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Artificial Intelligence (AI) and robotic technologies have been applied in healthcare sectors for many years. Along with the ageing trend rising globally, the use of AI and intelligent service robots in home care for elderly or disabled bedridden people has generated considerable attention. A robot that can physically assist care-workers and the elderly or disabled bedridden people in daily activities may help ease care-workers’ burden and help older people live independently for longer with higher quality of life. This survey gives a comprehensive overview in the latest AI and robotic technologies applications in elderly care, providing a detailed analysis on the current products in the market, and discusses the problems and the future trend in this topic.

Ikigai, a Japanese term often referred to as “purpose in life” or “the feeling that life is worthwhile”, is a commonly used indicator of well-being in studies focused on the elderly population in Japan. It can be identified within the different realms like family, work, and friendship. However, as individuals age, their sense of ikigai is prone to diminish. Teachable agents (TAs) have been employed in educational settings for decades to facilitate learning by teaching students. Previous research has indicated that TAs can have a positive impact on self-esteem which is an important aspect of ikigai. TAs may hold the potential to address issues related to declining ikigai that are often associated with aging. By engaging in the act of teaching the agent, elderly individuals may experience a sense of being needed, consequently enhancing their self-esteem and potentially leading to an improvement in their ikigai levels. In this paper, we introduce a TA designed around the concept of ikigai, aiming at assisting the elderly in maintaining a high level of ikigai to support healthy aging. Based on a user study conducted using the phenomenographical approach, we demonstrate the effectiveness of our proposed TA design.

Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem; then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed; and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators; finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected.

The multi-channel transcranial magnetic stimulator is commonly used for rehabilitation treatment of ischemic cerebrovascular disease, neurosis, and brain injury diseases in the elderly. And multiple high coherency signals are required as inputs for its test, which are with consistent frequency, adjustable amplitude, equal magnitude, and long-time phase consistency ⩽±1°. But ordinary signal sources have only one output channel, which is far from meeting the test requirements. If the traditional power division is used for the duplication, the phase difference between the channels will be larger than ±1°. If the multi-throw Radio Frequency (RF) switch is used, it will seriously affect the phase consistency of the input signal and increase the distortion of the signal. In order to solve the above problems, the method to extend the output of the microwave signal source has been proposed in the paper by using the integrated transceiver AD9361. The proposed method can realize the duplication of the number of signal channels according to user requirements. Then a signal replicator has been designed by combining AD9361 chip and Field-Programmable Gate Array (FPGA) control module. The experimental platform has been built to test the performance of the designed device. Experimental results show that the duplicated signals are highly consistent with the source signal in the power amplitude and frequency, meanwhile the phase of the duplicated signals are all highly consistent with each other.

As a typical representative of mental disorders, anxiety and depression disorders have occupied a large number of people after the outbreak of the new crown epidemic. However, anxiety and depressive disorders are detected by screening with clinical scales, potentially undermining the efficacy of detecting mental disorders, as it is heavily contingent upon the personal condition of the individual participant and the expertise of diagnosing physician. Therefore, to predict the feasibility of individual-level mental disorders, we developed a multi-bands joint graph convolution network (MBJ-GCN) framework based on electroencephalogram (EEG) brain functional connectivity (FC) networks. The functional connectivity networks are fused with orthogonal power envelopes correlation across five different frequency bands. Furthermore, the five frequency bands of brain FC networks were then integrated using locally weighted clustering coefficients (LWCC), which were then combined into feature vectors to represent the vertexes. After that, we design parallel GCN layers with different inputs via random embeddings, which can recognize intrinsic mental disorder maps from the embeddings in GCNs. Finally, we concatenate the outputs of all GCN layers in the fully connected layer for prediction. The experimental results have shown that our proposed method has low mean absolute error (MAE) and high Pearson correlation coefficient (PCC) performance compared with other prediction methods. It is possible to represent both individual characteristics and clinical information of potential patients using the proposed method. In conclusion, we extend traditional scale interviews to FC-based individual predictions, thereby taking a step towards the development of applicable techniques for quantitative real-world monitoring of mental disorders.

Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting the new disease-gene associations, due to its strong capability to capture the relevance of disease and gene in the new semantic embedding space. However, a single embedding vector cannot informatively represent a disease or gene and cannot characterize the uncertainty of their features. We propose to learn a distribution for a disease or gene under the variational autoencoder framework, which enables disease-gene associations to be modeled by the Kullback-Leibler divergence. The experiment results show that our model outperforms the state-of-the-art models significantly in various metrics.

The dynamical behavior of the Lakshamanan-Porsezian-Daniel (LPD) model with applications in nonlinear optics is uncovered in this paper. The incorporation of spatio-temporal dispersion into the model, addressing the internet bottleneck issue, is emphasized. A planar dynamical system (DS) is accounted for by applying the Galilean transformation to the considered model. All types of phase portraits are plotted based on specific values of parameters. For the generation of quasi-periodic and chaotic patterns, Acos⁡(Ωτ) with strength and frequency components is added. The resulting quasi-periodic and chaotic orbits are also plotted. Various bifurcation and chaos detecting tools, such as time series analysis, Poincare maps, Lyapunov exponent, bifurcation diagrams, and sensitivity, are employed to examine the behavior of the obtained dynamical system under proposed initial conditions. The Hamiltonian function is calculated and plotted for the unperturbed dynamical system. Multi-stability phenomena demonstrate the coexistence of multi-stable orbits in their dynamic features. The dynamics of pulse propagation are found to be significantly impacted by the spatio-temporal dispersion.

Handover (HO) management in Heterogeneous Networks (HetNets) poses challenges arising from network densification and dynamic environmental behaviors. Existing HO decision algorithms struggle to efficiently utilize network resources and ensure a high-quality user experience amidst the complexity of HetNets and the burgeoning growth of mobile users. This paper introduces a robust and data-driven HO decision model designed to enhance HO performance in HetNets. Initially, a conventional HO decision algorithm is developed based on users' Reference Signal Received Power (RSRP) values in MATLAB. Various simulation cases explore different HO parameters to observe their impact on handover performance. To address these challenges, a data-driven HO decision model leveraging Long Short-Term Memory (LSTM), a deep learning technique, is proposed for the regression task. The LSTM model is trained and tested using obtained RSRP values, and the future RSRP values predicted by the model are employed to trigger HO decisions in the proposed algorithm. Results from the traditional HO decision algorithm are compared with those of the proposed machine learning-based approach across various simulation runs, considering average Signal-to-Interference-plus-Noise Ratio (SINR), RSRP, user throughput values, the number of HOs and the Radio Link Failure (RLF) ratio. Different user speeds are also considered to establish a relationship between HO frequency and mobile user speed. The proposed model achieved reducing the rate of radio link failure to levels that are deemed acceptable in order to ensure a continuous connection.

Current source inverters (CSIs) have been widely used for renewable energy sources integration with the utility grid. However, traditional CSIs provide only voltage-boost power conversion, which can be considered a limitation for grid-interfacing applications when consistent tracking of a widely varying input voltage is necessary. Hence, an additional front-end converter is usually utilized in an attempt to step down the input source voltage. Nevertheless, adding the dc-dc bucking stage would reduce the system reliability and its overall efficiency, increase the converter cost, and complicate the control scheme. In order to address the aforementioned shortcomings, this paper proposes a novel three-phase single-stage inverter, suitable for low-power applications, called split-source current-type inverter (SSCTI). The proposed converter can achieve high current boosting capability with lower component count and high-quality output current in comparison with other single-stage topologies. In addition, it utilizes the conventional modulation scheme of the CSI, while maintaining low current stresses on the inverter switches. Comprehensive analysis, modelling, and closed-loop control scheme of the proposed converter are developed. Also, a comparison between the proposed topology and several single-stage topologies is carried out. Moreover, an experimental prototype is built to validate the proposed converter analysis under steady-state operation and different transient scenarios.

A significant attention has concentrated on systems with “hidden attractors” in recent years. The aim of this work is to study an oscillator displaying “hidden attractors”. The oscillator has all nonlinear terms but no equilibrium. The oscillator exhibits attractive dynamics such as chaos, bubble bifurcation, symmetrical attractors, and boosting attractors. Oscillator's feasibility is verified by a microcontroller. Utilizing the oscillator for biomedical image encryption illustrates its application. In addition, the adaptive control is designed for the oscillator.

The Bekaa Plain in Lebanon, is a major agricultural region known for its fertile soil and extensive crops cultivation using groundwater resources which is recently became under stress due to population growth, urbanization, and climate change. The main aim of this study is to investigate the rate of groundwater recharge (GwR) into this region using APLIS Model which principally depends on altitude, slope, lithology, infiltration, and soil type. Using GIS-based for a multicriteria mapping approach, in this study additional factors were added to the model, by incorporating rainfall rates, stream density, and fracture density. Results revealed various recharge potential area in the Bekaa Plain, with 52 % classified as high to very high GwR zones, equivalent to approximately 42.5 % of precipitated water, which is a testament to the exceptional capacity of the region to replenish groundwater aquifers. The geographic distribution of GwR categories primarily depends on altitude and lithological characteristics. The exceptional GwR potential of the Bekaa Plain holds immense promise for mitigating water scarcity, fostering agricultural productivity, and ensuring the socioeconomic well-being of communities reliant on groundwater resources.

Assessment of water quality is imperative for informed decision-making, ecological preservation, and sustainable water resource management, particularly in light of escalating anthropogenic pollution. Traditional approaches often face complexities and inaccuracies in forecasting, necessitating advancements in modelling techniques. Machine learning methods, particularly deep learning, present promising avenues for accurate water quality estimation, albeit with challenges. This study aims to enhance water quality forecasting through the integration of deep learning, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks, with computational fluid dynamics (CFD). The objective is to address the limitations of existing methods by leveraging the synergies between deep learning and fluid dynamics simulations, thereby improving predictive accuracy and efficiency. By proposing a novel framework that combines Bi-LSTM networks with computational fluid mechanics, this study introduces an innovative approach to water quality assessment. The integration of these techniques offers a comprehensive solution to overcome the complexities inherent in traditional methods, promising greater reliability and efficiency in predicting water quality parameters. The study employs Bi-LSTM networks alongside CFD simulations to model contaminant movement and diffusion in water bodies, particularly focusing on the Yamuna River. Utilizing empirical data on Chemical Oxygen Demand (COD) and Biological Oxygen Demand (BOD), the framework aims to predict key factors influencing water quality. The ANSYS software, operating within a Windows environment, facilitates the execution of the proposed methodology. Evaluation of the proposed model demonstrates superior performance compared to traditional methods, exhibiting higher predictive reliability and lower margins of error. Through a comprehensive comparison study, the Bi-LSTM and CFD-based approach surpasses existing techniques in accurately forecasting water quality parameters, thus highlighting its efficacy and potential for practical application. The integration of Bi-LSTM networks with computational fluid dynamics presents a promising avenue for enhancing water quality evaluation. The study underscores the significance of leveraging advanced computational techniques to address the challenges associated with forecasting water quality, ultimately contributing to informed decision-making and sustainable water resource management.

In the urban environment, the transient electromagnetic method often suffers from severe electromagnetic interference because it employs coils to perceive the electromagnetic field. To address this issue, the DC method employs electrodes to shield the electromagnetic noise in the urban environment. In this paper, a new geophysical resistivity imaging sensing approach based on a DC inversion model is proposed to explore and identify the path of underground water to solve the water disaster problem. A real-world case study is carried out in Xinjiang Region. In order to investigate the underground water conducted path, an electrode network is designed and installed for the buildings of interest to collect the DC electrical current and DC electrical potential data. Then, the proposed method establishes a DC resistivity inversion model based on the forward theory and finite-volume technique to extract the geological information from the collected data and visualize the underground water conducted path. Different from existing popular numerical techniques, such as the finite element and finite difference, the finite-volume technique has clear theoretical explanations to make the obtained geological images understandable. As a result, the underground water path can be directly observed from the geological images calculated by the proposed approach to successfully identify and locate the underground water disaster in real-world applications.

The fault dislocation produces severe additional deformation on cross-fault tunnels along the axial direction, seriously threatens tunnel safety. To this end, a simplified analytical model for evaluating the mechanical behavior of segmental tunnels subjected to buried fault dislocation was established. The segmental tunnel is treated as a Timoshenko beam acting on the Vlasov elastic foundation. The plastic yield of circumferential joints, the effect of frictional resistance along the axial direction, and the deformation characteristics of overburden soil after faulting were considered. Then, the reasonability of the analytical solution is proved by 3D numerical simulation. The tunnel safety state was evaluated based on the joint deformation of the segmental tunnel. Subsequently, the effects of plastic yield behavior between segmental rings, plastic equivalent bending stiffness ratio, segment dimensions, and longitudinal bolt on the longitudinal response of the segmental tunnel linings were investigated. The results show that the simplified analytical solution proposed is reasonable in predicting the joint deformation between segmental rings when the segmental tunnel is subjected to buried fault dislocation. When the normal faulting is imposed, the segmental tunnel is dominated by tensile deformation along the tunnel axial. Under 20 cm of normal faulting, the joint opening between segmental rings is close to the deformation control value of joint waterproofing. However, the shear deformation has been significantly weakened due to the effect of faulting in the propagation process to the surface. The calculation result is too small when the plastic deformation behavior is ignored. The plastic equivalent bending stiffness ratio η2 inversely correlated with the maximum joint opening. Increasing the strength grade or the number of longitudinal bolts has a relatively limited effect on reducing the opening between segment rings, where the joint still has a greater risk of water leakage.

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape.

In this paper, the fractional space-time nonlinear Chen-Lee-Liu equation has been considered using various methods. The investigation of the transition from periodic to quasi-periodic behavior has been conducted using a saddle-node bifurcation approach. The paper reports the conditions of multi-dimensional bifurcations of dynamical solutions. Additionally, a direct algebraic method has been used to calculate various 2D and 3D solitonic structures of the equation, and an analysis of their accuracy and effectiveness has been conducted. Furthermore, the Galilean transformation has been used to convert the equation into a planar dynamical system, which is further utilized to obtain bifurcations and chaotic structures. Chaotic structures of perturbed dynamical system are observed and detected through chaos detecting tools such as 2D-phase portrait, 3D-phase portrait, time series analysis, multistability and Lyapunov exponents over time. Further, sensitivity behavior for a range of initial conditions, both perturbed and unperturbed. The results suggest that the investigated equation exhibits a higher degree of multi-stability.

In this paper, we apply the variational approach for solving the time-space fractal Bogoyavlenskii equation. By the two-scale fractal complex transformation, the fractal modification of Bogoyarlenskii equation is equivalently rewritten as the original Bogoyavlenskii equation. By variational principle, different type solutions including fractal bright soliton solution, fractal bright-like soliton solution, fractal kinky-bright soliton solution and fractal periodic wave solution are provided, which are not touched in the existing literature. Numerical results are presented to show the propagation behaviours.

Gross Domestic Product (GDP) is one of the key macroeconomic aggregates that measures the added value produced in a country during a period. In the contemporary world, macroeconomic uncertainty, among others due to the COVID-19 pandemic and the conflict in Ukraine, and GDP prediction remain important goals in public policy making. This study aims to predict Benin's GDP through a unidimensional statistical approach and machine learning techniques. For this purpose, GDP data were collected from the Central Bank of the West African States (BCEAO) website from 1960 to 2021. The predictions are based on comparing classical statistical and machine learning methods. For the classical statistical methods, we investigated the Autoregressive Integrated Moving Average (ARIMA) and Error Trend Seasonality (ETS) forecasting models. As for the machine learning methods, the K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM) forecasting models proved to be sound. The findings revealed that the statistical models (ARIMA and ETS) better predict Benin's GDP. However, machine learning models (KNN and LSTM) also provide a wide range of results that can be used to analyze Benin's economic growth.

Commercial Buildings release large volumes of greenhouse gases, expediting global warming. The commercial building industry accounts for 37% of global CO2 emissions. Geothermal energy technologies are an intriguing solution that provides a low-impact, emission-free, and cost-effective thermal fuel source for baseload energy production. Geothermal energy systems offer a potential solution to dramatically halt global warming and facilitate the transition to a carbon-neutral civilization. This study develops a framework for assessing the geothermal power capabilities of innovative house and district heating systems. The energy requirements are first assessed, and then the spatial evaluation of district heating infrastructure is carried out using a Python-based program. Economic comparisons between geothermal energy and fossil fuels are made using LCC analysis. Environmental impacts are measured by Life Cycle Analysis (LCA). This study compares geothermal energy to fossil fuels, and the LCA results show that geothermal energy has a low environmental impact. With a Net Present Value (NPV) of 34.81 USD, it displays economic viability as well as a satisfactory financial return. The study's findings show that geothermal energy has the ability to compete with fossil fuels in commercial heating applications while still being environmentally beneficial.

Cilia flow plays a crucial role in biological systems such as the movement of mucus in the respiratory tract, circulation of cerebrospinal fluid in the brain, and propulsion of sperm cells. Understanding the cilia flow of viscous fluids is essential for elucidating the biomechanics of these processes and their implications for health and disease. Motivated by such numerous biomedical applications, this article aims to exhibit the impact of variable thermal conductivity on the mixed convective cilia beating transport of viscous fluid in a curved channel in the presence of radial magnetic field. The energy equation is also modulated with heat source/sink and viscous dissipation impacts. The constitutive equations are simplified by the hypothesis of lubrication approximation theory and then solved numerically using the implicit finite difference method (FDM). The numerical results concerning the impacts of various physical parameters on the velocity, temperature, pumping phenomena, and streamlines are graphed and explained. The obtained results indicate that as the Hartman number upsurges, fluid velocity reduces, and the Brickman number shows stronger viscous dissipation effects, leading to an increase in the fluid temperature.

This research investigates the impact of vortex generators integrated with a corrugated absorber plate of solar air heater on the enhancement of heat transfer and thermal-hydraulic performance utilizing a 3D numerical model for Reynolds numbers varying from 7,000 to 25,000. Three different types of vortex generators (rectangular, trapezoidal, and delta) are investigated separately. Vortex generators in two pairs with various angles of attack of 15º, 30º, 45º, and 60º are mounted on the entrance section's upper wall of the domain of computation. The current numerical models are validated by comparisons with experimentally published data. The velocity vectors, turbulent kinetic energy, friction factor, Nusselt number, and thermal-hydraulic performance parameters are examined for the considered current cases considered. The numerical results demonstrate a significant impact of vortex generators on friction loss and transmission of heat. The values of thermal-hydraulic performance for utilizing a corrugated absorber plate integrated with vortex generators are found to be considerably higher than those for using a corrugated absorber plate alone. The highest value of the thermal-hydraulic performance parameter is 0.91 in the case of rectangular vortex generators at Re of 7000 and an angle of attack of 30º.

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet Optimization Algorithm (POA-M), leveraging the strengths of the Arithmetic Optimization Algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm Optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-M-FOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

Perovskite quantum dots (PQDs) have shown great promise for use in photovoltaic devices, due to their ability to offer advantages such as tunability of visible light and efficient light emission. This study demonstrates the successful utilization of a triazine-based PO-T2T (2,4,6-Tris[3(diphenylphosphenyl)phenyl]-1,3,5-triazine; PO-T2T) as the electron transport layer (ETL) through an all-solution process. To improve the structural and optical characterization of CsPbBr3 and CsPbI3 QDs, three organic solvents, namely chlorobenzene, methyl acetate, and ethyl acetate, were selected to find an effective orthogonal solvent for PO-T2T. The all-solution processed devices incorporating PO-T2T and Methyl acetate (MeOAc) demonstrated impressive external quantum efficiencies of 5.57% for CsPbBr3 and 1.60% for CsPbI3 QDs. Notably, these devices also achieved the highest maximum luminance among all-solution processed PQDs-LEDs, with values of 33334 cd m–2 for CsPbBr3 and 224 cd m–2 for CsPbI3 QDs.

A method of trajectory planning with regards to joint velocity and acceleration constraints for industrial 6 DOF manipulator is presented. The task of the robot is to move to specified location in the workspace passing through intermediate waypoints. The proposed algorithm can be used to plan the task of the robot by autonomous systems in smart factories eliminating human participation in the robot programing process. Opposite to similar approaches it does not assume the type of function describing the motion of the robot. The trajectories generated using the proposed approach are smooth and provide smooth velocities and continuous joint accelerations. The motion is planned in such a way to fulfill joint velocity and acceleration constraints. Fulfillment of velocity limitations is accomplished by perturbing the manipulator motion close to velocity limits. To satisfy acceleration constraints a trajectory scaling approach carried out in limited periods of time is used. The results of the research are illustrated by simulations and experiments, in which an analysis of the method of performing robot tasks carried out using built-in algorithms and presented methods are performed.

One of the most attractive β-Ti alloys is Ti–35Nb–7Zr–5Ta wt% (TNZT) alloy, which has one of the minimal Young’s moduli among the β-Ti family (about 55 GPa) and contains no cytotoxic elements. On the other hand, β-type Ti alloys are susceptible to bacterial infection because they lack an antibacterial function and can get contaminated quickly after implantation, making surface modification a keyway to improve these alloys' antibacterial properties. A recently created technique called powder mixed-EDM can improve machining, mechanical, and biological properties at the same time. In this research, silver was added to the dielectric fluid during PM-EDM of Ti-35Nb-7Zr-5Ta wt% alloy prepared by powder metallurgy route. The surface composition, Brinell hardness, corrosion resistance, ion release, and antibacterial properties were evaluated for TNZT alloy before and after surface modification. The results show better hardness and corrosion resistance as well as lower ion release for the PM-EDMed specimen due to the presence of Ag, oxides, and carbides such as NbC, TiC, TiO2, ZrO2, and Nb2O5 that’s deposited and embedded as a hard phase in the recast layer of the machined surface. Also, the antibacterial property of the PM-EDMed specimen is effectively improved as silver is an antibiotic with a wide range, so it has favorable antibacterial properties for Gram-negative and Gram-positive bacteria.

This research paper explains in detail how well regular concrete works and how well concrete with fly ash and ground granulated blast furnace slag (GGBS) as a substitute for cement. Through a series of experiments, the objective of the study is to perform an experimental approach that promote the usage of partial replacement-based concrete that can replace the conventional concrete and to promote the sustainable development. a dedicated methodology is developed for the study, focussing on the mechanical and durability properties of the materials with inducing sustainable materials. The methodology study examines at the mechanical properties, durability, and microstructural attributes of the concrete blends. Cement concrete specimens with binder ratios (%) of 0.3, 0.4, and 0.5 were tested for compressive strength, rapid chloride permeability, SEM, and XRD at 28, 56, and 90 days. Fly ash and GGBS were used to partially replace cement at 0% to 70% for all binder ratios by weight of cement. There were optimal replacement percentages for each binder ratio and fly ash and GGBS partially substituted concrete had similar or enhanced mechanical properties to conventional concrete. The novelty of the study is to incorporate microstructure analysis for the same samples that shall enable to analyse the behaviour of the partial replaced materials with conventional concrete. In connection with the results, the study had found lower RCPT values in partial replacement concrete specimens, fly ash and GGBS increased chloride ion resistance. SEM and XRD analyses revealed the concrete mixtures' microstructural properties and phase composition, showing how supplementary cementitious materials refine pore structure and provide durable hydration products. This study shows that fly ash and GGBS can improve concrete performance and reduce impact on environment and applications in construction.

The purpose of the research was to analyze the experimental-numerical influence of the type of cross-section on the stability of thin-walled composite columns with closed (rectangular) cross-sections. The subject of the investigation was thin-walled composite structures made of CFRP composite (carbon fiber reinforced polimer), characterized by a closed rectangular cross-section shape of the profile and a identical ply configuration. In this study, experimental and numerical investigations of axially compressed columns were performed to determine the values of buckling loads and buckling forms. Experimental investigations were performed using a universal testing machine with an optical deformation measurement system. In parallel with the experimental tests, numerical simulations were made using the FEM (Finite Element Method). The numerical studies conducted using dedicated numerical models and the experimental studies made it possible to carry out a thorough analysis of the impact of the cross-sectional shape on the buckling phenomenon of the structure. The novelty of the present paper is the use of interdisciplinary testing methods to compare the effect of cross-sectional geometry on the stability of thin-walled composite columns.

The study offers an analytical approach for assessing production control system performance in terms of volume and variety features of the product being manufactured for the Flexible Manufacturing System. Throughput, resource utilisation, cycle time, and maximum completion time are the four performance indicators taken into account. The objective is to quantify the production system's performance and define the reactive capability of production control architectures in a market that is becoming increasingly competitive. The examination of control systems' production performances motivates the evaluation of these architectures through the introduction of scheduling approaches that deal with uncertainty. Results revealed the semi-heterarchical control architecture outperforms the hierarchical control architecture through multiple performance criteria. A case study with regard to a manufacturing control system is presented in order to highlight the significance of our methodology and the contribution of the research.

This paper investigates the implementation of a model-based predictive control (MPC) strategy to improve the performance of a semi-autogenous grinding (SAG) mill in a uranium mineral processing plant. The SAG mill, crucial in crushing and grinding uranium ore to the desired size, is currently managed using conventional proportional-integral-derivative (PID) controllers. However, to enhance production efficiency and control over the SAG mill's variables, this paper suggests the adoption of MPC. The proposed MPC controller is developed using a neural network (NN) model of the SAG mill, created in MATLAB with data collected over 21 days. The effectiveness of the MPC controller is assessed by contrasting its response with that of the real-time operator control. This comparison utilizes tools like MATLAB and the RSlinx remote server for accessing OPC real-time data. Findings reveal that the MPC controller exhibits a quicker reaction to alterations in the SAG mill's process outputs and proficiently regulates crucial outputs such as Mill mass, ensuring that the manipulated variables stay within their designated limits. Unlike operator control, which is slower and adjusts one variable at a time, the MPC approach can maximize the mill's throughput rate without impacting the ore feed rate. This demonstrates the MPC controller's superior ability to optimize SAG mill operations efficiently.

Energy harvesting from human body kinetics is a crucial issue. The primary challenge lies in designing and optimizing the energy converter. This paper presents an analysis of energy harvesting using three variants of electromagnetic harvesters designed for backpack integration. The first harvester comprises a single levitating magnet within a coil. The second concept involves a specially designed oscillating magnet consisting of two divided magnets with a separator. The third harvester variant utilizes two levitating magnets within the coil. The results indicate that, for harmonic excitation, the harvested power is the highest for the classical harvester with a single oscillating magnet. However, when integrated into a backpack, the concept of two levitating magnets proves to be more effective in lower speed ranges.

This study combined simulation and experimental tests to analyse the cutting performance of three solid carbide end mills with distinct geometries during the milling of the 7075 aluminium alloy. For the tests, three uncoated end mills were employed, which differed in rake angle, clearance angle, and helical pitch. Simulation tests revealed temperature distributions and the resultant cutting forces. The machining with a milling cutter with a higher blade angle was shown to cause an increase in the temperature in the cutting zone. However, during machining with a sharper blade of cutting tool, a decrease of cutting forces was not observed. The simulated temperature distribution on the cutting edge of the cutting tool may justify significant differences in the dynamics of changes in the cutting force components during the period of operational wear.

The bottleneck in the widespread use of carbon multiwall nanotube polymer composites is the lack of manufacturing technology that can be used on an industrial scale. In this article, we describe a two-step composite manufacturing technology based on screw extrusion that produces composites characterizing with good dispersion of carbon nanotube filler in polylactide matrix. The first stage involved the fabrication of highly filled masterbatches of 25 wt% of carbon nanotubes. In the second stage, by screw extrusion of the masterbatch mixture with neat polymer, we obtained homogeneous composites with the target filler concentration. The resulting composites with nanotube content ranging from 0.1 to 2 wt%. Mechanical tests including static tension, tensile strength, tensile modulus, three-point bending and impact strength has shown that optimal concentration of the carbon nanotube filler is ranged between 0.5 and 1 wt%. Samples were examined also by SEM, FTIR-ATR, DSC and MFR methods.

The present work deals with the enhancement of the surface characteristics of stainless steel 316L as a result of shot peening treatment using ceramic balls. In accordance with our own research and information available in literature, as a result of shot peening process, the shot balls can penetrate to the surface layer (permanently depositing) and modify the mechanical performance and the corrosion resistance in the products being treated in this way. Shot peening leads to a significant change to the surface hardness and topography, and consequently, to the change in corrosion behaviour dependent on the choice of processing parameters. Therefore, in this paper, steel samples were treated using two variable parameters of peening pressure (0.3 and 0.4 MPa) and peening time (30 and 60s). In the research, the reference surface were the samples subjected to mechanical polishing. The surface morphology of the samples was investigated by scanning electron microscopy (SEM). The potentiodynamic polarization tests were performed with 1 mV/s scan rate in 0.9% NalCl solution. The improved corrosion resistance (lowest current density Icorr=0.35µA/cm2 and highest corrosion potential Ecorr=-0.164V) was obtained for specimens with longer time (60s) and higher pressure of shot peening treatment (0.4MPa). Greater changes in surface roughness were observed with an increase in peening pressure than with an increase in the processing time. The treatment of the surface with ceramic shots results in an increase in the hardness of the treated surface by more than 110% (for sample 316L/0.4/60) compared to the reference surface. Moreover, an increase in average hardness values was recorded for all surfaces after shot peening (by more than 42% relative to reference samples).

Demand for bent pipe profiles in various industrial sectors is increasing due to considerations necessitating lightweight construction, safety, as well as space and cost savings. Lightweight construction is becoming increasingly important for economic and ecological reasons. This can be seen in the automotive and aerospace industries, as well as in civil engineering, where curved structures are often required. In order to reduce the weight of the aircraft, the aviation industry often uses spatial truss structures with bent elements. The challenge is to meet all of the stringent requirements regarding design, safety, structure, size, cost, etc., without compromising a structure's stiffness. Profiles with complicated cross-sections and so-called tailored tubes are increasingly used in the production of automotive and aircraft structures (General Aviation). Pipe and profile bending technologies are constantly being improved upon to meet the growing expectations of customers and ensure greater process efficiency. Currently, efforts are being made to increase the level of automation in this area by combining the functionality of modern bending machines with the capabilities of industrial robots. CNC bending machines currently dominate the industrial pipe bending process. A technologically advanced bending machine allows for the production of increasingly complex shapes and profiles.

This study investigates the influence of temperature variations on the buckling properties of thin-walled omega-profiles fabricated from carbon-epoxy composite materials. Utilizing a MTS testing machine, compression tests were conducted on these profiles at temperatures ranging from -20°C to 80°C, in 20°C increments. The primary objective was to assess how temperature fluctuations impact the buckling load and load-bearing capacity of these composite profiles under axial compression. The experimental setup allowed for precise measurement of load-displacement and load-deflection characteristics, and the critical load at which buckling initiation occurred. Observations revealed that the buckling resistance of the profiles exhibited a complex dependence on temperature. At lower temperatures, the composite material demonstrated enhanced stiffness and strength, marginally increasing buckling resistance. Conversely, at elevated temperatures, a noticeable degradation in mechanical properties was observed, leading to a reduced buckling load and altered failure modes. To complement the experimental findings, a comprehensive finite element (FE) analysis was conducted for sample in room temperature. The FE model, developed to replicate the experimental conditions closely, employed an eigenvalue-based approach to predict the buckling initiation and progression accurately. The presented results are the results of only preliminary tests and they will be expand about more samples number as well as to determine material properties for various temperatures.

The work presents the results of research on the effect of deformation parameters on hot ductility of high-Mn austenitic steel with niobium and titanium. The investigations were carried out on steel with 0.05% C, 24% Mn, 3.5% Si, 1.5% Al, 0.030% Nb and 0.075% Ti. Hot static tensile test was performed using Gleeble 3800 thermomechanical simulator. Samples were deformed in a temperature range from 1050°C to 1200°C with a strain rate of 3·10-3 s-1. The reduction in area (RA), determined in the static tensile test, was the basis for determining the hot ductility of the examined steel. Reduction in area of examined steel decreases from 88% at the temperature of 1050°C to 59% at 1200°C. High hot ductility of the investigated steel is the result of the synergy of chemical composition optimization, properly conducted modification of non-metallic inclusions and formed fine-grained microstructure of dynamically recrystallized austenite. In addition to hot ductility, parameters characterizing susceptibility of studied steel to high temperature cracking were also defined, namely: ductility recovery temperature (DRT), nil ductility temperature (NDT) and nil strength temperature (NST) were determined. The values of these temperatures are 1240°C, 1250°C and 1270°C, respectively. This means that the temperature of the beginning of plastic deformation of ingots of this steel may be equal even slightly above 1200°C. In addition, the high-temperature brittleness range (HTBR) was determined, which is equal 30°C.

The aim of the research was to design and build a research stand that can be used to compare different types of tire tread profiles of the Mars rover (made using 3D printing) and to select the one that meets the criterion of the lowest power consumption of the drive motor, which corresponds to the minimum rolling resistance in paved area. As part of the task, a research stand was designed and built, consisting of two drive units of the tested drive system: one as a driving unit, the other as a driven unit, generating resistance when rolling one tire after another. During the tests, the following parameters were measured: the amplitude of the supply current, the rotational speed, information about which was obtained from the motor controller and the encoder located in the BLDC motor, and their variability over time. Additionally, the amplitude of the force pressing the wheel against the supporting surface, which generated tire deflection, was also measured. A relationship was demonstrated between the type of tire tread used, the force pressing the tires against the supporting surface and the rolling resistance forces of the Mars rover tires. The tire with the lowest rolling resistance under given operating conditions was selected.

Hot storage tanks (HST) are known for their high energy consumption, attributed to variations in usage, heat dissipation within the tank, and heat losses to the surroundings. This study proposes a chimney-type electrically heated HST, which is investigated under static mode to enhance its thermal performance. Different natural circulation areas (chimney areas) with large (9.5 cm diameter), medium (2.5 cm diameter), and small (1.5 cm diameter) sizes were utilized to examine the effect of natural circulation on the HST performance. Additionally, the influence of chimney insulation on the HST performance was also studied. The experiments revealed that the chimney significantly affected the thermal stratification within the tank. Different chimney contact diameters (9.5 cm, 2.5 cm, and 1.5 cm) were tested, showing varying degrees of thermal stratification. The results indicated that smaller chimney contact diameters led to higher thermal stratification and more rapid heating of the top layer temperatures. However, the impact of insulation on thermal performance was inconclusive, suggesting the need for more effective insulation and further investigation into the dynamic mode of operation. The findings also highlighted the faster heating of the top outer layer compared to the larger diameter, emphasizing the significance of the chimney type electrical heater in the hot storage tank.

Data files, photos, and videos on the internet are vast sources of information about the person who posted them. These files contain content about appearance, behaviour, views, and material status. Analyzing these files helps verify the accuracy of the content and helps verify the creation method. Social media platforms like Facebook, Twitter, and Instagram often post this information. Public databases provide information about enterprises, corporations, and public figures, enabling access to government trips, scientific articles, and company reputations. These resources help in understanding potential collaborations and identifying potential partners. Open-source intelligence (OSINT) is a collection of tools and methods for extracting information from publicly available sources. It helps verify the accuracy and authenticity of information, as seen in the FBI's 2020 investigation of a Philadelphia woman involved in protests and preparing precise attacks like spear phishing. In this manuscript, we present an up-to-date overview of research that uses open-source methods and techniques. We will concentrate on the tools and methods advancing the cybersecurity industry. Studying the manuscript of OSINT opportunities and challenges can help readers understand the state of the art in theory and practice. We will also highlight the future directions and requirements for OSINT methods and the newly designed tools using these methods.

Intensive research is currently being conducted on WAAM (Wire Arc Additive Manufacturing) processes. Previous studies have demonstrated the impact of current parameters on altering the structure and properties of 316L stainless steel. However, there is a lack of comprehensive information in the literature regarding the influence of electrode extension length (contact to tube distance) on changes in the structure and geometry of parts made of 316L steel using the CMT (Cold Metal Transfer) method. This parameter was often assumed to be constant in research experiments. The study aimed to determine how the length of the electrode extension affects the geometric properties of steel walls produced in the WAAM CMT additive manufacturing process. The experiment used 316LSi stainless steel to build 3D structures in the shape of straight walls. The chosen shape of the parts yielded the most benefits for preparing samples from the resulting structures for destructive testing. The research demonstrated that the length of the electrode extension is a crucial parameter in the additive manufacturing process of structures using the WAAM method. Modifying the electrode extension length in the WAAM process with a CMT machine impacts the bead geometry and, consequently, the overall model geometry. A 6 mm increase in the electrode extension length resulted in a model that was over 8 mm taller, despite using the same number of layers.

The emerging trend of employing 4 or more axes multi-purpose and gantry industrial robots in large format additive manufacturing presents numerous opportunities as well as challenges. The capacity to handle substantial material quantities and rapidly produce prototypes, jigs, and final products of considerable dimensions necessitates the formulation of a well-suited production strategy. This involves setting production parameters to minimize material consumption and production time, considering the limitations of the utilized technologies, and ensuring the final product's quality. While slicers are commonly employed for establishing manufacturing strategies and production parameters, most additive manufacturing slicers are optimized for planar 3 axes 3D printing. This limitation hinders their ability to generate non-planar and freeform toolpaths. To overcome this constraint, this paper delves into the utilization of parametric modelling as a potent tool in the realm of non-planar additive manufacturing. It explores the possibilities offered by Rhinoceros Grasshopper software in designing toolpath strategies and fabricating non-planar layers. The paper addresses the associated challenges and limitations of parametric modelling, including computational complexity and the requirement for specialized software and expertise. It emphasizes the crucial need to strike a balance between design complexity and manufacturability to ensure the successful implementation of non-planar additive manufacturing processes.

The article presents the concepts of control systems that use methods of automatic analysis and interpretation of signals. These issues are presented through the description of three control strategies: simple closed-loop control, control using a process of classification, and using a signal understanding technique. The systems based on the discussed concepts are illustrated with examples of controlling a milling machine and an autonomous vehicle. In addition, in more detail, a task of learning human motor activities is described. This task, due to the nature of the controlled object, which is a human being, is extremely difficult. The article shows that the advanced control process, in which the control algorithm is selected and its parameters are adapted to the current situation, may be implemented through the use of the classification process and machine learning methods in general. Changing the algorithm is also possible using signal understanding techniques. These techniques, utilizing models of the objects, allow to predict the long-term effects of the control process. The ability to build control systems that operate in the above manner is of huge practical importance. The aim of this article is to describe the methods of automatic signal interpretation used in control processes and identify the main problems related to their use. The key problems refer to the acquisition of expert knowledge by the system. In order for this knowledge to be effectively transferred, the methods used in the system should have a high level of explainability. Showing the essential nature of this feature is the main outcome of this work.

This paper presents an analysis of the design of the bogie pivot of an electric multiple unit, which is cast in cast steel to replace the forged pivot used to date. The change in pivot manufacturing technology is justified for economic reasons, but may affect the reliability and operational safety of multiple units. The authors analysed to what extent the change in pivot manufacturing technology from forged to cast can affect the operational reliability of the upgraded structural component and what requirements should be met so that the introduced upgrade does not reduce the operational safety of the electric multiple unit. An important part of the analysis is the consideration of the effect of preventive replacements of the rubber element of the vibration damper on damage to the pivot and vibration damper system. The dependence of damage to the cooperating components of the pivot elements and vibration damper in the system is demonstrated. The lifespan of the rubber element of the damper is considerably less than that of the bogie pivot of the electric traction unit, and its damage increases the probability of damage to the cast steel pivot. This relationship is critical and, in order to ensure the required reliability of the traction unit, a strategy of preventive replacement of the rubber element of the damper was proposed and achieved in the simulation model developed. This issue is presented in this paper because it is fundamental to the safe operation of rail vehicles.

This study optimized the integrated die-casting of an aluminum alloy longitudinal beam and tower component using no heat-treated aluminum alloy. Leveraging material properties and part geometry, a pouring system was designed and refined through mold flow analysis. This optimized the velocity, temperature distribution, and air entrapment to reduce defects like shrinkage porosity. The optimized pouring parameters were 695 °C melt temperature, 210 °C initial mold temperature, and 4.9 m/s shot speed. This reduced shrinkage porosity by 10.4% versus the original design. Die-casting trials with the optimized pouring system produced defect-free castings. The critical load-bearing section of the die casting had a yield strength of 184 MPa and elongation of 10.9%, which can meet the production requirements. In summary, based on the optimization of pouring system by mold flow analysis, by developing the integrated die casting process for aluminum alloy. Not only the defects are eliminated, but also the castings with sufficient mechanical properties are produced.

This paper presents a machine learning and image segmentation based advanced quality assessment technique for thin Refill Friction Stir Spot Welded (RFSSW) joints. In particular, the research focuses on developing a predictive support vector machines (SVM) model. The purpose of this model is to facilitate the selection of RFSSW process parameters in order to increase the shear load capacity of joints. In addition, an improved weld quality assessment algorithm based on optical analysis was developed. The research methodology includes specimen preparation stages, mechanical tests, and algorithmic analysis, culminating in a machine learning model trained on experimental data. The results demonstrate the effectiveness of the model in selecting welding process parameters and assessing weld quality, offering significant improvements compared to standard techniques. This research not only proposes a novel approach to optimizing welding parameters but also facilitates automatic quality assessment, potentially revolutionizing and spreading the application of the RFSSW technique in various industries.

The examinations of concrete involved partial substitution of cement with Cement Kiln Dust (CKD - 0, 5, 10, 20 and 30%). The water/cement (w/c) ratio amounted to 0.36. The obtained findings pertaining to open porosity, density, and volumetric density were found to correlate with the capillary action and absorptivity of the analyzed types of concrete. With the maximum addition of CKD, i.e. 30%, open porosity decreased by 35%. In turn, CKD added in the amount of 5% resulted in a slightly reduced addition compressive strength, amounting to 1.3% and 2.1% following 28 days and 56 days of concrete curing, respectively. After 28 days, the differences in strength were greater when the additive was supplied in higher amounts, i.e. 10%, 20%, and 30%, resulting in 6.5%, 13.4%, and 22.9% decrease, respectively, in spite of strength improvement. In terms of flexural and splitting tensile strengths, comparable relations were noted. As far as the frost resistance test results are concerned, the mass losses in all examined concretes were not significant, reaching up to 0.5%. The strength reduction in the case of the first three series of concretes was below 3%. When the CKD addition was increased to 20% and 30%, the value of the dynamic modulus of elasticity Ec,s decreased to a greater degree, by 8.0% and 14.7%, respectively. The hydrophobization effect is best seen in CKD-free concrete. After the impregnation, the loss of mass following the frost test of the samples is reduced by half. With a higher CKD content, the hydrophobization effectiveness is insignificant. The most favourable results were observed for hydrophobization with the oligomer-based preparation A1.

The article presents the application of the time domain reflectometry (TDR) technique for measuring the moisture of porous building materials used in construction. The work is focused on using the potential of artificial intelligence to improve the quality of TDR measurements through a new approach to the interpretation of data obtained from the TDR readings. Machine learning is a data analysis technique, used nowadays in many scientific disciplines. The authors performed a measurement data analysis using the artificial intelligence algorithms to assess moisture of aerated concrete samples tested with a TDR multimeter using two non-invasive sensors which differ in thickness. Data analysis was carried out using supervised machine learning to analyse a series of reflectograms obtained during the measurement. For the data achieved by the classical and machine learning method interpretation, correlation analysis was conducted to confirm the potential of artificial intelligence to improve the quality of TDR measurement. The summary of the work discusses the obtained analytical results and highlights the effectiveness of moisture assessment using the Gaussian Process Regression method, which allowed achieving a level of 0.2 - 0.3% of the RMSE errors value, which is about 10 times lower than the traditional approach.

One of the most straightforward and affordable ways to produce hydrogen is by alkaline water electrolysis. In order to split water molecules into hydrogen and oxygen, an electrolyser is often subjected to current levels of 1.23V. The electrodes in an electrolytic cell are the primary structural component. The cathode electrode type is the one where hydrogen is created via the reduction reaction between the two types of electrodes. LPG is combined with hydrogen at a 4:1 ratio to lower the combustion energy because hydrogen cannot be used directly in a traditional SI engine due to its higher energy production during combustion. With the aid of a vaporizer unit, the hydrogen and LPG are combined in the necessary ratio. Through the bypass line created on the input manifold before the carburettor, where air is also mixed with the hydrogen-LPG fuel with the A/F ratio of 17:1 (stoichiometric ratio) for complete combustion, the fuel mixture is transported to the engine's combustion chamber. Due to the usage of LPG and hydrogen, full combustion may occur as a result of the production of a blue flame during combustion. Better mixing of the fuel and air can be achieved since the fuel mixture is conveyed in va-por state instead of semi-liquid form as in a conventional SI engine. This approach of using mix fuel (LPG+H2) for con-ventional SI engines can lower nitrogen oxide and hydrocarbon values in the exhaust gas more effectively.

There have been investigated potential evaluation of equilibrium adsorption isotherm for the removal of nitrates from water solutions using two types of char produced in commercial-scale pyrolysis based on recycled waste rubber tires. Liquid phase adsorption studies were performed under batch conditions and maximum adsorption capacity was determined. Equilibrium data were mathematically modelled using two-parameters Langmuir, Freundlich, three-parameters Redlich-Peterson, Toth, Dubinin-Radushkevich, Radke-Praushnitz, combined Langmuir-Freundlich and four-parameters Fritz-Schlunder, Marczewski-Jaroniec, Bi-Langmuir adsorption models. Obtained results revealed the potential use of the studied char adsorbents for nitrates removal from aqueous media (the maximum adsorption capacity at equilibrium 10.07 mg/g, have been achieved for CH-1 char). The Langmuir-Freundlich isotherm had the best fit for the adsorption experimental data over the whole concentration range. The highest percentage of NO3 removal efficiency onto CH-2 char achieved in NO3 initial concentration range from 26.44 to 66.55mg/L reaching values in the range of from 80.74 to 78.7%.

Pin fins have the potential to improve the thermal performance of various engineering devices. Modified pin fins could further increase their thermal performance in a passive way at lower cost. This study is aimed to numerically investigate the thermal performance of trenched hemispherical pin fins heat sink (THPFHS) and the influence of parameters including the trench number (N=1, 3 and 5) and thickness (e =1 to 5 mm). The simulations are performed using a CFD software considering turbulent air flow conditions. Results show that the use of aluminum fins fitted with one trench in the middle of the hemispherical pin fin considerably increases the local heat transfer. Furthermore, all studied configurations show high thermal performance factor (HTPF) compared with the conventional CPFHS (cylindrical pin fins heat sink). For this new configuration (THPFHS), Nu increases by 45% while the thermal resistance reduces by 42%, compared to the baseline case. On the other hand, this improved performance results in 50% pressure drop penalty. Moreover, the obtained results show a significant improvement in the performance mainly at high Re.