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Improving the intrinsic pharmacodynamics or pharmacokinetic of drugs is regarded as a crucial step in developing potential drug therapies. In this context, the development of nanostructured-based drug delivery systems (DDSs) responsive to exogenous or endogenous stimuli demonstrate an outstanding platform to clinical applications. Polymeric micelles (PM)-based DDSs are often modified with selective short peptides like cell penetrating peptides (CPPs) for highly efficient cellular internalization. In this review, we discussed recent achievements in the design and development of nanoscale pH-responsive micelles modified with CPPs to provide controlled drug release in the tumor cells. An overview on the application of micelles, especially PMs as promising engineered nanocarriers to combine pharmaceutical agents with CPPs are presented. Afterwards, the peptide-based transfection method as one of the promising approaches for drug delivery and therapeutic agents was explained. Subsequently, conjugation of pH-responsive micelles with CPPs as a novel DDS in cancer therapy was surveyed. Finally, the current challenges and future application of stimuli-sensitive nanoplatforms conjugated with CPP were discussed. In conclusion, the application of controlled delivery using pH-sensitive PMs-based DDSs conjugated with peptides can be considered as a potential approach to improve the therapeutic index of anticancer drugs.

A bilinear form of the (2+1)-dimensional nonlinear Calogero–Bogoyavlenskii–Schiff (CBS) model is derived using a transformation of dependent variable, which contain a controlling parameter. This parameter can control the direction, wave height and angle of the traveling wave. Based on the Hirota bilinear form and ansatz functions, we build many types of novel structures and manifold periodic-soliton solutions to the CBS model. In particular, we obtain entirely exciting periodic-soliton, cross-kinky-lump wave, double kinky-lump wave, periodic cross-kinky-lump wave, periodic two-solitary wave solutions as well as breather style of two-solitary wave solutions. We present their propagation features via changing the existence parametric values in graphically. In addition, we estimate a condition that the waves are propagated obliquely for η≠0 , and orthogonally for η=0.

We study a scenario where multiple drone-mounted base stations cruise freely over a macro hotspot to serve mobile users on the ground. The drone base stations move constantly and update their moving directions following our proposed mobility control algorithm. The constant movement of drones reduces the distance between the base stations and users, which in turn improves the probability of having a line of sight connection. We consider a practical user association scheme for the moving base stations, which enables user equipments to switch their serving base stations based only on the received signal strength. via extensive simulations, we demonstrate that the drone base stations moving according to our proposed algorithms can improve the average packet throughput by 82% and the 5th-percentile packet throughput by 430% compared to a baseline scenario, where drones hover over fixed locations. These improvements can be realized regardless of users' and base stations' density. The constant movement of the drones also helps reduce the total number of drones required to cover the macro hotspot.

In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

There is a growing interest in developing energy harvesting solutions for wearable devices so they can self-power themselves without relying on batteries. Piezoelectric energy harvesters (PEHs) can convert kinetic energy released from human activities into usable electrical energy for powering various electronic circuits inside the wearable device. Intuitively, the kinetic energy is produced because the user expends some calories during the physical activities. We therefore postulate that the voltage output of a PEH in a wearable device should contain information that can be used to estimate the amount of calorie expended. If this is true, then the PEH can be used as a new source for calorie estimation. Unlike conventional sensors, such as accelerometers, a PEH does not consume any power, which would make this new source very attractive. In this paper, using real PEH hardware and the data collected from ten real subjects, we conduct an experimental study to assess the suitability of PEH voltage in estimating calorie expenditure for two different activities, walking and running. We find that, for most subjects, the calorie estimations obtained from the output voltage of PEH is very close to those obtained from a 3-axial accelerometer.

This paper presents a system based on pedestrian dead reckoning (PDR) for localization of networked mobile users, which relies only on sensors embedded in the devices and device- to-device connectivity. The user trajectory is reconstructed by measuring step by step the user displacements. Though step length can be estimated rather accurately, heading evaluation is extremely problematic in indoor environments. Magnetometer is typically used, however measurements are strongly perturbed. To improve the location accuracy, this paper proposes a novel cooperative system to estimate the direction of motion based on a machine learning approach for perturbation detection and filtering, combined with a consensus algorithm for performance augmentation by cooperative data fusion at multiple devices. A first algorithm filters out perturbed magnetometer measurements based on a-priori information on the Earth’s magnetic field. A second algorithm aggregates groups of users walking in the same direction, while a third one combines the measurements of the aggregated users in a distributed way to extract a more accurate heading estimate. To the best of our knowledge, this is the first approach that combines machine learning with consensus algorithms for cooperative PDR. Compared to other methods in the literature, the method has the advantage of being infrastructure-free, fully distributed and robust to sensor failures thanks to the pre-filtering of perturbed measurements. Extensive indoor experiments show that the heading error is highly reduced by the proposed approach thus leading to noticeable enhancements in localization performance.

Thanks to nanotechnology, it is now possible to fabricatesensor nodes below 100 nanometers in size. Although wireless communication at this scale has not been successfullydemonstrated yet, simulations confirm that these sensor nodeswould be able to communicate in the terahertz band usinggraphene as a transmission antenna. These developmentssuggest that deployment of wireless nanoscale sensor networks (WNSNs) inside human body could be a reality one day. In this paper, we design and analyse a WNSN for monitoring human lung cells. We find that respiration, i.e., theperiodic inhalation and exhalation of oxygen and carbon dioxide, is the major process that influences the terahertz channel inside lung cells. The channel is characterized as a two-state channel, where it periodically switches betweengood and bad states. Using real human respiratory data, we find that the channel absorbs terahertz signal much faster when it is in bad state compared to good state. Our simula-tion experiments confirm that we could reduce transmission power of the nanosensors, and hence the electromagnetic radiation inside lungs due to deployment of WNSN, by afactor of 20 if we could schedule all communication only during good channel states. We propose two duty cycling protocols along with a simple channel estimation algorithm that enables nanosensors to achieve such scheduling.

Background. For the treatment of visceral leishmaniasis in Bangladesh, single dose liposomal amphotericin B (ambisome) is supposed to be the safest and most effective treatment. Specific needs for application and storage raise questions about feasibility of its implementation and acceptance by patients and health staff. Methods. The study was carried out in the most endemic district of Bangladesh. Study population includes patients treated with ambisome or miltefosine, hospital staff, and a director of the national visceral leishmaniasis program. Study methods include direct observation (subdistrict hospitals), open interviews (heath staff and program personnel), structured questionnaires, and focus group discussions (patients). Results. Politicalcommitment for ambisome is strong; the general hospital infrastructure favours implementation but further strengthening is required, particularly for drug storage below 25°C (refrigerators), back-up energy (fuel for generators), and supplies for ambisome administration (like 5% dextrose solution). Ambisome created high satisfaction in patients and hospital staff, less adverse events, and less income loss for patients compared to miltefosine. Conclusions. High political commitment, general capacities of subdistrict hospitals, and high acceptability favour the implementation of ambisome treatment in Bangladesh. However, strengthening of the infrastructure and uninterrupted supplies of essential accessories is mandatory before introducing sLAB in Bangladesh.