Discrete Variables Research Articles

Two-Dimensional and Doppler trans-thoracic echocardiographic patterns of suspected pediatric heart diseases at Tibebe--Ghion specialized Teaching Hospital and Adinas General Hospital, Bahir Dar, North-west Ethiopia:-An experience from an LMIC.

Transthoracic Echocardiography is the first-line, non-invasive, and accessible imaging modality to evaluate heart disease anatomy, physiology, and hemodynamics. We aim to describe the trans-thoracic echocardiography pattern of pediatric heart diseases and reasons for referral in children referred to Bahir Dar University Tibebe-Ghion Hospital and Adinas General Hospital. A descriptive cross-sectional study of the archived Transthoracic, Two Dimensional, and Doppler Echocardiography assessments of children from birth to fifteen years of age performed between June 2019 to May 2023 was done. Data were collected retrospectively from February 01, 2023 -May 31, 2023. Categorical variables like gender, referral reasons for echocardiography, and patterns of pediatric heart lesions were analyzed in the form of proportions and presented in tables and figures. Discrete variables including age were summarized as means (SD) and medians(IQR). Out of 3,647 Children enrolled; 1,917 (52.6%) were males and 1,730 (47.4%) were females. The median (IQR) age of children enrolled was 24 months (5 to 96). Cardiac murmur (33%) was the most common reason for echocardiography followed by, Respiratory Distress (18%), Syndromic Child (15%), easy fatigability/ Diaphoresis (14.3%), congestive heart failure (14%), and rheumatic fever (13.2%). Congenital heart defect (CHD) accounts for 70% of all heart diseases, followed by rheumatic heart disease (21%). Isolated ventricular septal defect(VSD) was the most common CHD (21%) followed by isolated Patent ductus arteriosus (15%), isolated atrial septal defect (10%), Isolated atrioventricular septal defect (6%) and isolated pulmonary stenosis (5%). Cyanotic CHD accounts for 11.5% of all heart diseases. Tetralogy of Fallot (30%), d-TGA (20%), and double outlet right ventricle (19%) were the most common cyanotic CHDs. In our study, congenital heart lesions are the most common diagnosis and cardiac murmurs are the most common presenting reasons for echocardiography evaluation.

HP3O algorithm-based all electric ship energy management strategy integrating demand-side adjustment

To tackle the energy management challenge that integrates power generation scheduling and demand-side adjustment for all-electric ship in uncertain marine environment, a hybrid penalized proximal policy optimization algorithm (HP3O)-based energy management strategy is proposed. First, demand-side adjustment, which involves adjusting the power of the ship's electric propulsion motors and flexible service loads, is integrated into the energy management problem. Second, HP3O algorithm is employed to obtain both continuous and discrete variables simultaneously. It utilizes a continuous actor network to obtain continuous variables, such as the generator's power and ship cruising speed, while employing a discrete actor network to determine discrete variables, i.e., the on/off status of the generators. Third, to handle complex constraints reasonably, the energy management problem is formulated as a constrained Markov decision process (CMDP), and an action mask mechanism is also integrated into the energy management framework to make agent's actions more reliable. The simulation results of an all-electric cruise ship validate the effectiveness and superiority of the proposed strategy in achieving near-optimal scheduling while satisfying operation constraints. Furthermore, a case study on a hybrid diesel-electric ferry confirms its generalization performance.

Stabilization of continuous-time Markovian jump systems: A mode separation but optimization method

This paper addresses the stabilization problem of continuous-time Markovian jump systems (MJSs) by applying an optimization controller. A new stabilizing method based on a mode separation algorithm is proposed, whose separation will be optimized by minimizing the established cost function value. It will be seen that the presented optimal controller will have hybrid decision variables such as continuous and discrete variables. Meanwhile, the related cost function is in contrast to the traditionally linear quadratic functions. The conditions for the existence of the developed controller are established via using rigorous theory analysis, and its detailed computation method is presented too. It can be concluded that traditionally mode-dependent and -independent controllers about MJSs are contained as two special situations. A practical example is offered so as to verify the effectiveness and superiority of the methods proposed in this study.

Conditional probability table limit-based quantization for Bayesian networks: model quality, data fidelity and structure score

Bayesian Networks (BN) are robust probabilistic graphical models mainly used with discrete random variables requiring discretization and quantization of continuous data. Quantization is known to affect model accuracy, speed and interpretability, and there are various quantization methods and performance comparisons proposed in literature. Therefore, this paper introduces a novel approach called CPT limit-based quantization (CLBQ) aimed to address the trade-off among model quality, data fidelity and structure score. CLBQ sets CPT size limitation based on how large the dataset is so as to optimize the balance between the structure score of BNs and mean squared error. For such a purpose, a range of quantization values for each variable was evaluated and a Pareto set was designed considering structure score and mean squared error (MSE). A quantization value was selected from the Pareto set in order to balance MSE and structure score, and the method’s effectiveness was tested using different datasets, such as discrete variables with added noise, continuous variables and real continuous data. In all tests, CLBQ was compared to another quantization method known as Dynamic Discretization. Moreover, this study assesses the suitability of CLBQ for the search and score of BN structure learning, in addition to examining the landscape of BN structures while varying dataset sizes and confirming its consistency. It was sought to find the expected structure location through a landscape analysis and optimal BNs on it so as to confirm whether the expected results were actually achieved in the search and score of BN structure learning. Results demonstrate that CLBQ is quite capable of striking a balance between model quality, data fidelity and structure score, in addition to evidencing its potential application in the search and score of BN structure learning, thus further research should explore different structure scores and quantization methods through CLBQ. Furthermore, its code and used datasets have all been made available.

Parental stress, depression, anxiety and participation in care in neonatal intensive care unit: a cross-sectional study in Italy comparing mothers versus fathers

BackgroundThis study aimed at documenting the levels of stress, depression, anxiety and participation in care among mothers versus fathers of newborns hospitalised in a third-level neonatal intensive care unit (NICU)...

Compactness property of the linearized Boltzmann collision operator for a multicomponent polyatomic gas

The linearized Boltzmann collision operator is fundamental in many studies of the Boltzmann equation and its main properties are of substantial importance. The decomposition into a sum of a positive multiplication operator, the collision frequency, and an integral operator is trivial. Compactness of the integral operator for monatomic single species is a classical result, while corresponding results for monatomic mixtures and polyatomic single species are more recently obtained. This work concerns the compactness of the operator for a multicomponent mixture of polyatomic species, where the polyatomicity is modeled by a discrete internal energy variable. With a probabilistic formulation of the collision operator as a starting point, compactness is obtained by proving that the integral operator is a sum of Hilbert-Schmidt integral operators and operators, which are uniform limits of Hilbert-Schmidt integral operators, under some assumptions on the collision kernel. The assumptions are essentially generalizations of the Grad's assumptions for monatomic single species. Self-adjointness of the linearized collision operator follows. Moreover, bounds on - including coercivity of - the collision frequency are obtained for a hard sphere like model. Then it follows that the linearized collision operator is a Fredholm operator, and its domain is also obtained.

Neuromuscular Electrical Stimulation Improves Frontal Ankle Motor Control in Individuals With Chronic Ankle Instability During Drop Landing.

This study investigated the effect of neuromuscular electrical stimulation on the frontal ankle motor control in individuals with chronic ankle instability during drop landing. This was a randomized, controlled, double-blind trial. Thirty-six individuals with chronic ankle instability were randomly assigned to each group. Participants received 6-wk neuromuscular electrical stimulation intervention and sham stimulation in the neuromuscular electrical stimulation and control groups, respectively. Data were collected at week 0 and week 6 . A mixed-effects model and analysis of covariance were employed to investigate the between-group differences in continuous and discrete outcome variables at week 6 , with the outcome variables at week 0 as covariates. Compared to control group, neuromuscular electrical stimulation group exhibited a 2.66° (2.45, 2.86) reduction in frontal ankle inversion angle, a 47.41°/sec (-16.05, -78.77) decrease in peak ankle inversion angular velocity, and a 0.43 Nm/kg (0.18, 0.68) increase in peak ankle eversion moment during drop landing at week 6 . Applying 6-wk neuromuscular electrical stimulation to the fibularis longus resulted in decreased ankle inversion angle and ankle inversion angular velocity and increased peak ankle eversion moment during drop landing. Consequently, neuromuscular electrical stimulation could be considered an effective modality for individuals with chronic ankle instability to enhance the frontal ankle movement patterns and overall ankle motor control.

Is tooth loss associated with oral health-related quality of life among young men? Findings from southern Brazil.

To determine the association between tooth loss and oral health-related quality of life (OHRQoL) among young men from southern Brazil. Cross-sectional study of 518 young men aged 18-19 years enlisted for mandatory military service in the Brazilian Army in two cities of southern Brazil in 2019-2021. Participants were clinically examined regarding gingivitis, dental caries experience (caries lesions, missing and filled surfaces), and malocclusion. A questionnaire collected socioeconomic information and the Oral Health Impact Profile 14 (OHIP-14). The outcome was OHRQoL, modeled as a discrete variable. The main explanatory variable was tooth loss due to caries (0 or ≥1). Negative binomial regression analysis was used; rate ratios (RR) and 95% confidence intervals (CI) were estimated. Tooth loss prevalence was 22.2%. Associations were detected between tooth loss and OHRQoL in the physical pain and handicap domains. Individuals with at least one missing tooth had 17% higher OHIP-14 scores in the physical pain domain (adjusted RR=1.17; 95%CI=1.01, 1.35) and 49% higher in the handicap domain (adjusted RR=1.49; 95%CI=1.03, 2.15) than those with no tooth loss. Tooth loss was negatively associated with OHRQoL among 18-19-year-old Brazilian Army conscripts, and this association was related to physical pain and handicap domains.

BinDMO: a new Binary Dwarf Mongoose Optimization algorithm on based Z-shaped, U-shaped, and taper-shaped transfer functions for CEC-2017 benchmarks

Intelligent swarm optimization algorithms have become increasingly common due to their success in solving real-world problems. Dwarf Mongoose Optimization (DMO) algorithm is a newly proposed intelligent swarm optimization algorithm in recent years. It was developed for continuous optimization problem solutions in its original paper. But real-world problems are not always problems that take continuously variable values. Real-world problems are often problems with discrete variables. Therefore, heuristic algorithms proposed for continuous optimization problems need to be updated to solve discrete optimization problems. In this study, DMO has been updated for binary optimization problems and the Binary DMO (BinDMO) algorithm has been proposed. In binary optimization, the search space consists of binary variable values. Transfer functions are often used in the conversion of continuous variable values to binary variable values. In this study, twelve different transfer functions were used (four Z-shaped, four U-shaped, and four Taper-shaped). Thus, twelve different BinDMO variations were obtained (BinDMO1, BinDMO2, …, BinDMO12). The achievements of BinDMO variations were tested on thirteen different unimodal and multimodal classical benchmark functions. The effectiveness of population sizes on the effectiveness of BinDMO was also investigated. When the results were examined, it was determined that the most successful BinDMO variation was BinDMO1 (with Z1-shaped transfer function). The most successful BinDMO variation was compared with three different binary heuristic algorithms selected from the literature (SO, PDO, and AFT) on CEC-2017 benchmark functions. According to the average results, BinDMO was the most successful binary heuristic algorithm. This has proven that BinDMO can be chosen as an alternative algorithm for binary optimization problems.

Compactness Property of the Linearized Boltzmann Collision Operator for a Mixture of Monatomic and Polyatomic Species

The linearized Boltzmann collision operator has a central role in many important applications of the Boltzmann equation. Recently some important classical properties of the linearized collision operator for monatomic single species were extended to multicomponent monatomic gases and polyatomic single species. For multicomponent polyatomic gases, the case where the polyatomicity is modelled by a discrete internal energy variable was considered lately. Here we consider the corresponding case for a continuous internal energy variable. Compactness results, stating that the linearized operator can be decomposed into a sum of a positive multiplication operator, the collision frequency, and a compact operator, bringing e.g., self-adjointness, is extended from the classical result for monatomic single species, under reasonable assumptions on the collision kernel. With a probabilistic formulation of the collision operator as a starting point, the compactness property is shown by a decomposition, such that the terms are, or at least are uniform limits of, Hilbert–Schmidt integral operators and therefore are compact operators. Moreover, bounds on—including coercivity of—the collision frequency are obtained for hard sphere like, as well as hard potentials with cutoff like, models, from which Fredholmness of the linearized collision operator follows, as well as its domain.

Temperament Clusters in Patients With Panic Disorder in Relation to Character Maturity.

This study explored whether temperament profiles are associated with psychological functioning and whether character maturity affects this association in patients with panic disorders (PD). A total of 270 patients with PD were enrolled in this study. Measurements included the Temperament and Character Inventory-revised-short (TCI-RS), a self-report version of the Panic Disorder Severity Scale (PDSS-SR), Beck Depression Inventory-II (BDI-II), and Spielberger State-Trait Anxiety Inventory (STAI). Cluster analysis was used to define the patients' temperament profiles, and the differences in discrete variables among temperament clusters were calculated using a one-way analysis of variance. An analysis of covariance was conducted to control for the impact of character maturity on psychological functioning among clusters. We identified four temperament clusters of patients with PD. Significant differences in the PDSS-SR, BDI-II, STAI-state, and STAI-trait scores among the four clusters were detected [F(3, 262)=9.16, p<0.001; F(3, 266)=33.78, p<0.001; F(3, 266)=19.12, p<0.001; F(3, 266)=39.46, p<0.001]. However, after controlling for the effect of character maturity, the effect of cluster type was either eliminated or reduced ([STAI-state] cluster type: F(3, 262)=0.94, p>0.05; SD+CO: F(1, 262)=65.95, p<0.001, ηp2 =0.20). This study enabled a more comprehensive and integrated understanding of patients by exploring the configuration of all temperament dimensions together rather than each temperament separately. Furthermore, we revealed that depending on the degree of character maturity, the psychological functioning might differ even within the same temperament cluster. These results imply that character maturity can complement inherently vulnerable temperament expression.

High-quality matched transfer generation adversarial network for synthetic cross-material surface defect images

Generating Adversarial Network based on style transfer is an effective method to expand sample data. Nonetheless, an urgent issue that demands resolution is the fusion of cross-material defects and backgrounds to generate high-quality defect samples. In this paper, we propose the High-quality Matching Transfer Generative Adversarial Network (HMTGAN), an innovative framework. This network is based on the principles of CycleGAN and StyleGAN, specifically designed for advanced image generation and migration tasks. The network model incorporates multidimensional latent variables within the input image. It employs a unique method of latent space random sampling to effectively combine source defects and background materials. Additionally, a novel loss function is designed, leveraging the straight-through gradient with discrete random variables back-propagation, to effectively compare the binary defects between the source and synthetic images. Following this process, some realistic-looking defect samples are generated. We obtained a KID score of 120.76 and a FID score of 0.16, along with a Class IOU score of 0.11 on the crack defect dataset, by guiding the image generation through training style transformations. We conducted defect migration experiments on various textured surfaces to explore crack generation and migration. Extensive experiments on aluminum plate defect and crack datasets show that our method achieves state-of-the-art performance for image generation.

Polyhedral Relaxations for Optimal Pump Scheduling of Potable Water Distribution Networks

The classic pump scheduling or optimal water flow (OWF) problem for water distribution networks (WDNs) minimizes the cost of power consumption for a given WDN over a fixed time horizon. In its exact form, the OWF is a computationally challenging mixed-integer nonlinear program (MINLP). It is complicated by nonlinear equality constraints that model network physics, discrete variables that model operational controls, and intertemporal constraints that model changes to storage devices. To address the computational challenges of the OWF, this paper develops tight polyhedral relaxations of the original MINLP, derives novel valid inequalities (or cuts) using duality theory, and implements novel optimization-based bound tightening and cut generation procedures. The efficacy of each new method is rigorously evaluated by measuring empirical improvements in OWF primal and dual bounds over 45 literature instances. The evaluation suggests that our relaxation improvements, model strengthening techniques, and a thoughtfully selected polyhedral relaxation partitioning scheme can substantially improve OWF primal and dual bounds, especially when compared with similar relaxation-based techniques that do not leverage these new methods. History: Accepted by David Alderson, Area Editor for Network Optimization: Algorithms &amp; Applications. Funding: This work was supported by the U.S. Department of Energy (DOE) Advanced Grid Modeling project, Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability. Incorporation of the PolyhedralRelaxations Julia package was supported by Los Alamos National Laboratory’s Directed Research and Development program under the project Fast, Linear Programming-Based Algorithms with Solution Quality Guarantees for Nonlinear Optimal Control Problems [Grant 20220006ER]. All work at Los Alamos National Laboratory was conducted under the auspices of the National Nuclear Security Administration of the U.S. DOE, Contract No. 89233218CNA000001. This work was also authored in part by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. DOE, Contract No. DE-AC36-08GO28308. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0233 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0233 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Default risk and stock returns: From a perspective of measurement errors

The risk-return tradeoff underlies the conceptual framework for the asset pricing model and investment decisions under the efficient markets hypothesis. Many empirical study, however, demonstrate negative relation between credit risk and cross-section of stock returns. The main effort of this study is to attempt detangling the puzzle from the perspective of measurement error by representing the first treatment of a wide class of nonlinear models with discrete variables using instruments. We found measurement error has great impact on the relationship between stock returns and firm's risk of default. In addition, this relation found to be crucially dependent on credit cycles and firm financial status.

Probabilistic Neural-Symbolic Models With Inductive Posterior Constraints.

Neural-symbolic models provide a powerful tool to tackle complex visual reasoning tasks by combining symbolic program execution for reasoning and deep representation learning for visual recognition. A probabilistic formulation of such models with stochastic latent variables can obtain an interpretable and legible reasoning system with less supervision. However, it is still nontrivial to generate reasonable symbolic structures without the guidance of domain knowledge, since it generally involves an optimization problem with both continuous and discrete variables. Despite the challenges, the interpretability of such symbolic structures provides an interface to regularize their generation by domain knowledge. In this article, we propose to incorporate the available domain knowledge into the learning process of probabilistic neural-symbolic (PNS) models via posterior constraints that directly regularize the structure posterior. In this way, our model is able to identify a middle point where the structure generation process mainly learns from data but also selectively borrows information from domain knowledge. We further present inductive reasoning where the posterior constraints can be automatically reweighted to handle noisy annotations. The experimental results show that our method achieves state-of-the-art performance on major abstract reasoning datasets and enjoys good generalization capability and data efficiency.

Performance of the F-PSMA 1007 PET/CT for high-risk prostate cancer staging: Experience from a Latin American reference center.

286 Background: Diagnostic staging in high-risk prostate cancer patients seeks to define the local extent of the disease as well as determine the presence of lymph nodes and distant metastases. Molecular imaging, such as PET PSMA, provides higher sensitivity and specificity for the detection of nodal and distant metastases compared to conventional imaging. 18F-PSMA-1007 is radiotracer that represents a cost-effective alternative to 68Ga-PSMA-11, although evidence is limited. We aim to describe the performance of PSMA-1007 in the staging of patients with high risk for prostate cancer in a Latin America reference center. Methods: We performed a cross-sectional study that included prostate cancer patients who underwent 18F-PSMA1007 PET/CT for initial staging in our center of reference between 2020-2023. Descriptive analysis was performed with frequencies for discrete variables, and central tendency and dispersion measures for continuous variables. High risk was defined using the NCCN prostate cancer guidelines. Contingency tables were designed to evaluate associations between groups. To compare categorical variables, we use Chi-square and ANOVA for continuous variables. This analysis was performed using Jamovi 2.3.21 statistical software. Results: Two hundred and seventeen patients were included. The median age was 67 years. The median PSA distribution was 12 ng/ml. The most frequent Gleason group was group 4 (42,7%), followed by group 3 (25,5%). The most prevalent results on the PET pattern are presented in Table. 32% of the patients presented positive lymph node findings. Before the F-PSMA 1007 PET/CT, 24% of the patients had lymph node involvement identified and 33.2% had negative lymph nodes with conventional imaging. These values changed to 39% and 56% respectively, with the F-PSMA 1007 PET/CT. Bone was the most frequent metastasis site (14,3%), followed by extrapelvic lymph node metastasis (12%). A favorable correlation between higher PSA levels and stratified risk assessment by PSMA pattern was evident (p&lt; 0.001). Conclusions: 18F-PSMA-1007 is an innovative radiotracer that can be used to evaluate the extension of prostate cancer involvement. Future studies with prospective cohorts should be carried out to establish analyses between 18F-PSMA-1007 use and its influence on the oncological management of these patients.[Table: see text]

Visualizing Space–Time Multivariate Data Consisting of Discrete and Continuous Variables: A Method for the General Public

Visualizing multivariate data can be challenging, especially for the general public. The difficulties extend beyond determining how to present the data; they also involve comprehension. Early literature has identified various methods, including Chernoff’s face, but these methods often have significant drawbacks, making them challenging to interpret. Subsequently, other techniques, such as scatterplots, parallel coordinate plots, and dynamic graphics, have been introduced. However, many of these methods can be intricate to create and interpret, particularly when visualizing high-dimensional data. Additionally, simultaneously representing discrete aspects (including “space”) and continuous aspects (including “time”) presents another challenge. This study proposes a novel approach named the “Δ table” (delta table), which transforms space–time multivariate data consisting of discrete and continuous variables into a tabular format. The Δ table is believed to be more user-friendly for the general public, which is its most significant advantage compared to previous methods. Finally, we used a case study of the decoupling of the world’s developed, newly industrialized, and developing economies in recent decades as an example of an attempt to apply the Δ table.

A discrete adjoint gradient approach for equality and inequality constraints in dynamics

The optimization of multibody systems requires accurate and efficient methods for sensitivity analysis. The adjoint method is probably the most efficient way to analyze sensitivities, especially for optimization problems with numerous optimization variables. This paper discusses sensitivity analysis for dynamic systems in gradient-based optimization problems. A discrete adjoint gradient approach is presented to compute sensitivities of equality and inequality constraints in dynamic simulations. The constraints are combined with the dynamic system equations, and the sensitivities are computed straightforwardly by solving discrete adjoint algebraic equations. The computation of these discrete adjoint gradients can be easily adapted to deal with different time integrators. This paper demonstrates discrete adjoint gradients for two different time-integration schemes and highlights efficiency and easy applicability. The proposed approach is particularly suitable for problems involving large-scale models or high-dimensional optimization spaces, where the computational effort of computing gradients by finite differences can be enormous. Three examples are investigated to validate the proposed discrete adjoint gradient approach. The sensitivity analysis of an academic example discusses the role of discrete adjoint variables. The energy optimal control problem of a nonlinear spring pendulum is analyzed to discuss the efficiency of the proposed approach. In addition, a flexible multibody system is investigated in a combined optimal control and design optimization problem. The combined optimization provides the best possible mechanical structure regarding an optimal control problem within one optimization.

Penerapan Model Regresi Hurdle Binomial Negatif Menggunakan Algoritma Broyden-Fletcher-Goldfarb-Shanno pada Data Jumlah Kematian Bayi di Kota Makassar Tahun 2017

Poisson regression is a nonlinear regression method used to analyse the relationship between discrete response variables. Equidispersion is the assumption that must be met in the Poisson regression. Furthermore, there are cases in which the equidispersion assumption is invalidated when using the Poisson regression model to analyze data. One such case is overdispersion, which occurs when there is an excess of zero. As a result, the Negative Hurdle Binomial (HBN) regression is implemented to solve the overdispersion issue. Maximum Likelihood Estimation (MLE) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was applied in this study to perform parameter estimation. In addition, the HBN regression model was used to analyze the data on the number of infant mortality cases in Makassar in 2017 with the variables assumed to be significant with infant mortality. The percentage of infants who were exclusively breastfed was the variable that had a significant impact on the outcome of HBN regression on the data on the number of infant mortality that experienced overdispersion.

Automated self-optimization, intensification, and scale-up of photocatalysis in flow.

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in a manufacturing environment geared primarily toward thermal chemistry. In this work, we present a versatile flow-based robotic platform to address these challenges through the integration of readily available hardware and custom software. Our open-source platform combines a liquid handler, syringe pumps, a tunable continuous-flow photoreactor, inexpensive Internet of Things devices, and an in-line benchtop nuclear magnetic resonance spectrometer to enable automated, data-rich optimization with a closed-loop Bayesian optimization strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise to easily monitor, analyze, and improve photocatalytic reactions with respect to both continuous and discrete variables. The system's effectiveness was demonstrated by increasing overall reaction yields and improving space-time yields compared with those of previously reported processes.