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This paper presents a novel approach for a low-cost simulator-based driving assessment system incorporating a speech-based assistant, using pre-generated messages from Generative AI to achieve real-time interaction during the assessment. Simulator-based assessment is a crucial apparatus in the research toolkit for various fields. Traditional assessment approaches, like on-road evaluation, though reliable, can be risky, costly, and inaccessible. Simulator-based assessment using stationary driving simulators offers a safer evaluation and can be tailored to specific needs. However, these simulators are often only available to research-focused institutions due to their cost. To address this issue, our study proposes a system with the aforementioned properties aiming to enhance drivers' situational awareness, and foster positive emotional states, i.e., high valence and medium arousal, while assessing participants to prevent subpar performers from proceeding to the next stages of assessment and/or rehabilitation. In addition, this study introduces the speech-based assistant which provides timely guidance adaptable to the ever-changing context of the driving environment and vehicle state. The study's preliminary outcomes reveal encouraging progress, highlighting improved driving performance and positive emotional states when participants are engaged with the assistant during the assessment.

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The environmental impact of livestock is often evaluated separately from the other agricultural activities involved in an integrated system, such as that of the rearing of Piemontese cattle in the area of origin of the breed. The most frequently used assessment methods (e.g. Footprint approaches or a Life Cycle Assessment) are in fact often used, through a product-based approach, to analyse a single productive process, but such methods do not consider the production of agro-ecological services, and they neglect the interactions that characterise complex systems. Moreover, such methods often only consider the negative aspects of the environmental impact and misrepresent less intensive agriculture practices. However the current gaps in knowledge about the carbon sequestration of agricultural ecosystems, which are complex and integrated systems, require further investigation and other types of analysis tools. A carbon (C) balance of 1223 Piemontese breed beef farms, located in North-West Italy, has been calculated to evaluate whether such a method could be applied to overcome the aforementioned limitations, to evaluate whether it could be used to describe a complex and integrated system, to highlight the relationships that exist between rearing and agricultural activities and to characterise their environmental roles. Conducting a mass balance involves considering the input and output material flows and their accumulation within a system. Thus, the data necessary to quantify the C input, output and internal fluxes of a system at the farm gate, pertaining to the vegetable and animal production processes (productive factors, crop yields, animal performances, productions and sales, reuses), were collected from official documentation, and were then completed and verified through site visits. The mass balance of the system was transformed into C fluxes using stoichiometric coefficients. The fluxes evaluated for the balance were then used to estimate the changes in the C stocks to highlight not only the C emissions or losses from the system, but also their contributions towards reducing environmental hazards. A sensitivity analysis was carried out to evaluate the uncertainty and the robustness of the obtained results. The net C exchange from plants was the flux that contributed the most, amounting to 94.3 % of the inputs, and this was followed by soil losses and animal gases released through respiration and enteric fermentation, which amounted to 42.8 and 36.2 % of the outputs, respectively. The C stored and released by the considered system was calculated considering the C fluxes. Plant, animal and soil storage sites were included in the system, whereas the air site was left out. A constant C content was assumed for the soil. The productive activities of the selected group of beef farms in the Consortium were calculated to remove 96.1 103 t of C from the atmosphere (air site) over a period of one year, and that this amount of C was transferred to plant growth and agricultural products (plant site) and to an increase in live weight (LW) of the animals (animal site). The rates of the stored C to agricultural and wooded areas and to the LW of the animals slaughtered in one year were 1.18 t ha-1 and 2.24 t C t-1 LW, respectively. The sensitivity analysis demonstrated that the C balance was always positive, even for the worst scenario. This study has shown that the examined beef production system, when analysed as an integrated and complex system, can be considered an important C sink and that it is necessary to reconsider the role that livestock, and ruminants in particular, play in the global greenhouse effect.

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Unlabelled: The integration of health and activity data from various wearable devices into research studies presents technical and operational challenges. The Awesome Data Acquisition Method (ADAM) is a versatile, web-based system that was designed for integrating data from various sources and managing a large-scale multiphase research study. As a data collecting system, ADAM allows real-time data collection from wearable devices through the device's application programmable interface and the mobile app's adaptive real-time questionnaires. As a clinical trial management system, ADAM integrates clinical trial management processes and efficiently supports recruitment, screening, randomization, data tracking, data reporting, and data analysis during the entire research study process. We used a behavioral weight-loss intervention study (SMARTER trial) as a test case to evaluate the ADAM system. SMARTER was a randomized controlled trial that screened 1741 participants and enrolled 502 adults. As a result, the ADAM system was efficiently and successfully deployed to organize and manage the SMARTER trial. Moreover, with its versatile integration capability, the ADAM system made the necessary switch to fully remote assessments and tracking that are performed seamlessly and promptly when the COVID-19 pandemic ceased in-person contact. The remote-native features afforded by the ADAM system minimized the effects of the COVID-19 lockdown on the SMARTER trial. The success of SMARTER proved the comprehensiveness and efficiency of the ADAM system. Moreover, ADAM was designed to be generalizable and scalable to fit other studies with minimal editing, redevelopment, and customization. The ADAM system can benefit various behavioral interventions and different populations.

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Driven by the rapid advancement and practical implementation of biomaterials, fabrication technologies, and artificial intelligence, artificial neuron devices and systems have emerged as a promising technology for interpreting and transmitting neurological signals. These systems are equipped with multi-modal bio-integrable sensing capabilities, and can facilitate the benefits of neurological monitoring and modulation through accurate physiological recognition. In this article, we provide an overview of recent progress in artificial neuron technology, with a particular focus on the high-tech applications made possible by innovations in material engineering, new designs and technologies, and potential application areas. As a rapidly expanding field, these advancements have a promising potential to revolutionize personalized healthcare, human enhancement, and a wide range of other applications, making artificial neuron devices the future of brain-machine interfaces.

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Modular reconfigurable robots exhibit prominent advantages in the reconnaissance and exploration tasks within unstructured environments for their characteristics of high adaptability and high robustness. However, due to the limitations in locomotion mechanism and integration requirements, the modular design of miniature robots in the aquatic environment encounters significant challenges. Here, a modular strategy based on the synthetic jet principle is proposed, and a modular reconfigurable robot system is developed. Specialized bottom and side jet actuators are designed with vibration motors as excitation sources, and a motion module is developed incorporating the jet actuators to realize three-dimensional agile motion. Its linear, rotational, and ascending motion speeds reach 70.7 mm s-1, 3.3 rad s-1, and 28.7 mm s-1, respectively. The module integrates the power supply, communication, and control system with a small size of 48 mm × 38 mm × 38 mm, which ensures a wireless controllable motion. Then, various configurations of the multi-module robot system are established with corresponding motion schemes, and the experiments with replaceable intermediate modules are further conducted to verify the transportation and image-capturing functions. This work demonstrates the effectiveness of synthetic jet propulsion for aquatic modular reconfigurable robot systems, and it exhibits profound potential in future underwater applications.

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This paper presents an innovative approach towards space-ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods.

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The integrated "perception-memory" system is receiving increasing attention due to its crucial applications in humanoid robots, as well as in the simulation of the human retina and brain. Here, a Field Programmable Gate Array (FPGA) platform-boosted system that enables the sensing, recognition, and memory for human-computer interaction is reported by the combination of ultra-thin Ag/Al/Paster-based electronic tattoos (AAP) and Tantalum Oxide/Indium Gallium Zinc Oxide (Ta2O5/IGZO)-based memristors. Notably, the AAP demonstrates exceptional capabilities in accommodating the strain caused by skin deformation, thanks to its unique structural design, which ensures a secure fit to the skin and enables the prolonged monitoring of physiological signals. By utilizing Ta2O5/IGZO as the functional layer, a high switching ratio is conferred to the memristor, and an integrated system for sensing, distinguishing, storing, and controlling the machine hand of multiple human physiological signals is constructed together with the AAP. Further, the proposed system implements emergency calls and smart homes using facial electromyogram signals and utilizing logical entailment to realize the control of the music interface. This innovative "perception-memory" integrated system not only serves the disabled, enhancing human-computer interaction but also provides an alternative avenue to enhance the quality of life and autonomy of individuals with disabilities.

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Venous blood collection systems (VBCSs) are combinations of in-vitro diagnostics and medical devices, usually available as integrated set. However, purchasing and using a combination of devices from different sets is considered by clinical laboratories as an option to achieve specific sampling tasks or reduce costs. This systematic review aimed to retrieve available evidence regarding safety, efficacy, and economic aspects of VBCSs, focusing on differences between integrated and combined systems. The literature review was carried out in PubMed. Cited documents and resources made available by scientific organisations were also screened. Extracted evidence was clustered according to Quality/Efficacy/Performance, Safety, and Costs/Procurement domains and discussed in the current European regulatory framework. Twenty documents published between 2010 and 2021 were included. There was no evidence to suggest equivalence between combined and integrated VBCSs in terms of safety and efficacy. Scientific society's consensus documents and product standards report that combined VBCS can impact operators' and patients' safety. Analytical performances and overall efficacy of combined VBCSs are not guaranteed without whole system validation and verification. EU regulatory framework clearly allocates responsibilities for the validation and verification of an integrated VBCS, but not for combined VBCSs, lacking information about the management of product nonconformities and post-market surveillance. Laboratory validation of combined VBCS demands risk-benefit and cost-benefit analyses, a non-negligible organisational and economic burden, and investment in knowledge acquisition. Implications in terms of laboratory responsibility and legal liability should be part of a comprehensive assessment of safety, efficacy, and cost carried out during device procurement.

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When INS/GNSS (inertial navigation system/global navigation satellite system) integrated system is applied, it will be affected by the insufficient number of visible satellites, and even the satellite signal will be lost completely. At this time, the positioning error of INS accumulates with time, and the navigation accuracy decreases rapidly. Therefore, in order to improve the performance of INS/GNSS integration during the satellite signals interruption, a novel learning algorithm for neural network has been presented and used for intelligence integrated system in this article. First of all, determine the input and output of neural network for intelligent integrated system and a nonlinear model for weighs updating during neural network learning has been established. Then, the neural network learning based on strong tracking and square root UKF (unscented Kalman filter) is proposed for iterations of the nonlinear model. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical UKF to avoid the filter divergence caused by the negative definite state covariance matrix. Meanwhile, the strong tracking coefficient is introduced to adjust the filter gain in real-time and improve the tracking capability to mutation state. Finally, an improved calculation method of strong tracking coefficient is presented to reduce the computational complexity in this algorithm. The results of the simulation test and the field-positioning data show that the proposed learning algorithm could improve the calculation stability and robustness of neural network. Therefore, the error accumulation of INS/GNSS integration is effectively compensated, and then the positioning accuracy of INS/GNSS intelligence integrated system has been improved.

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The integrated smart electronics for real-time monitoring and personalized therapy of disease-related analytes have been gradually gaining tremendous attention. However, human tissue barriers, including the skin barrier and brain-blood barrier, pose significant challenges for effective biomarker detection and drug delivery. Microneedle (MN) electronics present a promising solution to overcome these tissue barriers due to their semi-invasive structures, enabling effective drug delivery and target-analyte detection without compromising the tissue configuration. Furthermore, MNs can be fabricated through solution processing, facilitating large-scale manufacturing. This review provides a comprehensive summary of the recent three-year advancements in smart MNs development, categorized as follows. First, the solution-processed technology for MNs is introduced, with a focus on various printing technologies. Subsequently, smart MNs designed for sensing, drug delivery, and integrated systems combining diagnosis and treatment are separately summarized. Finally, the prospective and promising applications of next-generation MNs within mediated diagnosis and treatment systems are discussed.

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In recent years, soft robotic sensors have rapidly advanced to endow robots with the ability to interact with the external environment. Here, we propose a polymer optical fiber (POF) sensor with sensitive and stable detection performance for strain, bending, twisting, and pressing. Thus, we can map the real-time output light intensity of POF sensors to the spatial morphology of the elastomer. By leveraging the intrinsic correlations of neighboring sensors and machine learning algorithms, we realize the spatially resolved detection of the pressing and multi-dimensional deformation of elastomers. Specifically, the developed intelligent sensing system can effectively recognize the two-dimensional indentation position with a prediction accuracy as large as ~99.17%. The average prediction accuracy of combined strain and twist is ~98.4% using the random forest algorithm. In addition, we demonstrate an integrated intelligent glove for the recognition of hand gestures with a high recognition accuracy of 99.38%. Our work holds promise for applications in soft robots for interactive tasks in complex environments, providing robots with multidimensional proprioceptive perception. And it also can be applied in smart wearable sensing, human prosthetics, and human-machine interaction interfaces.

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Life cycle assessment (LCA) is widely used to evaluate product's life cycle environmental impact and identify the environmental weaknesses. However, it is difficult for existing LCA software to perform flexible LCA analysis based on the product life cycle characteristics and industry background. Meanwhile, under the existing LCA research model, product designers and manufacturers are usually not LCA evaluators, resulting in a certain time gap between the evaluation results and product improvement. Designers with less experience in green design often find it difficult to identify high environmental impact links in products at different life cycle stages and product levels, and updated products are challenging to meet various environmental restrictions. This paper establishes a multi-module product life cycle analysis model that combines product industry background that includes basic information, assessment information, structural information, and restriction information to achieve the multi-scenario of product LCA in different dimensions in a typical domain. The calculated mechanism of the dynamic power emission factor is built according to the service time and space dimensions. The proposed method forms an integrated environmental performance evaluation of household appliance (EPEHA) system. A software assessment and an optimization method are proposed to improve the EPEHA system. The results of this study show that these proposed methods can improve the timeliness and diversity of results analysis of product LCA in the field of household appliances in China. The universal data exchange format and simple operation interface of the EPEHA system enable people related to the product to quickly understand the environmental impact of the product in different scenarios, even if they lack green design knowledge and professional software training.

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MXenes have shown great potential for micro-supercapacitors (MSCs) due to the high metallic conductivity, tunable interlayer spacing and intercalation pseudocapacitance. In particular, the negative surface charge and high hydrophilicity of MXenes make them suitable for various solution processing strategies. Nevertheless, a comprehensive review of solution processing of MXene MSCs has not been conducted. In this review, we present a comprehensive summary of the state-of-the-art of MXene MSCs in terms of ink rheology, microelectrode design and integrated system. The ink formulation and rheological behavior of MXenes for different solution processing strategies, which are essential for high quality printed/coated films, are presented. The effects of MXene and its compounds, 3D electrode structure, and asymmetric design on the electrochemical properties of MXene MSCs are discussed in detail. Equally important, we summarize the integrated system and intelligent applications of MXene MSCs and present the current challenges and prospects for the development of high-performance MXene MSCs.

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Due to the advantages of ease of use, less motion disturbance, and low cost, wearable systems have been widely used in the human-machine interaction (HRI) field. However, HRI in complex clinical rehabilitation scenarios has further requirements for wearable sensor systems, which has aroused the interest of many researchers. However, the traditional wearable system has problems such as low integration, limited types of measurement data, and low accuracy, causing a gap with the actual needs of HRI. This paper will introduce the latest progress in the current wearable systems of HRI from four aspects. First of all, it introduces the breakthroughs of current research in system integration, which includes processing chips and flexible sensing modules to reduce the system's volume and increase battery life. After that, this paper reviews the latest progress of wearable systems in electrochemical measurement, which can extract single or multiple biomarkers from biological fluids such as sweat. In addition, the clinical application of non-invasive wearable systems is introduced, which solves the pain and discomfort problems caused by traditional clinical invasive measurement equipment. Finally, progress in the combination of current wearable systems and the latest machine-learning methods is shown, where higher accuracy and indirect acquisition of data that cannot be directly measured is achieved. From the evidence presented, we believe that the development trend of wearable systems in HRI is heading towards high integration, multi-electrochemical measurement data, and clinical and intelligent development.

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BACKGROUND: Investment in the implementation of hospital ePrescribing systems has been a priority in many economically-developed countries in order to modernise the delivery of healthcare. However, maximum gains in the safety, quality and efficiency of care are unlikely to be fully realised unless ePrescribing systems are further optimised in a local context. Typical barriers to optimal use are often encountered in relation to a lack of systemic capacity and preparedness to meet various levels of interoperability requirements, including at the data, systems and services levels. This lack of systemic interoperability may in turn limit the opportunities and benefits potentially arising from implementing novel digital heath systems. METHODS: We undertook n = 54 qualitative interviews with key stakeholders at nine digitally advanced hospital sites across the UK, US, Norway and the Netherlands. We included hospitals featuring 'standalone, best of breed' systems, which were interfaced locally, and multi-component and integrated electronic health record enterprise systems. We analysed the data inductively, looking at strategies and constraints for ePrescribing interoperability within and beyond hospital systems. RESULTS: Our thematic analysis identified 4 main drivers for increasing ePrescribing systems interoperability: (1) improving patient safety (2) improving integration & continuity of care (3) optimising care pathways and providing tailored decision support to meet local and contextualised care priorities and (4) to enable full patient care services interoperability in a variety of settings and contexts. These 4 interoperability dimensions were not always pursued equally at each implementation site, and these were often dependent on the specific national, policy, organisational or technical contexts of the ePrescribing implementations. Safety and efficiency objectives drove optimisation targeted at infrastructure and governance at all levels. Constraints to interoperability came from factors such as legacy systems, but barriers to interoperability of processes came from system capability, hospital policy and staff culture. CONCLUSIONS: Achieving interoperability is key in making ePrescribing systems both safe and useable. Data resources exist at macro, meso and micro levels, as do the governance interventions necessary to achieve system interoperability. Strategic objectives, most notably improved safety, often motivated hospitals to push for evolution across the entire data architecture of which they formed a part. However, hospitals negotiated this terrain with varying degrees of centralised coordination. Hospitals were heavily reliant on staff buy-in to ensure that systems interoperability was built upon to achieve effective data sharing and use. Positive outcomes were founded on a culture of agreement about the usefulness of access by stakeholders, including prescribers, policymakers, vendors and lab technicians, which was reflected in an alignment of governance goals with system design.

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Developing cost-efficient bifunctional water splitting catalysts is crucial for sustainable hydrogen energy applications. Herein, ruthenium (Ru)-incorporated and phosphorus (P)-doped nickel molybdate (Ru-NiMoO(P)4 ) nanosheet array catalysts are synthesized. Due to the synergy of Ru clusters and NiMoO(P)4 by the modulated electronic structure and the rich active sites, impressively, Ru-NiMoO(P)4 exhibits superior OER (194 mV @ 50 mA cm-2 ) and HER (24 mV @ 10 mA cm-2 ) activity in alkaline media, far exceeding that of commercial Pt/C and RuO2 catalysts. Meanwhile, as bifunctional catalyst, to drive the overall water splitting at the current density of 10 mA cm-2 , Ru-NiMoO(P)4 requires only 1.45 V and maintaining stable output for 100 h. Furthermore, Ru-NiMoO(P)4 also possesses excellent capability for seawater electrolysis hydrogen production. Moreover, the successful demonstration of wind and solar hydrogen production systems provide the feasibility of the ultra-low Ru loading catalyst for large-scale hydrogen production in the future.

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Solidification of soluble arsenic from extremely acidic water and direct use of recovery water have been the major challenges in global water management, with the urgent need for new treatment system development. Thus, magnetic adsorption - fertilizer drawn forward osmosis (FDFO) hybrid system with a novel adsorbent and fertilizer mixture to solve the drawbacks of each process was developed with the ultimate goals of metal removal and direct reuse for hydroponic irrigation. Magnetic metal-organic framework-based adsorbent (CMM) was synthesized with various promising capabilities, i.e., wide pH range efficiency, strong pH adjustment, good stability, fast adsorption (1 h), and oxidation (40 min), high capacity (175 and 126 mg/g for As(III), As(V)), strong magnetization (75 emu/g), complete separation by a magnet, excellent interference-tolerance and reusability. In the FDFO system, a massive water volume (50 times higher than the initial draw solution with suitable nutrients for hydroponics irrigation with acceptable NaCl levels was obtained for the first time up to now. However, low As(III) rejection (50%) required the FDFO process to improve more. After integrating with magnetic adsorption, nearly 100% of As was removed. The pH of feed solutions adjusted from extremely acidic to close to neutral conditions further solidified metal by precipitation and membrane separation processes, leading to almost no detection of metals in the final draw solution. Also, favorable nutrients and excellent reusability were obtained. This hybrid process would generally offer an environmentally sustainable and high efficiency for decontaminating As-containing heavy metal water for hydroponic irrigation.

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Modules, toolboxes, and synthetic biology systems may be designed to address environmental bioremediation. However, weak and decentralized functional modules require complex control. To address this issue, an integrated system for toxicant detection and biodegradation, and subsequent suicide in chronological order without exogenous inducers is constructed. Salicylic acid, a typical pollutant in industrial wastewater, is selected as an example to demonstrate this design. Biosensors are optimized by regulating the expression of receptors and reporters to get 2-fold sensitivity and 6-fold maximum output. Several stationary phase promoters are compared, and promoter Pfic is chosen to express the degradation enzyme. Two concepts for suicide circuits are developed, with the toxin/antitoxin circuit showing potent lethality. The three modules are coupled in a stepwise manner. Detection and biodegradation, and suicide are sequentially completed with partial attenuation compared to pre-integration, except for biodegradation, being improved by the replacements of ribosome binding site. Finally, a long-term stability test reveals that the engineered strain maintained its function for ten generations. The study provides a novel concept for integrating and controlling functional modules that can accelerate the transition of synthetic biology from conceptual to practical applications.

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At present, self-powered, lightweight, and flexible sensors are widely applied, especially in the fields of wearable devices and human health monitoring. Nevertheless, conventional self-powered flexible sensor systems rely on power supply components such as supercapacitors, nanofriction generators, and solar cells, which present certain limitations, such as high dependence on external environmental factors and the inability to provide long-term stable energy supply. Thus, a paramount exigency emerges for the development of wearable sensors endowed with enduring battery life to enable continuous monitoring of human motion for extended periods. In our academic study, we present an innovative self-powered sensing system that seamlessly combines a pliable zinc-air battery with a strain sensor. This approach offers a stable output signal over extended periods without an external energy device, which is crucial for long-term, continuous human motion monitoring. Through the incorporation of various carbon materials, we realized the multifunction of poly(vinyl alcohol) (PVA)/poly(acrylic acid) (PAA) dual network hydrogels and prepared zinc-air battery electrolytes and strain sensors. Notably, the batteries exhibit impressive power density (82.5 mW cm-2), high open-circuit voltage (1.42 V), and remarkable environmental stability. Even when subjected to puncture and breakage, the batteries remain operational without suffering from electrolyte leakage. Similarly, our strain sensor boasts a broad working range spanning from 0 to 1400%, coupled with a remarkable sensitivity (GF = 2.99) and exceptional capacity to accurately detect various mechanical deformations. When integrated into a single system, the integrated system can monitor human movement for up to 10 h, which has broad prospects in wearable sensor applications.