Optimization Approach Research Articles

Comparing chemical transfection, electroporation, and lentiviral vector transduction to achieve optimal transfection conditions in the Vero cell line

BackgroundTransfection is an important analytical method for studying gene expression in the cellular environment. There are some barriers to efficient DNA transfection in host cells, including circumventing the plasma membrane, escaping endosomal compartmentalization, autophagy, immune sensing pathways, and translocating the nuclear envelope. Therefore, it would be very useful to introduce an optimum transfection approach to achieve a high transfection efficiency in the Vero cell line. The aim of this study was to compare various transfection techniques and introduce a highly efficient method for gene delivery in Vero cells.MethodsIn the current study, three transfection methods were used, including chemical transfection, electroporation, and lentiviral vector transduction, to obtain the optimum transfection conditions in the Vero cell line. Vero cells were cultured and transfected with chemical transfection reagents, electroporation, or HIV-1-based lentivectors under different experimental conditions. Transfection efficiency was assessed using flow cytometry and fluorescence microscopy to detect GFP-positive cells.ResultsAmong the tested methods, TurboFect™ chemical transfection exhibited the highest efficiency. Optimal transfection conditions were achieved using 1 µg DNA and 4 µL TurboFect™ in 6 × 104 Vero cells.ConclusionTurboFect™, a cationic polymer transfection reagent, demonstrated superior transfection efficiency in Vero cells compared with electroporation and lentivirus particles, and is the optimal choice for chemical transfection in the Vero cell line.

Machine learning based data governance methods for demand response databases

With the increasing level of grid intelligence and the related demand response database expanding, it is important to study a compound problem data governance method for demand response, while the traditional data governance methods have problems such as not considering data temporality and ignoring the impact of noise and duplicate data on data repair. As a result, this project will develop an anomaly data extraction and repair model based on two-way long and short memory networks, and repair the anomaly data by respective noise smoothing, missing data filling, and duplicate data cleaning. The paper also provides an adaptive moment estimation approach for optimisation to raise the model’s accuracy. The outcomes demonstrated that the study model’s precision for anomalous data extraction was 100% and its recall rate was 80%, which was a significant improvement over the previous state. In terms of anomalous data repair, the research model had the root mean square error value and lowest mean absolute percentage error value when compared with related models, at 0.0049 MPa and 1.375% respectively. Both the abnormal data extraction and repair performance of the research model are greatly improved over the related models, and have important value in the abnormal data governance of demand response databases.

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Cross validation based transfer learning for cross-sectional non-linear shrinkage: A data-driven approach in portfolio optimization

Enhanced covariance estimation approaches, such as (non-)linear shrinkage, are well established in the literature. Non-linear shrinkage estimators generally minimize a certain loss function regarding statistical assumptions about the future covariance matrix. At the same time, the problem of covariance estimation is traditionally considered from a rather restrictive view since the only available data to determine the estimation parameters is given by the return history of the actual portfolio constituents. In this study, we propose a novel and purely data-driven perspective on covariance estimation. We present a non-linear shrinkage estimator that determines the estimation parameters using cross validation to be historically optimal on a disjoint dataset of assets according to the given objective, such as minimum variance or maximum risk-adjusted return. We then transfer the historically optimal estimation parameters learned on the disjoint dataset to the actual covariance estimation problem. Thereby, the sample eigenvalues are corrected in a purely data-driven way, agnostic to theoretically derived parameters. Another benefit of focusing on disjoint data is that we address the problem of limited data availability in high-dimensional estimation problems when the number of assets exceeds the history length. Our empirical evaluation, based on a total of six stock market indices and various problem dimensions, shows that our approach outperforms existing cross-sectional estimators in minimizing variance and maximizing risk-adjusted return. While our study is limited to the cross-section, the method of parameter selection using cross validation and transfer learning can also be combined with other estimators, such as time-series methods.

Human resources development: Evaluating perceptions against policy

The objective of sustaining smart, sustainable and cohesive inclusive growth, framed by the European strategy, is in line with the global commitments of the Sustainable Development Goals. Thus, an analysis of people’s attitudes towards social policy and perceptions of professional development in Poland against European development policy is crucial. The nationwide representative sample included 1,000 respondents aged 18-64. The study used principal component analysis (PCA), an approach for the multi-response optimisation. The results indicate that respondents’ evaluations are in line with the direction of EU labour policy, where their decisions correspond to the planned priorities oriented towards inclusion and cohesion. The PCA analysis showed that employee competencies and priorities for individual standing can be considered strongly correlated. Competencies were also found to be important for the platform to support systemic development. However, the items included in the analysis that are beyond the scope of the ESF+, although even correlated, are of lesser importance. The research results allow to expand knowledge on the perception of the development policy facets promoted by the EU, supplemented by themes of leadership or immigrants. The study may become a starting point for inclusive and cohesive working climate research.

A multi-objective scheduling model in medical tourism centers considering multi-task staff training

In the present era, hospitals are actively encouraging their International Patient Departments (IPD) to provide high-quality medical services at a cost-effective rate for international patients. To accomplish this objective, several crucial factors must be taken into account, including capacity constraints, hospital waiting lists, and the presence of a highly skilled workforce. The waiting time experienced by patients is a significant indicator of the quality of hospital services and is often used by patients to evaluate healthcare professionals, sometimes even surpassing their knowledge and expertise. This becomes particularly critical when dealing with international patients as longer waiting times indirectly lead to additional accommodation costs. One potential solution is to schedule international patients on different days within a specific timeframe to minimize patient flow time. However, this can only be realistically achieved if staff productivity is enhanced through in-service training. These challenges, coupled with the uncertainty surrounding treatment duration, create a complex problem for IPDs. This study proposes an optimization approach for scheduling medical tourists who visit destination medical centers with two primary objectives: 1) reducing patient waiting times and 2) decreasing training costs while enhancing the skill level of the staff serving patients. The proposed model is formulated as a bi-objective nonlinear integer programming problem, which is subsequently transformed into a linear model using theoretical techniques. Given that this problem is NP-hard, two efficient meta-heuristic methods are developed to solve it for real-world scenarios of varying sizes. The findings of this study present a comparative analysis between the outcomes generated by the proposed algorithms and an exact method (CPLEX), showcasing their effectiveness and superiority. Additionally, a sensitivity analysis highlights that despite the costs associated with in-service training, enhancing staff skills can substantially reduce patient waiting times, resulting in significant cost savings for medical services while ensuring the delivery of high-quality care. Moreover, the improved staff skills enable IPD managers to gain valuable insights into the financial well-being of the medical center, including its ability to attract medical tourists.

A Survey of Internet of Things based Indoor Positioning Systems based on Bluetooth Low Energy Beacon

The global positioning systems (GPS) and cellular systems face inherent limitations when it comes to enclosed environments like shopping malls, hospitals, warehouses, as they have significant shadowing effects that cause the signals to be distorted. There have been many studies on different technologies and techniques aiming to find the optimum approach to be used for indoor positioning systems (IPS). Among all the technologies adapted by researchers all around the world, Bluetooth Low Energy (BLE) beacons have been a prevalent choice for many, due to its deploying flexibility, low cost, low power consumption and ability to provide very advanced services to users. It is arduous to obtain RSSI values with great accuracy because the signals can be easily intervened by many factors like humidity, temperature, physical barriers, and interference from decoherent signals. In this paper, we presented a comparative analysis of different, fairly recent IPS that are based on BLE beacons by classifying the different proposed approaches in a structured and systematic manner and traversing the issues and challenges that exist with each technique. The accuracy of the results can be enhanced by using the most suitable positioning algorithm, the orientation of the beacon setup, and optimization methods used. A preliminary result from a case study using BLE beacons with K-Nearest Neighbours (KNN) based fingerprinting approach for indoor positioning estimation shows 86.67% accuracy.

Modeling Spatiotemporal Heterogeneity of Customer Preferences With Small-Scale Aggregated Data: A Spatial Panel Modeling Approach

Abstract Customer preferences are found to evolve over time and correlate with geographical locations. Studying the spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for understanding the trend of customer preferences. However, existing choice models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. Learning-based spatiotemporal data modeling methods usually require large-scale datasets for model training, which are not applicable to small aggregated data, such as the sale records of a product in several regions and years. To fill this research gap, we propose a spatial panel modeling approach to investigate the spatiotemporal heterogeneity of customer preferences. Product and regional attributes varying in time are included as model inputs to support demand forecasting in engineering design. With case studies using the dataset of small SUVs and compact sedans in China's automotive market, we demonstrate that the spatial panel modeling approach outperforms other statistical spatiotemporal data models and non-parametric regression methods in goodness of fit and prediction accuracy. We also illustrate a potential design application of the proposed approach in a portfolio optimization of two vehicles from the same producer. While the spatial panel modeling approach exists in econometrics, applying this approach to support engineering decisions by considering spatiotemporal heterogeneity and introducing engineering attributes in demand forecasting is the contribution of this work. Our paper is focused on presenting the approach rather than the results per se.

A Mesh-Based Approach for Computational Fluid Dynamics-Free Aerodynamic Optimisation of Complex Geometries Using Area Ruling

In this paper, an optimisation procedure is introduced that uses a significantly cheaper, and CFD-free, objective function for aerodynamic optimisation than conventional CFD-driven approaches. Despite the reduced computational cost, we show that this approach can still drive the optimisation scheme towards a design with a similar reduction in drag coefficient for wave drag-dominated problems. The approach used is ‘CFD-free’, i.e., it does not require any computational aerodynamic analysis. It can be applied to geometries discretised using meshes more conventionally used for ‘standard’ CFD-based optimisation approaches. The approach outlined in this paper makes use of the transonic area rule and its supersonic extension, exploiting a mesh-based parameterisation and mesh morphing methodology. The paper addresses the following question: ‘To what extent can an optimiser perform (wave) drag minimisation if using ‘area ruling’ alone as the objective (fitness) function measurement?’. A summary of the wave drag approximation in transonic and supersonic regimes is outlined along with the methodology for exploiting this theory on a typical CFD surface mesh to construct an objective function evaluation for a given geometry. The implementation is presented including notes on the considerations required to ensure stability, and error minimisation, of the numerical scheme. The paper concludes with the results from a number of (simple and complex geometry) examples of a drag-minimisation optimisation study and the results are compared with an approach using full-fidelity CFD simulation. The overall conclusions from this study suggest that the approach presented is capable of driving a geometry towards a similar shape to when using full-fidelity CFD at a significantly lower computational cost. However, it cannot account for any constraints, driven by other aerodynamic factors, that might be present within the problem.

Recurrence and Survival after Minimally Invasive and Open Esophagectomy for Esophageal Cancer - A Post Hoc Analysis of the Ensure Study.

The aim of this study was to determine the impact of operative approach (open [OE], hybrid [HMIE] and total minimally invasive esophagectomy [TMIE]) on operative and oncologic outcomes for patients treated with curative intent for esophageal and junctional cancer. The optimum oncologic surgical approach to esophageal and junctional cancer is unclear. This secondary analysis of the European multicenter ENSURE study includes patients undergoing curative-intent esophagectomy for cancer between 2009-2015 across 20 high-volume centers. Primary endpoints were disease-free survival (DFS) and the incidence and location of disease recurrence. Secondary endpoints included among others R0 resection rate, lymph node yield and overall survival (OS). In total, 3,199 patients were included. Of these, 55% underwent OE, 17% HMIE and 29% TMIE. DFS was independently increased post TMIE (HR 0.86 [95% CI 0.76-0.98], P=0.022) compared with OE. Multivariable regression demonstrated no difference in absolute locoregional recurrence risk according to operative approach (HMIE vs. OE OR 0.79, P=0.257, TMIE vs. OE OR 0.84, P=0.243). The probability of systemic recurrence was independently increased post HMIE (OR 2.07, P=0.031), but not TMIE (OR 0.86, P=0.508). R0 resection rates (P=0.005) and nodal yield (P<0.001) were independently increased after TMIE, but not HMIE (P=0.424; P=0.512) compared with OE. OS was independently improved following both HMIE (HR 0.79, P=0.009) and TMIE (HR 0.82, P=0.003) as compared with OE. In this European multicenter study, TMIE was associated with improved surgical quality and DFS, while both TMIE and HMIE were associated with improved OS as compared with OE for esophageal cancer.

A review on agrowaste based activated carbons for pollutant removal in wastewater systems

Environmental pollution from chemicals utilized in manufacturing, pharmaceuticals, and chemical process industries is of serious concern nowadays due to the contamination that ensues when these chemicals are discharged into water bodies. Activated carbon adsorption provides an efficient and economically viable means for mitigation of toxic chemicals (i.e., heavy metals, dyes, pharmaceutics, and antibiotics). However, the exorbitant cost of commercial activated carbons has resulted in the search for low-cost alternatives for the treatment of contaminated effluents. An exhaustive literature survey in this area is necessary to know the extent of work done in this area and seek out the gaps that future research will provide answers to. In this review, various works on activated carbon utilization, batch adsorption, fixed-bed adsorption (experimental and numerical studies) are summarized. This review elucidates the different kinetic and isotherm models of agrowastes-derived activated carbon materials in context with pollutants (dyes, heavy metals, pharmaceuticals, miscellaneous adsorbates) removal through batch and column methods. In addition, fixed-bed column adsorption/regeneration methods using various activated carbons derived from agrowastes are discussed. Among these methods, heavy metal adsorption from aqueous solutions by the activated carbons is the most efficient. The deployment of mathematical and machine learning approaches (ANN and novel GMDH algorithms) in optimization of batch and continuous adsorption processes are also highlighted. Numerical simulation of fixed-column adsorption systems for more improved industrial-scale column designs is described. Conclusions and future challenges of chemicals removal from polluted wastewater utilizing agrowaste-derived activated carbons are also presented.

Sizing and Shape Optimization of Discrete Truss Employing a Target-Oriented Krill Herd Algorithm

Abstract The krill herd (KH) algorithm is widely used for optimizing truss structures as no gradient information is necessary, and only a few parameters require adjustment. However, when the truss structure becomes discrete and complex, KH tends to fall into a local optimum. Therefore, a novel target-oriented KH (TOKH) algorithm is proposed in this study to optimize the design of discrete truss structures. Initially, a crossover operator is established between the “best krill” and “suboptimal krill” to generate a robust “cross krill” for global exploration. Additionally, an improved local mutation and crossover (ILMC) operator is introduced to fine-tune the “center of food” and candidate solutions for local exploitation. The proposed method and other optimization approaches are experimentally compared considering 15 benchmark functions. Then, the performance of the TOKH algorithm is evaluated based on four discrete truss structure optimization problems under multiple loading conditions. The obtained optimization results indicate that the proposed method presents competitive solutions in terms of accuracy, unlike other algorithms in the literature, and avoids falling into a local minimum.

A Clustering-Based Optimization Approach for Hospital Miscoding Correction

This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects’ features reviewed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical coders’ coding practices. Furthermore, the proposed approach allows the decision-maker to balance the cost-benefit and the requirement of public health institutions (i.e., miscoding rate).

Optimizing Job Offloading Schedule for Collaborative DNN Inference

Deep Neural Networks (DNNs) have been widely deployed in mobile applications. DNN inference latency is a critical metric to measure the service quality of those applications. Collaborative inference is a promising approach for latency optimization, where partial inference workloads are offloaded from mobile devices to cloud servers. Model partition problems for collaborative inference have been well studied. However, little attention has been paid to optimizing offloading pipeline for multiple DNN inference jobs. In practice, mobile devices usually need to process multiple DNN inference jobs simultaneously. We propose to jointly optimize the DNN partitioning and pipeline scheduling for multiple inference jobs. We theoretically analyze the optimal scheduling conditions for homogeneous chain-structure DNNs. Based on the analysis, we proposed near-optimal partitioning and scheduling methods for chain-structure DNNs. We also extend those methods for general-structure DNNs. In addition, we extend our problem scenario to handle heterogeneous DNN inference jobs. A layer-level scheduling algorithm is proposed. Theoretical analyses show that our proposed method is optimal when computation graphs are tree-structure. Our joint optimization methods are evaluated in a real-world testbed. Experiment results show that our methods can significantly reduce the overall inference latency of multiple inference jobs compared to partition-only or schedule-only approaches.

Neurodynamic Flow Approach for Convex and Quasi-Convex Optimization on Riemannian Manifolds With Diagonal Metrics

Neurodynamic Flow Approach for Convex and Quasi-Convex Optimization on Riemannian Manifolds With Diagonal Metrics

Disentangling the Diagnoses of Psychotic Depression, Catatonia, Neurocognitive Disorders, and Delirium: A Multidisciplinary Approach

An ambiguous clinical presentation can create diagnostic uncertainty, leading to delays in effective interventions and increasing the likelihood of adverse clinical outcomes. Unfortunately, many psychiatric conditions share overlapping clinical symptoms. Psychotic depression, catatonia, neurocognitive disorders, and delirium are all examples of distinct clinical conditions with overlapping symptomatology, and all require decidedly different therapeutic interventions. While much literature is devoted to the diagnostic criteria and management of these clinical conditions, little is dedicated to the challenge of disentangling these diagnoses when faced with a complex clinical presentation.We present here a complicated psychiatric inpatient hospitalization of a 77-year-old male who exemplified this diagnostic challenge. He primarily presented with severely depressed mood and decreased functionality, remaining in bed for much of the day with little attention to basic hygiene and oral intake. While records from a recent outside hospitalization carried a historical diagnosis of catatonia, his moderate level of engagement with providers and somatic delusion of disseminated prostate cancer suggested an alternate diagnosis of psychotic depression. However, before a diagnostic conclusion could be reached, other clinical characteristics surfaced. Following a singular administration of olanzapine shortly after his initial assessment, he developed Lilliputian hallucinations, prominent parkinsonism, and significantly altered mentation.Neuroleptic sensitivity raised clinical suspicion for an underlying neurocognitive disorder such as dementia with Lewy bodies, and a consult was placed to the neuropsychology service. While dementia could not be fully ruled out, they did recommend additional workup to rule out herpes simplex encephalitis, autoimmune limbic encephalitis, and paraneoplastic syndromes. After his MRI imaging revealed moderate microvascular ischemic changes, a true multidisciplinary approach to care was taken, as neurology was consulted to also help narrow the differential. An EEG was unremarkable and a serum encephalitis panel was also unremarkable. Olanzapine was discontinued with significant improvement in parkinsonism and cognition; however, severe neurovegetative symptoms and somatic and nihilistic delusions persisted. While antipsychotics remained clinically indicated for a suspected depression with psychotic features, caution was taken due to established neuroleptic sensitivity, subtle catatonic features, and the related risks of neuroleptic malignant syndrome. Ultimately, low-dose quetiapine was initiated with some benefit; however, he continued to remain acutely depressed and withdrawn with prominent negativism. Trials with benzodiazepines for these catatonic features were also approached with caution to avoid potential risks of delirium.Over time, a multidisciplinary approach to psychopharmacologic interventions and medication trials led the team to a principal diagnosis of major depressive disorder with psychotic features and subtle catatonia. The abrupt changes observed early in the hospital course were determined to be related to olanzapine-induced anticholinergic delirium. With greater diagnostic certainty, the team was able to counsel the patient and family about the importance of pursuing electroconvulsive therapy (ECT). The patient's cardiovascular risk factors and known microvascular changes were considered in the determination of the appropriateness for ECT. After starting ECT, he was immediately noted to have objective improvement, and following his fifth treatment, he was deemed appropriate for discharge after exhibiting significant improvement in depression and functionality and reporting near-resolution of somatic delusions. He was able to complete ECT on an outpatient basis and follow-up visits note durable improvements in mood, functionality, and quality of life, continuing to enjoy an active lifestyle of socializing with family and playing tennis. We use this case to further explore the clinical challenges that stem from the symptomatic similarity between psychotic depression, catatonia, neurocognitive disorders, and delirium and discuss optimal therapeutic approaches when the presentation appears complex and equivocal.

Data-Driven Linear Quadratic Optimization for Controller Synthesis With Structural Constraints.

For various typical cases and situations where the formulation results in an optimal control problem, the linear quadratic regulator (LQR) approach and its variants continue to be highly attractive. In certain scenarios, it can happen that some prescribed structural constraints on the gain matrix would arise. Consequently then, the algebraic Riccati equation (ARE) is no longer applicable in a straightforward way to obtain the optimal solution. This work presents a rather effective alternative optimization approach based on gradient projection. The utilized gradient is obtained through a data-driven methodology, and then projected onto applicable constrained hyperplanes. Essentially, this projection gradient determines a direction of progression and computation for the gain matrix update with a decreasing functional cost; and then the gain matrix is further refined in an iterative framework. With this formulation, a data-driven optimization algorithm is summarized for controller synthesis with structural constraints. This data-driven approach has the key advantage that it avoids the necessity of precise modeling which is always required in the classical model-based counterpart; and thus the approach can additionally accommodate various model uncertainties. Illustrative examples are also provided in the work to validate the theoretical results.

The Physical Cell Identity Assignment Problem: A Practical Optimization Approach

The Physical Cell Identity (PCI) is a critical parameter to radio access networks used by the user equipment, like a smartphone, to identify the cell to connect. The PCI misassignment leads to call drops, no voice/data connections, and other sorts of impairments. Many works in the literature only treat the PCI assignment problem from the technological constraints viewpoint. In this work, we take a realistic approach and incorporate several operational constraints found daily in carrier-grade networks. For that, we model the problem with multiple objectives with a strict lexicographical dominance relation. We solve it using a hybrid memetic algorithm with warm start solutions obtained by a constraint programming solver and intensification with path-relinking and custom local search procedures. The experimental results suggest that our approach is superior to commercial solver packages in several scenarios.

A Distributionally Robust Optimization Approach for Local Electrical Market Interaction With Wholesale Electrical Market and Prosumers Within the Framework of Transactive Energy Concepts

A Distributionally Robust Optimization Approach for Local Electrical Market Interaction With Wholesale Electrical Market and Prosumers Within the Framework of Transactive Energy Concepts