Cracks are omnipresent in materials and lead to billions of dollars in losses annually due to catastrophic and spectacular failures. Nondestructive wave-based methods are used to identify cracks, but these methods are cumbersome and require experts, leading to limited investigation. This research propose MicroCracksAttNet50E model that leverages numerical data to detect and localize damage in materials and structures, with a particular focus on microcracks that are imperceptible to the naked eye or conventional imaging methods but have the potential to develop into larger, hazardous fissures. The paper also includes a comparative analysis between the current study and the previous work, specifically evaluating the model that performed best in the prior paper (1D-DenseNet-Resize&Conv). Despite having approximately eight times fewer layers and over 200,000 fewer trainable parameters than DENSE variants, MicroCracksAttNet50E achieves similar or even better performance, with an accuracy of 0.860 and a precision of 0.881, compared to the best-performing DENSE model with an accuracy of 0.836 and a precision of 0.875. This improvement primarily highlights the effectiveness of the attention mechanism in MicroCracksAttNet50E, which focuses on critical areas to detect smaller cracks more accurately.

Damage in metals, composites, and cemented porous solids, in the form of cracks, inclusions, and voids, is a nontrivial problem. Many experimental, numerical, and analytical methods have been proposed in the past, with some recent models deploying neural networks. However, past methods often lack the accuracy and precision needed to identify microcracks. This paper presents the MicroCracksMetaNet50E (MCMN) deep learning model, inspired by Meta's Segment Anything Model (SAM). MCMN is trained with numerical data produced by an advanced mesoscale numerical model for spatial crack detection inside various materials. MicroCracksMetaNet50E achieves an accuracy of 0.867% and a precision of 0.906% in identifying microcracks. The robust performance of MCMN is highlighted, showcasing a notable advancement that its capabilities and propels the field into uncharted territories by expanding oppor-tunities for the comprehensive exploration of additional datasets. The method could be adopted for damage detection in metals and composites in manufacturing as well as structural health monitoring.

In this paper, we study the upper bounds of memory storage for two different allocators. In the first case, we consider a general allocator that can allocate memory blocks anywhere in the available heap space. In the second case, a more economical allocator constrained by the address-ordered first-fit allocation policy is considered. We derive the upper bound of memory usage for all allocators and present a systematic approach to search for allocation/deallocation patterns that might lead to the largest fragmentation. These results are beneficial in embedded systems where memory usage must be reduced and predictable because of lack of swapping facility. They are also useful in other types of computing systems.

Dynamic memory management can make up to 30% of total program execution time. Object oriented languages like C++ allocate and free dynamic memory prolifically. Since computer memory is a limited resource its efficient utilization is required to minimize wastage and keep costs down. Memory management algorithms such as best fit seem to perform most efficiently in terms of space cost while simple segregated storage seems to minimize the time cost. There is a trade-off between time and space costs. We have developed a new general purpose hybrid algorithm that shows excellent performance with respect to both time and space in comparison to the Doug Lea version 2.7.0 dynamic memory allocator.

Dynamic memory management has been a high cost component in many software systems. Studies have shown that memory intensive C programs can consume up to 30% of the program runtime in memory allocation and liberation. The OOP language system tends to perform object creation and deletion prolifically. An empirical study shown that C++ programs can have ten times more memory allocation and deallocation than comparable C programs. However, the allocation behavior of C++ programs is rarely reported. This paper attempts to locate where the dynamic memory allocations are coming from and report an empirical study of dynamic memory invocations in C++ programs. Firstly, this paper summarizes the hypothesis of situations that invoke the dynamic memory management explicitly and implicitly. They are: constructors, copy constructors, overloading assignment operator=, type conversions and application specific member functions. Secondly, the development of a source code level tracing tool is reported as the procedure to investigate the hypothesis. Thirdly, results include behavioral patterns of memory allocations. With these patterns, we may increase the reusability of the resources. For example, a profile-based strategy can be used to improve the performance of dynamic memory management. The C++ programs that were traced include Java compiler, CORBA compliant and visual framework.