Programming Solution Research Articles

Optimal refinement of strata to balance covariates.

What is the best way to split one stratum into two to maximally reduce the within-stratum imbalance in many covariates? We formulate this as an integer program and approximate the solution by randomized rounding of a linear program. A linear program may assign a fraction of a person to each refined stratum. Randomized rounding views fractional people as probabilities, assigning intact people to strata using biased coins. Randomized rounding is a well-studied theoretical technique for approximating the optimal solution of certain insoluble integer programs. When the number of people in a stratum is large relative to the number of covariates, we prove the following new results: (i) randomized rounding to split a stratum does very little randomizing, so it closely resembles the linear programming relaxation without splitting intact people; (ii) the linear relaxation and the randomly rounded solution place lower and upper bounds on the unattainable integer programming solution; and because of (i), these bounds are often close, thereby ratifying the usable randomly rounded solution. We illustrate using an observational study that balanced many covariates by forming matched pairs composed of 2016 patients selected from 5735 using a propensity score. Instead, we form 5 propensity score strata and refine them into 10 strata, obtaining excellent covariate balance while retaining all patients. An R package optrefine at CRAN implements the method. Supplementary materials are available online.

An evaluation of challenges and solutions in dryland farming training programs: Insights from Extension Functionaries at CRIDA in Bhoothpur Block, Mahbubnagar District, Telangana

An evaluation of challenges and solutions in dryland farming training programs: Insights from Extension Functionaries at CRIDA in Bhoothpur Block, Mahbubnagar District, Telangana

Structured Planning for Strengthening Marketing and Distribution Capacity of Cilembu Sweet Potato Products

The method of implementing this community service is systematically designed to overcome the problems faced by the target partner, Ma Utik, in developing the Cilembu yam industry and its processed products. The preparation and planning stages as well as socialization and initial training are the main focuses in this program. The preparation stage begins with an initial survey to identify partner needs related to marketing, business management, distribution network development, and product innovation. A service team consisting of experts in economics, management, agribusiness, and informatics was formed with a division of tasks according to competence. A structured work plan includes a schedule of activities, implementation methods, and indicators of success, as well as careful management of budgets and resources. Socialization and training materials were developed based on partner needs to ensure in-depth understanding and mastery of relevant skills. The socialization phase begins with an initial meeting to introduce the program's objectives, benefits and solutions to partners, while accommodating their inputs. Initial training was conducted to provide partners with a foundation of knowledge and skills. Marketing strategy training covers the use of social media, digital marketing, branding, and promotion. Business management training covers business planning, financial management, and strategic decision-making using business management software. Distribution network development training includes negotiating and building relationships with distributors, as well as utilizing communication technology. Product innovation training includes identification of market trends and development of new products using market research technology. Through this approach, it is expected that partners can understand the program objectives and acquire the necessary skills to improve their competitiveness. With effective coordination between the service team, community partners, and supporting institutions, this program can run smoothly and have a significant impact. The implementation of a digital marketing application specifically for Cilembu yam products is one of the main results that is expected to increase product visibility, facilitate customer access, strengthen branding, and increase interaction with customers and business partners

Advocating for transportation equity: A critical examination of paratransit service reductions in St. Louis and its impact on health and community social participation

Social participation is associated with better health, quality of life, physical activity, and engagement in community living and is thus an emerging health priority. Transportation plays an important role in facilitating social participation. Our team recently reported in the Journal of Disability and Health that Missouri-dwelling adults aging with long-term physical disabilities who use paratransit services as their primary transportation mode are more likely to participate in social roles and activities outside the home compared to those who do not use paratransit. In March of 2023, the paratransit company Metro Call-A-Ride that serves St. Louis announced major scale backs to their coverage zones due in part to staffing shortages. This decision has been met with a formal complaint filed to the U.S. Department of Justice as well as protest from the St. Louis disability community and advocates. Thousands of individuals who relied on Call-A-Ride for their routine community outings—to work, grocery stores, or medical appointments, for example—have been affected by the cuts. In this commentary, we will summarize the media coverage this decision has received, including the perspectives of disability rights advocates and individuals who have been directly affected. We will then present an overview of our original research findings in the context of these recent events and a brief synthesis of existing literature on paratransit services in the U.S. The commentary will end with proposed policy, research, and programming solutions for St. Louis's Metro Call-A-Ride and public transportation at large.

Using Constraint Propagation to Bound Linear Programs

We present an approach to compute bounds on the optimal value of linear programs based on constraint propagation. Given a feasible dual solution, we apply constraint propagation to the complementary slackness conditions and, if propagation succeeds to prove these conditions infeasible, the infeasibility certificate (in the sense of Farkas’ lemma) is reconstructed from the propagation history. This certificate is a dual-improving direction, which allows us to improve the bound. As constraint propagation need not always detect infeasibility of a linear inequality system, the method is not guaranteed to converge to a global solution of the linear program but only to an upper bound, whose tightness depends on the structure of the program and the used propagation method. The approach is suited for large sparse linear programs (such as LP relaxations of combinatorial optimization problems), for which the classical LP algorithms may be infeasible, if only for their super-linear space complexity. The approach can be seen as a generalization of the Virtual Arc Consistency (VAC) algorithm to bound the LP relaxation of the Weighted CSP (WCSP). We newly apply it to the LP relaxation of the Weighted Max-SAT problem, experimentally showing that the obtained bounds are often not far from optima of the relaxation and proving that they are exact for known tractable subclasses of Weighted Max-SAT.

Cost optimization of electricity in energy storage system by dynamic programming

This paper presents a dynamic programming solution for the cost optimization of an electric storage system. The objective is to minimize the total cost of meeting electricity demand over a specified time interval, considering energy constraints and costs. The proposed algorithm efficiently determines the optimal energy discharge and charge strategies for the storage system, resulting in reduced overall costs. The effectiveness and efficiency of the algorithm are demonstrated through various test cases, highlighting its potential for real-world applications in energy storage systems and electric grid management. It also provides an overview of different types of electrical storage systems, review recent research on optimization techniques for energy storage, and examines recent studies on the optimization of electrical storage systems for specific applications, such as peak load shaving and grid stability. Through this comprehensive analysis, we hope to shed light on the current state of the field and identify areas for further research and improvement.

Fault-tolerant control against actuator faults based on extended state observer for quadrotor UAV

Abstract A model predictive control (MPC) strategy is devised to handle the issue of multi-actuators partial failure faults of quadrotor vehicles. Firstly, considering a quadrotor system with actuator partial failure faults and unknown disturbances, the quadrotor dynamics model is analyzed and established. Then, the MPC attitude fault-tolerant controller is devised to transform the issue of controlling the attitude angle of a quadrotor into a quadratic programming solution problem, and the extended state observer (ESO) is devised to evaluate the faults and perturbations, which highly and greatly improves the system’s suppression capability against disturbances. Finally, numerical simulation experiments demonstrate the scheme’s fault-tolerance to actuator faults and its ability to suppress unknown perturbations, while the results of semi-physical simulation experiments also prove that the method is effective and feasible.

The Role of Artificial Intelligence in Learning and Development

Artificial Intelligence (AI) is transforming corporate learning and development by offering personalized, efficient, and scalable training solutions. This paper explores how AI technologies, such as machine learning, natural language processing, and data analytics, enhance learning experiences and outcomes in corporate environments. Key benefits include personalized learning paths, automated content creation, and real-time feedback. Challenges such as data privacy, implementation costs, and the need for human oversight are also discussed. By examining current applications and future trends, this paper highlights AI’s potential to revolutionize corporate training and suggests best practices for integrating AI-driven solutions in learning and development programs. Key Words: Artificial Intelligence (AI), Corporate Learning and Development, Personalized Learning, Real-Time Feedback, Adaptive Learning Technologies, Data Privacy, Emotional Intelligence in AI, AI and Augmented Reality (AR), Social Learning. Predictive Analytics, Human Oversight

A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm.

The set-covering problem aims to find the smallest possible set of subsets that cover all the elements of a larger set. The difficulty of solving the set-covering problem increases as the number of elements and sets grows, making it a complex problem for which traditional integer programming solutions may become inefficient in real-life instances. Given this complexity, various metaheuristics have been successfully applied to solve the set-covering problem and related issues. This study introduces, implements, and analyzes a novel metaheuristic inspired by the well-established Growth Optimizer algorithm. Drawing insights from human behavioral patterns, this approach has shown promise in optimizing complex problems in continuous domains, where experimental results demonstrate the effectiveness and competitiveness of the metaheuristic compared to other strategies. The Growth Optimizer algorithm is modified and adapted to the realm of binary optimization for solving the set-covering problem, resulting in the creation of the Binary Growth Optimizer algorithm. Upon the implementation and analysis of its outcomes, the findings illustrate its capability to achieve competitive and efficient solutions in terms of resolution time and result quality.

ADVANCED HOSPITAL MANAGEMENT SYSTEM

The Advanced Hospital Management System (AHMS) is a comprehensive program solution that coordinating different functionalities significant for effective hospital management. It includes modules for patient enrollment, appointment planning, electronic health records (EHR), charging and invoicing, stock administration, and detailing. One of the key highlights of AHMS is its interoperability, permitting consistent communication and data exchange between diverse departments inside the hospital as well as with outside partners such as laboratories, pharmacies, and insurance companies. This interoperability guarantees real-time access to patient data, encouraging convenient decision-making and progressing overall care coordination. Key Words: Django, Html, CSS, SQLite3.

The nurse-led equitable learning framework for training programs: A framework to grow, bolster and diversify the nursing and public health workforce

BackgroundAddressing threats to the nursing and public health workforce, while also strengthening the skills of current and future workers, requires programmatic solutions. Training programs should be guided by frameworks, which leverage nursing expertise and leadership, partnerships, and integrate ongoing evaluation. Purpose statementThis article provides a replicable framework to grow, bolster, and diversify the nursing and public health workforces, known as the Nurse-led Equitable Learning (NEL) Framework for Training Programs. The framework has been applied by several multipronged, federally funded training programs led by investigators embedded in an academic nursing institution. MethodsThe NEL framework focuses on: (1) increasing equitable access to the knowledge, skills, and competencies needed to prepare a diverse workforce to deliver effective interventions; (2) fostering academic-practice linkages and community partnerships to facilitate the deployment of newly gained knowledge and skills to address ongoing and emerging challenges in care delivery; and (3) continuously evaluating and disseminating findings to inform expansion and replication of programs. ResultsTen programs using this framework have successfully leveraged $18.3 million in extramural funding to support over 1000 public health professionals and trainees. Longitudinal evaluation efforts indicate that public health workers, including nurses, are benefiting from the programs' workplace trainings, future clinicians are being rigorously trained to identify and address determinants of health to improve patient and community well-being, and educators are engaging in novel pedagogical opportunities to enhance their ability to deliver high quality public health education. ConclusionsTraining programs may apply the NEL framework to ensure that the nursing and public health workforces achieve equitable, sustainable growth and deliver high quality evidence-based care.

Long-term upgrade strategies in multiband and multifiber optical transport networks

Flexible-grid optical transport networks (OTNs) are currently undergoing a massive scale upgrade to bandwidth variable transceivers (BVTs) to cater to the exponential Internet traffic growth. Network operators seeking to plan future capacity in a scalable manner can also add additional bands of operations or additional fibers in the OTN. We propose two strategies for planning multiband upgrades in OTNs, which use an integer linear programming (ILP) solution and a heuristic solution with a reinforcement learning (RL)-based extension. Network simulation studies on three differently sized OTNs show that the heuristic and its RL-based extension perform extremely close to the optimal ILP-based solution while reducing execution times drastically. Finally, a techno-economic study based on what we believe to be a novel cost model highlights the cost-per-bit-per-second savings through carefully planned upgrades.

Large-W limit of the knapsack problem.

We formulate the knapsack problem (KP) as a statistical physics system and compute the corresponding partition function as an integral in the complex plane. The introduced formalism allows us to derive three statistical-physics-based algorithms for the KP: one based on the recursive definition of the exact partition function, another based on the large weight limit of that partition function, and a final one based on the zero-temperature limit of the second. Comparing the performances of the algorithms, we find that they do not consistently outperform (in terms of runtime and accuracy) dynamic programming, annealing, or standard greedy algorithms. However, the exact partition function is shown to reproduce the dynamic programming solution to the KP, and the zero-temperature algorithm is shown to produce a greedy solution. Therefore, although dynamic programming and greedy solutions to the KP are conceptually distinct, a statistical physics formalism introduced reveals that the large weight-constraint limit of the former leads to the latter. We conclude by discussing how to extend this formalism in order to obtain more accurate versions of the introduced algorithms and other similar combinatorial optimization problems.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

Automated Trace Clustering Pipeline Synthesis in Process Mining

Business processes have undergone a significant transformation with the advent of the process-oriented view in organizations. The increasing complexity of business processes and the abundance of event data have driven the development and widespread adoption of process mining techniques. However, the size and noise of event logs pose challenges that require careful analysis. The inclusion of different sets of behaviors within the same business process further complicates data representation, highlighting the continued need for innovative solutions in the evolving field of process mining. Trace clustering is emerging as a solution to improve the interpretation of underlying business processes. Trace clustering offers benefits such as mitigating the impact of outliers, providing valuable insights, reducing data dimensionality, and serving as a preprocessing step in robust pipelines. However, designing an appropriate clustering pipeline can be challenging for non-experts due to the complexity of the process and the number of steps involved. For experts, it can be time-consuming and costly, requiring careful consideration of trade-offs. To address the challenge of pipeline creation, the paper proposes a genetic programming solution for trace clustering pipeline synthesis that optimizes a multi-objective function matching clustering and process quality metrics. The solution is applied to real event logs, and the results demonstrate improved performance in downstream tasks through the identification of sub-logs.

The Floor Is Lava: Halving Natural Genomes with Viaducts, Piers, and Pontoons.

Whole Genome Duplications (WGDs) are events that double the content and structure of a genome. In some organisms, multiple WGD events have been observed while loss of genetic material is a typical occurrence following a WGD event. The requirement of classic rearrangement models that every genetic marker has to occur exactly two times in a given problem instance, therefore, poses a serious restriction in this context. The Double-Cut and Join (DCJ) model is a simple and powerful model for the analysis of large structural rearrangements. After being extended to the DCJ-Indel model, capable of handling gains and losses of genetic material, research has shifted in recent years toward enabling it to handle natural genomes, for which no assumption about the distribution of markers has to be made. The traditional theoretical framework for studying WGD events is the Genome Halving Problem (GHP). While the GHP is solved for the DCJ model for genomes without losses, there are currently no exact algorithms utilizing the DCJ-Indel model that are able to handle natural genomes. In this work, we present a general view on the DCJ-Indel model that we apply to derive an exact polynomial time and space solution for the GHP on genomes with at most two genes per family before generalizing the problem to an integer linear program solution for natural genomes.

An approach to duality via image space analysis and applications to linear vector optimization problems

In Yao et al. [Yao CL, Wang SQ, Tammer C. Construction of vector-valued weak separation functions with applications to conjugate duality in vector optimization; 2024. Submitted.], based on the idea of image space analysis and abstract convexity, we proposed a uniform framework of conjugate duality for the constrained vector optimization problems. This paper aims to apply this framework to linear vector optimizations. With the aid of a special collection of separation functions, a set-valued dual problem, which was also derived by Heyde, Löhne, Tammer in [Heyde F, Löhne A, Tammer C. The attainment of the solution of the dual program in vertices for vectorial linear programs. In: Barichard V, Ehrgott M, Gandibleux X, T'Kindt V, editors. Multiobjective programming and goal programming. 2009. p. 13–24. (Lecture Notes in Economics and Mathematical Systems; 618).; Heyde F, Löhne A, Tammer C. Set-valued duality theory for multiple objective linear programs and application to mathematical finance. Math Meth Oper Res. 2009;69:159–179.], is obtained by this approach. Within the uniform framework, we provide a new way to construct this dual model, and to investigate the duality theory of it. In this method, the strong duality is established by the separation of sets in the image space and the generalized saddle points.

Advantages of Low-Rise Construction as a Factor in Increasing Life Standards of the Population

The development of the construction complex in the regions is a key factor in providing energy-efficient, safe, affordable and comfortable housing for citizens living in small towns and settlements. The purpose of the study is to develop a socio-economic justification for program decisions in the field of development of low-rise construction in small towns and settlements. The research methodology is based on a theoretical analysis of the basic scientific principles of low-rise construction management. Statistical data on statistical reporting on low-rise construction served as an information and empirical base of the study. The scientific novelty lies in the formation of the author's conceptual approach to the socio-economic justification of program solutions for small towns and settlements. The study showed that the developed program measures are effective and will ensure the intensification of low-rise housing construction with a high level of comfort and quality in small towns and settlements.

Relational Programming with Foundational Models

Foundation models have vast potential to enable diverse AI applications. The powerful yet incomplete nature of these models has spurred a wide range of mechanisms to augment them with capabilities such as in-context learning, information retrieval, and code interpreting. We propose Vieira, a declarative framework that unifies these mechanisms in a general solution for programming with foundation models. Vieira follows a probabilistic relational paradigm and treats foundation models as stateless functions with relational inputs and outputs. It supports neuro-symbolic applications by enabling the seamless combination of such models with logic programs, as well as complex, multi-modal applications by streamlining the composition of diverse sub-models. We implement Vieira by extending the Scallop compiler with a foreign interface that supports foundation models as plugins. We implement plugins for 12 foundation models including GPT, CLIP, and SAM. We evaluate Vieira on 9 challenging tasks that span language, vision, and structured and vector databases. Our evaluation shows that programs in Vieira are concise, can incorporate modern foundation models, and have comparable or better accuracy than competitive baselines.

A Stochastic Goal-Based Economic Order Quantity Model for Dairy Products

Resilient efforts are still sought to optimize the inventory of dairy products under demand uncertainty. Several industries operate under uncertainties that severely affect company performance. In this paper, a multi-objective goal programming model is proposed to optimize the economic order quantity of dairy products under stochastic demand. Adopting a Markov chain approach, the model initially defines the demand transition matrix, inventory cost matrix, objective function, priorities, and goal constraints of dairy products. The model seeks to minimize the deviation variables from the targeted economic order quantity and total inventory costs of dairy products over a finite period planning horizon. The sum of deviations is minimized so that the actual economic order quantity and inventory costs meet the targeted levels of dairy products. Using the simplex method, an analytical solution is obtained and a numerical example is presented for illustration; demonstrating the overachievement or underachievement of the targeted economic order quantity and inventory costs of dairy products. Results indicate how priority-based goal programming solution is sensitive to the highest priority to be achieved. The proposed model can be effective where the relevant economic order quantity and inventory costs can be prioritized if necessary.