Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

This study focuses on the optimal deployment problem, and determines the optimal size of a military force to send to the battle field. The decision is optimized, based on an objective function, that considers the cost of deployment, the cost of the time it takes to win the battle, and the costs of killed and wounded soldiers with equipment. The cost of deployment is modeled as an explicit function of the number of deployed troops and the value of a victory with access to a free territory, is modeled as a function of the length of the time it takes to win the battle. The cost of lost troops and equipment, is a function of the size of the reduction of these lives and resources. An objective function, based on these values and costs, is optimized, under different parameter assumptions. The battle dynamics is modeled via the Lanchester differential equation system based on the principles of directed fire. First, the deterministic problem is solved analytically, via derivations and comparative statics analysis. General mathematical results are reported, including the directions of changes of the optimal deployment decisions, under the influence of alternative types of parameter changes. Then, the first order optimum condition from the analytical model, in combination with numerically specified parameter values, is used to determine optimal values of the levels of deployment in different situations. A concrete numerical case, based on documented facts from the Battle of Iwo Jima, during WW II, is analyzed, and the optimal US deployment decisions are determined under different assumptions. The known attrition coefficients of both armies, from USA and Japan, and the initial size of the Japanese force, are parameters. The analysis is also based on some parameters without empirical documentation, that are necessary to include to make optimization possible. These parameter values are motivated in the text. The optimal solutions are found via Newton- Raphson iteration. Finally, a stochastic version of the optimal deployment problem is defined. The attrition parameters are considered as stochastic, before the deployment decisions have been made. The attrition parameters of the two armies have the same expected values as in the deterministic analysis, are independent of each other, have correlation zero, and have relative standard deviations of 20%. All possible deployment decisions, with 5000 units intervals, from 0 to 150000 troops, are investigated, and the optimal decisions are selected. The analytical, and the two numerical, methods, all show that the optimal deployment level is a decreasing function of the marginal deployment cost, an increasing function of the marginal cost of the time to win the battle, an increasing function of the marginal cost of killed and wounded soldiers and lost equipment, an increasing function of the initial size of the opposing army, an increasing function of the efficiency of the soldiers in the opposing army and a decreasing function of the efficiency of the soldiers in the deployed army. With stochastic attrition parameters, the stochastic model also shows that the probability to win the battle is an increasing function of the size of the deployed army. When the optimal deployment level is selected, the probability of a victory is usually less than 100%, since it would be too expensive to guarantee a victory with 100% probability. Some of many results of relevance to the Battle of Iwo Jima, are the following: In the deterministic Case 0 analysis, the optimal US deployment level is 66200, the time to win the battle is 30 days and 14000 US soldiers are killed or wounded. If the marginal cost of the time it takes to win a victory doubles, the optimal deployment increases to 75400, the time to win a victory is reduced to 26 days, and less than 12000 soldiers are killed or wounded. In the stochastic Case 0 analysis, the optimal US deployment level is 65000, the expected time to win the battle is 46 days and almost 25000 US soldiers are expected to be killed or wounded. If the cost per killed or wounded soldier increases from 0 to 5 M $US, the optimal deployment level increases to 75000. Then, the victory is expected to appear after 35 days and 19900 US soldiers are expected to be killed or wounded.

We consider the financial planning problem of a retiree wishing to enter a retirement village at a future uncertain date. The date of entry is determined by the retiree's utility and bequest maximisation problem within the context of uncertain future health states. In addition, the retiree must choose optimal consumption, investment, bequest and purchase of insurance products prior to her full annuitisation on entry to the retirement village. A hyperbolic absolute risk-aversion (HARA) utility function is used to allow necessary consumption for basic living and medical costs. The retirement village will typically require an initial deposit upon entry. This threshold wealth requirement leads to exercising the replication of an American put option at the uncertain stopping time. From our numerical results, active insurance and annuity markets are shown to be a critical aspect in retirement planning.

Nowadays, wearable devices for human health monitoring are increasingly become popular and widely used. Typically, the wearable device is small size and operates with batteries. Therefore, the wearable device acquires bio-signals and transfers to smartphones or personal computers (PC) via WiFi/ Bluetooth for processing data. To reduce power consumption is one of the most important challenges of designing wearable devices. To solve this problem, the proposed signal quality valuation (SQV) method can be select the high-quality signal and reduce the transfer time to other devices. In this paper, the proposed SQV and data compression method rely on real-time PPG signals analysis to retain important information of PPG signal, improve performance and power consumption of PPG devices. Besides, we also proposed Heuristic rules for heart rate (HR) estimation with compression data. The experimental results show that the highest compression ratio (CR) is 387.8 with BIDMC Physionet database (sampling frequencies of 125 Hz) and HR error as 1.43 bpm for averaging absolute error (avAE), the standard deviation absolute error (sdAE as 0.4) and relative error mean (avRE as 0.019). The proposed real-time PPG measurement system (sampling frequency as 100/ 200/ 400 Hz) reduce power consumption and open the new structure for healthcare application systems

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

This article systematically studied the A-optimal designs of a high-order mixture central polynomial model, which is an advancement of the research results for the A-optimal design of the most recent third-order mixture central polynomial model and a more accurate response surface model. This article is based on the simplex center design, obtaining measurement values at various design points, and verifying them through the A-optimal equivalence theorem. We also compare the A-efficiency of designs through examples, which can be applied to the A-optimal precise design of the fourth-order mixture model.

The impact of the agriculture net carbon effect is crucial for climate change mitigation, yet research on this topic in China is currently insufficient. In this study, the carbon balance ratio (CBR) is used to characterise the net carbon effect of agriculture, and the spatial Durbin model and threshold regression model were used to empirically analyse the CBR with a sample of 30 provincial-level regions in China from 2004 to 2019. The aim was to investigate the mechanism of the influence of the land management scale (SCALE) of farming on the CBR. The results of the study indicate that national farming generally has a net sink effect, with large changes in interannual fluctuations; the expansion of SCALE has an obvious role in increasing sinks and reducing emissions, and has a positive spatial spillover effect on CBR, with significant spatial heterogeneity. Furthermore, as the SCALE expands, the direction of the influence of rural residents’ income and education level on CBR changes in the opposite direction, showing a significant non-linear relationship characteristic. However, in most areas, the SCALE threshold has not yet been reached, suggesting that the improvement in education levels can still offset the negative impact of economic income growth in the short term. The study recommends paying attention to the role of farmers’ human capital, changing production methods, and implementing carbon peaking and carbon neutrality goals to enhance the net sink effect of the farming industry, which would be achieved by increasing land management scale.

To facilitate the computation of the uniaxial compressive strength value of the cemented paste backfill under the condition of high sand-cement ratio, and to analyze the size effect of the cemented paste backfill with high sand-cement ratio under varied aspect ratios, the cemented paste backfill specimens with sand-cement ratios of 8:1-24:1 and aspect ratios of 0.5, 1, and 2 were prepared. Through response surface experiments and finite element analysis, the influence of three aspect ratios on the cemented paste backfill strength was explored, and the failure characteristics and size effect mechanisms of cemented paste backfill with various aspect ratios were analyzed. The model of strength conversion between cemented paste backfill with different aspect ratios was established. The results show that the compressive strength of the cemented paste backfill decreases with the increase of the aspect ratio under the same mass concentration and sand-cement ratio. When the aspect ratio is 0.5 and 1, the splitting failure is the main factor; when the aspect ratio is 2, the shearing failure is the main factor. The three-dimensional stress caused by the end effect of the cemented paste backfill is the main reason for the high strength when the aspect ratio is 0.5. Due to the one-dimensional stress zone generated in the middle of the cemented paste backfill with an aspect ratio of 2 in the laboratory, its strength value is closer to the actual uniaxial compressive strength value of the cemented paste backfill. The strength values of the cemented paste backfill with aspect ratios of 0.5 and 1 were higher than the actual uniaxial compressive strength values in the laboratory. The rationality of the theoretical analysis was verified by the ABAQUS analysis results. According to the experimental results, a strength conversion model between different aspect ratios is established, which can provide a reference for the design of the uniaxial compressive strength of the cemented paste backfill.

Milk serves as a crucial source of natural bioactive compounds essential for human nutrition and health. The gastrointestinal digestion of milk proteins releases various biologically active peptides that positively impact human health and enhance physiological performance. With the growing production of cheese, Greek yogurt, and other dairy products, the volume of unutilized acid whey is significantly increasing. Acid whey, containing almost all whey proteins and approximately one-quarter of casein, presents a valuable raw material for generating peptides with potential health benefits. These peptides exhibit properties such as antioxidant, antimicrobial, anti-inflammatory, anticarcinogenic, antihypertensive, antithrombotic, opioid, mineral-binding, and growth-stimulating activities, contributing to improved human immunity and the treatment of chronic diseases. Enzymatic hydrolysis of milk proteins has been developed as a method to produce these peptides, facilitating their use in food fortification. To incorporate whey into functional beverages, it undergoes fermentation by lactic acid bacteria with proteolytic activity. To specifically enrich these beverages with peptides exhibiting targeted bioactivities, it is essential to select lactic acid bacteria strains possessing the requisite protease profiles. Additionally, the sensory properties of the final product can be enhanced using starter cultures with diverse metabolic capabilities, even in the absence of added flavoring agents. Given the imperative to develop waste-free technologies and enhance the profitability of the dairy industry, there is a growing need to efficiently utilize and transform acid whey into a broad spectrum of valuable products and food additives. This approach is justified not only by the nutritional benefits of the bioactive components in whey but also by the commercial potential of safe and health-enhancing products.

Introduction: The identification of some prognostic markers in head and neck cancers (HNC) is necessary for the stratification of the therapeutic approach with benefits in increasing treatment response rates and limiting the toxic side effects of treatments. Neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR) and pallets-to-lymphocyte ratio (PLR) are currently validated as cheap and accessible biomarkers in different types of solid tumors. The purpose of the study was to evaluate the possible biomarker value of NLR, PLR and MLR recorded pre-treatment (radiotherapy/chemotherapy) for prognostic purposes in HNC. Materials and methods: The study included 190 patients with HNC included in the oncology record in the oncology outpatient clinic of the Craiova County Emergency Hospital starting from January 2002 and deceased until December 2022. Among them, 40 patients treated multimodally in the Clinic were included in the study of Oncology of the Craiova County Emergency Hospital for which the pre-treatment value (chemotherapy/radiotherapy) of NLR, PLR and MLR could be calculated. Overall survival (OS) values were correlated with NLR, PLR and MLR. Results: Median values for NLR, PLR and MLR were 6.15 (1.24-69), 200.79 (61.3- 1775.0) and 0.53 (0.12-5.5) respectively. In the study, the mean values for NLR, PLR and MLR of 2.88, 142.97 and 0.36 respectively were obtained. Median OS in the study group was 11 months (1-120). Although a negative Pearson`s correlation, the relationship between your variables was only weak, values R =.07, p=.67; R=.02, p=.31 and R=07, p=.62 being related with NLR, PLR and MLR respectively correlation with overall survival (OS). The median values of NLR, PLR and MLR were calculated (1.53, 90.32 and 0.18 respectively) for HNC cases with pre-treatment values of NLR <2 and for HNC cases with NLR values ≥6 (23.5, 232.78 and 0.79 respectively). Median OS for cases with NLR<2 and NLR≥6 were 17.4 and 13 months, respectively. Conclusions: The negative correlations of NLR, PLR and MLR with OS reported in the study are in accordance with the data reported in the literature for locally advanced recurrent and metastatic HNSCC cases. The comparative analysis of the data in the group with NLR<2 and NLR≥6 highlights an advantage of 4.4 months in median OS in favor of the group with low values of NLR. Being cheap and accessible, these markers could change the therapeutic approach even in centers with limited resources. The role of borderline NLR values as a prognostic factor in HNSCC must also be defined. PLR and MLR are less evaluated as biomarkers but the study demonstrates their potential to be used as prognostic biomarkers, it remains to be clarified if their inclusion in multivariable models along with NLR would bring a benefit

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

To assure the opacity, an optimal problem is to retain the occurring events sequences as many as possible. Contrary to such problem, the other optimal problem is to preserve the minimal occurring events sequences. In the paper, based on the choosing cost, an optimal opacity-enforcing model is presented to minimize the sum of discount choosing cost, which is not only to preserve the opacity, but also to retain the secret to the maximum. To solve the model, two scenarios on opacity are considered. For the two scenarios, some algorithms are presented to achieve the optimal solution for the model by using the method of dynamic programming. And then, the solutions produced by the algorithms are proved correctly by theoretical proof. Finally, some illustrations for the algorithms are given.

The study comprehensively evaluated the prognostic roles of PLR, NLR, MLR, BLR, and ELR in patients with acute exacerbation chronic obstructive pulmonary disease (AECOPD). 619 patients with AECOPD and 300 healthy volunteers were retrospectively included into the study. The clinical characteristics containing laboratory findings of the AECOPD patients and the blood cell counts (CBCs) of the healthy volunteers were collected. Compared with the healthy volunteers, PLR, NLR, and MLR were elevated in COPD patients in stable condition, and were further ele-vated during exacerbation. ELR showed the opposite trend. PLR, NLR, and MLR were all posi-tively correlated with hospital LOS as well as CRP. In contrast, ELR was negatively correlated with hospital LOS as well as CRP. Elevated PLR, NLR, and MLR were all associated with more serious airflow limitation in AECOPD. Elevated PLR, NLR, and MLR were all associated with increased in-hospital mortality while Elevated ELR was associated with decreased in-hospital mortality in AECOPD. A nomogram was construct to predict in-hospital mortality in AECOPD. The nomo-gram had a C-index of 0.850 (95% CI: 0.799 – 0.901) with good predictive value and clinical ap-plicability. In summary, PLR, NLR, MLR, and ELR served as predictors for clinical outcomes in patients with AECOPD.

Breast cancer in patients with prior breast augmentation poses unique challenges for detection, diagnosis, and management. Mastectomy rates are increasing, and patients with prior augmen-tation often have a lower body mass index, making autologous techniques unsuitable. This study aims to assess the best reconstructive option in patients with a history of subglandular or dual plane breast augmentation. Materials and methods: a prospective analysis was conducted on pa-tients who underwent breast reconstruction after mastectomy. Patients with subglandular or dual plane breast augmentation were included. Patients were divided into submuscular breast recon-struction (group 2) or prepectoral breast reconstruction (group 1) groups. Demographic and sur-gical data were collected. Results: A total of 47 patients were included, with 23 in Group 1 and 24 in the Group 2. Complications occurred in 11 patients (23.4%), with significant differences be-tween groups. The most common complication was seroma formation. Implant loss occurred in 4.3% of cases in the Group 1, while no implant loss was observed in the Group 2. Patient-reported satisfaction scores were similar between groups at 12 months postoperatively. Conclusions: sub-pectoral breast reconstruction with a tissue expander seems a safer and effective technique for pa-tients with prior breast augmentation. It resulted in fewer complications. This approach should be considered as an option for breast reconstruction after mastectomy in this cohort of patients.

(1) Background: This retrospective cohort study aimed to investigate the cephalometric evaluation of facial height ratio (FHR) and growth patterns. (2) Methods: We assessed facial height ratios, Y-axis to SN angle, and growth patterns in 94 participants from Timis county using digital cephalograms. Participants were categorized based on Angle’s classification. Cephalograms were digitally traced and analyzed using WebCeph imaging software. Statistical analysis was conducted using Python version 3.11.9. (3) Results: Significant correlations were observed between FHR and craniofacial development, particularly regarding hypodivergent growth patterns. In a sample of 94, molar classes showed varying patterns: Class I—33.3% hyperdivergent, 61.5% normodivergent, 33.3% hypodivergent. Class II—50% hyperdivergent, 30.8% normodivergent, 52% hypodivergent. Class III—16.7% hyperdivergent, 7.7% normodivergent, 14.7% hypodivergent. Y-axis to SN angle values were within normal limits across groups (Group I: 65.0 (IQR: 61.2-69.0), Group II: 65.0 (IQR: 62.0-69.0), Group III: 67.0 (IQR: 59.7- 69.0). A strong negative correlation was found between Y-axis to SN angle and Jarabak’s ratio (r = -0.72, p &lt; 0.001), highlighting the need for holistic assessment methods in orthodontic practice. (4) Conclusion: Patients from Timis county predominantly exhibit a hypodivergent growth pattern across all malocclusion types. Understanding these patterns is essential for comprehensive orthodontic treatment planning. Further research is needed to explore the implications of these findings on treatment outcomes and patient care.

In this study, the impact of the addition of high-aspect-ratio nano-ettringite to photocurable epoxy acrylate resin was explored, with a specific focus on the changes observed in viscosity, shrinkage, tensile strength, and elongation at the break of composites formed. Herein, the nano-ettringite samples were modified using KH-570. The 3 wt% or 6 wt% KH-570-modified and unmodified nano-ettringite were dispersed into the resin by ultrasonic treatment in conjunction with mechanical stirring. The grafting effects of nano-ettringite onto KH-570 were analyzed through scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric (TG) analysis, and Fourier transform infrared spectroscopy (FTIR). The results demonstrate that increased dosages of unmodified and modified nano-ettringite led to increased viscosity of the composite while reducing shrinkage. At the same dosage, the photocurable resin with modified nano-ettringite demonstrated a lower shrinkage and a higher tensile strength. Notably, when the amount of modified nano-ettringite was 3 wt%, the highest tensile strength of the composite was 64.61 MPa, presenting a 72.57% increase compared to the blank sample. Furthermore, the incorporation of modified nano-ettringite as a filler provides a new perspective for improving the performance of photocurable epoxy acrylate resin composites.

Tissue engineering has the potential for repairing large bone defects, which impose a heavy financial burden on the public health. However, difficulties with O₂ delivery to the cells residing in the interior of tissue engineering scaffolds make it challenging to grow artificial tissues of clinically-relevant sizes. This study uses image-based simulation in order to provide insight into how to better optimize the scaffold manufacturing parameters, and the culturing conditions, in order to resolve the O₂ bottleneck. To do this, high resolution 3D X-ray images of two common scaffold types (salt leached foam and non-woven fiber mesh) are fed into a Lattice Boltzmann Method fluid dynamics and reactive Lagrangian Scalar Tracking mass transfer solvers. The obtained findings indicate that the scaffolds should have maximal surface area-to-solid volume ratios, for higher chances of the molecular collisions with the cells. Furthermore, the cell culture media should be flown through the scaffold pores as fast as practically possible (without detaching or killing the cells). Finally, we have provided a parametric sweep that maps how the molecular transport within the scaffolds is affected by variations in rates of O₂ consumption by the cells. Ultimately, the results of this study are expected to benefit the computer-assisted design of tissue engineering scaffolds and culturing experiments.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

The concept of {\it quantum ratio} emerged in the recent efforts to understand how Newton's equations appear for the center of mass (CM) of an isolated macroscopic body at finite body-temperatures, as the first approximation to quantum-mechanical equations. It is defined as $Q\equiv R_q/L_0$, where the quantum fluctuation range $R_q$ is the spatial extension of the pure-state CM wave function, whereas $L_0$ stands for the body's linear size (the space support of the internal, bound-state wave function). The two cases $R_q /L_0 \lesssim 1$ or $R_q/ L_0 \gg 1$, roughly correspond to the body's CM behaving classically or quantum mechanically, respectively. In the present note we elaborate more on this concept, illustrating it in several examples. An important notion following from introduction of the quantum ratio is that the elementary particles (thus the electron and the photon) are quantum mechanical, even when the environment-induced decoherence turns them into a mixed state. Decoherence and classical state should not be identified. This simple observation, further illustrationed by the consideration of a few atomic or molecular processes, may have significant implications on the way quantum mechanics works in biological systems.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE₁ = 1.865, ISE₂ = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

Background: Psoriasis is a chronic immune-mediated skin disease in which systemic inflammation plays an important role in the pathogenesis. In recent years, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-monocyte ratio (NMR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) have been shown to be important indicators of inflammation. In this study, our aim is to investigate NLR, NMR, PLR, and SII levels in psoriasis patients treated with biologic agents. Method: Clinical and biochemical data of 209 patients who received systemic therapy for psoriasis were obtained by retrospectively reviewing their medical records. NLR, NMR, PLR, and SII values were calculated from the hemogram values of the patients. Results: In the third month of follow-up, the mean CRP, NLR, NMR, PLR, and SII values were significantly decreased compared to the baseline values. SII values showed strong positive correlations with NLR, NMR and PLR. Adalimumab, etanercept, and infliximab, which are TNF-α blockers, were observed to be more effective on PLR and NLR and especially NMR. Conclusion: NLR, NMR, PLR, and SII, which are data obtained from routine blood tests, can be used in the monitoring of treatment of psoriasis, especially with TNF- α blockers.

Since surface relief transmission gratings have very strict requirements on operators and use environment, according to the semiconductor laser external cavity spectral beam combining system, this paper proposes a design scheme of semiconductor laser array spectral beam combining system based on grating-external cavity. The finite element approach was used to create a wideband, high-efficiency fill-in multilayer dielectric transmission grating structure for a high-power spectrum beam combining system. The incidence angle, ridge height, duty cycle, and sidewall inclination angle of the transmission grating were tuned and evaluated, and a link between the transmission grating's diffraction efficiency and grating characteristics was discovered. The calculated design of the high-power fused silica transmission grating has a negative first-order peak diffraction efficiency of 99.5% in the 800nm range. In the spectral region of 765-872 nm, the transmission grating's diffraction effectiveness exceeds 92%. The filled ultra-high diffraction efficiency multilayer dielectric transmission grating design addresses the issue of resistance to high-power lasers under complicated operating settings, it’s intended to maintain a high diffraction efficiency even after several cleaning cycles, and it’s an ideal component for high-power spectrum beam combining systems.

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

With covid infection having a periodic occurrence and each wave affecting millions of people, there is an urgent need for healthcare providers to develop readily available biological markers to predict the severity and mortality of COVID-19 infection at an early stage of the disease to predict the most optimal management. The Neutrophil – lymphocyte Ratio (NLR) and Platelet-lymphocyte ratio (PLR), are novel inflammatory markers suggested to predict the severity of illness in COVID -19 patients. We evaluated a retrospective cohort of 224 patients with covid 19 illnesses from August 2020 to October 2020. We categorized the patients into 'mild' and 'moderate to severe as per the severity of their illnesses. The optimal cut-off values of the continuous NLR, PLR, and Age were calculated by applying the receiver operating characteristic (ROC) curve analysis. Sensitivity, specificity, and likelihood ratios were calculated from ROC analysis. The results showed that Age, Hb, and NLR are significant predictors of the severity of illness. So we quantified the predictive contribution of those variables by its Z- score and showed that NLR is the strongest predictor followed by Age.ROC curve analysis revealed NLR to be an excellent predictor of disease severity (sensitivity 88.30% and specificity 82.60%) at a cut-off of > 4.24. A cut-off of 165.63 was obtained for PLR, which showed good predictive value for the severity of illness. A NLR > 6 was used as a predictor for mortality due to covid illness with good sensitivity and specificity. Conclusion: NLR and PLR can be used as prognostic markers to identify the severity of covid illness and the probability of mortality in the future in low-resource settings. Given its low cost, easily available, accessible, and reproducible data, it can be used as a useful marker to assist the physician in early referral and treatment of covid 19 infected patients.

The golden ratio, also known as Phi (ϕ ≈ 1.618034), attracted the interest of mathematicians, artists, and intellectuals for many centuries, probably from when it was discovered in human anthropometry. Even in recent times, researchers found the presence of golden ratio in Renaissance paintings and aesthetic preferences. The reasons behind the fascination with the golden ratio remain unclear, but it has been suggested that stimuli containing this proportion are often perceived as beautiful. However, evidence is conflicting, and the literature struggles to establish the existence of individual preferences for golden ratio. To gain new insights into the nature of these preferences, one hundred participants completed an implicit association task, with either golden ratio or random stimuli presented with positive or negative words. Participants initially categorized the stimuli based on their assigned categories. Then, we assessed their explicit preferences by asking to rate the stimuli in terms of pleasantness and by completing a line bisection task and the Ultimate Game. The results revealed the typical effects observed in implicit association tasks, with improved response times and accuracy when golden ratio stimuli were associated with positive word categories. In contrast, explicit ratings yielded mixed results. We discuss our findings in relation to previous studies that have explored this issue, highlighting the ongoing debate surrounding preferences for the golden ratio.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combines model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods.

Indoor environmental comfort is fundamental to human health as people spend 90% of their time indoors. This aspect is even more crucial in hospitals, where the concept of health is closely linked to well-being, ethics, and environmental aspects. Emerging methodologies and technologies such as Digital Twin, Building Information Modeling, the Internet of Things, sensing technologies, and data analytics offer new opportunities to ensure healthier environments and more efficient building management. This paper provides an assessment of how digitalization can support decision-making processes related to maintaining high levels of indoor environmental comfort in hospital settings, particularly by analyzing how real-time data processing and the application of machine learning can promote proactive interventions in these facilities. The methodological approach was based on four phases: defining the objectives of the digital twin, identifying the input data to build and feed the digital model, defining the KPIs to evaluate the system's correct functioning, and identifying the enabling technologies to be integrated into the system to achieve the set goal. The result is a digital twin for managing the operating room and its related services, with the aim of guiding decisions based on accurate data and improving operational efficiency, levels of environmental comfort, and safety regarding the diffusion of medical gases.

This study uses a mass-spring-damping system to simulate the repeated strain of liquefaction cyclic triaxial tests. The two results are compared in this paper to understand the feasibility of the mass-spring-damping system developed in this paper and the feasibility of future research on this related topic and development potential. The main factors affecting this mode's repeated strain are the spring coefficient k and the external force Q0. The spring coefficient has an inverse relationship but does not increase in multiples. The external force has a direct proportional relationship but does not increase the result- a non-multiple increase. When the pore water pressure of the liquefied cyclic triaxial test specimen rises, it will cause the specimen to liquefy, decreasing effective stress, shear modulus G, and damping ratio D, causing an increase in strain. The shear modulus and damping ratio are related to the spring coefficients k and c. Both are variables that change with time. This article takes an original thin-tube specimen at point A in the Yunlin area in Taiwan as a testing example. Through Mathematica software, it can be obtained that the mass m=1kg, the spring coefficient k=244e-0.1t kgf/cm, and the damping coefficient c=0.739e-0.257t kgf-s/cm and external force Q0=10.1sin2πt kg; Finally, this study selected the original thin-tube specimens from four locations in the Yunlin area, and simulated the repeated strain amount of the cyclic triaxial test specimens through a spring-damping system. The results show that the spring-damping system is feasible for simulated cyclic triaxial tests because the model is simple and the parameters are easy to understand and obtain, which also shows the extensibility of this model. Preliminary results of the research show that this model can further simulate the repeated strain obtained by cyclic triaxial tests without considering the increased trend in pore water pressure and the decrease in effective stress during cyclic loading.

Background: Since the previous study dealing with the case fatality ratio and infection fatality ratio caused by COVID-19, the author has received many comments that prompted the question: "Why did an optimistic prognosis fail?" To answer this question, a more detailed and expanded analysis was carried out in a new study. Objective: To evaluate the dynamics of monthly numbers of cases, deaths, tests and CFR worldwide during three phases of the COVID-19 pandemic. Material and Methods: Twenty three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, CFR, IFR, etc. were counted for various periods of time for each of the 213 countries, then results related to different periods of time were compared. Results: The analysis of the main epidemiological parameters led to the division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases, and mortality due to COVID-19. By the end of the second phase, the worldwide statistics indicated the imminent end of the pandemic, but the third phase was characterized by a sudden rise in the number of new cases and deaths that could not be explained rationally. The most dramatic evolution of the epidemic curve occurred in the countries where doctors had successfully battled COVID-19 during the first two phases of the pandemic. Conclusions: Despite the decrease in overall death numbers during the latest months analyzed, additional study is necessary to identify the cause for the increase in the number of new cases and deaths during the third phase of the pandemic. Only complete information regarding the positive and negative impact of medical and non-medical methods of diagnostics and prophylaxis of COVID-19 can help to organize effective measures to end the current pandemic and prevent a similar one from occurring in the future. Presumably, there are several causes of the negative evolution of the current pandemic, including the overreliance on PCR tests, application of non-specialized premises for quarantine and treatment, decrease in herd and individual immunity, inadequate change of therapeutic protocols, and ignoring prophylactic treatment. It can be suggested that the use of immunemodulatory drugs, for example, thymus extract or thymic peptides, in groups of people with compromised immunity is necessary, and prophylactic and therapeutic protocols should be changed from the 'standard' types to 'personalized' ones.

This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Sixteen features are extracted and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and that collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values being greater than 95%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

This study fabricated piezoelectric fibers of polyvinylidene fluoride (PVDF) with graphene using near-field electrospinning (NFES) technology. A uniform experimental design table U*7 (7^4 ) was applied, considering weight percentage (1-13 wt%), distance between needle and disk collector (2.1-3.9 mm), and applied voltage (14.5-17.5 kV). We optimized the parameters using electrical property measurements and the Kriging response surface method. Adding 13 wt% graphene significantly improved electrical conductivity, increasing from 17.7 μS/cm for pure PVDF to 187.5 μS/cm. The fiber diameter decreased from 21.4 μm in PVDF/1% graphene to 9.1 μm in PVDF/13% graphene. Adding 5 wt% graphene increased the β-phase content by 6.9%, reaching 65.4% compared to pure PVDF fibers. Material characteristics were investigated using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), contact angle measurements, and tensile testing. Optimal parameters included 3.47 wt% graphene, yielding 15.82 mV voltage at 5 Hz and 5 N force (2.04 times pure PVDF). Force testing showed a sensitivity (S) of 7.67493 log(mV/N). Fibers were attached to electrodes for piezoelectric sensor applications. Results affirmed enhanced electrical conductivity, piezoelectric performance, and mechanical strength. The optimized piezoelectric sensor could be applied to measure physiological signals, such as attaching it to the throat under different conditions to measure the output voltage. The force-to-voltage conversion facilitated subsequent analysis.

The objective of this study is to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the equivalence ratio distributions and the injected fuel droplet behavior in the cylinder of the RCCI engine. The spray simulation was conducted by dividing two-part. First, the spray validation simulations progressed to find the spray-influenced factor of the test injector. Next, the engine simulations were performed with the spray-influenced factor obtained from spray validation simulations to investigate the effect of the premixed ethanol ratio based on the same total heating value in-cylinder on the injected fuel atomization and the equivalence ratio distributions. The introduced total heating value was fixed at 595J based on the lower heating value of diesel 14mg. The heating value of the premixed ethanol ratio varied from 0% to 40% based on the same total heating value in-cylinder in steps of 10%. It was revealed that when the premixed ethanol ratio based on the same total heating value in-cylinder was increased, the spray tip penetration value was reduced from the time after 4deg of diesel injection start because of the short injection duration by the small amount of diesel fuel. The SMD value was also more enormous up to 32.58% with increasing the premixed ethanol ratio because of the low kinetic energy of the injected fuel by the short injection duration and the slow evaporation of the injected fuel by the low cylinder temperature.

The coexistence of SAH with T2DM is a common comorbidity. In this present study, we investigated the link between altered plasma antioxidant trace elements (ATE: Manganese, Selenium, Zinc and Copper) and fatty acids ratio (FAR: polyunsaturated/saturated) imbalance as transition biomarkers between vascular pathology (SAH) to metabolic pathology (T2DM). Our data revealed strong correlation between plasma ATE and FAR profile which is modified during SAH-T2DM association compared to healthy group. This relationship is mediated by lipotoxicity (simultaneously prominent visceral adipose tissue lipolysis, significant flow of non-esterified free fatty acids release, TG-Chol-dyslipidemia, high association of Total SFA, Palmitic acid, Arachidonic acid and PUFA 6/ PUFA 3; drop in tandem of PUFA / SFA and EPA+DHA); oxidative stress (lipid peroxidation confirmed by TAS depletion and MDA raise, concurrently drop of Zn/Cu-SOD, GPx, GSH, Se, Zn, Se/Mn, Zn/Cu; concomitantly enhance Cu, Mn and Fe); endothelial dysfunction (endotheline-1 increase); athero-thrombogenesis risk (concomitantly raise of ApoB100/ApoA1, Ox-LDL, tHcy and Lp(a)) and inflammation (higher of Hs-CRP, Fibrinogen and Ferritin). Our study open to new therapeutic targets and to better dietary management, such as to establish dietary ATE and PUFA 6 / PUFA 3 or PUFA / SFA reference values for atherosclerotic risk prevention in hypertensive-diabetics patients.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

Achieving the thermal conductivity required for efficient heat management in semiconductors and other devices requires the integration of thermally conductive ceramic fillers at concentrations of 60 vol% or higher. However, the increased filler content often negatively affects the mechanical properties of the composite matrix, limiting its practical applicability. To address this issue, in this paper, we present a new strategy to reduce the required ceramic filler content: the use of a thermally conductive ceramic composite filler with carbon nanotubes (CNT) grown on aluminum nitride (AlN). We have combined catalyst coating technology with vacuum filtration to ensure that the catalyst is uniformly applied to micrometer-sized AlN particles, followed by efficient and uniform synthesis of CNTs using a water-assisted process in a vertical furnace. By carefully controlling the number of vacuum filtration cycles and the growth time of the CNTs, we demonstrate precise control over the number and length of the CNT layers, thereby adjusting the properties of the composite to the intended specifications. When AlN/CNT hybrid fillers are incorporated into silicone rubber, while maintaining the mechanical properties of rubber, the thermal diffusivity achieved at reduced filler levels exceeds that of composites using AlN-only or simultaneous AlN and CNTs formulations. This demonstrates the critical role of CNTs on AlN surfaces. Our study represents a significant advancement in the design of thermally conductive materials with potential implications for a wide range of applications.

In this paper we derive a unified perspective for Optimal Transport (OT) theory and Mean Field Control (MFC) theory to analyse the learning process for Neural Networks algorithms in a high-dimensional framework. We consider Mean Field Neural Networks in the context of MFC theory, specifically the mean field formulation of OT theory that allows the development of highly efficient algorithms while providing a powerful tool in the context of explainable Artificial Intelligence.

The human microbiome is an extremely complex ecosystem considering the amount of bacterial species, their interactions, and its variability over time. Here we untangle the complexity of the human microbiome for the Irritable Bowel Syndrome (IBS) that is the most prevalent functional gastrointestinal disorder in human populations. Based on a novel information theoretic network inference model we detect species interaction networks that are functionally and structurally different for healthy and unhealthy individuals. Healthy networks are characterized by a neutral symmetrical pattern of species interactions and scale-free topology versus random unhealthy networks. We detect an inverse scaling relationship between species total outgoing information flow, meaningful of node interactivity, and relative species abundance (RSA). The top ten interacting species are also the least relatively abundant for the healthy microbiome and the most detrimental. These findings support the idea about the diminishing role of network hubs and hubs should be defined considering the total outgoing information flow rather than the node degree. Macroecologically, the healthy microbiome is characterized by the highest total species diversity growth rate, the lowest species turnover, and the smallest variability of RSA for all species. This result challenges current views that posit a universal association between healthy states and the highest absolute species diversity in ecosystems. Additionally, we show how the transitory microbiome is unstable and microbiome criticality is not at the phase transition between healthy and unhealthy states. We stress out the importance of considering interacting pairs versus single node dynamics when characterizing the microbiome and of ranking these pairs in terms of their dynamics. Interactions (i.e. species collective behavior) shape transition from healthy to unhealthy states.\\ The macroecological characterization of the microbiome is useful for diagnostic purposes and disease etiognosis, while species-specific analyses can detect species that are more beneficial leading to personalized design of pre- and pro-biotic treatments and microbiome engineering.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

Acoustic Radiation Forced Impulse (ARFI) is an ultrasound integrated method, measuring stiff-ness by point shear wave elastography. To evaluate the diagnostic performance of ARFI of the liver and of the spleen, combined with spleen dimension and platelet count, in predicting clinical events in cirrhosis, a prospective and cross-sectional study was conducted between February 2017 and February 2021. The following ratio scores with ARFI measurements were performed: ALSDP (ARFI Liver-Spleen Diameter-to-Platelets ratio score), ASSDP (ARFI Spleen-Spleen Diam-eter-to-Platelets ratio score), ASSAP (ARFI Spleen-Spleen Area-to-Platelets ratio score), ALSAP (ARFI Liver-Spleen Area-to-Platelets ratio score). In 100 enrolled subjects, spleen ARFI, ASSDP, ASSAP, were significantly associated with high-risk oesophageal varices (HRVs) in the prospec-tive short- and long-term follow-up and in the cross-sectional study (p 0.7). ARFI-ratio scores can predict, in cirrhotic patients, the risk of developing HVRs in short and long-term period.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

Background and objective: Analysis of inflammatory biomarkers, along with the neutrophil/lymphocyte ratio (NLR) or platelets/lymphocyte ratio (PLR), supports the connection between inflammation and carcinogenesis. Methods: We conducted a retrospective observational study at the Clinical County Hospital Mureș, including patients with lung cancer. The parameters analyzed included: histopathological type (NSCLC - squamous cell carcinoma or adenocarcinoma; SCLC), molecular mutations (EGFR, ALK, PD-L1), parameters from the complete blood count, inflammatory parameters and associated comorbidities. Results: 380 patients were included: 115 patients in the cancer group and 265 patients in the control group. Among patients in the lung cancer group, 88 were diagnosed with NSCLC (44 adenocarcinomas, 44 squamous cell carcinomas) and 27 with SCLC. Both NLR and PLR were significantly higher in cancer patients than in the control group (5,30 versus 2,60, p&lt;0,001; 217 versus 136, p&lt; 0.001, respectively). NLR and PLR differs between men and women (p = 0.005, respectively p=0.056). C-reactive protein was not correlated with either NLR (p-value: 0.0669) or PLR (p-value: 0.6733) in lung cancer patients. Conclusion: The NLR and PLR values ​​may serve as new predictive biomarkers for the diagnostic of disease in patients with lung cancer, especially those with NSCLC.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

Due to advancement in hacking/reverse engineering tools, threat against transfer of sensitive data or highly classified information is always at risk of being intercepted by an attacker. Covert communication outwit this malicious breach of privacy act better than cryptography as it camouflage secret information inside another innocent looking information, while cryptography shows scrambled information that might arouse attention of an attacker. However, the challenges in Steganography are the modification of carrier that causes some abnormalities, which is detectable and often the methods are not optimize. This paper presents an approach in Covert communication Chanel, which utilizes mathematical concept of combination to optimize time of transmission using sets of multiple transmitter’s, and receiver’s addresses where each abstractly represents a set of bits or characters combination without modifying the address. To minimize the number of physical address to be use, a combinatorial and permutation concept of virtual address generation from physical address is introduce. The paper in addition presents some technique like relationship and their application in both re-enforcing resistivity against Steganalysis and generating combinations. Furthermore, a concept of dynamical clockwise and anti-clockwise rotation of combination over addresses after every transmission is introduce to further improve on resistivity against Steganalysis. A simple test was performed for demonstrating relay address, combination and permutation concepts. Based on test results and analysis, the method is effective as expected and it is quite easy to use as it can be implemented in different platform without much difficulties.

In this paper we analyze and propose new method and algorithm of selecting the optimal labor time as a function of skills following our main references Mirrlees (1971), Saez (2001) and Stancheva (2014). The optimal labor time is a situation when the utility function of individual reaches a maximum. One of the main differences with Saez (2001) is our algorithm for creation the skill distribution

Photothermal therapy (PTT) can overcome cancer treatment resistance by enhancing cell membrane permeability, facilitating drug accumulation, and promoting drug release within the tumor tissue. Iron oxide nanoparticles (IONPs) have emerged as effective agents for PTT due to their unique properties and biocompatibility. Approved for the treatment of anemia, as MRI contrast agents, and as magnetic hyperthermia mediators, IONPs also offer excellent light-to-heat conversion and can be manipulated using external magnetic field for targeted accumulation in specific tissues. Optimizing parameters such as laser wavelength, power density, shape, size, iron oxidation state, functionalization, and concentration is crucial for IONPs effectiveness. In addition to PTT, IONPs enhance other cancer treatment modalities. They improve tumor oxygenation enhancing the efficacy of radiotherapy and photodynamic therapy. IONPs can also trigger ferroptosis, a programmed cell death pathway mediated by iron-dependent lipid peroxidation. Their magneto-mechanical effect can exert mechanical force on cancer cells to destroy tumors, minimizing damage to healthy tissues. This review outlines strategies for managing photothermal performance and PTT efficiency with iron oxide nanoparticles, as well as synergies with other cancer therapies.

Forest resources are renewable,rational exploitation and utilization of forest resources are not only conducive to sustainable development of population, but also can obtain higher economic benefits. Based on actual timber harvesting problems,a combined harvesting model of timber and non-timber with nonlinear harvesting term was established.Firstly, using the qualitative theory of ordinary differential equations, the dynamic behavior of the model is studied, and the existence and stability of the equilibrium point of the model are proved .Secondly, the optimal control solution is obtained by using the optimal control theory.Finally, the optimal harvesting strategy of timber and non-timber products is given based on the numerical simulation results, which provides a certain theoretical basis for the sustainable development of forestry ecological economy.

The paper deals with two new properties of the linear time-optimal control problems with real non-positive eigenvalues of the system. They are the foundation of a new method of synthesizing the time-optimal control without the need to describe the switching hyper-surfaces. The so called “axes initialization” and the synthesis technique are illustrated on the already classical example of the time-optimal control of a double integrator.

: Inflammation may mediate the association of mitral valve prolapse (MVP) with ventricular scarring and arrhythmia. The purpose of this study is to investigate the association of neutrophil- and platelet-to-lymphocyte ratios (NLR, PLR) with ventricular arrhythmia in MVP. Clinical and demographic data were extracted from electronic medical records for all patients aged 18-90 years with MVP on echocardiography between 2016-19 at our institution. We studied 65 patients who had a differential blood count within six months prior to ambulatory ECG (A- ECG) (medi-an age [IQR] 70 [61-77] years; 62% female; 12% African American). Patients with surgery or in-fection within six months prior to CBC were excluded. Complex or frequent ventricular ectopy (cfVE) was defined as the presence of two or more premature ventricular contractions (PVCs) on a 10-second ECG tracing or greater than 1% PVC burden or NSVT on ambulatory ECG monitoring. Logistic and bivariate regression was used to analyze clinical, electrocardiographic and echo-cardiographic factors associated with NLR and PLR. Factors associated with higher NLR were congestive heart failure (p=0.005), male sex (p=0.01), age (p=0.003), and bileaflet MVP (p=0.04). NLR was associated with higher absolute PVC burden (rs=0.28, p=0.03) and cfVE (median NLR [IQR] 3.0 [1.6 – 6.3]) with cfVE versus (2.4 [1.4-3.4], p=0.04) without cfVE. An NLR of 5.6 yielded 50% sensitivity and 90% specificity for PVC burden >1% (AUC 0.69). No significant association was found between PLR and PVC burden or cfVE. NLR is associated with PVC burden and com-plex ventricular ectopy in MVP, suggesting that inflammation is a significant factor in the patho-genesis of ventricular arrhythmia in this condition.

Autonomous navigation of spacecraft necessitates innovative technologies, methods, and algorithms, particularly when orbiting in proximity of other space objects. Optimization methods are useful for autonomous spacecraft navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some proposed methods use noisy sensor data to learn the optimal guidance and control solutions real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aims to highlight efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimalization with singular switching plus nonlinear transport theorem decoupling proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. State tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (e.g., fuel) is reduced eighty four percent, while computational burden in reduced ten percent simultaneously.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

Aquaculture contributes to the sustainable development of food security, marine resource conservation, and economy. Shifting aquaculture feed from fish meal and oil to terrestrial plant-derivatives may result in cost savings. However, many carnivorous fish cannot be sustained on plant-derived materials, necessitating the need for the identification of important factors for farmed fish growth and to investigate whether components derived from terrestrial plants can be used in feed. Herein, we focused on the carnivorous fish leopard coral grouper (P. leopardus) to identify the essential growth factors and clarify their intake timing from feeds. Furthermore, we evaluated the functionality of starch, which are easily produced by terrestrial plants. Results reveal that carbohydrates, which are not considered essential for carnivorous fish, can be introduced as a major part of artificial diet. The development of artificial feed using starch offers the possibility of increasing the growth of carnivorous fish in aquaculture.

The eyes serve as a window into underlying physical and cognitive processes. Although factors such as pupil size have been studied extensively, a less explored yet potentially informative aspect is blinking. Given its novelty, blink detection techniques are far less available compared to eye-tracking and pupil size estimation tools. In this work, we present a new unsupervised machine learning blink detection strategy using existing eye-tracking technology. The method is compared to two existing techniques. All three algorithms make use of eye aspect ratio values for blink detection. Accurate and rapid blink detection complements existing eye-tracking research and may provide a new informative index of physical and mental status.

The figures of merit of luminescence intensity ratio (LIR) thermometry for Er3+ in 40 different crystals and glasses have been calculated and compared. For calculations, the relevant data has been collected from the literature while the missing data were derived from available absorption and emission spectra. The calculated parameters include Judd-Ofelt parameters, refractive indexes, Slater integrals, spin-orbit coupling parameters, reduced matrix elements (RMEs), energy differences between emitting levels used for LIR, absolute and relative sensitivities. We found a slight variation of RMEs between hosts as a result of variations in values of Slater integrals and spin-orbit coupling parameters, and we calculated their average values over 40 hosts. The calculations showed that crystals perform better than glasses in Er3+ based thermometry, and we identified hosts that have large values of both absolute and relative sensitivity.

In public health literature the risk of death or disease associated with a dietary, environmental of behavioral factor is most commonly denoted by odds ratio (OR), hazard ratio (HR) or risk ratio (RR). The ratio indices have several desirable statistical properties. However, the most important question is whether there are some evolved innate norms of perception of risk that people use and what they are. We conducted a simple one question survey of 98 individuals with different age, sex, educational and professional backgrounds. The respondents were asked to judge the relative perceived risk of four different hypothetical habits for which data on the percentage of people affected by the disease with and without the habit was given. They were asked to rank the risks for the four habits. Results showed that the habits that had the highest difference between probability of acquiring the disease were ranked high on risk perception. The probability ratios did not affect risk perception significantly. Further age, sex, profession or formal training in statistics did not affect the response significantly. Even individuals that were formally trained to use OR and HR as risk indicators, preferred using probability differences over ratios for judging their own risk in the perceived context. This preliminary inquiry into intuitive statistical perception suggests that designing statistical indices based on people’s innate perception may be a better strategy than trying to train people to understand the indices designed by expert statisticians.

This paper considers estimating the ratio of two distributions with different parameters and common supports. We consider a Bayesian approach based on the Log--Huber loss function which is resistant to outliers and useful to find robust M-estimators. We propose two different types of Bayesian density ratio estimators and compare their performance in terms of Bayesian risk function with themselves as well as the usual plug-in density ratio estimators. Some applications such as classification and divergence function estimation are addressed.

Efficient fixed crane position analysis is critical in precast buildings construction to maximize lifting efficiency for precast elements. The primary objective is to minimize the travel distances of fixed cranes, thereby enhancing operational efficiency and cost control. This involves strategically reducing the distances between the transport truck parking, where elements are stored, the fixed crane, and the construction installation point. Despite its significance, existing studies often overlook the dynamic movement efficiency of transport trucks, particularly when dealing with multiple transport trucks and various models of fixed cranes, rendering the opti-mization of this scenario a challenging NP-hard problem susceptible to combinatorial explosion. This study presents a novel mathematical model leveraging the Genetic Algorithm (GA). The model aims to solve optimal model and position of fixed cranes, along with identifying ideal positions for transport truck parking. The study's methodology is validated and demonstrated through a project case study. Comparative analyses between the proposed model and the original planning underscore a noteworthy 6% reduction in both duration and costs when lifting elements directly from transport trucks, as opposed to establishing an on-site stock yard. Furthermore, the developed GA exhibits a substantial improvement in final solutions, achieving a remarkable 30% reduction in both operational duration and costs. This research contributes valuable insights into fixed crane position analysis of precast building construction, providing foundation for enhanced operational efficiency and cost-effectiveness in the industry.

Driven by mounting environmental apprehensions stemming from the over dependence on fossil fuels in energy and transportation sectors, extensive exploration into alternative energy sources, particularly biomass, has stimulated profound research on harnessing the potential of computational and mathematical techniques. This comprehensive survey delves into optimal models and strategies, unveiling their pivotal role in the design of biomass gasification processes.of hydrogen through biomass gasification. In this study, we aim to provide a comprehensive overview of the computational and mathematical techniques employed in optimizing biomass gasification processes, with a specific focus on enhancing hydrogen yield. Through an extensive literature review, various models and strategies will be examined, including thermodynamic analysis, kinetic modeling, reactor design, and process optimization. By uncovering the power of these techniques, we aim to contribute to the advancement of sustainable and efficient biomass utilization for the hydrogen-based future economy.This paper aims to provide an updated and comprehensive coverage of the investigations conducted on the potential of producing hydrogen from biomass through gasification. To achieve this, we incorporate the latest works that have utilized numerical modeling, simulation, optimization techniques, process heat integration, and co-generation in their studies. By encompassing these aspects, we aim to offer a broader and more in-depth re-appraisal of the subject matter. This will ensure that readers gain a holistic understanding of the advances made in the field and the potential for sustainable hydrogen production of biomass gasification.Through a meticulous re-appraisal and analysis of each subject, we can identify their respective strengths and areas that require further research effort. By thoroughly examining numerical modeling, simulation, optimization techniques, process heat integration, and co-generation, we can assess their effectiveness and applicability in the context of biomass gasification for hydrogen production. This analysis shed light on the areas where these techniques excel, as well as pinpoint limitations or gaps in current understanding. By identifying these areas, we can highlight the need for further research and development, enabling us to make significant advancements in biomass gasification processes and pave the way for a sustainable and efficient hydrogen-based future economy..

Decision tree algorithms have been among the most popular algorithms for interpretable (transparent) machine learning since the early 1980s. On the other hand, deep learning methods have boosted the capacity of machine learning algorithms and are now being used for non-trivial applications in various applied domains. But training a fully-connected deep feed-forward network by gradient-descent backpropagation is slow and requires arbitrary choices regarding the number of hidden units and layers. In this paper, we propose near-optimal neural regression trees, intending to make it much faster than deep feed-forward networks and for which it is not essential to specify the number of hidden units in the hidden layers of the neural network in advance. The key idea is to construct a decision tree and then simulate the decision tree with a neural network. This work aims to build a mathematical formulation of neural trees and gain the complementary benefits of both sparse optimal decision trees and neural trees. We propose near-optimal sparse neural trees (NSNT) that is shown to be asymptotically consistent and robust in nature. Additionally, the proposed NSNT model obtain a fast rate of convergence which is near-optimal up to some logarithmic factor. We comprehensively benchmark the proposed method on a sample of 80 datasets (40 classification datasets and 40 regression datasets) from the UCI machine learning repository. We establish that the proposed method is likely to outperform the current state-of-the-art methods (random forest, XGBoost, optimal classification tree, and near-optimal nonlinear trees) for the majority of the datasets.

Upper confidence bound multi-armed bandit algorithms (UCB) typically rely on concentration in- equalities (such as Hoeffding’s inequality) for the creation of the upper confidence bound. Intu- itively, the tighter the bound is, the more likely the respective arm is or isn’t judged appropriately for selection. Hence we derive and utilise an optimal inequality. Usually the sample mean (and sometimes the sample variance) of previous rewards are the information which are used in the bounds which drive the algorithm, but intuitively the more infor- mation that taken from the previous rewards, the tighter the bound could be. Hence our inequality explicitly considers the values of each and every past reward into the upper bound expression which drives the method. We show how this UCB method fits into the broader scope of other information theoretic UCB algorithms, but unlike them is free from assumptions about the distribution of the data, We conclude by reporting some already established regret information, and give some numerical simulations to demonstrate the method’s effectiveness.

What are relevant levels of description when investigating human language? How are these levels connected to each other? Does one description yield smoothly into the next one such that different models lie naturally along a hierarchy containing each other? Or, instead, are there sharp transitions between one description and the next, such that to gain a little bit accuracy it is necessary to change our framework radically? Do different levels describe the same linguistic aspects with increasing (or decreasing) accuracy? Historically, answers to these questions were guided by intuition and resulted in subfields of study, from phonetics to syntax and semantics. Need for research at each level is acknowledged, but seldom are these different aspects brought together (with notable exceptions). Here we propose a methodology to inspect empirical corpora systematically, and to extract from them, blindly, relevant phenomenological scales and interactions between them. Our methodology is rigorously grounded in information theory, multi-objective optimization, and statistical physics. Salient levels of linguistic description are readily interpretable in terms of energies, entropies, phase transitions, or criticality. Our results suggest a critical point in the description of human language, indicating that several complementary models are simultaneously necessary (and unavoidable) to describe it.

Motivated by South Africa's need in the transition to a net-zero economy; this study 10 investigates the integration of renewable energy sources (RES) into oil refineries, considering 11 therein unique challenges and opportunities. The research focuses on optimizing RES allocation 12 using Particle Swarm Optimization (PSO), a data-driven approach that adapts to real-time 13 operational conditions. Traditional energy management systems often struggle with the inherent 14variability of RES, leading to suboptimal energy distribution and increased emissions. Therefore, 15 this study proposes a PSO-based renewable energy allocation strategy specifically designed for oil 16 refineries. It considers factors like the levelized cost of energy, geographical location, and available 17 technology. The methodology involves formulating the optimization problem, developing a PSO 18 model, and implementing it in a simulated oil refinery environment. The results demonstrate 19 significant convergence of the PSO algorithm, leading to an optimal configuration for integrating 20 RES and achieving cost reductions and sustainability goals. The optimization result of 4,457,527.00 21 Rand achieved by iterations is much better than the result of 4,829,638.88 Rand acquired using 22Linear Programming as the baseline model. The mean cost, indicating consistent performance, has 23 remained at its original value of 4,457,527.00 Rand, highlighting the convergence. Key findings 24 include the average distance measure decreasing from 4.2 to 3.4, indicating particle convergence; 25 swarm diameter decreasing from 4.7 to 3.8, showing swarm concentration on promising solutions; 26 average velocity decreasing from 7.8 to 4.25, demonstrating refined particle movement; and the 27 optimum cost function achieved at 4,457,527 Rand with zero standard deviation, highlighting 28 stability and optimal solution identification. This research offers a valuable solution for oil 29 refineries seeking to integrate RES effectively, contributing to South Africa's transition to a 30sustainable energy future.

Nitrogen (N) strongly affects plant growth and metabolism. For shikonin biosynthesis, although the ammonium toxicity phenomenon has been reported, the effects of nitrogen on the shikonin synthesis remains obscure. In this study, we carried out four different concentrations of NH4+ treatments on Arnebia euchroma hairy roots (AEHR) to clarify the influence of NH4+ on the growth and shikonin accumulation in A. euchroma and the possible mechanism. The results showed that compared with the 0% NH4+ treatment (only nitrate as nitrogen source), 10% NH4+ treatment increased the fresh weight and the dry weight of AEHR, and promoted the synthesis of shikonins; 20% NH4+ treatment started to show the inhibition effects on growth and shikonin accumulation of AEHR and 30% NH4+ treatment exhibited the strongest inhibition effects. With increased percentage of NH4+, AEHRs became shorter and thicker with more branches. To further elucidate the mechanism, we analyzed the time-course of nitrogen assimilation, gene expression level of key enzymes involved in the shikonin biosynthesis pathway, and contents of various endogenous hormones under NH4+ toxicity condition. Results indicated that auxin and cytokinin might regulate the growth and architecture of AEHR under NH4+ toxicity, and jasmonate level was reduced along with the inhibition of shikonin biosynthesis. This first comprehensive investigation into the effects of ammonium/nitrate ratio on shikonin biosynthesis not only provided valuable data for optimizing the in vitro culture and shikonin production in A. euchroma, but also suggested potential fertilizer strategies for its cultivation.

The temporal evolution of pandemics described by the susceptible-infectious-recovered (SIR)-compartment model is sensitively determined by the time dependence of the infection (a(t)) and recovery (u(t)) rates regulating the transitions from the susceptible to the infected and from the infected to the recovered compartment, respectively. Here approximated SIR-solutions for different time dependencies of the infection and recovery rates are derived which are based on the adiabatic approximation assuming time-dependent ratios k(t)=u(t)/a(t) varying slowly in comparison to the typical time characteristics of the pandemic wave. For such slow variations the available analytical approximations from the KSSIR-model, valid for a stationary value of the ratio k, are used to insert a-posteriori the adopted time-dependent ratio of the two rates. Instead of investigating endless different combinations of the time dependencies of the two rates a(t) and u(t) a suitably parameterized reduced time dependence of the ratio k(tau) is adopted. Together with the definition of the reduced time this parameterized ratio k(tau) allows us to cover a great variety of different time dependencies of the infection and recovery rates. The agreement between the solutions from the adiabatic approximation in its four different studied variants and the exact numerical solutions of the SIR-equations is remarkably good providing strong confidence in the accuracy of the proposed adiabatic approximation.

This paper evaluates the usefulness of measuring the concentration of dissolved hydrogen in an anaerobic fermenter for maintaining the process stability. A laboratory test of two-stage psychrophilic anaerobic digestion of food leftovers from a university canteen was carried out in a vertical reactor with a total working volume of 0.255 m3 without stirring. In the course of the experiment lasting 1050 days, with an average organic loading of 15.45 kgVS m-3 d-1 for the 1st stage and 0.657 kgVS m-3 d-1 for the 2nd stage, a specific biogas production of 0.123 Nm3 per kilogram of substrate and 0.448 Nm3 per kilogram of total solids and 0.480 Nm3 per kilogram of volatile solids, respectively, was achieved. The average methane content in biogas was 55.9 % vol. Slightly higher gas productions were measured in the batch BMP test. The concentration of hydrogen in the mixed biogas from both reactor stages occasionally exceeded 1000 ppm and averaged 134 ppm, the concentration of dissolved hydrogen measured by AMT MS 08 sensor in the overloaded second stage was often 0.10-0.23 mg dm-3 and correlated with the total concentration of lower fatty acids and with the VFA/ TIC parameter. The dissolved hydrogen concentration from AMT instrument was not found to be a reliable timely indicator of overload or process stability.

Type two diabetes mellitus (T2DM) represents a chronic condition with increasing prevalence worldwide among the older population. T2DM condition increases the risk of micro and macro-vascular complications as well as the risk of geriatric syndromes as falls, fractures and cognitive impairment. The management of T2DM in the older population represents a challenge for the cli-nician, and a Comprehensive Geriatric Assessment should always be prioritized, in order to tailor the glycate haemoglobin target according to functional and cognitive status comorbidities, life ex-pectancy and type of therapy. According to the most recent guidelines, older adults with T2DM should be cathegorized in three groups: healthy patients with good functional status, patients with complications and reduced functionality and patients at the end of life; for each group the target for the glycemic control is different, also according to the type of treatment drug. The therapeutic ap-proach should always begin with lifestyle changes; after that, several lines of therapies are available, with different mechanism of action and potential effect other than glucose level reduction. Partic-ular interest is growing around sodium-glucose cotransporter-2 inhibitors, due to their effect on the cardiovascular system. In this review we evaluate the therapeutic options available for the treat-ment of older diabetic patients, to ensure a correct treatment approach

Noise reduction is the major issue in the loudspeaker for the application of the musical instruments and related areas. In this paper, a noise disturbance control of a loudspeaker with optimal and robust controllers has been done successfully. The noise of the loudspeaker has been analyzed by simply track a reference cone displacement with the actual cone displacement. Static output feedback and H infinity optimal loop shaping controllers have been used to compare the actual and reference cone displacements by using a sine wave and random cone displacement signals and a promising results have been analyzed.

Currently, maritime traffic is increasing with economic growth in several regions worldwide. However, this growth in maritime traffic has led to increased risk of marine accidents. These accidents have a higher probability of occurring in regions where geographical features, such as islands, are present. Further, the positioning of rescue ships in a particular ocean region with a high level of maritime activity is critical for rescue operations. This paper proposes a method for determining an optimal set of locations for stationing rescue ships in an ocean region with numerous accident sites in the Wando islands of South Korea. The computational challenge in this problem is identified as the positioning of numerous islands of varying sizes located in the region. Thus, the proposed method combines a clustering-based optimization method and an image processing approach that incorporates flood filling to calculate the shortest distance between two points in the ocean that detours around the islands. Experimental results indicate that the proposed method reduces the distance from rescue ships and each accident site by 5.0 km compared to the original rescue ship locations. Thus, rescue time is reduced.

Lap joints were friction stir welded in AA 7075-T6 at 1320 rpm and using two welding speeds: 70 mm/min and 120 mm/min. The temperature profiles of the weld zone were registered during the process; furthermore, the microhardness, tensile strength, and microstructure of the joints produced were examined. This study brings new insights into the thermomechanical behavior of the material during the process. The peak temperature obtained with the lower welding speed (70 mm/min) is 10% higher than that obtained with the higher speed (120 mm/min). Moreover, a 5% increase in microhardness and a 6% increase in tensile strength were were observed increasing the welding speed. Addition-ally, the microstructure examination demonstrated that, by decreasing the welding speed, the larger interaction between the tool and the material results in a deeper stir zone due to the increased heat diffusion downwards to the material. This heat flow affects the thermal profile and influences the resulting mechanical properties of the welded joints.

On the basis of the golden ratio and the compasseses, every angle was divided into three equal parts. In general, it has been proven that the problem of triangulation of an angle cannot be solved on the basis of the golden ratio and the expanding of the angle. The relationship of golden relativity with Pi number and Euler's number has not been investigated to solve impossible problems. According to the investigation of events in six-dimensional space-time, with the simultaneous movement of two arms of the compasses based on the golden ratio, every angle was divided into three equal parts. The growth rate based on the golden ratio is the key to solving the most intractable mathematical and physical problems.

1) Background: Ultrasound-based strain imaging is now available in several ultrasound (US) scanners. Strain ratio (SR) can be used to quantify strain recorded simultaneously in two different user-selected areas, ideally exposed to the same amount of stress. The aim of this study was to evaluate SR variability when assessed in an in-vitro setup with a tissue-mimicking phantom, on resected tissue samples and in live tissue scanning with endoscopic applications. 2) Material and methods: Retrospective analysis of SR for quantification of elastic contrasts in a tissue-mimicking phantom containing four homogenous inclusions, in 38 resected bowel wall lesions and in 48 focal pancreatic lesions examined in vivo. Median SR and the inter-quartile range (IQR) was calculated on all external and endoscopic US-applications. The IQR/median provides a measure of the SR variability focusing on the two percentiles of the data closest to the median value. 3) Results: The overall variability of SR was lowest in a tissue-mimicking phantom (mean QR/median SR: 0.07). In resected bowel wall lesions representing adenomas, adenocarcinomas or Crohn lesions, the variability increased (mean IQR/Median: 0.62). During an endoscopic examination of focal pancreatic lesions in vivo, the variability increased further (mean IQR/Median: 2.04). 4) Conclusion: SR variability increased when assessed on different targets with growing heterogeneity and biological variability as one moved from homogeneous media to live tissues and endoscopic application. This may indicate a limitation for the accuracy SR evaluation in clinical applications.

There are few results about the internal damping ratio of normal and high strength concretes. Hence, this paper presents the expansion of the investigation about internal damping ratio under undamaged and damaged conditions, for usual concretes (NSC) and high strength concretes (HSC). In this way, the study cover a strength range of 42 to 83 MPa. Cyclic load was employed by a servo-controlled hydraulic testing machine and for each cyclic step the dynamic elastic modulus (Ed) and internal damping ratio (ξ) were determinate by acoustic tests. Normal strength concretes (fc = 42MPa) presented undamaged internal damping ratio ξ = 0.5% and achieved the maximum value of ξ = 2.5 for a damage index equal to 0.8. High strength concretes mixtures (fc = 83MPa) presented an undamaged internal damping of ξ=0.29% and maximum internal damping equal to ξ=0.93% (under damage index of 0.32). Porosity and the transition zones were the responsible to the initial internal damping values and how the material behavior under each cyclic load. The progressive damage generates the increase of coulomb damping. There are few studies allowing the quantification and comprehension of internal damping ratio under damage cyclic loading and this approach will lead to improve the understanding of NSC and HSC under dynamical excitation subject to damage evolution, mainly in impact situations.

A new approach to the measurement with elastic lidar of depolarization produced by atmospheric aerosols is presented. The system uses two different telescopes: one for depolarization measurements and another for total-power measurements. The system architecture and principle of operation are described. The first experimental results are also presented, corresponding to a collection of atmospheric conditions over the city of Barcelona.

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from conventional heuristic-based methods, commonly used to achieve timely solutions in large-scale ride-matching. We propose a novel approach that provides snapshot optimal solutions for various forms of one-to-one matching, ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant where the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset.

Pharmacogenomics (PGx) is the study of how variations of the patient genetic makeup could influence the selection and dosing of medications, including urate-lowering therapies (ULTs). Patients with gout present with several comorbidities, warranting the use of several long-term medications that increase their pill burden and the risk of adverse drug events. PGx testing can identify individuals who are more or less likely to benefit from a given treatment and ultimately improve adherence and reduce polypharmacy. Gout management involves using various ULTs associated with specific genetic variations that could modulate medication safety and efficacy. Individuals carrying the HLA-B*58:01 variant are at a higher risk of serious and life-threatening skin reactions from taking allopurinol. Besides, racial disparities in HLA-B distribution warrant testing for its status in high-risk populations, specifically some Asian subgroups and African Americans. G6PD-deficient individuals are prone to develop hemolytic anemia and methemoglobinemia with pegloticase and probenecid use. Patients with the less active form of the drug-metabolizing CYP2C9 are at higher risk for NSAID-related upper gastrointestinal (GI) bleeding. The drug-gene pair interactions among various gout therapies were also reviewed. Allopurinol-related genes included AOX, ABCG2, and SLC22A12. Febuxostat-related genes included UGT1A1. Benzbromarone- and probenecid- related genes included SLC22A12 and ABCB1. Colchicine-related genes included CYP2D6, ABCB1, rs6916345 G>A, and SEPHS1. Finally, the corticosteroid-related gene included HCG22; and the anakinra-related gene included IL1RA. This review synthesizes the most contemporary level of evidence for using PGx in gout pharmacotherapy, as referenced in the CPIC guidelines, FDA, PharmGKB, and current literature.

Fish schooling implies an awareness of the swimmers for their companions. In flow mediated environments, in addition to visual cues, pressure and shear sensors on the fish body are critical for providing quantitative information that assists the quantification of proximity to other swimmers. Here we examine the distribution of sensors on the surface of an artificial swimmer so that it can optimally identify a leading group of swimmers. We employ Bayesian experimental design coupled with two-dimensional Navier Stokes equations for multiple self-propelled swimmers. The follower tracks the school using information from its own surface pressure and shear stress. We demonstrate that the optimal sensor distribution of the follower is qualitatively similar to the distribution of neuromasts on fish. Our results show that it is possible to identify accurately the center of mass and even the number of the leading swimmers using surface only information.

Introduction: The COVID-19 pandemic highlighted the necessity for rapid, efficient testing at large-scale events. Developing rapid and efficient testing methods was critical, especially for events with a high risk of virus spread. The queueing theory offers a powerful tool to manage the flow of people through testing centers. This study uses the M/M/c model to examine how different floor plan configurations impact the efficiency of RADT testing centers. Aim of the study: This research applies queueing theory to optimize the Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. The study uses the M/M/c queueing model to evaluate the dynamics of RADT centers configured in U-shaped and straight-line layouts. The primary goal is to assess the feasibility of using RADTs efficiently at events and mass gathering entrances, aiming to enhance throughput and minimize wait times. Methodology: The study involved 500 healthy participants, managed by medical staff across a U-shaped and a straight floor plans. Agile management techniques were employed to enhance operational efficiency. The process stages included queue number issuance, registration, sample collection, sample mixing, and results dissemination. Results: Both floor plans enabled the completion of RADT screening in approximately 2 minutes per participant. The U-shaped layout slightly outperformed the straight-line setup in efficiency, demonstrating its effectiveness in optimizing the testing process. Conclusion: This study confirms the feasibility of implementing Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. It was found that the U-shaped floor plan configuration is particularly effective, indicating its potential suitability for future mass testing scenarios.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

Since its introduction almost 50 years ago, the concept of ‘flow’ has been descriptive of optimal experiences, also in relation to play. However, the explorative nature of play leads to some discrepancies between flow and the optimal experience of play. In this paper the differences between flow and play are explored, leading to proposing the state of ‘wonder’ (directed at exploration) as an alternative to ‘flow’. From this perspective, the study further explores how we may design toys that enable meaningful experiences of play, identifying opportunities in designing for toys as the enablers of immersed experiences of wonder.

This paper presents findings of a study on continuous feed-in management and continuous synthetic inertia contribution with wind turbines. A realistic case study, based on real measurements, is outlined. A wind turbine feeds into a weak feeder, such that its power has to be adapted to the permissible loading of this feeder. At the same time the wind turbine is to provide inertia to the grid by applying the previously published variable inertia constant controller. It is discussed that optimal grid utilisation and simultaneous inertia contribution are mandatory for the frequency control in power systems that are heavily penetrated with renewable energies. The study shows that continuous feed-in management can be combined well with continuous inertia provision. There are hardly any negative consequences for the wind turbine. The benefits for the grid are convincing, both in terms of increased system utilisation and in terms of provided inertia. It is concluded that wind turbines can enhance angular stability in a power system to a larger extent than conventional power plants.

In this study, the determination of static E50 and υ parameters of rock materials was investigated using P-S wave velocities and Shore hardness (SH), non-destructive measurement methods. We used multiple linear regression (MLR), multiple non-linear regression (MNLR) and artificial neural network (ANN) models to estimate and determine these parameters. When the models defined by MLR, MNLR, and ANN were compared to the R2 values, ANN models that estimate E50 and υ parameters of rock materials using non-destructive methods (Vp, Vs, Vp/Vs, d, and SH) could be determined with higher accuracy than MLR and MNLR models. The study’s originality is based on the fact that ores such as galena, chromite, and barite have been studied for the first time on rock mechanics, and in this respect, it offers an innovative perspective. In addition, the use of all non-destructive measurement methods, Vp, Vs, and Shore hardness tests, also increases the originality of the study.

Background: The proportion of dietary protein may play a key role in the development of obesity in children and adolescents. Methods: Cross-sectional data of 4,336 children and adolescents from the National Health and Nutrition Survey (NHANES) during 2011 to March 2020 were analyzed. Multivariate logistic regression analysis was used to estimate odds ratios (OR) and 95% confidence intervals, and restriction cubic spline was uesd to explore nonlinear relationships of dietary protein energy supply ratio and obesity in children and adolescents. Results:In the adjusted logistic regression model, for every 1% increase in dietary protein energy ratio in children and adolescents, the increased risk of obesity was 4% (OR=1.04,95%CI:1.01-1.08). The restrictive cubic spline showed a nonlinear relationship between dietary protein energy ratio and obesity in children aged 6-11 years (P <0.05). The dietary protein energy supply ratio was included in the logistic regression model after converting to quartiles into categorical variables. The adjusted OR of children aged 6-11 years was 2.08 (95%CI:1.34, 3.23,P <0.01) for the highest quartile of dietary protein energy supply ratio compared with the lowest quartile. Conclusion: In American children, dietary protein energy supply ratios was positively correlated with obesity, independent of individual characteristics and energy.

During the last forty years, the use of strontium isotopes in archaeology and biogeochemical research has spread widely. These isotopes, alone or in combination with others, can contribute to trace past and present environmental conditions. However, the interpretation of the isotopic values of strontium is not always simple and requires good knowledge of geochemistry and geology. This short paper on the use of strontium isotopes is aimed at those who use this tool (archaeologists, but not only) but who do not have a thorough knowledge of mineralogy, geology, and geochemistry necessary for a good understanding of natural processes involving these isotopes. We report basic knowledge and suggestions for a correct use of these isotopes. The isotopic characteristics of bio-assimilable strontium depend not so much on the isotopic characteristics of the bulk rock as, rather, on that of its more soluble minerals. Before studying human, animal and plant remains, the state of conservation and any conditions of isotopic pollution should be carefully checked. Samples should be collected according to random sampling rules. The data should be treated by a statistical approach. To make comparisons between different areas, it should be borne in mind that the study of current soils can be misleading since the mineralogical modification of the soil over time can be very rapid.

This article introduces an innovative design method for public lighting systems that surpasses the limitations of conventional approaches, which rely on predefined lamp characteristics and spatial arrangement. By employing a multi-criteria decision algorithm, our study proposes an optimal design methodology considering several key aspects, including the distance between lamps, their type, power, and light distribution. The goal is to achieve optimal illumination that enhances visibility on public roads for drivers and pedestrians while simultaneously minimizing glare, installation costs and maximizing energy efficiency. The proposed methodology is implemented through an algorithm developed in Matlab, with results validated through simulations in Dialux. This information is used to construct a decision matrix, assessed using the CRITIC method across 180 different scenarios within a specific case study. The findings demonstrate the effectiveness of multi-criteria decision-making as a tool for significantly improving the planning and design of lighting in public illumination systems, allowing for selecting the optimal combination of parameters that ensure the best lighting conditions.

Wind turbines (WTs) are clean a renewable energy source that has been growing in popularity in recent years. Gearboxes are complex, expensive, and critical components of WT, which are subject to high maintenance costs and several stresses including high loads and harsh environments that can lead to failure with significant downtime and financial losses. This paper focuses on the development of a digital twin-based approach for the modelling and simulation of WT gearboxes with the aim to improve their design, diagnosis, operation, and maintenance by providing insights into their behavior under different operating conditions. Pow-erful commercial computer-aided design tools (CAD) and computer-aided engineering (CAE) software are embedded into a computationally efficient multi-objective optimization framework (modeFrontier) with the aim of maximizing the power density, compactness, performance, and reliability of the WT gearbox. This entails minimizing its weight, volume, and maximum stresses and strains achieved without compromising efficiency. The 3D CAD model of the WT gearbox is carried out using SolidWorks, the Finite Element Analysis (FEA) to obtain the stresses and strains fields are modelled using Ansys Workbench, while the multibody kinematic and dynamic system is analyzed using MSC Adams. The method has been successfully applied to different case studies to find the optimal design and analyze the performance of the WT gearboxes. Simulation results can be used to identify potential failure modes and optimize gearbox design to improve performance, extend service life and relia-bility, thereby ensuring proper operation over its lifetime and reducing maintenance costs.

The Golden ratio is an irrational number that has a tendency to appear in many different scientific and artistic fields. It may be found in natural phenomena across a vast range of length scales; from galactic to atomic. In this review, the mathematical properties of the Golden ratio are discussed before exploring where in nature it has been found; beginning at astronomical scales and progressing to smaller lengths, until reaching those of atomic and quantum physics. In making such a tour across length scales, it is illustrated just how prevalent this single number is within the natural universe.

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH where the latter contribute a time independent amplitude to the waveforms. Additionally we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. We discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters, and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency and accuracy of the EMRI search algorithm.

The objective of this study is to assess the hepatic/renal and splenic/renal echointensity ratios in ultrasonography in patients with and without diabetic nephropathy. This retrospective study included patients with diabetes mellitus who underwent ultrasound examinations at our hospital between January 2023 and May 2023. Ultrasound examinations done with renal cortical echogenicity and cortico-medullary differentiation by using B-mode ultrasonography. The hepatic/renal and splenic/renal echo intensity ratios were compared among study groups (diabetic patients with diabetic nephropathy and without nephropathy). The diabetic nephropathy group exhibited significantly higher right renal echointensity and left renal echointensity compared to the non-nephropathic group. Additionally, the splenic/renal echointensity ratio and hepatic/renal echointensity ratio were significantly lower in the diabetic nephropathy group. Urinary microalbumin levels was significantly correlated with Right renal echointensity (r=0.65, p&lt;0.001) and Left renal echointensity (r=0.69, p&lt;0.001). There was also a significant inverse correlation between urinary albumin and splenic/renal echointensity ratio (r=-0.58, p&lt;0.001). Ultrasonography, specifically the assessment of hepatic/renal and splenic/renal echointensity ratios, shows promise as a non-invasive and cost-effective method for evaluating morphological changes in the kidneys in patients with diabetic nephropathy. These findings suggest that ultrasonography can be a valuable tool for monitoring the progression of diabetic nephropathy and contributing to its early detection and management.

We consider a model of predator-prey interaction at fractional-order where the predation obeys the ratio-dependent functional response and the prey is linearly harvested. For the proposed model, we show the existence, uniqueness, non-negativity as well as the boundedness of the solutions. Conditions for the existence of all possible equilibrium points and their stability criteria, both locally and globally, are also investigated. The local stability conditions are derived using the Magtinon's theorem, while the global stability is proven by formulating an appropriate Lyapunov function. The occurance of Hopf bifurcation around the interior point is also shown analytically. At the end, we implement the Predictor-Corrector scheme to perform some numerical simulations.

As a renewable industrial crop, Miscanthus offers numerous advantages, namely high photosynthesis activity (as a C4 plant) and exceptional CO2 fixation rate. These properties make Miscanthus very attractive for industrial exploitation, such as lignin generation. Here, we present a systematic study analyzing the correlation of the lignin structure with Miscanthus genotype and plant portion (stem versus leaf). Specifically, the ratio of the three monolignols and corresponding building blocks as well as the linkages formed between the units have been studied. Depending on the Miscanthus genotype and plant component (leaf versus stem), correlations between chemical structure and properties of the lignins have been determined, i.e. correlations in molecular weight, polydispersity and decomposition temperature. Lignin isolation was performed using non-catalyzed organosolv pulping and the structure analysis includes NREL, FTIR, UV-Vis, HSQC-NMR, TGA, pyrolysis GC/MS. Structural differences were found for stem and leaf-derived lignins. Compared to beech wood lignins, Miscanthus lignins possess lower molecular weight and narrow polydispersities (< 1.5 Miscanthus vs. > 2.5 beech) corresponding to improved homogeneity. In addition to conventional univariate analysis of FTIR spectra, multivariate chemometrics revealed distinct differences for aromatic in-plane deformations of stem versus leaf-derived lignins. These results emphasize the potential of Miscanthus as low-input resource and Miscanthus-derived lignin as promising agricultural feedstock.

Unmanned Aerial Vehicles (UAVs) in particular multirotors are becoming the {\it de facto} tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult to access structures, such chimneys, deposits and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as $15$\% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 the time taken by a centralized method.

The Standard Genetic Code (SGC) exists in every organism known on Earth. SGC evolution via early unique codon assignment, then later wobble, yields coding resembling the near-universal code. Below, later wobble also creates an optimal route to accurate codon assignment. This assignment time matches a previous mean time for ordered codes, exhibiting ≥ 90% of SGC order. Accurate evolution is also accessible, sufficiently frequent to appear in populations of 10³ to 10⁴ codes. SGC-like coding capacity, code order and assignments therefore arise together, in one attainable evolutionary intermediate. Examples, which plausibly resemble coding at evolutionary domain separation, are characterized.

For the time being, public sector innovation gains new and complex forms of expression: managerial, institutional, technological or communication. This fact is also due to national and international important bodies’ interest for using innovation as resource and tool for public sector development. Characterised by complexity and adaptation, the innovative processes in the public sector embrace the form of medium and long term innovation strategies, holding high key socio-economic impact on the social utility of public sector innovation. The optimality of innovation strategies becomes a tool for improved decisions in public sector management, providing the methodology for their evaluation related to the objectives of development in the public sector.

Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, I present guidelines to optimize the design of these instruments starting from the basic sensitivity equations. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit transmit power. Finally, a set of example designs and associated performance are presented.

In this paper we present the numerical and experimental results of a design optimization of electromagnetic cantilevers. In particular, a cost-effective technique of evolutionary computing enabling the simultaneous minimization of multiple criteria is applied. A set of optimal solutions are subsequently fabricated and measured. The designed structures are fabricated in arrays, which makes the comparison and measurements of the sensor properties reliable. The microfabrication process, based on the silicon on insulator (SOI) technology, is proposed in order to minimize parasitic phenomena and enable efficient electromagnetic actuation. Measurements on the fabricated prototypes assessed the proposed methodological approach.