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Psoriasis is a chronic, non-contagious, immune-mediated skin disorder. Inflammation of the skin's surface is characterised by scaly white, red, or silvery spots that occur due to the hyper-proliferation of keratinocytes in the epidermal layer. Primarily, pharmaceutical drugs or immune therapy are used to treat psoriasis. We are all aware that, certain therapeutic strategies can have some adverse effects, and over time, that hidden inflammation may manifest. This article introduces a mathematical model for psoriasis, formulated by employing a set of nonlinear ordinary differential equations (ODEs) that describe the densities of T-cells, dendritic cells (DCs), keratinocytes, and mesenchymal stromal cells (MSCs) as basic cell populations. A tumor necrosis factor- α ( T N F - α ) inhibitor has been imposed from the initial stage of the treatment regime, using the optimal control theoretic approach, and the numerical results have been observed. After 80 days of monitoring using only biologic T N F - α inhibitors, if this approach did not provide the intended outcomes (when severity arises), stem cells are administered a few times in a pulsed manner as a cell replacement technique in addition to this anti T N F - α medicine. We have observed the combined therapeutic benefit of stem cell replacement with a T N F - α inhibitor from a mathematical point of view. The theoretical analysis and the numerical results revealed that stem cell transplantation, along with a T N F - α inhibitor, is a promising psoriasis treatment option moving forward.

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This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

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Optimal control theory deals with finding protocols to steer a system between assigned initial and final states, such that a trajectory-dependent cost function is minimized. The application of optimal control to stochastic systems is an open and challenging research frontier, with a spectrum of applications ranging from stochastic thermodynamics to biophysics and data science. Among these, the design of nanoscale electronic components motivates the study of underdamped dynamics, leading to practical and conceptual difficulties. In this work, we develop analytic techniques to determine protocols steering finite time transitions at a minimum thermodynamic cost for stochastic underdamped dynamics. As cost functions, we consider two paradigmatic thermodynamic indicators. The first is the Kullback-Leibler divergence between the probability measure of the controlled process and that of a reference process. The corresponding optimization problem is the underdamped version of the Schrödinger diffusion problem that has been widely studied in the overdamped regime. The second is the mean entropy production during the transition, corresponding to the second law of modern stochastic thermodynamics. For transitions between Gaussian states, we show that optimal protocols satisfy a Lyapunov equation, a central tool in stability analysis of dynamical systems. For transitions between states described by general Maxwell-Boltzmann distributions, we introduce an infinite-dimensional version of the Poincaré-Lindstedt multiscale perturbation theory around the overdamped limit. This technique fundamentally improves the standard multiscale expansion. Indeed, it enables the explicit computation of momentum cumulants, whose variation in time is a distinctive trait of underdamped dynamics and is directly accessible to experimental observation. Our results allow us to numerically study cost asymmetries in expansion and compression processes and make predictions for inertial corrections to optimal protocols in the Landauer erasure problem at the nanoscale.

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The advancement in technology, with the "Internet of Things (IoT) is continuing a crucial task to accomplish distance medical care observation, where the effective and secure healthcare information retrieval is complex. However, the IoT systems have restricted resources hence it is complex to attain effective and secure healthcare information acquisition. The idea of smart healthcare has developed in diverse regions, where small-scale implementations of medical facilities are evaluated. In the IoT-aided medical devices, the security of the IoT systems and related information is highly essential on the other hand, the edge computing is a significant framework that rectifies their processing and computational issues. The edge computing is inexpensive, and it is a powerful framework to offer low latency information assistance by enhancing the computation and the transmission speed of the IoT systems in the medical sectors. The main intention of this work is to design a secure framework for Edge computing in IoT-enabled healthcare systems using heuristic-based authentication and "Named Data Networking (NDN)". There are three layers in the proposed model. In the first layer, many IoT devices are connected together, and using the cluster head formation, the patients are transmitting their data to the edge cloud layer. The edge cloud layer is responsible for storage and computing resources for rapidly caching and providing medical data. Hence, the patient layer is a new heuristic-based sanitization algorithm called Revised Position of Cat Swarm Optimization (RPCSO) with NDN for hiding the sensitive data that should not be leaked to unauthorized users. This authentication procedure is adopted as a multi-objective function key generation procedure considering constraints like hiding failure rate, information preservation rate, and degree of modification. Further, the data from the edge cloud layer is transferred to the user layer, where the optimal key generation with NDN-based restoration is adopted, thus achieving efficient and secure medical data retrieval. The framework is evaluated quantitatively on diverse healthcare datasets from University of California (UCI) and Kaggle repository and experimental analysis shows the superior performance of the proposed model in terms of latency and cost when compared to existing solutions. The proposed model performs the comparative analysis of the existing algorithms such as Cat Swarm Optimization (CSO), Osprey Optimization Algorithm (OOA), Mexican Axolotl Optimization (MAO), Single candidate optimizer (SCO). Similarly, the cryptography tasks like "Rivest-Shamir-Adleman (RSA), Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Data sanitization and Restoration (DSR) are applied and compared with the RPCSO in the proposed work. The results of the proposed model is compared on the basis of the best, worst, mean, median and standard deviation. The proposed RPCSO outperforms all other models with values of 0.018069361, 0.50564046, 0.112643119, 0.018069361, 0.156968355 and 0.283597992, 0.467442652, 0.32920734, 0.328581887, 0.063687386 for both dataset 1 and dataset 2 respectively.

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Purposes: The magnesium alloy bionic cannulated screw (MABCS) was designed in a previous study promoting cortical-cancellous biphasic healing of femoral neck fractures. The main purpose was to analyze the bore diameters that satisfy the torsion standards and further analyze the optimal pore and implantation direction for stabilizing femoral neck fractures. Methods: The MABCS design with bionic holes with a screw diameter of less than 20% met the torsion standard for metal screws. The MABCS was utilized to repair the femoral neck fracture via Abaqus 6.14 software, which simulated the various stages of fracture healing to identify the optimal biomechanical environment for bionic hole size (5%, 10%, 15%, and 20%) and implantation direction (0°, 45°, 90°, and 135°). Results: The stress distribution of the MABCS fracture fixation model is significantly improved with an implantation orientation of 90°. The MABCS with a bionic hole and a screw diameter of 10% provides optimal stress distribution compared with the bionic cannulated screw with diameters of 5%, 15%, and 20%. In addition, the cannulated screw fixation model with a 10% bionic hole size has optimal bone stress distribution and better internal fixation than the MABCS fixation models with 5%, 15%, and 20% screw diameters. Conclusion: In summary, the MABCS with 10% screw diameter bionic holes has favorable biomechanical characteristics for stabilizing femoral neck fractures. This study provides a biomechanical foundation for further optimization of the bionic cannulated screw.

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Obtaining a representative sample of disease vectors (mosquitoes, flies, ticks, etc.) is essential for researchers to draw meaningful conclusions about the entire vector population in a target study area and during a specific study period. To achieve this, a carefully chosen surveillance design is required to ensure that the sample captures essential spatial and temporal variations in the target vector population(s) and/or that the study results can be generalized to the entire population. Designed-based and model-based spatiotemporal sampling (or in our context surveillance) designs can be used to maximize information gain within given resource constraints. In this paper, we aim to offer a concise overview of common spatiotemporal field sampling designs, their advantages and disadvantages and their practical applications in the context of surveillance and management of vector-borne diseases. At the end of the article, we offer guidance to help vector-borne disease surveillance planners design effective spatiotemporal surveillance interventions.

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In the contemporary era of information technology, copious amounts of data are ubiquitous, generated across various sectors on a daily basis. Analyzing every unit of data is impractical due to constraints such as limited resources in terms of time, labor, and cost. In such scenarios, survey sampling becomes a recommended approach for extracting information about population parameters. The primary goal of this study is to devise an estimation method for acquiring information about population parameters. We propose an optimal estimator for an improved estimation of the population mean in stratified random sampling by leveraging the information from two auxiliary attributes. The proposed estimator's bias, mean squared error (MSE), and minimum mean squared error are determined up to the first-order approximation. It is demonstrated that, under the derived conditions, the proposed estimator theoretically outperforms existing estimators. Four population are utilized to evaluate both the performance and applicability of the proposed estimator. The percentage relative efficiency (PRE) of proposed estimator for all the populations is 178.389, 142.881, 181.383, and 152.679 respectively. The suggested estimator superior to existing estimators, as demonstrated by the numerical examples.

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We develop a provably efficient importance sampling scheme that estimates exit probabilities of solutions to small-noise stochastic reaction-diffusion equations from scaled neighborhoods of a stable equilibrium. The moderate deviation scaling allows for a local approximation of the nonlinear dynamics by their linearized version. In addition, we identify a finite-dimensional subspace where exits take place with high probability. Using stochastic control and variational methods we show that our scheme performs well both in the zero noise limit and pre-asymptotically. Simulation studies for stochastically perturbed bistable dynamics illustrate the theoretical results.

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The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

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An economical approach for incorporating Battery Energy Storage Systems (BESS) onto DP-2 vessels is presented in this research. The paper deals with developing a Battery Optimization for Optimal Sizing and Throughput Energy Regulation (BOOSTER) framework for putting research findings into practice by optimizing battery size, technology choice and power generation scheduling while considering battery degradation. Twelve battery sizes are analyzed based on three key performance metrics: return on investment, payback period, and years of profitability. A Mixed Integer Linear Programming (MILP) is developed to operate the energy and power management system of the vessel in a fuel and economically efficient manner. The study considers two load profiles of a DP-2 vessel operating near Taiwan and the North Sea. Our findings emphasize the significance of taking battery ownership costs in the form of energy throughput cost and fuel price into account, resulting in a longer battery lifetime and higher return on investment. The research also proposes a BESS operation matrix that provides vessel operators with valuable information on BESS usage for economic benefits. This matrix translates analytics and decision-making into tangible actions that can be implemented in real-time operations. Based on the findings, energy systems may be optimized for a sustainable future, which benefits vessel operators and industry stakeholders.

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In cancer treatment, chemotherapy has the disadvantage of killing both healthy and cancerous cells. Hence, the mixed-treatment of cancer such as chemo-immunotherapy is recommended. However, deriving the optimal dosage of each drug is a challenging issue. Although metaheuristic algorithms have received more attention in solving engineering problems due to their simplicity and flexibility, they have not consistently produced the best results for every problem. Thus, the need to introduce novel algorithms or extend the previous ones is felt for important optimization problems. Hence, in this paper, the multi-objective Equilibrium Optimizer algorithm, as an extension of the single-objective Equilibrium Optimizer algorithm, is recommended for cancer treatment problems. Then, the performance of the proposed algorithm is considered in both chemotherapy and mixed chemo-immunotherapy of cancer, considering the constraints of the tumor-immune dynamic system and the health level of the patients. For this purpose, two different patients with real clinical data are considered. The Pareto front obtained from the multi-objective optimization algorithm shows the points that can be selected for treatment under different criteria. Using the proposed multi-objective algorithm has reduced the total chemo-drug dose to 138.92 and 5.84 in the first patient, and 16.9 and 0.4384 in the second patient, for chemotherapy and chemo-immunotherapy, respectively. Comparing the results with previous studies demonstrates MOEO's superior performance in both chemotherapy and chemo-immunotherapy. However, it is shown that the proposed algorithm is more suitable for mixed-treatment approaches.

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Many researchers have addressed the challenge of optimal pressure sensor placement for different purposes, such as leakage detection, model calibration, state estimation, etc. However, pressure data often need to serve multiple purposes, and a method to optimize sensor locations with versatility for various objectives is still lacking. In this paper, a graph-based optimal sensor placement (GOSP) framework is proposed, which aims to provide a robust and all-purpose approach to identify critical points for pressure monitoring. By analysing the spatial variation frequencies of WDN pressures, the relationship between measurements and the global variation of original pressures is established. On this basis, the D-optimality criterion is adopted to formulate the objective of GOSP, which aims to maximize the information on the spatial distribution of pressures that can be obtained from measurements. The new-proposed objective ensures that the sensor locations are compatible with various application scenarios. The proposed method was applied to a real-life distribution network, and was compared with other optimal sensor placement methods oriented towards burst detection and pipe roughness calibration. Based on comparative studies in different scenarios including unknown pressure estimation, burst detection, and model calibration, the effectiveness and robustness of the proposed method have been proved.

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The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

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In isolated English word reading, readers have the optimal performance when their initial eye fixation is directed to the area between the beginning and word center, that is, the optimal viewing position (OVP). Thus, how well readers voluntarily direct eye gaze to this OVP during isolated word reading may be associated with reading performance. Using Eye Movement analysis with Hidden Markov Models, we discovered two representative eye movement patterns during lexical decisions through clustering, which focused at the OVP and the word center, respectively. Higher eye movement similarity to the OVP-focusing pattern predicted faster lexical decision time in addition to cognitive abilities and lexical knowledge. However, the OVP-focusing pattern was associated with longer isolated single letter naming time, suggesting conflicting visual abilities required for identifying isolated letters and multi-letter words. In contrast, in both word and pseudoword naming, although clustering did not reveal an OVP-focused pattern, higher consistency of the first fixation as measured in entropy predicted faster naming time in addition to cognitive abilities and lexical knowledge. Thus, developing a consistent eye movement pattern focusing on the OVP is essential for word orthographic processing and reading fluency. This finding has important implications for interventions for reading difficulties.

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Objective: This article aims to assess the adherence level to second-line therapy for cardiovascular prevention in a tertiary hospital in Mexico City and identify key barriers to adequate pharmacological adherence. Methods: A single-center prospective cross-sectional study was conducted between August 2018 and February 2020. Sociodemographic data were collected, and the Morisky medication adherence scale was performed. Directed interviews during medical consultations were also conducted to determine reasons for non-adherence. Results: Showed that out of 991 patients included with a median age of 65 (58.72) years, 70.3% exhibited inadequate adherence, with forgetfulness being the most common reason (55.4%). Patients receiving combined therapy with coronary revascularization showed higher adherence compared to those on optimal medical therapy alone. Low educational level (OR 1.68, IC 95% 1.23-2.23, p = 0.0001) and the use of optimal medical therapy alone (OR 1.2, I 95% 1.11-2.007 p = 0.007) were identified as predictors of poor adherence. Conclusion: Among patients with ischemic heart disease and pharmacological therapy for secondary prevention, inadequate adherence is observed in 70% of cases. Factors associated with poor pharmacological adherence were low educational level and prescription of medical therapy without revascularization.

Objetivo: Determinar el nivel de adherencia a la terapia secundaria de prevención cardiovascular en un hospital terciario de la Ciudad de México e identificar las barreras que contribuyen a la inadecuada adherencia farmacológica. Métodos: Se realizó un estudio transversal entre agosto de 2018 y febrero de 2020. Se obtuvieron los datos sociodemográficos, la escala de adherencia a la medicación de Morisky, y se realizó una entrevista sobre las razones de la no adherencia. Resultados: 991 pacientes fueron incluidos con una mediana de edad de 65 (58,72) años. La adherencia inadecuada fue de 70.3%, siendo el olvido la causa más frecuente (55.4%). Aquellos pacientes en terapia farmacológica combinada con revascularización coronaria fueron más adherentes que aquellos en terapia médica óptima. El bajo nivel educativo (OR 1.68, IC95%1.23-2.3, p = 0.001) y el uso de tratamiento médico óptimo solo (OR 1.52, IC95%1.11-2.07, p = 0.007) fueron predictores de mala adherencia. Conclusión: En pacientes con cardiopatia isquemica y terapia farmacológica para prevención secundaria se observa adherencia inadecuada en 70%. Los factores asociados a mala adherencia farmacológica fueron el bajo nivel educativo y la prescripción de tratamiento médico sin revascularización.

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Optimal irrigation water depth is a crucial parameter in irrigation engineering, often referred to as root zone depth. It is typically assumed to lie between 1 and 1.5 m below the ground surface, depending on the crop and soil types as well as the practitioner's skill and experience. This approach can lead to inefficient irrigation scheduling. Coupling Richards' equation with the Soil Conservation Service Curve Number (SCS-CN) concept and using the three-phase diagram of soil column widely used in geotechnical engineering, this paper suggests an analytical expression for optimal irrigation water depth providing the maximum storage capacity of a soil depending on its hydraulic/storage properties. The results for winter wheat crop in different hydrologic soil groups show that the use of the proposed concept can lead to savings of 71.79% and 57.69% of irrigation water in sandy soils (HSG-A) compared to that used in traditional irrigation considering lump-sum 1.5 m and 1 m optimal irrigation water depths, respectively. In the case of silty loam soils (HSG-C), these savings can assume 52.42% and 28.62%, respectively. The proposed relation can also be of great help in volumetric assessment of field capacity, moisture content, maximum water storage capacity (of different agricultural soils), and avoiding the issue of waterlogging that may arise from over-irrigation and thus is useful in efficient irrigation scheduling as well as in sustainable agricultural water management.

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Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated.

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BACKGROUND: No universal protocol exists for treating cerebral abscesses in Down syndrome. An illustrative case supplemented with a systematic literature review on brain abscesses in Down syndrome is presented, comprising a total of 16 cases. Preoperative infectious disease workups, cardiac examinations including echocardiography, as well as reported surgical and antibiotic treatments were correlated in the reported cohorts. OBSERVATIONS: Overall, 18.8% of cases (n = 3) had no reported cardiac evaluation. The majority of cases were treated surgically (n = 8), with aspiration (n = 3), drainage (n = 2), or other operations (n = 3); 25% (n = 4) were treated with antibiotics only. Strikingly, 25% of cases (n = 4) reported neither surgical nor antibiotic therapy, a significantly higher rate compared to 0%-3% of patients with brain abscess in other reported cohorts. Half of the patients (n = 8) who died either lacked a cardiac evaluation or had existing heart conditions. This mortality rate was about 4 times higher than the rates observed in other studies. LESSONS: Down syndrome patients with cerebral abscess have a high morbidity rate, mainly due to cardiac disease. Therefore, early diagnostic workup, including echocardiography, allows proactive management with an improved outcome. https://thejns.org/doi/10.3171/CASE23394.

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Density-dependent population dynamic models strongly influence many of the world's most important harvest policies. Nearly all classic models (e.g. Beverton-Holt and Ricker) recommend that managers maintain a population size of roughly 40-50 percent of carrying capacity to maximize sustainable harvest, no matter the species' population growth rate. Such insights are the foundational logic behind most sustainability targets and biomass reference points for fisheries. However, a simple, less-commonly used model, called the Hockey-Stick model, yields very different recommendations. We show that the optimal population size to maintain in this model, as a proportion of carrying capacity, is one over the population growth rate. This leads to more conservative optimal harvest policies for slow-growing species, compared to other models, if all models use the same growth rate and carrying capacity values. However, parameters typically are not fixed; they are estimated after model-fitting. If the Hockey-Stick model leads to lower estimates of carrying capacity than other models, then the Hockey-Stick policy could yield lower absolute population size targets in practice. Therefore, to better understand the population size targets that may be recommended across real fisheries, we fit the Hockey-Stick, Ricker and Beverton-Holt models to population time series data across 284 fished species from the RAM Stock Assessment database. We found that the Hockey-Stick model usually recommended fisheries maintain population sizes higher than all other models (in 69-81% of the data sets). Furthermore, in 77% of the datasets, the Hockey-Stick model recommended an optimal population target even higher than 60% of carrying capacity (a widely used target, thought to be conservative). However, there was considerable uncertainty in the model fitting. While Beverton-Holt fit several of the data sets best, Hockey-Stick also frequently fit similarly well. In general, the best-fitting model rarely had overwhelming support (a model probability of greater than 95% was achieved in less than five percent of the datasets). A computational experiment, where time series data were simulated from all three models, revealed that Beverton-Holt often fit best even when it was not the true model, suggesting that fisheries data are likely too small and too noisy to resolve uncertainties in the functional forms of density-dependent growth. Therefore, sustainability targets may warrant revisiting, especially for slow-growing species.

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Platelet-rich plasma (PRP) has become an important regenerative therapy. However, the preparation method of PRP has not been standardized, and the optimal platelet concentration for PRP used in skin wound repair is unclear, leading to inconsistent clinical efficacy of PRP. Therefore, the development of standardized preparation methods for PRP and the investigation of the dose-response relationship between PRP with different platelet concentrations and tissue regeneration plays an important role in the development and clinical application of PRP technology. This study has developed an integrated blood collection device from blood drawing to centrifugation. Response surface methodology was employed to optimize the preparation conditions, ultimately achieving a platelet recovery rate as high as 95.74% for PRP (with optimal parameters: centrifugation force 1730× g, centrifugation time 10 min, and serum separation gel dosage 1.4 g). Both in vitro and in vivo experimental results indicate that PRP with a (2×) enrichment ratio is the most effective in promoting fibroblast proliferation and skin wound healing, with a cell proliferation rate of over 150% and a wound healing rate of 78% on day 7.

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