Critical Weight Research Articles

Pooled Population Pharmacokinetic Analysis and Dose Recommendations for Ciprofloxacin in Intensive Care Unit Patients with Obesity.

Recent studies have explored the influence of obesity and critical illness on ciprofloxacin pharmacokinetics. However, variation across the subpopulation of individuals with obesity admitted to the intensive care unit (ICU) with varying renal function remains unexamined. This study aims to characterize ciprofloxacin pharmacokinetics in ICU patients with obesity and provide dose recommendations for this special population. Individual patient data of 34 ICU patients with obesity (BMI >30 kg/m2) from four studies evaluating ciprofloxacin pharmacokinetics in ICU patients were pooled and combined with data from a study involving 10 individuals with obesity undergoing bariatric surgery. All samples were collected after intravenous administration. Non-linear mixed effects modeling and simulation were used to develop a population pharmacokinetic model and describe ciprofloxacin exposure in plasma. Model-based dose evaluations were performed using a pharmacokinetic/pharmacodynamic target of AUC/MIC >125. The data from patients with BMI ranging from 30.2 to 58.1 were best described by a two-compartment model with first-order elimination and a proportional error model. The inclusion of Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) as a covariate on clearance reduced inter-individual variability from 57.3% to 38.5% (P <.001). Neither body weight nor ICU admission significantly influenced clearance or volume of distribution. Renal function is a viable predictor for ciprofloxacin clearance in ICU patients with obesity, while critical illness and body weight do not significantly alter clearance. As such, body weight and critical illness do not need to be accounted for when dosing ciprofloxacin in ICU patients with obesity. Individuals with CKD-EPI >60 mL/min/1.73 m2 may require higher dosages for the treatment of pathogens with minimal inhibitory concentration ≥0.25 mg/L.

Hypoxia delays steroid-induced developmental maturation in Drosophila by suppressing EGF signaling.

Animals often grow and develop in unpredictable environments where factors like food availability, temperature, and oxygen levels can fluctuate dramatically. To ensure proper sexual maturation into adulthood, juvenile animals need to adapt their growth and developmental rates to these fluctuating environmental conditions. Failure to do so can result in impaired maturation and incorrect body size. Here we describe a mechanism by which Drosophila larvae adapt their development in low oxygen (hypoxia). During normal development, larvae grow and increase in mass until they reach critical weight (CW), after which point a neuroendocrine circuit triggers the production of the steroid hormone ecdysone from the prothoracic gland (PG), which promotes maturation to the pupal stage. However, when raised in hypoxia (5% oxygen), larvae slow their growth and delay their maturation to the pupal stage. We find that, although hypoxia delays the attainment of CW, the maturation delay occurs mainly because of hypoxia acting late in development to suppress ecdysone production. This suppression operates through a distinct mechanism from nutrient deprivation, occurs independently of HIF-1 alpha and does not involve dilp8 or modulation of Ptth, the main neuropeptide that initiates ecdysone production in the PG. Instead, we find that hypoxia lowers the expression of the EGF ligand, spitz, and that the delay in maturation occurs due to reduced EGFR/ERK signaling in the PG. Our study sheds light on how animals can adjust their development rate in response to changing oxygen levels in their environment. Given that hypoxia is a feature of both normal physiology and many diseases, our findings have important implications for understanding how low oxygen levels may impact animal development in both normal and pathological situations.

Protein kinase C-inhibition reduces critical weight loss and improves functional outcome after experimental subarachnoid haemorrhage

ObjectivesSubarachnoid haemorrhage (SAH) carries a high burden of morbidity and mortality. One in three patients develop vasospasm, which is associated with Delayed Cerebral Ischemia. The pathophysiology includes vasoconstrictor receptor upregulation in cerebral arteries. The protein kinase C – inhibitor RO-31-7549 reduces the expression of several vasoconstrictor receptors and normalizes cerebral blood flow in experimental SAH but functional and behavioural effects are unknown. This study was undertaken to analyse functional outcomes up to 14 days after experimental SAH. Materials and methods54 male rats were randomised to experimental SAH or sham, using the pre-chiasmatic, single injection model, and subsequent treatment or vehicle. 42 remained for final analysis. The animals were euthanized on day 14 or when reaching a humane endpoint. The primary endpoint was overall survival, defined as either spontaneous mortality or when reaching a predefined humane endpoint. The secondary outcomes were differences in the rotating pole test, weight, open field test, novel object recognition and qPCR of selected inflammatory markers. ResultsIn the vehicle group 6/15 rats reached the humane endpoint of >20 % weight loss compared to 1/14 in the treatment group. This resulted in a significant reduced risk of early euthanasia due to >20 % weight loss of HR 0.15 (0.03–0.66, p = 0.04). Furthermore, the treatment group did significantly better on the rotating pole test, RR 0.64 (0.47–0.91, p = 0.02). ConclusionRO-31-7549 improved outcomes in terms >20 % weight loss and rotating pole performance after experimental SAH and could be investigated.

The quantitative molecular weight-antimicrobial activity relationship for chitosan polymers, oligomers, and derivatives

Chitosan and chitosan derivatives can kill pathogenic microorganisms including bacteria and fungi. The antimicrobial activity is dependent on the degree of acetylation, substituent structure, and molecular weight. Over the past four decades, numerous studies have endeavored to elucidate the relationship between molecular weight and the activity against microorganisms. However, investigators have reported divergent and, at times, conflicting conclusions. Here a bilinear equation is proposed, delineating the relationship between antimicrobial activity, defined as log (1/MIC), and the molecular weight of chitosan and chitosan derivatives. Three constants AMin, AMax, and CMW govern the shape of the curve determined by the equation. The constant AMin denotes the minimal activity expected as the molecular weight tends towards zero while AMax represents the maximal activity observed for molecular weights exceeding CMW, the critical molecular weight required for max activity. This equation was applied to analyze data from seven studies conducted between 1984 and 2019, which reported MIC (Minimum Inhibitory Concentration) values against bacteria and fungi for various molecular weights of chitosan and its derivatives. All the 29 datasets exhibited a good fit (R2 ≥ 0.5) and half excellent (R2 ≥ 0.95) fit to the equation. The CMW generally ranged from 4 to 10 KD for datasets with an excellent fit to the equation.

Integral reinforcement learning-based dynamic event-triggered safety control for multiplayer Stackelberg–Nash games with time-varying state constraints

In this article, an integral reinforcement learning-based dynamic event-triggered safety control scheme is proposed to tackle the multiplayer Stackelberg–Nash games (MSNG) problem in continuous-time nonlinear systems with time-varying state constraints. Initially, a new barrier function (BF) is introduced by integrating traditional barrier functions with a novel smooth function to address the challenge of time-varying state constraints. Then, the constrained MSNG system is transformed into an unconstrained system through the application of the state transformation technique, which helps to characterize hierarchical decision problems as MSNG problems with the leader and followers. Meanwhile, the integral reinforcement learning (IRL) technique is also applied to ease the demand for precise system dynamics. Moreover, a new dynamic event-triggered control (DETC) mechanism is designed, resulting in coupled dynamic event-triggered Hamilton–Jacobi (HJ) equations. A single critic neural network (NN) is constructed to learn the optimal control laws for the leader and followers. By employing Lyapunov theory, the integral reinforcement learning-based dynamic event-triggered safety control method ensures that the system state and the critic NN weight errors are uniformly ultimately bounded (UUB). Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed algorithm.

Power to the (Right) People: Reply to Critics

ABSTRACT This article responds to four critics of Democracy in Spite of the Demos and reiterates its central thesis. Christopher Holman and Théophile Pénigaud attempt to maintain the critical value of democracy by invoking different elements of the deliberative tradition, while Benjamin Schupmann answers my charges by appealing to a strong liberal constitutionalism. I argue that these attempts repeat the ambivalence described and criticized in the book: democracy is taken as an end in itself, but with asterisks that introduce conditions and qualifications. As long as democracy is only desirable given certain caveats, the critical weight is placed on these caveats and not on the figure of democracy. Andrew Norris takes a different approach, interrogating the book’s use of ideology critique and the concept of “socially necessary delusion.” This intervention presents difficulties to the concluding suggestion of the book, but I maintain that that these difficulties can be productive and generative rather than limiting or prohibitive.

Adaptive Virotherapy Strategy for Organism With Constrained Input Using Medicine Dosage Regulation Mechanism.

In this article, the constrained adaptive control strategy based on virotherapy is investigated for organism using the medicine dosage regulation mechanism (MDRM). First, the tumor-virus-immune interaction dynamics is established to model the relations among the tumor cells (TCs), virus particles, and the immune response. The adaptive dynamic programming (ADP) method is extended to approximately obtain the optimal strategy for the interaction system to reduce the populations of TCs. Due to the consideration of asymmetric control constraints, the nonquadratic functions are proposed to formulate the value function such that the corresponding Hamilton-Jacobi-Bellman equation (HJBE) is derived which can be deemed as the cornerstone of ADP algorithms. Then, the ADP method of a single-critic network architecture which integrates MDRM is proposed to obtain the approximate solutions of HJBE and eventually derive the optimal strategy. The design of MDRM makes it possible for the dosage of the agentia containing oncolytic virus particles to be regulated timely and necessarily. Furthermore, the uniform ultimate boundedness of the system states and critic weight estimation errors is validated by Lyapunov stability analysis. Finally, simulation results are given to show the effectiveness of the derived therapeutic strategy.

A novel wind power deterministic and interval prediction framework based on the critic weight method, improved northern goshawk optimization, and kernel density estimation

Wind power prediction holds significant importance for the stable operation of power systems and the enhancement of energy utilization efficiency. Interval prediction, capable of providing more uncertain information, has garnered widespread attention. In this study we have designed a hybrid prediction framework for both deterministic and interval prediction. Initially, the original wind power data undergo cleaning using the quartile method and the fuzzy C-means (FCM) clustering algorithm. This approach not only eliminates scattered outliers but also identifies concentrated outliers more effectively. Subsequently, five distinct single prediction models were employed for point predictions. The critic weight method was applied to yield point forecasting results with higher prediction accuracy. Nonparametric kernel density estimation (KDE) and normal distribution (ND) were incorporated to calculate the prediction interval (PI) under varying confidence levels. Finally, an improved northern goshawk optimization (INGO) algorithm was proposed, integrating three enhancement methods: levy flight, sinusoidal mapping, and a reverse learning strategy. This optimization aims to fine-tune the weight coefficients of the aforementioned two interval prediction methods. The resulting PI overcomes the limitations of a single-interval prediction model. To effectively demonstrate the forecasting performance of the proposed prediction framework, two datasets from a wind turbine in northwest China were selected for simulation verification. In deterministic prediction, the designed hybrid forecast model demonstrates a reduction in root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) by 8.5%, 16.98%, and 11.49%, respectively, compared to other single prediction models. In interval prediction, the prediction interval coverage probability (PICP) is enhanced, while the prediction interval normalized average width (PINAW) is effectively reduced. The designed interval prediction model can reduce the comprehensive evaluation index coverage width-based criterion (CWC) by 10.51%, 9.62%, and 12.95% on average at 95%, 90%, and 80% confidence levels, respectively. Simulation results verified the validity and feasibility of the proposed prediction framework.

Design Improvement Using Topology Optimization for the Structural Frame Design of a 40 Ft LNG ISO Container Tank

LNG ISO tank containers are a solution for bulk liquefied natural gas (LNG) delivery to the outer islands of Indonesia that are not connected to the gas pipeline network. The design of an ISO tank frame must consider two critical parameters, strength/rigidity and weight saving, which affect the operational performance of the distribution process. The current investigation aims to numerically optimize the design of the structural frame of a 40 ft LNG ISO tank for a mini LNG carrier operation using a topology optimization framework. Two design solutions are used in the topology optimization framework: reducing the strain energy and mass retained. Mass retained was selected as the objective function to be minimized, which was assumed to be 60–80%. The proposed frame design is tested using three operational loading scenarios, including racking, lifting, and stacking tests based on the ISO 1496 standard. The convergence mesh tests were initially evaluated to obtain the appropriate mesh density in the finite element analysis (FEA). The simulation findings show that the topology optimization method of the frame design resulted in an improved design, with an increase in the strength-to-weight saving ratio. A promising result from the optimization scenario demonstrates weight savings of about 18.4–37.3%, with experienced stress below the limit criteria. It is found that decreasing mass retained causes a significant stress increase in the structural frame and ISO corner castings, especially in the stacking load. The critical recommendation in the frame design of the LNG ISO tank can be improved by eliminating the saddle support and bottom frame and increasing the thickness of the vertical frame.

Predictive Factors for Critical Weight Loss in Saudi Head and Neck Cancer Patients Undergoing (Chemo)Radiotherapy.

Weight loss is a significant health problem among patients with head and neck cancer (HNC) that is attributable primarily to the tumor or tumor therapy. Critical weight loss (CWL) is defined as the unintentional loss of ≥5% of weight. Therefore, this study's goal was to investigate and determine the possible factors influencing CWL among patients with HNC who have received radiotherapy or concurrent chemoradiotherapy (CCRT). We conducted a retrospective analysis of 175 patients who received radiotherapy or CCRT as either their primary, adjuvant, or combined treatment at the Oncology Center in King Abdullah Medical City. All patients were ≥18 years of age and diagnosed with HNC with no metastasis. The study results showed that 107 patients (61%) had CWL, while 68 (39%) did not. The following factors were significantly predictive of CWL with a multivariate regression analysis: pretreatment BMI (AOR = 1.1, 95% CI = 1.02-1.17), oral cavity cancer (AOR = 10.36, 95% CI = 1.13-94.55), and male sex (AOR = 3.15, 95% CI = 1.39-7.11). In conclusion, weight loss is highly prevalent among HNC patients during treatment. Accordingly, pretreatment BMI, cancer in the oral cavity, and being male can be considered predictive factors for CWL.

Assessing the level of innovativeness and digitalization of enterprises in the European Union States

Innovativeness and digitalization of enterprises of various countries is fundamental to their development and building a competitive knowledge-based economy. These issues are well understood in the European Union (EU), which promotes sustainable development of its economy, for which innovativeness and digitalization are of vital importance. The paper addresses this issue by presenting the results of a study of the level of innovativeness and digitalization of enterprises in the countries of the EU. The objectives of the research were to assess the level of innovativeness and digitalization of enterprises in the EU-27 states and to determine a relationship between digitalization and the level of innovativeness of these enterprises, as well as the impact of selected economic, energy, social and environmental parameters on these processes. The importance and interaction of these areas, as well as their impact on the economy, causes the methodology developed and the research conducted to consider both these dimensions of business activity simultaneously. To achieve these goals, an original research methodology was developed. Its basis was a set of 18 selected indicators characterizing the state of digitalization and innovativeness of enterprises in the EU-27 member states. Three methods of Multiple-criteria decision-making (EDAS, MOORA, VIKOR) and the Hurwicz criterion were used for the primary research. On the other hand, the CRITIC, Entropy and equal weights methods and the Laplace criterion were used to determine the weights of indicators. The multi-criteria approach to the issue under study made it possible to achieve the set objectives and formulate conclusions and answers to the research questions posed. The results unequivocally indicate a link between the innovativeness of enterprises and the process of their digitalization and R&D expenditures in the EU countries. The level of innovativeness and digitalization is definitely higher in the group of the so-called "old EU-14' states than in the "new EU-13" states. The research and its results provide ample opportunities for conclusions as to the state of both studied processes (digitalization and innovativeness) in the enterprises of the EU-27 states. The wide scope of the research carried out makes it possible to use the findings to build strategies and future economic policies of the entire EU and individual countries. The universality of the developed methodology also gives a wide range of possibilities for its application for further research.

An integrated decision-making method for corporate culture influence evaluation with intuitionistic fuzzy multiple attribute group decision-making

Corporate culture is an objective existence that arises with the rise and development of enterprises. It originates from enterprise practice and influences the behavior of employees. Whether it is intentional identification or unintentional avoidance, corporate culture is not a question of absence, but a question of quality; It’s not about non-existent issues, but about the magnitude of their influence. Therefore, building a corporate culture that conforms to the characteristics of the enterprise and is recognized by the majority of employees, continuously enhancing the influence of corporate culture, is a very important topic in the construction of corporate culture. The corporate culture influence evaluation is looked as the multiple attribute group decision-making (MAGDM) problem. The intuitionistic fuzzy sets (IFSs) are easy to depict the uncertain information during the corporate culture influence evaluation. Then, intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs. Furthermore, IF-CoCoSo is used to cope with the MAGDM. At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to verify the effectiveness of IF-CoCoSo method. Thus, the main objectives of this paper are outlined as follows: (1) the CRITIC method is used to obtain the weight information; (2) intuitionistic fuzzy Combined Compromise Solution (IF-CoCoSo) method is designed under IFSs; (3) IF-CoCoSo is used to cope with the MAGDM based on CRITIC weight information and Euclidean distance; (4) At last, an example is supplied for corporate culture influence evaluation to prove the practicability of the IF-CoCoSo method and some comparative analysis are conducted to show the effectiveness of IF-CoCoSo method.

Guaranteed cost tracking control of constrained input nonlinear uncertain systems using event-based ADP

This article investigates the guaranteed cost robust tracking control problem of nonlinear systems subjected to input constraint and unmatched uncertainty. The event-based adaptive dynamic programming (ADP) approach is utilized to address this problem. First, the tracking error and reference trajectory are combined to form an augmented uncertain system. Then, by decomposing the uncertainty into the matched and unmatched parts, the original tracking problem is converted into the optimal regulation problem of an auxiliary system. The cost function for the auxiliary system is defined, and the associated Hamilton–Jacobi–Bellman (HJB) equation is solved using a single critic neural network (NN). Moreover, a novel event-triggering rule is formulated, and it is shown that the designed event-based controller guarantees that the tracking error is uniformly ultimately bounded. The derivation of event-based guaranteed cost and its relation with the time-based counterpart is presented. The exclusion of the infamous Zeno behavior is guaranteed. The uniform ultimate boundedness of the critic weight estimation error is shown. Finally, the effectiveness of the proposed event-triggered ADP method is illustrated through simulations of the spring–mass–damper system and Van der Pol’s oscillator with unmatched uncertainty.

Robust Approximate Dynamic Programming for Nonlinear Systems With Both Model Error and External Disturbance.

Model error and external disturbance have been separately addressed by optimizing the definite H∞ performance in standard linear H∞ control problems. However, the concurrent handling of both introduces uncertainty and nonconvexity into the H∞ performance, posing a huge challenge for solving nonlinear problems. This article introduces an additional cost function in the augmented Hamilton-Jacobi-Isaacs (HJI) equation of zero-sum games to simultaneously manage the model error and external disturbance in nonlinear robust performance problems. For satisfying the Hamilton-Jacobi inequality in nonlinear robust control theory under all considered model errors, the relationship between the additional cost function and model uncertainty is revealed. A critic online learning algorithm, applying Lyapunov stabilizing terms and historical states to reinforce training stability and achieve persistent learning, is proposed to approximate the solution of the augmented HJI equation. By constructing a joint Lyapunov candidate about the critic weight and system state, both stability and convergence are proved by the second method of Lyapunov. Theoretical results also show that introducing historical data reduces the ultimate bounds of system state and critic error. Three numerical examples are conducted to demonstrate the effectiveness of the proposed method.

Optimal Adaptive Tracking Control of Partially Uncertain Nonlinear Discrete-Time Systems Using Lifelong Hybrid Learning.

This article addresses a multilayer neural network (MNN)-based optimal adaptive tracking of partially uncertain nonlinear discrete-time (DT) systems in affine form. By employing an actor-critic neural network (NN) to approximate the value function and optimal control policy, the critic NN is updated via a novel hybrid learning scheme, where its weights are adjusted once at a sampling instant and also in a finite iterative manner within the instants to enhance the convergence rate. Moreover, to deal with the persistency of excitation (PE) condition, a replay buffer is incorporated into the critic update law through concurrent learning. To address the vanishing gradient issue, the actor and critic MNN weights are tuned using control input and temporal difference errors (TDEs), respectively. In addition, a weight consolidation scheme is incorporated into the critic MNN update law to attain lifelong learning and overcome catastrophic forgetting, thus lowering the cumulative cost. The tracking error, and the actor and critic weight estimation errors are shown to be bounded using the Lyapunov analysis. Simulation results using the proposed approach on a two-link robot manipulator show a significant reduction in tracking error by 44% and cumulative cost by 31% in a multitask environment.

Dynamic Event-Sampled Control of Interconnected Nonlinear Systems Using Reinforcement Learning.

We develop a decentralized dynamic event-based control strategy for nonlinear systems subject to matched interconnections. To begin with, we introduce a dynamic event-based sampling mechanism, which relies on the system's states and the variables generated by time-based differential equations. Then, we prove that the decentralized event-based controller for the whole system is composed of all the optimal event-based control policies of nominal subsystems. To derive these optimal event-based control policies, we design a critic-only architecture to solve the related event-based Hamilton-Jacobi-Bellman equations in the reinforcement learning framework. The implementation of such an architecture uses only critic neural networks (NNs) with their weight vectors being updated through the gradient descent method together with concurrent learning. After that, we demonstrate that the asymptotic stability of closed-loop nominal subsystems and the uniformly ultimate boundedness stability of critic NNs' weight estimation errors are guaranteed by using Lyapunov's approach. Finally, we provide simulations of a matched nonlinear-interconnected plant to validate the present theoretical claims.

Comprehensive Ecological Environment Quality Assessment Based on AHP and CRITIC Weight Method—Taking Turpan and Hami Cities as Examples

Comprehensive Ecological Environment Quality Assessment Based on AHP and CRITIC Weight Method—Taking Turpan and Hami Cities as Examples

An efficient intrusion detection model based on neutrosophic logic for optimal response from the arranged response set

While an Automated Intrusion Response System (AIRS) chooses and initiates a suitable reaction from the pool of response groups based on specific response choice requirements to reduce the intrusion immediately, an Intrusion Detection System (IDS) finds the intrusions and generates alerts. The accurate assessment of the critical weight of all responses chosen and the prioritization of the incursion response set are the biggest hurdles when creating an AIRS. This study suggested a multi-criteria decision-making (MCDM) method for ranking intrusion responses. The TOPSIS method is an MCDM method used to rank the alternatives. The TOPSIS method integrated with the single-valued neutrosophic set (SVNS) to overcome uncertainty. This study used 16 criteria and 10 alternatives to be evaluated by experts and decision-makers. The sensitivity analysis shows the rank of other options under different cases. The criteria weights are changed under 17 cases. The results of sensitivity analysis show the rank of alternatives is stable. The suggested method was compared with other MCDM methods to show its effectiveness and robustness.

EVALUATION OF FINANCIAL PERFORMANCE OF TURKISH BANKS BEFORE AND AFTER COVID-19 USING INTEGRATED TOPSIS METHODS

The effects of the COVID-19 pandemic on the financial performance of 10 banks operating in Turkey, including five with domestic capital and five with foreign capital, were investigated in this study. 15 criteria obtained with various numerical data related to these 10 banks were determined. Entropy, CRITIC, and Mean Weight methods were used for criteria weighting. The performance rankings were obtained by these three weights scores using the TOPSIS method. A fourth ranking was obtained by taking arithmetic averages of the rankings obtained from these three methods. According to this ranking for 2019, Fibabank was the bank with the best performance. For 2020, Garanti was the bank with the best performance.

An efficient model‐free adaptive optimal control of continuous‐time nonlinear non‐zero‐sum games based on integral reinforcement learning with exploration

AbstractTo reduce the learning time and space occupation, this study presents a novel model‐free algorithm for obtaining the Nash equilibrium solution of continuous‐time nonlinear non‐zero‐sum games. Based on the integral reinforcement learning method, a new integral HJ equation that can quickly and cooperatively determine the Nash equilibrium strategies of all players is proposed. By leveraging the neural network approximation and gradient descent method, simultaneous continuous‐time adaptive tuning laws are provided for both critic and actor neural network weights. These laws facilitate the estimation of the optimal value function and optimal policy without requiring knowledge or identification of the system's dynamics. The closed‐loop system stability and convergence of weights are guaranteed through the Lyapunov analysis. Additionally, the algorithm is enhanced to reduce the number of auxiliary NNs used in the critic. The simulation results for a two‐player non‐zero‐sum game validate the effectiveness of the proposed algorithm.