Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence.

PURPOSE: The key problem raised in the paper is the change in the position of the breast tumor due to magnetic resonance imaging examinations in the abdominal position relative to the supine position during the surgical procedure. Changing the position of the patient leads to significant deformation of the breast, which leads to the inability to indicate the location of the neoplastic lesion correctly. METHODS: This study outlines a methodological process for treating cancer patients. Pre-qualification assessments are conducted for magnetic resonance imaging (MRI), and 3D scans are taken in three positions: supine with arms raised, supine surgical position (SS), and standing. MRI and standard ultrasonography (USG) imaging are performed, and breast and cancer tissue are segmented from the MRI images. Finite element analysis is used to simulate tissue behavior in different positions, and an artificial neural network is trained to predict tumor dislocation. Based on the model, a 3D-printed breast with a highlighted tumor is manufactured. This computer-aided analysis is used to create a detailed surgical plan, and lumpectomy surgery is performed in the SS. In addition, the geometry of the tumor is presented to the medical staff as a 3D-printed element. RESULTS: By utilizing a comprehensive range of techniques, including pre-qualification assessment, 3D scanning, MRI and USG imaging, segmentation of breast and cancer tissue, model analysis, image fusion, finite element analysis, artificial neural network training, and additive manufacturing, a detailed surgical plan can be created for performing lumpectomy surgery in the supine surgical position. CONCLUSION: The new approach developed for the pre-operative assessment and surgical planning of breast cancer patients has demonstrated significant potential for improving the accuracy and efficacy of surgical procedures. This procedure may also help the pathomorphological justification. Moreover, transparent 3D-printed breast models can benefit breast cancer operation assistance. The physical and computational models can help surgeons visualize the breast and the tumor more accurately and detailedly, allowing them to plan the surgery with greater precision and accuracy.

Pulses outweigh cumulative effects of water diversion from river to lake on lacustrine phytoplankton communities.

Due to the allochthonous input of nutrients and species, the cumulative effects of water diversion on water-receiving lakes deserve attention. Taking the water diversion project from the Yangtze River to Lake Taihu (WDYT) as an example, we explored the temporal effects of WDYT on the phytoplankton community and physicochemical habitat of Lake Taihu in autumn and winter from 2013 to 2018. Although the short-term diversion significantly increased the risk of importing nutrients, the relatively high quality of the diversion water compared with other inflow rivers had improved the water quality of the water-receiving lake region. The seasonal water diversion significantly increased phytoplankton diversity and community network complexity and reshaped the lacustrine community to be diatom-dominated with their relative proportions of 24.1-64.9% during water diversion periods. The contributions of physicochemical habitat changes induced by water diversion to variations in phytoplankton communities were 24.0-28.0%. The differences in phytoplankton diversity, community composition and physicochemical habitat in the water-receiving lake region between the diversion and non-diversion years were more evident than those between the non-diversion years in the same season, when comparing the multivariate dispersion indices among them. However, the lacustrine phytoplankton community during non-diversion periods still has not been essentially altered after several years of diversion, so the pulse effects of short-term water diversion were more obvious than the long-term cumulative impacts. Better control of allochthonous nutrients, appropriate increase in inflow water, adhering to the long-term operation, should be effective to enhance ecological benefits of such water diversion projects.

Application of the Laser Linear Distance-Speed-Acceleration Measurement System and Sport Kinematic Analysis Software.

The industrial development of technology, with appropriate adaptation, enables us to discover possibilities in sport training control. Therefore, we have developed a new approach to linear running analysis. This study aims to determine the measurement possibilities using an LDM301A laser system in obtaining basic kinematic parameters. The second goal is the application of specialized computer programs based on appropriate algorithms to calculate a vast number of variables that can be used to adjust the training and the rivalry. It is a non-invasive, non-contact measurement method. We can also determine the influence of both subjective and objective external factors. In this way, we can also conduct training with real-time scientific feedback. This method is easy to use and requires very little time to set up and use. The efficiency and running economy can be calculated with various time, speed, acceleration, and length indexes. Calculating the symmetries between the left and right leg in velocity, stride lengths, support phase times, flight phase times, and step frequency are possible. Using the laser measurement method and detailed kinematic analysis may constitute a new chapter in measuring speed. However, it still has to compete with classic photocell measurement methods. This is mainly due to their high frequency of measurement used, despite some reservations about the scale of measurement errors.

Exploring and quantifying the impact of climate change on surface water temperature of a high mountain lake in Central Europe.

Lake surface water temperature (LSWT) is a key indicator which drives ecosystem structure and function. Quantifying the impact of climate change on LSWT variations is thus of great significance. In this study, observed data of LSWT during the period 1969-2018 in a high mountain lake (Morskie Oko Lake, Central Europe) were analyzed. The results showed that the prominent warming of the LSWT and air temperature began around 1997. A logistic non-linear S-curve function was used to model monthly average LSWT. The non-linear model performed well to capture monthly average LSWT and air temperature relationships (Nash-Sutcliffe efficiency coefficient 0.86 and the root mean squared error 1.63 °C). Using the 2009-2018 period as base scenario, a sensitivity analysis was conducted. The results showed that the annual mean LSWT will likely increase about + 1.29 °C and + 2.64 °C with air temperature increases of + 2 °C and + 4 °C respectively at the end of the twenty-first century. If realized, such a scenario will cause serious consequences on lake ecosystem.

An optimized NARX-based model for predicting thermal dynamics and heatwaves in rivers.

The thermal dynamics within river ecosystems represent critical areas of study due to their profound impact on overall aquatic health. With the rising prevalence of heatwaves in rivers, a consequence of climate change, it is imperative to deepen our understanding through comprehensive research efforts. Despite this urgency, there remains a noticeable dearth in studies aimed at refining modeling techniques to precisely characterize the duration and intensity of these events. In response to this gap, the present study endeavors to augment the NARX-based model (Nonlinear Autoregressive network with Exogenous Inputs) to enhance predictive capabilities regarding thermal dynamics and river heatwaves. The optimized NARX-based model included the Bayesian Optimization (BO) algorithm, which allows fine-tuning the number of NARX hidden nodes and lagged input/target values, and the Bayesian Regularization (BR) backpropagation algorithm to improve the NARX calibration process. A long-term dataset spanning from 1991 to 2021, encompassing 18 rivers across the expansive Vistula River Basin, one of Europe's largest river systems, was employed for this study. The performance of the BO-NARX-BR model was compared with that of the widely utilized air2stream model for modeling river water temperature (RWT). The results unequivocally demonstrated the superior performance of the NARX-based model across the calibration and validation periods, and four heatwave years. In the context of river heatwaves, the study revealed an escalating frequency and intensity within the Vistula River Basin. Furthermore, the NARX-based model exhibited superior proficiency in characterizing river heatwaves compared to the air2stream model. This study, as the inaugural examination of river heatwaves in Poland and one of the few globally, furnishes crucial reference points for subsequent research endeavors on this phenomenon.

Accuracy and efficiency of finite element head models: The role of finite element formulation and material laws.

Traumatic brain injury is a significant problem worldwide. In the United States of America, around 1.7 million cases are documented annually, displaying the need for a deeper understanding of the effects on the human brain. The tests required for this assessment are very complex. Tests on cadavers may raise serious ethical questions, and in vivo crash tests are not viable. In this context, there is a great need to developing finite element head models (FEHM) to study the biomechanics of the tissues when submitted to a certain impact or acceleration/deceleration scenario. An excellent compromise between accuracy and CPU efficiency is always desirable for a FEHM, For this reason, this work focuses on the improvement of an existing head model, including the study of the behavior of the brain using distinct finite element types. The finite element type and formulation is of utmost importance for the general accuracy and efficiency of the models. Several validations were performed, comparing the simulation results against experimental data. The simulations with hexahedral elements, under specific conditions, obtained more accurate results with a lower computational cost. Using hexahedrals, a comparison was also performed using two material characterizations with more than 10 years apart, using the latest finite element head model validation experiment. Overall, the newer material model displays a less stiff response, although its implementation must always depend on the overall purpose of the model it is being applied to.

Characteristics of river heatwaves in the Vistula River basin, Europe.

Rivers worldwide are warming due to the impact of climate change and human interventions. This study investigated river heatwaves in the Vistula River Basin, one of the largest river systems in Europe using long-term observed daily river water temperatures from the past 30 years (1991-2020). The results showed that river heatwaves are increased in frequency and intensity in the Vistula River Basin. The total number of river heatwaves showed clear increasing trend with an average rate of 1.400 times/decade, the duration of river heatwaves increased at an average rate of 14.506 days/decade, and the cumulative intensity of river heatwaves increased at an average rate of 53.169 °C/decade. The Mann-Kendall (MK) test was also employed, showing statistically significant increasing trends in the total number, duration, and intensity of heatwaves for all rivers, including the main watercourse of the Vistula River and its tributaries, with few exceptions. Air temperature is the major controller of river heatwaves for each hydrological station, and with the increase of air temperatures, river heatwaves will increase in frequency and intensity. Another impacting factor is flow, and with the increase of flow, river heatwaves tend to decrease in number, duration and intensity. The results suggested that mitigation measures shall be taken to reduce the effect of climate change on river systems.

Eco-Friendly Cork-Polyurethane Biocomposites for Enhanced Impact Performance: Experimental and Numerical Analysis.

Cork composites are byproducts from wine stopper production, resulting from the agglomeration of cork granules with a thermoset resin. The resulting compound is a versatile and durable material with numerous industrial applications. Due to its unique properties, such as low-density, high-strength, excellent energy absorption, and good thermal and acoustic insulators, cork composites find room for application in demanding industries such as automotive, construction, and aerospace. However, agglomerated cork typically has a polyurethane matrix derived from petrochemical sources. This study focuses on developing eco-friendly porous polyurethane biocomposites manufactured with the used cooking oil polyol modified with cork. Since cork and polyurethane foam are typically used for impact shock absorption, the manufactured samples were subjected to impact loading. The assessment of crashworthiness is performed through 100 J impact tests. A finite element numerical model was developed to simulate the compression of these new composites under impact, and the model validation was performed. The highest specific absorbed energy was obtained for petrochemical polyol composites with the 3% addition of natural or modified cork. The research conducted in this study showcased the feasibility of substituting certain petrochemical components used for the synthesis of the polyurethane matrix with ecological waste vegetable oil components.

A novel optimized model based on NARX networks for predicting thermal anomalies in Polish lakes during heatwaves, with special reference to the 2018 heatwave.

In 2018, Europe experienced one of the most severe heatwaves ever recorded. This extreme event's impact on lake surface water temperature (LSWT) in Polish lakes has largely remained unknown. In this study, the impact of the 2018 European heatwave on LSWT in 24 Polish lakes was investigated based on a long-term observed dataset (1987-2020). To capture the LSWT dynamics during the heatwave period and reproduce lake heatwaves, a novel BO-NARX-BR model was developed and evaluated. This model combines the capabilities of the Nonlinear Autoregressive network with Exogenous Inputs (NARX) neural network, the Bayesian Optimization (BO) algorithm for optimizing the number of NARX hidden nodes and lagged input/target values, and the Bayesian Regularization (BR) backpropagation algorithm for the NARX training. The results showed that from April to October 2018, the mean and maximum LSWTs were 2.35 and 3.38 °C warmer than the base-period average (1987-2010) due to the impact of the extreme heatwave. The NARX-based model outperformed another widely used model called air2water in calibration and validation periods. The results also revealed that the BO-NARX-BR model produced significantly better results in capturing lake heatwaves, with computed duration and intensity of lake heatwaves close to the in-situ data. Additionally, LSWT anomaly significantly impacted the duration and intensity of heatwaves that occurred in lakes. Extreme climatic events are gaining increasing importance for the functioning of various elements of the hydrosphere. Such a situation encourages the search for more accurate methods and tools for their prediction. The model applied in the paper corresponds with these assumptions, and its good performance allows for its adaptation to lakes in other regions.

A stacked machine learning model for multi-step ahead prediction of lake surface water temperature.

Lake surface water temperature is one of the most important physical and ecological indices of lakes, which has frequently been used as the indicator to evaluate the impact of climate change on lakes. Knowing the dynamics of lake surface water temperature is thus of great significance. The past decades have witnessed the development of different modeling tools to forecast lake surface water temperature, yet, simple models with fewer input variables, while maintaining high forecasting accuracy are scarce. Impact of forecast horizons on model performance has seldom been investigated. To fill the gap, in this study, a novel machine learning algorithm by stacking multilayer perceptron and random forest (MLP-RF) was employed to forecast daily lake surface water temperature using daily air temperature as the exogenous input variable, with the Bayesian Optimization procedure applied for tuning the hyperparameters. Prediction models were developed using long-term observed data from eight Polish lakes. The MLP-RF stacked model showed very good forecasting capabilities for all lakes and forecast horizons, far better than shallow multilayer perceptron neural network, a model coupling wavelet transform and multilayer perceptron neural network, non-linear regression and air2water models. A reduction in model performance was observed as the forecast horizon increased. However, the model also performs well with a forecast horizon of several days (e.g., 7 days ahead, testing stage: R2 - [0.932, 0.990], RMSE °C - [0.77, 1.83], MAE °C - [0.55, 1.38]). In addition, the MLP-RF stacked model has proven to be reliable for both intermediate temperatures and minimum and maximum peaks. The model proposed in this study will be useful to the scientific community in predicting lake surface water temperature, thus contributing to studies on such sensitive aquatic ecosystems as lakes.

The Relationship between Mortality from Cardiovascular Diseases and Total Drinking Water Hardness: Systematic Review with Meta-Analysis.

BACKGROUND: Interest in water chemical activity, its content, and its impact on human health has greatly increased throughout the last decade. Some studies suggest that drinking water with high hardness may have preventative effects on cardiovascular diseases. This study aims to investigate the association between drinking water hardness and cardiovascular disease (CVD) mortality. METHODS: The study selection process was designed to find the association between drinking water hardness and CVDs mortality. The search included both qualitative and quantitative research and was performed in three databases: Web of Science (Clarivate Analytics, Ann Arbor, MI, USA), PubMed (National Institute of Health, Bethesda, MA, USA), and Scopus (Elsevier, RELX Group plc, London, UK). The project was registered in the International Prospective Register of Systematic Reviews (PROSPERO), registration number: CRD42020213102. RESULTS: Seventeen studies out of a total of twenty-five studies qualitatively analyzed indicated a significant relation between total water hardness and protection from CVD mortality. The quantitative analysis concluded that high drinking water hardness has a significantly lowering effect on mortality from CVDs, however, the heterogeneity was high. CONCLUSIONS: This systematic literature review shows that total water hardness could affect CVD prevention and mortality. Due to the many confounding factors in the studies, more research is needed.

Development, validation and a case study: The female finite element head model (FeFEHM).

BACKGROUND AND OBJECTIVE: Traumatic brain injuries are one of the leading causes of death and disability in the world. To better understand the interactions and forces applied in different constituents of the human head, several finite element head models have been developed throughout the years, for offering a good cost-effective and ethical approach compared to experimental tests. Once validated, the female finite element head model (FeFEHM) will allow a better understanding of injury mechanisms resulting in neuronal damage, which can later evolve into neurodegenerative diseases. METHODS: This work encompasses the approached methodology starting from medical images and finite element modelling until the validation process using novel experimental data of brain displacements conducted on human cadavers. The material modelling of the brain is performed using an age-specific characterization of the brain using microindentation at dynamic rates and under large deformation, with a similar age to the patient used to model the FeFEHM. RESULTS: The numerical displacement curves are in good accordance with the experimental data, displaying similar peak times and values, in all three anatomical planes. The case study result shows a similarity between the pressure fields of the FeFEHM compared to another model, highlighting the future potential of the model. CONCLUSIONS: The initial objective was met, and a new female finite element head model has been developed with biofidelic brain motion. This model will be used for the assessment of repetitive impact scenarios and its repercussions on the female brain.

Assessment of child safety on bicycles in baby carriers - The importance of evaluating both head and neck injuries.

INTRODUCTION: The paper addresses an important accident type that involves children in bicycle seats - the bicycle fall over. It is a significant and common accident type and many parents have been reported to experience this type of "close call." The fall over occurs at low velocities and even while a bicycle is standing still, and may result from a split-second lack of attention on behalf of the accompanying adult (e.g. while loading groceries, i.e. while not being exposed to traffic per se). Moreover, irrespective of the low velocities involved, the trauma that may result to the head of the child is considerable and may be life-threatening, as shown in the study. METHOD: The paper presents two methods to address this accident scenario in a quantitative way: in-situ accelerometer-based measurement and numerical modeling approaches. It is shown that the methods produce consistent results under the prerequisites of the study. They are therefore promising methods to be used in the study of this type of accident. RESULTS: The importanance of the protective role of a child helmet is without discussion in everyday traffic.However, this study draws attention to one particular effect observed in this accident type: that the geometry of the helmet may at times expose the child's head to considerably larger forces, by having contact with the ground. The study also highlights the importance of neck bending injuries during bicycle fall over, which are often neglected in the safety assessment - not only for children in bicycle seats. The study concludes that considering only head acceleration may lead to biased conclusions about using helmets as protective devices.

Improvement and validation of a female finite element model of the cervical spine.

Although the cervical spine supports and controls the kinematics of the head, it is vulnerable to injuries during mechanical loading. Severe injuries often result in damage to the spinal cord, leading to significant ramifications. The role of gender in determining the outcome of such injuries has been established as significant. In order to better understand the essential mechanics and develop treatments or preventative measures, various forms of research have been conducted. Computational modelling is one of the most useful and extensively utilised methods, as it provides information that would otherwise be difficult to obtain. As such, the primary goal of this research is to create a new finite element of the female cervical spine that will more accurately represent the group most affected by such injuries. This work is a continuation of a previous study where a model was created from the computer tomography scans of a 46-year-old female. A functioning spinal unit consisting of the C6-C7 segment was simulated as a validation procedure. The experimental data obtained from cadaveric specimens, that assessed the range of motion of different cervical segments in flexion-extension, axial rotation, and lateral bending, was used to validate the reduced model.

Cork Porous Biocomposites with Polyurethane Matrix Modified with Polyol Based on Used Cooking Oil.

Renewable materials are materials that are replenished naturally and can be used again and again. These materials include things such as bamboo, cork, hemp, and recycled plastic. The use of renewable components helps to reduce the dependence on petrochemical resources and reduce waste. Adopting these materials in various industries such as construction, packaging, and textiles can lead to a more sustainable future and decrease the carbon footprint. The presented research describes new porous polyurethane biocomposites based on used cooking oil polyol (50 per hundred polyol-php) modified with cork (3, 6, 9, and 12 php). The research described here demonstrated that it is possible to replace some petrochemical raw materials with raw materials of renewable origin. This was achieved by replacing one of the petrochemical components used for the synthesis of the polyurethane matrix with a waste vegetable oil component. The modified foams were analyzed in terms of their apparent density, coefficient of thermal conductivity, compressive strength at 10% of deformation, brittleness, short-term water absorption, thermal stability, and water vapor permeability, while their morphology was examined using scanning electron microscopy and the content of closed cells. After the successful introduction of a bio-filler, it was found that the thermal insulation properties of the modified biomaterials were comparable to those of the reference material. It was concluded that it is possible to replace some petrochemical raw materials with raw materials of renewable origin.

A kinematic model of a humanoid lower limb exoskeleton with pneumatic actuators.

PURPOSE: Although it is well-established that exoskeletons as robots attached to the human body's extremities increase their strength, limited studies presented a computer and mathematical model of a human leg pneumatic exoskeleton based on anthropometric data. METHODS: By using Inertial Measurement Units a lower limb joint angles (hip, knee and ankle in sagittal plane) during walking and running were calculated. The geometric model of a human leg pneumatic exoskeleton was presented. Joint angle data acquired during experiments were used in the mathematical model. RESULTS: The position and velocity of exoskeleton actuators in each phase of the movement were calculated using the MATLAB package (Matlab_R2017b, The MathWorks Company, Novi, MI, USA). CONCLUSIONS: The obtained results demonstrate the efficiency of the proposed approach that can be utilized to analyze the kinematics of pneumatic exoskeletons using the dedicated design process. The developed mathematical model makes it possible to determine the position of lower limb segments and exoskeleton elements. The proposed model allows for calculating the position of the human leg and actuators' characteristic points.

Ecological and Health Risk Assessments of Heavy Metals Contained in Sediments of Polish Dam Reservoirs.

This study aimed at investigating the distribution of heavy metals (HMs: Zn, Pb, Cd, Ni, Cr, and Cu) in the bottom sediments of 28 reservoirs covered area of Poland. The paper evaluates the pollution of sediments with HMs and their potential toxic effects on aquatic organisms and human health on the basis of results provided by the Chief Inspectorate of Environmental Protection in Poland. The average concentrations of HMs in the bottom sediments of the reservoirs were as follows: Cd < Ni < Cr < Cu < Pb < Zn. (0.187, 7.30, 7.74, 10.62, 12.47, and 52.67 mg∙dm−3). The pollution load index values were from 0.05 to 2.45. They indicate contamination of the bottom sediments in seven reservoirs. The contamination-factor values suggest pollution with individual HMs in 19 reservoirs, primarily Cr, Ni, Cu, and Pb. The analysis showed that only two reservoirs had the potential for toxic effects on aquatic organisms due to high concentrations of Cd and Pb. The hazard index values for all the analyzed HMs were less than one. Therefore, there was no non-carcinogenic risk for dredging workers. The reservoirs were divided into two groups in terms of composition and concentration values. Reservoirs with higher concentrations of HMs in bottom sediments are dispersed, suggesting local pollution sources. For the second group of reservoirs, HMs' concentrations may be determined by regional pollution sources. The analysis showed that Pb, Zn, and Cd concentrations are higher in older reservoirs and those with higher proportions of artificial areas in their catchments. Concentrations of Ni, Cu, and Cr are higher in reservoirs in south Poland and those with higher Schindler's ratios.

Heavy metals in lake surface sediments in protected areas in Poland: concentration, pollution, ecological risk, sources and spatial distribution.

This paper presents the state and spatial distribution of surface sediment contamination of 77 lakes in Poland by Cr, Ni, Cd, Pb, Zn, and Cu. The analyzed lakes were located within a network of nature protection areas in the territory of the European Union (EU). Spatial distribution of the heavy metals (HMs), factors favoring the delivery/accumulation of HMs in surface sediments, and pollution sources were analyzed. The results indicate the contamination of lake sediments by HMs, but the potentially toxic effects of HMs are only found in single lakes. The spatial distribution of Cr indicates predominant impacts of point sources, while for Pb, Ni, and Zn, the impact of non-point sources. The analysis showed the presence of areas with very high values of particular HMs (hot spots) in the western part of Poland, while a group of 5 lakes with very low values of Ni, Pb, and Zn (cold spots) was identified in the central part of Poland. Principal component analysis showed that presence of wetlands is a factor limiting HMs inflow to lakes. Also, lower HMs concentrations were found in lake surface sediments located in catchments with a higher proportion of national parks and nature reserves. Higher HMs concentrations were found in lakes with a high proportion of Special Protection Areas designated under the EU Birds Directive. The positive matrix factorization analysis identified four sources of HMs. High values of HMs concentrations indicate their delivery from industrial, urbanized, and agricultural areas. However, these impacts overlap, which disturbs the characteristic quantitative profiles assigned to these pollution sources.

Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.

Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Ta). The proposed models relied on a nonlinear relationship between the Tw and Ta and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river Tw modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor's regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the Ta as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R2) and Nash-Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river Tw with an overall accuracy of 0.956 for R2 and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.

Injury Biomechanics Evaluation of a Driver with Disabilities during a Road Accident-A Numerical Approach.

Numerical methods are often a robust way to predict how external mechanical loads affect individual biological structures. Computational models of biological systems have been developed over the years, reaching high levels of detail, complexity, and precision. In this study, two cases were analysed, differing in the airbag operation; in the first, the airbag was normally activated, and in the second case, the airbag was disabled. We analysed a model of a disabled person without a left leg who steers a vehicle using a specialized knob on the steering wheel. In both cases, a head-on collision between a car moving at an initial speed of 50 km/h and a rigid obstacle was analysed. We concluded that the activated airbag for a person with disabilities reduces the effects of asymmetries in the positioning of the belts and body support points. Moreover, all the biomechanical parameters, analysed on the 50th percentile dummy, i.e., HIC, seat belt contact force and neck injury criterion (Nij) support the use of an airbag. The resulting accelerations, measured in the head of the dummy, were induced into a finite element head model (YEAHM) to kinematically drive the head and simulate both accidents, with and without the airbag. In the latter, the subsequent head injury prediction revealed a form of contrecoup injury, more specifically cerebral contusion based on the intracranial pressure levels that were achieved. Therefore, based on the in-depth investigation, a frontal airbag can significantly lower the possibility of injuries for disabled drivers, including cerebral contusions.