Optimization Criteria Research Articles

Optimisation of the performance of alkali-activated mortars using CDW binders from different sources

This study investigates the fresh-state properties and the hardened-state physical and mechanical properties of a total of 64 alkali-activated mortars incorporating different wastes as binders, such as fly ash (FA), recycled concrete (RC), recycled brick (RB), and recycled tile (RT). These waste materials were activated using sodium hydroxide (NaOH) and sodium silicate (Na2SiO3). Linear regression analysis was employed to evaluate the impact and significance of Na2O concentration (9 % or 15 %), modulus of silica (0.5 or 1), curing time in the oven (24 or 48 h), and temperature (50 °C or 70 °C) on the properties of the mortars. The results, analysed using ANOVA, indicate that all properties were affected by these parameters, except for the initial curing time in the oven for mortars made with construction and demolition waste (CDW), which showed no significant differences between periods of 24 and 48 h. Compared to mortars produced with RC, the contribution of these parameters to mechanical properties was more prominent in the mixes with RT and RB. The highest compressive strength at 28 days, for the mortars with CDW binders, was obtained by incorporating RT with 12 % of Na2O, modulus of silica of 1 and 70 °C for 48 h of initial curing, reaching 12.5 MPa. This value is 2.1 times higher than the strength obtained in mixes with RB or RC. The results also show that the performance of the mortars is highly related to the reactivity of the waste material (mortars with RT performed better) and prove that each raw material has its own optimum criterion.

Fetoscopic Release of Amniotic Bands Based on the Evidence-A Systematic Review.

The purpose of this review is to provide an overview of the perinatal outcomes of fetuses who underwent fetal surgery for the management of Amniotic Band Syndrome (ABS). A systematic review of studies reporting on the perinatal outcome of fetuses undergoing fetoscopic release of amniotic bands according to the (PRISMA) guidelines was performed. The MEDLINE, Embase, Scopus, and Cochrane Library databases were systematically searched. In total, 17 studies reporting 37 cases of ABS that underwent amniotic band release by fetoscopy were included. The median gestational age at which fetal surgery was performed was 22weeks (range 18-29weeks). PPROM occurred in 51.3%, while fetal survival reached 89.2%. The success of fetal surgery was 75.7% in preserving and maintaining the functionality of the affected limb. Fetoscopic release of amniotic bands can preserve the affected limb and its function in cases of ABS and prevent fetal death in cases of ABS involving the umbilical cord. Further studies are needed to determine the optimal criteria for selecting patients who can benefit from fetal surgery, considering that it is an intervention that is not free of perinatal complications.

Разработка алгоритма оперативного планирования серийного дискретного производства с альтернативными цепочками операций в технологических процессах

The problem of the theory of schedules with an additional condition – the need to choose an alternative chain of operations in the technical processes of products is considered. A two-stage planning algorithm is proposed, the first stage of which is the selection of chains of operations suitable for a certain criterion from the given alterna-tives, after which the task is reduced to the classic JSSP (Job-Shop Scheduling Problem) problem. At the second stage, the selected production operations are arranged on the machines, taking into account the order of the technological process and other restrictions. Minimization of changeover time in production was chosen as an optimization criterion. The description of the algorithm and its implementation is given on the example of the cable industry (production of wiring harnesses). Both stages of planning are implemented on the basis of greedy algorithms, the results of test measurements on various amounts of data (up to tens of thousands of operations) are presented. The implementation is made in C# 10 using a free platform .NET 6. The vector of further research is the implementation of more complex algorithms (in particular, based on evolutionary methods) in order to obtain more optimal plans.

Analysis of different objective functions in petroleum field development optimization

Oilfield development optimization plays a vital role in maximizing the potential of hydrocarbon reservoirs. Decision-making in this complex domain can rely on various objective functions, including net present value (NPV), expected monetary value (EMV), cumulative oil production (COP), cumulative gas production (CGP), cumulative water production (CWP), project costs, and risks. However, EMV is often the main function when optimization is performed under uncertainty. The behavior and performance of different objective functions has been investigated in this paper, when EMV is the primary criterion for optimization under reservoir and economic uncertainty. One of the goals of this study is to provide insights into the advantages and limitations of employing EMV as the sole objective function in oil field development decision-making. The designed optimization problem included sequential optimization of design variables including well positions, well quantity, well type, platform capacity, and internal control valve placements. A comparative analysis is presented, contrasting the outcomes obtained from optimizing the EMV-based objective function against traditional objective functions. The study underscores the importance of incorporating multiple objective functions alongside EMV to guide decision-making in oilfield development. Potential benefits in minimizing CGP and CWP are revealed, aiding in the mitigation of environmental impact and optimization of resource utilization. A strong correlation between EMV and COP is identified, highlighting EMV’s role in improving COP and RF.

Consensus on glycemic management for patients with coronary heart disease and type 2 diabetes.

The prevalence of patients with coronary heart disease (CHD) and diabetes mellitus is notably high, posing significant residual cardiovascular risks even after routine interventions such as antihypertensive, lipid-lowering, and antithrombotic treatments. Recent studies have demonstrated that certain glucose-lowering medications confer cardiovascular benefits for patients with type 2 diabetes. However, a survey indicates that cardiologists may not be fully acquainted with the optimal screening timing, indicators, and diagnostic criteria for type 2 diabetes, and there is insufficient awareness and a low rate of prescription of novel glucose-lowering medications with proven cardiovascular efficacy, such as glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and sodium-glucose co-transporter-2 inhibitors (SGLT-2i). In this context, based on domestic and international guidelines or consensus and the latest evidence-based evidence, this consensus aims to standardize the glycemic management for patients with acute coronary syndrome, chronic coronary syndrome, and perioperative management for percutaneous coronary intervention. It highlights the key points of screening and diagnosis of type 2 diabetes, and the comprehensive management of cardiovascular risk in patients with CHD. The consensus elaborates on the principles and algorithms of glycemic management for CHD patients, without involving acute complications of diabetes, clarifies the clinical practice of glucose-lowering medications with cardiovascular benefits, and promotes the standardized use of these medications in cardiovascular and other related specialty fields. Additionally, it addresses the glucose-lowering treatment to comprehensively reduce cardiovascular risks.

Transarterial Embolization of Geniculate Arteries Reduces Pain and Improves Physical Function in Knee Osteoarthritis-A Prospective Cohort Study.

Knee osteoarthritis (OA) affects millions worldwide, leading to pain and reduced quality of life. Conventional treatments often fail to provide adequate relief, necessitating new therapeutic approaches. This study evaluated the efficacy and safety of genicular artery embolization (GAE) using permanent microspheres in patients with mild-to-moderate knee OA. In this prospective, single-center study, 17 participants underwent GAE. KOOS (Knee injury and Osteoarthritis Outcome Score), WOMAC (The Western Ontario and McMaster Universities Arthritis Index), and IPAQ (International Physical Activity Questionnaire) scores, along with physical performance tests, medication use, and dual-energy X-ray absorptiometry (DEXA) scans, were assessed at baseline and at multiple follow-up points over six months. The primary endpoint, VAS at six months, showed significant improvement (median reduction from 66 mm to 40 mm, p = 0.0004). All pain and function scores, as well as physical performance tests, improved significantly. No clinically relevant changes in medication use or DEXA parameters were observed after six months. Only minor, self-limiting adverse events occurred. This study indicates that GAE is a promising minimally invasive treatment for knee OA, providing significant pain relief and functional improvement. However, further long-term, randomized trials are needed to confirm these findings and establish optimal patient selection criteria.

Optimal Design of Plane Elastic Membranes Using the Convexified Föppl’s Model

This work puts forth a new optimal design formulation for planar elastic membranes. The goal is to minimize the membrane’s compliance through choosing the material distribution described by a positive Radon measure. The deformation of the membrane itself is governed by the convexified Föppl’s model. The uniqueness of this model lies in the convexity of its variational formulation despite the inherent nonlinearity of the strain–displacement relation. It makes it possible to rewrite the optimization problem as a pair of mutually dual convex variational problems. The primal variables are displacement functions, whilst in the dual one seeks stresses being Radon measures. The pair of problems is analysed: existence and regularity results are provided, together with the system of optimality criteria. To demonstrate the computational potential of the pair, a finite element scheme is developed around it. Upon reformulation to a conic-quadratic & semi-definite programming problem, the method is employed to produce numerical simulations for several load case scenarios.

Survey of nutrition screening practices in pediatric hospitals across the United States.

Nutrition screening on admission is critically important to systematically identify patients with undernutrition given the known relationship with deleterious clinical outcomes. Limited data exist regarding optimal processes and criteria for pediatric nutrition screening. Therefore, we sought to characterize nutrition screening practices in pediatric hospitals. A total of 365 inpatient pediatric hospitals in the United States were identified, eligible, and contacted. Eligible hospitals included general pediatric hospitals, adult hospitals with pediatric units, and specialty pediatric hospitals. One respondent at each eligible hospital was asked to complete a 33-question survey of admission nutrition screening practices. Of 268 survey respondents, 37% represented pediatric units in adult hospitals, 35% general pediatric hospitals, and 28% pediatric specialty or psychiatric hospitals. A total of 98.5% endorsed the existence of a screening process on admission. Anthropometrics (eg, body mass index z score, 84%) and nutrition status (eg, change in intake, 67%) were the most common screening criteria applied. A nutrition screening instrument was used in 37% of institutions, and only 31% of institutions reported using pediatric-specific screening instruments. Pediatric units within adult hospitals were 1.38 times more likely to use a screening instrument validated in any population. Barriers to nutrition screening included the lack of a standard screening procedure and insufficient staff to conduct screening. Fifty-four percent of respondents reported a desire to change their hospital's nutrition screening process. Most pediatric hospitals screen for nutrition risk on admission. However, methods and criteria varied widely across pediatric hospitals, highlighting the importance of standardized best practices.

Shortest path or random walks? A framework for path weights in network meta-analysis.

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in NMA.

Static and dynamic behaviours of a self-expanding nitinol stent real contact area within a PTFE catheter: A multiscale approach

Current research on self-expanding super-elastic nitinol stents is mainly focused on designing geometries suited to the human venous and arterial systems. This study specifically considers a patented one-piece double cross-sectional stent designed to address venous stenosis affecting the vena cava, iliac veins, and their bifurcations. Before its placement within the vein, numerous tribological challenges arise as the stent slides along the guide-wire (so-called catheter). These are mainly connected to the lack of knowledge related to (i) its frictional behaviour and (ii) the level of contact pressure linked to the unknown real contact area between the compressed stent and the polytetrafluoroethylene (PTFE) catheter.This paper describes an original multi-scale approach, based on the Persson’s contact theory, that allows determination of (i) the contact pressure, (ii) the evolution of the real contact area, and (iii) the shear stresses at the interface during the stent sliding. A topographical optimization criterion will finally be provided, enabling control of both friction and stick–slip phenomena at the stent-catheter interface.

Mathematical models and algorithms for designing main pipeline for transporting georesources

Relevance. The practical importance of the tasks of utility networks design, namely, the problems of optimizing the structure of the main pipeline according to given criteria, such as efficiency, reliability etc., under conditions of limitations, for example, the compatibility of various types of utilities. Since the main pipeline is laid on the ground with various physical and geological factors, natural and situational conditions, it is advisable to take the reliability of its operation as a global criterion. The task of network optimization is proposed in the form of displaying the main pipeline along the selected routes in three-dimensional space, which considers various existing communications and objects, as well as elevation marks of the area. The paper presents the problems of optimizing networks, both in the continuous case and in the discrete case, and also studies various indicators of the reliability of the operation of the main pipeline. Aim. To develop a model for laying the main pipeline in three-dimensional space, considering the reliability of the pipeline transport; to conduct a comparative analysis for various reliability indicators and topologies of the main pipeline. Objects. Utility communications and networks laid in three-dimensional space. Methods. Calculus of variations, discrete optimization methods, graph theory and hypernet theory methods, network reliability analysis methods. Results. The task of optimizing the main pipeline transport is given taking into account its nesting along the route in three-dimensional space with the choice of an optimization criterion (economic efficiency, reliability, etc.). The problem is presented in the form of continuous and discrete formulations, which is important for its development both within the theory of the calculus of variations and discrete optimization. In this work, the problem was studied within the framework of the theory of graphs and hypernets, which allow, firstly, taking into account the nesting of one structure (main pipeline) into another (a discrete analogue of three-dimensional space) and, secondly, clearly illustrating the results of numerical experiments. It is shown that under the conditions of a given variants for laying a secondary network along the primary channels, various optimal structures are obtained when considering various reliability indicators as a criterion, which can be used to implement a design solution for the construction and operation of pipeline transport for various purposes.

Multi-Objective Optimization of Surface Integrity in the Grind-Hardening Process

Grind-hardening machining is a new integrated manufacturing technology that integrates the theory of material surface quenching and grinding machining. The surface integrity of grind-hardening directly affects the performance and reliability of the parts. Improving the grind-hardening quality has always been the focus and difficulty in this field. Based on the surface integrity theory and the characteristics of the grind-hardening process, this paper proposed four optimization criteria for grinding parameters according to the engineering application requirements of materials. Using the expectation function, the burr cross-sectional area, depth of the effective hardened layer, and surface roughness were comprehensively analyzed under each optimization criterion to obtain an optimal combination of grinding parameters. The results revealed a significant inconsistency in the optimized grinding parameters under each optimization criterion. When considering the depth of the effective hardened layer as the primary optimization parameter and ignoring the surface roughness and burr cross-sectional area, the highest overall desirability was 0.926395. In practical application, the optimization criteria should be reasonably selected according to the actual engineering requirements.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method’s effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods.

Theoretical and experimental investigations on a data-driven trajectory planning scheme for dynamic shape control of piezo-actuated compliant morphing structures

Piezo-actuated compliant morphing structures can transmit motion and force via elastic deformation with lower weight, simplified design, and higher accuracy, a typical example is the variable camber wing. However, the rapid shape change of compliant structures is a challenging control problem because it often results in a high level of vibration due to the structural flexibility necessary to achieve the morphing requirement. This study presents a model-free data-driven trajectory planning approach based on spline curves and surrogate-based optimization (SBO) to obtain smooth “rest-to-rest” morphing trajectories. The macro-fiber composite (MFC) patches are used for piezoelectric actuators to generate elastic deformation. The flexible beam and variable camber wing are used for both linear numerical simulation and experimental tests with various nonlinearities and uncertainties. An optimization criterion is proposed to minimize both transient and residual vibrations during the “rest-to-rest” motion. An extraordinarily terminal displacement error function is added to eliminate the effects of the hysteresis and creep of the actuator. The control inputs, i.e., voltage profiles for the MFCs, are defined using cubic spline curves via only a few control points. Then, the optimization problem is solved by employing a Kriging surrogate model-based algorithm integrated with the expected improvement (EI) infilling-sampling criterion, which constructs an efficient algorithm similar to reinforcement learning. The optimal morphing trajectories can be highly efficiently obtained by using only 4 control points and less than 150 samplings. The experimental results of both the plate model and the variable camber wing model show that the proposed method can not only achieve a smooth dynamic morphing trajectory with minimized vibration but also automatically compensate for hysteresis and creep. The proposed method provides an effective means for the rapid realization of an optimized morphing trajectory for compliant morphing structures.

Topology optimization of blazed gratings under conical incidence

A topology optimization method is presented and applied to a blazed diffraction grating in reflection under conical incidence. This type of grating is meant to disperse the incident light on one particular diffraction order, and this property is fundamental in spectroscopy. Conventionally, a blazed metallic grating is made of a sawtooth profile designed to work with the ±1st diffraction order in reflection. In this paper, we question this intuitive triangular pattern and look for optimal opto-geometric characteristics using topology optimization based on finite element modelling of Maxwell’s equations. In practical contexts, the grating geometry is mono-periodic, but it is enlightened by a 3D plane wave with a wave vector outside of the plane of invariance. Consequently, this study deals with the resolution of direct and inverse problems using the finite element method in this intermediate state between 2D and 3D: the so-called conical incidence. A multi-wavelength objective is used in order to obtain a broadband blazed effect. Finally, several numerical experiments are detailed. Our numerical results show that it is possible to reach a 98% diffraction efficiency on the −1st diffraction order if the optimization is performed on a single wavelength, and that the reflection integrated over the [400,1500] nm wavelength range can be 29% higher in absolute terms, 56% in relative terms, than that of the sawtooth blazed grating when using a multi-wavelength optimization criterion (from 52% to 81%).

Improved MTPA and MTPV Optimal Criteria Analysis Based on IPMSM Nonlinear Flux-Linkage Model

The use of interior permanent-magnet synchronous machines (IPMSMs) is prevalent in automotive and vehicle traction applications due to their high efficiency over a wide speed range. Given the high-power-density requirements of automotive IPMSMs, it is imperative to consider the effect of nonlinearities, such as saturation and cross-coupling, on the motor model. The aforementioned nonlinearities render conventional linear motor models incapable of accurately describing the operating characteristics of the IPMSM, including the maximum torque per ampere (MTPA) trajectory, the flux-weakening (FW) trajectory, and the maximum torque per volt (MTPV) trajectory. With respect to the linear motor model, the nonlinear flux-linkage model is gradually receiving attention from researchers. This modeling method represents the nonlinear behavior of the motor through the direct establishment of a bidirectional mapping relationship between flux-linkage and current. It is capable of naturally incorporating the effects of magnetic saturation and cross-coupling factors. However, the analysis of the current trajectory optimal criteria based on this model has not yet been reported. In this paper, the optimal criteria for the MTPA and MTPV current trajectories are analyzed based on the nonlinear flux-linkage model of IPMSMs. Firstly, the nonlinear flux-linkage model of the tested IPMSM is established by the experimental calibration method. The mathematical analytical expressions of the MTPA and MTPV optimal criteria are then analyzed by constructing and solving optimal problems with different objectives. Finally, the current command table applicable to actual motor control is constructed by calculating the current command for different operating conditions according to the optimal criteria proposed in this paper. The validity and feasibility of the optimal criteria proposed in this paper are verified through experimental tests on different operating conditions.

Super-Resolution Image Reconstruction of Wavefront Coding Imaging System Based on Deep Learning Network

Wavefront Coding (WFC) is an innovative technique aimed at extending the depth of focus (DOF) of optics imaging systems. In digital imaging systems, super-resolution digital reconstruction close to the diffraction limit of optical systems has always been a hot research topic. With the design of a point spread function (PSF) generated by a suitably phase mask, WFC could also be used in super-resolution image reconstruction. In this paper, we use a deep learning network combined with WFC as a general framework for images reconstruction, and verify its possibility and effectiveness. Considering the blur and additive noise simultaneously, we proposed three super-resolution image reconstruction procedures utilizing convolutional neural networks (CNN) based on mean square error (MSE) loss, conditional Generative Adversarial Networks (CGAN), and Swin Transformer Networks (SwinIR) based on mean absolute error (MAE) loss. We verified their effectiveness by simulation experiments. A comparison of experimental results shows that the SwinIR deep residual network structure based on MAE loss optimization criteria can generate more realistic super-resolution images with more details. In addition, we used a WFC camera to obtain a resolution test target and real scene images for experiments. Using the resolution test target, we demonstrated that the spatial resolution could be improved from 55.6 lp/mm to 124 lp/mm by the proposed super-resolution reconstruction procedure. The reconstruction results show that the proposed deep learning network model is superior to the traditional method in reconstructing high-frequency details and effectively suppressing noise, with the resolution approaching the diffraction limit.

Optimal life-testing plans for joint progressively Type-II censored Birnbaum-Saunders distributions with compound optimal criteria

ABSTRACT In this paper, we first derive the maximum likelihood estimates (MLEs) of the parameters of Birnbaum-Saunders (BISA) distributions based on the joint progressive Type-II censored (JPC) samples. We also discuss using the EM algorithm to obtain the MLEs of the model parameters. Then, we determine the single-objective-criterion optimal JPC schemes for BISA distributions based on the cost minimization criterion, A -optimality criterion, and D -optimality criterion by the complete search method, variable neighborhood search (VNS) algorithm and modified VNS (MVNS) algorithm. These algorithms are compared in terms of accuracy and computation time. In addition, we determine the compound-criterion optimal JPC schemes based on different optimal criteria and the reasonably efficient compound optimal JPC schemes for two competing statistical models, such as the inverse Gaussian and BISA models. The advantages of using the compound optimal scheme over the single-objective-criterion optimal scheme are demonstrated through a real-life data set on the micro-indentation test about the hardness of polymeric bone cement.

Thromboinflammation in COVID-19: Unraveling the interplay of coagulation and inflammation.

The coronavirus disease 2019 (COVID-19) pandemic has not only challenged global health systems but also spurred intense scientific inquiry into its pathophysiology. Among the multifaceted aspects of the disease, coagulation abnormalities have emerged as a significant contributor to morbidity and mortality. From endothelial dysfunction to dysregulated immune responses, various factors contribute to the hypercoagulable state seen in severe COVID-19 cases. The dysregulation of coagulation in COVID-19 extends beyond traditional thromboembolic events, encompassing a spectrum of abnormalities ranging from microvascular thrombosis to disseminated intravascular coagulation (DIC). Endothelial injury induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers a cascade of events involving platelet activation, coagulation factor consumption, and fibrinolysis impairment. Moreover, the virus direct effects on immune cells and the cytokine storm further exacerbate the prothrombotic milieu. Unraveling this intricate web of interactions between viral pathogenesis and host responses is essential for elucidating novel therapeutic targets and refining existing management strategies for COVID-19-associated coagulopathy. In the quest to unravel the complex interplay between coagulation and COVID-19, numerous clinical and laboratory studies have yielded invaluable insights into potential biomarkers, prognostic indicators, and therapeutic avenues. Anticoagulation therapy has emerged as a cornerstone in the management of severe COVID-19, although optimal dosing regimens and patient selection criteria remain subjects of ongoing investigation. Additionally, innovative approaches such as targeting specific components of the coagulation cascade or modulating endothelial function hold promise for future therapeutic development.