Robust Set Research Articles

NCBI RefSeq: reference sequence standards through 25 years of curation and annotation

Abstract Reference sequences and annotations serve as the foundation for many lines of research today, from organism and sequence identification to providing a core description of the genes, transcripts and proteins found in an organism's genome. Interpretation of data including transcriptomics, proteomics, sequence variation and comparative analyses based on reference gene annotations informs our understanding of gene function and possible disease mechanisms, leading to new biomedical discoveries. The Reference Sequence (RefSeq) resource created at the National Center for Biotechnology Information (NCBI) leverages both automatic processes and expert curation to create a robust set of reference sequences of genomic, transcript and protein data spanning the tree of life. RefSeq continues to refine its annotation and quality control processes and utilize better quality genomes resulting from advances in sequencing technologies as well as RNA-Seq data to produce high-quality annotated genomes, ortholog predictions across more organisms and other products that are easily accessible through multiple NCBI resources. This report summarizes the current status of the eukaryotic, prokaryotic and viral RefSeq resources, with a focus on eukaryotic annotation, the increase in taxonomic representation and the effect it will have on comparative genomics. The RefSeq resource is publicly accessible at https://www.ncbi.nlm.nih.gov/refseq.

Interdisciplinary Scientific Evidence for a New Agenda on The Challenges of Operations Management in the Built Environment in Brazil

Objective: This study aimed to identify scientific evidence concerning the legal dimension of Artificial Intelligence (AI) in Brazil, serving as a foundation for guidelines on a new agenda addressing the challenges of operations management with a specific focus on university intellectual property (IP) in the built environment. Theoretical Framework: Two theoretical axes were addressed: (1) Innovation and the legal dimension of AI and (2) Intellectual property (IP) in the university context. Method: The research was exploratory and descriptive, employing both qualitative and quantitative approaches, carried out through: (a) a bibliographic review; (b) empirical research, with textual analysis. Voyant Tools software was used for the analysis. Results and Discussion: The results of the textual analysis, including Cirrus and Link, revealed six scientific pieces of evidence, enabling the development of a robust set of guidelines on the subject. Research Implications: The research provided insights that supported the proposal of a new agenda, fostering a feedback loop between theory and practice on the topic. Originality/Value: The interdisciplinary approach to the topic underscores the originality of this research. Its value lies in broadening the debate on management in the built environment, highlighting the role of AI and university IP, topics with great potential to contribute to innovations in the bidirectional flow between academia and the labor market.

Identification of a Novel Biomarker Panel for Breast Cancer Screening

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.

Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs

The rapid expansion of the internet has led to a corresponding surge in malicious online activities, posing significant threats to users and organizations. Cybercriminals exploit malicious uniform resource locators (URLs) to disseminate harmful content, execute phishing schemes, and orchestrate various cyber attacks. As these threats evolve, detecting malicious URLs (MURLs) has become crucial for safeguarding internet users and ensuring a secure online environment. In response to this urgent need, we propose a novel machine learning-driven framework designed to identify known and unknown MURLs effectively. Our approach leverages a comprehensive dataset encompassing various labels—including benign, phishing, defacement, and malware—to engineer a robust set of features validated through extensive statistical analyses. The resulting malicious URL detection system (MUDS) combines supervised machine learning techniques, tree-based algorithms, and advanced data preprocessing, achieving a high detection accuracy of 96.83% for known MURLs. For unknown MURLs, the proposed framework utilizes CL_K-means, a modified k-means clustering algorithm, alongside two additional biased classifiers, achieving 92.54% accuracy on simulated zero-day datasets. With an average processing time of under 14 milliseconds per instance, MUDS is optimized for real-time integration into network endpoint systems. These outcomes highlight the efficacy and efficiency of the proposed MUDS in fortifying online security by identifying and mitigating MURLs, thereby reinforcing the digital landscape against cyber threats.

Accelerated ensemble optimization using momentum methods

We investigate the use of various momentum methods in combination with an ensemble approximation of gradients, for accelerated optimization. Although momentum gradient descent methods are popular in machine learning, it is unclear how they perform when applied to time-consuming dynamic problems such as production optimization for petroleum reservoir management. Four different momentum methods are extensively tested on a reservoir test case in one deterministic and one robust setting. The numerical experiments show that momentum strategies yield, on average, a higher net present value with fewer simulations needed.

Exploring the Chemopreventive Effect of Medication on Gene Expression Linked to Colorectal Cancer: An Observational and Mendelian Randomization Analysis in Healthy Colon Mucosa

Gene expression appears altered in apparently normal tissue surrounding tumor tissue. The observed biological alterations in the tumor microenvironment play a crucial role in cancer development and are named the cancer field effect (FE). A robust set of overexpressed FE genes in tissue surrounding colorectal cancer (CRC) tumor were identified in previous studies. Our study aimed to investigate the influence of common medication intake and modifiable risk factors on FE gene expression using a colonic mucosa sample dataset of healthy individuals (BarcUVa-Seq). We applied expression enrichment analysis of the FE genes for each studied medication and factor. Both observational and instrumental (Mendelian randomization) analysis were conducted, and the results were validated using independent datasets. The findings from the observational and instrumental analyses consistently showed that medication intake, especially metformin, considerably downregulated the FE genes. Chemopreventive effects were also noted for antihypertensive drugs targeting the renin–angiotensin system. Conversely, benzodiazepines usage might upregulate FE genes, thus fostering a tumor-promoting microenvironment. In contrast, the findings from the observational and instrumental analyses on modifiable risk factors showed some discrepancies. The instrumental results indicated that obesity and smoking might promote a tumor-favorable microenvironment. These findings offer insights into the biological mechanisms through which risk factors might influence CRC development and highlight the potential chemopreventive roles of metformin and antihypertensive drugs in CRC risk.

Robust Computation and Analysis of Vibrational Spectra of Layered Framework Materials Including Host-Guest Interactions.

Layered framework materials, a rapidly advancing class of porous materials, are composed of molecular components stitched together via covalent bonds and are usually synthesized through wet-chemical methods. Computational infrared (IR) and Raman spectra are among the most important characterization tools for this material class. Besides the a priori known spectra of the molecular building blocks and the solvent, they allow for in situ monitoring of the framework formation during synthesis. Therefore, they need to capture the additional peaks from host-guest interactions and the bands from emerging bonds between the molecular building blocks, verifying the successful synthesis of the desired material. In this work, we propose a robust computational framework based on ab initio molecular dynamics (AIMD), where we compute IR and Raman spectra from the time-correlation functions of dipole moments and polarizability tensors, respectively. As a case study, we apply our methodology to a covalent organic framework (COF) material, COF-1, and present its AIMD-computed IR and Raman spectra with and without 1,4-dioxane solvent molecules in its pores. To determine robust settings, we meticulously validate our model and explore how stacking disorder and different methods for computing dipole moments and polarizabilities affect IR and Raman intensities. Using our robust computational protocol, we achieve excellent agreement with experimental data. Furthermore, we illustrate how the computed spectra can be dissected into individual contributions from the solvent molecules, the molecular building blocks of COF-1, and the bonds connecting them.

Indoor occupancy monitoring using environmental feature fusion and semi-supervised machine learning models

Smart buildings optimize energy consumption and occupant comfort through Heating, Ventilation and Air Conditioning, and lighting management. Nevertheless, large venues require data fusion techniques to improve analysis and forecasting. This study aims to evaluate the effectiveness of using different feature fusion techniques, environmental sensors, and semi-supervised learning to estimate indoor occupancy in a 230 m2 office. Using five Internet of Things devices measuring air temperature, relative humidity, and barometric pressure, data was collected for 99 days with 6800 entries (on average) and only 14% labeled. Eight feature selection methods were evaluated along with three supervised and two semi-supervised classification methods. Results indicate that the Chi-squared-based approach for feature fusion outperformed others. Similarly, the semi-supervised Self-Training model achieved better performance than the supervised methods. This research shows that combining semi-supervised learning and data fusion allows for estimating the occupancy level in large indoor spaces with high accuracy and low labeling costs. Highlights This study pioneers in exploring semi-supervised learning and distinct feature fusion methods for estimating indoor occupancy levels in a 230 m 2 open office using only Internet of Things (IoT) environmental sensors (air temperature, relative humidity, and barometric pressure). A comprehensive comparison of statistical methods, feature selection, and dimensionality reduction techniques are conducted to determine their ability to generate robust feature fusion sets. The feature fusion selected through the Chi-squared test stood out with a high accuracy F1-score (average of 0.95) and an average accuracy of 0.99. The Self-Training model reached the best performance from semi-supervised learning, with an average F1-Score of 0.90 and an average accuracy of 0.97, based on a dataset with a large proportion of unlabelled data (16,847 entries) and only 9367 labels. For supervised learning, Random Forest achieved a high accuracy (average of 0.98) and F1-score (average of 0.93) across various feature sets.

Harmonizing Multisource Data to Inform Vector-Borne Disease Risk Management Strategies.

In the last few decades, we have witnessed the emergence of new vector-borne diseases (VBDs), the globalization of endemic VBDs, and the urbanization of previously rural VBDs. Data harmonization forms the basis of robust decision-support systems designed to protect at-risk communities from VBD threats. Strong interdisciplinary partnerships, protocols, digital infrastructure, and capacity-building initiatives are essential for facilitating the coproduction of robust multisource data sets. This review provides a foundation for researchers and practitioners embarking on data harmonization efforts to (a) better understand the links among environmental degradation, climate change, socioeconomic inequalities, and VBD risk; (b) conduct risk assessments, health impact attribution, and projection studies; and (c) develop robust early warning and response systems. We draw upon best practices in harmonizing data for two well-studied VBDs, dengue and malaria, and provide recommendations for the evolution of research and digital technology to improve data harmonization for VBD risk management.

Does Company Size and Profitability Matter? Investigating the Moderating Effects of Growth in Cash Flow on Stock Performance

This research examines the intricate relationships between company size, growth in cash flow, and stock performance, revealing complexities that challenge traditional financial analysis. While company size is often associated with stable stock performance due to advantages such as economies of scale and market power, the findings indicate that size alone does not positively impact stock performance. Furthermore, the study demonstrates that growth in cash flow does not significantly moderate the relationship between company size and stock performance. This suggests that external factors, such as regulatory changes or market sentiment, may play a more decisive role. The results underscore that cash flow, while an important indicator of financial health, does not enhance the influence of company size on stock performance, particularly in certain industries where external conditions prevail. This underscores the need for a more comprehensive evaluation approach that considers a broader range of factors when assessing stock performance. It's time to move beyond traditional metrics like profitability and cash flow growth and equip ourselves with a more robust set of tools for analysis. Ultimately, this research advocates for a multifactorial approach to stock performance evaluation, emphasizing the importance of understanding the interplay between various variables, including industry trends and macroeconomic conditions. By adopting this comprehensive perspective, investors and analysts can make more informed decisions and strategies, enhancing their ability to navigate the complexities of the financial markets.

Physiological regulation of oral saliva ion composition and flow rate are not coupled in healthy humans-Partial revision of our current knowledge required.

Appropriate composition of oral saliva is essential for a healthy milieu that protects mucosa and teeth. Only few studies, with small sample numbers, investigated physiological saliva ion composition in humans. We determined saliva ion composition in a sufficiently large cohort of healthy adults and analyzed the effect of physiological stimulation. We collected saliva from 102 adults under non-stimulated and physiologically stimulated conditions (chewing). Individual flow rates, pH, osmolality, Na+, K+, Cl-, and HCO3- concentrations under both conditions as well as the individual changes due to stimulation (Δvalues) were determined. Non-stimulated saliva was hypoosmolal and acidic. Na+, Cl-, and HCO3- concentrations remained well below physiological plasma values, whereas K+ concentrations exceeded plasma values more than twofold. Stimulation resulted in a doubling of flow rates and substantial increases in pH, HCO3-, and Na+ concentrations. Overall, stimulation did not considerably affect osmolality nor K+ or Cl- concentrations of saliva. An in-depth analysis of stimulation effects, using individual Δvalues, showed no correlation of Δflow rate with Δion concentrations, indicating independent regulation of acinar volume and ductal ion transport. Stimulation-induced Δ[Na+] correlated with Δ[HCO3-] and Δ[Cl-] but not with Δ[K+], indicating common regulation of ductal Na+, Cl-, and HCO3- transport. We present a robust data set of human oral saliva ion composition in healthy adults and functional insights into physiological stimulation. Our data show (i) that flow-dependence exists for Na+ and HCO3- but not for K+ and Cl- concentrations, (ii) osmolality is flow-independent, (iii) regulation of Na+, Cl-, and HCO3- transport is coupled, (iv) regulation of flow rate and ion concentrations are independent and (v) spatially separated between acini and ducts, respectively.

Non-invasive regional parameter identification of degenerated human meniscus

Accurate identification of local changes in the biomechanical properties of the normal and degenerative meniscus is critical to better understand knee joint osteoarthritis onset and progression. Ex-vivo material characterization is typically performed on specimens obtained from different locations, compromising the tissue's structural integrity and thus altering its mechanical behavior. Therefore, the aim of this in-silico study was to establish a non-invasive method to determine the region-specific material properties of the degenerated human meniscus. In a previous experimental magnetic resonance imaging (MRI) study, the spatial displacement of the meniscus and its root attachments in mildly degenerated (n = 12) and severely degenerated (n = 12) cadaveric knee joints was determined under controlled subject-specific axial joint loading. To simulate the experimental response of the lateral and medial menisci, individual finite element models were created utilizing a transverse isotropic hyper-poroelastic constitutive material formulation. The superficial displacements were applied to the individual models to calculate the femoral reaction force in an inverse finite element analysis. During particle swarm optimization, the four most sensitive material parameters were varied to minimize the error between the femoral reaction force and the force applied in the MRI loading experiment. Individual global and regional parameter sets were identified. In addition to in-depth model verification, prediction errors were determined to quantify the reliability of the identified parameter sets. Both compressibility of the solid meniscus matrix (+141 %, p ≤ 0.04) and hydraulic permeability (+53 %, p ≤ 0.04) were significantly increased in the menisci of severely degenerated knees compared to mildly degenerated knees, irrespective of the meniscus region. By contrast, tensile and shear properties were unaffected by progressive knee joint degeneration. Overall, the optimization procedure resulted in reliable and robust parameter sets, as evidenced by mean prediction errors of <1 %. In conclusion, the proposed approach demonstrated high potential for application in clinical practice, where it might provide a non-invasive diagnostic tool for the early detection of osteoarthritic changes within the knee joint.

Influence of Preoperative Motor Score and Patient Comorbidities on Transcranial Motor-Evoked Potential Acquisition in Intracranial Surgery: A Retrospective Cohort Study.

Intraoperative neurophysiological monitoring plays a pivotal role in modern neurosurgery, aiding in real-time assessment of eloquent neural structures to mitigate iatrogenic neural injury. This study represents the largest retrospective series to date in monitoring corticospinal tract integrity during intracranial surgery with transcranial motor-evoked potentials (TCMEPs), focusing on the influence of demographic factors, comorbidities, and preoperative motor deficits on the reliability of intraoperative neurophysiological monitoring. While the impact of patient-specific factors affecting TCMEP monitoring in spine surgery is well-documented, similar insights for intracranial surgery are lacking. A total of 420 craniotomy patients were retrospectively analyzed from electronic medical records from December 2017 to February 2023, excluding patients without preoperative Medical Research Council scores or medical histories. Using intrinsic hand muscles as a robust data set, 840 hand TCMEPs acquired during intracranial surgery were assessed. Demographic and clinical factors, including preoperative motor scores, were analyzed to identify associations with TCMEP acquisition and amplitude. Nonparametric statistics and multivariate regression analysis were employed. TCMEPs were successfully acquired in 734 (87.7%) patient hands, even in the presence of preoperative motor deficits in 13.9% of total patient hands. Preoperative motor scores did not predict the ability to acquire baseline TCMEPs ( P = .6). Notably, older age ( P < .001) and hypertension ( P = .01) were independent predictors of lower TCMEP acquisition rates. Preoperative motor scores significantly influenced TCMEP amplitudes, with higher scores correlating with higher amplitudes (1771 [SD = 1550] eve vs 882 [SD = 856] μV, P < .0001). Older age ( P < .001) and chronic kidney disease ( P = .04) were also associated with reduced TCMEP amplitudes. Our investigation into TCMEPs during intracranial surgery demonstrated a notably high acquisition rate in hand muscles, irrespective of preoperative motor deficits. Preoperative motor scores reliably correlated with TCMEP amplitudes in a linear fashion while advanced age and renal disease emerged as independent predictors of lower TCMEP amplitudes.

Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on unreliable network channel

During the batch process, the need for security control is becoming increasingly urgent with the gradual penetration of network control technology. For small time delay batch processes subject to deception attacks, an iterative learning robust security predictive tracking control approach is developed. Reviewing previous studies for iterative learning control, the issue that the repetitive character of the batch process would mask the characteristics of deception attacks was not considered. Moreover, the gains of iterative learning control law are utilized offline, which makes large deviations for the system state as the operating time increases. This will lead to “excessive-input” conditions for control inputs, which means more consumption of energy and production resources. To address these challenges, we introduce robust security invariant sets to improve the robustness of the system by constraining the system state to be in a safe range. Also, the design of the iterative learning controller incorporates deception attack data for historical batches, which is continuously improved and optimized to withstand deception attacks. Meanwhile, by calculating stability conditions online, the real-time control law gain is obtained, which could make the control inputs adjusted online based on the real-time system’s dynamic characteristics, avoiding excessive inputs and improving the efficiency of energy and resource utilization. Additionally, the stability analysis proves that the system is input to state stability. Ultimately, simulation verifies the effectiveness and feasibility of the developed method.

Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.

Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventions. This study aims to utilize feature selection process to identify a robust and minimal set of predictors that can aid in the early prediction of short- and long-term weight problems in children and adolescents. We utilized demographic, physical, and psychological wellbeing predictors to model weight status (normal, underweight, overweight, and obese) for 1-, 3-, and 5-year periods. To select the most influential features, we employed four feature selection methods: (1) Chi-Square test; (2) Information Gain; (3) Random Forest; (4) eXtreme Gradient Boosting (XGBoost) with six ML approaches. The stability of the feature selection methods was assessed by Jaccard's index, Spearman's rank correlation and Pearson's correlation. Model evaluation was performed by various accuracy metrics. With 3,862,820 million student-visits were included in this population-based study, the mean age of 11.6 (SD = 3.64) for the training set and 10.8 years (SD = 3.50) for the temporal test set. From the initial set of 38 predictors, we identified 6, 9, and 13 features for 1-, 3-, and 5-year predictions, respectively, by the best performed feature selection method of Chi-Square test in XGBoost models. These feature sets demonstrated excellent stability and achieved prediction accuracies of 0.82, 0.73, and 0.70; macro-AUCs of 0.94, 0.86, and 0.83; micro-AUCs of 0.96, 0.93, and 0.92 for different prediction windows, respectively. Weight, height, sex, total score of self-esteem, and age were consistently the most influential predictors across all prediction windows. Additionally, several psychological and social wellbeing predictors showed relatively high importance in long-term weight status prediction. We demonstrate the potential of ML in identifying key predictors of weight status in children and adolescents. While traditional anthropometric measures remain important, psychological and social wellbeing factors also emerge as crucial predictors, potentially informing targeted interventions to address childhood and adolescence weight problems.

Predictive Neuromarker Patterns for Calcification Metaplasia in Early Tendon Healing.

Unsuccessful tendon healing leads to fibrosis and occasionally calcification. In these metaplastic drifts, the mouse AT preclinical injury model represents a robust experimental setting for studying tendon calcifications. Previously, calcium deposits were found in about 30% of tendons after 28 days post-injury. Although a neuromediated healing process has previously been documented, the expression patterns of NF200, NGF, NPY, GAL, and CGRP in mouse AT and their roles in metaplastic calcific repair remain to be explored. This study included a spatiotemporal analysis of these neuromarkers during the inflammatory phase (7 days p.i.) and the proliferative/early-remodelling phase (28 days p.i.). While the inflammatory phase is characterised by NF200 and CGRP upregulation, in the 28 days p.i., the non-calcified tendons (n = 16/24) showed overall NGF, NPY, GAL, and CGRP upregulation (compared to 7 days post-injury) and a return of NF200 expression to values similar to pre-injury. Presenting a different picture, in calcified tendons (n = 8), NF200 persisted at high levels, while NGF and NPY significantly increased, resulting in a higher NPY/CGRP ratio. Therefore, high levels of NF200 and imbalance between vasoconstrictive (NPY) and vasodilatory (CGRP) neuromarkers may be indicative of calcification. Tendon cells contributed to the synthesis of neuromarkers, suggesting that their neuro-autocrine/paracrine role is exerted by coordinating growth factors, cytokines, and neuropeptides. These findings offer insights into the neurobiological mechanisms of early tendon healing and identify new neuromarker profiles predictive of tendon healing outcomes.

Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework

Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the process of designing specific classifiers for various astronomical objects. We present a novel deep-learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on data sets obtained from Kepler, TESS, and Zwicky Transient Facility multiband observations of variable stars and transients. We achieve an accuracy of 87% for combined variable and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly for other missions, such as the All-sky Automated Survey for Supernovae, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true-label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github (https://github.com/ckm3/Deep-LC) and PyPI.

THE NEW ECONOMIC SOCIOLOGY AND TERRITORIAL DEVELOPMENT: AN INTERDISCIPLINARY APPROACH

This article investigates the intersection between economic theory and sociology through a qualitative methodology, aiming to deepen the interdisciplinary debate between economics and the New Economic Sociology (NES). The analysis focuses on the contributions of Granovetter, Fligstein, and DiMaggio, highlighting how NES concepts can enrich the understanding of territorial development, especially regarding the role of social relations in territories. Sociologists, in turn, demonstrate diligence and creativity in addressing cultural and political issues concerning collective representations and social coercion, fundamental concepts that define institutions. Based on a robust set of authors and bibliographic references, the article seeks to complement the debate on NES. The research reveals that NES offers valuable insights for analyzing territorial dynamics and reinforces the importance of a territorial approach in formulating development strategies. In the end, the article demonstrates that the themes discussed by NES confirm the need for a more integrated territorial perspective in constructing sustainable development projects.

Solving multi-objective robust optimization problems via Stakelberg-based game model

Real-world multi-objective engineering problems frequently involve uncertainties stemming from environmental factors, production inaccuracies, and other sources. A critical aspect of addressing these problems, termed Multi-Objective Robust Optimization (MORO) problems, is the development of solutions that are both optimal and resilient to uncertainties. This paper proposes addressing these uncertainties through the application of Stackelberg game models, a novel approach involving the interaction of two players. The Leader searches for optimal and robust solutions and the Follower generates uncertainties based on the Leader’s chosen solutions. The Follower seeks to tackle the most challenging uncertainties associated with the Leader’s candidate solutions. Additionally, this paper introduces a novel metric to assess the robustness of a given set of solutions concerning specified uncertainties.Based on the proposed approach, a co-evolutionary algorithm is developed. A numerical study is then conducted to evaluate the algorithm by comparing its performance with those obtained by four benchmark algorithms on nine benchmark MORO problems. The numerical study also aims to assess its sensitivity to run parameter variations. The experimental results demonstrate the proposed approach’s effectiveness in identifying a non-dominated robust set of solutions.

Cost-efficient payoffs under model ambiguity

Dybvig (1988a, 1988b) solves in a complete market setting the problem of finding a payoff that is cheapest possible in reaching a given target distribution (“cost-efficient payoff”). In the presence of ambiguity, the distribution of a payoff is, however, no longer known with certainty. We study the problem of finding the cheapest possible payoff whose worst-case distribution stochastically dominates a given target distribution (“robust cost-efficient payoff”) and determine solutions under certain conditions. We study the link between “robust cost-efficiency” and the maxmin expected utility setting of Gilboa and Schmeidler (1989), as well as more generally in a possibly nonexpected robust utility setting. Specifically, we show that solutions to maxmin robust expected utility are necessarily robust cost-efficient. We illustrate our study with examples involving uncertainty both on the drift and on the volatility of the risky asset.