Optimization Approach Research Articles

Supervised Feature Selection via Collaborative Neurodynamic Optimization.

As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In this article, supervised feature selection is at first formulated as a mixed-integer optimization problem with an objective function of weighted feature redundancy and relevancy subject to a cardinality constraint on the number of selected features. It is equivalently reformulated as a bound-constrained mixed-integer optimization problem by augmenting the objective function with a penalty function for realizing the cardinality constraint. With additional bilinear and linear equality constraints for realizing the integrality constraints, it is further reformulated as a bound-constrained biconvex optimization problem with two more penalty terms. Two collaborative neurodynamic optimization (CNO) approaches are proposed for solving the formulated and reformulated feature selection problems. One of the proposed CNO approaches uses a population of discrete-time recurrent neural networks (RNNs), and the other use a pair of continuous-time projection networks operating concurrently on two timescales. Experimental results on 13 benchmark datasets are elaborated to substantiate the superiority of the CNO approaches to several mainstream methods in terms of average classification accuracy with three commonly used classifiers.

A Collaborative Neurodynamic Optimization Approach to Distributed Nash-Equilibrium Seeking in Multicluster Games With Nonconvex Functions.

In this article, we propose a collaborative neurodynamic optimization (CNO) method for the distributed seeking of generalized Nash equilibriums (GNEs) in multicluster games with nonconvex functions. Based on an augmented Lagrangian function, we develop a projection neural network for the local search of GNEs, and its convergence to a local GNE is proven. We formulate a global optimization problem to which a global optimal solution is a high-quality local GNE, and we adopt a CNO approach consisting of multiple recurrent neural networks for scattering searches and a metaheuristic rule for reinitializing states. We elaborate on an example of a price-bidding problem in an electricity market to demonstrate the viability of the proposed approach.

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.

Impact of Prevascularization on Immunological Environment and Early Engraftment in Subcutaneous Islet Transplantation.

The utilization of islet-like cells derived from pluripotent stem cells may resolve the scarcity of islet transplantation donors. The subcutaneous space is a promising transplantation site because of its capacity for graft observation and removal, thereby ensuring safety. To guarantee subcutaneous islet transplantation, physicians should ensure ample blood supply. Numerous methodologies, including prevascularization, have been investigated to augment blood flow, but the optimal approach remains undetermined. From C57BL/6 mice, 500 syngeneic islets were transplanted into the prevascularized subcutaneous site of recipient mice by implanting agarose rods with basic fibroblast growth factor at 1 and 2 wk. Before transplantation, the blood glucose levels, cell infiltration, and cytokine levels at the transplant site were evaluated. Furthermore, we examined the impact of the extracellular matrix capsule on graft function and the inflammatory response. Compared with the 1-wk group, the 2-wk group exhibited improved glycemic control, indicating that longer prevascularization enhanced transplant success. Flow cytometry analysis detected immune cells, such as neutrophils and macrophages, in the extracellular matrix capsules, whereas cytometric bead array analysis indicated the release of inflammatory and proinflammatory cytokines. Treatment with antitumor necrosis factor and anti-interleukin-6R antibodies in the 1-wk group improved graft survival, similar to the 2-wk group. In early prevascularization before subcutaneous transplantation, neutrophil and macrophage accumulation prevented early engraftment owing to inflammatory cytokine production.

SMoGW-based deep CNN: Plant disease detection and classification using SMoGW-deep CNN classifier

Diagnosing plant disease is a major role to reduce adequate losses in yield production, which further leads to economic losses. The various disease control measures are accessible without a proper diagnosis of the disease which results in a waste of expenses and time. The diagnosis of disease using images leads to unsatisfactory results in the prevalent methods due to the image clarity. It is mainly caused by the worst performance of the existing pre-trained image classifiers. This issue can be controlled by the SMoGW-deep convolutional neural network (deep CNN) classifier for the accurate and precise classification of plant leaf disease. The developed method transforms the poor-quality captured images into high quality by the preprocessing technique. The preprocessed input images contain pixels on their dimension and also the value of the threshold is analyzed by the Otsu method by which the particular disease-affected region is extracted based on the image pixels. The region of interest is separated from the other parts of the input leaf image using the K-means segmentation technique. The stored features in the feature vector are fed forward to the deep CNN classifier for training and are optimized by the SMoGW optimization approach. The experiments are done and achieved an accuracy of 94.5% sensitivity of 94.525%, specificity of 94.6%, precision of 95% with 90% of training data and under K-fold training with 95% of accuracy, 95% of sensitivity, 94.1% of specificity, and 92.1% of precession is achieved for the SMoGW-optimized classifier approach that is superior to the prevalent techniques for disease classification and detection. The potential, as well as the capability of the proposed method, is experimentally demonstrated for plant leaf disease classification and identification.

Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques

Big Data Analytics (BDA) is an unavoidable technique in today’s digital world for dealing with massive amounts of digital data generated by online and internet sources. It is kept in repositories for data processing via cluster nodes that are distributed throughout the wider network. Because of its magnitude and real-time creation, big data processing faces challenges with latency and throughput. Modern systems such as Hadoop and SPARK manage large amounts of data with their HDFS, Map Reduce, and In-Memory analytics approaches, but the migration cost is higher than usual. With Genetic Algorithm-based Optimization (GABO), Map Reduce Scheduling (MRS) and Data Replication have provided answers to this challenge. With multi objective solutions provided by Genetic Algorithm, resource utilization and node availability improve processing performance in large data environments. This work develops a novel creative strategy for enhancing data processing performance in big data analytics called Map Reduce Scheduling Based Non-Dominated Sorting Genetic Algorithm (MRSNSGA). The Hadoop-Map Reduce paradigm handles the placement of data in distributed blocks as a chunk and their scheduling among the cluster nodes in a wider network. Best fit solutions with high latency and low accessing time are extracted from the findings of various objective solutions. Experiments were carried out as a simulation with several inputs of varied location node data and cluster racks. Finally, the results show that the speed of data processing in big data analytics was enhanced by 30–35% over previous methodologies. Optimization approaches developed to locate the best solutions from multi-objective solutions at a rate of 24–30% among cluster nodes.

Imbalance data: The application of RUS FCM K-RBFNN Smote with XGBoost in the elderly well-being identification

Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling.

Secure deep learning-based energy efficient routing with intrusion detection system for wireless sensor networks

In general, wireless sensor networks are used in various industries, including environmental monitoring, military applications, and queue tracking. To support vital applications, it is crucial to ensure effectiveness and security. To prolong the network lifetime, most current works either introduce energy-preserving and dynamic clustering strategies to maintain the optimal energy level or attempt to address intrusion detection to fix attacks. In addition, some strategies use routing algorithms to secure the network from one or two attacks to meet this requirement, but many fewer solutions can withstand multiple types of attacks. So, this paper proposes a secure deep learning-based energy-efficient routing (SDLEER) mechanism for WSNs that comes with an intrusion detection system for detecting attacks in the network. The proposed system overcomes the existing solutions’ drawbacks by including energy-efficient intrusion detection and prevention mechanisms in a single network. The system transfers the network’s data in an energy-aware manner and detects various kinds of network attacks in WSNs. The proposed system mainly comprises two phases, such as optimal cluster-based energy-aware routing and deep learning-based intrusion detection system. Initially, the cluster of sensor nodes is formed using the density peak k-mean clustering algorithm. After that, the proposed system applies an improved pelican optimization approach to select the cluster heads optimally. The data are transmitted to the base station via the chosen optimal cluster heads. Next, in the attack detection phase, the preprocessing operations, such as missing value imputation and normalization, are done on the gathered dataset. Next, the proposed system applies principal component analysis to reduce the dimensionality of the dataset. Finally, intrusion classification is performed by Smish activation included recurrent neural networks. The proposed system uses the NSL-KDD dataset to train and test it. The proposed one consumes a minimum energy of 49.67 mJ, achieves a better delivery rate of 99.92%, takes less lifetime of 5902 rounds, 0.057 s delay, and achieves a higher throughput of 0.99 Mbps when considering a maximum of 500 nodes in the network. Also, the proposed one achieves 99.76% accuracy for the intrusion detection. Thus, the simulation outcomes prove the superiority of the proposed SDLEER system over the existing schemes for routing and attack detection.

Machine learning and evolutionary optimization approach for solving the flexible job-shop scheduling problem

This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP.

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.

Enhancing Electrochemical Properties of Walnut Shell Activated Carbon with Embedded MnO Clusters for Supercapacitor Applications

AbstractActivated carbon (AC) materials from renewable sources are widely used in electrochemical applications due to their well‐known high surface area. However, their application as electrode material in double‐layer electrochemical devices may be limited due to their relatively low electrical conductivity and lightweight. To overcome these limitations, the incorporation of pseudocapacitance metal oxide nanoparticles is an optimum approach. These nanoparticles can provide a second energy storage mechanism to the composite, mitigating the loss of surface area associated with their incorporation. As a result, the composite material is endowed with increased conductivity and higher density, making it more suitable for practical implementation in real devices. In this study, we have incorporated a fine dispersion of 1 % of MnO clusters into a highly porous activated carbon synthesized from walnut shells (WAC). The high‐resolution electron microscopy studies, combined with their related analytical techniques, allow us to determine the presence of the cluster within the matrix carbon precisely. The resulting MnO@WAC composite demonstrated significantly improved capacitive behavior compared with the WAC material, with increased volumetric capacitance and higher charge retention at higher current densities. The composite‘s electrochemical performance suggests its potential as a promising electrode material for supercapacitors, addressing drawbacks associated with traditional AC materials.

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

To determine the impact of operative approach [open (OE), hybrid minimally invasive (HMIE), and total minimally invasive (TMIE) esophagectomy] 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 and 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, 3199 patients were included. Of these, 55% underwent OE, 17% HMIE, and 29% TMIE. DFS was independently increased post-TMIE [hazard ratio (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 the operative approach [HMIE vs OE, odds ratio (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, whereas 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.