Ridge Features Research Articles

Formation and preservation mechanisms of magnetofossils in the surface sediments of muddy areas in the yellow and Bohai Seas, China

Magnetofossils in the continental shelf sediments of the Yellow and Bohai Seas have long been overlooked. Based on the magnetic results of 88 surface sediments (0–10 cm depth), first-order reversal curve (FORC) diagrams, isothermal remanent magnetization (IRM) acquisition curves from 6 representative samples, and transmission electron microscopy (TEM) observations of 2 samples, the formation and preservation mechanisms of magnetofossils in this region are elucidated. The FORC diagrams consistently show a clear central ridge feature, which indicates the presence of intact magnetofossils in all representative samples. The morphologies observed by TEM are primarily equant and elongated, with minimal or no bullet-shaped (magnetite) magnetofossils. Analysis further reveals a widespread distribution of magnetofossils in the mud areas of the Bohai Sea, North Yellow Sea, and South Yellow Sea, with proportions (contribution to SIRM; SIRM is defined as the remanent magnetization that remains constant as the external magnetic field increases) of <32.5 %, 40.9 % ∼ 44.6 %, and 59.9 % ∼ 66.5 %, respectively. Despite the presence of non-biogenic single domain magnetite, the proportion of magnetofossils can be estimated by the χARM/SIRM value, as they are positively correlated. The surface sedimentary environment of these mud areas is primarily suboxic and characterized by abundant dissolved iron, which facilitate the formation of magnetofossils by magnetotactic bacteria (MTB). It is unlikely that the surface sedimentary environment becomes sulphidic, thereby enabling the preservation of magnetofossils after their formation. The redox state of the study area, crucial for magnetofossil formation, is mainly controlled by the total organic carbon (TOC) content. From north to south, the higher proportion of magnetofossils is coupled with higher TOC content, possibly due to the intensified reducing degree of the suboxic environment, promoting MTB proliferation and thus forming more magnetofossils. The mechanisms governing the formation and preservation of magnetofossils proposed in this study may also be applicable to geological records.

Identification of Decedents by Restoring Mummified Fingerprints: Forensic Dermatology in the Investigation of Mummy Dermatoglyphics

Fingerprints are created by elevations and depressions on the fingertip pads. Each person has their own unique fingerprints which can be used in the identification of that individual when alive, during the immediate postmortem period, or even after the digits have become mummified. Mummification can occur naturally; it can be partial (such as localized to only the hands and feet, extensive, or complete. Obtaining fingerprints after the skin has become mummified can be attempted while the digits remaining intact with the hand; however, the digits may need to be removed from the hand and the finger pads may also need to be separated from the underlying bone to secure an adequate fingerprint. Frequently, the mummified tissue needs to be rehydrated; numerous solutions have been used that increase the turgor of the digits, provide softening and pliability of the tissue, and enhance the details of the finger pad ridges. An aqueous solution of sodium carbonate (either combined with acetic acid or combined with 95 percent ethanol and distilled water) was found to be most effective for rehydration. Thereafter, various techniques can be attempted to obtain the fingerprint. These include the traditional method of inking and rolling of the finger or photographing the finger. Powders (such as aluminum powder, black fingerprint powder, white cornstarch-based powder, or fluorescent powder) can be used to enhance the ridge features; adhesive tape can be pressed against the powdered digit and the print pattern preserved by applying the adhesive tape to a clear transparency sheet. In addition, molds (using modeling clay or silicone rubber) and casts (using plaster of Paris, dental casting materials, or putty) can be made of the digits; either the molds or the casts or both can be photographed with or without prior application of fingerprint powder. Transillumination, using a fiber optic light source to illuminate the epidermis and underlying remaining dermis of a scraped and defleshed finger pad can be used to demonstrate the finger ridge pattern when the photographing the tip of the digit. In summary, forensic dermatology can have an integral role in obtaining fingerprints from mummified digits, which can be successfully used for the identification of the decedent.

Formation and hydrothermal alteration of a volcanic center: Melt pooling and mass transfers at Langseth Ridge (Gakkel Ridge, Arctic Ocean)

Volcanic centers are characteristic features of ultraslow-spreading mid-ocean ridges, the least-explored parts of the global ridge system. Volcanic centers can provide insights into deep magmatic and metamorphic processes at these ridges. Here, we present data from the largest volcanic center on the Gakkel Ridge, the Langseth Ridge, situated at 60–62°E. Langseth is ∼10 km wide, consisting of three peaks that rise to 585 m water depth, some 3–4 km above the surrounding seafloor. It strikes perpendicular to Gakkel's spreading direction and can be traced for ∼40 km, which translates to an age of ∼8 Myr. Seafloor imaging revealed abundant (pillow) basalt but also fissures and geologic faults across the Langseth Ridge. Basaltic rocks were sampled at all summits and diabase at the slope of the northern summit that dips into the rift valley.Our samples are of normal to depleted mid-ocean ridge basalt composition and exhibit a wide range of major and trace element contents, due to magmatic processes, accumulation of macrocrysts, and hydrothermal alteration. Radiogenic isotope ratios, most notably 143Nd/144Nd and 208Pb/206Pb, trend from typical rift valley compositions to isotopically enriched values with increasing distance to the rift valley. This trend may imply melt pooling from different sources, potentially representing a shift from shallow melting beneath the rift valley to deeper melting of enriched sources and higher degrees of melting underneath Langseth. Mineral compositions and plagioclase sieve textures imply prolonged storage of magma at depth prior to eruption. Hydrothermal alteration occurred over a range of conditions. Basalt from the summits is weakly altered at temperatures ≪100 °C, which likely occurred in situ at the summit sites. Diabase samples experienced chloritization and albitization and display epidote and quartz veins, which formed at >300 °C. These assemblages and temperatures are typical for lower crustal levels and imply uplift of the samples of >1 km. Diabase samples from the Afanasenkov Seamount, another volcanic center on the Gakkel Ridge that we investigated for comparison, were altered under comparable conditions.Our findings suggest a combined volcanic–tectonic origin of the studied volcanic centers, potentially implying that such complexes may generally form due to the interplay of magmatism and tectonics. Researching volcanic centers has the potential to further our understanding of both deep and shallow crustal processes at ultraslow-spreading ridges, providing further insights into the role of these centers as linkages between lithosphere and hydrosphere and the (deep) biosphere they sustain.

Biophysical characterization of summer Arctic sea-ice habitats using a remotely operated vehicle-mounted underwater hyperspectral imager

The impact of a rapidly shifting sea-ice cover on climate, ecosystem processes and biophysical habitat properties is not yet fully understood, particularly in the central Arctic Ocean, due to a lack of spatially representative observations. From June to July 2020 during the year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, leg 4) in the Transpolar Drift, we deployed an underwater hyperspectral imager (UHI) mounted on a remotely operated vehicle (ROV) to characterize the biophysical properties of different sea-ice habitats. We conducted UHI surveys along two transects: i) under level first-year sea ice (FYI), which had a mean sea-ice draft of 1.4 m and was composed of primarily level FYI but also had a relatively shallow ridge (keel depth ∼2.6 m); and ii) under the flank of a ridge, named Jaridge, with a mean ice draft of 1.7 m, which was composed of a mix of level ice and thicker ridge blocks with over 3 m draft. We present a new unsupervised bio-optical quantification algorithm for hyperspectral surveys, the relative ice algal biomass index (RBI), using spectral mixture analysis (SMA). We compare this method to the supervised machine learning habitat classification algorithm, Support Vector Machine (SVM). The RBI showed good agreement to literature-based normalized difference indices (NDI) and PCA analyses, which confirm the RBI as a reliable unsupervised index for ice algal biomass. Our biophysical characterization of the two surveyed regions showed an association of sea-ice algal biomass with sea-ice ridge features. Our surveys also indicate that ice algal spatial distribution may be influenced by ice melt rates, and the formation of under ice meltwater layers and false bottoms. With high spatial coverage (>100 m) at microscale resolution (∼cm) we documented large spatial variability of summer Arctic sea-ice algal biomass and different patterns between adjacent ice habitats. We further demonstrate the need for improved understanding of sea-ice algal spatial variability as a complementary tool for sea-ice biogeochemical sampling using destructive ice core sampling.

Hydrazo Pyrazole-Pyridone Fluorescent tag for NLO, Live cell imaging, LFPs visualization, Photophysical probing, and Electrochemical sensor for Dopamine detection.

The present communication reports on the synthesis of a novel methyl-pyridone azo fluorescent tag (MPAFT) were proven through 1H (NMR), FT-IR, UV-vis, and high-resolution mass spectrometry. The quantum chemical parameters of MPAFT were evaluated using density functional theory (DFT) analysis. It was further investigated for its latent fingerprint (LFPs) in various surfaces and anticounterfeiting applications. By exposing Level I-Level III, ridge features to UV light with a wavelength of 365 nm, a bioimaging investigation has also demonstrated the potential of MPAFT's emission behaviour. The cyclic voltammetry (CV) and linear sweep voltammetry (LSV) at MPAFT/MGCE (modified glassy carbon electrode) were used to explore the electrochemical sensitivity and reliable detection of dopamine (DA) in neutral PBS (pH7) electrolyte solution, and the results show good sensitivity and detection. The lower detection limit for LSV was 0.81 μM under optimum conditions.

Anatomical features of the sphenoid ridge in the pediatric population.

To describe the relation of the sphenoid ridge (SR) with the surrounding anatomical structures in healthy children when approaching the anterior and middle fossae. Computed tomography of 180 pediatric patients (90 males / 90 females), aged 1-18 years were included the study. The size of the lesser wing (LW) and the distances of the LW to certain landmarks were measured. The LW length was 28.48 ± 8.15mm. The LW widths at the midline and the midpoint and lateral point of the SR were 7.78 ± 1.74mm, 2.84 ± 0.81mm, and 1.91 ± 0.64mm, respectively. The distance between the midpoint of the SR and the crista galli was 28.22 ± 5.56mm, and the distance between the crista alaris and internal auditory meatus was 51.73 ± 5.79mm. The linear function was calculated as y = 18.748 + 1.024 × age for SR length, y = 6.046 + 0.182 × age for the midline width of SR, y = 2.367 + 0.050 × age for the midpoint width of SR, y = 1.249 + 0.069 × age for the crista alaris width, y = 21.727 + 0.683 × age for the distance between the SR midpoint and the crista galli, and y = 43.614 + 0.855 × age for the distance between the crista alaris and internal auditory meatus. All measured parameters increased irregularly with advancing age. Furthermore, our regression equations representing the growth dynamics of SR may be used to estimate these parameters.

Classification of Blood Cells with Convolutional Neural Network Model

Among the blood cells, white blood cells (WBC), which play a crucial role in forming our body's defense system, are essential components. Originating in the bone marrow, these cells serve as the fundamental components of the immune system, shouldering the responsibility of safeguarding the body against foreign microbes and diseases. Insufficient WBC counts may compromise the body's skill to resist infections, a status known as leukopenia. White blood cell counting is a specialty procedure that is usually carried out by qualified physicians and radiologists. Thanks to recent advances, image processing techniques are frequently used in biological systems to identify a wide spectrum of illnesses. In this work, image processing techniques were applied to enhance the white blood cell deep learning models' classification accuracy. To expedite the classification process, Convolutional Neural Network (CNN) models were combined with Ridge feature selection and Maximal Information Coefficient techniques. These tactics successfully determined the most important characteristics. The selected feature set was then applied to the classification procedure. ResNet-50, VGG19, and our suggested model were used as feature extractors in this study. The categorizing of white blood cells was completed with an amazing 98.27% success rate. Results from the experiments demonstrated a considerable improvement in classification accuracy using the proposed CNN model.

Ultra-bright green emitting Sr2MgSi2O7:Tb3+ nanophosphors: unveiling multi-security applications in anti-counterfeiting, flexible films, optical thermometry and latent fingerprint visualization

In this study, we synthesized facile green-emitting Sr2MgSi2O7:Tb3+ (1–11 mol %) nanophosphors (SMSO:Tb3+ NPs) using a solid-state reaction method. Characterization through X-ray diffraction (XRD) revealed a tetragonal crystal structure in the as-synthesized SMSO:Tb3+ NPs. Crystallographic data obtained from Rietveld refinements identified the specific sites of Tb3+ ions in the host material. Chemical and photo-luminescent properties are explored using X-ray photo-electron spectroscopy (XPS) and Photoluminescence (PL) spectroscopy, respectively. The NPs exhibited vibrant green emission at 547 nm, attributed to the Tb3+ ions' 5D4→7F5 transition, with a high quantum yield of 63.10 %. Optimal doping concentration is determined as 7 mol % (SMSO:7 Tb3+), overcoming concentration quenching primarily due to dipole-dipole (d-d) interaction. Impressively, SMSO:7 Tb3+ NPs demonstrated a correlated colour temperature (CCT) of 5549 K, colour purity (CP) of 91.60 %, and excellent colour stability. These phosphors are effectively applied in latent fingerprints (LFPs) development, revealing distinct ridge features useful for personal identification. Additionally, SMSO:7 Tb3+ NPs, combined with soluble PVA, are used to create a transparent anti-counterfeiting (AC) film, exhibiting intense green emission under UV 365 nm light. Notably, the film flexibility, durability, and foldability enhance its applicability in AC techniques. This study showcases SMSO:Tb3+ NPs as versatile luminous platforms with applications in flexible displays, high-colour quality lighting, fingerprint (FP) visualization, and anti-counterfeiting.

Discovery of late Quaternary giant magnetofossils in the Bay of Bengal

Unusually large and enigmatic giant magnetofossils are well-documented in sediments deposited during and surrounding climatic events such as the Paleocene-Eocene Thermal Maximum (~56 Ma) and Middle Eocene Climatic Optimum (~40 Ma). However, the absence of giant magnetofossil modern analogues raises intriguing questions about their origin and formation mechanism. In this study, we report the discovery of giant magnetofossils within late Quaternary sediments retrieved from the Bay of Bengal (BoB), the youngest giant magnetofossils yet reported. First-order reversal curve diagrams of BoB sediments display a central ridge feature, a characteristic magnetic signature of non-interacting single domain magnetofossils. Scanning electron microscopy examinations reveal giant magnetofossils with needle, spindle, bullet, and spearhead shapes. We use high-resolution transmission electron microscopy, with electron diffraction and energy dispersive spectrometry, to confirm their distinctive morphologies and magnetite crystal structure. Our environmental magnetic and geochemical data suggest that an influx of fluvially derived reactive iron and organic carbon, combined with persistent suboxic conditions, supported the proliferation of giant magnetofossil-producing organisms. The discovery of a modern analogue to ancient giant magnetofossils expands our understanding of their temporal distribution within the geological record and is a promising step toward elucidating the nature of the organisms responsible for their formation.

Study of the fingerprints of a Spanish sample for the determination of the hand and finger

The individuality and permanence of fingerprints make of them a very useful feature in the identification of individuals. There are now automated computer programmes that allow a quick comparison between a fingermark and a database. However, in order to assess the strength of evidence in fingerprint identification, complementary information on the frequencies of the different morphological features of the dermopapillary ridges is required. This idea is used in this work as a starting point to evaluate the frequencies of the parameters used in the determination of the hand and finger in a large sample of 2600 fingerprints taken from 134 male and 127 female Spanish population. Based on these fingerprints, the frequencies of different categories of the following parameters were obtained: type of pattern, slope of the apex ridge, subtype of two-delta pattern, ridge tracing, major angle, major ridge count, bisector, rotation of the central ridge, assimilation to loops and slant. Moreover, the results have shown that these characters are useful for the determination of the hand in whorls (two-delta pattern) and loops (one-delta pattern), but not for the determination of the finger. The most useful and classificatory parameter when determining the hand of origin of a two-delta fingerprint is the slope of the apex ridge, and for the one-delta pattern, knowing the location of the delta allows the correct estimation of the hand of a fingerprint in more than 93% of the cases. The data presented in this paper are novel and can be used by latent print examiners to improve the statistical basis of their decisions in reaching conclusions.

A segmentation network for farmland ridge based on encoder-decoder architecture in combined with strip pooling module and ASPP.

In order to effectively support wheat breeding, farmland ridge segmentation can be used to visualize the size and spacing of a wheat field. At the same time, accurate ridge information collecting can deliver useful data support for farmland management. However, in the farming ridge segmentation scenarios based on remote sensing photos, the commonly used semantic segmentation methods tend to overlook the ridge edges and ridge strip features, which impair the segmentation effect. In order to efficiently collect ridge information, this paper proposes a segmentation method based on encoder-decoder of network with strip pooling module and ASPP module. First, in order to extract context information for multi-scale features, ASPP module are integrated in the deepest feature map. Second, the remote dependence of the ridge features is improved in both horizontal and vertical directions by using the strip pooling module. The final segmentation map is generated by fusing the boundary features and semantic features using an encoder and decoder architecture. As a result, the accuracy of the proposed method in the validation set is 98.0% and mIoU is 94.6%. The results of the experiments demonstrate that the method suggested in this paper can precisely segment the ridge information, as well as its value in obtaining data on the distribution of farmland and its potential for practical application.

An empirical analysis of feature selection techniques for Software Defect Prediction

&lt;p&gt;Detecting software defects before they occur is crucial in software engineering as it impacts software system quality and reliability. Previous studies on predicting software defects have typically employed software features, such as code size, complexity, coupling, cohesion, inheritance, and other software metrics., to forecast whether a code file or commit is prone to defects in the future. However, it is advantageous to restrict the number of features employed in a defect prediction model to avoid the challenges associated with multicollinearity and the “curse of dimensionality” and to simplify the data analysis process. By using a reduced number of features, the defect prediction model can concentrate on the most significant variables and improve its accuracy. This research paper investigates the impact of eight feature selection methods on the accuracy and stability of six supervised learning models. This study is novel as it is based on exhaustive experimentation of each of the eight feature selection techniques with each of the six supervised learning models. Two notable findings have been obtained. First, we discovered that the association and coherence-based techniques have demonstrated the highest level of accuracy when it comes to defect prediction. The models that utilized these selected features outperformed those using the original features. Second, the feature selection techniques, namely Correlation feature selection, Recursive feature elimination, and Ridge feature selection when combined with the Support vector machine and Decision tree classifier, consistently selected low-variance features across multiple supervised defect prediction models. When combined with different classifiers, these techniques achieved exceptional performance on the publicly available NASA datasets CM1 and PC2. The findings revealed a remarkable accuracy rate of over 85% for CM1 and 95% for PC2, accompanied by precision, recall, and f-measure values exceeding 95%. These exceptional results indicate the achievement of the highest level of performance in the evaluation.&lt;/p&gt;

Multi-dimensional approach for interpreting the structure of barrier island morphology

Barrier islands are complex polygenetic landscapes, but contemporary models used to extract and visualize morphometric patterns tend to characterize them as two-dimensional landforms, leading to an idealized morphology in which the dune crest, dune toe, and dune heel are viewed as alongshore continuous features. A new exploratory approach to the extraction of topographic features explicitly recognizes the complex nature of barrier island morphology using surface normal vectors of the continuous surface. Complex alongshore and cross-shore ridge and swale features not visible in traditional elevation models are extracted using the directional components of the vectors. This transferable approach accounts for cross-shore and alongshore features and does not depend on a requirement of an alongshore continuous landform and provides a more objective approach to the extraction of beach and dune morphology by recognizing the polygenetic history of barrier islands.

Naive Bayesian classifier target recognition based on ridge features

Traditional time-frequency algorithms do not have a high feature recognition rate for targets in a low signal-to-noise background, and the number of operations for classification using plain Bayes is large and slow. To solve the above problems and achieve recognition of different ship echo signals in water, this paper proposes a target recognition method based on time-frequency ridge features by using collected audio data of different ship types, combined with Fourier Simultaneous Compression Transform (abbreviation: FSST), starting from time-frequency features, by extracting the FSST time-frequency ridge and finally using the Hopper Parsimonious Bayes classifier to obtain respectively. The recognition rate of ships under FSST and the recognition rate of ships based on ridge line features are obtained respectively. The final comparison shows that the correct recognition rate of the plain Bayesian classifier using the spine features of the time-frequency map is much higher than that of the conventional FSST time-frequency method, and it also outperforms the FSST in terms of computational power and speed.

Ancient silicic volcanism and incipient rift propagation during Gondwana breakup; geophysical insights from the Bumbeni ridge, South Africa

A detailed understanding of the association between magmatism and rift initiation is key to defining the prevailing tectonic conditions active during the initial break-up of Gondwana. The Bumbeni Complex in northern KwaZulu-Natal, South Africa represents an early Cretaceous silicic volcanic centre exposed at the southwestern termination of a linear zone of magnetic anomalies which are buried beneath sedimentary cover. Newly acquired high resolution aeromagnetic data combined with legacy seismic and borehole data is analysed to define the structural architecture of the basement beneath the Zululand Basin in northern KwaZulu-Natal. Multiple prominent positive and negative anomalies ranging from −200 nT to +300 nT based on International Geomagnetic Reference Field (IGRF) and corrected Reduced to Pole (RTP) data are resolved and form a distinct geophysical lineament. Geophysical and borehole core data indicate that the lineament represents a chain of silicic central volcanoes developed as a ridge feature, the Bumbeni Ridge, along the axial zone of an incipient rift structure. Detailed geological mapping and high resolution geophysical data are analysed and compared with analogous contemporary continental rift environments, as well as published data within the adjacent offshore Northern Natal Valley. Results are presented which propose a continental rift evolutionary model for Gondwana breakup in the Zululand Basin in northern KwaZulu-Natal.

Investigating the influence of mono-, di-, and trivalent co-dopants (Li+, Na+, K+, Ca2+, Bi3+) on the photoluminescent properties and their prospective role in data security applications for SrAl2O4:Tb3+ nanophosphors synthesized via an eco-friendly combustion method

Nanostructured SrAl2O4:Tb3+/M (M = Li+, Na+, K+, Ca2+, Bi3+) green nanophosphors (NPs) were synthesized through an environmentally friendly combustion process, employing Areca nut (A.N) as a sustainable fuel source. Alkali metal co-dopants were introduced to optimize luminescent intensity and assess their suitability for data security applications. Photoluminescence (PL) intensity increased with rising Tb3+ concentrations (1–5 mol %) until reaching a maximum at 3 mol %, after which quenching occurred. The phenomenon of Tb3+ concentration quenching at 3 mol % was explained using the Van Uitert equation, which accounts for dipole–dipole interactions. To enhance luminescence efficiency, various metal ions, including Li+, Na+, K+, Ca2+, and Bi3+, were explored. Notably, SrAl2O4:Tb3+ (SAO:Tb3+) co-doped with Li+ ions exhibited the highest PL intensity, surpassing that of Tb3+ by a factor of 41. The estimated CIE value for green emission are x = 0.3541 and y = 0.5562. It is observed that the CIE coordinates of Tb3+ activated SAO NPs are situated in the green region. The CCT value of SAO:Tb3+/Li+ was found to be 5117 K, which is useful for cool LEDs. The Li+ doped samples proved exceptionally effective in latent fingerprints (LFPs) detection, revealing level I-III ridge features crucial for personal identification. Under NUV light, the PDMS wrapping of the SAO:Tb3+/Li+ NPs accomplished a green glow. Based on the results, SAO:Tb3+/Li+ green-emitting phosphors is a promising material for white LEDs, optical temperature sensors, and flexible display applications. This comprehensive study underscores the outstanding multifunctional potential of Li+ co-dopants among the various ions considered.

Transforming human hair fibers into carbon dots: Utilization in flexible films, fingerprint detection, counterfeit prevention and Fe3+ detection

Creating environmentally-friendly fluorescent carbon quantum dots (HCQDs) from human hair is a straightforward process with versatile applications. These HCQDs, derived from non-toxic hair, are produced through a one-step microwave (MW) method and have gained increasing attention due to their exceptional properties. Despite the availability of various advanced HCQDs, their full potential remains untapped. Thanks to their remarkable optical characteristics, HCQDs serve as highly sensitive nano-probe agents for the fluorometric measurement of Fe3+ ions. The fluorescence (FL) intensity of the nano-probe exhibits a linear correlation with Fe3+ ion concentration ranging from 0 to 50 μM, boasting a limit of detection (LoD) of approximately 0.13 μM. This sensitivity is achieved through resonance energy/electron transfer-induced FL quenching. Remarkably, carbon dots (CDs) obtained from human hair function as ink-free patterned substrates, making them ideal for printing and the nondestructive collection and detection of latent fingerprints (LFPs). Immersing LFPs in HCQDs@poly(vinyl alcohol) (HCQDs@PVA) solution yields flexible, transparent films with consistent luminescent LFPs and clear ridge features for accurate personal identification. Importantly, this technique allows for the lifting and identification of long-term exposed LFPs from various surfaces without causing any damage. The segregation of HCQDs within the polymer matrix during film formation is the mechanism responsible for LFP collection and visualization. Overall, HCQDs@PVA composites exhibit outstanding performance, suitable for applications in fluorescent patterns, flat panel displays, LFP analysis, and anti-counterfeiting (AC) labeling.

Designing energy transfer-based color-tunable, information-encrypting, luminous hydro-gels, and latent fingerprint detection systems using BaLa2ZnO5:Tb3+, Bi3+ nanophosphors

Synthesizing cost-effective blue-green emitting BaLa2ZnO5:Tb3+, Bi3+ nanophosphors (BLZO:Tb3+, Bi3+ NPs) through an eco-friendly urea-aided combustion method. Thorough examination of phase purity, structure, surface characteristics, and luminescent properties of the resulting products reveals a robust blue-green emission linked to the 5D4 → 7F5 transition of Tb3+ under 365 nm UV excitation, as well as evident energy transfer from Bi3+ to Tb3+ within the co-doped BLZO, discussed in detail. Optimal doping concentrations are determined to be 5 mol% for Tb3+ and 0.3 mol% for Bi3+ in BLZO phosphor, with non-radiative transition mechanisms explaining concentration quenching. BLZO:Tb3+ NPs emits green light, while co-doping with Bi3+ shifted to the blue-green region. The optimized phosphor is employed for latent fingerprint (LFP) development on porous, semi-porous, and non-porous surfaces, with clear visualization under 365 nm UV light. The generated fingerprints (FPs) display level I–III ridge features with exceptional sensitivity, selectivity, and contrast. The luminescent hydro-gels exhibit outstanding photo-stability, chemical stability, and long-term durability, making them ideal for anti-counterfeiting (AC) applications. Furthermore, BLZO:Tb3+, Bi3+ NPs demonstrate the ability to mitigate oxidative stress and thrombosis, evidenced by their DPPH scavenging activity and their capacity to normalize stress indicators in Red Blood Cells (RBC) exposed to NaNO2-induced oxidative stress. These NPs also reduce ADP-induced platelet aggregation. Collectively, these results highlight the effective utilization of the optimized phosphor in display devices, advanced forensics, anti-counterfeiting measures, and biomedical applications.

Boruta based feature selection model for heart disease prediction

In today’s time the rate of heart disease is increasing at a very fast pace and because of that it is becoming the reason for major cause of deaths worldwide. It is very important to give treatment for heart disease or predict any such disease beforehand but there are some medical centers where experts lack appropriate or fair expertise to diagnose and treat the patient on time. So often they assume their readings and as a result, poor outcome is shown which sometimes lead to death of the patient. This paper identifies the relevant attributes of heart diseases using Boruta, Lasso and Ridge feature selection method. It also presents valuable insight on effectiveness of various machine learning algorithms to predict heart disease. The feature selection method reduces number of features and at the same time maintaining comparable accuracy of the model. Experimental results demonstrate that Boruta feature selection with Random Forest classifier outperforms all the other state-of-art methods used in this study.

Tailored auto-combustion synthesis and optical and structural characterization of TiO2:Dy3+(1–11 mol%) nanostructures for wLED and latent finger print applications

Un-doped and dysprosium (Dy3+) doped (1–11 mol%) titanium dioxide (TiO2) nanoparticles (NPs) were synthesized by low temperature solution combustion route. The structural, morphological and nanostructural characterization of NPs were performed by powder x-ray diffraction (PXRD), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM) analysis, respectively. Rarely found rutile to anatase phase transition was observed by Dy3+ doping in TiO2 NPs using PXRD. The energy gap (Eg) of un-doped and Dy3+ doped TiO2 NPs were estimated by diffused reflectance spectra (DRS) between 2.92 and 3.07 eV. The active vibrational Raman modes of pure TiO2 were observed at 141, 446 and 608 cm−1. The mode alleged at 229 cm−1 indicates the rutile phase of TiO2 caused by second order effect. Studies of CIE and CCT confirmed that the NPs may be used in wLED applications. A rise of asymmetry ratio from 1.326 to 1.3453 with dopant concentration was observed in the photoluminescence (PL) spectra. The optimized TiO2:Dy3+ (9 mol%) nanostructures were used for the visualization of latent finger prints (LFPs) on porous and non-porous surfaces from level I to level III of ridge features.