Traditional Techniques Research Articles

Current Trends on Innovative Technologies in Topical Wound Care for Advanced Healing and Management.

To investigate critically traditional and modern techniques for cutaneous wound healing and to provide comprehensive information on these novel techniques to encounter the challenges with the existing wound healing methods. The financial burden and mortality associated with wounds is increasing, so managing wounds is essential. Traditional wound treatments include surgical and non-surgical methods, while modern techniques are advancing rapidly. This review examines the various traditional and modern techniques used for cutaneous wound healing. Traditional wound treatments include surgical techniques such as debridement, skin flaps, and grafts. Non-surgical treatments include skin replacements, topical formulations, scaffold-based skin grafts, and hydrogel-based skin dressings. More modern techniques include using nanoparticles, growth factors, and bioactive substances in wound dressings. Bioengineered skin substitutes using biomaterials, cells, and growth factors are also being developed. Other techniques include stem cell therapy, growth factor/cytokine therapy, vacuum-assisted wound closure, and 3D-printed/bio-printed wound dressings. Traditional wound treatments have been replaced by modern techniques such as stem cell therapy, growth factor/cytokine therapy, vacuum-assisted wound closure, and bioengineered skin substitutes. However, most of these strategies lack effectiveness and thorough evaluation. Therefore, further research is required to develop new techniques for cutaneous wound healing that are effective, cost-efficient, and appealing to patients.

The prevention and management of complications in parotid and other ​ major salivary gland surgeries.

The prevention and management of complications that appear after the application of the classical techniques of traditional surgery of the major salivary glands, during the intra-operative or post-operative period, are not a common subject in the international literature and the number of scientific articles is relatively small. The most frequently occurring and most serious postoperative complications, early or late, are the partial or complete transection of the facial nerve or its branches, the development of neuroma on the transection of the greater auricular nerve, Frey’s syndrome, the deformed scars on the face, the numbness of the cheek and auricle, salivary fistula, sialocele, seroma, parotid removal skin intrusion and facial asymmetry, surgical incision keloid, and surgical wound inflammation-infiltration,which have been studied and adequately documented (30, 35). The increasingly frequently applied modern,advanced and minimally invasive-endoscopic surgical procedures which tend to replace traditional surgical techniques, are distinguished by their rapid recovery and significantly lower rate of complications. Endoscopic operations of the major salivary glands and the complica- tions associated with them are minor and usually belong to the general postoperative complications such as local inflammation, hematoma, injury, rupture and stricture of the excretory duct.

A Survey on Information Bottleneck.

This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999. It provides an elegant balance between data compression and information preservation, and improves its prediction or representation ability accordingly. This survey summarizes both the theoretical progress and practical applications on IB over the past 20-plus years, where its basic theory, optimization, extensive models and task-oriented algorithms are systematically explored. Existing IB methods are roughly divided into two parts: traditional and deep IB, where the former contains the IBs optimized by traditional machine learning analysis techniques without involving any neural networks, and the latter includes the IBs involving the interpretation, optimization and improvement of deep neural works (DNNs). Specifically, based on the technique taxonomy, traditional IBs are further classified into three categories: Basic, Informative and Propagating IB; While the deep IBs, based on the taxonomy of problem settings, contain Debate: Understanding DNNs with IB, Optimizing DNNs Using IB, and DNN-based IB methods. Furthermore, some potential issues deserving future research are discussed. This survey attempts to draw a more complete picture of IB, from which the subsequent studies can benefit.

Overcoming Data Deficiency for Multi-Person Pose Estimation.

Building multi-person pose estimation (MPPE) models that can handle complex foreground and uncommon scenes is an important challenge in computer vision. Aside from designing novel models, strengthening training data is a promising direction but remains largely unexploited for the MPPE task. In this article, we systematically identify the key deficiencies of existing pose datasets that prevent the power of well-designed models from being fully exploited and propose the corresponding solutions. Specifically, we find that the traditional data augmentation techniques are inadequate in addressing the two key deficiencies, imbalanced instance complexity (IC) (evaluated by our new metric IC) and insufficient realistic scenes. To overcome these deficiencies, we propose a model-agnostic full-view data generation (Full-DG) method to enrich the training data from the perspectives of both poses and scenes. By hallucinating images with more balanced pose complexity and richer real-world scenes, Full-DG can help improve pose estimators' robustness and generalizability. In addition, we introduce a plug-and-play adaptive category-aware loss (AC-loss) to alleviate the severe pixel-level imbalance between keypoints and backgrounds (i.e., around 1:600). Full-DG together with AC-loss can be readily applied to both the bottom-up and top-down models to improve their accuracy. Notably, plugging into the representative estimators HigherHRNet and HRNet, our method achieves substantial performance gains of 1.0%-2.9% AP on the COCO benchmark, and 1.0%-5.1% AP on the CrowdPose benchmark.

An Update on the Application of Nano Phytomedicine as an Emerging Therapeutic Tool for Neurodegenerative Diseases

Abstract: The existence of the blood-brain barrier (BBB), a densely woven network of blood vessels and endothelial cells designed to prevent the infiltration of foreign substances into the brain, the methods employed in developing treatments for neurodegenerative disorders (NDs) such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Multiple sclerosis, Amyotrophic lateral sclerosis, and others, pose significant challenges and complexities. These illnesses have had a terrible impact on the human population's health. Because early detection of these problems is poor and no good therapy has been established, they have emerged as the biggest lifethreatening healthcare burden worldwide compared to other significant illnesses. Traditional drug delivery techniques do not offer efficient treatment for NDs due to constraints in the BBB design, efflux pumps, and metabolic enzyme expression. Nanotechnology has the potential to significantly enhance ND therapy by utilizing systems that have been bioengineered to engage with living organisms at the cellular range. Compared to traditional techniques, nanotechnological technologies have several potential ways for crossing the BBB and increasing therapeutic efficacy in the brain. The introduction and growth of nanotechnology indicate promising potential for overcoming this issue. Engineered nanoparticles coupled with therapeutic moieties and imaging agents with dimensions ranging from 1-100 nm can improve effectiveness, cellular uptake, selective transport, and drug delivery to the brain due to their changed physicochemical properties. Conjugates of nanoparticles and medicinal plants, or their constituents known as nano phytomedicine, have recently gained importance in developing cutting-edge neuro-therapeutics due to their abundant natural supply, promising targeted delivery to the brain, and lower potential for adverse effects. This study summarizes the common NDs, their prevalence and pathogenesis, and potential herbal nanoformulation for treating NDs.

Synthesis, Characterization, and Optimization of Novel Furan-ring Fused Chalcones via Radical Cyclization of α,β-Unsaturated Ketones and Cyclic Ketone

Abstract: Novel Furan-ring Fused Chalcones (FFC) were synthesized using a radical cyclization reaction of α,β-unsaturated ketones with cyclic ketone as the model reaction to attain this goal. In this study, traditional and microwave-assisted methods for the efficient and cost-effective synthesis of furan-ring fused chalcones in mild reaction conditions are compared and optimized. The goal is to develop a reliable and adaptable synthetic technique that may be used to produce these useful chalcone derivatives quickly and effectively. The optimal experimental conditions for these reactions were carefully determined using two independent methodologies: conventional (Method A) and microwave (Method B). The results indicated that the proposed method B could be used effectively in the future to synthesize novel furans with short reaction times and acceptable yields (87-94 %), and products were purified by column chromatography and preparative thin layer chromatography (PTLC). All new compounds were characterized by 1H-NMR, 13C-NMR, LC-MS, and elemental analyses.

An IoMT-based Federated Learning Survey in Smart Transportation

Abstract: Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL

Multiscale and multidisciplinary data-driven reservoir characterization of a fractured carbonate field in Kurdistan

Abstract The combination of traditional subsurface interpretation techniques with advanced data analytics is a key stepping stone for better predicting reservoir quality, especially in heterogeneous and complex geological systems. The Peshkabir oil and gas field, located in the north of the Kurdistan Region of Iraq and within the Tawke Production Sharing Contract, is one such heterogeneous system. Well oil rates vary significantly across the field and cannot be simply correlated to fracture densities measured at the wells. Understanding which fractures matter and what influences reservoir deliverability is a question of major importance for maximizing oil production. The carbonate reservoirs include karstified vuggy zones and hydrothermal dolostones, in addition to an extensively developed fractured network. This paper presents a geological conceptual model for the Peshkabir field, and an application of Python based data science techniques to identify key predictors of reservoir deliverability from drilling, logging and production data. We demonstrate that the major advantage of the application of advanced data analytics is that it can enable the recognition of patterns and associations in a complex, high-dimensional parameter environment whereas traditional interpretation methods typically only allow for the comparison of two or three parameters at a time. This method allows the integration of dynamic and static data effectively and empowers the interpreter to incorporate all the available insights which, coupled with domain knowledge, allows for data-driven decision-making.

Effectiveness of NOTES versus traditional techniques on surgical outcomes in adults with diverticulitis: a systematic review protocol.

This review will evaluate the effectiveness of natural orifice transluminal endoscopic surgery (NOTES)/hybrid NOTES versus traditional surgical techniques in the management of diverticular disease. Diverticular disease can have a significant impact on a patient's quality of life, especially the changes affecting bowel function. Recurrent/severe symptoms may require surgery. Resections are invasive and can have significant operative complications. There has been a shift from an open resection to a more minimally invasive technique such as laparoscopy. Furthermore, the use of natural orifice transluminal endoscopic resection, using a transanal/transvaginal approach, has begun to be used in colorectal resections. Eligible studies will include patients who underwent surgical resection for diverticular disease or diverticulitis. The review will consider studies that evaluate NOTES in the management of diverticular disease or diverticulitis. This will include hybrid procedures involving both NOTES and natural orifice specimen extraction (NOSE), with the aid of laparoscopy/endoscopy/robotic equipment. Studies on participants under 18 years of age and presenting with non-diverticular pathologies will be excluded. This review will follow the JBI methodology for systematic reviews of effectiveness and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Databases to be searched will include PubMed, MEDLINE (Ovid), CINAHL (EBSCOhost), Cochrane Library, Web of Science, Scopus, ProQuest, ClinicalTrials.gov, and ANZCTR, with no language limitations. The reference lists of included studies will be screened for additional studies. This review will preferentially consider experimental and quasi-experimental study designs. Two reviewers will conduct critical appraisal and data extraction. Studies will, where possible, be pooled in statistical meta-analyses. PROSPERO CRD42023420771.

CONTROL DE MICRO MECANIZADO NO CONVENCIONAL BASADO EN ANÁLISIS DE PARÁMETROS MULTIVARIABLES UTILIZANDO UN SISTEMA DE INFERENCIA DIFUSA

Non-conventional manufacturing processes, such as pulsed electrochemical machining, are strategies for manufacturing metal components that optimize tool life. This is achieved by implementing non-traditional techniques based on electrolysis to remove metal in a controlled environment without generating contact between the tool and the workpiece. These characteristics allow a reduction in the resources required for the development of components with complex morphology on high-strength materials in many application fields, such as aerospace, medical, and precision tools. However, the characteristic features of non-linearity in this kind of process increase the complexity of the development of automatic control through traditional algebraic techniques in the regulation of uniform material wear. In this context, a non-traditional solution based on techniques derived from artificial intelligence bounded by fuzzy logic and genetic algorithms is presented for the tuning and automatic control of the pulsed electrochemical machining process. Furthermore, the efficiency is evaluated by analyzing the over-cutting phenomenon and the precision in developing linear polygonal machining on a micrometer scale. Keywords: Modern manufacturing, fuzzy logic, automatic control, electrolysis, genetic algorithms.

Generative Adversarial Network for Wireless Communication: Principle, Application, and Trends

Generative adversarial network (GAN) has attracted wide attention because of its remarkable ability to learn highdimensional and complex data distributions based on game theory and machine learning. In this paper, we focus on the role that GANs play in wireless communications, which has been well researched and developed in recent years. With the gradual increase in communication rates and the complicacy of communication scenarios, some urgent issues have emerged, such as complex channel generation, high-dimensional channel estimation, and insufficient real-world signal acquisition. The powerful nonlinear fitting property of GAN can break through the bottleneck of traditional communication techniques, whose applications can be categorized into data generation, performance optimization, and data classification according to different purposes and network structures. For each category, a universal network structure summarizing the characteristics and targets is investigated, and the performance of GANs in communication is also analyzed with simulation examples. Moreover, the open research issues are discussed at the end to provide some directions for future study.

Distributed Estimation of Support Vector Machines for Matrix Data.

Discrimination problems are of significant interest in the machine learning literature. There has been growing interest in extending traditional vector-based machine learning techniques to their matrix forms. In this article, we investigate the statistical properties of the nuclear-norm-based regularized linear support vector machines (SVMs), in particular establishing the convergence rate of the estimator in the high-dimensional setting. Furthermore, within the distributed estimation paradigm, we propose a communication-efficient estimator that can achieve the same convergence rate. We illustrate the performances of the estimators via some simulation examples and an empirical data analysis.

Identification Of Traditional Foods In West Pasaman District

This study aims to learn more about traditional food in West Pasaman District. The research was conducted August-December 2023 in West Pasaman District.The purpose of this study was to identify the types of food, ingredients used and traditional food processing techniques in West Pasaman District. The type of research used is a mix method (qualitative and quantitative). This research data was obtained from two ways, namely primary data and secondary data. Primary data was conducted to identify the types of traditional food, ingredients used and traditional food processing techniques in West Pasaman District. While secondary data is in the form of demographics of West Pasaman District. Data collection techniques are carried out by observation, interviews, and documentation. Data analysis is done by reducing data, presenting data, and concluding. The conclusion of the research results is that traditional food in West Pasaman Regency is a typical food that has existed for a long time. The ingredients used are food ingredients developed by yourself. As well as traditional food processing techniques that are typical in West Pasaman District.

Comparative study of surface roughness between electropolishing process and traditional sanding for low carbon steel

Steel is a versatile material utilized across various sectors, encompassing both industrial and decorative applications. AISI (American Iron and Steel Institute) 1005 steel, recognized for its low carbon content (approximately 0.05% carbon), is commonly referred to as mild steel and finds extensive use in society. This study aims to investigate the influence of electropolishing parameters, specifically current density, and time, on the surface roughness of steel. The methodology involved the preparation of samples for electropolishing tests, systematic variation of the parameters, and subsequent analysis of the resulting surface finish through roughness measurements. Samples subjected to higher current densities for shorter durations exhibited substantial reductions in roughness, while longer durations resulted in minimal or negligible changes in roughness regardless of the current density employed. This comparative analysis between electropolishing and tradi-tional sanding techniques provides valuable insights into optimizing surface finishing processes for low carbon steel applications.

StegEraser: Defending cybersecurity against malicious covert communications

Traditionally, the mission of intercepting malicious traffic between the Internet and the internal network of entities like organizations and corporations, is largely fulfilled by techniques such as deep packet inspection (DPI). However, steganography, the methodology of hiding secret data in seemingly benign public mediums (e.g., images), has been leveraged by advanced persistent threat (APT) groups in recent years, and is almost impossible to be detected and intercepted by traditional techniques, posing a pervasive and realistic threat to cybersecurity. Additionally, internal networks’ vulnerability to steganography is further exacerbated by the connectivity and large attack surface of the Internet of Things (IoT), whose adoption and deployment are quickly expanding. To protect computer systems against malicious communications that apply steganographic methods potentially unknown to cybersecurity stakeholders, we propose StegEraser, an approach to removing the secret information embedded in public mediums by adversaries, that is fundamentally distinct from existing research which is primarily designed for known steganographic methods. Implemented for images, StegEraser injects an excessively huge amount of random binary data with a novel steganographic method into the images, by utilizing the information-merging capabilities of invertible neural networks (INNs), in order to “overload” adversaries’ steganographic hiding capacity of images transmitted through the firewall performing DPI. In the meantime, StegEraser preserves the perceptual quality of the images. In other words, StegEraser “defeats unknown steganography with steganography”. Extensive evaluation verifies that StegEraser significantly outperforms state-of-the-art (SOTA) methods in terms of removing secret information embedded with both traditional and neural network-based steganographic methods, while visually maintaining the image quality.

GeoNLPlify: A spatial data augmentation enhancing text classification for crisis monitoring

Crises such as natural disasters and public health emergencies generate vast amounts of text data, making it challenging to classify the information into relevant categories. Acquiring expert-labeled data for such scenarios can be difficult, leading to limited training datasets for text classification by fine-tuning BERT-like models. Unfortunately, traditional data augmentation techniques only slightly improve F1-scores. How can data augmentation be used to obtain better results in this applied domain? In this paper, using neural network explicability methods, we aim to highlight that fine-tuned BERT-like models on crisis corpora give too much importance to spatial information to make their predictions. This overfitting of spatial information limits their ability to generalize especially when the event which occurs in a place has evolved and changed since the training dataset has been built. To reduce this bias, we propose GeoNLPlify,1 a novel data augmentation technique that leverages spatial information to generate new labeled data for text classification related to crises. Our approach aims to address overfitting without necessitating modifications to the underlying model architecture, distinguishing it from other prevalent methods employed to combat overfitting. Our results show that GeoNLPlify significantly improves F1-scores, demonstrating the potential of the spatial information for data augmentation for crisis-related text classification tasks. In order to evaluate the contribution of our method, GeoNLPlify is applied to three public datasets (PADI-web, CrisisNLP and SST2) and compared with classical natural language processing data augmentations.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

An advanced ensemble load balancing approach for fog computing applications

Fog computing has emerged as a viable concept for expanding the capabilities of cloud computing to the periphery of the network allowing for efficient data processing and analysis from internet of things (IoT) devices. Load balancing is essential in fog computing because it ensures optimal resource utilization and performance among distributed fog nodes. This paper proposed an ensemble-based load-balancing approach for fog computing environments. An advanced ensemble load balancing approach (AELBA) uses real-time monitoring and analysis of fog node metrics, such as resource utilization, network congestion, and service response times, to facilitate effective load distribution. Based on the ensemble's collective decision-making, these metrics are fed into a centralized load-balancing controller, which dynamically adjusts the load distribution across fog nodes. Performance of the proposed ensemble load-balancing approach is evaluated and compared it to traditional load-balancing techniques in fog using extensive simulation experiments. The results demonstrate that our ensemble-based approach outperforms individual load-balancing algorithms regarding response time, resource utilization, and scalability. It adapts to dynamic fog environments, providing efficient load balancing even under varying workload conditions.

Deep Learning Approaches and Biomarkers in Medical Diagnosis

Abstract: The size of the medical information system is growing gradually. Due to this, traditional data analysis for extracting helpful information for any disease has become inefficient in providing accurate real-time valid information. Traditional data mining and statistical learning techniques, which lack sufficient domain knowledge on a complicatedly colossal amount of data, need to stop adopting new advances in deep learning technologies. Thus, the use of sophisticated machines with in-depth understanding and analysis capability is vital to provide real-time information in detecting and diagnosing diseases in the healthcare system. In this paper, we study recent deep learning approaches which are capable of working on high-dimensional and multi-dimensional data. These approaches have been deployed to identify the root cause of various diseases like Cancer, Lung Diseases, Heart Diseases, Diabetes, Hepatitis, Alzheimer’s, Dengue, Parkinson’s, etc. : In this study, our key contributions are: 1: A decent overview of the deep learning techniques. 2: Several modalities in the diagnosis of various diseases. 3: Set out recent trends in various applications of deep learning in healthcare, some of which are analysis and diagnosis of medical images, precision medicine, drug discovery, predictive analysis to support clinical decisions, and sustainable public health. 4: Several deep learning approaches with their performance are described in detail. 5: Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially the availability of the patient’s data for training deep learning models on a particular disease is comparatively much less than required.

Robust Policy Iteration of Uncertain Interconnected Systems With Imperfect Data

This paper investigates the robust optimal control problem of a class of continuous-time, partially linear, interconnected systems. In addition to the dynamic uncertainties resulted from the interconnected dynamic system, unknown bounded disturbances are taken into account throughout the learning process, wherein the system’s dynamics and the disturbances are assumed unknown. These challenges lead the collected online data to be imperfect. In this scenario, traditional data-driven control techniques, such as adaptive dynamic programming (ADP) and robust ADP, encounter a challenge in approximating the optimal control policy precisely due to imperfect data and computational errors. In this paper, a novel data-driven robust policy iteration method is proposed to simultaneously solve the robust optimal control problems. Without relying on the knowledge of the system’s dynamics, the external disturbances or the complete state, the implementation of the proposed method only needs to access the input and partial state information. Based on the small-gain theorem, the notions of strong unboundedness observability and input-to-output stability, it is guaranteed that the learned robust optimal control gain is stabilizing and that the solution of the closed-loop system is uniformly ultimately bounded despite the existence of dynamic uncertainties and unknown external disturbances. The simulation results reveal the efficiency and practicality of the proposed data-driven control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the use of reinforcement learning to improve the quality of designing adaptive optimal controllers for engineering applications. Adaptive dynamic programming methods, in particular policy iteration (PI), are widely used in solving optimal control problems. However, due to the iterative nature of PI, the approximated optimal control policy may be inaccurate and imprecise. Especially, when using imperfect system’s measurements instead of the modelling information. This can result in causing the learned control policy to deviate from the actual optimal policy. This becomes more challenging in the existence of dynamic uncertainties and unknown external disturbances which corrupt the measurements and result in imperfect data. This work investigates the conditions on the uncertainties such that the proposed novel data-driven PI algorithm is robust to system’s uncertainties, unknown external disturbances and imperfect measurements. The approximated robust optimal control policy performs robustly in the existences of imperfect data and uncertainties, and at the same time is close enough to the optimal control policy.