Robert Bosch (China)

Abstract Weak binding and affinity between the conductive support and iodine species leads to inadequate electron transfer and the shuttle effect. Herein, redox kinetics and duration are significantly boosted by introducing a Nb 2 CT X host that is classified as a layered 2D Nb‐based MXene. With a facile electrodeposition strategy, initial I − ions are electrically driven to insert in the nanosized interlayers and are electro‐oxidized in situ. Linear I 2 is firmly confined inside and benefits from the rapid charge supply from the MXene. Consequently, an aqueous Zn battery based on a Zn metal anode and ZnSO 4 electrolyte delivers an ultraflat plateau at 1.3 V, which contributes to 84.5% of the capacity and 89.1% of the energy density. Record rate capability (143 mAh g −1 at 18 A g −1 ) and lifespan (23 000) cycles are achieved, which are far superior to those of all reported aqueous MXenes and I 2 –metal batteries. Moreover, the low voltage decay rate of 5.6 mV h −1 indicates its superior anti‐self‐discharge properties. Physicochemical analyses and density functional theory calculations elucidate that the localized electron transfer and trapping effect of the Nb 2 CT X MXene host are responsible for enhanced kinetics and suppressed shuttle behavior. This work can be extended to the fabrication of other I 2 –metal batteries with long‐life‐time expectations.

MXenes are two‐dimensional transition metal carbides and/or nitrides with unique physiochemical properties and have attracted extensive interest in numerous fields. However, current MXene synthesis methods are limited by hazardous synthesis conditions, high production costs, or difficulty in large‐scale production. Therefore, a general, safe, cost‐effective, and scalable synthesis method for MXenes is crucial. Here, we report the fast synthesis of MXenes in the open air using a molten salt‐shielded synthesis (MS 3 ) method, which uses Lewis‐acid salts as etchants and a low‐melting‐point eutectic salt mixture as the reaction medium and shield to prevent MXene oxidation at high temperatures. Carbide and nitride MXenes, including Ti 3 C 2 T x , Ti 2 CT x , Ti 3 CNT x , and Ti 4 N 3 T x , were successfully synthesized using the MS 3 method. We also present the flexibility of the MS 3 method by scaling the etching process to large batches of 20 and 60 g of Ti 3 AlC 2 MAX precursor in one pot. When used as negative electrodes, the prepared MS 3 ‐MXenes delivered excellent electrochemical properties for high‐rate Li‐ion storage.

This paper reports a novel optical ballistocardiography technique, which is non-invasive, for the simultaneous measurement of cardiac and respiratory activities using a Fiber Bragg Grating Heart Beat Device (FBGHBD). The unique design of FBGHBD offers additional capabilities such as monitoring nascent morphology of cardiac and breathing activity, heart rate variability, heart beat rhythm, etc., which can assist in early clinical diagnosis of many conditions associated with heart and lung malfunctioning. The results obtained from the FBGHBD positioned around the pulmonic area on the chest have been evaluated against an electronic stethoscope which detects and records sound pulses originated from the cardiac activity. In order to evaluate the performance of the FBGHBD, quantitative and qualitative studies have been carried out and the results are found to be reliable and accurate, validating its potential as a standalone medical diagnostic device. The developed FBGHBD is simple in design, robust, portable, EMI proof, shock proof and non-electric in its operation which are desired features for any clinical diagnostic tool used in hospital environment.

We discuss the design and preliminary results of an IoT based system for management of the water distribution system in a large campus. In particular, we focus on two specific components of the system: a low cost ultrasonic based water level sensor and a sub-GHz based campus scale wireless network to connect the sensors. We describe techniques to achieve a large sensing distance that makes them suitable for installation across overhead tanks (OHT) and ground level reservoirs (GLR). The wireless network, which uses sub-GHz radios, connects to a gateway that can upload the data online for visualisation and analytics.

This article presents an Internet-of-Things (IoT) anti-counterfeiting system that uses visual features combined with the quick response (QR) code. The visual features guarantee the authenticity of a product with the QR code for tracking and tracing. Two visual features, i.e., natural texture features and printed micro features are exploited in the proposed system. The natural texture features use the texture of fiber paper to achieve physical unclonable function (PUF), while the micro features are artificially generated for improved industrial manufacturability and reliability. Features are generated and registered in the production phase when the QR code is printed. In the anti-counterfeiting verification phase, the feature obtained through the feature extraction algorithm is compared with the record to calculate similarity, which indicates the verification result. Such an approach is fully compatible with the QR code-based logistic process without any additional manufacturing cost. A user-friendly application has been developed on a mobile platform that facilitates easy-to-use and affordable devices for verification, such as a mobile phone or a handheld code reader. The experimental results show 99.6% and 99.9% accuracy of anti-counterfeiting verification for texture features and micro features, respectively. The system with corresponding algorithms and software has been demonstrated in real-life products.

Escherichia coli (E. coli) bacteria have been identified to be the cause of variety of health outbreaks resulting from contamination of food and water. Timely and rapid detection of the bacteria is thus crucial to maintain desired quality of food products and water resources. A novel methodology proposed in this paper demonstrates for the first time, the feasibility of employing a bare fiber Bragg grating (bFBG) sensor for detection of E. coli bacteria. The sensor was fabricated in a photo-sensitive optical fiber (4.2 µm/80 µm). Anti-E. coli antibody was immobilized on the sensor surface to enable the capture of target cells/bacteria present in the sample solution. Strain induced on the sensor surface as a result of antibody immobilization and subsequent binding of E. coli bacteria resulted in unique wavelength shifts in the respective recording of the reflected Bragg wavelength, which can be exploited for the application of biosensing. Functionalization and antibody binding on to the fiber surface was cross validated by the color development resulting from the reaction of an appropriate substrate solution with the enzyme label conjugated to the anti-E. coli antibody. Scanning electron microscope image of the fiber, further verified the E. coli cells bound to the antibody immobilized sensor surface.

Causal inference has recently garnered significant interest among recommender system (RS) researchers due to its ability to dissect cause-and-effect relationships and its broad applicability across multiple fields. It offers a framework to model the causality in RSs such as confounding effects and deal with counterfactual problems such as offline policy evaluation and data augmentation. Although there are already some valuable surveys on causal recommendations, they typically classify approaches based on the practical issues faced in RS, a classification that may disperse and fragment the unified causal theories. Considering RS researchers' unfamiliarity with causality, it is necessary yet challenging to comprehensively review relevant studies from a coherent causal theoretical perspective, thereby facilitating a deeper integration of causal inference in RS. This survey provides a systematic review of up-to-date papers in this area from a causal theory standpoint and traces the evolutionary development of RS methods within the same causal strategy. First, we introduce the fundamental concepts of causal inference as the basis of the following review. Subsequently, we propose a novel theory-driven taxonomy, categorizing existing methods based on the causal theory employed, namely those based on the potential outcome framework, the structural causal model, and general counterfactuals. The review then delves into the technical details of how existing methods apply causal inference to address particular recommender issues. Finally, we highlight some promising directions for future research in this field. Representative papers and open-source resources will be progressively available at https://github.com/Chrissie-Law/Causal-Inference-for-Recommendation.

The localization accuracy of ultra-wide band (UWB) system could be dramatically degraded, if the signal is propagated under non-line-of-sight (NLOS) condition. The detection of the NLOS propagation is very important to guarantee the accuracy of the UWB system. Based on the channel impulse response (CIR) sample, the NLOS condition could be identified. However, for the decawave chips, each CIR sample contains 1015 points. Thus the real-time realization of the NLOS detection with CIR is very hard, since the import and calculation of such a large amount of data cause to huge delay. In order to reduce the delay, the minimal needed size of the points in CIR for accurate NLOS identification is discussed in this paper. The support vector machine (SVM) is used for the classification based on the original CIR points or the eight different features extracted from each CIR. Furthermore, a new method is proposed for the identification based on the convolution algorithm. Compared to the existing approach with CIR, the needed CIR points for the detection are dramatically reduced, which makes the on-line identification realization possible. The accuracy of the NLOS identification with less CIR points is even better. The new proposed method using convolution algorithm also shows very promising results compared the other approaches.

Non-line-of-sight (NLOS) identification is very important for accurate localization based on ultra-wide band (UWB) system. One of the most widely used approach for NLOS detection is based on machine learning algorithms with features extracted from the channel impulse response (CIR). Features, such as kurtosis, mean excess delay, root mean delay, energy and rise time are discussed in a lot of papers. Other features, like signal to noise ratio, form factor and crest factor etc. are barely discussed but they are also very useful parameters for NLOS detection. In this paper 18 useful features are discussed in total. The support vector machine (SVM) is used for the identification of the NLOS condition. Since the identification accuracy does not always improve with an increase in the number of used features, in this paper the best feature combination is selected based on genetic algorithm. By reducing the used features, not only the accuracy improves, but also the computation complexity is reduced. The experimental results show that, the RMS delay, maximal amplitude, received signal energy, distance between MS and BS, peak to start of the received pulses time delay are the optimal combination leading to best accuracy.

The probing of individual cells at specific frequency regimes in a microfluidic impedance flow cytometer led to the observation of unusual "double peak" features in the reactive component of the resulting signal. The phenomenon was restricted to the lower frequencies (400-800 kHz) of the β-dispersion regime and its occurrence was facilitated by the co-planar microelectrode geometry in the device. To understand the reasons for this anomalous behaviour, the system was modelled using COMSOL. The simulated model agreed well with experimental observations and provided insight into the origins of this signal profile and the effect of various parameters on its behaviour. One of the most significant observations of this study was the high sensitivity of the features in the "double peak" profile to changes in cell membrane capacitance (CMC), compared to conventional "single peaks" of reactive impedance. This was consequently exploited to accurately distinguish populations of normal and glutaraldehyde treated erythrocytes based on variations in their CMC, indicating a drastic decrease in the CMC of treated cells. Additionally, we demonstrate the applicability of using this double peak effect to identify cell populations within a mixture of PBMCs. This study is an improvement over conventional approaches of measuring CMC via impedance flow cytometry by enabling the measurement of both cell size and cell membrane properties at a single frequency rather than using multiple frequencies. Using a single frequency significantly simplifies the system and reduces the associated costs. Additionally, this technique enables the measurement of CMC at relatively low frequencies.

Getting the location information of a mobile station (MS) is very important in many applications. The position of a MS in a ultra-wideband system can be determined based on the distance measurements between the MS and several base stations (BSs), using the time of arrival (TOA) method. However, the Non-Line-of-Sight (NLOS) cause inaccuracy position estimation. In this paper a novel NLOS identification and accurate range measurements selection approach based on the acceleration measurements is proposed. Additionally, the Iterative Extended Kalman Filter (IEKF) is combined with the proposed approach to effectively reduce the NLOS errors. The position estimation accuracy of the system based on IEKF with and without the proposed selection approach is compared. The performance of an ultra-wideband/inertial measurement unit (UWB/IMU) tightly coupled approach is also used as comparison. The most accurate results are achieved with the IEKF with proposed approach both in simulation and real field tests among all the approaches.

This study presents a novel approach to real-time water quality state (chlorine concentration) and reaction parameter estimation in water distribution systems (WDSs) using ensemble Kalman filter (EnKF)–based data assimilation techniques. Two different types of EnKF-based methods are used in this study: noniterative restart-EnKF (NIR-EnKF) and iterative restart-EnKF (IR-EnKF). The use of these data assimilation frameworks for addressing key uncertainties in water quality models, such as uncertainty in the source or initial concentration of chlorine and uncertainty in the wall reaction parameter, is studied. The effect of ensemble size, number and location of measurement nodes, measurement error, and noise are also studied extensively in this work. The performance of the proposed methodology is tested on two different water networks: a brushy plains network and a large, citywide WDS, the Bangalore inflow network. The results of the simulation study show that both the NIR-EnKF and IR-EnKF methods are appropriate for dealing with uncertainty in source chlorine concentration, but the IR-EnKF method performs better than the NIR-EnKF method in the case of reaction parameter uncertainty.

We report the implementation of a micro-patterned, glass-based photonic sensing element that is capable of label-free biosensing. The diffractive optical analyzer is based on the differential response of diffracted orders to bulk as well as surface refractive index changes. The differential read-out suppresses signal drifts and enables time-resolved determination of refractive index changes in the sample cell. A remarkable feature of this device is that under appropriate conditions, the measurement sensitivity of the sensor can be enhanced by more than two orders of magnitude due to interference between multiply reflected diffracted orders. A noise-equivalent limit of detection (LoD) of 6 × 10(-7) was achieved with this technique with scope for further improvement.

Accurate indoor human localization without requiring any pre-installed infrastructure is essential for many applications, such as search and rescue in fire disaster areas or human social interaction. Ultra-wideband (UWB) is a very promising technology for accurate indoor positioning with pre-installed receivers. An infrastructure-free methodology, called Pedestrian Dead-Reckoning (PDR), which uses an inertial measurement unit (IMU), can also be used for position estimation. In this approach, the drift errors of IMU in each step length estimation are compensated based on zero-velocity update (ZUPT), zero angular rate update (ZARU) and heuristic heading drift reduction (HDR) algorithms. An accurate step detection can be achieved by relying on the data provided by accelerometers and gyroscopes. In order to further improve the accuracy, a novel approach, which combines IMU PDR and UWB ranging measurements by Extended Kalman filter (EKF) without any pre-installed infrastructure, is proposed. All the components in this approach, the IMU, the mobile station (MS) and the receiver of the UWB are mounted on the feet. The biases in the IMU measurements, which cause inaccurate step length estimation, can be compensated by range measurements provided by UWB. The performance of the normal PDR with EKF is evaluated as comparison to the proposed approach. The real test results show that the proposed approach with EKF is the most effective way to reduce the error.

For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation. Constraints enforcing persistently exciting closed loop control actions are introduced for a set-membership parameter identification scheme. The algorithm requires the online solution of a convex program, satisfies constraints robustly, and ensures recursive feasibility and input-to-state stability. Almost sure convergence to the actual system parameters is demonstrated under assumptions on stabilizability, reachability, and tight disturbance bounds.

BACKGROUND: Individuals with autism spectrum disorder (ASD) have inhibitory control deficits. The combination of transcranial direct current stimulation (tDCS) and inhibitory control training produces good transfer effects and improves neuroplasticity. However, no studies have explored whether applying tDCS over the dlPFC improves inhibitory control and produces transfer effects in children with ASD. OBJECTIVE: To explore whether multisession tDCS could enhance inhibitory control training (response inhibition), near-transfer (interference control) and far-transfer effects (sustained attention; stability of attention) in children with ASD and the generalizability of training effects in daily life and the class, as reflected by behavioral performance and neural activity measured by functional near-infrared spectroscopy (fNIRS). METHODS: Twenty-eight autistic children were randomly assigned to either the true or sham tDCS group. The experimental group received bifrontal tDCS stimulation at 1.5 mA, administered for 15 min daily across eight consecutive days. tDCS was delivered during a computerized Go/No-go training task. Behavioral performance in terms of inhibitory control (Dog/Monkey and Day/Night Stroop tasks), sustained attention (Continuous Performance and Cancellation tests), prefrontal cortex (PFC) neural activity and inhibitory control and sustained attention in the class and at home were evaluated. RESULTS: Training (response inhibition) and transfer effects (interference control; sustained attention) were significantly greater after receiving tDCS during the Go/No-go training task than after receiving sham tDCS. Changes in oxyhemoglobin (HbO) concentrations in the dlPFC and FPA associated with consistent conditions in the Day/Night Stroop and Continuous Performance test were observed after applying tDCS during the inhibitory control training task. Notably, transfer effects can be generalized to classroom environments. CONCLUSION: Inhibitory control training combined with tDCS may be a promising, safe, and effective method for improving inhibitory control and sustained attention in children with ASD.

This article proposes a production system framework that synthesises lean production, business excellence, and factory physics. The framework, which draws on a deep state-of-the-art understanding, consists of a performance measurement system supporting the achievement of a target condition based on variability and lead time reduction, as well as approaches of continuous improvement. Based on four types of excellence, a System Excellence value is calculated, indicating the distance from a target condition and thus displaying relevant improvement potential. As a key result, the framework proposed provides a contribution to knowledge, as it combines the aforementioned schools of thought, resulting in a holistic framework for action. The measurement system offers a high level of robustness, as it draws on diverse data sources and reflects on the dynamic behaviour over time. It has been successfully implemented in automotive manufacturing plants worldwide, which may suggest considerable practical relevance. Another key result of this research is that through applying the framework, important bottom-line indicators, such as lead time, failure costs, or productivity, could be improved. As the plants are typical automotive industry high-volume plants, it is proposed that the solutions presented offer a suitable standard for this industry and type of plant.

Rice and wheat are two major staple food crops in India and worldwide. Over the years the yield potential of the crops has been affected by abiotic factors, which is further projected to increase due to climate change induced environmental adversities. Typically these two crops have different growing conditions, rice requiring high water for cultivation unlike wheat which is water demanding and sensitive to larger variability in temperature regimes. In the recent past drought and disease stress, besides several other stresses, are considered to be critical factors affecting the growth and yield of crops, which is evident in the recent decades. Admittedly, drought stress coupled with biotic stress will further contribute for declining performance of crop varieties and difficult to alleviate even with innovative technological innovations. Few of the technological innovations like high yielding varieties, genetically modified cultivars, integrated nutrient management, integrated pest management, water conservation strategies and prophylactic measures to avoid the disease/pest outbreak, though with potential to augment the yield losses is affected by the stresses. Attempts have also been made to utilize transgenic technologies to build intrinsic tolerance mechanisms by the plants through alteration to functional genes. However, sustainable technologies like classical breeding approaches and integrated farming principles are also being considered to develop crops adaptation and/or enhance the adaptive mechanisms by aligning with technological interventions. Though, several technologies show promise but constrained by the limitations to achieve ‘one-fits-all’ model to overcome the interactive effects of abiotic stressors. Visibly, the crop growth and yield enhancement through technological innovations is call of the day as climate change induced aggravation of these stressors on crop production is imminent. Skilful integration of technological innovations to suit the local and regional scale crop husbandry systems may have promise to address the abiotic stress to realize economic yield of crops like rice and wheat. The review will argumentatively analyse few critical stressors that limit the successful implementation of technological innovations to sustain the rice/wheat crop production and resilience building in the millennia.

Remote biomonitoring of vital parameters in hospitals and homes has the potential to improve coverage and quality of maternal and neonatal health. Wearable sensors coupled with modern information and communication technology now offer an opportunity to monitor temperatures and kangaroo mother care (KMC) adherence in a continuous and real-time manner remotely for several days’ duration in hospital and home settings. Using an innovative remote biomonitoring device to measure both temperature and baby position, we undertook a techno-feasibility study in preparation for a clinical trial. We designed and developed a wearable sensor for tracking KMC adherence and neonatal temperature, using social innovation design principles. After screening mother-infant dyads using clinical and logistic eligibility criteria, we piloted this wearable sensor along with a gateway device and the commercial cellular network. The dyads were recruited during hospitalization and followed up in the hospital and home phases for several days. Simple descriptive statistical analysis was undertaken. Recruitment rate was 50% (6/12), and consenting rate was 83% (5/6) during a 2-month period. These five neonates contributed a total of 39 study days (15 hospital days and 24 home days). Their mean [± standard deviation (S.D.)] birth weight was 1490 (± 244) g. The mean (± S.D.) of the vital signs for the five babies was temperature [36.5 °C (± 0.3)], heart rate [146.5/min (± 14)], and oxygen saturation [94% (± 4)]. No severe or moderate side-effects were noted; one baby developed mild dermatitis under the device that was transient and self-limiting, yielding an incidence proportion of 20% and incidence rate of 2.6/100 person-days. None of the mothers reported any discomfort with the use of the device. Temperatures detected from 81 paired readings revealed that those from the wearable sensor were 0.2 °C lower than those detected by clinical thermometers [36.4 (± 0.7) vs 36.6 (± 0.3); < 0.001]. There was also iterative feedback that was useful for hardware and software design specifications of the wearable sensor, the gateway device, and the analytics platform. Lastly, lessons were learnt with regard to the logistics of research team interactions with healthcare professionals and study participants during the hospitalization and post-discharge home phases of the study. The pilot study has shown that it is feasible and acceptable to track KMC adherence as well as maternal and newborn temperatures in a potentially safe manner on a real-time mode for several days’ duration during hospitalization and home phases. The pilot has also helped inform modifications in clinical monitoring, technological modifications, and logistics planning in preparation for the definitive clinical trial. Clinical Trials Registry of India, CTRI/2017/09/009789

Trajectory prediction in autonomous driving relies on accurate representation of all relevant contexts of the driving scene, including traffic participants, road topology, traffic signs, as well as their semantic relations to each other. Despite increased attention to this issue, most approaches in trajectory prediction do not consider all of these factors sufficiently. We present <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SemanticFormer</b>, an approach for predicting multimodal trajectories by reasoning over a semantic traffic scene graph using a hybrid approach. It utilizes high-level information in the form of meta-paths, i.e. trajectories on which an agent is allowed to drive from a knowledge graph which is then processed by a novel pipeline based on multiple attention mechanisms to predict accurate trajectories. SemanticFormer comprises a hierarchical heterogeneous graph encoder to capture spatio-temporal and relational information across agents as well as between agents and road elements. Further, it includes a predictor to fuse different encodings and decode trajectories with probabilities. Finally, a refinement module assesses permitted meta-paths of trajectories and speed profiles to obtain final predicted trajectories. Evaluation of the nuScenes benchmark demonstrates improved performance compared to several SOTA methods. In addition, we demonstrate that our knowledge graph can be easily added to two graph-based existing SOTA methods, namely VectorNet and LaFormer, replacing their original homogeneous graphs. The evaluation results suggest that by adding our knowledge graph the performance of the original methods is enhanced by 5% and 4%, respectively.