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Pulmonary tuberculosis (TB) causes progressive and irreversible damage to lung tissue, a damage that may not fully resolve after treatment. Mycobacterial vesicles (MVs), which are poorly understood, may contribute to TB pathology. This study investigated the effects of stress, such as treatment with conventional TB antibiotics rifampicin, isoniazid, ethambutol, or treatment with an antimycobacterial peptide (NZX), on mycobacterial vesiculation. Stress from minimal inhibitory concentrations of antibiotics, or peptide all increased MV formation. Electron microscopy and lipid profiling revealed that these vesicles, about 40 nm in size, were released from the bacterial inner membrane and consisted of apolar lipids. Using mass spectrometry, the study identified key differences in MVs protein cargo dependent on the antibiotic used, especially with ethambutol-induced MVs that contained proteins from several mycobacterial pathways. Additionally, toxicology analysis using different concentrations of MVs on primary human macrophages and the monocytic cells indicated that MVs from the different treatments were not toxic to human cells, however induced specific inflammatory profiles. In conclusion, this study identified mycobacterial vesicles to be a potential contributor to tuberculosis pathology. [ABSTRACT FROM AUTHOR]

Traditional IN-718 superalloy inspires the novel AlCrFeMoNbNi high-entropy alloy developed in this work. Major constituents of the IN-718 are selected in equiatomic proportions and processed via vacuum arc melting route. Alloy in the as-cast and heat-treated (1373 K for 24 h and 1373 K for 24 h + 1473 K for 48 h) conditions showed a multi-phase structure (BCC1, B2, Sigma, and BCC2) with high hardness of 11.94–9.08 GPa at indentation loads of 25–5000 g. Further, a significant indentation size effect (ISE) has been observed, which was confirmed by the Meyer's power law. Strain gradient plasticity theory based on geometrically necessary dislocations, proposed by Nix and Gao, was adapted to understand the ISE. Further, depth-independent hardness values of 9.30 ± 0.9, 9.26 ± 0.9, and 10.41 ± 0.9 GPa and characteristic length scales of 0.52 ± 0.01, 0.53 ± 0.01, and 0.25 ± 0.02 µm were evaluated at different processing conditions in the current work. At 5000 g. load, KIFT values (in MPa m) of 4.68, 6.61 and 6.29 were realized for as-cast, single heat-treated and double heat-treated conditions respectively. [ABSTRACT FROM AUTHOR]

The next-generation of batteries need be both energy dense and environment friendly. Lithium sulfur batteries (LSBs) satisfy both criteria but their practical implementation is marred by the highly resistive nature of sulfur. Carbon-based cathodes play a vital role in mitigating the issue because their high conductivity allows for effective electron transfer during electrochemical cycling. Synthesis and electrochemical evaluation of carbon-based cathodes from two different sources for LSBs was carried out. Herein, two kinds of carbon, namely bio-derived carbon from coconut shells (CC500) and N-doped carbon (NC) from polyacrylonitrile fibers were synthesized and sulfur was incorporated via the melt diffusion route. The composites are characterized by PXRD and TGA, which determined 80 wt% mass loading of sulfur. The higher intensity of G-band over D-band in Raman spectroscopy indicates greater graphitic character for CC500 compared to NC. SEM images show large macro-pore like tunnels in CC500 while NC appears are irregular chunks. EDAX spectra showed 20 wt% N content in NC while CC500 is largely carbon with some minor surface oxygen. In galvanostatic charge–discharge cycling of coin cells, bare CC500/S shows better specific capacity compared to NC/S samples but the trend flips once a separator modified with 4 mg of graphene oxide (GO) is introduced (indicated as NC/S/GO4 and CC500/S/GO4). This points towards synergy between N-doped carbon and GO layer in retaining the soluble polysulfides in the catholyte region. NC/S/GO4 exhibited better capacity i.e., 1453, 1024, 866, 787, 697 mAh/g versus 1016, 779, 672, 551, 441 mAh/g offered by CC500/S/GO4 when discharged at 50, 100, 200, 300 and 500 mA/g, respectively. [ABSTRACT FROM AUTHOR]

Seaweed endophytes are a rich source of microbial diversity and bioactive compounds. This review provides a comprehensive analysis of the microbial diversity associated with seaweeds and their interaction between them. These diverse bacteria and fungi have distinct metabolic pathways, which result in the synthesis of bioactive compounds with potential applications in a variety of health fields. We examine many types of seaweed-associated microorganisms, their bioactive metabolites, and their potential role in cancer treatment using a comprehensive literature review. By incorporating recent findings, we hope to highlight the importance of seaweed endophytes as a prospective source of novel anticancer drugs and promote additional studies in this area. We also investigate the pharmacokinetic and pharmacodynamic profiles of these bioactive compounds because understanding their absorption, distribution, metabolism, excretion (ADMET), and toxicity profiles is critical for developing bioactive compounds with anticancer potential into effective cancer drugs. This knowledge ensures the safety and efficacy of proposed medications prior to clinical trials. This study not only provides promise for novel and more effective treatments for cancer with fewer side effects, but it also emphasizes the necessity of sustainable harvesting procedures and ethical considerations for protecting the delicate marine ecology during bioprospecting activities. [ABSTRACT FROM AUTHOR]

If dynamic loads propagate in the rock mass, they can generate vibrations or cyclic movements along the joints with different frequency and displacement. Hence, evaluation of rock joints behaviour under different cyclic movements significantly contributes to increasing the safety and stability of rock mass. In this study a series of cyclic shear tests was conducted on soft synthetic rock joints (with asperity angle of 30°- 30°) by shear apparatus to investigate the effects of shear load frequency and amplitude of displacement on the joint. Varying load frequencies (0.01, 0.05 and 0.1 Hz,) and displacement amplitudes (± 4, ± 6, and ± 8 mm) for the three different normal stresses (0.1, 0.5 and 1 MPa) were applied. The results indicated that the first peak shear stress on the joint was significantly affected by the frequency of cyclic shear load and the subsequent peaks of shear stress were largely influenced by the amplitude of displacement. Reduction of post peak shear stress was significant for the first cycle as the load frequency increased. For low normal stress, shear load frequency of 0.05 Hz and one-third of asperity base length as displacement were the certain values that shear mechanism changed on slopes of the joint asperities. Shear mechanism also demonstrated that asperity diminishing did not depend on load direction at high frequency. Whereas, at low frequency of loads, it depended on the direction of starting cyclic load. Comparison of shear strength envelopes indicated an opposite behaviour of the joint between load frequencies and displacement amplitudes. [ABSTRACT FROM AUTHOR]

Rising population and urban expansion have ushered drastic transformations in land use patterns, which can considerably affect soil components and processes. Peri-urban regions at the rural-urban interface are of specific concern because of their dynamic and complex land use pattern. Here, we investigated the spatial variability in soil physicochemical parameters of the surface soil (0–30 cm) in peri-urban landscape of Ghaziabad district, located in the upper Gangetic plain of Uttar Pradesh, India. A total of 45 sites belonging to five different land use classes namely agriculture, park, residential, industrial and bare land were sampled from December 2019 to January 2020. Descriptive analysis of data showed wide variability in the soil properties, with chemical properties being more variable than physical properties. We observed significant differences in soil properties across land use types and their heterogeneous distribution across the three sites. Industrial land use with low vegetation cover was distinctly differentiated from other anthropogenic land use and land cover by high bulk density. Residential land use with high nutrient input from domestic waste and physical disturbance from construction activities was separated from other overlapping land use types by high water-holding capacity and calcium (Ca) concentration. Overall, the results of the study suggest that multifaceted anthropogenic disturbances including modification of parent material, extensive fertilization and irrigation, transportation and waste dumping entail significant changes in soil characteristics and may have serious implications on soil structure and function. Such regional and local studies would help in developing scientifically informed practical measures to improve and maintain the soil quality. Hence, further detailed research exploring the impact of anthropogenic land uses on soil characteristics is needed for management and preservation of the soil mantle. [ABSTRACT FROM AUTHOR]

Actinobacteria emerge as significant producers of bioactive compounds. These compounds exhibit diverse beneficial properties, such as antioxidative, anti-inflammatory, anticancer, antimicrobial, and antiviral attributes. Hot springs, with their high temperatures and unique geochemical conditions, serve as fascinating ecosystems harboring diverse microbial life. In these environments, actinomycetes thrive, utilizing the available organic matter and nutrients. The extreme conditions, characterized by high temperatures and pH levels, likely exert selective pressure, favoring the survival of resilient microorganisms like actinomycetes. This study specifically identified Actinomycetalesbacterium spp. (Genbank ID: OP863023), isolated from Rajgir hot spring, as a noteworthy producer of a brown pigment at 43 °C. This isolated strain is known to produce various metabolites identified through GC–MS analysis. Following ADME/T screening of all the metabolites, Diethyl Phthalate (DEP) stands out among those produced by the Actinomycetales bacterium species. The isolated strain demonstrated in vitro antibacterial activity, inhibiting Listeria monocytogenes MTCC1143 with a 10-mm zone of inhibition, while the reference Ampicillin disc showed a 15 mm inhibition zone. In the in silico analysis targeting the Internalin A protein of L. monocytogenes, the Actinomycetales-produced compound DEP displayed an inhibition potential of − 5.4 kcal/Mol, while the reference drug Ampicillin exhibited a higher inhibition potential of − 7.4 kcal/Mol. These findings suggest that the isolated actionobacteria holds promise for antibacterial applications, particularly against L. monocytogenes. [ABSTRACT FROM AUTHOR]

COVID-19 all over the world has given an option to employees to work from home. As a result, the number of computer users has increased drastically. According to international market tracker Data Corporation, in 2020, the sales of computer devices exceeded 302 million. The survey conducted on computer users indicates that there was increase in neck pain and back pain. The increase in musculoskeletal disorder is mainly due to bad ergonomic design of computer workstation. The present work is focused on design of computer user chair based on Indian anthropometric standard data. Three different kinds of chair have been modeled in CATIA V5. The bio-mechanical analysis and rapid upper limb assessment analysis were carried out. The structural analyses of chairs have been carried out in ANSYS. The results showed that the chairs were structurally strong for static condition. [ABSTRACT FROM AUTHOR]

The current study aims to determine the optimal ferrite composition for a substrate material with good magneto-dielectric properties and low losses in order to fabricate high-frequency miniaturized antennas. Cobalt–zinc ferrite samples with different concentration of zinc were synthesized using the sol–gel method and polyvinyl alcohol as the chelating agent. The lattice constant obtained through experimentation agrees with the one obtained from the suggested cation distribution and Rietveld refinement, indicating that zinc has entered the tetrahedral sites of the spinel structure. A higher zinc concentration was associated with a lower average grain size, ranging from 96.7 to 62.4 nm. The investigation will examine complex permittivity, complex permeability, dielectric and magnetic loss tangents, and AC conductivity in order to evaluate the effects of frequencies between 1 MHz and 2 GHz. The material with the composition Co0.4Zn0.6Fe2O4 exhibits low AC conductivity (1.28 × 10−6 S/cm), low values of magnetic and dielectric loss tangents (0.047, 0.013) at 100 MHz frequency, and a stable miniaturization factor (n = 4.2) over a broad frequency range of 10 MHz to 1 GHz. The persistent magneto-dielectric behavior and low-loss properties of cobalt–zinc nanoferrite confirm that it is a suitable substrate material for high-frequency antenna design. [ABSTRACT FROM AUTHOR]

Capacitive sensor is best suitable sensor for the fabrication. In this study, we used Finite Element Method (FEM) tool to design and simulate capacitive pressure sensor based on the nob-linearity and sensitivity aspects. The sensor consists of parallel plates which is electrodes made up by optimizing two sensing devices that is microbridge and micro-cantilever with three different materials aluminium, gold and platinum. Among those aluminium is better due to its non-linearity and cost. The suitable sensing element is integrated into pressure sensor in order to measure the variation in glucose concentration levels. [ABSTRACT FROM AUTHOR]

Traffic safety has been a significant concern for ever-increasing injuries and fatalities. The advancement in technology and ITS infrastructure implementation has made traffic data collection and storage easier. This data can be further utilized to identify and suggest measures in preventing potential crashes. Despite the availability of huge vehicle trajectory data sets, assessing potential conflicts by segregating all the leader-follower (LF) pairs often becomes a tedious task. Heavy braking (HB) is a variable that is narrowly explored as a crash surrogate for safety assessment. In this study, the empirical vehicular trajectories are utilized to analyze HB events. HB events are based on the deceleration values of vehicles in the stream exceeding a chosen threshold. The vehicle trajectories are also used to estimate Deceleration Rate to Avoid Collision (DRAC) as a Surrogate Safety Measure (SSM). The empirical microscopic trajectory data for three cities in India, namely, Delhi, Surat, and Chennai, are utilized in this study. This study hypothesized that DRAC events are highly correlated with the heavy braking events within the stream. Thus, this paper attempts to understand the correlation between DRAC and HB events. The R2 value at each site is greater than 0.80, thus suggesting a strong correlation between the heavy braking events and the conflicting DRAC events. HB events can be used as a surrogate safety measure in identifying potential crashes or near misses. [ABSTRACT FROM AUTHOR]

Rice, serving as a crucial staple food for more than half of the global population, confronts significant challenges from insect pests, particularly the rice leaf folder (Cnaphalocrocis medinalis), which leads to substantial yield losses. This study meticulously evaluates 94 rice landraces for leaffolder resistance under diverse environmental conditions usisng genotype × environment (G × E) interaction analysis. The additive main effects and multiplicative interaction model and genotype and genotype × environment interaction (GGE) biplot are employed to assess stability and resistance traits. The findings reveal that 44 genotypes demonstrate stable resistance across three test environments (Kharif, 2021, Rabi, 2021–2022 and Kharif, 2022). Resistance is primarily governed by genotypic factors (89.93%), with minimal influence from the environment (5.22%). The GGE biplot highlights distinct mega-environments, identifying Kharif, 2021, followed by Kharif, 2022, as the ideal test environments. This study identifies specific stable resistant genotypes that could prove valuable for sustainable pest management and contribute to further crop improvement programs. [ABSTRACT FROM AUTHOR]

Strawberries are highly perishable, leading to significant postharvest losses and reduced fruit quality during storage and distribution. Conventional preservation methods are often insufficient, necessitating the development of innovative and natural approaches to improve the longevity and quality of these fruits. This study investigates the use of sericin, chitosan, and Aloe vera blended formulation (SBF) coating to extend the shelf life and quality of strawberries. The coated strawberries were stored at 14 ± 1 °C and 40–45% humidity, with their properties monitored over 12 days. Scanning electron microscopy (SEM) and fourier transform infrared (FTIR) spectroscopy analyses of strawberries confirmed the uniform application and chemical interaction of the coating, while contact angle measurements demonstrated improved wettability (contact angle 50.73 ± 0.6 °). The SBF coating significantly reduced weight loss and maintained higher firmness i.e. with only 7 ± 1.14% weight loss resulted after 9 days compared to a 16.5 ± 0.7% weight loss in uncoated strawberries. It also preserved higher levels of total soluble solids and titratable acidity compared to uncoated strawberries. Additionally, the SBF coating effectively slowed moisture loss, prevented infection, and regulated pH and respiration rate, demonstrating its potential for postharvest preservation of strawberries. [ABSTRACT FROM AUTHOR]

Emotional speech has some dependency on language or within a language itself, there are certain variations due to accents. The presence of accents degrades the performance of the speech emotion recognition (SER) system. A pre-trained accent identification system (AID) could effectively capture the characteristics of accent variations in emotional speech which is an important factor to develop a more reliable SER system. In this work, we investigate the dependencies between accent identification and emotion recognition to enhance the performance of SER. This paper proposes a novel transfer learning-based approach utilizing accent identification knowledge for SER. In the proposed method, the deep neural network (DNN) is used to model the accent identification system, which uses statistical aggregation functions (mean, std, median, etc.,) of spectral subband centroid (SSC) features and Mel-frequency discrete wavelet coefficients (MFDWC). To build the SER, the deep convolutional recurrent autoencoder produces the attention-based latent representation, and the acoustic features are extracted by the openSMILE toolkit. A separate DNN model is used to learn the mapping between attention features and acoustic features for SER. In addition, the a priori knowledge of accent can lead the SER to effect the improvement which is possible through transfer learning (TL). The performance of the proposed method is assessed using the accented emotional speech utterances of the Crema-D dataset and also compared with state-of-the-art techniques. The experimental results show that transferring AID learning improves the recognition rate of the SER and results in around 8% relative improvement in accuracy as compared to the existing SER techniques. [ABSTRACT FROM AUTHOR]

The synthesis and the structural and luminescence studies of samarium-doped calcium aluminate phosphors are reported. These phosphors are synthesized by the solid-state reaction method with a variable concentration of doping ions (0.5–3.0 mol%). The synthesized phosphors are characterized by the X-ray diffraction (XRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) techniques. The XRD studies show that the doped phosphors have a cubic structure. The SEM images reveal that the Sm3+- doped Ca3Al2O6 phosphor represents a combination of some rod-like and flower-like structures. The FTIR studies confirm the formation of a Ca3Al2O6:Sm3+ phosphor. The photoluminescence (PL) emission measurements upon excitation by 273 nm (ultraviolet light) show the characteristic emission peaks of Sm3+ at 579, 589, 600 and 619 nm. The characteristic orange-red emission is observed for samarium ion. The excitation spectra of the Ca3Al2O6:Sm3+ phosphor mainly consist of a more intense charge transfer band (CTB) of Sm3+ located at 273 and 395 nm with some peaks centred at 319 and 362 nm. The CIE coordinates are calculated by the spectrophotometric method using the spectral energy distribution of the Ca3Al2O6:Sm3+ sample. The prepared phosphors can act as a single host for orange-red light emission in display devices. [ABSTRACT FROM AUTHOR]

Landslide susceptibility zonation mapping is important to identify the places susceptible to landslides according to the failure probability of slopes. Recently, several techniques for assessing the landslide susceptibility zone (LSZ) have been documented with a unique evaluation and validation strategy. Functionally, in LSZ mapping, susceptibility predictions are typically generated in terms of probabilities and likelihoods. The novelty of the current study is a hybrid integration of analytical hierarchy process (AHP) and multi objective optimization on the basis of ratio analysis (MOORA) multi-criteria decision-making to prepare landslide susceptibility zones of Aizawl district of the state of Mizoram, India. The region falls under the tectonically active belt of the Himalayas which makes it landslide prone. Eight morphometric indices, including terrain surface texture, topographic wetness index, slope steepness and length (LS) factor, terrain surface convexity, topographic openness, topographic ruggedness index, morphometric protection index, and stream power index, have been chosen for preparing LSZ after being examined by a multi-collinearity test using variance inflation factors and tolerances. As a result, AHP subjective weighting with an acceptable consistency ratio was used to solve the criteria weight selection problem, and MOORA for weighted overlay with inverse distance weighting interpolation was used. The landslide susceptibility raster was categorised into five landslide susceptibility zones by the natural breaking classification system. As a result, the district comprises a very high landslide susceptibility zone about 8.51%, a high landslide susceptibility zone about 28.30%, a moderate landslide susceptibility zone about 35.75%, a low landslide susceptibility zone about 23.29%, and a very low landslide susceptibility zone about 4.15%. The significance of the study is that integration of AHP and MOORA has shown very high accuracy at 0.981 and the same was evaluated using the ROC (receiver operating characteristics) curve. Overall, the use of the AHP approach in combination with certain other MCDM models, like a hybrid model to develop LSZs, is suggested as the fundamentals of participatory decision-making in AHP for land-use design and landslide hazard management. [ABSTRACT FROM AUTHOR]

In this paper, a wide stopband low-pass filter (LPF) is proposed using the one-cell triangle and cascaded triangle defected ground structure (DGS). In this proposed structure, no coupling load is applied. The performance characteristics of the proposed low-pass filter were studied in both electromagnetic and equivalent simulations for the one-cell and cascaded triangle DGS. The equivalent circuit of the proposed low-pass filter is extracted with the help of electromagnetic (EM) simulation. The proposed low-pass filter is analyzed with both EM and equivalent simulations using the HFSS and ADS FEM tools, respectively. The performance characteristics of the proposed one-cell triangle arrowhead, such as cut-off frequency, stopband attenuation at the resonance frequency, passband width, stop bandwidth, and insertion losses, are 14 GHz, − 29 dB, 11.4 GHz, 15.5 GHz, and < 0.2 dB, respectively. Additionally, the performance characteristics of the 3-cell triangle arrowhead, such as cut-off frequency, stopband attenuation at the resonance frequency, passband width, stop bandwidth, and insertion losses, are 17.4 GHz, − 28 dB, 13.5 GHz, 20.1 GHz, and < 0.12 dB, respectively. The proposed structure shows good agreement between both EM and equivalent simulations. With this, the proposed LPF structure is utilized in wide stopband applications for satellite and radar communications. [ABSTRACT FROM AUTHOR]

This study explores the therapeutic potential of phytochemicals derived from Morus alba for colorectal cancer (CRC) treatment. Colorectal cancer is a global health concern with increasing mortality rates, necessitating innovative strategies for prevention and therapy. Employing in silico analysis, molecular docking techniques (MDT), and molecular dynamics simulations (MDS), the study investigates the interactions between Morus alba-derived phytochemicals and key proteins (AKT1, Src, STAT3, EGFR) implicated in CRC progression. ADME/T analysis screens 78 phytochemicals for drug-like and pharmacokinetic properties. The study integrates Lipinski's Rule of Five and comprehensive bioactivity assessments, providing a nuanced understanding of Morus alba phytoconstituent's potential as CRC therapeutic agents. Notably, 14 phytochemicals out of 78 emerge as potential candidates, demonstrating oral bioavailability and favorable bioactivity scores. Autodock 1.5.7 is employed for energy minimization followed by molecular docking with the highest binding energy observed to be − 11.7 kcal/mol exhibited by Kuwanon A against AKT1. Molecular dynamics simulations and trajectory path analysis were conducted between Kuwanon A and AKT1 at the Pleckstrin homology (PH) domain region (TRP80), revealing minimal deviations. In comparison to the standard drug Capivasertib, the phytochemical Kuwanon A emerges as a standout candidate based on computational analysis. This suggests its potential as an alternative to mitigate the limitations associated with the standard drug. The research aims to provide insights for future experimental validations and to stimulate the development of Kuwanon A as a novel, effective therapeutic agent for managing colorectal cancer. [ABSTRACT FROM AUTHOR]

Lithium-Sulfur batteries with high sulfur loading cathodes are highly desirable to achieve greater energy density. In this work, a high loading composite cathode is reported that is made by scalable vacuum filtration route, eliminating the need for a binder, conducting carbon, and metallic current collector. Moreover, capacity fade due to peeling off of coated active material from current collector is avoided, leading to excellent capacity retention. The coated separator affords a porous framework to (i) accommodate volume changes during cycling, (ii) allow higher sulfur loading, (iii) allow electrolyte wetting of active material in addition to (iv) functioning as a traditional separator and current collector by pressing against stainless steel casing. The MXene/reduced graphene oxide/S8 composite with an areal loading of 2.52 mg/cm2 exhibits a specific capacity of 495 mAh/g with a capacity retention of 97.5% after 200 cycles at a charge/discharge rate of 100 mA/g. At 500 mA/g discharge rate, an initial specific capacity of 378 mAh/g was obtained with 239 mAh/g retained after 750 cycles. [ABSTRACT FROM AUTHOR]

Occupancy sensing is one of the integral parts of modern evolving security surveillance and monitoring systems used over different types of infrastructure. With multiple forms of occupancy sensors, the prime idea of occupancy sensing is to identify the presence or absence of occupants in a precisely monitored area, followed by transmitting the sensing information for storage or prompting a set of commands from the connected control units. A review of existing schemes exhibits the presence of the adoption of multiple methodologies over different variants of use cases; however, they are quite case-specific, use expensive deployment processes, and perform highly sophisticated operations. Currently, no studies specifically reported using multi-scale occupancy sensing suitable for large and distributed Internet-of-Things (IoT) environments. Therefore, the proposed study introduces a mechanism of novel multi-scale occupancy sensing considering a smart university campus use case. However, it can be implemented over different infrastructures connected to an IoT environment. The proposed scheme is implemented considering varied forms of cost-effective sensors, handheld devices and access points for determining the state of occupancy in a large number of rooms on campus. The sensed data from distributed connected campuses are aggregated over a cloud server subjected to suitable preprocessing to increase the data quality suitable for reliable prediction. Multiple potential learning-based schemes are integrated with a proposed model to explore the best-fit model. This assessment scenario is not reported in the existing scheme to classify states of occupancy. The study outcome shows Convolution Neural Network and Long Short-Term Memory to accomplish higher accuracy than other learning approaches. [ABSTRACT FROM AUTHOR]

The novel non-linear optical materials of pure and l-serine (LS) doped potassium sulphato oxalate (KSO) single crystals at different concentration levels (1 mol%, 2 mol% and 3 mol%) are grown by conventional slow evaporation solution growth technique (SEST). The grown crystals were studied for their structural, optical, thermal and second harmonic generation (SHG) efficiency properties. The Powder X-ray diffraction (PXRD) spectrum confirms the grown crystals are triclinic crystalline system with space group P1 for pure KSO and it remains unchanged for LS-doped KSO single crystals at different concentration levels except small angular shift for few intense peaks. As the doping concentration increases, the intensity of (201) peak increases due to the preferred orientation of c-axis and the absence of (− 100), (− 101) peaks. The presence of doped molecules was identified through infrared spectroscopy. The absorbance was decreased with increasing doping concentration and indirect band gap energy increased from 3.9 eV for pure KSO to 4.6 eV for 3 mol% LS-doped KSO which indicates that these materials are one of the best choices for efficient light conversion materials. The crystalline perfection of grown crystals was assessed by High-resolution X-ray diffraction (HRXRD) technique. At an optimum doping concentration, the crystalline perfection was increased due to predominant occupation of doped molecules at the sites of grain boundaries. The thermal stability of the samples was carried out using thermo gravimetric analysis (TGA) and differential thermal analysis (DTA) study. The sharp increase in photo luminescent intensity for 3 mol% LS-doped KSO at 563 nm is preferable for green light fluorescence applications. The relative second harmonic generation (SHG) efficiency was increased with increasing doping concentration. At 3 mol% of LS doping, KSO crystals showed an enhanced efficiency by 1.45 times to that of standard KDP crystals. Thus, the grown crystals were found to have potential applications for optoelectronic device applications. [ABSTRACT FROM AUTHOR]

Identifying pre-seismic atmospheric and ionospheric anomalies is of research importance but also meets difficulties, especially for earthquakes with varying magnitudes, focal depths and focal mechanisms. In this paper, atmospheric‒ionospheric disturbances associated with earthquakes in Nepal (April 25, 2015, M = 7.8 and May 12, 2015, M = 7.3) are investigated using atmospheric and ionospheric parameters. Ionospheric (vertical total electron content (VTEC)) and atmospheric (outgoing long wave radiation (OLR), cloud mask, vertical temperature gradient (VTG)) parameters are archived from IGS GPS stations and INSAT 3D data from Indian Meteorological Department (IMD) of Ministry of Earth Sciences, Government of India. The abnormal VTEC signal was noticed 3 days and 10 days prior to April 25, 2015 event and 2 days and 6 days prior to the May 12, 2015 event. Inter-quartile range (IQR) and associated running median over one day were determined as the upper limit reference to a signature of VTEC for the 51-days period of the Nepal earthquake, it can be clearly observed that the total electron content (TEC) has increased from the limits of UB (upper bound) at the stations closest to the earthquake epicentre such as LCK-4, LHAZ than the far stations such as IISC, HYDE, SGOC and URUM. Prior to these earthquakes, UB observed a 54‒60% increase in relative amplitude of VTEC. The overall geomagnetic storm condition was thoroughly examined using the global planetary index (Kp) and storm time disturbance index (Dst) over the 51-days period. The IQR range method was used to analyse its abnormal positive and negative signals. We found no geomagnetic signatures caused by geomagnetic storms during the seismic regime The OLR varied from 240 to 340 watts/m2 observed 4 days before the event. The vertical temperature gradient varied from 4.3 to 23.2 K. Daily variations of 51 days for the OLR showed good anomalous atmospheric responses a few days before the event. The shallow depth of the earthquake gives the best coupling, releasing a large amount of energy from the seismic zones, and could be a causal factor in the enhancement of anomalous VTEC patterns. [ABSTRACT FROM AUTHOR]

Gujarat region (India) was struck by an earthquake of magnitude 5.3 on June 14, 2020 at 14:43 UTC near Bhachau city in Kachchh district in the state of Gujarat, with a depth of 20 km at the epicentre of 23.38° N, 70.36° E. In order to study the earthquake precursors for this event, data from the Induction Coil Magnetometer (LEMI-30) installed at the Badargadh Multi-Parameter Geophysical Observatory (MPGO) was analyzed for the period from January 1 to June 16, 2020. This station is located ~20 km from the epicentre of this earthquake. We observed that a clear geomagnetic burst was identified in the raw data of the Bx and By components in the LEMI-30 data before this earthquake. Geomagnetic amplitude bursts were identified 6 to 18 days and 2 days before this earthquake with a frequency of 0.01 to 0.02 Hz. Polarization ratio (PR) analysis revealed an anomalous signal on June 11, 2020 with PR values increasing to 1.4. The planetary index (Kp) and disturbance storm time index (Dst) due to the Sun‒Earth interaction are also very low (Kp = 0.3 and Dst = –6 nT) from June 10 to 16, 2020. In order to understand the dynamics of seismic processes, fractal dimensional analysis is also applied to magnetic data. Fractal dimension values also corroborate with the results of PR analysis, which showed a similar anomaly on June 11, 2020. The ULF geomagnetic data was further analyzed by applying the band-pass filtered data instead of the raw data in the period range from 10 to 45 seconds and derived the Z/X amplitude ratio in the Pc3 band. We found an upward trend and a downward trend from June 10, 2020. Enhanced polarization ratios were detected in the reconstructed components using the EMD technique which are linked to the current earthquake. It has been clearly demonstrated that the EMD method can be used to isolate noise and thus improve the identification of simultaneous short-term geomagnetic variations/anomalies. Therefore, in our study, we have clearly differentiated their origin, whether external (the Sun‒Earth interactions) or internal (local changes in conductivity in the area of the preparation). [ABSTRACT FROM AUTHOR]

Influence of compression on the cyclic mechanical properties of rock leaves a significant scope for research to judiciously design surface and subsurface structures in the Himalaya. The paper aims at analysis of the cyclic strength and mechanical properties of Disang sandstone from Indian Himalaya under complete and incomplete uniaxial cyclic loading tests. Two series of cyclic loading tests were performed. First series comprises of complete cyclic loading test under stress-controlled mode to determine the fatigue limit of the rock. Second series comprises of incomplete cyclic loading tests under amplitude and cycle controlled mode performed below the determined fatigue limit of the rock. The key findings from the study indicate that under incomplete cyclic loading, with increase in both amplitude and number of cycles, static compressive strength of the rock decreases. Cyclic elastic properties decrease with increase in peak stress under complete cyclic loading, whereas the incomplete test shows a progressive decline in its elastic properties during initial cycles followed by an asymptotic behavior with increasing number of cycles. Progressive evolution of irreversible axial strain and hysteresis energy under complete cyclic loading shows primary, secondary, and tertiary stages of deformation behavior whereas in incomplete cyclic loading, only primary and secondary stages are visible. Three non-linear strain–based empirical models were proposed to characterize all the three stages of cyclic deformation and hysteresis behavior of rock. [ABSTRACT FROM AUTHOR]

Due to technological development, the mass production of video and its storage on the Internet has increased. This made a huge amount of videos to be available on websites from various sources. Thus, the retrieval of essential lecture videos from multimedia is difficult. So, an effective way of indexing and retrieving the video by considering various similarities in the video features is suggested using the deep learning method in this paper. From the standardized set of data, the videos containing lectures are obtained for training. The optimal keyframes are selected from the obtained videos employing the Adaptive Anti-Corona virus Optimization Algorithm. Then the video contents are segmented and arranged on the basis of the optimized keyframes. The optical characters, such as semantic words and keywords, are recognized by means of Optical Character Reorganization, and the image features are extracted from the segmented frames with the help of a Multi-scale Residual Attention Network (MRAN). The generated pool of features is arranged and stored in the database according to the contents. Text and video queries are given as the input for testing the trained model. The features from the text query and the features of the optimized keyframes from the video query are obtained with the help of MRAN in the testing phase. The generated pool features from the text and video queries are compared with the features that are stored in the database for analyzing the similarities using Cosine, Jacquard, and Euclidean similarity indices. From this, the multi-similarity features are used for retrieval of the relevant videos in accordance with the provided query. The experimental results show that the performance of the proposed system for video indexing and retrieval is better and more efficient than the existing methods of video retrieval. [ABSTRACT FROM AUTHOR]

Teriflunomide is an immunomodulatory agent used in the treatment of rheumatoid arthritis. Our present research work depicts the origin, preparation and characterization of impurities of teriflunomide: (2Z)-4-chloro-2-cyano-N-[4-(trifluoromethyl]phenyl]-3-hydroxy-2-butenamide (chloromethyl teriflunomide), (2Z)-2-cyano-3-hydroxy-N-[4-(trifluoromethyl)phenyl]-2-pentenamide (ethyl teriflunomide), two positional isomers namely (2Z)-2-cyano-3-hydroxy-N-[2-(trifluoromethyl)phenyl]-2-butenamide (2-isomer of teriflunomide) and (2Z)-2-cyano-3-hydroxy-N-[3-(trifluoromethyl)phenyl]-2-butenamide (3-isomer of teriflunomide). These impurities are process related impurities which are useful to finalize the specifications of teriflunomide drug substance. These synthesized impurities were characterized by 13C, 1H NMR, HRMS and HPLC analysis. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

The study investigates that a high-entropy alloy based on AlCoCrFeNi is mechanically alloyed (MA) for 40 h with 1 wt% Y2O3 dispersed and then consolidated through vacuum hot pressing (VHP). In VHP, three temperatures are used for consolidation: 800 °C, 900 °C, and 1000 °C. Microanalysis of the powder aggregate formed by MA revealed the presence of mostly face-centered cubic (FCC) and body-centered cubic (BCC) phases, along with a metastable AlFe4O12Y3 garnet phase. The enthalpy of mixing Y and Al induced the development of the garnet phase. In addition, after the consolidation of the powder sample at different temperatures, microstructural investigations indicated that the FCC and BCC phases generated during MA were stable owing to sluggish diffusion. By contrast, the garnet phase generated during the MA dissociated into an intermetallic phase based on Yttrium. The slower heating rates associated with VHP (in comparison to the rapid heating rates associated with spark plasma sintering) may be responsible for the dissociation of the garnet phase. The sample sintered at 1000 °C exhibited a bimodal grain distribution, with grain sizes ranging from 100 to 800 nm, as determined by transmission electron microscopy. In addition, this sample had the highest measured hardness of 1353 ± 20 Hv. [ABSTRACT FROM AUTHOR]

Features related to the glottal closure instants (GCI) exhibit different patterns for different emotions. In this work, our main objective was to explore the effectiveness of these features in speech emotion recognition (SER). In this regard, we had proposed two distance-based classifiers based on four features related to GCI. This was the first phase of our work. In two later phases of this work, we extended this idea to develop hierarchical two-stage SER systems in order to couple the GCI features with other features to improve our SER systems. The first stage in phase 2 was based on prosodic features and for phase 3, we had used power spectral features in the first stage. The second stage in both the systems was based on the GCI features. The best performance was observed for the phase 3 systems, which outperformed the phase 1 systems by as much as about 10% for the IEMOCAP corpus and by nearly 20% for the EMO-DB and the IITKGP-SEHSC datasets. It also outperformed a related and recent work by Kadiri et al. (Circ Syst Signal Process 39(9):4459–4481, 2020) by 9.6% for the EMO-DB corpus. [ABSTRACT FROM AUTHOR]

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD. [ABSTRACT FROM AUTHOR]

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the "HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures. [ABSTRACT FROM AUTHOR]

The development of an energy-efficient wireless sensor network is a difficult problem since batteries are used to energize the sensor nodes. In certain circumstances, charging a battery is extremely difficult or even impossible. If the heterogeneity of sensor nodes is not correctly used, it can result in unequal energy consumption and lowering network performance. By combining power control and data aggregation, clustering has the ability to reduce energy consumption and extend network life. Many routing methods have been suggested for network optimization, with a major focus on energy efficiency, network longevity, and clustering processes. We proposed the Adaptive Active Sleep Energy Efficient Method (AASEEM) for Wireless Sensor Networks (WSNs), which takes into account network heterogeneity. We examine and improve some difficulties including network stability and cluster head selection procedure. The principle of providing a detailed pairing among sensor nodes is used to maximize energy usage. The results of the simulations show that the suggested method improves network performance significantly and it might be a beneficial technique for WSNs. [ABSTRACT FROM AUTHOR]

Biosurfactants are naturally occurring, surface-active chemicals generated by microorganisms and have attracted interest recently because of their numerous industrial uses. Compared to their chemical equivalents, they exhibit qualities that include lower toxic levels, increased biodegradable properties, and unique physiochemical properties. Due to these traits, biosurfactants have become attractive substitutes for synthetic surfactants in the pharmaceutical industry. In-depth research has been done in the last few decades, demonstrating their vast use in various industries. This review article includes a thorough description of the various types of biosurfactants and their production processes. The production process discussed here is from oil-contaminated waste, agro-industrial waste, dairy, and sugar industry waste, and also how biosurfactants can be produced from animal fat. Various purification methods such as ultrafiltration, liquid–liquid extraction, acid precipitation, foam fraction, and adsorption are required to acquire a purified product, which is necessary in the pharmaceutical industry, are also discussed here. Alternative ways for large-scale production of biosurfactants using different statistical experimental designs such as CCD, ANN, and RSM are described here. Several uses of biosurfactants, including drug delivery systems, antibacterial and antifungal agents, wound healing, and cancer therapy, are discussed. Additionally, in this review, the future challenges and aspects of biosurfactant utilization in the pharmaceutical industry and how to overcome them are also discussed. [ABSTRACT FROM AUTHOR]

This work presents the design of a modified split push–pull balanced (SPPB) operational transconductance amplifier (OTA) for the analog frontend (AFE) of Brain-Machine Interface (BMI) applications. The proposed OTA architecture enables the development of a low-power amplifier with a wide bandwidth and high output swing. Comprehensive post-layout simulations and analyses were conducted in Cadence Virtuoso with reference to the technology library UMC 180 nm and very less deviation is observed between post and pre layout simulation. The simulated results show that the highest gain is 51.66 dB, the average power consumption is 1.69 µW, the phase margin is 60.25 degrees, and the noise level is 918nV/sqrt (Hz) at 1 Hz. Moreover, in this paper using the proposed OTA neural amplifier with capacitive feedback topology is designed which exhibits a maximum mid-band gain of 51.06 dB and a bandwidth ranging from 378.481 mHz to 619.17 Hz. The neural amplifier also found to have acceptable Noise efficiency factor (NEF) which is 2.3. These findings showcase the potential of the neural amplifier with the proposed OTA design for achieving low-power, high-performance amplification in BMI of analog frontends. [ABSTRACT FROM AUTHOR]

In this work, we report magnetic and optical anisotropies in tilted nickel (Ni) nanocolumns and their dependencies on the film growth conditions. The Ni nanocolumns were prepared using electron beam evaporation technique in conjunction with glancing angle deposition methodology. The film depositions were performed at various in-situ substrate temperatures viz. 30 °C, 150 °C, 250 °C and 300 °C. An increase in material density with in-situ substrate temperature due to the thermal diffusion mediated coalescence of neighbouring grains was observed. The results were further corroborated with the alterations in surface morphology of the films. The modification in tilt angle of the Ni nanocolumns was also observed with in-situ substrate heating due to the non-ballistic growth approach. The presence of uniaxial optical anisotropy was established in the films deposited at low substrate temperatures (< 250 °C) using generalized ellipsometry (GE). However, the uniaxial optical anisotropy was found to be negligible for the high temperature deposited films due to thermal diffusion effect. SQUID measurements were performed for quantification of magnetic anisotropy in the films. The estimated magnetic anisotropy energy confirmed low substrate temperature deposited films to be more anisotropic as compared to others. Magnetic coercivity of the films showed film porosity dependent changes in the field values. Overall, the present investigation demonstrates anisotropic behaviour of nanocolumnar nickel thin films for non-ballistic glancing angle deposition. [ABSTRACT FROM AUTHOR]

The effect of phase transformations on the steel/quenchant interfacial heat flux during quench hardening heat treatment is investigated in the present work. Experimental and modeling approaches comprising the inverse heat conduction problem (IHCP) were employed to analyze the thermal behavior of different steel grades with varying section thicknesses. The results revealed that phase transformation led to a distinctive pattern of the interfacial heat flux, characterized by a dip and subsequent rise. We observed that increasing the section thickness increases the surface heat flux for stainless steel probes without phase transformation. In contrast, the surface heat flux decreased with thicker sections in phase transformation. The increased heat evolved due to the latent heat liberation during phase transformation, and a reduction in thermal diffusivity due to increased specific heat caused a fall in the heat flow rates. Furthermore, the study proposed a phase transformation enthalpy parameter (ΔQ) to access the enthalpy change during quenching. ΔQ was consistent for a specific steel grade and independent of section thickness but varied with the cooling rate or quench media. The incorporation of phase transformation in the quenching heat transfer model is complex due to the required material data, including TTT/CCT diagrams and thermophysical properties that vary with steel grade. The study suggests directly incorporating the ΔQ values into the heat conduction equation or the IHCP model with phase transformation, simplifying the simulation process and minimizing data inputs. A database on ΔQ as a function of temperature and cooling rate would facilitate heat transfer modeling during quench hardening. [ABSTRACT FROM AUTHOR]

This study investigated the adsorption performance of biochar produced from different types of urban biowaste material viz., sugarcane bagasse (SB), brinjal stem (BS), and citrus peel (CP) for removal of heavy metal ions (Pb, Cu, Cr, and Cd) from aqueous solution. The effects of biowaste material, dosage of biochar, solution pH, and initial concentration of heavy metal ions and isotherm models were performed to understand the possible adsorption mechanisms. The results showed that the biochar derived from BS and SB removes Cu (99.94%), Cr (99.57%), and Cd (99.77%) whereas biochar derived from CP removes Pb (99.59%) and Cu (99.90%) more efficiently from the aqueous solution. Biochar derived from BS showed maximum adsorption capacity for Cu (246.31 mg g−1), Pb (183.15 mg g−1), and Cr (71.89 mg g−1) while the biochar derived from CP showed highest for Cd (15.46 mg g−1). Moreover, biochar derived from BS and SB has more polar functional groups and less hydrophobicity than the biochar derived from CP. This study reveals that solution pH and biochar doses play a major role in removal of heavy metal ions from aqueous solution. The results of Langmuir model fitted well for Pb and Cu while the Freundlich model for Cr and Cd. Our study concludes that the biochar derived from different biowaste materials adsorbs heavy metal ions majorly through surface complexation and precipitation processes. The results of this study will be very useful in selecting the effective urban biowaste material for making biochar for heavy metal removal from the aqueous environment. Highlights: Sugarcane bagasse, brinjal stem, and citrus peel from metropolitan areas have potential for production of biochar. Biochar derived from SB and BS adsorb more Cu, Cr, and Cd ions. Biochar adsorption performance varies with different pH levels and biowaste materials. Langmuir isotherm model was well fitted for Pb and Cu while Freundlich model for Cr and Cd ions. Biochars mostly adsorb heavy metal ions by surface complexation and precipitation. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation. [ABSTRACT FROM AUTHOR]

Investing in stock market is considered a high-risk monetary motion where investor may not take into version the factor of stock charge fluctuations or destroy their profits without gaining professional knowledge and investment experience. In addition, when working with stock information, people increase the importance of available and self-correcting information, a habit that runs counter to objective and reasonable investment decisions. In recent years, various methods of stock market forecasting have been proposed to enhance the quality and profitability of investor decision making. Recent machine learning models have been able to reduce the risk of stock market forecasting. However, diversity remains a major challenge in developing better learning models and in identifying intellectually valuable traits to further improve diagnosis. In this paper, we propose an optimal characteristic selection with hybrid machine learning classifier for stock market price forecasting (OFML-SMF) using social media and secondary data sources. First, we introduce a multi-objective earthworm optimization (MOEO) algorithm for optimal feature selection among multiple features i.e. called feature optimization which reduces the data dimensionality problem. Second, we develop a hybrid Quantile regression learning based deep belief neural network (HQR-DBNN) to classify the stock market price movement prediction which enhances the statistical metrics. Finally, to appraise the presentation of planned OFML-SMF model through the influential international stock market indices and the performance can be compare with the existing state-of-art models in conditions of accuracy, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]