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Wearable smart glasses are an emerging technology gaining popularity in the assistive technologies industry. Smart glasses aids typically leverage computer vision and other sensory information to translate the wearer's surrounding into computer-synthesized speech. In this work, we explored the potential of a new technique known as "acoustic touch" to provide a wearable spatial audio solution for assisting people who are blind in finding objects. In contrast to traditional systems, this technique uses smart glasses to sonify objects into distinct sound auditory icons when the object enters the device's field of view. We developed a wearable Foveated Audio Device to study the efficacy and usability of using acoustic touch to search, memorize, and reach items. Our evaluation study involved 14 participants, 7 blind or low-visioned and 7 blindfolded sighted (as a control group) participants. We compared the wearable device to two idealized conditions, a verbal clock face description and a sequential audio presentation through external speakers. We found that the wearable device can effectively aid the recognition and reaching of an object. We also observed that the device does not significantly increase the user's cognitive workload. These promising results suggest that acoustic touch can provide a wearable and effective method of sensory augmentation.

Lung cancer is a leading cause of cancer-related deaths globally. The availability of successful anticancer agents in the market is limited, and the development process of a new drug molecule is slow and difficult. The currently available commercial formulations are not sufficient to produce the desired therapeutic response within a specific time limit. Therefore, there is an urgent need to develop novel nanocarrier-based therapies to defeat the restrictions of existing therapeutics. Nanoparticles have been investigated as novel formulations but are often inefficient in practical applications. However, several unanswered questions and challenges exist in their clinical development; thus, a better understanding of their influence on cancer biology, stability, and toxicity needs to be gained. This review discusses different types of lung cancers as well as diagnostic approaches to lung cancer. The review also explores the drug targeting mechanisms to cancer cells through nanoparticles and multi-drug resistance-associated challenges in lung cancer therapy. Various nanocarrier systems that are safe and effective for drug delivery in the treatment of lung cancer have been discussed. This communication will be of high relevance to the biological, formulation, and translational scientists working in the field of cancer biology and drug delivery.

Smoothing filters are widely used in EEG signal processing for noise removal while preserving important features. Unlike common approaches in the time domain, a recent effective algorithm using the Upscale and Downscale Representation (UDR) technique has been introduced to process the signal in the image domain. The idea of UDR is to visualize the input with an appropriate line width, convert it to a binary image, and then smooth it by skeletonizing the signal object to a unit width and projecting it back to the time domain. We propose in this paper a cascaded UDR (CUDR) where the interested signal is filtered twice. CUDR's performance is verified on simulated data with added white Gaussian noise and compared with the cascaded arrangement of some conventional techniques. Experimental results have demonstrated the outperformance of CUDR in terms of the fitting error when dealing with noisy signals, especially at a low signal-to-noise ratio.

Reconciling machine learning with individual privacy is one of the main motivations behind federated learning (FL), a decentralized machine learning technique that aggregates partial models trained by clients on their own private data to obtain a global deep learning model. Even if FL provides stronger privacy guarantees to the participating clients than centralized learning collecting the clients’ data in a central server, FL is vulnerable to some attacks whereby malicious clients submit bad updates in order to prevent the model from converging or, more subtly, to introduce artificial bias in the classification (poisoning). Poisoning detection techniques compute statistics on the updates to identify malicious clients. A downside of anti-poisoning techniques is that they might lead to discriminate minority groups whose data are significantly and legitimately different from those of the majority of clients. This would not only be unfair, but would yield poorer models that would fail to capture the knowledge in the training data, especially when data are not independent and identically distributed (non-i.i.d.). In this work, we strive to strike a balance between fighting poisoning and accommodating diversity to help learning fairer and less discriminatory federated learning models. In this way, we forestall the exclusion of diverse clients while still ensuring detection of poisoning attacks. Empirical work on three data sets shows that employing our approach to tell legitimate from malicious updates produces models that are more accurate than those obtained with state-of-the-art poisoning detection techniques. Additionally, we explore the impact of our proposal on the performance of models on non-i.i.d local training data.

Adoption of single-fraction lung stereotactic body radiation therapy (SBRT) for patients with medically inoperable early stage non-small-cell lung cancer (NSCLC) or oligometastatic lung disease, even during the coronavirus disease 2019 (COVID-19) pandemic, was limited despite encouraging phase II trial results. Barriers to using single-fraction SBRT may include lack of familiarity with the regimen and lack of clarity about the expected toxicity. To address these concerns, we performed a systematic review of prospective literature on single-fraction SBRT for definitive treatment of early stage and oligometastatic lung cancer. A PubMed search of prospective studies in English on single-fraction lung SBRT was conducted. A systematic review was performed of the studies that reported clinical outcomes of single-fraction SBRT in the treatment of early stage non-small-cell lung cancer and lung oligometastases. The current prospective literature including nine trials supports the use of single-fraction SBRT in the definitive treatment of early stage peripheral NSCLC and lung oligometastases. Most studies cite local control rates of >90%, mild toxicity profiles, and favorable survival outcomes. Most toxicities reported were grade 1-2, with grade ≥3 toxicity in 0-17% of patients. Prospective trial results suggest potential consideration of utilizing single-fraction SBRT beyond the COVID-19 pandemic.

Integration of supercapacitor alongwith battery in electric vehicles (EVs) improves the life cycle of the battery. Additionally, supercapacitor supplies or absorbs a large amount of instantaneous power during sudden demand such as acceleration or regenerative braking operation, hence also improve the dynamics of the internal power system. However, a major challenge with supercapacitor is that its terminal voltage is low and varies in a wide range during charging and discharging operation. Thus, a high voltage conversion ratio based bidirectional DC-DC converters are required to connect the lower supercapacitor voltage to higher DC-link voltage. The steep voltage conversion ratio with continuous gain based bidirectional DC-DC converters are an integral part of such applications. Various high gain converters exist in the literature such as isolated, non-isolated, cascaded, switched, flying capacitor, and coupled inductor based DC-DC converters, however all these converters have some limitations in context to high gain/high power applications. Therefore, to understand the development of next-generation high voltage gain bidirectional DC-DC converter, this paper focus to comprehensively review and classify various bidirectional step-up DC-DC converters based on their characteristics and voltage-boosting techniques. Further, practical suitability of the reviewed converters and future research directions in switched capacitor converters in context to EVs are also discussed in detail.

Analyzing the effects landmarks have on spatial learning is an active area of research in the study of human navigation processes and one that is key to understanding the links between human brain dynamics, landmark encoding, and spatial learning outcomes. This article presents a study on whether electroencephalography (EEG) signals related to virtual global landmarks combined with deep learning can be used to predict the accuracy and efficacy of spatial learning. Virtual global landmarks are silhouettes of actual landmarks projected into the navigator's vision via a heads-up display. They serve as a notable frame of reference in addition to the local landmarks we all typically use for route navigation. From a mobile virtual reality scenario involving 55 participants, the results of the study suggest that the EEG data associated with those who were exposed to global landmarks shows a visibly better capacity for predicting the quality of spatial learning levels than those who were not. As such, the EEG features associated with processing VGLs have a greater functional relation to the quality of spatial learning. This finding opens up a future direction of enquiry into landmark encoding and navigational ability. It may also provide a potential avenue for the early diagnosis of Alzheimer's disease.

Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).

IN THE above article [1], we detected an error in reporting the 10-fold cross-validation result. The correct 10-fold cross-validation result in Table IV is uploaded in this letter. The corrected result of the cross-validation is consistent with our findings [1] that the EEG data associated with virtual global landmark (VGL) [2], [3] stimuli from the VGL group had an overall improvement in Acc and F1 scores compared to local landmarks from the non-VGL group, where Acc and F1 scores improved averagely 14.89% and 21.77%, respectively. (Table Presented).

Robots for physical Human-Robot Collaboration (pHRC) often need to adapt their admittance and how they operate due to several factors. As the admittance of the system becomes variable throughout the workspace, it is not always straightforward for the operator to predict the robot’s behaviour. Previous work demonstrated that cognitive conflicts can be detected during one-dimensional tasks. This work assesses whether cognitive conflicts can also be detected during 2D tasks in pHRC and a classification problem is formulated. Different robot admittance profiles anticipating the stimulus translated into different levels of cognitive conflict. Several commonly used classification algorithms for EEG signals were evaluated to classify different levels of cognitive conflict. Results demonstrate that cognitive conflict level is lower when the admittance smoothly decreases before unexpected events when compared to conditions in which the admittance abruptly decreases before the stimulus. Among the classification algorithms, Convolutional Neural Network has shown the best results to classify different levels of cognitive conflict. Results suggest the feasibility of adaptive approaches for future pHRC control systems that close the loop on users through EEG signals. The detected human cognitive state can also be used to assess and improve the predictability of Human-Robot teams in various pHRC applications.

Modern work environments have extensive interactions with technology and greater cognitive complexity of the tasks, which results in human operators experiencing increased mental workload. Air traffic control operators routinely work in such complex environments, and we designed tracking and collision prediction tasks to emulate their elementary tasks. The physiological response to the workload variations in these tasks was elucidated to untangle the impact of workload variations experienced by operators. Electroencephalogram (EEG), eye activity, and heart rate variability (HRV) data were recorded from 24 participants performing tracking and collision prediction tasks with three levels of difficulty. Our findings indicate that variations in task load in both these tasks are sensitively reflected in EEG, eye activity and HRV data. Multiple regression results also show that operators' performance in both tasks can be predicted using the corresponding EEG, eye activity and HRV data. The results also demonstrate that the brain dynamics during each of these tasks can be estimated from the corresponding eye activity, HRV and performance data. Furthermore, the markedly distinct neurometrics of workload variations in the tracking and collision prediction tasks indicate that neurometrics can provide insights on the type of mental workload. These findings have applicability to the design of future mental workload adaptive systems that integrate neurometrics in deciding not just "when" but also "what" to adapt. Our study provides compelling evidence in the viability of developing intelligent closed-loop mental workload adaptive systems that ensure efficiency and safety in complex work environments.

Although beacon- and map-based spatial strategies are the default strategies for navigation activities, today's navigational aids mostly follow a beacon-based design where one is provided with turn-by-turn instructions. Recent research, however, shows that our reliance on these navigational aids is causing a decline in our spatial skills. We are processing less of our surrounding environment and relying too heavily on the instructions given. To reverse this decline, we need to engage more in map-based learning, which encourages the user to process and integrate spatial knowledge into a cognitive map built to benefit flexible and independent spatial navigation behaviour. In an attempt to curb our loss of skills, we proposed a navigation assistant to support map-based learning during active navigation. Called the virtual global landmark (VGL) system, this augmented reality (AR) system is based on the kinds of techniques used in traditional orienteering. Specifically, a notable landmark is always present in the user's sight, allowing the user to continuously compute where they are in relation to that specific location. The efficacy of the unit as a navigational aid was tested in an experiment with 27 students from the University of Technology Sydney via a comparison of brain dynamics and behaviour. From an analysis of behaviour and event-related spectral perturbation, we found that participants were encouraged to process more spatial information with a map-based strategy where a silhouette of the compass-like landmark was perpetually in view. As a result of this technique, they consistently navigated with greater efficiency and better accuracy.

The merit of switched-capacitors-based multilevel inverters (SCMLIs) is generally quantified in terms of a 'cost function' (CF) that incorporates parameters such as voltage gain, component count, total standing voltage (TSV), and number of levels. In this article, a 13-level inverter is proposed with the aim of achieving a low value of CF. The proposed single-stage SCMLI uses one input source and three capacitors to attain a voltage gain of 3. It requires 13 power switches, of which the peak inverse voltage (PIV) of nine switches is restricted to the source voltage. The remaining four switches have PIV equal to twice the source voltage and they operate at low frequency. Thus, for all switches, the PIV is less than the amplitude of the output voltage. Moreover, the capacitors are self-balanced at all regions of modulation index values. The proposed inverter is validated through simulation and experimental results. A comparison of the proposed topology with other contemporary SCMLIs shows that it is highly competent in terms of CF, PIV and TSV requirements, waveform resolution, and capability to achieve voltage balancing of capacitors at low values of modulation index.