Seamless Fusion: Multi-Modal Localization for First Responders in Challenging Environments.

In dynamic and unpredictable environments, the precise localization of first responders and rescuers is crucial for effective incident response. This paper introduces a novel approach leveraging three complementary localization modalities: visual-based, Galileo-based, and inertial-based. Each modality contributes uniquely to the final Fusion tool, facilitating seamless indoor and outdoor localization, offering a robust and accurate localization solution without reliance on pre-existing infrastructure, essential for maintaining responder safety and optimizing operational effectiveness. The visual-based localization method utilizes an RGB camera coupled with a modified implementation of the ORB-SLAM2 method, enabling operation with or without prior area scanning. The Galileo-based localization method employs a lightweight prototype equipped with a high-accuracy GNSS receiver board, tailored to meet the specific needs of first responders. The inertial-based localization method utilizes sensor fusion, primarily leveraging smartphone inertial measurement units, to predict and adjust first responders' positions incrementally, compensating for the GPS signal attenuation indoors. A comprehensive validation test involving various environmental conditions was carried out to demonstrate the efficacy of the proposed fused localization tool. Our results show that our proposed solution always provides a location regardless of the conditions (indoors, outdoors, etc.), with an overall mean error of 1.73 m.

AndroDex: Android Dex Images of Obfuscated Malware.

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that are based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.

Independent co-delivery of model actives with different degrees of hydrophilicity from oil-in-water and water-in-oil emulsions stabilised by solid lipid particles via a Pickering mechanism: a-proof-of-principle study.

HYPOTHESIS: The development of vehicles for the co-encapsulation of actives with diverse characteristics and their subsequent controllable co-delivery is gaining increasing research interest. Predominantly centred around pharmaceutical applications, the majority of such co-delivery approaches have been focusing on solid formulations and less so on liquid-based systems. Simple emulsions can be designed to offer a liquid-based microstructural platform for the compartmentalised multi-delivery of actives. EXPERIMENTS: In this work, solid lipid nanoparticle stabilised Pickering emulsions were used for the co-encapsulation/co-delivery of two model actives with different degrees of hydrophilicity. Lipid particles containing a model hydrophobic active were prepared in the presence of either Tween 20 or whey protein isolate, and were then used to stabilise water-in-oil or oil-in-water emulsions, containing a secondary model active within their dispersed phase. FINDINGS: Solid lipid nanoparticles prepared with either type of emulsifier were able to provide stable emulsions. Release kinetic data fitting revealed that different co-delivery profiles can be achieved by controlling the surface properties of the lipid nanoparticles. The current proof-of-principle study presents preliminary data that confirm the potential of this approach to be utilised as a flexible liquid-based platform for the segregated co-encapsulation and independent co-release of different combinations of actives, either hydrophobic/hydrophilic or hydrophobic/hydrophobic, with diverse release profiles.

Formulation design, production and characterisation of solid lipid nanoparticles (SLN) and nanostructured lipid carriers (NLC) for the encapsulation of a model hydrophobic active.

Lipid nanoparticles have been widely investigated for their use as either carriers for poorly water soluble actives or as (Pickering) emulsion stabilisers. Recent studies have suggested that the fabrication of lipid nanostructures that can display both these performances concurrently, can enable the development of liquid formulations for multi-active encapsulation and release. Understanding the effects of different formulation variables on the microstructural attributes that underline both these functionalities is crucial in developing such lipid nanostructures. In this study, two types of lipid-based nanoparticles, solid lipid nanoparticles and nanostructured lipid carriers, were fabricated using varying formulation parameters, namely type of solid lipid, concentration of liquid lipid and type/concentration of surface active species. The impact of these formulation parameters on the size, thermal properties, encapsulation efficiency, loading capacity and long-term storage stability of the developed lipid systems, was studied. Preliminary lipid screening and processing conditions studies, focused on creating a suitable lipid host matrix of appropriate dimensions that could enable the high loading of a model hydrophobic active (curcumin). Informed by this, selected lipid nanostructures were then produced. These were characterised by encapsulation efficiency and loading capacity values as high as 99% and 5%, respectively, and particle dimensions within the desirable size range (100-200 nm) required to enable Pickering functionality. Compatibility between the lipid matrix components, and liquid lipid/active addition were shown to greatly influence the polymorphism/crystallinity of the fabricated particles, with the latter demonstrating a liquid lipid concentration-dependent behaviour. Successful long-term storage stability of up to 28 weeks was confirmed for certain formulations.