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Topic Review
Complexity of Needs Model (DEA)
Data Envelopment Analysis (DEA) is a powerful non-parametric engineering tool for estimating technical efficiency and the production capacity of service units. The Complex-of-Needs Allocation Model proposed by Nepomuceno et al. (2020) is a two-step methodology for prioritizing hospital bed vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through Data Envelopment Analysis and incorporates the Complexity of Needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient health-care units presenting less complex bed slacks to be reduced, i.e. to be allocated for patients presenting more severe conditions.
  • 701
  • 08 Apr 2021
Topic Review
Homothetic Behavior of Betweenness Centralities
       In mathematics, a homothetic behavior is characterized by a transformation of an affine space by a factor λ and results in an invariance of this space form or configuration, albeit its overall scale changes. In this sense, if two objects or parts of those objects have distinct sizes, but conserve the same appearance, they can be considered homothetic. In networks, the occurrence of homothetic behaviors would imply that a section of the network, when modelled independently, ought to retain a certain regularity in their distribution of centrality hierarchies (visual similitude) when compared to a larger section, independently modelled as well, that contains it. Hence, the smaller network maintains its overall proportions (configuration, hierarchies and values) across scales. This visual similitude was perceived while apposing several Normalized Angular Choice (NACH) models, a Space Syntax’ derivative from mathematical betweenness. Network homotheties, due to their invariability in form and value, can be used as an alternative to extensive network generalization for the construction of large spatial networks. Hence, data maps can be constructed sooner and more accurately as “pieces of a puzzle”, since each individual lesser scale graph possesses a faster processing time.
  • 642
  • 21 Feb 2021
Topic Review
Global Financial Crisis Impact on SA Car Sales
In both developed and developing nations, with South Africa (SA) being one of the latter, the motor vehicle industry is one of the most important sectors. The SA automobile industry was not unaffected by the 2007/2008 global financial crisis (GFC).
  • 543
  • 16 May 2023
Topic Review
Chatbot-Based Natural Language Interfaces for Data Visualisation
Reality (AR) or Virtual Reality (VR), particularly for advanced visualisations, expanding guidance strategies beyond current limitations, adopting intelligent visual mapping techniques, and incorporating more sophisticated interaction methods. 
  • 437
  • 28 Jun 2023
Topic Review
Resampling under Complex Sampling Designs
In principle, survey data are an ideal context to apply resampling methods to approximate the (unknown) sampling distribution of statistics, due to both a usually large sample size and data of controlled quality. However, survey data cannot be generally assumed independent and identically distributed (i.i.d.) so that any resampling methodologies to be used in sampling from finite populations must be adapted to account for the sample design effect. A principled appraisal is given and discussed here.
  • 427
  • 31 Mar 2022
Topic Review
Machine Learning for Hydropower Generation
Hydropower is the most prevalent source of renewable energy production worldwide. As the global demand for robust and ecologically sustainable energy production increases, developing and enhancing the current energy production processes is essential. In the past decade, machine learning has contributed significantly to various fields, and hydropower is no exception. All three horizons of hydropower models could benefit from machine learning: short-term, medium-term, and long-term. Dynamic programming is used in the majority of hydropower scheduling models.
  • 421
  • 27 Jun 2023
Topic Review
Non-Malleable Codes
The notion of non-malleable codes was introduced in 2010 by Dziembowski, Pietrzak, and Wichs, for relaxing the notion of error-correction and error-detection. Informally, a code is non-malleable if the message contained in a modified code-word is either the original message, or a completely unrelated value. Non-malleable codes provide a useful and meaningful security guarantee in situations where traditional error-correction and error-detection is impossible; for example, when the attacker can completely overwrite the encoded message. Although such codes do not exist if the family of "tampering functions" F is completely unrestricted, they are known to exist for many broad tampering families F.
  • 377
  • 16 Nov 2022
Topic Review
Requirements of Compression in Key-Value Stores
A key–value store is a de facto standard database for unstructured big data. Key–value stores, such as Google’s LevelDB and Meta’s RocksDB, have emerged as a popular solution for managing unstructured data due to their ability to handle diverse data types with a simple key–value abstraction. Simultaneously, a multitude of data management tools have actively adopted compression techniques, such as Snappy and Zstd, to effectively reduce data volume.
  • 342
  • 27 Oct 2023
Topic Review
Generating Primes
In computational number theory, a variety of algorithms make it possible to generate prime numbers efficiently. These are used in various applications, for example hashing, public-key cryptography, and search of prime factors in large numbers. For relatively small numbers, it is possible to just apply trial division to each successive odd number. Prime sieves are almost always faster.
  • 339
  • 07 Nov 2022
Topic Review
Mathematical Background of 5D model of the aorta
Visualization is crucial for the display and understanding of medical image data. For diagnostic and surgical planning, radiologists and surgeons must be able to evaluate the data appropriately. Many imaging systems’ data can incorporate both functional and structural information, resulting in 4D datasets. When the image contains spectral information, it can be extended to 5D in some circumstances. Overall, 5D imaging reveals more information than 4D imaging.
  • 333
  • 17 Jan 2022
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