Search Results (911)

Organic-rich shale, as a significant alternative energy source, possesses abundant resources. Classified by maturity, it comprises three categories: medium-high maturity shale oil, medium-low maturity shale oil, and oil shale. Medium-high maturity shale oil faces challenges such as tight reservoirs and poor fluidity; medium-low maturity shale oil is characterized by a high proportion of retained hydrocarbons and poor mobility; and oil shale requires high-temperature conversion. Addressing the inherent characteristics of these three resource types, this paper systematically reviews the theoretical foundations and key technologies from two dimensions: “CO₂ injection for medium-high maturity shale oil extraction” and “in situ conversion of medium-low maturity shale/oil shale”. The results indicate that CO₂ injection technology for medium-high maturity shale oil utilizes its supercritical diffusion properties to reduce miscibility pressure by 40–60% compared to conventional reservoirs, efficiently displacing crude oil in nanopores while establishing a geological storage system for greenhouse gases, thereby pioneering an integrated “displacement–drive–storage” model for carbon-reduced oil production. The autothermic pyrolysis in situ conversion process for medium-low maturity shale/oil shale significantly reduces costs by leveraging the oxidation latent heat of kerogen. Under temperature and pressure conditions of 350–450 °C, the shale pore network expansion rate reaches 200–300%, with permeability increasing by two orders of magnitude. Assisted natural gas injection further optimizes the thermal field distribution within the reservoir. Future research should focus on two key directions: synergistic cost reduction and carbon sequestration through CO₂ injection, and the matching of in situ conversion with complex fracture networks. This study delineates key technological pathways for the low-carbon and efficient development of different types of organic-rich shale, contributing to energy security. Full article

3D-printed hand exoskeletons are important because they enable the creation of affordable, lightweight, and highly customizable assistive and rehabilitation devices tailored to individual patient needs. Their rapid production and design flexibility accelerate innovation, improve access to therapies, and accelerate functional recovery for people with hand impairments. This article discusses the development of a hand exoskeleton using advanced additive manufacturing. It highlights how Industry 4.0 principles such as digital design, automation, and smart manufacturing enable precise prototyping and efficient use of materials. Moving on to Industry 5.0, the study highlights the role of human–machine collaboration, where customization and ergonomics are prioritized to ensure user comfort and rehabilitation effectiveness. The integration of AI-based generative design and digital twins (DTs) is explored as a path to Industry 6.0, where adaptive and self-optimizing systems support continuous improvement. The perspective of personal experience provides insight into practical challenges, including material selection, printing accuracy, and wearability. The results show how technological optimization can be used to reduce costs, improves efficiency and sustainability, and accelerates the personalization of medical devices. The article shows how evolving industrial paradigms are driving the design, manufacture, and refinement of 3D-printed hand exoskeletons, combining technological innovation with human-centered outcomes. Full article

Functional foods are designed to provide health benefits beyond basic nutrition; however, the incorporation of bioactive compounds often impacts flavor, stability, and consumer acceptance, making flavor science a critical challenge in product development. This review explores the biochemical and biotechnological mechanisms underlying the formation and modulation of flavor in functional foods. Advances in biotechnology, including microbial fermentation, enzyme engineering, biocatalyst immobilization, and metabolic optimization, have facilitated the sustainable production of natural flavor compounds with improved sensory profiles. Emerging technologies, including nanoencapsulation, ultrasound-assisted extraction, nanotechnology, artificial intelligence-driven flavor design, and 3D food printing, are also discussed for their roles in enhancing the stability, bioavailability, and controlled release of bioactive and flavor compounds. By integrating biotechnology and flavor science, these approaches offer promising strategies for developing clean-label, sensory-optimized functional foods that meet nutritional needs while satisfying consumer expectations, thereby driving innovation toward healthier and more sustainable food systems. Full article

The growing global population and the rapid increase in older adults are driving healthcare costs upward. In response, the healthcare system is shifting toward models that enable continuous monitoring of individuals without requiring hospital admission. Advances in sensing technologies, embedded systems, wireless communication, nanotechnology, and device miniaturization have made these smart systems possible. Wearable sensors can monitor physiological indicators and other symptoms, helping to detect unusual or unexpected events. This allows for the provision of timely assistance when it is needed most. This paper outlines the challenges associated with these systems and reviews recent developments in wearable, sensor-based human activity monitoring. The focus is on health monitoring applications, including relevant biomarkers, wearable and implantable sensors, and established sensor technologies currently used in healthcare, as well as future prospects. It also discusses the challenges involved in researching, developing, and applying these sensors. The goal is to promote the widespread use of these sensors in human health monitoring. Full article

This work presents the design and experimental validation of a 2 × 2 MIMO communication system assisted by a directly modulated analog radio-over-fiber (A-RoF) fronthaul, targeting low-complexity connectivity solutions for underserved/remote regions. The study details the complete end-to-end architecture, including a wireless access segment to complement the 20-km optical fronthaul link. The system is implemented on an software defined radio (SDR) platform using GNU Radio 3.7.11, running on Ubuntu 18.04 with kernel 4.15.0-213-generic. It also employs adaptive modulation driven by real-time signal-to-noise ratio (SNR) estimation to keep bit error rate (BER) close to zero while maximizing throughput. Performance is characterized over 20 km of single-mode fiber (SMF) using coarse wavelength division multiplexing (WDM) and assessed through root mean square error vector magnitude (${\mathrm{EVM}}_{\mathrm{RMS}}$), throughput, and spectral integrity. The results identify an optimum radio-frequency drive region around $-16$ dBm enabling high-order modulation (e.g., 256-QAM), whereas RF input powers above approximately $-10$ dBm increase ${\mathrm{EVM}}_{\mathrm{RMS}}$ due to nonlinearity in the RF front-end/low-noise amplifier (LNA) and direct modulation stage, forcing the adaptive scheme to reduce modulation order and throughput. Over the optical-power sweep, when the incident optical power exceeds approximately $-8$ dBm, the system reaches ∼130 Mbps (24-MHz channel) with ${\mathrm{EVM}}_{\mathrm{RMS}}$ approaching ∼1%, highlighting the need for careful joint tuning of RF drive, optical launch power, and wavelength allocation across transceivers. Finally, the integrated access link employs diplexers for transmitter/receiver separation in a 2 × 2 configuration with 2.8 m antenna separation and low channel correlation, demonstrating a 10 m proof-of-concept range and enabling end-to-end spectrum/EVM/throughput observations across the full communication chain. Full article

Level 2 driving automation requires continuous driver supervision, yet common attention metrics often capture gaze allocation rather than the structure of supervisory scanning. This study proposes a quantitative approach for describing supervisory gaze organisation using first-order Markov chain analysis of gaze transitions. Forty-three licensed drivers ($N=43$) completed a simulator drive with Level 2 automation for either 5 or 15 min (between-subjects), representing typical Japanese expressway intervals between service areas. Supervisory behaviour was analysed at the scenario level, without introducing secondary tasks, allowing attentional drift to emerge naturally under automation. Eye-tracking data were manually annotated frame-by-frame at 60 Hz and modelled as transition probability matrices across key Areas of Interest (AOIs): road centre, mirrors, periphery, and the human–machine interface. Compared with the 5 min condition, the 15 min condition showed fewer mirror-to-road-centre recovery transitions and slower System-Recognised Reaction Time (SRRT) at the takeover request. These patterns suggest a gradual weakening of supervisory gaze organisation rather than a simple loss of attention. The proposed framework offers a reproducible way to calibrate driver monitoring and evaluate human–machine interfaces by linking gaze transition probabilities to takeover readiness. By quantifying how supervisory behaviour reorganises under extended automation in realistic driving scenarios, this study provides a practical basis for the development of safety-relevant driver monitoring indicators in Level 2 driver assistance systems. Full article

Enzyme technology, characterized by high efficiency, environmental compatibility, and precise controllability, has become a pivotal biocatalytic approach for quality enhancement and nutritional improvement in modern food industries. This review summarizes recent advances and underlying mechanisms of enzyme applications in food processing optimization, nutritional enhancement, and functional food development. In terms of process optimization, enzymes such as transglutaminase, laccase, and peroxidase enhance protein crosslinking, thereby markedly improving the texture and stability of dairy products, meat products, and plant-based protein systems. Proteases and lipases play essential roles in flavor development, maturation, and modulation of sensory attributes. From a nutritional perspective, enzymatic hydrolysis significantly improves the bioavailability of proteins, minerals, and dietary fibers, while simultaneously degrading antinutritional factors and harmful compounds, including phytic acid, tannins, food allergens, and acrylamide, thus contributing to improved food safety and nutritional balance. With respect to functional innovation, enzyme-directed production of bioactive peptides has demonstrated notable antihypertensive, antioxidant, and immunomodulatory activities. In addition, enzymatic synthesis of functional oligosaccharides and rare sugars, glycosylation-based modification of polyphenols, and enzyme-assisted extraction of plant bioactive compounds provide novel strategies and technological support for the development of functional foods. Owing to their high specificity and eco-friendly nature, enzyme technologies are driving food and nutrition sciences toward more precise, personalized, and sustainable development pathways. Despite these advances, critical research gaps remain, particularly in the limited mechanistic understanding of enzyme behavior in complex food matrices, the insufficient integration of multi-omics data with enzymatic process design, and the challenges associated with translating laboratory-scale enzymatic strategies into robust, data-driven, and scalable industrial applications. Full article

Driving safety under low-visibility or distracted conditions remains a critical challenge for intelligent transportation systems. This paper presents Edge-VisionGuard, a lightweight framework that integrates signal processing and edge artificial intelligence to enhance real-time driver monitoring and hazard detection. The system fuses multi-modal sensor data—including visual, inertial, and illumination cues—to jointly estimate driver attention and environmental visibility. A hybrid temporal–spatial feature extractor (TS-FE) is introduced, combining convolutional and B-spline reconstruction filters to improve robustness against illumination changes and sensor noise. To enable deployment on resource-constrained automotive hardware, a structured pruning and quantization pipeline is proposed. Experiments on synthetic VR-based driving scenes demonstrate that the full-precision model achieves 89.6% driver-state accuracy (F1 = 0.893) and 100% visibility accuracy, with an average inference latency of 16.5 ms. After 60% parameter reduction and short fine-tuning, the pruned model preserves 87.1% accuracy (F1 = 0.866) and <3 ms latency overhead. These results confirm that Edge-VisionGuard maintains near-baseline performance under strict computational constraints, advancing the integration of computer vision and Edge AI for next-generation safe and reliable driving assistance systems. Full article

This paper reviews recent developments in the design optimization of electrical drive systems for electric vehicles (EVs) and proposes a pathway to develop next-generation AI design platforms that integrate system-level optimization methods and digital twins. First, a comprehensive review is presented to five design optimization models for EV motors, including multiphysics, multiobjective, multimode, robust, and topology optimization, as well as six efficient optimization strategies, such as multilevel optimization and AI-based approaches. Several recommendations on the practical application of these optimization strategies are also presented. Second, representative optimization methods for power converters and control systems of EV drives are summarized. Third, application-oriented and robust system-level design optimization strategies for EV drive systems are discussed. Finally, two proposals are presented and discussed for the design of next-generation EV drive systems and their integration with battery management systems. They are AI-powered automatic design optimization platforms that integrate large language models and a digital-twin-assisted system-level optimization framework. Two case studies on in-wheel motors and drive systems are also included to demonstrate the performance and effectiveness of various optimization methods. Full article

With the growing consumer demand for enhanced driving dynamics in vehicles, optimizing powertrain configurations to balance performance, energy efficiency, and cost has become a critical challenge. Traditional internal combustion engine vehicles (ICEVs) suffer from significant energy consumption and cost penalties when improving acceleration performance. This study systematically evaluates the trade-offs between dynamic performance, energy consumption, and direct manufacturing costs across six powertrain configurations: ICEV, 48 V mild hybrid (48 V), hybrid electric vehicle (HEV), plug-in hybrid electric vehicle (PHEV), range-extended electric vehicle (REV), and battery electric vehicle (BEV). By developing a comprehensive parameterized model, we quantify the impacts of acceleration improvement on vehicle mass, energy consumption, and costs. Key findings reveal that electrified powertrains (PHEV, REV, BEV) exhibit superior cost-effectiveness and energy efficiency. For instance, improving 0–100 km/h acceleration time from 9 to 5 s reduces direct manufacturing costs by only 5.72% for BEV versus 13.38% for ICEV, while PHEV achieves a balanced compromise with 3.40% lower fuel consumption and 10.43% cost increase compared to conventional counterparts. Mechanistic analysis attributes these advantages to higher power density of electric motors and simplified energy transmission in electrified systems. This work provides data-driven insights for consumers and automakers to prioritize powertrain technologies under dynamic performance requirements, highlighting PHEV with driving range of 50 km as the optimal choice for harmonizing driving experience, energy economy, and affordability. The results of this study assist automakers in optimizing the technology pathways of vehicle powertrain, within the consumer demand for dynamic performance. This plays a crucial role in advancing the automotive industry’s overall fuel consumption and energy consumption, thereby contributing to sustainable development. Full article

The energy sector is counted among the environmentally unfriendly branches in many global economies, including in Poland. However, it has been pivoting towards alternatives to traditional, high-emission energy generation from non-renewable sources for years. Renewable energy sources, or renewables, are a responsible response to today’s expectations concerning country-level sustainable development, driving the global green energy transition. However, the success of increasing the share of renewables in energy mixes hinges to a large extent on the public perceptions of the changes. In the broadest perspective, research today focuses on global energy transition policy and its funding, problems with the availability of energy carriers, and the adequacy of specific energy production and transfer systems from a technical and technological point of view. Academics tend to concentrate slightly less on investigating the public opinion regarding the challenges of energy transition. This aligns with a relevant research gap for Poland, particularly in rural areas. Therefore, the present article aims to analyse public opinion on environmental protection challenges and the ensuing need to improve energy sourcing to promote the growth of renewable energy in rural Poland, with a case study of five districts in Małopolskie Voivodeship, to contribute to the body of knowledge on these issues. The goal was pursued through a survey of 300 randomly selected inhabitants of the five districts in Malopolska, conducted using Computer-Assisted Personal Interviewing (CAPI) in 2024. The results were analysed with quantitative techniques and qualitative instruments. The detailed investigation involved descriptive statistics and tests proposed by Fisher, Shapiro–Wilk, and Kruskal–Wallis, using IBM SPSS v.25. The use of the indicated methodological approach to achieve the adopted goal distinguishes the study from the approach of other authors. The primary findings reveal acceptance of the ongoing transition processes among the rural population. It is relatively well aware of the role of renewables, but there is still room for improvement, therefore it is necessary to disseminate knowledge in this area and monitor changes in sustainable awareness. We have also established that, overall, educational background is not a significant discriminative feature in rural perceptions of the energy transition. The conclusions can inform policy models to promote green transformation processes, enabling their adaptation to the current challenges and needs of rural residents. Full article

With an aging population and the high incidence of neurological diseases, rehabilitative lower limb exoskeleton robots, as a wearable assistance device, present important application prospects in gait training and human function recovery. As the core of human–computer interaction, control strategy directly determines the exoskeleton’s ability to perceive and respond to human movement intentions. This paper focuses on the control strategies of rehabilitative lower limb exoskeleton robots. Based on the typical hierarchical control architecture of “perception–decision–execution,” it systematically reviews recent research progress centered around four typical control tasks: trajectory reproduction, motion following, Assist-As-Needed (AAN), and motion intention prediction. It emphasizes analyzing the core mechanisms, applicable scenarios, and technical characteristics of different control strategies. Furthermore, from the perspectives of drive system and control coupling, multi-source perception, and the universality and individual adaptability of control algorithms, it summarizes the key challenges and common technical constraints currently faced by control strategies. This article innovatively separates the end-effector control strategy from the hardware implementation to provide support for a universal control framework for exoskeletons. Full article

The rapid expansion of lithium-ion battery applications calls for efficient and reliable thermal management to ensure safety and performance. Liquid thermal management systems (LTMS) offer high cooling efficiency and uniform temperature control, effectively preventing thermal runaway. This review focuses on composite LTMS that integrate phase change materials and nanofluids and discusses how thermal modeling optimizes key material parameters. Despite notable progress, challenges remain in compatibility, stability, and sustainability. Emerging smart, self-healing, and AI-assisted materials are expected to drive the next generation of intelligent battery cooling systems. Compared with air-cooling systems (maximum temperature ≈ 55 °C, temperature difference ΔT ≈ 10 °C), liquid-based systems can reduce the peak temperature to below 42 °C and improve temperature uniformity (ΔT ≤ 5 °C). Particularly, nanofluid-enhanced LTMS achieve up to 15%~20% higher heat transfer efficiency and 3~5 °C lower surface temperature compared with conventional water-glycol cooling. Direct immersion cooling using dielectric fluids such as HFE-7000 further decreases the maximum temperature to ≈37 °C with ΔT ≈ 3.5 °C, achieving a cooling efficiency above 88%. Thermal modeling results show that accurate representation of material parameters (e.g., interfacial thermal resistance R₍int₎ and thermal conductivity k) can reduce simulation error by more than 30%. This work uniquely bridges materials science with thermal system engineering through AI-driven innovation, providing a data-guided route for next-generation adaptive LTMS design. Full article

Accurate detection and localization of traffic objects are essential for autonomous driving tasks such as path planning. While semantic segmentation is able to provide pixel-level classification, existing networks often fail under challenging conditions like nighttime or rain. In this paper, we introduce a new training framework that combines unsupervised domain adaptation with high dynamic range imaging. The proposed network uses labeled daytime images along with unlabeled nighttime HDR images. By utilizing the fine details typically lost in conventional SDR images due to dynamic range compression, and incorporating the UDA training strategy, the framework effectively trains a model that is capable of semantic segmentation across adverse weather conditions. Experiments conducted on four datasets have demonstrated substantial improvements in inference performance under nighttime and rainy scenarios. The accuracy for daytime images is also enhanced through expanded training diversity. Full article

The sideslip angle is a key state for evaluating the lateral stability of a vehicle. Its accurate estimation is crucial for active safety and intelligent driving assistance systems. To improve the estimation accuracy and robustness of the sideslip angle for distributed drive electric vehicles (DDEV) under extreme maneuvering conditions, this paper proposes a redundant estimation scheme based on multi-source sensor information fusion. Firstly, a dynamic model of the DDEV is established, including the vehicle body dynamics model, wheel rotation dynamics model, tire model, and hub motor model. Subsequently, robust unscented particle filtering (RUPF) and backpropagation (BP) neural network algorithms are developed to estimate the sideslip angle from both the vehicle dynamics and data-driven perspectives. Based on this, a redundant estimation scheme for the sideslip angle is developed. Finally, the effectiveness of the redundant estimation scheme is validated through the Matlab/Simulink-CarSim co-simulation platform using MATLAB R2022b and CarSim 2020.0. Full article