A user‐oriented interface for generalised informetric analysis based on applying advanced data modelling techniques

In this paper, the authors' goal was to report their novel presurgical simulation method applying interactive virtual simulation (IVS) using 3D computer graphics (CG) data and microscopic observation of color-printed plaster models based on these CG data in surgery for skull base and deep tumors. For 25 operations in 23 patients with skull base or deep intracranial tumors (meningiomas, schwannomas, epidermoid tumors, chordomas, and others), the authors carried out presurgical simulation based on 3D CG data created by image analysis for radiological data. Interactive virtual simulation was performed by modifying the 3D CG data to imitate various surgical procedures, such as bone drilling, brain retraction, and tumor removal, with manipulation of a haptic device. The authors also produced color-printed plaster models of modified 3D CG data by a selective laser sintering method and observed them under the operative microscope. In all patients, IVS provided detailed and realistic surgical perspectives of sufficient quality, thereby allowing surgeons to determine an appropriate and feasible surgical approach. Surgeons agreed that in 44% of the 25 operations IVS showed high utility (as indicated by a rating of "prominent") in comprehending 3D microsurgical anatomies for which reconstruction using only 2D images was complicated. Microscopic observation of color-printed plaster models in 12 patients provided further utility in confirming realistic surgical anatomies. The authors' presurgical simulation method applying advanced 3D imaging and modeling techniques provided a realistic environment for practicing microsurgical procedures virtually and enabled the authors to ascertain complex microsurgical anatomy, to determine the optimal surgical strategies, and also to efficiently educate neurosurgical trainees, especially during surgery for skull base and deep tumors.

In the past thirty years, modeling techniques in computer graphics have evolved significantly as the field has matured and attempted to portray the complexities of nature. Polygonal models, patches, points, and lines are insufficient to represent the complexities of natural objects and intricate man-made objects in a manageable and controllable fashion. Higher-level modeling techniques have been developed to provide an abstraction of the model, encode classes of objects, and allow high-level control and specification of the model. The goal of these advanced modeling techniques is to provide a concise, efficient, flexible, and controllable mechanism for specifying and animating models of complex objects and natural phenomena. Most of these advanced modeling techniques can be considered procedural modeling techniques: code segments or algorithms are used to abstract and encode the details of the model instead of explicitly storing vast numbers of low-level primitives. The use of algorithms unburdens the modeler/animator of low-level control, provides great flexibility, and allows amplification of his efforts through parametric control: a few parameters to the model yield large amounts of geometric details (Smith [1984] called this “database amplification”). This survey examines several types of procedural techniques, including fractals, grammar-based models, volumetric procedural models, implicit surfaces, and particle systems.

Radiators play an important role in providing a comfortable and safe indoor environment while maintaining high-energy efficiency. In the perspective of future climate change with expected larger temperature fluctuations and the rapidly changing heat supply and demand, it is required that the current radiator technology is adaptable. The heat supply is changing towards a lower supply temperature to enable an increase in energy efficiency and an increase in the share of renewable energy. Simultaneously, both the heat supply and demand are expected to have more variations in the future. An additional concern that has come into more focus after the experience with the COVID 19 pandemic is the prevention of the spread of infection in indoor environments. Researchers have extensively studied several innovations in radiator technologies and their deployment that addresses these challenges. Some of the solutions available in the literature include floor heating, ceiling heating, ventilation radiator, stratum ventilation. Researchers have used advanced modeling and experimental techniques to understand how to deploy different types of radiator technologies. This review summarizes solutions in the literature that address these challenges and identifies knowledge gaps that need to be addressed. In particular, this study explores the gaps in knowledge of practical issues, such as the position of furniture and the position of people, which have received less attention in the literature. Research that addresses the effect of radiators on ventilation and a healthy indoor environment is also of particular interest in this review.

Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT. This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem. Experiments show that with the specially designed deep transition modules, our DTMT can achieve remarkable improvements on translation quality. Experimental results on Chinese⇒English translation task show that DTMT can outperform the Transformer model by +2.09 BLEU points and achieve the best results ever reported in the same dataset. On WMT14 English⇒German and English⇒French translation tasks, DTMT shows superior quality to the state-of-the-art NMT systems, including the Transformer and the RNMT+.

Micro-characterization and three dimensional modeling of very large waveguide arrays by selective area growth for photonic integrated circuits

High demand of Liquefied Natural Gas (LNG) in recent time requires carriers in more frequent operations in order to meet customers' needs. To ensure that the carriers are always reliable in service, it has become necessary to adopt various advanced modelling techniques such as Genetic Algorithm (GA), fuzzy logic and Evidential Reasoning (ER) for risk/safety assessment and maintenance modelling of carrier operations. These advanced computational techniques can help to overcome challenges posed by uncertainties associated with the carrier operations. Their usefulness is demonstrated using case studies in this research. Firstly, two major hazards of carrier operations such as of containment and LNG spill from transfer are identified and estimated as high risk ones using a risk matrix technique and expert judgement. The causes (failure modeslbasic events) of these high risk hazards are analysed using a Fault Tree Analysis (FTA). The failure logics of their failure modes are established and Boolean algebra is applied to facilitate the evaluation of the failure probabilities and frequencies. Secondly, a GA model is developed to improve the safety levels of the containment system and transfer arm, to minimise their maintenance costs and to realise optimal resource management. The GA is used to optimise a risk model that is developed with exponential distribution and parameters such as failure frequencies, unit costs of maintenance and new maintenance costs of the containment system and transfer arm. Thirdly, the uncertainties of some parameters in the GA model such as unit costs of maintenance are subdued using the strength of Fuzzy Rule Base (FRB) in combination with GA. 125 fuzzy rules of carrier system maintenance cost are developed, which makes it possible to facilitate the evaluation of maintenance cost in any specific risk-based operation. The outcomes of unit costs of maintenance are used in the GA based risk model to update the optimal management of maintenance cost. Finally, the uncertainties of failure modes of the containment system and transfer arm are investigated and treated based on the Formal Safety Assessment (FSA) principle using a Fuzzy ER (FER) approach. The fuzzy logic is used to estimate the safety/risk levels of those failure modes while the ER is used to synthesise them to facilitate the estimation of safety/risk levels of the top events. Risk Control Options (RCOs) are developed to manage high level risks. The costs for each of the RCOs are estimated and synthesised using ER, which facilitated the investigation of the best RCOs in risk-based decision making. There is no doubt that the methodologies proposed possess significant potential for use in improving safety and maintenance of carrier operations based on the verifications of their corresponding test cases. Accordingly, the developed models can be integrated to formulate a platform to facilitate risk assessment and maintenance management of carrier systems in situations where traditional techniques cannot be applied with confidence.

Stimulation fluids injected into a reservoir take the path of least resistance, leaving behind under-stimulated areas. As a result, the stimulation efficiency and effectiveness are reduced, affecting the overall production potential. The placement efficiency of a stimulation treatment depends on maximizing fluids contact in the zone of interest (good penetration) and uniform distribution across the section. To achieve uniform distribution, existing higher permeability areas or natural fractures must be efficiently and temporarily blocked, diverting the treatment towards the section with low permeability and higher skin. This process is known as diversion.In this paper, we utilize an analytical model based on computational fluid dynamics and discrete element modeling to simulate treatment placement efficiency and diversion effectiveness in high-pressure/high-temperature (HPHT) carbonate formations, from a long open hole highly fractured reservoir interval in a well in South America. The case study demonstrates that parameters playing a key role were flow rate, spacers between diverting pills, type of diversion fluid carrier, rheology of carrier fluid, number of diversion stages, and displacement rate. The modeling calibrated against an actual field case demonstrates the space for improvement that can be customized depending upon the type of application.The results indicate that without using diversion, the lower part of the openhole section is left under-stimulated in comparison to the top section as the top section had the presence of natural fractures. When diversion was utilized to optimize the design, the stimulation efficiency in the lower section improved considerably and ultimately had a high impact on the production of the well. This paper uses an actual case study to demonstrate the value created and overall production enhancement despite the excellent results achieved. It also summarizes the engineering workflow to optimize diversion design in carbonates HPHT openhole formations.We believe that an engineering approach is critical in the design of a successful stimulation in an open hole with considerable presence of natural fractures in some parts of the section of interest. The results demonstrate the effectiveness of advanced modeling in evenly distributing the stimulation fluid and thereby increasing its effectiveness enhancing the production across the target zone. A case study from a field job in South America will be presented based on job evaluation and actual well production performance. Actual well intervention and treatment design are discussed. The lessons learned from this case study can be applied for stimulation design and planning for future jobs in the area.

This paper describes the general properties of complex objects in engineering designs. There are two types of complex objects: (i) the complex structural objects which describe the physical composition of the design, and (ii) the complex functional objects which describe the behavior of the design and its components. Data manipulation operations on complex structural objects are governed by a set of structural invariants. Similarly, the validation of functional abstraction is governed by a set of functional invariants. The structure-function interactions are represented by interaction objects that describe a set of mappings. These three object types constitute the Structure-Function paradigm. The S-F paradigm can be used to represent engineering designs and active environments, monitor manufacturing operations and industrial processes, and carry out simulations.

Based on the particular case of the average impact factor of a subfield versus the impact factor of this subfield as a whole, the difference is studied between an average of quotients, denoted as AQ, and a global average, obtained as a quotient of averages, and denoted as GQ. In the case of impact factors, AQ becomes the average impact factor of a field, and GQ becomes its global impact factor.

The application of health systems analysis points to the need to specify theoretically the circumstances under which health disparities exist in the USA as well as in other countries. This paper highlights the importance of developing major themes in global health research and formulating research strategies to investigate the dynamic forces of health status change and human adaptation in small geographical areas. The concepts based on systems theory are used to build a theoretically informed framework to guide ecological research on global health. The use of advanced statistical and dynamic modelling techniques enables researchers to better understand the determinants and consequences of global health and population change. The research foci should be extended beyond the conventional bounds of health disparity studies. Intervention research should be designed and executed to produce new knowledge for improving medical practice and global health.

A strategic review on enhanced DEM simulation and advanced 3-D particle printing techniques to improve pipe-jacking force prediction

Digital twin technology is the talking point of academia and industry. When defining a digital twin, new modeling paradigms and computational methods are needed. Developments in the Internet of Things and advanced simulation and modeling techniques have provided new strategies for building complex digital twins. The digital twin is a virtual entity representation of the physical entity, such as a product or a process. This virtual entity is a collection of computationally complex knowledge models that embeds all the information of the physical world. To that end, this article proposes a graph-based representation of the virtual entity. This graph-based representation provides a method to visualize the parameter and their interactions across different modeling domains. However, the virtual entity graph becomes inherently complex with multiple parameters for a complex multidimensional physical system. This research contributes to the body of knowledge with a novel graph-based model reduction method that simplifies the virtual entity analysis. The graph-based model reduction method uses graph structure preserving algorithms and Dempster–Shaffer Theory to provide the importance of the parameters in the virtual entity. The graph-based model reduction method is validated by benchmarking it against the random forest regressor method. The method is tested on a turbo compressor case study. In the future, a method such as graph-based model reduction needs to be integrated with digital twin frameworks to provide digital services by the twin efficiently.

Improved element-particle coupling strategy with δ-SPH and particle shifting for modeling sloshing with rigid or deformable structures

To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay. Observational cohort study. One hundred and four ICUs in 45 U.S. hospitals. Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria. None. The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (< or =1.7 days) or long (> or =9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1. The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay.

An investigation of consumer intention to use pick-up point services for last-mile distribution in a developing country