known as shallow learning-based models. These models require the computation and selection of molecular descriptors to characterize each peptide sequence and train the models. The second generation, known as deep learning-based models, which no longer requires the explicit computation and selection of those descriptors, started to be used in the prediction task of AMPs just four years ago. The superior performance claimed by deep models regarding shallow models has created a prevalent inertia to using deep learning to identify AMPs. However, methodological flaws and/or modeling biases in the building of deep models do not support such superiority. Here, we analyze the main pitfalls that led to establish biased conclusions on the leading performance of deep models. Also, we analyze whether deep models truly contribute to achieve better predictions than shallow models by performing fair studies on different state-of-the-art benchmarking datasets. The experiments reveal that deep models do not outperform shallow models in the classification of AMPs, and that both types of models codify similar chemical information since their predictions are highly similar. Thus, according to the currently available datasets, we conclude that the use of deep learning could not be the most suitable approach to develop models to identify AMPs, mainly because shallow models achieve comparable-to-superior performances and are simpler (Ockham's razor principle). Even so, we suggest the use of deep learning only when its capabilities lead to obtaining significantly better performance gains worth the additional computational cost.

Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering.

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements.

Is learning for the unit commitment problem a low-hanging fruit?

The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to perceive a glimmer of Occam’s razor principle. In this letter, we use the unit commitment problem (UCP), an NP-hard mathematical program that is fundamental to power system operations, to show that simplicity must guide any strategy to solve it, in particular those that are based on learning from past UCP instances. To this end, we apply a naive algorithm to produce candidate solutions to the UCP and show, using a variety of realistically sized power systems, that we are able to find optimal or quasi-optimal solutions with remarkable speedups. To the best of our knowledge, this is the first work in the technical literature that quantifies how challenging learning the solution of the UCP actually is for real-size power systems. Our claim is thus that any sophistication of the learning method must be backed up with a statistically significant improvement of the results in this letter.

Hemiarch Versus Arch Replacement in Acute Type A Aortic Dissection: Is the Occam’s Razor Principle Applicable?

Background and aim of the study: In patients with acute Type A aortic dissection (A-AAD) whether repair should be limited to ascending aorta/hemiarch replacement or extended to include the aortic arch is still debated. We have analyzed our experience to compare outcomes of patients with A-AAD treated with these 2 different surgical strategies. Methods: From 2006 to 2020, a total of 213 patients have undergone repair of A-AAD at our Center; in 163 of them ascending aorta/hemiarch replacement (Group 1) and in 75 ascending aorta and arch replacement (Group 2) were performed. The primary endpoint was early survival and secondary endpoints late survival, freedom from late complications and reoperations. Patients were compared according to era of operation: 2006 to 2013 (Era 1) and 2014 to 2020 (Era 2). Results: Overall hospital mortality was 12% and 5% in Group 1 and 2; mortality remained stable in Era 1 and 2 for Group 1 (15%), while it decreased from 8% to 1% in Group 2 patients (p = 0.24). Actuarial survival at 5 and 10 years is 72 ± 4% and 49 ± 5% in Group 1 and 77 ± 6% and 66 ± 9% in Group 2 (p = 0.073). Actuarial freedom from reoperation in the entire series is 94 ± 2% and 92 ± 3% at 5 and 10 years. Freedom from reoperation at 5 and 10 years is 92 ± 2% and 89 ± 3% in Group 1 and 98 ± 1% at all intervals in Group 2 (p = 0.068). Conclusions: An aggressive approach to A-AAD provides superior long-term results without increasing mortality. Furthermore, arch replacement during A-AAD repair represents a more stable solution with lower incidence of late aortic-related complications. Immediate aortic arch replacement should be considered in the treatment of A-AAD especially in experienced centers.