Search

NiTi smart alloys are known for their characteristic shape memory behavior. The current work focuses on the physical and mechanical characterization of Ni(50−X)Ti50FeX shape memory alloys prepared by the spark plasma sintering (SPS) process and their dependence on the concentration of Fe. The physical characterization of the samples confirmed the presence of the FeNiTi phase along with the Ti- and Ni-rich phases. Enhanced mechanical properties were observed in 8 at. pct Fe samples, which contained secondary intermetallic phases such as Ti2Ni, Ni3Ti, Fe2Ti, and Ni4Ti3. Higher fraction of NiTi phase in the 8 at. pct Fe sample resulted in better shape memory properties while showing a higher friction coefficient. Ball on disk wear tests were done to identify the mechanisms contributing to the wear in the sintered sample. It is observed that the abrasive wear as well as the adhesive wear are the most prominent contributors for the surface material removal, and the dependence of characterization is observed with the variation of Fe content in NiTiFe alloy. [ABSTRACT FROM AUTHOR]

Characterizing forest fuel properties in terms of their distribution and flammability behaviour offers useful information for effective fire management. Satellite images provide synoptic images capturing the vegetation state and their change including burn area loss, enabling stratification, and narrowing down the search for further investigation on ground or laboratory. The study investigated the flammability of various forest fuels including sal, teak, bamboo and grass species, by analysing different fuel variables such as moisture content (MC), volatile matter (VM), ash content (AC), fixed carbon (FC), higher heating value (HHV) and minimum ignition temperature (MIT), and exploring the intrinsic relationships between and among them. The teak wood showed low MC (5.08%), high VM (81.91%), low AC (1.1%), high HHV (19.93 MJ/kg), high CF (93.82) indicating high sustainability, combustibility and consumability characteristics. Teak indicated higher flammability characteristics compared to sal, while grass indicated higher sustainability and combustibility characteristics compared to bamboo. Overall woody species (sal and teak) indicated high ignitability and sustainability characteristics over grass and bamboo. Among the plant components, leaves indicated high ignitability, wood indicated high sustainability, combustibility and consumability, and barks indicated low flammability characteristics. This study registers a maiden and early attempt using satellite and laboratory-based analysis to characterize the flammability properties of dominant species of tropical dry forests in eastern India. [ABSTRACT FROM AUTHOR]

The global outbreak of the COVID-19 pandemic has given rise to a significant health emergency to adverse impact on environment, and human society. The COVID-19 post-pandemic not only affects human beings but also creates pollution crisis in environment. The post-pandemic situation has shown a drastic change in nature due to biomedical waste load and other components. The inadequate segregation of untreated healthcare wastes, chemical disinfectants, and single-use plastics leads to contamination of the water, air, and agricultural fields. These materials allow the growth of disease-causing agents and transmission. Particularly, the COVID-19 outbreak has posed a severe environmental and health concern in many developing countries for infectious waste. In 2030, plastic enhances a transboundary menace to natural ecological communities and public health. This review provides a complete overview of the COVID-19 pandemic on environmental pollution and its anthropogenic impacts to public health and natural ecosystem considering short- and long-term scenarios. The review thoroughly assesses the impacts on ecosystem in the terrestrial, marine, and atmospheric realms. The information from this evaluation can be utilized to assess the short-term and long-term solutions for minimizing any unfavorable effects. Especially, this topic focuses on the excessive use of plastics and their products, subsequently with the involvement of the scientific community, and policymakers will develop the proper management plan for the upcoming generation. This article also provides crucial research gap knowledge to boost national disaster preparedness in future perspectives. [ABSTRACT FROM AUTHOR]

BRICS nations as emerging economies have undergone a substantial structural transformation; however, achieving economic growth while sustaining the ecology is a pressing challenge for these economies. Nonetheless, one of the possible ways to achieve ecological sustainability is to promote clean energy use and green innovation backed by a strengthened political system. Thus, this study assesses the effect of disaggregated clean energy use (hydro and nuclear energy), green technology innovation, and political stability on the ecological footprint in BRICS countries from 1993 to 2022 while controlling for the impacts of natural resources rent, population growth, and GDP per capita. Using dynamic fixed effect and CS-ARDL estimators, the outcomes reveal that hydro and nuclear energy use and political stability induce the ecological footprint in BRICS countries. However, the empirical findings indicate that green technology innovation substantially reduces the ecological footprint. Therefore, from a policy perspective, the study encourages policy coordination among BRICS countries to embrace targeted policies to strengthen regulatory frameworks, promote innovations in technological know-how, and encourage the utilization of clean and sustainable energy to foster environmental sustainability. [ABSTRACT FROM AUTHOR]

Ferritins are multimeric nanocage proteins that sequester/concentrate excess of free iron and catalytically synthesize a hydrated ferric oxyhydroxide bio-mineral. Besides functioning as the primary intracellular iron storehouses, these supramolecular assemblies also oversee the controlled release of iron to meet physiologic demands. By virtue of the reducing nature of the cytosol, reductive dissolution of ferritin-iron bio-mineral by physiologic reducing agents might be a probable pathway operating in vivo. Herein, to explore this reductive iron-release pathway, a series of quinone analogs differing in size, position/nature of substituents and redox potentials were employed to relay electrons from physiologic reducing agent, NADH, to the ferritin core. Quinones are well known natural electron/proton mediators capable of facilitating both 1/2 electron transfer processes and have been implicated in iron/nutrient acquisition in plants and energy transduction. Our findings on the structure–reactivity of quinone mediators highlight that iron release from ferritin is dictated by electron-relay capability (dependent on E1/2 values) of quinones, their molecular structure (i.e., the presence of iron-chelation sites and the propensity for H-bonding) and the type/amount of reactive oxygen species (ROS) they generate in situ. Juglone/Plumbagin released maximum iron due to their intermediate E1/2 values, presence of iron chelation sites, the ability to inhibit in situ generation of H2O2 and form intramolecular H-bonding (possibly promotes semiquinone formation). This study may strengthen our understanding of the ferritin-iron-release process and their significance in bioenergetics/O2-based cellular metabolism/toxicity while providing insights on microbial/plant iron acquisition and the dynamic host–pathogen interactions. [ABSTRACT FROM AUTHOR]

This article proposed an off-board bidirectional battery charger for electric vehicles (EVs) that have been designed to perform various modes of operation of EVs like grid-to-vehicle (G2V) and vehicle-to-grid (V2G) while improving the grid power quality (PQ). During the charging process, the charger operates in the G2V mode. In this mode, power flows from the utility grid to the EV, ensuring a smooth and efficient transfer, which has the ability to maintain a sinusoidal grid current waveform and a unity power factor throughout the operation. This not only ensures efficient power transfer but also minimizes disruptions and distortions in the grid. On the other side of the operational spectrum lies the discharging phase, governed by the V2G mode. This mode allows the charger to deliver power from the EV's batteries back to the utility grid. This capability is a significant step toward establishing a two-way relationship between EVs and the grid, enabling EVs to act as energy reservoirs and support grid stability during peak demand or emergencies. This paper presents the proposed adaptive direct power control (ADPC) theory for the proposed electric vehicle charger (EVC), which consists of a grid-side converter and an EV side converter. The proposed control algorithm on both sides of the converters plays a significant role in various operating modes. The ADPC employs a control strategy referred to as the second-order generalized integrator to estimate the actual synchronizing voltage templates specifically designed for single-phase grid-side converters. Consequently, the appropriate current reference used by the charger is to ensure that the power factor remains unity throughout various operating modes. Similarly, for the EV side converter, the integration of the power delivery error further enhances steady-state responses with the proposed ADPC by generating actual phase shifted pulses for the DAB. The overall performance under similar conditions is studied with virtual direct power control to further check the robustness of the proposed ADPC. The adopted topology and control technique further implemented in the prototype of an off-board EVC to verify the various operating conditions to demonstrate its effectiveness and justified as per the IEEE 519-2022 standard. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) provides valuable input for modeling climate and ecosystem processes. However, ground-based observations are necessitated across various phenophases from dense tropical forests for a better understanding in terms of their contribution to carbon fixation. In this study, Digital Hemispherical Photography (DHP) was used for LAI observation from Similipal Biosphere Reserve, and to predict high-resolution LAI using Random Forest Machine Learning approach. Observations were taken from ninety-three Elementary sampling units (ESUs) corresponding to the beginning and end of leaf fall seasons across moist deciduous, dry deciduous, and semi-evergreen forests. LAI demonstrated high values for dry deciduous, followed by semi-evergreen and moist deciduous forests for the start of the leaf fall season, whereas moist deciduous forests demonstrated high values during the end of the leaf fall season. Satellite-based spectral reflectance bands of Sentinel-2 and vegetation indices (VIs) were used as predictor variables, wherein the band-7, band-8, band-12, enhanced vegetation index (EVI), and Red-edge based EVI were evaluated as the most dominant responsive variables for LAI estimation. Random Forest (RF) model provided good accuracy (R2 = 0.64, RMSE = 0.62) with observed DHP-based LAI. However, a comparison of RF model-based predicted LAI with global LAI products (MOD15A2H and VNP15A2H) provided a moderate correlation. Such studies demonstrate the potential of site or region-specific case studies to evaluate coarser-resolution global LAI products for possible improvement. [ABSTRACT FROM AUTHOR]

In weight-sensitive applications, the widespread use of honeycomb composites underscores the significance of enhancing their specific strength and energy absorption capacity. In this pursuit, various hybrid honeycomb structures have been developed, with a particular focus on their cell wall buckling behaviour. This study involved testing six different specimen types, incorporating intralayer hybridization with materials namely, Kevlar, Glass, Dyneema, Sisal, Hemp, and Jute. The incorporation of the intralayer hybrid technique examined various aspects of honeycomb structures, leading to improvements in mechanical performance. In addition, the effects of specific energy absorption and crush force efficiency on the compressive and flexural strength were investigated. Among all the samples, the honeycomb core with a height of 15 mm demonstrated the highest compressive strength and specific energy absorption values. This enhancement is attributed to the synergistic effects of intralayer hybridization, emphasizing the potential for utilizing natural alternatives such as sisal, hemp, and jute, which may offer pronounced advantages in impact stress propagation within hybrid composites. [ABSTRACT FROM AUTHOR]

The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively. [ABSTRACT FROM AUTHOR]

Carbon sequestration in aboveground biomass remains understudied because of the difficulties in conducting field observations and saturation of remote sensing datasets. This study aimed to address the challenges associated with estimating forest AGB using remote sensing data from a dense mangrove ecosystem that has reached saturation limits. A mangrove ecosystem can reach saturation limits when the vegetation density and AGB are so high that remote sensing instruments, such as radar and lidar, cannot accurately measure further increases in biomass because of the sensors' limited penetration and resolution capabilities. This study evaluated the potential and limitations of using dual-polarised microwave data from Sentinel-1A and PALSAR-2, as well as spectral reflectance data from Sentinel-2, to estimate the AGB of the Bhitarkanika Wildlife Sanctuary (BWS), which is the second largest mangrove site in India. Using stratified random sampling, 314 elementary sampling units of 20 m × 20 m (0.04 ha) were used to record the diameter at breast height, tree height, and stand density for estimating AGB. Different band combinations of multisensor datasets were utilised to identify the best predictor variables for estimating the AGB and their corresponding saturation limits. Sentinel-1A demonstrated saturation at 123 Mg/ha (R2 = 0.17) for AGB using VV polarisation, followed by 93 Mg/ha (R2 = 0.55), 91 Mg/ha (R2 = 0.26), and 96 Mg/ha (R2 = 0.17) for the three red-edge bands at wavelengths of 705, 749, and 783 nm, respectively, of Sentinel-2 data. Red-edge bands are sensitive to chlorophyll and vegetation structure, and therefore, S2REP and REIP attained the maximum saturation limit at an AGB of 80 Mg/ha (R2 = 0.26, 0.3, respectively). The highest correlation (R2 = 0.9) and the maximum AGB of 326.06 Mg/ha was captured by the variable HH × HV of the L-band of the PALSAR-2 due to its penetration and interaction capacity with biomass components and less susceptible to interference from surface conditions and atmospheric effects. This study confirmed the advantages of longer-wavelength L-band data over C-band and multispectral optical bands for AGB estimation and identified the best predictor variables. This approach highlights the efficacy of different predictor variables for AGB estimation and the complementary strengths of multisensor datasets for navigating saturation limits. This framework could offer a practical guide for variable selection based on forest density and canopy structure from upcoming SAR missions, including NISAR. [ABSTRACT FROM AUTHOR]

We report the synthesis of the polycrystalline Cu 3 N phase via a diffusion reaction between copper and nitrogen under extreme conditions. Herein, the solid-state diffusion mechanism for the synthesis of the Cu 3 N phase was analyzed. Structural indexing of the new phase was confirmed using the X-ray diffraction technique. Synthesis of the above Cu 3 N phase applying this novel technique is an inexpensive method for the industrial production of the material. The microstructure of the above solid was investigated under a high-resolution scanning electron microscope. [ABSTRACT FROM AUTHOR]

Temperature variability has substantial socioeconomic impacts through its association with the frequency and severity of heat extremes. Under anthropogenic influence, climate models project seasonally-dependent amplifications of near-surface temperature variability over some sectors of the Southern Hemisphere, and robust positive trends have already been observed in recent decades. Here we show that the amplification of subweekly temperature variability simulated by the multi-model ensemble mean of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) over South Africa, Australia, and South America is often substantially smaller than in reanalyses in recent decades, reaching a similar amplification only at the end of the 21st century due to a weaker amplification of subweekly variance generation efficiency. Analysis of a large model ensemble indicates that this discrepancy may be due to internal climatic variability suggesting that the recent rapid amplification seen in reanalyses may slow down or even temporarily reverse in the near future. [ABSTRACT FROM AUTHOR]

Satellite-based quantum communications including quantum key distribution (QKD) represent one of the most promising approaches toward global-scale quantum communications. To determine the viability of transmitting quantum signals through the atmosphere, it is essential to conduct atmospheric simulations for both uplink and downlink quantum communications. In the case of the uplink scenario, the initial phase of the beam's propagation involves interaction with the atmosphere, making simulation particularly critical. To analyze the atmosphere over the Indian subcontinent, we begin by validating our approach by utilizing atmospheric data obtained from the experiments carried out in the Canary Islands within the framework of Quantum Communication (QC). We also verify our simulation methodology by reproducing simulation outcomes from diverse Canadian locations, taking into account both uplink and downlink scenarios in Low Earth Orbit (LEO). In this manuscript, we explore the practicality of utilizing three different ground station locations in India for uplink-based QC, while also considering beacon signals for both uplink and downlink scenarios. The atmospheric conditions of various geographical regions in India are simulated, and a dedicated link budget analysis is performed for each location, specifically focusing on three renowned observatories: IAO Hanle, Aries Nainital, and Mount Abu. The analysis involves computing the overall losses of the signal and beacon beams. The findings indicate that the IAO Hanle site is a more suitable choice for uplink-based QC when compared to the other two sites. [ABSTRACT FROM AUTHOR]

The presence of somatic mutations, including copy number variants (CNVs), in the brain is well recognized. Comprehensive study requires single-cell whole genome amplification, with several methods available, prior to sequencing. Here we compare PicoPLEX with two recent adaptations of multiple displacement amplification (MDA): primary template-directed amplification (PTA) and droplet MDA, across 93 human brain cortical nuclei. We demonstrate different properties for each, with PTA providing the broadest amplification, PicoPLEX the most even, and distinct chimeric profiles. Furthermore, we perform CNV calling on two brains with multiple system atrophy and one control brain using different reference genomes. We find that 20.6% of brain cells have at least one Mb-scale CNV, with some supported by bulk sequencing or single-cells from other brain regions. Our study highlights the importance of selecting whole genome amplification method and reference genome for CNV calling, while supporting the existence of somatic CNVs in healthy and diseased human brain. Comparison of single cell whole genome amplification methods using human brain nuclei determines suitability for detection of large somatic copy number variants by low coverage whole genome sequencing aligned to different reference genome versions. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are generated from diverse categories of plastic debris disposed in open. Its entry into the terrestrial ecosystem could not only impact soil physico-chemical attributes but also endanger the lives of biotas including the earthworms which play significant role in the decomposition of organics and sustaining the nutrient pool. There have been consistent efforts by various workers across the globe to study the impact of MPs on the terrestrial environment and exploring potential mitigation strategies to minimize contamination levels. This review presents a bibliometric analysis of scientific publications on impact of MPs on the earthworms from 2017 to 2023. The primary objective is to discern trends among authors, institutions, and countries contributing to research on MPs, particularly concerning their interaction with earthworms. The analysis reveals a steady escalation in the number of publications up to 2022. The VOS viewer software was utilized for data visualization and cluster analysis, unveiling three clusters highlighting keyword groups associated with "microplastics", "soil", and "earthworms". Notably, "microplastics" and "earthworm" emerged as prominent research hotspots. [ABSTRACT FROM AUTHOR]

Background: Cucumber (Cucumis sativus L.) is a model crop to study cell biology, including the development of haploids and doubled haploids in vegetable crops. In plant breeding, haploid and doubled haploids are valuable tools for developing pure homozygous inbred lines and accelerating genetic progress by reducing the time required for breeding cycles. Besides, the haploids are also valuable in genomic studies. We are reporting the induction of haploids in cucumber involving gynoecious and parthenocarpic genotypes for the first time. This study aimed to assess the efficient induction of haploids through pollination with gamma-irradiated pollen in cucumber. The effect of gamma irradiation dose on pollen viability and germination, fruit setting percentage, seed development, and haploid embryo development in cucumber hybrid genotypes were studied in detail. The goal was to utilize this information to produce haploid plants for genomics and transformation works in this model vegetable crop. Results: Pollination was done on six cucumber genotypes using varying doses of gamma rays (100, 200, 300, 400, and 500 Gy). Genotypes, doses of irradiation, and embryo developmental stage influenced the successful generation of in-vitro haploid plants. The optimal timeframe for embryo rescue was found to be 25 to 30 days after pollination. Haploid embryos were effectively induced using irradiated pollen at 400 to 500 Gy doses. Parthenogenetic plantlets were analyzed, and their ploidy level was confirmed through stomatal physiology, cytology (mitosis), and flow cytometry methods. Conclusion: Through parthenogenic embryo development, it is possible to induce a large number of haploids in cucumber. This technique's power lies in its ability to streamline the breeding process, enhance genetic gain, and produce superior cultivars that contribute to sustainable agriculture and food security. [ABSTRACT FROM AUTHOR]

Purpose: Considering the dynamic influence of environmental, social, economic, and political factors in the emergence and growth of the BRICS countries (Brazil, Russia, India, China, and South Africa) over the years and pre-existing differences, the adverse effects of air pollution on the health and well-being of the people have remained major areas of academic inquiry and policy interventions. The present study examines the global trend of deaths and Disability Adjusted Life Years (DALYs) attributable to air pollution with particular reference to the BRICS countries for the period 1990 to 2019. Methods: This study has used the global burden of disease estimates by using different rounds of the Global Burden of Disease (GBD) study report published by the Institute of Health Metrics Evaluation. This study has calculated the cause of death and DALYs due to environmental risk factors (i.e. Air pollution). Data analysis has been done by using the standard formula for the calculation of death (mortality) rate and DALYs rate. Similarly, we calculated the age and gender-wise death and DALYs rate by using the appropriate numerator and denominator. Results: The study discovered a significant shift in disease patterns over this period, as communicable diseases like respiratory infections and tuberculosis were replaced by non-communicable diseases such as ischemic heart disease (17.2 million), chronic obstructive pulmonary disease (14.59 million), and stroke (17.02 million) as the primary causes of air pollution-related deaths in 2019 at the global level. Additionally, the study identified a worrying increase in deaths linked to neonatal disorders and respiratory infections caused by ambient particulate matter pollution in South Africa, India, and Brazil. The impact of air pollution on public health is evident across different age groups and genders, with people aged 50-69 years, those aged 70 and above, and children under 5 years being more vulnerable. Furthermore, the male population is disproportionately affected by communicable and noncommunicable diseases caused by air pollution. Conclusion: The study highlights the need for policymakers to implement evidence-based interventions to tackle this global health problem. The interventions should aim to reduce the emerging crisis of non-communicable diseases related to air pollution, particularly among vulnerable age groups and the male population, ultimately improving public health outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Perinatal depression significantly impacts maternal and child health, with further complexities arising during the COVID-19 pandemic. This review is the first to comprehensively synthesize evidence on the prevalence of perinatal depression and its associated risk factors in Low- and Middle-Income Countries (LMICs) during the pandemic period. Methods: The study protocol was registered in PROSPERO (CRD42022326991). This review followed the Joanna Briggs Institute (JBI) guideline for prevalence studies. A comprehensive literature search was conducted in six databases: PubMed, Scopus, Web of Science, PsycInfo, CINAHL, and ProQuest. Pooled prevalence estimates were computed for both prenatal and postnatal depression. Identified risk factors were summarized narratively. Results: A total of 5169 studies were screened, out of which 58 were included in the narrative review and 48 [prenatal (n = 36) and postnatal (n = 17)] were included in the meta-analysis. The pooled depression prevalence for prenatal women was 23% (95% CI: 19–27%), and for the postnatal women was 23% (95% CI: 18–30%). Maternal age, education, perceived fear of COVID-19 infection, week of pregnancy, pregnancy complications, and social and family support were identified as associated risk factors for depression. Conclusions: Our review demonstrates an increased prevalence of perinatal depression during the COVID-19 pandemic in LMICs. It sheds light on the significant burden faced by pregnant and postnatal women and emphasizes the necessity for targeted interventions during the ongoing and potential future crisis. [ABSTRACT FROM AUTHOR]

Skill in predicting monthly mean daily maximum air temperature (MonTmax) over India during the pre-monsoon months (March to May) at lead times of 1–3 months by the Scale Interaction Experiment-Frontier Research Center for Global Change Version 2 (SINTEX-F2) model was evaluated. Analysis revealed that MonTmax in April over northwest and south India could be skillfully predicted by the model with a two-month lead time. The model predictions could capture most of the extreme MonTmax events over the two regions. Analysis of the extreme events in April in La Niña and El Niño years shows that the anomalous Matsuno-Gill atmospheric response to the precipitation anomalies over the northwest Pacific plays a crucial role in enhancing MonTmax anomalies over India. In the El Niño-Southern Oscillation (ENSO) neutral years and in some ENSO years with sparse precipitation anomalies over the northwest and central Pacific, the atmospheric waves from higher latitudes play a role in enhancing MonTmax anomalies over northwest India. The SINTEX-F2 model could capture the processes realistically with minimal bias. [ABSTRACT FROM AUTHOR]

Adapting the power and energy systems by integrating renewable sources is necessary to address climate change. On the other hand, microgrids are gaining prominence in meeting power and energy requirements, including in remote locations. Consequently, the power system's penetration of renewable energy-based microgrids is increasing. Planning an isolated microgrid necessitates cost-effective capacity sizing of energy sources and storage systems for maintaining continuity in power supply. Considering the variability and uncertainty of photovoltaic (PV), wind energies, and load variations, deciding the optimal size of renewable-rich, isolated microgrids is challenging. Batteries and fuel cells are potential storage solutions for managing variability. However, a more trustworthy sizing approach is necessary to maximize power availability at the lowest possible cost, even in the face of uncertainty. Moreover, providing the microgrid owner with the opportunity to choose from a range of optimal solutions is also essential. Therefore, incorporating the uncertainty handling feature with the help of robust assessment under worst-case scenarios in the multi-objective optimization method can provide a more trustworthy solution. In this connection, a novel algorithm is proposed that instills robustness in the solutions provided by traditional non-dominated sorting genetic algorithm-II (NSGA-II), which can offer multiple break-even solutions. The isolated microgrids with PV, wind as sources, and storage technologies such as Lithium-ion (Li-ion) batteries, fuel cells, and a combination of both, a less explored scenario, have been compared to determine the effective storage option over the long run while considering uncertainties in renewable energy and load variations. The NSGA-II benchmark solutions developed under these uncertainties and variations are used to validate the robustness of the solutions obtained from the proposed robust algorithm. With a good trade-off between the cost and availability aspects, the proposed algorithm is found to be superior in getting maximum availability with minimum cost under uncertainties. [ABSTRACT FROM AUTHOR]

Software failure prediction is a crucial task in software quality assurance. Although time series forecasting techniques and conventional software reliability models are used for the prediction of software reliability, very often these models fail to provide accurate predictions. Therefore, an accurate model for software reliability prediction is imperative. The objective of this paper is to introduce a hybrid model that merges a recurrent adaptive neuro-fuzzy inference system (RANFIS) and modified differential evolution (MDE) for software reliability prediction. The model employs inner spatial feedback loops and delayed output feedback to improve the conventional neuro-fuzzy system's prediction abilities while dealing with time series software failure data. The hybrid model is trained by MDE, where a new scheme of crossover and mutation has been proposed. We conduct an extensive simulation on a few publicly available benchmark datasets for computing the predicting ability of our hybrid model. Simulation results along with statistical analysis illustrate that our hybrid model predicts more precisely the time between successive failures in software and outperforms other traditional models. [ABSTRACT FROM AUTHOR]

Pointed gourd (Trichosanthes dioica Roxb.) is an economically important dioecious vegetable crop having separate plants growing male and female flowers. Cross-pollination is imperative for its fruit settings and insects play a pivotal role in this. Although a nitidulid beetle was reported to be a primary pollinator in pointed gourd, its species identification and validation were needed to enhance the fruit production. The beetles collected from the pointed gourd have revealed presence of abundant round or spheroidal, trizonopororate, sticky pointed gourd pollens. Further, role of beetle was validated in farmers field where no fruit set was recorded when beetle's visit was curtailed. Twenty adult beetles were artificially released into a caged pot, and the maximum fruit setting (30.43%) indicated its potential role in pointed gourd pollination in vegetable ecosystem. Beetle-pollinated fruits had higher fruit length (6.88 cm), width (2.83 cm), fruit weight (22.63 g), and lesser number of seeds (16.75) than the hand-pollinated fruits (6.21 cm, 2.77 cm, 19.41 g, and 21.75 numbers, respectively). The morphological and molecular analysis identified the species as Epuraea motschulskyi and the study hereby reports for the first time that E. motschulskyi is the primary pollinator in pointed gourd. The study also suggests that the release and protection of the beetle in pointed gourd field would lead to enhanced fruit set. [ABSTRACT FROM AUTHOR]

The present study investigates the flower-anthesis mechanism in two closely related plant species, Cestrum diurnum and Cestrum nocturnum. These plants are morphologically similar, but exhibit distinct flowering times: C. diurnum flowers during the day, whereas C. nocturnum blooms at night. This contrast in flowering time makes them ideal subjects for this study. Physiological parameters like photosynthetic rate, floral development, and chlorophyll content were analyzed along with transcriptome sequencing. Transcripts related to blooming time were analyzed using de novo sequencing analysis, with emphasis on photoperiod, autonomous, circadian clock, and vernalization pathways. Gene Ontology and KEGG pathway enrichment analysis revealed differences in morphology and physiology. Transcription factors (bHLH, ERF, MYB, and C2H2) involved in regulating flowering time were identified between these two species. The delayed flowering of C. nocturnum was validated by qRT-PCR, which showed the involvement of FT, FLC, and CO genes. Physical attributes such as larger leaves and higher chlorophyll content were two distinctive characteristics of C. nocturnum attributed to higher PHYA levels. Explaining the different flowering events between these two species, the study implies that C. nocturnum focuses on flower formation, photoperiodic responses, and meristem management, whereas C. diurnum prioritizes growth and development. [ABSTRACT FROM AUTHOR]

The use of environmental-stress resilient plant growth promoting rhizobacteria (PGPR) offers an organic solution to sustainable agriculture, under rapid climate change. Our laboratory has previously reported the drought-ameliorating property of an exopolysaccharide-secreting PGPR strain, Bacillus endophyticus J13 on Arabidopsis thaliana. In this study, A. thaliana roots were inoculated with J13 under well-watered and water-stressed conditions (under controlled plant growth conditions). To understand the mechanism of drought amelioration, impact of J13 on plant ABA biosynthesis and signaling was analyzed. It was found that the ABA levels in water-stress, inoculated plants were lower than the water-stressed plants, without inoculation. Also, the expression of ABA biosynthesis genes: NCED3, AAO3 and ABA2, was downregulated in inoculated treatments under water-stress as compared to non-inoculated, water-stress treatments. J13 did not cause any modulation in the expression of ABA-dependent signaling gene SnRK2 and the ABA-independent signaling gene, DREB2A, under water-stress in Arabidopsis shoots. On treating the bacteria with EDTA (which negatively impacts biofilm levels), we observed that J13 fails to impart stress tolerance to plants under water deficit conditions. The soil moisture content in soil adhered to roots and exopolysaccharide (EPS) content was significantly higher in the plants under water-stressed, inoculated treatments than the non-inoculated plants. This study unravels the mechanism of drought amelioration by J-13 on Arabidopsis by modulating soil moisture content through EPS secretion, thereby eliminating the need for enhanced ABA biosynthesis and signaling. [ABSTRACT FROM AUTHOR]

Calling structural variations (SVs) is technically challenging, but using long reads remains the most accurate way to identify complex genomic alterations. Here we present Sniffles2, which improves over current methods by implementing a repeat aware clustering coupled with a fast consensus sequence and coverage-adaptive filtering. Sniffles2 is 11.8 times faster and 29% more accurate than state-of-the-art SV callers across different coverages (5–50×), sequencing technologies (ONT and HiFi) and SV types. Furthermore, Sniffles2 solves the problem of family-level to population-level SV calling to produce fully genotyped VCF files. Across 11 probands, we accurately identified causative SVs around MECP2, including highly complex alleles with three overlapping SVs. Sniffles2 also enables the detection of mosaic SVs in bulk long-read data. As a result, we identified multiple mosaic SVs in brain tissue from a patient with multiple system atrophy. The identified SV showed a remarkable diversity within the cingulate cortex, impacting both genes involved in neuron function and repetitive elements. Sniffles2 detects mosaic structural variation from bulk long-read sequencing data. [ABSTRACT FROM AUTHOR]

CM247LC alloy has been processed through two different routes, viz. investment casting and powder metallurgical route. The powder metallurgical route involves production of powder through inert gas atomization followed by consolidation using hot isostatic pressing (HIP). A comparative study has been carried out on microstructure and mechanical properties of the material processed through both routes to assess the efficacy of the powder metallurgical process as a viable alternative to the investment casting route. The HIPed material is free from deleterious prior particle boundaries and coarse primary γ′ and has finer grain size compared to cast material. The HIPed material exhibits higher strength and ductility at room temperature, whereas ductility of HIPed alloy at 760°C is less than for the cast alloy. Detailed microstructural analysis and thermodynamic calculation reveal that network of carbides at the grain boundary of HIPed material causes lower ductility although HIPed material exhibits better strength than cast material, even at higher temperature. This study establishes the potential of HIP process to manufacture near net shape components of complex configuration through powder metallurgy route. [ABSTRACT FROM AUTHOR]

The testing for distinctiveness, uniformity and stability (DUS) is a prerequisite for release and intellectual property rights (IPRs) protection of new cultivars. Ninety eight bitter gourd genotypes were characterized for 12 agro-morphological traits using DUS guidelines developed by UPOV. Shannon–Wiener diversity index across genotypes varied from 0.00 to 1.32 with mean value of 0.52, demonstrating the adequate phenotypic diversity. Estimates of diversity indices across Indian origins (0.52) and botanical variety, Momordica charantia var. charantia (0.53) genotypes exceeds exotic origins (0.50) and Momordica charantia var. muricata genotypes (0.34). The traits like leaf blade: depth of lobing, fruit color of skin and fruit tubercles were most diverse traits that can distinguish genotypes of bitter gourd. The other traits like sex forms and ridge patterns can be used for identifying and groping of bitter gourd cultivars. The wide variation was also recorded for quantitative traits like node number of first female flower (9.14–27.48), days to first female flower opening (38.62–60.30), days to first fruit harvest (45.68–73.40), fruit length (4.65–21.45 cm), fruit diameter (2.76–6.23 cm), fruit weight (23.14–88.63 g), fruits per plant (12.36–44.40), yield per plant (546.87–2354.51 g) and vine length (118.63–171.28 cm). High heritability for most of the quantitative traits indicated that selection would be effective in improving these traits. This is the first comprehensive study on DUS characterization and identification of a unique trait like white flower (DBGS-54-18) in bitter gourd. The presence of considerable levels of phenotypic variation could be exploited as a source of valuable gene pool for future breeding programme. [ABSTRACT FROM AUTHOR]

Background: Delirium poses a significant challenge in musculoskeletal trauma patients, particularly the elderly, contributing to elevated morbidity and mortality. Despite unclear pathogenesis, various risk factors have been identified. This prospective observational study, conducted in a tertiary center, aims to estimate delirium incidence and identify associated risk factors in adult patients undergoing lower limb trauma surgeries. Methods: Between August 2021 and December 2022, 150 patients with lower limb trauma admitted for surgery were included. Initial assessments recorded Mini-Mental State Examination scores, Injury Severity Scores, and potential risk factors. Confusion Assessment Method scoring done preoperatively (excluding emergencies) and postoperatively on days 2 and 5 or at discharge. Daily delirium screening utilized Nursing 4-Abbreviated Trauma score, with severity assessed using CAM-Severity score. Risk factor analysis categorized patients into Group A (delirium) and Group B (non-delirium), with primary endpoint being delirium occurrence. Results: The study revealed a 10% delirium incidence. Delirium severity ranged from mild to severe. The median age (in years) was 79 vs 46 in delirium and non-delirium group respectively. Delirium patients had longer hospital stay (13 vs 8, p value 0.011). Similarly, factors like female gender, delayed surgery, hyponatremia, hypoproteinaemia, increased injury severity, midazolam use during induction, multiple blood transfusions, and heightened postoperative pain intensity were found significant (p value < 0.05). Conclusions: This study enhances our understanding of delirium in lower limb trauma patients undergoing surgeries. Identified risk factors offer insights for targeted interventions, emphasizing the need for comprehensive preoperative assessments and management strategies to reduce delirium incidence and improve patient outcomes. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable economic development, this study investigates the nexus between carbon emissions, renewable energy utilization, trade openness, foreign direct investment and output volatility across selected East Asia–Pacific nations. Employing advanced ARDL bound tests, our results reveal a robust long-term relationship among these selected variables. Notably, our findings underscore the significant impact of carbon emissions on output volatility across all examined nations, except for Malaysia. Moreover, the study highlights the positive impact of renewable energy on output volatility, signaling a compelling imperative for policymakers to prioritize initiatives aimed at curbing carbon emissions and fostering the widespread adoption of renewable energy sources. [ABSTRACT FROM AUTHOR]

Stroke remains a critical global health issue, significantly impacting India with substantial contributions to mortality and disability. This study comprehensively analyses stroke incidence, mortality, and disability-adjusted life years (DALYs) across India from 1990 to 2021, using the latest Global Burden of Disease (GBD) 2021 data. We evaluate how Universal Health Coverage (UHC), health expenditure, human development index (HDI), and gross national income (GNI) influence stroke outcomes. Our findings reveal significant regional disparities, with higher stroke rates in urban areas and states like Goa and Kerala. Higher health expenditure and HDI are linked to lower stroke rates, while higher GNI per capita correlates with increased stroke incidence, likely due to lifestyle changes. Risk factors include air pollution, tobacco use, dietary risks, and high blood pressure. Air pollution notably impacts stroke mortality in Bihar and Jharkhand, while tobacco use is a major risk factor in Mizoram and Manipur. Dietary risks and hypertension are prevalent in Maharashtra and Jammu & Kashmir. The study highlights the need for targeted public health strategies addressing regional disparities and socioeconomic factors. Policymakers should focus on lifestyle modification programs, public awareness campaigns, and enhanced access to quality stroke care to reduce stroke-related morbidity and mortality effectively. [ABSTRACT FROM AUTHOR]

Breast carcinoma or breast cancer is the most common type of cancer in women. It is very dangerous, and early detection is important for proper diagnosis and reduction of mortality rate. Cancer starts in a benign state, and if there is no proper treatment, it becomes malignant. Computer-aided diagnosis (CAD) techniques can be used for early detection of cancers. These techniques can significantly and effectively help in the diagnosis of cancer. Deep learning is the most popular technique used for accurate data analysis. It is also a powerful tool for categorizing histopathological images of breast cancer and analyzing breast cell shapes and densities. This paper proposes a hybrid EfficientNet with an SVM to categorize breast mass density with consideration of tumor types. (i.e., malignant or benign). In this hybrid, EfficientNet, the mammography images are applied to EfficientNet, and then the classification is carried out using a Support Vector Machine (SVM). The proposed model achieved accuracy of 94.48%, precision of 94.79%, specificity of 99.21%, Precision of 94.79%, FPR of 0.79%, score of 94.45%, MCC of 93.78%, and Kappa of 74.76%. The model outperformed state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Triple Negative Breast Cancer (TNBC) is a highly aggressive and treatment-resistant subtype of breast cancer, lacking the expression of estrogen, progesterone, and HER2 receptors. Conventional chemotherapy remains the primary treatment option, but its efficacy is often compromised by the development of drug resistance. Nanoquercetin has garnered the attention of researchers due to its potential in combating cancer. This antioxidant exhibits significant efficacy against various types of cancer, including blood, breast, pancreatic, prostate, colon, and oral cancers. Functioning as a potential anti-cancer agent, nanoquercetin impedes the development and proliferation of cancer cells, induces apoptosis and autophagy, and prevents cancer cell invasion and metastasis. Numerous processes, such as the inhibition of pathways linked to angiogenesis, inflammation, and cell survival, are responsible for these anticancer actions. Moreover, it shields DNA from degradation caused by radiation and other carcinogens. The cost-effectiveness of current cancer treatments remains a significant challenge in healthcare, imposing a substantial economic burden on societies worldwide. Preclinical studies and early-phase clinical trials indicate that nanoquercetin-based therapies could offer a significant advancement in the management of TNBC, providing a foundation for future research and clinical application in overcoming drug resistance and improving patient outcomes. This article examines the latest data on nanoquercetin's potent anti-cancer properties and interprets the accumulated research findings within the framework of preventive, predictive, and personalized (3P) medicine. [ABSTRACT FROM AUTHOR]

This study investigates olfaction, a complex and not well-understood sensory modality. The chemical mechanism behind smell can be described by so far proposed two theories: vibrational and docking theories. The vibrational theory has been gaining acceptance lately but needs more extensive validation. To fill this gap for the first time, we, with the help of data-driven classification, clustering, and Explainable AI techniques, systematically analyze a large dataset of vibrational spectra (VS) of 3018 molecules obtained from the atomistic simulation. The study utlizes image representations of VS using Gramian Angular Fields and Markov Transition Fields, allowing computer vision techniques to be applied for better feature extraction and improved odor classification. Furthermore, we fuse the PCA-reduced fingerprint features with image features, which show additional improvement in classification results. We use two clustering methods, agglomerative hierarchical (AHC) and k-means, on dimensionality reduced (UMAP, MDS, t-SNE, and PCA) VS and image features, which shed further insight into the connections between molecular structure, VS, and odor. Additionally, we contrast our method with an earlier work that employed traditional machine learning on fingerprint features for the same dataset, and demonstrate that even with a representative subset of 3018 molecules, our deep learning model outperforms previous results. This comprehensive and systematic analysis highlights the potential of deep learning in furthering the field of olfactory research while confirming the vibrational theory of olfaction. [ABSTRACT FROM AUTHOR]

In response to the growing population and increasing demand for cattle products, enhancing sorghum forage yield is essential for ensuring food security. This study aimed to identify stable genotypes with high forage yields and key yield traits for sorghum breeding programs. Ninety-five forage sorghum lines were evaluated under five distinct climatic conditions over two years (2020–2021), revealing significant genotype × environment interaction (GEI) effects for 14 agronomic traits. Two BLUP-based mixed model stability methods, weight average absolute score based on BLUP (WAASB) and the multi-trait stability index (MTSI), were employed for stability analysis. Three genotypes, G90 (424B), G80 (382B) and G3 (349B) were identified stable and high yielding for forage yield based on WAASB based methods. The MTSI, a novel simultaneous selection index, effectively selected genotypes based on multiple agro-morphological traits, except for the leaf-to-stem ratio. Genotypes G81, G90, G80, and G89 were identified as desirable based on the MTSI. The strength and weakness plot is highlighted as a valuable graphical tool for identifying and selecting genotypes based on trait strengths and weaknesses. Among these, G90 (424B) and G80 (382B) stood out as superior, excelling in both forage yield and early maturity, as determined by WAASB based methods and MTSI method. These genotypes warrant further comprehensive investigation across diverse environments and show significant potential for future breeding programs. [ABSTRACT FROM AUTHOR]

This study presents a nonordinary state-based (NOSB) peridynamic (PD) modeling of creep deformation and damage. The force density vectors in PD equilibrium equations are derived by considering the Liu and Murakami creep model with a damage parameter. The bond-associated (BA) deformation gradient is derived by using the PD differential operator (PDDO). Traction and displacement boundary conditions are directly imposed through a novel strategy while solving for the strong form of PD equilibrium equations. The PD form of traction components enables the imposition of traction conditions in the actual "boundary layer" region without any unphysical displacement kinks near the boundaries. The approach is validated under uniaxial and 2D plane stress assumptions by considering creep deformation due to constant stress at high temperatures. The creep strain predictions are in excellent agreement with the experimental data and analytical solutions. Subsequently, creep crack growth in a compact tension (CT) specimen is simulated by using the damage variable in Liu and Murakami constitutive model. [ABSTRACT FROM AUTHOR]

Background: Mental and physical non-communicable diseases (NCDs) coexist, because they share common environmental and behavioral risk factors. The treatment gap for common mental disorders, such as depression, anxiety, and substance use, is large compared to other NCDs. Objective: To determine the prevalence and treatment gap of common mental disorders among patients with non-communicable diseases. Methodology: The community-based cross-sectional study was conducted in the rural parts of East India, in people aged 30 years and older with NCDs. A simple random sample was chosen to select the villages and participants. Eligible participants administered with screening and diagnostic questionnaire for depression, anxiety, and substance use. Those diagnosed with mental disorders were again assessed for treatment status in the last 12 months. Non-receipt of treatment was considered as treatment gap. Results: A total of 515 participants were included in the analysis. The overall prevalence of common mental disorders among the study population was 46.4% (95% CI 42.0–50.8), and excluding substance use, the prevalence was 11.7% (95% CI 9.0–14.7). The treatment gap for common mental disorders among patients with non-communicable diseases, including and excluding substance use, was 98.3% (95% CI 95.8–99.5) and 93.3% (95% CI 83.8–98.2), respectively. Conclusion: The prevalence and treatment gap of common mental disorders among persons with NCDs was high. Public health interventions need to be emphasized for the integration of mental health care into NCD care. [ABSTRACT FROM AUTHOR]

In this study, we examine the modulation of light transmission via a one-dimensional polymeric symmetrical Fibonacci-multilayer. The multilayer is constructed using substitutional generalised Fibonacci using PMMA and PS as the constituent materials. The widely recognised transfer matrix approach is utilised to derive theoretical results. The results of our investigation indicate that the width, spacing, and depth of the PBG can be modified by choosing the proper generation number. Remarkably, the count of significant PBGs remains consistent throughout all generation numbers, with the exception of the 10 th generation symmetrical Fibonacci-multilayer, which exhibits a pronounced localization. As the generation number grows, a symmetrical shift of the PBGs is noticed in relation to their position. Moreover, it has been shown that the forbidden frequency range can be adjusted by changing the hydrostatic pressure applied. The rise in pressure causes a shift in the frequency spectrum towards higher frequencies. Furthermore, our findings indicate that augmenting the polymer thickness leads to a shift towards longer wavelengths, known as a redshift, in the PBGs. This paper presents a new method for creating optical devices, specifically multi-band tunable optical filters and durable optical sensors, using one-dimensional polymeric symmetrical Fibonacci multilayers. [ABSTRACT FROM AUTHOR]

This research focuses on the stability analysis of an odd viscosity-induced shear-imposed Newtonian fluid flowing down an inclined slippery bed having an insoluble surfactant at the top of the liquid surface. The Orr-Sommerfeld boundary value problem is developed by applying the normal mode approach to the infinitesimal perturbed fluid flow and solved using the numerical method Chebyshev spectral collocation. The numerical results confirm the existence of Yih mode and Marangoni mode in the longwave zone. For the clean/contaminated surface of the film flow, the presence of an odd or Hall viscosity coefficient reduces the surface wave energy and delays the transition from laminar to perturbed flow. Also, it has stabilizing nature on the unstable Marangoni mode as well. The growth rate of both clean and contaminated liquid surfaces becomes more/less when the stronger external shear acts along the downstream/upstream direction of fluid flow. Further, the slip parameter leads to a lower critical Reynolds number and makes the liquid surface more unstable. An increase in the critical Reynolds number due to the stronger Marangoni force ensures that the insoluble surfactant has the potential to dampen the Yih mode instability. Moreover, the unstable shear mode occurs in the finite wavenumber regime with very high inertial force and a small angle of inclination. The two-fold variation of the shear mode instability is possible with respect to the imposed shear. However, the inclusion of the odd viscosity coefficient in the viscous falling film may advance the shear mode instability. [ABSTRACT FROM AUTHOR]

This study presents exceptional perception into the phase transition behavior of binary mixtures containing 3-chloro-2-hydroxypropyl methacrylate (3C2HM) or 2-N-morpholinoethyl methacrylate (2NMEM) in supercritical CO2 at different operating temperatures (313.2–393.2 K) and pressures (3.36–33.90 MPa). The findings are expected to significantly contribute to the evolution of advanced materials and technologies in several industrial sectors. As temperature increases at constant pressure, carbon dioxide (CO2) solubility in the monomer aqueous phase decreases. However, the solvability of the binary systems improved with temperature and mole fraction at steady pressure. The 2NMEM component exhibited higher polarizability and lower surface tension than the 3C2HM monomer, making it less soluble in CO2, which is a nonpolar compound. The solution phase of the binary systems exhibited Type I phase behavior, and the phase diagrams were nearly identical. The experimental solubility data were adequately correlated with the Peng–Robinson equation of state with the aid of molecular interaction parameters (IPs) which was evaluated at 353.2 K. The optimized molecular IPs were nearly zero, confirming that both binary systems were nearly ideal mixture systems as the temperature increased. The model precision was evaluated by calculating the percentage of root-mean-square deviation (RSD%) at five temperatures using the molecular IPs. The calculated RSD% of the CO2 + 3C2HM and CO2 + 2NMEM systems were 4.70% and 4.91%, respectively, indicating that the model values fit reasonably well. Therefore, the predicted phase behavior agrees well with the experimental phase transitions of both systems. The characteristics of the critical solution curve were simulated to realise the interactions and transition behavior of the studied binary systems. This is the first study to demonstrate the solubility of CO2 + 3C2HM and CO2 + 2NMEM chemical mixtures, and it will be significant for chemical industries. [ABSTRACT FROM AUTHOR]

This study focuses on numerically optimizing key process parameters related to the liquid–liquid extraction batch process (LLEBP) technique for carrying out batch runs to remove methyl red effectively (MR) from dye effluent. LLEBP, a suitable industrial process for treating dye effluents, depends on the number of reaction parameters such as feed concentration, extraction time, and dye ratio (solution/solvent). The current research utilized a central composite design (CCD) of experiments along with numerical optimization techniques to optimize process parameters over a range of dye concentrations: (20–100) ppm, extraction time range 10–30 min, and dye ratio 1–3 mL/mL (solution/solvent). The batch runs performed at room temperature and a constant pH of 3, according to the experimental design criteria, suggest that maximum dye removal efficiency and distribution coefficient value could be achieved within the feed concentration range of (20–30) ppm, 20–30 min of extraction time, and 1–3 mL/mL of dye ratio (solution/solvent). Solvent capacity increases significantly within the (60–100) ppm feed concentration range. Numerical optimization with desirability function criteria identified optimal conditions: 20 ppm dye concentration, 30 min extraction time, and 3 mL/mL dye ratio ensuring maximum LLEBP yield. The current investigation achieved a 4% higher dye removal (%) of 85.682 compared to the previous study. The distribution coefficient and solvent capacity attained were 5.287 and 4.504 mg/L, respectively. The research enhances understanding of the optimization process for LLEBP in MR dye removal from textile effluent, surpassing previous findings within the same input range. The manuscript aims to maximize process optimization using CCD, promoting sustainable industry progress in line with UN sustainable development goals. [ABSTRACT FROM AUTHOR]

High polyhydroxyalkanoate (PHA) yield from selected substrates associated with minimal accumulation of residual biomass can improve the process economy. In this study, different carbon (glucose, sucrose, glycerol, and acetic acid) and nitrogen (NH4Cl and urea) sources were screened for PHA production by Cupriavidus necator. The effects of incubation time, nitrogen, and phosphate concentration on biomass growth and PHA production were co-optimized through response surface methodology (RSM) and genetic algorithm-optimized artificial neural network (GA-ANN). Sucrose and urea were found to offer significantly better (p <0.001) biomass (1.468 ± 0.007 g l-1) and PHA (0.924 ± 0.02 g l-1) yield when compared with other carbon and nitrogen sources. Though the performance of both the models remains similar for biomass (R2 = 0.97–0.98), GA-ANN (with six neurones in a hidden layer) seems exceptionally better in predicting PHA yield (R2 = 0.97) when compared to the polynomial model (R2 = 0.92). The maximum PHA concentration of 2.69 g l-1 was predicted by the ANN model at an incubation time of 62.80 h with 2.0 g l-1 of nitrogen and 4.0 g l-1 of phosphate concentration. The multi-composite desirability using the GA-ANN model projected a better polymer-to-biomass ratio compared to the polynomial model. The inclusion of a cost-benefit analysis framework may be warranted before recommending the optimal conditions obtained through multivariate regression and GA-ANN models. [ABSTRACT FROM AUTHOR]

This article describes the solution procedure of the fractional Pade-II equation and generalized Zakharov equation(GSEs) using the sine-cosine method. Pade-II is an important nonlinear wave equation modeling unidirectional propagation of long-wave in dispersive media and GSEs are used to model the interaction between one-dimensional high, and low-frequency waves. Classes of trigonometric and hyperbolic function solutions in fractional calculus are discussed. Graphical simulations of the numerical solutions are flaunted by MATLAB. [ABSTRACT FROM AUTHOR]

Silicon nanowalls atop the (100) – oriented boron doped P-type (1–10 Ω-cm) single crystalline silicon wafers were prepared using a metal assisted chemical etching route for different durations 1, 5, 15, and 30 min. The scanning electron microscopy results revealed that the structure evolved up on etching is in the form of vertical silicon nanowalls with mean wall thickness of 70 nm. It can be observed that as the etching time increases, the height of the SiNWs increases linearly at an etching rate of ~ 301 nm per minute. The transmission electron microscopy results combined with FTIR spectroscopy results indicate that about one nanometer thick Si–O-Si- bonded amorphous layer formed at the surface of the grown silicon nanowalls. A defect sensitive Variable energy positron beam Doppler broadening technique used to study the etched silicon wafers confirms that the defect structures that are evolved in the silicon nanowalls with etching are different from that of planar Silicon. Analysis of the Doppler broadened line-shape profiles shows that the effective positron diffusion length and height of the silicon nanowalls are related logarithmically with a scaling exponent of—1/2, indicating that the implanted positrons that are thermalized in the silicon nanowalls diffuse back to the wall surfaces and are annihilated at the defects linked (Si–O-Si)/Si interface region. The present positron experimental results abound with literature reports suggest that understanding the microstructure of the surface layer in SiNWs is significantly important in determining their performance for producing efficient solar cells. [ABSTRACT FROM AUTHOR]

For many years, spectral remote sensing has been essential for research on the Earth's surface. The data from a single satellite sensor is sometimes insufficient to fulfil the expanding needs of remote sensing applications. Spatial-temporal fusion techniques have become an effective way for merging spectral data from many sources and times, enabling improved data analysis and interpretation. The goal of this review paper is to offer a thorough examination of the historical growth of spatio-temporal fusion techniques for spectral remote sensing. The classification of all currently used fusion approaches, such as Unmixing, Weight-based, Bayesian-based, machine learning-based, and hybrid methods, is covered in detail. Additionally, it evaluates pixel-level, decision-level, and feature-level-based data fusion techniques and compares and contrasts their advantages and disadvantages. The report also discusses spatiotemporal fusion's difficulties and recommends future advances. For those working in remote sensing research and practice, it offers an invaluable resource. In conclusion, this review paper provides a comprehensive overview of spatio-temporal fusion systems for spectral remote sensing, including an analysis of their comparative benefits and drawbacks and a description of their historical development. It aims to stimulate further research and development of spatio-temporal fusion methods for spectral remote sensing. In summary, this review paper presents a comprehensive overview of spatio-temporal fusion methods for spectral remote sensing, including their historical development, categorization of existing techniques and applications, and a comparative analysis of their strengths and limitations. It also discusses the current challenges and future research directions, providing a valuable resource for the remote sensing community. [ABSTRACT FROM AUTHOR]

Arsenic (As) pollution in soil poses a significant risk to human health and sustainable development goals. Accurate measurement of the degree of As contamination and mapping in soil are important for identifying high-risk areas and developing remediation strategies. In this study, 201 soil samples (0–15 cm) were collected from As contaminated rice growing regions in Nadia, West Bengal, India. Various pollution indices (geoaccumulation index, Igeo; contamination factor, CF; ecological risk index, Eri) were computed to understand the contamination level and its health implications. Geostatistical analysis using ArcGIS mapped the spatial distribution of As contamination in soil. The accuracy of different interpolation techniques were evaluated using root mean square error (RMSE) and mean CV (cross-validation) parameters. Results showed that total As content in soil ranged from 3.47 to 53.1 mg kg‒1 (mean-19.3 mg kg‒1). The pollution indices such as Igeo, CF, and Eri varied from ‒0.22 to 3.72 (mean-2.11 mg kg‒1), 0.23 to 3.54 (mean-1.29 mg kg‒1), and 2.32 to 35.4 (mean-12.9 mg kg‒1), respectively. The northern side of Nadia showed higher As contamination, while comparatively lower levels were observed in southern side. Two interpolation techniques, viz. ordinary kriging (OK) and inverse distance weighting (IDW) were identified to precisely predict the spatial distribution of As in soil based on RMSE and mean CV values. Health risk assessment showed that children were more vulnerable to As toxicity in the study region. The findings support the development of location-specific remediation strategies and policies to minimize health risks associated with As-contaminated soils. [ABSTRACT FROM AUTHOR]