Digitization of pathology has been proposed as an essential mitigation strategy for the severe staffing crisis facing most pathology departments. Despite its benefits, several barriers have prevented widespread adoption of digital workflows, including cost and pathologist reluctance due to subjective image quality concerns. In this work, we quantitatively determine the minimum image quality requirements for binary classification of histopathology images of breast tissue in terms of spatial and sampling resolution. We train an ensemble of deep learning classifier models on publicly available datasets to obtain a baseline accuracy and computationally degrade these images according to our derived theoretical model to identify the minimum resolution necessary for acceptable diagnostic accuracy. Our results show that images can be degraded significantly below the resolution of most commercial whole-slide imaging systems while maintaining reasonable accuracy, demonstrating that macroscopic features are sufficient for binary classification of stained breast tissue. A rapid low-cost imaging system capable of identifying healthy tissue not requiring human assessment could serve as a triage system for reducing caseloads and alleviating the significant strain on the current workforce.

Optoelectronic tweezer-driven microrobots (OETdMs) are a versatile micromanipulation technology based on the application of light induced dielectrophoresis to move small dielectric structures (microrobots) across a photoconductive substrate. The microrobots in turn can be used to exert forces on secondary objects and carry out a wide range of micromanipulation operations, including collecting, transporting and depositing microscopic cargos. In contrast to alternative (direct) micromanipulation techniques, OETdMs are relatively gentle, making them particularly well suited to interacting with sensitive objects such as biological cells. However, at present such systems are used exclusively under manual control by a human operator. This limits the capacity for simultaneous control of multiple microrobots, reducing both experimental throughput and the possibility of cooperative multi-robot operations. In this article, we describe an approach to automated targeting and path planning to enable open-loop control of multiple microrobots. We demonstrate the performance of the method in practice, using microrobots to simultaneously collect, transport and deposit silica microspheres. Using computational simulations based on real microscopic image data, we investigate the capacity of microrobots to collect target cells from within a dissociated tissue culture. Our results indicate the feasibility of using OETdMs to autonomously carry out micromanipulation tasks within complex, unstructured environments.

The continued growth of healthcare information systems (HCIS) promises to improve quality of care, lower costs, and streamline the entire healthcare system. But the resulting dependence on electronic medical records (EMRs) has also kindled patient concern about who has access to sensitive medical records. Healthcare organizations are obliged to protect patient records under HIPAA. The purpose of this study is to develop a formal privacy policy to protect the privacy and security of EMRs. This article describes the impact of EMRs and HIPAA on patient privacy in healthcare. It proposes access control and audit log policies to safeguard patient privacy. To illustrate the best practices in the healthcare industry, this article presents the case of the University of Texas M. D. Anderson Cancer Center. The case demonstrates that it is critical for a healthcare organization to have a privacy policy.

This paper develops an adaptive scheduling policy for dynamic manufacturing systems. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. The inductive learning methodology used for constructing this state-dependent scheduling policy also provides an understanding of the relative importance of the various system parameters in determining the appropriate dispatching rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tight due date jobs and machine breakdowns.

Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

In this paper, we describe a machine learning technique based on a double-layered architecture and Genetic Algorithms (GAs), which can be used to learn decision rules for financial classification. Once the rules have been acquired, they can be stored in an expert system for future application. Genetic algorithms represent a class of learning algorithms modeled on the biological evolution process. Equipped with unique search behavior and solution-seeking properties, they provide an interesting technique for such classification tasks as bankruptcy prediction and credit analysis. However, some modifications to the basic genetic algorithms are necessary in order to make the method suitable for solving these classification problems. One of the objectives of this paper is to develop a representation scheme for the concepts to be learned that can be incorporated in a genetic algorithm. More importantly, we expand on the concept of the genetic algorithm and develop a learning method called the Double-layered Learning System (DLS) that integrates the genetic algorithm with a similarity-based learning technique called the Probabilistic Learning System (PLS1). By combining a learning paradigm that searches through the “hypothesis space” (GA) with a paradigm that searches through the “instance space” (PLS1), DLS permits synergistic improvement of the learning performance. We demonstrate the feasibility of such a hybrid approach computationally by presenting the design, implementation, and performance evaluation of DLS. DLS proves to be an effective improvement over both GA and PLS1. The efficacy of the results points towards the importance of such hybrid approaches in providing more robust machine learning methods. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Artificial Neural Networks present a new paradigm for decision support that is adaptive and capable of integrating knowledge acquisition, problem solving, and learning. In this paper, we show that the performance of the classical back-propagation algorithm for training artificial neural networks can be improved by applying modified approaches which have better convergence properties, and are faster than the commonly used steepest gradient search method. Simulation results on test problems show that the proposed algorithms perform better than the steepest gradient search algorithm in terms of faster convergence with learning accuracy at least as good as in classical back propagation. Inductive learning algorithms (ID3 and NEWQ) and Probit are also used to compare the relative performances of connectionist, inductive learning and statistical procedures. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

The possibility of applying explanation-based learning (EBL), a technique from machine learning, to intelligent process planning is explored. There are currently two major approaches to process planning: the variant approach and the generative approach. Each has advantages and deficiencies. The authors' hypothesis is that EBL could successfully unite these apparently disparate approaches. EBL can be used to transform a traditional weak method planner into a strong method skeletal planner by acquiring strong method concepts which are generalized weak-method explanations of observed episodes. It would seem to be a natural vehicle to unite variant and generative process planning. A learning process planner, called EXBLIPP is implemented to test the authors' hypothesis. It is found that the system possesses many of the intended advantages. It is demonstrated that the EBL capability enables the process planning system to learn new schemata which yield many of the advantages of variant process planning.>

Planning in flexible manufacturing systems must take into account both the multiprocessing environment and the dynamically changing states. This paper uses a knowledge-based approach to handle such a planning system, where three levels-i.e., the control, state, and operational levels-constitute its manufacturing knowledge. The construction of plans for multiple manufacturing jobs requires four steps: (1) linear plan generation, (2) conflict detection, (3) plan synthesis, and (4) plan revision. To achieve goals, these steps will search for planning operators using a backward chaining inference procedure. To perform the scheduling and sequencing of the multiple jobs within a flexible manufacturing cell, the planning system modifies the nonlinear planning approach and adds resources and durations information to the action formalism. Because the plan-generation process is goal-directed, the dynamically adjustable plan can be constructed on-line.