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The type 120 emergency valve is an essential braking component of railway freight trains, but corresponding diaphragms consisting of natural rubber (NR) and chloroprene rubber (CR) exhibit insufficient aging resistance and low-temperature resistance, respectively. In order to develop type 120 emergency valve rubber diaphragms with long-life and high-performance, low-temperatureresistant CR and NR were processed.

The physical properties of the low-temperature-resistant CR and NR were tested by low-temperature stretching, dynamic mechanical analysis, differential scanning calorimetry and thermogravimetric analysis. Single-valve and single-vehicle tests of type 120 emergency valves were carried out for emergency diaphragms consisting of NR and CR.

The low-temperature-resistant CR and NR exhibited excellent physical properties. The elasticity and low-temperature resistance of NR were superior to those of CR, whereas the mechanical properties of the two rubbers were similar in the temperature range of 0 °C–150 °C. The NR and CR emergency diaphragms met the requirements of the single-valve test. In the low-temperature single-vehicle test, only the low-temperature sensitivity test of the NR emergency diaphragm met the requirements.

The innovation of this study is that it provides valuable data and experience for future development of type 120 valve rubber diaphragms.

In this study, the authors examine the push and pull effects of extreme weather events on migration among governorates in Egypt.

To estimate the effect of extreme weather events on internal migration, the authors use migration gravity models and data from the 1996 and 2006 Population and Housing Censuses. The authors measure weather extremes by the number of months in the past 36 months with temperatures or precipitation of a governorate below the 5th percentile and above the 95th percentile of the distribution of monthly temperatures or precipitation of the corresponding governorate during the period 1900–2006.

This study’s results suggest that high temperatures in the origin area act as a push factor. High-temperature extremes have a positive effect on out-migration. A 1% increase in the number of months with high-temperature extremes in the original governorate results in a 0.1% increase in the number of out-migrants.

The study suggests that people may respond to weather extremes through migration. However, climate migrants in Egypt may encounter several significant risks that authorities must address.

This study is one of the first attempts to measure the push and pull effect of weather extremes on migration in Egypt.

Biodegradable polymers are widely used in personalized medical devices or scaffolds for tissue engineering. The manufacturing process should be finished with sterilization procedure. However, it is not clear how the different sterilization methods have an impact on the mechanical strength of the three-dimensional (3D)-printed parts, such as bone models or personalized mechanical devices. This paper aims to present the results of mechanical testing of polylactide-based bone models before and after sterilization.

Polylactide specimens prepared in fused filament fabrication technology were sterilized with different sterilization methods: ultraviolet (UV) and ethylene oxide. Mechanical properties were determined by testing tensile strength, Young’s modulus and toughness.

The tensile strength of material after sterilization was significantly higher after ethylene oxide sterilization compared to the UV sterilization, but in both sterilization methods, the specimens characterized lower tensile strength and Young’s modulus when compared to the control. In comparison of toughness results, there was no statistically significant differences. The findings are particularly significant in the perspective of using individual implants, bone grafts and dental guides.

Although fused filament fabrication (FFF) 3D printing devices equipped with UV light sterilization options are available, experimental results of the effect of selected sterilization methods on the mechanical strength of additively manufactured parts have not been described. This paper completes the present state of the art on the problem of sterilization of FFF parts from biodegradable materials.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Efficient thermal management of central processing unit (CPU) cooling systems is vital in the context of advancing information technology and the demand for enhanced data processing speeds. This study aims to explore the thermal performance of a CPU cooling setup using a cylindrical porous metal foam heat sink.

Nanofluid flow through the metal foam is simulated using the Darcy–Brinkman–Forschheimer equation, accounting for magnetic field effects. The temperature distribution is modeled through the local thermal equilibrium equation, considering viscous dissipation. The problem’s governing partial differential equations are solved using the similarity method. The CPU’s hot surface serves as a solid wall, with nanofluid entering the heat sink as an impinging jet. Verification of the numerical results involves comparison with existing research, demonstrating strong agreement across numerical, analytical and experimental findings. Ansys Fluent® software is used to assess temperature, velocity and streamlines, yielding satisfactory results from an engineering standpoint.

Investigating critical parameters such as Darcy number (10⁻⁴ ≤ Da_D ≤ 10⁻²), aspect ratio (0.5 ≤ H/D ≤ 1.5), Reynolds number (5 ≤ Re_D,bf ≤ 3500), Eckert number (0 ≤ EC_bf ≤ 0.1) , porosity (0.85 ≤ ε ≤ 0.95), Hartmann number (0 ≤ Ha_D,bf ≤ 300) and the volume fraction of nanofluid (0 ≤ φ ≤ 0.1) reveals their impact on fluid flow and heat sink performance. Notably, Nusselt number will reduce 45%, rise 19.2%, decrease 14.1%, and decrease 0.15% for Reynolds numbers of 600, with rising porosity from 0.85 to 0.95, Darcy numbers from 10⁻⁴ to 10⁻², Eckert numbers from 0 to 0.1, and Hartman numbers from 0 to 300.

Despite notable progress in studying thermal management in CPU cooling systems using porous media and nanofluids, there are still significant gaps in the existing literature. First, few studies have considered the Darcy–Brinkman–Forchheimer equation, which accounts for non-Darcy effects and the flow and geometric interactions between coolant and porous medium. The influence of viscous dissipation on heat transfer in this specific geometry has also been largely overlooked. Additionally, while nanofluids and impinging jets have demonstrated potential in enhancing thermal performance, their utilization within porous media remains underexplored. Furthermore, the unique thermal and structural characteristics of porous media, along with the incorporation of a magnetic field, have not been fully investigated in this particular configuration. Consequently, this study aims to address these literature gaps and introduce novel advancements in analytical modeling, non-Darcy flow, viscous dissipation, nanofluid utilization, impinging jets, porous media characteristics and the impact of a magnetic field. These contributions hold promising prospects for improving CPU cooling system thermal management and have broader implications across various applications in the field.

The purpose of this study is to cover the influence of selected printing parameters at a macro and micro-geometrical level, focusing on the dimensions, geometry and surface of printed parts with short carbon fibers reinforced PLA. For this case study, a hollow cylindrical shape is considered, aiming to cover the gap detected in previous works analyzed.

Nowadays, additive manufacturing plays a very important role in the manufacturing industry, as can be seen through its numerous research and applications that can be found. Within the engineering industry, geometrical tolerances are essential for the functionality of the parts and their assembly, but the variability in three-dimensional (3D) printing makes dimensional control a difficult task. Constant development in 3D printing allows, more and more, printed parts with controlled and narrowed geometrical deviations and tolerances. So, it is essential to continue narrowing the studies to achieve the optimal printed parts, optimizing the manufacturing process as well.

Results present the relation between the selected printing parameters and the resulting printed part, showing the main deviations and the eligible values to achieve a better tolerance control. Also, from these results obtained, we present a parametric model that relates the geometrical deviations considered in this study with the printing parameters. It can provide an overview of the piece before printing it and so, adjusting the printing parameters and reducing time and number of printings to achieve a good part.

The main contribution is the study of the geometry selected under a 3D printing process, which is important because it considers parts that are created to fit together and need to comply with the required tolerances. Also, we consider that the parametric model can be a suitable approach to selecting the optimal printing parameters before printing.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

The study endeavours to determine (1) whether the disposition effect exists among South African investor teams, (2) whether it is causally intensified by a set of psychosocial factors and (3) whether the disposition effect causally reduces investor welfare.

Following a natural field experimentation design involving a sample of investor teams participating in the 2019 run of the JSE University Investment Challenge, the authors use regression adjustments as well as bootstrap tests to investigate the casual implications of a set of psychosocial factors on the intensity of the disposition effect, as well on the attenuation of market-adjusted ex post returns (i.e. investor welfare).

South African investor teams are susceptible to the disposition effect, and their susceptibility to the bias is associated with attenuated investor welfare. Furthermore, low female representation in an investor team causally intensifies the disposition effect, subsequently leading to a causal reduction in investor welfare.

Using evidence from real-world observation, the authors contribute to the literature on team gender diversity and investment decision-making, and – using Hofstede's (2001) cultural dimensions – the authors offer a comprehensive account for how differences in culture may lead to differences in gender-related disposition effects across different nationalities. The authors also introduce to the literature experimental evidence from the field that clearly demonstrates that – among South African investor teams – a causal relationship exists (1) between female representation and the disposition effect, and (2) between the disposition effect and investor welfare.

Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.