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This paper aims to investigate the influence of droplet infiltration and sliding on the deposition size and make a uniform deposition by controlling the interaction between droplets, using the three-dimensional lattice Boltzmann method (LBM) based on the actual working condition.

D3Q19 Shan-Chen LB approach is developed and optimized based on the metal droplet deposition. The Carnahan-Starling equation of state and transition layers are introduced to maintain the greater stability and low pseudo velocities. In addition, an additional collision term is adopted to implement immersed moving boundary scheme to deal with no-slip boundaries on the front of the phase change.

The numerical results show that the new¬ incoming droplet wet and slide off the solidified surface and the rejection between droplets are the reasons for the deviation of the actual deposition length. The total length of the longitudinal section negatively correlates with the deposition distance. To improve the dimensional accuracy, the deposition distance and repulsion rate need to be guaranteed. The optimal deposition distance is found to have a negative linear correlation with wettability.

The numerical model developed in this paper will help predict the continuous metal droplet deposition and provide guidance for the selection of deposition distance.

This paper aims to apply the augmented reality (AR) technology to assembly design in the early design stage. A proof‐of‐concept system with AR interface is developed.

Through AR interface, designers can design the assembly on the real assembly platform. The system helps users to design the assembly features to provide proper part‐part constraints in the early design stage. The virtual assembly features are rendered on the real assembly platform using AR registration techniques. The new evaluated assembly parts can be generated in the AR interface and assembled to assembly platform through assembly features. The model‐based collision detection technique is implemented for assembly constraint evaluation.

With AR interface, it would be possible to combine some of the benefits of both physical and virtual prototyping (VP). The AR environment can save a lot of computation resource compared to a totally virtual environment. Working on real assembly platform, designers have more realistic feel and the ability to design an assembly in a more intuitive way.

More interaction tools need to be developed to support the complex assembly design efficiently.

The presented system encourages designers to consider the assembly issues in the early design stage. The primitive 3D models of assembly parts with proper part‐part constraints are generated using the system before doing detailed geometry design.

A new markerless registration approach for AR system is presented. This generic approach can be also used for other AR applications.

The purpose of this paper is to present topological derivatives-based reconstruction algorithms to solve an inverse scattering problem for penetrable obstacles.

The method consists in rewriting the inverse reconstruction problem as a topology optimization problem and then to use the concept of topological derivatives to seek a higher-order asymptotic expansion for the topologically perturbed cost functional. Such expansion is truncated and then minimized with respect to the parameters under consideration, which leads to noniterative second-order reconstruction algorithms.

In this paper, the authors develop two different classes of noniterative second-order reconstruction algorithms that are able to accurately recover the unknown penetrable obstacles from partial measurements of a field generated by incident waves.

The current paper is a pioneer work in developing a reconstruction method entirely based on topological derivatives for solving an inverse scattering problem with penetrable obstacles. Both algorithms proposed here are able to return the number, location and size of multiple hidden and unknown obstacles in just one step. In summary, the main features of these algorithms lie in the fact that they are noniterative and thus, very robust with respect to noisy data as well as independent of initial guesses.

The purpose of this study is to investigate the effects of the cementite morphology on the hydrogen trapping behavior in low-alloy pipeline steel.

In this study, the hydrogen trapping behavior in low-alloy pipeline steel was quantitatively studied by a combination of microstructural observations, electrochemical hydrogen permeation experiments and thermal desorption spectroscopy (TDS) analyses.

P-1 and P-2 steels are two samples with different microstructures. The morphology of cementite precipitates in the P-1 and P-2 steels was different. Lamellar cementite is present in P-2 steel and only granular cementite in P-1 steel, which led to a better irreversible hydrogen trapping ability of P-2 steel, which was confirmed by subsequent hydrogen permeation and TDS experiments.

The study of these deep hydrogen trap sites is helpful in improving the hydrogen embrittlement resistance of low-alloy pipeline steels.

Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features.

The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector.

Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1.

In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

Ineffective management inventory of medical products such as blood and vaccines can create severe repercussions for hospitals, clinics or medical enterprises, such as surgery delays and postponements. Inventory sharing is a form of horizontal collaboration that can provide solutions to key actors of the healthcare supply chain (HSC), yet no prior study reviewed this topic.

This study conducts a systematic literature review of thirty-nine inventory-sharing studies in the context of HSCs published from 2012 until early 2022. The descriptive and thematic analyses include chronological distribution, geographical location, comparison between developed/developing regions, stakeholder and incident analysis.

Thematic analysis classified inventory sharing among five product supply chains (blood, medical supplies, medicines, vaccines and generic medical products). Benefits include shortage reduction, cost minimisation, and wastage mitigation. Barriers include (1) IT infrastructure, (2) social systems, (3) cost and (4) supply chain operations. Perishable inventory policies include Fresher-First (FF), Last-Expire-First-Out (LEFO), First-In-First-Out (FIFO) and First-Expire-First-Out (FEFO). The analysis also showed differences between developed and developing countries. The study identifies several future research opportunities that include (1) product utilisation rate, (2) cost reductions, (3) shortage mitigation and (4) waste reduction.

No prior study has systematically reviewed inventory sharing in HSCs to reveal benefits, barriers, patterns and gaps in the current literature. It makes five propositions and develops a research model to guide future research. The study concludes with theoretical and managerial implications.

Gut microbial changes are associated with diseases such as obesity and type 2 diabetes and may be influenced by diet patterns. Thus, this paper aims to investigate the effects of alcohol, sodium chloride (NaCl) and dietary restriction on the composition of the gut microflora.

Five-week-old male C57BL/6N mice were orally administered by gavage with ethanol (ET; 4 g/kg), 200 µL of 6% NaCl (NC), or equivalent volumes of phosphate-buffered saline (ET-control and NC-control) every two days for 10 weeks. The mice were also orally administered by gavage with the recommended diet (RD-control; 3 g per mouse per day), or with 40% (RD-40) or 60% restricted level (RD-60). DNA samples obtained from mice ceca were sequenced using the Illumina MiSeq Sequencing system; raw data were analyzed using BIOiPLUG software.

This study shows that high ethanol and NaCl consumption, and diet restriction can change the composition of the intestinal microflora, especially Akkermansia muciniphila in ET (p = 0.0050) and NT (p = 0.0366) groups. In addition, the ratio of Bacteriodetes/Firmicutes and the diversity of microflora were generally reduced (p = 0.0487-0.4929).

These results raise the possibility of a relationship between diet patterns, change of intestinal microbiome and disease, which must be further evaluated.

COVID-19 has hard-hit the global economy when the majority of developed countries could not demonstrate effective resolutions even a year after the outbreak. This study aims to present how government or policymakers could minimize the negative impacts of major incidents (pandemic) and speed up the process of recovery. It was also intended to identify effective strategies for crisis management.

A mixed research method was applied. Qualitative research studies of interviews were used to collect primary data from the stakeholders while archival research method of textual and documental information was used for secondary data on the pandemic facts.

The impacts of the governmental and enterprise actions with a focus on effective mega-crisis management strategies were structured based on the three themes, namely, appropriate measures, timely decisions and the role of the government. These strategies can minimize the destruction to the economy (or organization) while stabilize the emotional and psychological aspects of the people during the crisis and lead the society back on track once the crisis starts to diminish.

The experience of Macao in successfully responding to the crisis of pandemic provides constructive references to policymakers on the set up of timely strategic plans and business management on remedy actions, particularly to regions where the pandemic is still out of control.