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Topical drug delivery against cutaneous leishmaniasis (CL) signifies an effective alternate for improving the availability and reducing the toxicity associated with the parenteral administration of conventional sodium stibogluconate (SSG) injection. The basic aim of the study was to develop nano-deformable liposomes (NDLs) for the dermal delivery of SSG against CL. NDLs were formulated by a modified thin film hydration method and optimized via Box-Behnken statistical design. The physicochemical properties of SSG-NDLs were established in terms of vesicle size (195.1 nm), polydispersity index (0.158), zeta potential (−32.8 mV), and entrapment efficiency (35.26%). Moreover, deformability index, in vitro release, and macrophage uptake studies were also accomplished. SSG-NDLs were entrapped within Carbopol gel network for the ease of skin application. The ex vivo skin permeation study revealed that SSG-NDLs gel provided 10-fold higher skin retention towards the deeper skin layers, attained without use of classical permeation enhancers. Moreover, in vivo skin irritation and histopathological studies verified safety of the topically applied formulation. Interestingly, the cytotoxic potential of SSG-NDLs (1.3 mg/ml) was higher than plain SSG (1.65 mg/ml). The anti-leishmanial activity on intramacrophage amastigote model of Leishmania tropica showed that IC 50 value of the SSG-NDLs was ∼ fourfold lower than the plain drug solution with marked increase in the selectivity index. The in vivo results displayed higher anti-leishmanial activity by efficiently healing lesion and successfully reducing parasite burden. Concisely, the outcomes indicated that the targeted delivery of SSG could be accomplished by using topically applied NDLs for the effective treatment of CL.

For geopolymer cement (GPC) concrete to become a viable building material in the main stream construction industry, reliable stress–strain curves need to be established. This paper presents the stress–strain curves for GPC concrete at ambient and elevated temperatures of up to 800 °C. Prediction models for capturing the stress–strain response of GPC concrete at ambient temperatures, based on widely accepted OPC models, are also presented here. At high temperatures testing only covered one type of temperature-load history; that is: samples were heated up to test temperatures then loaded to failure under displacement control. Between 20 and 200 °C all the tested samples underwent a decrease in strength. However, samples tested between 200 and 400 °C manifested a moderate to significant gain in strength. At 800 °C all samples underwent a decrease in strength. The initial loss of strength may be attributed to the loss of water from the GPC concrete samples, which is supported by thermogravimetric analysis of geopolymer samples. Between 200 and 400 °C, the increase in the compressive strength of all tested concrete mixtures is attributed to further geopolymerization, which has been proven by differential scanning calorimetry results. The loss of strength at 800 °C is attributed to possible disintegration of the geopolymer gel and formation of new phases within the geopolymer system.

Over the last couple of decades, the advancement in Microelectromechanical System (MEMS) devices is highly demanded for integrating the economically miniaturized sensors with fabricating technology. A sensor is a system that detects and responds to multiple physical inputs and converting them into analogue or digital forms. The sensor transforms these variations into a form which can be utilized as a marker to monitor the device variable. MEMS exhibits excellent feasibility in miniaturization sensors due to its small dimension, low power consumption, superior performance, and, batch-fabrication. This article presents the recent developments in standard actuation and sensing mechanisms that can serve MEMS-based devices, which is expected to revolutionize almost many product categories in the current era. The featured principles of actuating, sensing mechanisms and real-life applications have also been discussed. Proper understanding of the actuating and sensing mechanisms for the MEMS-based devices can play a vital role in effective selection for novel and complex application design.

Multifocal microscopy (MFM) offers high-speed three-dimensional imaging through the simultaneous image capture from multiple focal planes. Conventional MFM systems use a fabricated grating in the emission path for a single emission wavelength band and one set of focal plane separations. While a Spatial Light Modulator (SLM) can add more flexibility as a replacement to the fabricated grating, the relatively small number of pixels in the SLM chip, cross-talk between the pixels, and aberrations in the imaging system can produce non-uniform intensity in the different axially separated image planes. We present an in situ iterative SLM calibration algorithm that overcomes these optical- and hardware-related limitations to deliver near-uniform intensity across all focal planes. Using immobilized gold nanoparticles under darkfield illumination, we demonstrate superior intensity evenness compared to current methods. We also demonstrate applicability across emission wavelengths, axial plane separations, imaging modalities, SLM settings, and different SLM manufacturers. Therefore, our microscope design and algorithms provide an alternative to the use of fabricated gratings in MFM, as they are relatively simple and could find broad applications in the wider research community.

A bstract Recently, the CMS and ATLAS collaborations have announced the results for H → Z [→ ℓ + ℓ − ] γ with ℓ = e or μ [1, 2], where H → Zγ is a sub-process of H → ℓ + ℓ − γ . This semi-leptonic Higgs decay gets both resonant and non-resonant contributions from the loop-induced H → Z [→ ℓ + ℓ − ] γ . To probe further features coming from these contributions to H → ℓ + ℓ − γ , we argue that the polarization of the final state leptons is also an important parameter. We show that the interference of resonant and non-resonant contributions, which is negligible in the case of unpolarized leptons, plays a significant role when considering the polarization of the final state lepton. For this purpose, we have calculated the polarized decay rates and the longitudinal ( P L ), normal ( P N ), and transverse ( P T ) polarization asymmetries. We find that these asymmetries purely come from the loop contributions and are helpful to further investigate the resonant and non-resonant nature of H → Z [ → ℓ + ℓ − ] γ decay. We observe that for ℓ = e , μ , the longitudinal decay rate is highly suppressed around m ℓℓ ≈ 60 GeV when the final lepton spin is − 1 2 , dramatically increasing the corresponding lepton polarization asymmetries. Furthermore, we analyze another observable, the ratio of decay rates R i ± ll ′ , where ℓ and ℓ ′ refer to different final state lepton generations. Precise measurements of these observables at the HL-LHC and the planned e + e − collider can provide fertile ground to test not only the SM but also to examine the signatures of possible NP beyond the SM.

The fishery and aquaculture sectors are essential to the global food supply but face multiple challenges, including security, fish welfare, and feeding management. Unmanned aerial vehicles (UAVs) have emerged as a groundbreaking technology in these fields, providing innovative solutions for monitoring, management, and conservation. In addition to addressing these challenges, UAVs enhance operational efficiency and contribute to sustainable practices in fisheries and aquaculture. This review paper presents a comprehensive analysis of the current state of UAV applications in these sectors, with a particular focus on year wise publications and citation trends. The classification of UAV types is also examined, highlighting their varied uses ranging from fish stock assessment to habitat monitoring. The paper further explores the utility of UAVs in enhancing fish production processes and their potential role in conservation strategies. Looking forward, the review outlines future prospects, emphasising the pivotal role of UAVs in advancing fish production techniques and fostering sustainable aquaculture practices, as well as their contribution to effective conservation management in aquatic ecosystems. This paper aims to provide a critical overview of the existing research while offering insights into how UAV technology can be leveraged for long-term advancements in both the production and conservation of aquatic resources.

Multifocal microscopy enables high-speed three-dimensional (3D) volume imaging by using a multifocal grating in the emission path. This grating is typically designed to afford a uniform illumination of multifocal subimages for a single emission wavelength. Using the same grating for multicolor imaging results in non-uniform subimage intensities in emission wavelengths for which the grating is not designed. This has restricted multifocal microscopy applications for samples having multicolored fluorophores. In this paper, we present a multicolor multifocal microscope implementation which uses a Spatial Light Modulator (SLM) as a single multifocal grating to realize near-uniform multifocal subimage intensities across multiple wavelength emission bands. Using real-time control of an in-situ-optimized SLM implemented as a multifocal grating, we demonstrate multicolor multifocal 3D imaging over three emission bands by imaging multicolored particles as well as Escherichia coli ( E. coli ) interacting with human liver cancer cells, at ∼ 2.5 multicolor 3D volumes per second acquisition speed. Our multicolor multifocal method is adaptable across SLM hardware, emission wavelength band locations and number of emission bands, making it particularly suited for researchers investigating fast processes occurring across a volume where multiple species are involved.

Accurate prediction of concrete properties is a critical aspect of structural engineering. Conventional neural network models, while effective in various predictive tasks, often fall short in this context because they do not distinguish between the risks associated with underestimation and overestimation of strength. Existing codes of practice in civil engineering advocate a conservative estimation approach, prioritizing underestimation to preserve safety margins. Standard neural network models, which treat all prediction errors uniformly, are therefore not aligned with this conservative bias, which motivates the present study. The aim of this research is to design a custom loss function that embeds domain-specific safety requirements into artificial intelligence models. This function penalizes underestimations less severely than overestimations, reflecting the conservative principles of structural design. The problem formulation emphasizes the importance of conservative concrete strength prediction within the broader context of structural safety. Rubberized concrete is adopted as a case study to investigate the performance of the proposed custom loss function and to benchmark it against traditional loss functions. The study results show that the developed custom loss function effectively addresses conservative estimation needs by reducing the overestimation ratio from about 25–100% with traditional approaches to as low as 1%. At the same time, prediction accuracy remains comparable to conventional loss functions. Accordingly, the proposed custom loss function can serve as a technical enhancement that improves prediction behavior in line with conservative design philosophy and supports the reliable use of neural network models as one component within the structural design process.

Indeed, understanding the behavior of headed stud shear connectors in composite steel and concrete construction is essential for ensuring structural integrity and optimal performance. This research focuses on the sensitivity and parametric assessment of the behavior of headed stud shear connectors in composite steel and concrete construction using ensemble machine learning techniques. The study aims to uncover hidden correlations and patterns in the data using a detailed database from 464 push tests, where connectors are welded within the ribs of both trapezoidal and re-entrant steel decks. These patterns provide insights into the performance of shear connectors under various conditions, including different welding methods. The application of ensemble machine learning offers an opportunity to understand complex relationships between variables that may not be immediately evident through conventional analysis. Within the study context, eight types of ensemble machine learning models are implemented and applied to estimate the shear capacity of shear studs and conduct feature importance and partial dependence analysis. The outcomes of this research contribute to a deeper understanding of the factors influencing the performance of shear connectors, providing valuable input for structural design and evaluation in composite construction practices. As a result, this research not only enriches the current academic discourse on shear connectors but also offers pragmatic insights for professionals in the field, thereby bridging the gap between theoretical research and real-world applications in composite construction practices.

Over the past decade, extensive research has been conducted to investigate the properties and behavior of rubberized concrete as a sustainable green alternative to conventional concrete. This research involves replacing natural aggregates with rubber particles from discarded tires. Generally, these studies have shown an enhancement in ductility, energy dissipation, and the damping ratio of rubberized concrete. However, a significant reduction in mechanical properties, such as compressive and tensile strength, and modulus of elasticity, has been noted compared to standard concrete. Currently, the literature lacks a comprehensive numerical study that could provide structural engineers with a complete understanding of the seismic performance of rubberized concrete frames. Consequently, this study examines three low-rise RC frames subjected to sixty recorded ground motions (near-fault, pulse-like, and far-fault) using nonlinear response-history analysis, comparing rubberized concrete (RBC) with a control concrete (NC-C) and a similar-strength mix (NC-S). Across records, RBC exhibits lower base shear (mean reductions up to 11.6–13.8% versus NC-C and about 3–6% versus NC-S, depending on motion class), higher viscous damping energy (increases of 29–53%), and lower hysteretic energy (reductions of about 10–29%), while interstory drift ratios increase yet remain within ASCE 7 drift limits. Absolute floor accelerations reduce modestly (up to 11.8% in far-fault motions). The results indicate that substituting RBC can enhance damping efficiency and reduce seismic forces relative to both NC-C and NC-S under severe earthquakes at a drift trade-off.