Search Results (229)

This paper presents a critical and comprehensive review of the application of mining waste, specifically waste rock and tailings, in asphalt pavements, with the aim of synthesizing performance outcomes and identifying key research gaps. A systematic literature search yielded a final dataset of 41 peer-reviewed articles for detailed analysis. Bibliometric analysis indicates a notable upward trend in annual publications, reflecting growing academic and practical interest in this field. Performance-based evaluations demonstrate that mining wastes, particularly iron and copper tailings, have the potential to enhance the high-temperature performance (i.e., rutting resistance) of asphalt binders and mixtures when utilized as fillers or aggregates. However, their effects on fatigue life, low-temperature cracking, and moisture susceptibility are inconsistent, largely influenced by the physicochemical properties and dosage of the specific waste material. Despite promising results, critical knowledge gaps remain, particularly in relation to long-term durability, comprehensive environmental and economic Life-Cycle Assessments (LCA), and the inherent variability of waste materials. This review underscores the substantial potential of mining wastes as sustainable alternatives to conventional pavement materials, while emphasizing the need for further multidisciplinary research to support their broader implementation. Full article

The properties of Hardware-in-the-Loop (HIL) for the development of controllers, together with electronic emulation of physical process by Digital Twins (DT) significantly enhance the optimization of design and implementation in nonlinear control applications. The study emphasizes the use of the Raspberry Pi (RBP), a low-cost and portable electronic board for two interrelated goals: (a) the Electronic Control Unit (ECU-RBP) implementing a Lyapunov-based Controller (LBC) for nonlinear trajectory tracking of P3DX wheeled robots, and (b) the Digital Twin (DT-RPB) emulating the real robot behavior, which is remotely connected to the control unit. ECU-RBP, DT-RBP and real robot are connected as nodes within the same wireless network, enhancing interaction between the three physical elements. The development process is supported by the Matlab/Simulink environment and the associated packages for the specified electronic board. Following testing of the real robot from the ECU-RBP in an open loop, the model is identified and integrated into the DT-RBP to replicate its functionality. The LBC solution, which has also been validated through simulation, is implemented in the ECU-RBP to examine the closed-loop control according to the HIL strategy. Finally, the study evaluates the effectiveness of the HIL approach by comparing the results obtained from the application of the LBC, as implemented in the ECU-RBP to both the real robot and its DT. Full article

The rapid integration of complex sensors and electronic control units (ECUs) in autonomous vehicles significantly increases cybersecurity risks in vehicular networks. Although the Controller Area Network (CAN) is efficient, it lacks inherent security mechanisms and is vulnerable to various network attacks. The traditional Intrusion Detection System (IDS) makes it difficult to effectively deal with the dynamics and complexity of emerging threats. To solve these problems, a lightweight vehicular network intrusion detection framework based on Dynamic Feature Fusion Federated Learning (DFF-FL) is proposed. The proposed framework employs a two-stream architecture, including a transformer-augmented autoencoder for abstract feature extraction and a lightweight CNN-LSTM–Attention model for preserving temporal and local patterns. Compared with the traditional theoretical framework of the federated learning, DFF-FL first dynamically fuses the deep feature representation of each node through the transformer attention module to realize the fine-grained cross-node feature interaction in a heterogeneous data environment, thereby eliminating the performance degradation caused by the difference in feature distribution. Secondly, based on the final loss $L_{AE}\left(X,\widehat{X}\right)$ index of each node, an adaptive weight adjustment mechanism is used to make the nodes with excellent performance dominate the global model update, which significantly improves robustness against complex attacks. Experimental evaluation on the CAN-Hacking dataset shows that the proposed intrusion detection system achieves more than 99% F1 score with only 1.11 MB of memory and 81,863 trainable parameters, while maintaining low computational overheads and ensuring data privacy, which is very suitable for edge device deployment. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

This study investigates the impact of hydrogen supplementation on the performance, efficiency, and emissions of a spark-ignition internal combustion engine, with a specific focus on knock phenomena. A Nissan HR16DE engine was modified to operate in a dual-fuel mode using gasoline (E95) and high-purity hydrogen. Hydrogen was injected via secondary manifold injectors and managed through a reprogrammable MoTeC ECU, allowing precise control of ignition timing and fuel delivery. Experiments were conducted across various engine speeds and loads, with hydrogen mass fractions ranging from 0% to 30%. Results showed that increasing hydrogen content enhanced combustion intensity, thermal efficiency, and stability. Brake specific fuel consumption decreased by up to 43.4%, while brake thermal efficiency improved by 2–3%. CO, HC, and CO₂ emissions were significantly reduced. However, NO_x emissions increased with higher hydrogen concentrations due to elevated combustion temperatures. Knock tendency was effectively mitigated by retarding ignition timing, ensuring peak in-cylinder pressure occurred at 14–15° CAD aTDC. These findings demonstrate the potential of hydrogen supplementation to reduce fossil fuel use and greenhouse gas emissions in spark ignition engines, while highlighting the importance of precise combustion control to address challenges such as knock and NO_x formation. Full article

Casein Kinase II (CK2) is a ubiquitously present serine/threonine kinase essential for mammalian development. CK2 holoenzyme is a tetramer with two highly related catalytic subunits (α or α’) and two regulatory ß subunits. Global deletion of the α or β subunit in mice is embryonically lethal. We and others have shown that CK2 is overexpressed in leukemia cells and plays an important role in cell cycle, survival, and resistance to the apoptosis of leukemia stem cells (LSCs). To study the role of CK2α in adult mouse hematopoiesis, we generated hematopoietic cell-specific CK2α-conditional knockout mice (Vav-iCreCK2 ^f/f). Here we report the generation and validation of a novel mouse model that lacks CK2α in the hematopoietic compartment. Vav-iCreCK2α ^f/f mice were viable without dysmorphic features and showed a mild phenotype under baseline conditions. In Vav-iCreCK2α ^f/f mice, the blood count showed a significant decrease in total red blood cells and platelets. The spleen was enlarged in Vav-iCreCK2α ^f/f mice with evidence of extramedullary hematopoiesis. HSC and early progenitor cell compartments showed expansion in CK2α-null bone marrow, suggesting that the absence of CK2α impaired their proliferation and differentiation. Given the established roles of CK2 in cell cycle regulation and the findings reported here, further functional studies are warranted to investigate the role of CK2α in HSC self-renewal and differentiation. This mouse model serves as a valuable tool for understanding the role of CK2α in normal and malignant hematopoiesis. Full article

Energy and mobility are currently powered by conventional fuels, and for the specific case of spark ignition (SI) engines, gasoline is dominant. Converting these power-units to hydrogen is an efficient and cost-effective choice for achieving zero-carbon emissions. The use of this alternative fuel can be combined with a circular-economy approach that gives new life to the existing fleet of engines and minimizes the need for added components. In this context, the current work scrutinizes specific aspects of converting a small-size passenger car to hydrogen fueling. The approach combined measurements performed with gasoline and predictive 0D/1D models for correctly including fuel chemistry effects; the experimental data were used for calibration purposes. One particular aspect of H₂ is that it results in lower volumetric efficiency compared to gasoline, and therefore boosting requirements can feature significant changes. The results of the 0D/1D simulations show that one of the main conclusions is that only stoichiometric operation would ensure the reference peak power level; lean fueling featured relative air–fuel ratios too low for ensuring the minimum value of 2 that would allow mitigating NO_x formation. Top speed could be instead feasible in lean conditions, with the same gearbox, but with an extension of the engine speed operating range to 7000 rpm compared to the 3700 rpm reference point with gasoline. Full article

Endoscopic procedures are the cornerstone of intervention in gastroenterology—from evaluating common illnesses to non-surgically managing complex diseases. Expectedly, these procedures are linked to greenhouse gas (GHG) emissions globally and contribute significantly to the global climate change crisis. Professional gastroenterology societies globally raise awareness of this evolving crisis and suggest specific measures to appropriately measure the burden contributed by endoscopy units and mitigate the environmental impact of this common clinical practice. To the unsuspecting eye, the solution to this crisis is relatively simple: decrease the utilization of endoscopic procedures. However, the dependence of modern medicine on these procedures, both diagnostically and therapeutically, makes it significantly more challenging to reduce their utilization. Instead, a structured approach to systematically consider the specific indications for each procedure, minimize waste generation, promote recycling of waste products, and limit the number of repeat endoscopies until clinically necessary may be more pragmatic to reduce GHG emissions globally. In this narrative review, we discuss the perspectives of global gastroenterology societies on sustainable or “green” endoscopy and summarize their recommendations to aid the day-to-day gastroenterologist in making their contribution to environmental sustainability while providing optimal care to their patients. Full article

As consumer behavior increasingly shifts toward hyperlocal, digitally mediated retail journeys, community unmanned stores have emerged as a transformative model that integrates smart technologies with community proximity services. These fully automated stores offer convenient, contactless shopping and hybrid digital–physical interactions, playing an increasingly important role within broader omnichannel digital retail ecosystems. However, there remains a lack of validated instruments to assess customer experience in such autonomous and locally embedded retail formats. This study develops and validates an ECUS-scale (an experience in community unmanned store scale), a multidimensional measurement tool grounded in qualitative research and refined through exploratory and confirmatory factor analysis. The scale identifies nine key dimensions—convenient service, smooth transaction, preferential price, good quality, safe environment, secure payment, comfortable space, comfortable interaction, and friendly image—across 36 items. These dimensions reflect the technological, spatial, and emotional–social aspects of customer experience in unmanned retail settings. The findings demonstrate that the ECUS-scale offers a robust framework for evaluating consumer experience in low-staffed, tech-enabled community stores, with strong relevance to omnichannel digital retail strategies. Theoretically, it advances the literature on smart retail experience by capturing underexplored dimensions such as emotional engagement with technology and perceptions of safety in staff-free environments. Practically, it serves as a diagnostic tool for businesses to enhance experience design and optimize customer engagement across digital and physical touchpoints. Full article

With the integration of an increasing number of outward-facing components in intelligent and connected vehicles, the open controller area network (CAN) bus environment faces increasingly severe security threats. However, existing security measures remain inadequate, and CAN bus messages lack effective security mechanisms and are vulnerable to malicious attacks. Although encryption algorithms can enhance system security, their high bandwidth consumption negatively impacts the real-time performance of intelligent and connected vehicles. Moreover, the message authentication mechanism of the CAN bus requires lengthy authentication codes, further exacerbating the bandwidth burden. To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. Additionally, we have proposed a novel dynamic key management approach, incorporating a dynamic key distribution mechanism, which effectively resolves the challenges associated with key management. Each Electronic Control Unit (ECU) node independently performs compression, encryption, and authentication while periodically updating its keys to enhance system security and strengthen defense capabilities. Experimental results show that the proposed dynamic compression algorithm improves the average compression rate by 2.24% and enhances compression time efficiency by 10% compared to existing solutions. The proposed security protocol effectively defends against four different types of attacks. In hardware tests, using an ECU operating at a frequency of 30 MHz, the computation time for the security algorithm on a single message was 0.85 ms, while at 400 MHz, the computation time was reduced to 0.064 ms. Additionally, for different vehicle models, the average CAN bus load rate was reduced by 8.28%. The proposed security mechanism ensures the security, real-time performance, and freshness of CAN bus messages while reducing bus load, providing a more efficient and reliable solution for the cybersecurity of intelligent and connected vehicles. Full article

The proliferation of cyber–physical systems in modern vehicles, characterized by densely interconnected Electronic Control Units (ECUs) and heterogeneous communication networks, has significantly expanded the automotive attack surface. Traditional Threat Analysis and Risk Assessment (TARA) methodologies remain predominantly manual processes that exhibit limitations in scalability, and comprehensive threat identification. This research addresses these limitations by developing a formalized framework for automating attack path analysis within the automotive architecture. While attack graph methodologies have demonstrated efficacy in conventional information technology domains, their application within automotive cybersecurity contexts presents unique challenges stemming from domain-specific architectural constraints. We propose a novel Graph-based Attack Path Prioritization (GAPP) methodology that integrates Extended Finite State Machine (EFSM) modeling. Our implementation employs the Neo4j property graph database architecture to establish the mappings between architectural components, security states, and exploitation vectors. This research contributes a systematic approach to automotive security assessment, enhancing vulnerability identification capabilities while reducing analytical complexity. Full article

The automotive industry is fully shifting towards autonomous connected vehicles. By advancing vehicles’ intelligence and connectivity, the industry has enabled innovative functions such as advanced driver assistance systems (ADAS) in the direction of driverless cars. Such functions are often referred to as cyber-physical features, since almost all of them require collecting data from the physical environment to make automotive operation decisions and properly actuate in the physical world. However, increased functionalities result in increased complexity, which causes serious security vulnerabilities that are typically a result of mushrooming functionality and hence complexity. In a world where we keep seeing traditional mechanical systems shifting to x-by-wire solutions, the number of connected sensors, processing systems, and communication buses inside the car exponentially increases, raising several safety and security concerns. Because there is no safety without security, car manufacturers start struggling in making lightweight sensor and processing systems while keeping the security aspects a major priority. This article surveys the current technological challenges in securing autonomous vehicles and contributes a cross-layer analysis bridging hardware security primitives, real-world side-channel threats, and redundancy-based fault tolerance in automotive electronic control units (ECUs). It combines architectural insights with an evaluation of commercial support for TrustZone, trusted platform modules (TPMs), and lockstep platforms, offering both academic and industry audiences a grounded perspective on gaps in current hardware capabilities. Finally, it outlines future directions and presents a forward-looking vision for securing sensors and processing systems in the path toward fully safe and connected autonomous vehicles. Full article

A new class of spiro[1,2,4]triazolo[1,5-c]quinazoline derivatives is presented as promising modulators of diacylglycerol kinase α (DGK-α), a target implicated in cancer, neurological disorders, and immune dysfunction. Through structure-based computational design using the CB-Dock2 platform with human DGK-α (PDB ID: 6IIE), 40 novel compounds were systematically evaluated along with established inhibitors (ritanserin, R59022, R59949, BMS502, and (5Z,2E)-CU-3) across five distinct binding pockets. Several compounds demonstrated binding profiles at the level of or surpassing the reference compounds. The physicochemical analysis revealed balanced drug-like properties with favorable molecular weights (252–412 g/mol) and appropriate three-dimensionality. The toxicological assessment indicated reassuring safety profiles with predicted LD₅₀ values of 1000–2000 mg/kg and minimal hepatotoxicity, carcinogenicity, and mutagenicity potential. Notably, compound 33 (adamantyl-substituted) emerged as exceptionally promising, exhibiting strong binding affinity, moderate solubility, and selective CYP inhibition patterns that minimize drug–drug interaction risks. Detailed molecular interaction mapping identified critical binding determinants, including strategic hydrogen bonding with TRP151, GLU166, and ARG126. The multidimensional evaluation identified compounds 13, 18, 33, and 40 as particularly promising candidates that balance potent target engagement with favorable pharmaceutical profiles, establishing this scaffold as a valuable platform for developing next-generation therapeutics targeting DGK-α -mediated signaling pathways. Full article

Objectives: The present study aimed to investigate changes in forearm muscle activity associated with short-term go-kart driving (680 m) and its potential effect on muscle activation patterns. Methods: Eleven male karting league drivers (mean age: 23.18 ± 1.40 years; body mass: 83.27 ± 10.98 kg; height: 182.73 ± 5.66 cm) volunteered to participate. Electromyographic (EMG) activity was recorded from four muscles: extensor carpi radialis (ECR), extensor carpi ulnaris (ECU), flexor carpi radialis (FCR), and flexor carpi ulnaris (FCU). Baseline EMG was measured before the intervention, followed by two consecutive kart-driving sessions on a 680 m closed track. Post-exercise EMG data were then collected. A repeated-measures analysis of covariance (ANCOVA) was used to analyze the effects of time (pre vs. post) while controlling for cumulative race time as a covariate. Results: A significant time effect with cumulative time as a covariate was observed, particularly in the ECR and ECU muscles on both the left and right sides. Notable findings include increases in maximum and mean activity of the left and right ECR (e.g., ECR right max: F = 51.57; p < 0.001; η² = 0.851) and ECU (e.g., ECU right max: F = 36.170; p < 0.001; η² = 0.801). Additionally, a significant increase was found in the maximum activation of the left FCR (F = 11.019; p = 0.009; η² = 0.550, which remained significant after controlling for total driving time. This heightened activation likely reflects an acute neuromuscular fatigue response to the demands of kart steering, rather than a long-term adaptation. Conclusions: The findings suggest that even short bouts of kart driving can induce measurable changes in neuromuscular activation of the forearm muscles, particularly in those involved in grip control and steering stability. This highlights the physical demands of karting and its potential impact on the upper limb muscle conditioning. Full article

The MIPI (Mobile Industry Processor Interface) Alliance provides a security framework for in-vehicle network connections between sensors and processing electronic control units (ECUs). One approach within this framework is data integrity verification for sensors with limited hardware resources. In this paper, the security risks associated with image sensor data are described. Adversarial examples (AEs) targeting the MIPI interface can induce misclassification, making image data integrity verification essential. A CMOS image sensor with a message authentication code (CIS-MAC) is then proposed as a defense mechanism starting from the image sensor to protect image data from malicious manipulations, such as AE attacks. Evaluation results of the physically unclonable function (PUF) response and random number, which are utilized for generating the cryptographic key and MAC tag, are presented using a 2 Mpixel CMOS image sensor. The area of the CIS-MAC circuit is estimated based on a Verilog HDL design and synthesis using a 0.18 μm CMOS process. Various hash topologies are evaluated to select a hash function suitable for key generation and MAC tag generation within the CMOS image sensor. The estimated area of the CIS-MAC circuit is 67 kGE and $0.86{\mathrm{mm}}^2$, demonstrating feasibility for implementation in a CMOS image sensor typically fabricated using analog process technology. Full article