The human brain has been described as a complex system. Its study by means of neurophysiological signals has revealed the presence of linear and nonlinear interactions. In this context, entropy metrics have been used to uncover brain behavior in the presence and absence of neurological disturbances. Entropy mapping is of great interest for the study of progressive neurodegenerative diseases such as Alzheimer’s disease. The aim of this study was to characterize the dynamics of brain oscillations in such disease by means of entropy and amplitude of low frequency oscillations from Bold signals of the default network and the executive control network in Alzheimer’s patients and healthy individuals, using a database extracted from the Open Access Imaging Studies series. The results revealed higher discriminative power of entropy by permutations compared to low-frequency fluctuation amplitude and fractional amplitude of low-frequency fluctuations. Increased entropy by permutations was obtained in regions of the default network and the executive control network in patients. The posterior cingulate cortex and the precuneus showed differential characteristics when assessing entropy by permutations in both groups. There were no findings when correlating metrics with clinical scales. The results demonstrated that entropy by permutations allows characterizing brain function in Alzheimer’s patients, and also reveals information about nonlinear interactions complementary to the characteristics obtained by calculating the amplitude of low frequency oscillations.

Cross-border infrastructure projects offer significant economic and social benefits for the Asia-Pacific region. If the required investment of $8 trillion in pan-Asian connectivity was made in the region’s infrastructure during 2010–2020, the total net income gains for developing Asia could reach about $12.98 trillion (in 2008 US dollars) during 2010–2020 and beyond, of which more than $4.43 trillion would be gained during 2010–2020 and nearly $8.55 trillion after 2020. Indeed, infrastructure connectivity helps improve regional productivity and competitiveness by facilitating the movement of goods, services and human resources, producing economies of scale, promoting trade and foreign direct investments, creating new business opportunities, stimulating inclusive industrialization and narrowing development gaps between communities, countries or sub-regions. Unfortunately, due to limited financing, progress in the development of cross-border infrastructure in the region is low.

This paper examines the key challenges faced in financing cross-border projects and discusses the roles that different stakeholders—national governments, state-owned enterprises, private sector, regional entities, development financing institutions (DFIs), affected people and civil society organizations—can play in facilitating the development of cross-border infrastructure in the region. In particular, this paper highlights the major risks that deter private sector investments and FDIs and provides recommendations to address these risks.

We report on the measurement of the response of Rhodamine 6G (R6G) dye to enhanced local surface plasmon resonance (LSPR) using a plasmonic-active nanostructured thin gold film (PANTF) sensor. This sensor features an active area of approximately ≈ 2.5 × 10¹³ nm² and is immobilized with gold nanourchins (GNU) on a thin gold film substrate (TGFS). The hexane-functionalized TGFS was immobilized with a 90 nm diameter GNU via the strong sulfhydryl group (SH) thiol bond and excited by a 637 nm Raman probe. To collect both Raman and SERS spectra, 10 μL of R6G was used at concentrations of 1 μM (6 × 10¹² molecules) and 10 mM (600 × 10¹⁴ molecules), respectively. FT-NIR showed a higher reflectivity of PANTF than TGFS. SERS was performed three times at three different laser powers for TGFS and PANTF with R6G. Two PANTF substrates were prepared at different GNU incubation times of 10 and 60 min for the purpose of comparison. The code for processing the data was written in Python. The data was filtered using the filtfilt filter from scipy.signals, and baseline corrected using the Improved Asymmetric Least Squares (ISALS) function from the pybaselines.Whittaker library. The results were then normalized using the minmax_scale function from sklearn.preprocessing. Atomic force microscopy (AFM) was used to capture the topography of the substrates. Signals exhibited a stochastic fluctuation in intensity and shape. An average corresponding enhancement factor (EF) of 0.3 × 10⁵ and 0.14 × 10⁵ was determinedforPANTFincubated at 10 and 60 min, respectively.

This review provides an overview of the importance of nanoparticles in various fields of science, their classification, synthesis, reinforcements, and applications in numerous areas of interest. Normally nanoparticles are particles having a size of 100 nm or less that would be included in the larger category of nanoparticles. Generally, these materials are either 0-D, 1-D, 2-D, or 3-D. They are classified into groups based on their composition like being organic and inorganic, shapes, and sizes. These nanomaterials are synthesized with the help of top-down bottom and bottom-up methods. In case of plant-based synthesis i.e., the synthesis using plant extracts is non-toxic, making plants the best choice for producing nanoparticles. Several physicochemical characterization techniques are available such as ultraviolet spectrophotometry, Fourier transform infrared spectroscopy, the atomic force microscopy, the scanning electron microscopy, the vibrating specimen magnetometer, the superconducting complex optical device, the energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy to investigate the nanomaterials. In the meanwhile, there are some challenges associated with the use of nanoparticles, which need to be addressed for the sustainable environment.

In this study, nano-scale microstructural evolution in 6061-T6 alloy after laser shock processing (LSP) was studied. 6061-T6 alloy plate was subjected to multiple LSP. The LSP treated area was characterized by X-ray diffraction and the microstructure of the samples was analyzed by transmission electron microscopy. Focused Ion Beam (FIB) tools were used to prepare TEM samples in precise areas. It was found that even though aluminum had high stacking fault energy, LSP yielded to formation of ultrafine grains and deformation faults such as dislocation cells, stacking faults. The stacking fault probability (PSF) was obtained in LSP-treated alloy using X-Ray diffraction. Deformation induced stacking faults lead to the peak position shifts, broadening and asymmetry of diffraction. XRD analysis and TEM observations revealed significant densities of stacking faults in LSP-treated 6061-T6 alloy. And mechanical properties of LSP-treated alloy were also determined to understand the hardening behavior with high concentration of structural defects.