Search Results (641)

Currently, various pruning strategies including different methods and distribution types are commonly used to reduce the number of FLOPs and parameters in deep learning models. However, their impact on actual energy savings remains insufficiently studied, particularly in resource-constrained settings. To address this, we introduce PruneEnergyAnalyzer, an open-source Python tool designed to evaluate the energy efficiency of pruned models. Starting from the unpruned model, the tool calculates the energy savings achieved by pruned versions provided by the user, and generates comparative visualizations based on previously applied pruning hyperparameters such as method, distribution type (PD), compression ratio (CR), and batch size. These visual outputs enable the identification of the most favorable pruning configurations in terms of FLOPs, parameter count, and energy consumption. As a demonstration, we evaluated the tool with 180 models generated from three architectures, five pruning distributions, three pruning methods, and four batch sizes, using another previous library (e.g. FlexiPrune). This experiment revealed the significant impact of the network architecture on Energy Reduction, the non-linearity between FLOPs savings and energy savings, as well as between parameter reduction and energy efficiency. It also showed that the batch size strongly influences the energy consumption of the pruned model. Therefore, this tool can support researchers in making pruning policy decisions that also take into account the energy efficiency of the pruned model. Full article

Despite rapid advances in robot learning, the coordination of closed-loop manipulation in cluttered environments remains a challenging and relatively underexplored problem. We present a novel two-level hierarchical architecture for a depth vision-equipped robotic arm that integrates pushing, grasping, and high-level decision making. Central to our approach is a prioritised action–selection mechanism that facilitates efficient early-stage learning via behaviour cloning (BC), while enabling scalable exploration through reinforcement learning (RL). A high-level decision neural network (DNN) selects between grasping and pushing actions, and two low-level action neural networks (ANNs) execute the selected primitive. The DNN is trained with RL, while the ANNs follow a hybrid learning scheme combining BC and RL. Notably, we introduce an automated demonstration generator based on oriented bounding boxes, eliminating the need for manual data collection and enabling precise, reproducible BC training signals. We evaluate our method on a challenging manipulation task involving five closely packed cubic objects. Our system achieves a completion rate (CR) of 100%, an average grasping success (AGS) of 93.1% per completion, and only 7.8 average decisions taken for completion (DTC). Comparative analysis against three baselines—a grasping-only policy, a fixed grasp-then-push sequence, and a cloned demonstration policy—highlights the necessity of dynamic decision making and the efficiency of our hierarchical design. In particular, the baselines yield lower AGS (86.6%) and higher DTC (10.6 and 11.4) scores, underscoring the advantages of content-aware, closed-loop control. These results demonstrate that our architecture supports robust, adaptive manipulation and scalable learning, offering a promising direction for autonomous skill coordination in complex environments. Full article

This study analyzed the heavy metal tolerance and chromium reduction and the potential of plant growth to promote Rhizobium sp. OS-1. By genetic makeup, the Rhizobium strain is nitrogen-fixing and phosphate-solubilizing in metal-contaminated agricultural soil. Among the Rhizobium group, bacterial strain OS-1 showed a significant tolerance to heavy metals, particularly chromium (900 µg/mL), zinc (700 µg/mL), and copper. In the initial investigation, the bacteria strains were morphologically short-rod, Gram-negative, appeared as light pink colonies on media plates, and were biochemically positive for catalase reaction and the ability to ferment glucose, sucrose, and mannitol. Further, bacterial genomic DNA was isolated and amplified with the 16SrRNA gene and sequencing; the obtained 16S rRNA sequence achieved accession no. HE663761.1 from the NCBI GenBank, and it was confirmed that the strain belongs to the Rhizobium genus by phylogenetic analysis. The strain’s performance was best for high hexavalent chromium [Cr(VI)] reduction at 7–8 pH and a temperature of 30 °C, resulting in a total decrease in 96 h. Additionally, the adsorption isotherm Freundlich and Langmuir models fit best for this study, revealing a large biosorption capacity, with Cr(VI) having the highest affinity. Further bacterial chromium reduction was confirmed by an enzymatic test of nitro reductase and chromate reductase activity in bacterial extract. Further, from the metal biosorption study, an Artificial Neural Network (ANN) model was built to assess the metal reduction capability, considering the variables of pH, temperature, incubation duration, and initial metal concentration. The model attained an excellent expected accuracy (R² > 0.90). With these features, this bacterial strain is excellent for bioremediation and use for industrial purposes and agricultural sustainability in metal-contaminated agricultural fields. Full article

Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality. Full article

Reconfigurable Intelligent Surface (RIS) has emerged as a promising enabling technology for wireless communications, which significantly enhances system performance through real-time manipulation of electromagnetic wave reflection characteristics. In RIS-assisted communication systems, existing deep learning-based channel state information (CSI) feedback methods often suffer from excessive parameter requirements and high computational complexity. To address this challenge, this paper proposes LwCSI-Net, a lightweight autoencoder network specifically designed for RIS-assisted multiple-input single-output (MISO) systems, aiming to achieve efficient and low-complexity CSI feedback. The core contribution of this work lies in an innovative lightweight feedback architecture that deeply integrates multi-layer convolutional neural networks (CNNs) with attention mechanisms. Specifically, the network employs 1D convolutional operations with unidirectional kernel sliding, which effectively reduces trainable parameters while maintaining robust feature-extraction capabilities. Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. This approach not only improves network representational efficiency but also reduces redundant computations, leading to optimized computational complexity. Additionally, the proposed cross-channel residual block (CRBlock) establishes inter-channel information-exchange paths, strengthening feature fusion and ensuring outstanding stability and robustness under high compression ratio (CR) conditions. Our experimental results show that for CRs of 16, 32, and 64, LwCSI-Net significantly improves CSI reconstruction performance while maintaining fewer parameters and lower computational complexity, achieving an average complexity reduction of 35.63% compared to state-of-the-art (SOTA) CSI feedback autoencoder architectures. Full article

In the context of globalization and the Belt and Road Initiative, maintaining the stability and security of the China Railway Express network (CRN) is critical for international logistics operations. However, unexpected events can lead to node and edge failures within the CRN, potentially triggering cascading failures that critically compromise network performance. This study introduces a Coupled Map Lattice model that incorporates cargo flow dynamics, distributing cargo based on distance and the residual capacity of neighboring nodes. We analyze cascading failures in the CRN under three scenarios, isolated node failure, isolated edge disruption, and simultaneous node and edge failure, to assess the network’s vulnerability during emergencies. Our findings show that deliberate attacks targeting cities with high node strength result in more significant damage than attacks on cities with a high node degree or betweenness. Additionally, when edges are disrupted by unexpected events, the impact of edge removals on cascading failures depends on their strategic position and connections within the network, not just their betweenness and weight. The study further reveals that removing collinear edges can effectively slow the propagation of cascading failures in response to deliberate attacks. Furthermore, a single-factor cargo flow allocation method significantly enhances the network’s resilience against edge failures compared to node failures. These insights provide practical guidance and strategic support for the CR Express in mitigating the effects of both unforeseen events and intentional attacks. Full article

The microstructure and mechanical properties of welded joints of API 5L X-52 steel plates cladded with AISI 316L-Si austenitic stainless steel were evaluated. The gas metal arc welding process with pulsed arc (GMAW-P) and controlled arc oscillation were used to join the bimetallic plates. After the root welding pass, buttering with an ERNiCrMo-3 filler wire was performed and multi-pass welding followed using an ER70S-6 electrode. The results obtained by optical and scanning electron microscopy indicated that the shielding atmosphere, welding parameters, and electric arc oscillation enabled good arc stability and proper molten metal transfer from the filler wire to the sidewalls of the joint during welding. Vickers microhardness (HV) and tensile tests were performed for correlating microstructural and mechanical properties. The mixture of ERNiCrMo-3 and ER70S-6 filler materials presented fine interlocked grains with a honeycomb network shape of the Ni–Fe mixture with Ni-rich grain boundaries and a cellular-dendritic and equiaxed solidification. Variation of microhardness at the weld metal (WM) in the middle zone of the bimetallic welded joints (BWJ) is associated with the manipulation of the welding parameters, promoting precipitation of carbides in the austenitic matrix and formation of martensite during solidification of the weld pool and cooling of the WM. The BWJ exhibited a mechanical strength of 380 and 520 MPa for the yield stress and ultimate tensile strength, respectively. These values are close to those of the as-received API 5L X-52 steel. Full article

Nanoparticle floating gate (NPFG) transistors have gained attention as synaptic devices due to their discrete charge storage capability, which minimizes leakage currents and enhances the memory window. In this study, we propose and evaluate a mesh-type floating gate transistor (Mesh-FGT) designed to emulate the characteristics of NPFG transistors. Individual floating gates with dimensions of 3 µm × 3 µm are arranged in an array configuration to form the floating gate structure. The Mesh-FGT is composed of an Al/Pt/Cr/HfO₂/Pt/Cr/HfO₂/SiO₂/SOI (silicon-on-insulator) stack. Threshold voltages (Vth) extracted from the transfer and output curves followed Gaussian distributions with means of 0.063 V (σ = 0.100 V) and 1.810 V (σ = 0.190 V) for the erase (ERS) and program (PGM) states, respectively. Synaptic potentiation and depression were successfully demonstrated in a multi-level implementation by varying the drain current (I_ds) and Vth. The Mesh-FGT exhibited high immunity to leakage current, excellent repeatability and retention, and a stable memory window that initially measured 2.4 V. These findings underscore the potential of the Mesh-FGT as a high-performance neuromorphic device, with promising applications in array device architectures and neuromorphic neural network implementations. Full article

Encryption and decryption are essential components in signal processing and optical communication systems, providing data confidentiality, integrity, and secure high-speed transmission. We present a novel design and simulation of an all-optical encryption and decryption system operating at 120 Gb/s using carrier reservoir semiconductor optical amplifiers (CR-SOAs) embedded in Mach–Zehnder interferometers (MZIs). The architecture relies on two consecutive exclusive-OR (XOR) logic gates, implemented through phase-sensitive interference in the CR-SOA-MZI structure. The first XOR gate performs encryption by combining the input data signal with a secure optical key, while the second gate decrypts the encoded signal using the same key. The fast gain recovery and efficient carrier dynamics of CR-SOAs enable a high-speed, low-latency operation suitable for modern photonic networks. The system is modeled and simulated using Mathematica Wolfram, and the output quality factors of the encrypted and decrypted signals are found to be 28.57 and 14.48, respectively, confirming excellent signal integrity and logic performance. The influence of key operating parameters, including the impact of amplified spontaneous emission noise, on system behavior is also examined. This work highlights the potential of CR-SOA-MZI-based designs for scalable, ultrafast, and energy-efficient all-optical security applications. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

Wear, fatigue, and corrosion are just a few of the issues that mechanical components in engineering experience, leading to surface deterioration. Enhancing the surface characteristics of engineering components is therefore essential. The surface properties of engineering objects can be improved by applying different surface treatments. One of these processes is shot peening (SP). Process parameters are crucial for SP. This necessitates the optimization of SP process parameters. In this study, we applied SP and vibratory shot peening (VSP) processes to different steel materials (AISI 8620, AISI 5140, AISI 4140, and AISI 1020) using different process parameters, aiming to determine the effects of these parameters on hardness, residual stress, and surface roughness. The highest compressive residual stress (CRS) and hardness values for shot-peened samples were obtained at the 24–26 A intensity for all steels. For all steel-group VSP samples, the highest CRS and hardness values were obtained at the 60 s −4 mm parameter. This paper aims to predict Almen intensity values using CRS, surface roughness, and hardness values from various steels. The supplied experimental data was utilized to estimate the SP Almen intensity value using a number of machine learning (ML) methods, eliminating the need for costly and time-consuming experimentation. With an RMSE of 0.0731, R2 of 0.9665, and MAE of 0.0613, the deep neural network (DNN) surpassed the other models in terms of prediction accuracy. The results indicate that artificial intelligence technology could be utilized to accurately evaluate Almen intensity. Full article

The cold recycling (CR) technique is gaining traction, with an increasing demand for sustainable pavement construction practices. Cold in-place recycling (CIR) and cold central plant recycling (CCPR) are two strategies under the umbrella of cold recycling. These techniques use reclaimed asphalt pavement (RAP) to rehabilitate pavement, and CCPR offers the added advantage of utilizing stockpiled RAP. While many agencies have expertise in cold recycling techniques including CCPR, the lack of pavement performance data prevented the largescale implementation of these technologies. Recent studies in high-traffic volume applications demonstrate that CCPR technology can be implemented on the entire road network across all traffic levels. This reignited interest in the widespread implementation of CCPR. Therefore, the purpose of this study is to provide agencies with the most up-to-date information on CCPR to help them make informed decisions. To this end, this paper comprehensively reviews the mix-design for CCPR, the structural design of pavements containing CCPR layers, best construction practices, and the agency experience in using this technology on high-traffic volume roads to provide in-depth information on the steps to follow from project selection to field implementation. The findings specify the suitable laboratory curing conditions to achieve the optimum mix design and specimen preparation procedures to accurately capture the material properties. Additionally, this review synthesizes existing quantitative data from previous studies, providing context for the comparison of findings, where applicable. The empirical and mechanistic–empirical design inputs, along with the limitations of AASHTOWare Pavement ME software for analyzing this non-conventional material, are also presented. Full article

Social desirability is a widely studied phenomenon due to its impact on the validity of self-reported data. It refers to the tendency of individuals to respond to questions in a manner that they believe is socially acceptable or favorable rather than providing truthful or accurate answers. This study evaluated the psychometric properties of the Brief Social Desirability Scale (BSSD-4) in Ecuadorian youth, analyzing its reliability, factorial and convergent validity, and measurement invariance by sex, age group, and experiences of dating violence. An instrumental study was conducted with a non-probabilistic convenience sample of 836 participants (aged 14–26). Reliability was adequate (Ω = 0.75, α = 0.81, CR = 0.759). Confirmatory factor analysis showed good fit indices (CFI = 0.98, TLI = 0.97, RMSEA = 0.056, SRMR = 0.037). Convergent validity was acceptable (AVE = 0.50, VIF < 2.01). A network analysis confirmed the unidimensionality of the scale and structural differences between groups. Measurement invariance by sex and age was verified, but differences in the network structure were found based on victimization and perpetration of violence. The BSSD-4 is a valid and reliable instrument for assessing social desirability in Ecuadorian youth, useful for population studies and intergroup comparisons. Further research is recommended to explore its invariance in populations with a history of violence, as differences in scalar invariance were observed. Full article

This paper addresses the optimization problem for mobile jamming assistance schemes in cognitive covert communication (CR-CC), where cognitive users adopt the underlying mode for spectrum access, while an unmanned aerial vehicle (UAV) transmits the same-frequency noise signals to interfere with eavesdroppers. Leveraging the inherent dynamic game-theoretic characteristics of covert communication (CC) systems, we propose a novel covert communication optimization algorithm based on generative adversarial networks (GAN-CCs) to achieve system-wide optimization under the constraint of maximum detection error probability. In GAN-CC, the generator simulates legitimate users to generate UAV interference assistance schemes, while the discriminator simulates the optimal signal detection of eavesdroppers. Through the alternating iterative optimization of these two components, the dynamic game process in CC is simulated, ultimately achieving the Nash equilibrium. The numerical results show that, compared with the commonly used multi-objective optimization algorithm or nonlinear programming algorithm at present, this algorithm exhibits faster and more stable convergence, enabling the derivation of optimal mobile interference assistance schemes for cognitive CC systems. Full article

The direct energy deposition (DED) metal additive manufacturing process enables rapid deposition and repair, providing an efficient approach to producing durable tool steel components. Here, 5 wt% Cr cold-work tool steel (Caldie) was developed by reducing carbon and chromium to suppress coarse carbide formation and by increasing molybdenum and vanadium to enhance dimensional stability. In this study, Caldie tool steel was fabricated via DED for the first time, and the effects of post-heat treatment on its hierarchical microstructure and mechanical properties were investigated and compared with those of wrought (reference) material. The as-built sample exhibited a mixed microstructure comprising lath martensite, retained austenite, polygonal ferrite, and carbide networks, which transformed into full martensite with fine carbides after heat treatment (DED-HT). The tensile strength of the DED Caldie material increased from 1340 MPa to 1949 MPa after heat treatment, demonstrating superior strength compared to other heat-treated, DED-processed high-carbon tool steels. Compared to DED-HT, the wrought material exhibited finer martensite, a more uniform Bain group distribution, and finer carbides, resulting in higher strength. This study provides insights into the effects of heat treatment on the hierarchical microstructure and mechanical behavior of Caldie tool steel manufactured by DED. Full article