Binary Variables Research Articles

Efficient molecular conformation generation with quantum-inspired algorithm.

Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5Å), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware becomes mature. The objective function of MU is defined as the sum of all internal distances between atoms in the molecule, which is a high-order unconstrained binary optimization (HUBO) problem. The degree of freedom of variables is discretized and encoded with binary variables by the phase encoding method. We employ the quantum-inspired simulated bifurcation algorithm for optimization. The public QM9 dataset is generated by DFT. The simulation experiment of quantum computation is implemented by MindQuantum using QAOA.

Predicting Suicidal Ideation, Planning, and Attempts among the Adolescent Population of the United States.

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment.

Relationship between bioelectrical impedance analysis-derived fluid status and nutritional indicators and the prognosis in patients undergoing maintenance hemodialysis

Objective: To explore the relationship between bioelectrical impedance analysis (BIA)-derived fluid status and nutritional indicators and the prognosis in patients undergoing maintenance hemodialysis (MHD). Methods: A retrospective cohort study was conducted. The clinical data of MHD patients in Jiangsu Province Hospital between January 2014 and December 2016 were analyzed. BIA data of healthy volunteers in Gulou District, Nanjing City, collected between April and October 2022, were used to determine the cut-off value of body cell mass index (BCMI). Referring to previous research, using 0.15 as the cut-off value for the ratio of overhydration and extracellular water (OH/ECW). The data were transformed into binary variables based on these cut-off values to categorize patients into different groups. Kaplan-Meier analysis was used to plot survival curves, and the Cox proportional hazards model was performed to analyze risk factors for all-cause mortality. Results: A total of 706 MHD patients (407 males and 299 females) were included, aged (54±15) years. MHD patients were classified into four groups based on whether BCMI was<5.4 kg/m2 and OH/ECW was≥0.15, which included non-overhydration and non-malnutrition group, overhydration group, malnutrition group, and overhydration and malnutrition group, with 269, 186, 151, and 100 patients, respectively. During a median follow-up of [M(Q1, Q3)] 33 (26, 37) months, 162 patients died. Kaplan-Meier analysis showed that the median survival periods of the four groups were 52 months (95%CI: 41-54 months), 46 months (95%CI: 44-49 months), 37 months (95%CI: 34-40 months), and 34 months (95%CI: 30-38 months), respectively, with a statistically significant difference (P<0.001). The 1-year survival rates were 95.5%, 93.5%, 92.1%, and 88.0% (P<0.001), respectively, and the 2-year mortality rates were 92.6%, 87.1%, 83.4%, and 77.0% (P<0.001), respectively. Multivariate Cox regression analysis showed that compared with non-overhydration and non-malnutrition group, the risk of all-cause mortality increased by 1.18 times in the malnutrition group (HR=2.18, 95%CI: 1.29-3.71, P=0.004), and by 1.59 times in the overhydration and malnutrition group (HR=2.59, 95%CI: 1.48-4.54, P=0.001). Conclusions: BIA-derived fluid status and nutritional indicators are associated with the prognosis of MHD patients. Compared with patients without fluid overload and malnutrition, patients with malnutrition and fluid overload have an increased risk of all-cause mortality.

Robust co-planning of transmission expansion and merchant energy storage investments considering long- and short-term uncertainties

The decreasing cost of energy storage technologies and the increasing utilization of renewable energy sources have made distributed transmission-scale energy storage economically viable. Merchant energy storage investors can collect significant profits through spatiotemporal energy arbitrage from congested transmission lines. This paper formulates a three-stage, four-level min–min–max–min problem from the viewpoint of power system operators and planners such that they will build new transmission lines and identify potential locations for profitable merchant energy storage investments. The first level determines transmission expansion decisions from the system operator’s perspective. The second level plans potential investments in merchant energy storage to ensure profitability at a desired rate of return. The third level realizes the worst-case situations of long-term uncertainties related to the expansion of wind farms and peak load growth in each regional zone, while the fourth level simulates market clearing for representative days, modeling short-term uncertainties. A novel iterative nested column and constraint generation (C&CG) technique is adopted to cope with numerical issues caused by binary variables of transmission lines and storage blocks and the enormous size of the problem. The case study of a modified IEEE 24-bus test system was used to assess the effectiveness of the proposed algorithm.

Prediction of Disease Progression and Clinical Response in Systemic Sclerosis: Experience From a Proof-of-Concept Trial.

Using the modified Rodnan skin score (mRSS) as a surrogate for disease activity, a phase 2a study in patients with systemic sclerosis (SSc) measured efficacy of the autotaxin inhibitor ziritaxestat. Mathematical modeling of mRSS was used to predict disease progression, examine candidate trial designs, and predict the probability of successfully discriminating treatment effect. Patients with SSc receiving 600 mg of ziritaxestat or placebo for 24 weeks were included, in addition to data up to week 52 of the open-label extension (OLE). Longitudinal mRSS data were described using a disease progression model; drug effect was a binary variable. Parameters used to predict the OLE mRSS outcome were estimated using data from the 24-week double-blind phase and validated with observed data. Three trial designs were simulated to identify which had the highest probability of detecting a treatment effect. Power to detect a treatment effect was quantified using the simulations. Maximum decreases from baseline in mRSS were 50.4% (ziritaxestat) and 34.7% (placebo). Study designs based on 300 patients randomized 2:1 or 1:1 to 600 mg of ziritaxestat or placebo had similar probabilities of detecting a significant treatment effect. Power to detect a treatment effect was >80% for all simulations. Disease progression and drug effect could be predicted beyond the range of observed data. This modeling and simulation approach may inform future trial design, including study duration, and predict the probability of success.

Cox proportional hazards regression in small studies of predictive biomarkers

Predictive biomarkers are essential for personalized medicine since they select the best treatment for a specific patient. However, of all biomarkers that are evaluated, only few are eventually used in clinical practice. Many promising biomarkers may be erroneously abandoned because they are investigated in small studies using standard statistical techniques which can cause small sample bias or lack of power. The standard technique for failure time endpoints is Cox proportional hazards regression with a multiplicative interaction term between binary variables of biomarker and treatment. Properties of this model in small studies have not been evaluated so far, therefore we performed a simulation study to understand its small sample behavior. As a remedy, we applied a Firth correction to the score function of the Cox model and obtained confidence intervals (CI) using a profile likelihood (PL) approach. These methods are generally recommended for small studies of different design. Our results show that a Cox model estimates the biomarker-treatment interaction term and the treatment effect in one of the biomarker subgroups with bias, and overestimates their standard errors. Bias is however reduced and power is increased with Firth correction and PL CIs. Hence, the modified Cox model and PL CI should be used instead of a standard Cox model with Wald based CI in small studies of predictive biomarkers.

Dispatchable region for distributed renewable energy generation in reconfigurable AC–DC distribution networks with soft open points

The dispatchable region (DR) describes the ability of a power system to adapt to the variability of distributed renewable energy (DRE) generation. Switchable devices such as soft open points (SOPs) and tie switches enable the AC–DC distribution network to flexibly cope with DRE power fluctuations. Currently, research on DRs seldom considers the effects of SOPs and reconfiguration with tie switches in AC–DC distribution networks, which prevents accurate evaluation of DR for this type of network. To bridge this gap, this paper formulates DRs for DRE generation in an AC–DC distribution network considering SOPs and reconfiguration with tie switches. The original model for deriving DRs is typically nonconvex, and the DR boundaries are intractable because of nonlinear power flow equations and binary variables of tie switches. Accordingly, this paper employs the outer polyhedral approximation and convex hull relaxation to approximate the original DR. An efficient adaptive constraint generation algorithm is developed to search for boundaries of the approximated DR. The numerical results show that the proposed model can effectively adapt to the DR of reconfigurable AC–DC distribution networks with SOPs and verify the accuracy and computational efficiency of the proposed method.

Development of optimization method for truss structure by quantum annealing

In this study, we developed a new method of topology optimization for truss structures by quantum annealing. To perform quantum annealing analysis with real variables, representation of real numbers as a sum of random number combinations is employed. The nodal displacement is expressed with binary variables. The Hamiltonian H is formulated on the basis of the elastic strain energy and position energy of a truss structure. It is confirmed that truss deformation analysis is possible by quantum annealing. For the analysis of the optimization method for the truss structure, the cross-sectional area of the truss is expressed with binary variables. The iterative calculation for the changes in displacement and cross-sectional area leads to the optimal structure under the prescribed boundary conditions.

1290-P: Developing Machine Learning Model for Predicting Acute Coronary Syndrome in Type 2 Diabetes Mellitus Patients through Substitution of Propensity Scores for Binary Variables

1290-P: Developing Machine Learning Model for Predicting Acute Coronary Syndrome in Type 2 Diabetes Mellitus Patients through Substitution of Propensity Scores for Binary Variables

Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme

Electricity, gas and heating networks are increasingly coupled to form an integrated energy system (IES), which is beneficial in improving the operational performance of the system during power outages. Generally, IES involves multiple energy operators, making the centralized framework impracticable. To this end, a novel distributed restoration method for IES is proposed in this paper. The IES restoration problem is formulated as a mixed-integer second-order cone programming (MISOCP) model to minimize the total load losses, which considers the coupling effects among the electricity, gas and heating networks. Then, considering the independent management and information privacy of different energy networks, the distributed restoration model is formulated by decomposing shared boundary information using the alternating direction method of multipliers (ADMM). For the sake of the non-convexity caused by binary load state variables, an iterative loop scheme is introduced in ADMM procedure. This allows us to solve a relaxed distributed model in the first stage, and perform iterative loops in the second stage, which can improve the convergence of ADMM and make the distributed solution feasible. Finally, numerical studies are conducted on 33-7-6-node IES and 123-24-32-node IES to validate the effectiveness and feasibility of the proposed method.

The Terminator: An Integration of Inner and Outer Approximations for Solving Wasserstein Distributionally Robust Chance Constrained Programs via Variable Fixing

We present a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution. In general, these programs can be reformulated as mixed-integer programs (MIPs) by introducing binary variables for each scenario, indicating whether a scenario should be satisfied. Whereas existing methods have focused predominantly on either inner or outer approximations, this paper bridges this gap by studying a scheme that effectively combines these approximations via variable fixing. By checking the restricted outer approximations and comparing them with the inner approximations, we derive optimality cuts that can notably reduce the number of binary variables by effectively setting them to either one or zero. We conduct a theoretical analysis of variable fixing techniques, deriving an asymptotic closed-form expression. This expression quantifies the proportion of binary variables that should be optimally fixed to zero. Our empirical results showcase the advantages of our approach in terms of both computational efficiency and solution quality. Notably, we solve all the tested instances from literature to optimality, signifying the robustness and effectiveness of our proposed approach. History: Accepted by Andrea Lodi/Design &amp; Analysis of Algorithms — Discrete. Funding: This work was supported by Office of Naval Research [N00014-24-1-2066]; Division of Civil, Mechanical and Manufacturing Innovation [2246414]. 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.2023.0299 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0299 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Thrombectomy in young adults with embolic stroke of undetermined source: Analysis of the Young ESUS study

ObjectivesEmbolic Stroke of Undetermined Source (ESUS) is a distinct stroke entity that disproportionately affects young adults. We sought to describe characteristics, workup and outcomes of young adult ESUS patients who underwent thrombectomy, and compare outcomes to those reported in different age groups. Materials and methodsYoung-ESUS is a multicenter longitudinal cohort study that enrolled consecutive patients aged 21-50 years at 41 stroke centers in 13 countries between 2017- 2019. Between-group comparisons were performed using Wilcoxon rank sum test for continuous variables or Fisher's exact test for binary variables. Distribution of functional outcomes after thrombectomy for our young adult cohort versus pediatric and older adult cohorts reported in the literature were described using the Kruskal-Wallis test. ResultsOf 535 patients enrolled in Young-ESUS, 65 (12.1%) were treated with endovascular thrombectomy. Patients who underwent thrombectomy were more likely to undergo in-depth cardiac testing than those who did not, but cardiac abnormalities were not detected more often in this group. Among thrombectomy patients, 35/63 (55.6%) had minimal to no functional disability at follow up. When adjusted for age, stroke severity and IV alteplase, the odds of achieving favorable outcome did not differ between treated versus untreated patients. ConclusionsThrombectomy is not rare in young adults with ESUS. Despite extensive workup, cardiac abnormalities were not more prevalent in the thrombectomy group. More research to determine optimal workup, etiologic factors and favorable outcome of stroke across the lifespan is needed.

Identity-by-descent segments in large samples.

We show the asymptotic normality of the identity-by-descent rate, a mean of correlated binary random variables that arises in population genetics studies.We describe an efficient algorithm capable of simulating long identity-by-descent segments around a locus in large sample sizes.In enormous simulation studies, we use this algorithm to characterize the distributional properties of the identity-by-descent rate.In finite samples, we reject the null hypothesis of normality more often than the nominal significance level, indicating that genome-wide scans based on identity-by-descent rates may be anti-conservative.

Effect of different anesthetic modalities on postoperative delirium in elderly hip fractures: A meta-analysis.

Previous findings on the effect of general versus spinal anesthesia on postoperative delirium in elderly people with hip fractures are somewhat controversial. This article included the latest randomized controlled study for meta-analysis to evaluate the effect of general anesthesia (GA) and spinal anesthesia (SA) on delirium after hip fracture surgery in the elderly, so as to guide the clinical. Cochrane Library, PubMed, Web Of Science, and Embase were searched from inception up to January 16, 2024. Randomized controlled trial (RCT) was included to evaluate the postoperative results of GA and SA in elderly patients (≥50 years old) undergoing hip fracture surgery. Two researchers independently screened for inclusion in the study and extracted data. Heterogeneity was assessed by the I²and Chi-square tests, and P < .1 or I² ≥ 50% indicated marked heterogeneity among studies. The Mantel-Haenszel method was used to estimate the combined relative risk ratio (RR) and the corresponding 95% confidence interval (CI) for the binary variables. Nine randomized controlled trials were included. There was no significant difference (RR = 0.93, 95% CI = 0.774-1.111, P > .05) in the incidence of postoperative delirium between the GA group and the SA group. In intraoperative blood transfusion (RR = 1.0, 95% CI = 0.77-1.28, Z = 0.04, P = .971), pulmonary embolism (RR = 0.795, 95% CI = 0.332-1.904, Z = 0.59, P = .606), pneumonia (RR = 1.47, 95% CI = 0.75-2.87, P = .675), myocardial infarction (RR = 0.97, 95% CI = 0.24-3.86, Z = 0.05, P = .961), heart failure (RR = 0.80, 95% CI = 0.26-2.42, Z = 0.40, P = .961), urinary retention (RR = 1.42, 95% CI = 0.77-2.61, Z = 1.11, P = .267) were similar between the 2 anesthetic techniques. There is no significant difference in the effect of GA and SA on postoperative delirium in elderly patients with hip fracture, and their effects on postoperative complications are similar.

The use of Panax notoginseng saponins injections after intravenous thrombolysis in acute ischemic stroke: a systematic review and meta-analysis.

As a bioactive metabolite preparation widely used in acute ischemic stroke (AIS), the efficacy and safety of Panax notoginseng saponins injections (PNSI) in patients with AIS after intravenous thrombolysis remain to be evaluated. This study included randomized controlled trials published before 26 April 2024 in 8 databases. AIS patients who received intravenous thrombolysis were included. The control group receiving conventional treatment and the treatment group receiving additional PNSI. Primary outcomes were selected as mortality, disability, and adverse events. Secondary outcomes were selected as all-cause mortality, improvement of neurological deficit, quality of life, and cerebral injury indicators. The revised Cochrane Risk of Bias tool was used to assess risk of bias. Risk ratio (RR) and mean differences (MD) were calculated for binary variables and continuous variables, respectively, based on a 95% confidence interval (CI). A total of 20 trials involving 1,856 participants were included. None of them reported mortality or disability. There was no significant difference in the adverse events [RR: 1.04; 95% CI: 0.60 to 1.81] and hemorrhagic transformation [RR: 0.99; 95% CI: 0.36 to 2.70] between the two groups. Compared to the control group, the treatment group had a better effect in neurological improvement assessed by National Institutes of Health Stroke Scale [MD: -2.91; 95% CI: -4.76 to -1.06], a better effect in activities of daily living changes in Barthel Index [MD: 9.37; 95% CI: 1.86 to 16.88], and a lower serum neuron-specific enolase level [MD: -2.08; 95% CI: -2.67 to -1.49]. For AIS patients undergoing intravenous thrombolysis, the use of PNSI improved neurological deficits and enhanced activity of daily living in the short term without increasing the occurrence rate of adverse events. However, due to the moderate to very low certainty of evidence, it is advisable to conduct high-quality clinical trials to validate the findings of this study. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=466851, Identifier CRD42023466851.

A unified framework for symmetry handling

AbstractHandling symmetries in optimization problems is essential for devising efficient solution methods. In this article, we present a general framework that captures many of the already existing symmetry handling methods. While these methods are mostly discussed independently from each other, our framework allows to apply different methods simultaneously and thus outperforming their individual effect. Moreover, most existing symmetry handling methods only apply to binary variables. Our framework allows to easily generalize these methods to general variable types. Numerical experiments confirm that our novel framework is superior to the state-of-the-art symmetry handling methods as implemented in the solver on a broad set of instances.

Designing a multi-purpose network of sustainable and closed-loop renewable energy supply chain, considering reliability and circular economy

Since renewable energy systems have a limited lifespan and cannot be used after a while, it seems imperative to pay attention to recycling and sustainability in the supply chain network design of these systems. Also, modern power systems have high complexity and a large number of problems that affect the reliability of electricity supply to consumers. Therefore, organizations should consider broader goals, including the maximum use of resources and the minimization of waste, which is known as lean economy. In this research, a multi-objective non-linear forward optimization model has been considered for the design of the supply chain network of photovoltaic and wind systems based on the aspects of stability and taking into account the reliability. Also, adding the reverse flow, in addition to the forward flow, is also considered, which, in addition to economic advantages, also pays attention to environmental and social issues. The objective function includes minimizing all costs and negative environmental effects as well as increasing reliability. In order to convert the multi-objective planning model into a single-objective one, the epsilon method of the enhanced constraint is used. Uncertainty has been considered for all variables (except binary variables) and parameters of demand, intensity of sun radiation and amount of wind speed. Then, the efficiency of the model has been investigated through its implementation on a case study and the results have been obtained. Eventually, the model's efficiency is illustrated by discussing a real-world case, through which prominent managerial results are acquired.

Correlations reveal the hierarchical organization of biological networks with latent variables

Deciphering the functional organization of large biological networks is a major challenge for current mathematical methods. A common approach is to decompose networks into largely independent functional modules, but inferring these modules and their organization from network activity is difficult, given the uncertainties and incompleteness of measurements. Typically, some parts of the overall functional organization, such as intermediate processing steps, are latent. We show that the hidden structure can be determined from the statistical moments of observable network components alone, as long as the functional relevance of the network components lies in their mean values and the mean of each latent variable maps onto a scaled expectation of a binary variable. Whether the function of biological networks permits a hierarchical modularization can be falsified by a correlation-based statistical test that we derive. We apply the test to gene regulatory networks, dendrites of pyramidal neurons, and networks of spiking neurons.

The mental health impact of primary Sjögren Syndrome among Italian women

BackgroundThis cross-sectional online survey aims to investigate the impact of Primary Sjögren's Syndrome (pSS) on mental health by measuring anxiety, depressive symptoms and health-related quality of life (HR-QoL) in the Italian context. MethodsParticipants diagnosed with pSS were recruited via the National Association of Sjögren's Syndrome Patients (A.N.I.Ma.S.S.), composed by members across Italy, and completed an online survey using the LimeSurvey platform. Data collected encompassed demographic, clinical information (e.g., age, gender, disease duration), diagnostic details (suspecting and diagnosing specialists), and mental health assessments (HADS for depressive and anxiety symptoms, SF-36 for HR-QoL). Descriptive analyses were conducted, including frequencies for binary variables, mean and standard deviations for interval-level variables, alongside correlation assessments between clinical scales. ResultsThe study included 216 women with pSS who completed the survey (average age: 54 years). Mental health symptoms evaluated by the HADS subscales revealed a clinical level of anxiety in 50 % and depression in 41 % of the respondents Patients with pSS had a low score in all eight domains of the SF-36. LimitationsThe study has limitations, including its focus on female pSS patients due to limited male participants, potential biases in self-reported survey data, and the lack of certain demographic and clinical variables. ConclusionOur results emphasize the critical role of emotional distress in the progression of pSS, highlighting the need for its proper management. This insight contributes to the development of a rational therapeutic approach to pSS patients useful for all the healthcare professionals, pharmaceutical industry, and drug regulatory organizations.

Some new clique inequalities in four-index hub location models

Hub location problems can be modeled in several ways, one of which is the path-based family of models that make use of four-index variables. Clique inequalities are frequently used to describe solution characteristics for optimization problems with binary variables. In this study, new valid inequalities of the clique type are introduced for the path-based family of hub location models. Some of their properties and corresponding lifting are discussed, and a separation heuristic algorithm is proposed. We also report a set of computational experiments to evaluate the usefulness of the proposals when embedded in a commercial solver.