Testing Phase Research Articles

Prior familiarity enhances recognition memory of faces, not just images of faces, when accompanied by conceptual information.

A recent paper reported that recognition discriminability was improved for faces that were familiarized prior to study, but only if that familiarization protocol included conceptual information, like a name (Akan & Benjamin, Journal of Memory and Language,131, 104,433, 2023). In those experiments, familiarity with each facial identity was gained through exposures to the same facial image prior to study, and memory for each facial identity was tested using the same images across study and test. That design characteristic has a serious constraint on generality, since it is possible that prior conceptual information enhances memory for images (of faces), but not for the representation of the face itself. Here we evaluated whether this finding generalizes to a paradigm in which each exposure of a face is a novel image. In two experiments, faces were familiarized with orienting tasks that induced more perceptual or more conceptual processing prior to study and test phases. Results from recognition tests replicated the results from Akan and Benjamin (2023): (1) Discriminability was enhanced when prior familiarity involved conceptual processing but not when it involved perceptual processing, and (2) familiarity gained through either perceptual or conceptual processing led to an increase in both correct and false identifications. This successful replication in a design with exclusively novel images indicates that the discriminability advantage provided by conceptual familiarity goes beyond memory for facial images and applies to memory for faces. These findings have implications in practical contexts, such as eyewitness identification situations involving suspects who are previously known or familiar to the witness.

Energy consumption forecasting in PCM-integration buildings considering building and environmental parameters for future climate scenarios

Despite numerous machine learning methods being employed to predict the energy consumption of PCM-integrated buildings, key research gaps remain. Most studies focus solely on building parameters while omitting important environmental parameters, including precipitation and air pressure. No study evaluated and proposed prediction models for PCM-integrated buildings considering future climate scenarios. Also, as per the authors′ knowledge, no researcher has assessed the impact of variations in the hyperparameter, especially for decision tree-based prediction models to develop a reliable prediction model with less complexity and a high degree of interpretability between independent and dependent variables. This research addresses these gaps by evaluating fine, medium, and coarse decision trees for predicting energy consumption in PCM-integrated buildings under future climate scenarios by considering extensive building and environmental parameters simultaneously. A database for energy consumption was created through energy simulations for 11 cities in hot semi-arid climates. The Fine Decision Tree (FDT3) emerged as the most accurate prediction model, with R2 values over 94 % in training and testing phases based on model evaluation and validation processes. Parametric analysis further revealed that both environmental and building parameters are crucial in accurately predicting the energy consumption of PCM-integrated buildings using FDT3.

Adaptive Weighted Multi-kernel Learning for Blast-Induced Flyrock Distance Prediction

AbstractIn the field of civil and mining engineering, blasting operations are widely and frequently used for rock excavation, However, some undesirable environmental problems induced by blasting operations cannot be ignored. Blast-induced flyrock is one important issue induced by blasting operation, which needs to be well predicted to identify the blasting zone’s safety zone. This study introduces an adaptive weighted multi-kernel learning model (AW-MKL) to provide an accurate prediction of blast-induced flyrock distance in Sungun Copper Mine site. The proposed model uses a combination of multi-kernel learning (MKL) approach and adaptive weighting strategy based on weighted Euclidean distance and modified local outlier factor (MLOF) to maximally improve the predictive ability of kernel ridge regression (KRR). To demonstrate the superiority of the proposed approach, six machine learning models were developed as comparisons, i.e., KRR, RF, GBDT, SVM, M5 Tree, MARS and AdaBoost. The outcomes of the proposed method achieved the highest accuracy in testing phase, with RMSE of 2.05, MAE of 0.98 and VAF of 99.92, which confirmed the strong predictive capability of the proposed AW-MKL in predicting blast-induced flyrock distance.

PROTA: A Robust Tool for Protamine Prediction Using a Hybrid Approach of Machine Learning and Deep Learning

Protamines play a critical role in DNA compaction and stabilization in sperm cells, significantly influencing male fertility and various biotechnological applications. Traditionally, identifying these proteins is a challenging and time-consuming process due to their species-specific variability and complexity. Leveraging advancements in computational biology, we present PROTA, a novel tool that combines machine learning (ML) and deep learning (DL) techniques to predict protamines with high accuracy. For the first time, we integrate Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of protamine prediction. Our methodology evaluated multiple ML models, including Light Gradient-Boosting Machine (LIGHTGBM), Multilayer Perceptron (MLP), Random Forest (RF), eXtreme Gradient Boosting (XGBOOST), k-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Radial Basis Function-Support Vector Machine (RBF-SVM). During ten-fold cross-validation on our training dataset, the MLP model with GAN-augmented data demonstrated superior performance metrics: 0.997 accuracy, 0.997 F1 score, 0.998 precision, 0.997 sensitivity, and 1.0 AUC. In the independent testing phase, this model achieved 0.999 accuracy, 0.999 F1 score, 1.0 precision, 0.999 sensitivity, and 1.0 AUC. These results establish PROTA, accessible via a user-friendly web application. We anticipate that PROTA will be a crucial resource for researchers, enabling the rapid and reliable prediction of protamines, thereby advancing our understanding of their roles in reproductive biology, biotechnology, and medicine.

Personality may modulate learning and memory differences in two taxa of the African striped mouse genus Rhabdomys

AbstractDifferent environments place different cognitive demands on constituent taxa. Learning and memory involve cognitive processes with associated costs, and it is expected that different levels of learning will occur in taxa from different environments. Greater memory loads linked to increased environmental complexity require greater learning and memory capacities. We investigated the variation in learning and memory in sister taxa of striped mice (genus Rhabdomys). We studied two populations each of the mesic grassland‐occurring R. d. chakae and the mostly arid‐occurring R. pumilio. We conducted two sets of experiments. (1) In a novel object recognition (NOR) test, we assessed memory by recording the duration of exploration of similar and novel objects by test mice. (2) In an associative learning task, we assessed whether mice could associate specific scents with or without a food incentive or with different quantities of the food incentive in previous training phases. We measured the latency of mice to contact scents in a two‐sample choice in the test phase. In the NOR test, R. pumilio spent less time investigating similar objects in a training trial than R. d. chakae but increased absolute exploration of the novel object when presented with a novel and a familiar object in the retention trial, suggesting a sensitization to the novel object by R. pumilio. In the associative learning experiments, R. pumilio approached the stimuli faster than R. d. chakae, whereas mice from both taxa preferred scents associated with a seed versus no seed and scents associated with 5 seeds versus 1 seed. The data provide evidence of taxon‐level differences in learning and memory, likely related to environmentally modulated personality differences between the taxa.

Prediction of CO2 uptake in bio-waste based porous carbons using model agnostic explainable artificial intelligence

This study introduces comprehensive research on the prediction of the carbon dioxide (CO2) uptake from the biomass-waste derived-porous carbons (BWDPCs), by using scientometrics and model agnostic multi-layered explainable artificial intelligence (XAI) techniques. It aims to identify the main characteristics, and trends that are specific to this domain, and to establish, compare and analyse the four different black box machine learning (ML) models for CO2 uptake prediction. For this study, through model evaluation parameters, and scatter plots, statistical analysis supports the fact that the Extreme Gradient Boosting (XGBoost) model is found to be the best performing model for CO2 uptake prediction with low errors and high coefficient of correlation for both training (MSE: 0.157, RMSE: 0.397, MAE: 0.294, MAPE: 0.112, R2: 0.931) and testing phases (MSE: 0.345, RMSE: 0.588, MAE: 0.461, MAPE: 0.121, R2: 0.860). Now, with the best performing black box ML model as XGBoost model, it serves as the basis for the multi-layered XAI analysis. Using multi-layered XAI techniques to interpret the black box ML model and covert it to a white box model, it makes clearer insights into the significant key features that affect the CO2 uptake both at the global and local level. The study demonstrates that using multi-layered XAI analysis helps in improving the trust of the predictive model and provides a way forward for the application of white box models in CO2 uptake.

Assessing swelling-induced damage in shale samples during triaxial testing

In shale testing, understanding the impact of effective stress and saturation conditions is crucial for accurate material behaviour assessment and parameter determination. In some cases, saturation in triaxial testing starts at low effective stress before ramping up for shearing. However, when in contact with water (or saline water), shales are prone to swelling, particularly at low effective stress levels, which can induce fissures and alter material properties. This study investigates the influence of fluid saturation strategies and stress/pressure variations on the mechanical behaviour of shales, particularly under low effective confinement. Building upon the comprehensive testing campaign (>140 tests) in Crisci et al. (2024), additional tests were conducted on Opalinus Clay shale, focusing on sample saturation methods and loading histories before shearing. The conditions under which tested specimens experience damage were detected through diagnostic indicators such as differences in stress path and lower strength and stiffness compared to intact specimens with identical basic properties. Micro CT scanning confirms that damage is related to the development of fissures. The volumetric changes in specimens were quantified throughout the testing phases and thresholds for tolerable strains and effective stresses, specific to this material, were established. Comparative analysis with Opalinus Clay from shallower depths and other shales globally revealed consistent findings. Notably, it is shown that, for all shale types analyzed, a linear failure envelope emerges in the low to intermediate effective stress regime when filtering out "damaged" specimens. This suggests that non-linear failure envelopes observed in some cases may stem from exposing specimens to low effective stress before shearing.

Perancangan UI/UX Aplikasi E-learning Kampus Universitas Stikubank Dengan Menggunakan Metode Design Thinking

Stikubank University, located in Semarang, has implemented an E-learning application. However, there have been several user complaints regarding its UI (User Interface) and UX (User Experience). Consequently, this issue has been chosen as the subject of research. The research primarily focuses on redesigning the UI and UX using the Design Thinking methodology, which encompasses the stages of Empathize, Define, Ideate, Prototype, and Test. Data collection was conducted through interviews with a total of 6 participants, comprising 4 students and 2 lecturers. The objective of the research is to determine how appealing and user-friendly the current E-learning application at Stikubank University is. For the testing phase, the researcher utilized the System Usability Scale (SUS) to evaluate the usability of the prototype. The testing results using the SUS get a score of 75.75, which the 'good' category (grade B).

Scheduling of memory chips for final testing on parallel machines considering power constraints and deteriorating effects

This paper delves into the intricate scheduling strategies crucial for the final testing phase of memory chip manufacturing within the semiconductor industry and other related sectors. It specifically addresses the complex serial-batching scheduling problem, where memory chips are tested on parallel machines under power constraints and chip deterioration effects. The processing time for each chip is significantly influenced by both the cumulative processing time of preceding chips and the power requirements for testing. We formulate this real-world optimization problem using a mixed integer nonlinear programming model. Exact solutions for small instances are obtained using a commercial solver. However, due to the model's complexity, we also develop heuristic solutions to efficiently handle larger instances. Based on the derivation of structural properties, we develop two tailored heuristic algorithms to determine the schedule for the final testing of memory chips. Additionally, we propose a refined Variable Neighborhood Search algorithm (VNS-H) that seamlessly integrates five local search strategies with two supplementary heuristic algorithms, dynamically alternating between them to ensure a balance between computational efficiency and the quality of the solutions obtained. Additionally, we establish a lower bound to validate the effectiveness of these solutions, particularly for large-scale instances. To validate the efficacy and robustness of our proposed metaheuristic algorithm, we conduct a rigorous comparison of the VNS-H algorithm with five other metaheuristic algorithms that have promising performance in various optimization problems. The results highlight the superior performance of the VNS-H algorithm. In small-scale instances, our VNS-H algorithm achieves an average makespan that is 5.52% lower compared to the original VNS. For large-scale instances, the VNS-H algorithm reduces the average makespan by 13.46% compared to VNS. Finally, we discuss the managerial implications of our findings, providing insights specifically tailored to semiconductor manufacturing enterprises based on the outcomes of this study.

Interpretable machine learning methods to predict the mechanical properties of ABX3 perovskites

This paper proposes the utility of interpretable ensemble learning models for predicting the mechanical properties (bulk, shear and Young moduli) of ABX3 perovskite compounds with the A, B, and X referring to the 3 elements that make the cubic 3-dimensional framework of the perovskite compounds. These models consist of 3 ensemble learning techniques namely CatBoost, Random Forest, and XGBoost. To expand the feature space, robust first-principles density functional theory calculations were used to generate some of the input features, namely elastic constants, density, volume per atom, and ground state energy per atom. The order of the input feature ranking that influences the machine learning (ML) model decisions was then determined. For this, we performed correlation analysis on the multi-dimensional input feature space, suppressed features with high collinearity, and selected features with limited correlation. We trained the three ensemble learning techniques on the desired vectorial input feature representation to predict the mechanical properties. Furthermore, we employed the Shapley Additive Explanations (SHAP) algorithm for analysing the intrinsic decision-making rationality of the ensemble learning models. We measured the performance in the context of the error metrics and coefficient of determination, R2. The results show that XGBoost outperforms other approaches when predicting the shear modulus or Young modulus of the perovskite compounds yielding the least error metrics and the highest R2 value (0.97) in the testing phase. However, both CatBoost and Random Forest outperformed XGBoost when attempting to predict the bulk modulus in the testing phase. The deficiency of the XGBoost in predicting the bulk modulus can be ascribed to an overfitting problem which can occur when the ML model gives accurate predictions for training data but not for test data. Furthermore, the SHAP algorithm provides an insight into the order of feature importance (from highest to lowest). Additionally, we conducted a post-analysis using a holistic ranking to analyse the relative importance of the SHAP feature impact comprehension for the examined ensemble learning techniques. Our findings indicate that the elastic constants are the most important input features influencing the predictive decision of the ensemble learning models.

Does bumblebee preference of continuous over interrupted strings in string-pulling tasks indicate means-end comprehension?

Bumblebees (Bombus terrestris) have been shown to engage in string-pulling behavior to access rewards. The objective of this study was to elucidate whether bumblebees display means-end comprehension in a string-pulling task. We presented bumblebees with two options: one where a string was connected to an artificial flower containing a reward and the other presenting an interrupted string. Bumblebees displayed a consistent preference for pulling connected strings over interrupted ones after training with a stepwise pulling technique. When exposed to novel string colors, bees continued to exhibit a bias towards pulling the connected string. This suggests that bumblebees engage in featural generalization of the visual display of the string connected to the flower in this task. If the view of the string connected to the flower was restricted during the training phase, the proportion of bumblebees choosing the connected strings significantly decreased. Similarly, when the bumblebees were confronted with coiled connected strings during the testing phase, they failed to identify and reject the interrupted strings. This finding underscores the significance of visual consistency in enabling the bumblebees to perform the task successfully. Our results suggest that bumblebees' ability to distinguish between continuous strings and interrupted strings relies on a combination of image matching and associative learning, rather than means-end understanding. These insights contribute to a deeper understanding of the cognitive processes employed by bumblebees when tackling complex spatial tasks.

Study on biomass and polymer catalytic co-pyrolysis product characteristics using machine learning and shapley additive explanations (SHAP)

Biomass and polymer catalytic co-pyrolysis can convert waste into higher-quality fuels, thereby reducing the use of fossil fuels to some extent. However, this process is an extremely complex thermochemical conversion, influenced by numerous factors such as feedstock properties, operational variables, and catalyst. Currently, experimental methods require substantial time and resource investment. Machine learning (ML) can fit and match input and output features based on existing data, achieving extremely high accuracy in the co-pyrolysis process. This study applies advanced ML models to study the biomass and polymer catalytic co-pyrolysis process, with a focus on the yield of pyrolysis products and the variations of the oxygen-containing components in the pyrolysis oil. The best-performing model is used for feature analysis of the correlation between inputs and outputs, based on game theory SHAP analysis. The results indicate a significant negative correlation between the polymer addition ratio and the generation of oxygen-containing components during the co-pyrolysis process. The addition of catalysts promotes the generation of pyrolysis gas during co-pyrolysis but suppresses the yield of pyrolysis oil. Additionally, catalysts significantly inhibit the formation of oxygenates in the pyrolysis oil. The XGBR model shows the highest performance in predicting pyrolysis oil yield, achieving R2 values of 0.98 during training phase and 0.91 during testing phase. The GBR model performs well in predicting the oxygenate composition of pyrolysis oil from small datasets.

Does bumblebee preference of continuous over interrupted strings in string-pulling tasks indicate means-end comprehension?

Bumblebees (Bombus terrestris) have been shown to engage in string-pulling behavior to access rewards. The objective of this study was to elucidate whether bumblebees display means-end comprehension in a string-pulling task. We presented bumblebees with two options: one where a string was connected to an artificial flower containing a reward and the other presenting an interrupted string. Bumblebees displayed a consistent preference for pulling connected strings over interrupted ones after training with a stepwise pulling technique. When exposed to novel string colors, bees continued to exhibit a bias towards pulling the connected string. This suggests that bumblebees engage in featural generalization of the visual display of the string connected to the flower in this task. If the view of the string connected to the flower was restricted during the training phase, the proportion of bumblebees choosing the connected strings significantly decreased. Similarly, when the bumblebees were confronted with coiled connected strings during the testing phase, they failed to identify and reject the interrupted strings. This finding underscores the significance of visual consistency in enabling the bumblebees to perform the task successfully. Our results suggest that bumblebees’ ability to distinguish between continuous strings and interrupted strings relies on a combination of image matching and associative learning, rather than means-end understanding. These insights contribute to a deeper understanding of the cognitive processes employed by bumblebees when tackling complex spatial tasks.

Energy evolution and damage deformation behavior of cemented broken coal specimen under triaxial compression condition

Coal is a major energy source, and deep coal mining often leads to fractured coal around roadways. Grouting is a common method used to reinforce fractured rock masses, where the slurry encases coal particles, forming a cemented broken coal (CBC) body. The content of coal particles (CP) significantly affects the energy characteristics of CBC specimens. To investigate this, a series of triaxial compression tests and triaxial unloading tests were conducted on CBC specimens with varying CP contents. The results indicate that as CP content increases, both peak strength and cohesion decrease, while the internal friction angle increases in both tests. In the triaxial unloading tests, specimens with higher CP content showed tensile-shear failure. Additionally, energy trends during damage were similar in both tests. In triaxial compression tests, dissipated energy increased with CP content, while in the triaxial unloading test, it initially rose and then fell. During the unloading phase of the triaxial unloading tests, the CBC specimen with 40 % CP content exhibited the most significant change in energy increment and its rate. Analysis of damage and deformation characteristics indicated that damage, deformation, and dilation were effectively restrained in CBC specimens with 40 % CP content during unloading.

Thermal coal futures trading volume predictions through the neural network

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020. Design/methodology/approach The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases. Findings A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume. Originality/value The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

The rise and fall of durable color-induced attentional bias.

Target and distractor templates play a pivotal role in guiding attentional control during visual search, with the former template facilitating target search and the latter template leading distractor suppression. We first investigated whether task-irrelevant colors could earn their value through color-target contingency in the training phase and bias attention when they became a distractor in search for a singleton shape during the test phase. Colors provided useful information for target selection, with high- and low-informational values, respectively, in Experiments 1 and 2. Experience-based attentional biases were observed in the first half of the former experiment, and null results were observed in the latter. Experiment 3 verified whether the null results were elicited because the response-relevant feature inside of the singleton shape was also a singleton. Colors were task defined in the training phase, and the test display was the same as that used in Experiment 2. Experience-based attentional biases were observed in the first half of the test phase. In Experiment 4, we tested whether decreasing the consistency of distractor processing can lengthen the duration of experience-based attentional biases by increasing the number of possible response-relevant features inside of the colored distractor. The results showed experience-based attentional biases throughout the test phase. The results highlight the ideas that the informational value provided by a feature dimension for facilitating target selection can modify a target template and that the consistency of rejecting a distractor feature can play a role in the formation of a distractor template.

Modeling the impact of noise and uncertain operational environment on software release decisions considering testing coverage-based SRGM

The operational environment of software differs from the debugging environment. Therefore, the study explores the impact of irregular consumption by diverse users on the development cost of software, reliability of software, and release decisions. To accomplish this, a release model has been formulated considering logistic testing coverage-based reliability growth model built as a stochastic process including the error generation. The stochastic nature has been captured by considering the noise factor due to irregular fluctuations occurring during testing while usage uncertainty has been captured by introducing a constant parameter in the cost function of the operational phase. It is assumed that the testing phase cost is affected by the noise and the operation phase cost is affected by the severity of noise which is the result of uncertain usage by users. The model was evaluated against a real failure dataset. The release model creates a trade-off between software development cost, release timing, and reliability aspirations. This study contributes to software reliability literature and provides insights to practitioners to make software release decisions. The sensitivity analysis results give information about various aspects during the operational phase that affect the overall development cost.

Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning

In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. This limitation presents a substantial opportunity to enhance the precision and effectiveness of fashion recommendations. In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. The VAGCN framework employs a deep-stacked autoencoder to convert the input image’s high-dimensional raw CNN visual features into more manageable low-dimensional representations. In addition to improving feature representation, the GCN can also reason more intelligently about predictions, which would not be possible without this compression. The GCN encoder processes nodes in the graph to capture structural and feature correlation. Following the GCN encoder, the refined embeddings are input to a multi-layer perceptron (MLP) to calculate compatibility scores. The approach extends to using neighborhood information only during the testing phase to help with training efficiency and generalizability in practical scenarios, a key characteristic of our model. By leveraging its ability to capture latent visual features and neighborhood-based learning, VAGCN thoroughly investigates item compatibility across various categories. This method significantly improves predictive accuracy, consistently outperforming existing benchmarks. These contributions tackle significant scalability and computational efficiency challenges, showcasing the potential transformation of recommendation systems through enhanced feature representation, paving the way for further innovations in the fashion domain.

Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques

After precipitation, reference evapotranspiration (ETO) plays a crucial role in the hydrological cycle as it quantifies water loss. ETO significantly impacts the water balance and holds great importance at the basin level because of the spatial distribution of managing water resources. Large scale teleconnection indices (LSTIs) play a vital role by influencing climatic variables and can be pivotal in determining ETO and its predictive variables. This study aimed to model and forecast annual ETO in Iran’s basins by utilizing LSTIs and employing various machine learning models (MLMs) such as least squares support vector machine, generalized regression neural network, multi-linear regression (MLR), and multi-layer perceptron (MLP). Initially, climate data from 122 synoptic stations covering six and 30, main and sub basins were collected, and annual ETO values were computed using the Food and Agriculture Organization 56 (PMF 56) Penman–Monteith equation. The correlations between these values and 37 LSTIs were examined within lead times ranging from 7 to 12 months. Through a stepwise approach, the most influential predictor indices (LSTIs) were selected as input datasets for the MLMs. The findings revealed the significant influence of factors such as carbon dioxide (CO2), Atlantic multidecadal oscillation, Atlantic Meridional Mode, and East Atlantic on annual ETO. Overall, all MLMs performed well in terms of the Scatter Index during both training and testing phases across all sub-basins. Furthermore, the MLP and MLR models displayed superior performance compared to other models in the training and testing evaluations based on various assessment metrics.

Composite skin substitutes, 3D skin bioprinting and the “BioMask” concept in regenerating skin defects - review

The treatment of skin trauma, especially facial skin trauma, is a major challenge due to its complex structure, the presence of appendages, color, texture, and the large area to be reconstructed in extensive trauma. “The gold standard” for treating trauma is autologous intermediate thickness skin grafting. An alternative solution is the usage of bioengineered skin substitutes. Tissue engineering is intended to provide patients with better treatment options and more effective pain reduction. Unique skin lesions are those related to the face. To fulfill the need to improve the results of facial skin reconstruction, the “Biomask” concept was introduced for the treatment of facial wounds. The purpose of this review is to analyze composite dermal-epidermal substitutes already on the market for clinical use, as well as briefly discussing materials in the testing phase, focusing on 3D skin bioprinting and facial trauma regeneration using “BioMask”. PubMed and Google Scholar databases were searched for relevant sources. Search terms included “skin substitutes”, “synthetic skin substitutes”, “bioengineered skin”, “composite skin substitutes” and additionally each analyzed unit of composite skin substitutes was searched. Bioengineered skin substitutes effectively fulfill the role of dressings during the reconstruction of skin injuries. The development of 3D skin bioprinting is enabling the increasing and effective use of these materials. The high requirements in the treatment of facial skin injuries are the trigger for the development of new materials such as "BioMask". The synergy of new technologies makes it possible to create improved methods of wound dressing and reconstruction of skin defects.