Ultrahigh-pressure isostructural electronic transitions in hydrogen.

High-pressure transitions are thought to modify hydrogen molecules to a molecular metallic solid and finally to an atomic metal1, which is predicted to have exotic physical properties and the topology of a two-component (electron and proton) superconducting superfluid condensate2,3. Therefore, understanding such transitions remains an important objective in condensed matter physics4,5. However, measurements of the crystal structure of solid hydrogen, which provides crucial information about the metallization of hydrogen under compression, are lacking for most high-pressure phases, owing to the considerable technical challenges involved in X-ray and neutron diffraction measurements under extreme conditions. Here we present a single-crystal X-ray diffraction study of solid hydrogen at pressures of up to 254 gigapascals that reveals the crystallographic nature of the transitions from phase I to phases III and IV. Under compression, hydrogen molecules remain in the hexagonal close-packed (hcp) crystal lattice structure, accompanied by a monotonic increase in anisotropy. In addition, the pressure-dependent decrease of the unit cell volume exhibits a slope change when entering phase IV, suggesting a second-order isostructural phase transition. Our results indicate that the precursor to the exotic two-component atomic hydrogen may consist of electronic transitions caused by a highly distorted hcp Brillouin zone and molecular-symmetry breaking.

Selenium - An environmentally friendly micronutrient in agroecosystem in the modern era: An overview of 50-year findings.

Agricultural productivity is constantly being forced to maintain yield stability to feed the enormously growing world population. However, shrinking arable and nutrient-deprived soil and abiotic and biotic stressor (s) in different magnitudes put additional challenges to achieving global food security. Though well-defined, the concept of macro, micronutrients, and beneficial elements is from a plant nutritional perspective. Among various micronutrients, selenium (Se) is essential in small amounts for the life cycle of organisms, including crops. Selenium has the potential to improve soil health, leading to the improvement of productivity and crop quality. However, Se possesses an immense encouraging phenomenon when supplied within the threshold limit, also having wide variations. The supplementation of Se has exhibited promising outcomes in lessening biotic and abiotic stress in various crops. Besides, bulk form, nano-Se, and biogenic-Se also revealed some merits and limitations. Literature suggests that the possibilities of biogenic-Se in stress alleviation and fortifying foods are encouraging. In this article, apart from adopting a combination of a conventional extensive review of the literature and bibliometric analysis, the authors have assessed the journey of Se in the "soil to spoon" perspective in a diverse agroecosystem to highlight the research gap area. There is no doubt that the time has come to seriously consider the tag of beneficial elements associated with Se, especially in the drastic global climate change era.

Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.

Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.

A Framework for Selecting and Assessing Wearable Sensors Deployed in Safety Critical Scenarios.

Wearable sensors for psychophysiological monitoring are becoming increasingly mainstream in safety critical contexts. They offer a novel solution to capturing sub-optimal states and can help identify when workers in safety critical environments are suffering from states such as fatigue and stress. However, sensors can differ widely in their application, design, usability, and measurement and there is a lack of guidance on what should be prioritized or considered when selecting a sensor. The paper aims to highlight which concepts are important when creating or selecting a device regarding the optimization of both measurement and usability. Additionally, the paper discusses how design choices can enhance both the usability and measurement capabilities of wearable sensors. The hopes are that this paper will provide researchers and practitioners in human factors and related fields with a framework to help guide them in building and selecting wearable sensors that are well suited for deployment in safety critical contexts.

Temporally Correlated Deep Learning-Based Horizontal Wind-Speed Prediction.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

Computation of X-ray and Neutron Scattering Patterns to Benchmark Atomistic Simulations against Experiments.

Molecular Dynamics simulations study material structure and dynamics at the atomic level. X-ray and neutron scattering experiments probe exactly the same time- and length scales as the simulations. In order to benchmark simulations against measured scattering data, a program is required that computes scattering patterns from simulations with good single-core performance and support for parallelization. In this work, the existing program Sassena is used as a potent solution to this requirement for a range of scattering methods, covering pico- to nanosecond dynamics, as well as the structure from some Ångströms to hundreds of nanometers. In the case of nanometer-level structures, the finite size of the simulation box, which is referred to as the finite size effect, has to be factored into the computations for which a method is described and implemented into Sassena. Additionally, the single-core and parallelization performance of Sassena is investigated, and several improvements are introduced.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Assessing Cognitive Workload Using Cardiovascular Measures and Voice.

Monitoring cognitive workload has the potential to improve both the performance and fidelity of human decision making. However, previous efforts towards discriminating further than binary levels (e.g., low/high or neutral/high) in cognitive workload classification have not been successful. This lack of sensitivity in cognitive workload measurements might be due to individual differences as well as inadequate methodology used to analyse the measured signal. In this paper, a method that combines the speech signal with cardiovascular measurements for screen and heartbeat classification is introduced. For validation, speech and cardiovascular signals from 97 university participants and 20 airline pilot participants were collected while cognitive stimuli of varying difficulty level were induced with the Stroop colour/word test. For the trinary classification scheme (low, medium, high cognitive workload) the prominent result using classifiers trained on each participant achieved 15.17 ± 0.79% and 17.38 ± 1.85% average misclassification rates indicating good discrimination at three levels of cognitive workload. Combining cardiovascular and speech measures synchronized to each heartbeat and consolidated with short-term dynamic measures might therefore provide enhanced sensitivity in cognitive workload monitoring. The results show that the influence of individual differences is a limiting factor for a generic classification and highlights the need for research to focus on methods that incorporate individual differences to achieve even better results. This method can potentially be used to measure and monitor workload in real time in operational environments.

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks.

Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications' execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

Potent Blockchain-Enabled Socket RPC Internet of Healthcare Things (IoHT) Framework for Medical Enterprises.

Present-day intelligent healthcare applications offer digital healthcare services to users in a distributed manner. The Internet of Healthcare Things (IoHT) is the mechanism of the Internet of Things (IoT) found in different healthcare applications, with devices that are attached to external fog cloud networks. Using different mobile applications connecting to cloud computing, the applications of the IoHT are remote healthcare monitoring systems, high blood pressure monitoring, online medical counseling, and others. These applications are designed based on a client-server architecture based on various standards such as the common object request broker (CORBA), a service-oriented architecture (SOA), remote method invocation (RMI), and others. However, these applications do not directly support the many healthcare nodes and blockchain technology in the current standard. Thus, this study devises a potent blockchain-enabled socket RPC IoHT framework for medical enterprises (e.g., healthcare applications). The goal is to minimize service costs, blockchain security costs, and data storage costs in distributed mobile cloud networks. Simulation results show that the proposed blockchain-enabled socket RPC minimized the service cost by 40%, the blockchain cost by 49%, and the storage cost by 23% for healthcare applications.

A Long Short-Term Memory Biomarker-Based Prediction Framework for Alzheimer's Disease.

The early prediction of Alzheimer's disease (AD) can be vital for the endurance of patients and establishes as an accommodating and facilitative factor for specialists. The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Pressure-induced order-disorder transitions in ß-In2S3: an experimental and theoretical study of structural and vibrational properties.

This joint experimental and theoretical study of the structural and vibrational properties of ß-In2S3 upon compression shows that this tetragonal defect spinel undergoes two reversible pressure-induced order-disorder transitions up to 20 GPa. We propose that the first high-pressure phase above 5.0 GPa has the cubic defect spinel structure of α-In2S3 and the second high-pressure phase (ϕ-In2S3) above 10.5 GPa has a defect α-NaFeO2-type (R3̄m) structure. This phase, related to the NaCl structure, has not been previously observed in spinels under compression and is related to both the tetradymite structure of topological insulators and to the defect LiTiO2 phase observed at high pressure in other thiospinels. Structural characterization of the three phases shows that α-In2S3 is softer than ß-In2S3 while ϕ-In2S3 is harder than ß-In2S3. Vibrational characterization of the three phases is also provided, and their Raman-active modes are tentatively assigned. Our work shows that the metastable α phase of In2S3 can be accessed not only by high temperature or varying composition, but also by high pressure. On top of that, the pressure-induced ß-α-ϕ sequence of phase transitions evidences that ß-In2S3, a BIII2XV3 compound with an intriguing structure typical of AIIBIII2XVI4 compounds (intermediate between thiospinels and ordered-vacancy compounds) undergoes: (i) a first phase transition at ambient pressure to a disordered spinel-type structure (α-In2S3), isostructural with those found at high pressure and high temperature in other BIII2XV3 compounds; and (ii) a second phase transition to the defect α-NaFeO2-type structure (ϕ-In2S3), a distorted NaCl-type structure that is related to the defect NaCl phase found at high pressure in AIIBIII2XVI4 ordered-vacancy compounds and to the defect LiTiO2-type phase found at high pressure in AIIBIII2XVI4 thiospinels. This result shows that In2S3 (with its intrinsic vacancies) has a similar pressure behaviour to thiospinels and ordered-vacancy compounds of the AIIBIII2XVI4 family, making ß-In2S3 the union link between such families of compounds and showing that group-13 thiospinels have more in common with ordered-vacancy compounds than with oxospinels and thiospinels with transition metals.

Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch.

Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices-namely, the apnoea-hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson's correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients' OSA severity. The high supine percentage of sleep may cause OSA indices' overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption.

The climate forcing of contrails and induced-cirrus cloudiness is thought to be comparable to the cumulative impacts of aviation CO2 emissions. This paper estimates the impact of aviation contrails on climate forcing for flight track data in Japanese airspace and propagates uncertainties arising from meteorology and aircraft black carbon (BC) particle number emissions. Uncertainties in the contrail age, coverage, optical properties, radiative forcing, and energy forcing (EF) from individual flights can be 2 orders of magnitude larger than the fleet-average values. Only 2.2% [2.0, 2.5%] of flights contribute to 80% of the contrail EF in this region. A small-scale strategy of selectively diverting 1.7% of the fleet could reduce the contrail EF by up to 59.3% [52.4, 65.6%], with only a 0.014% [0.010, 0.017%] increase in total fuel consumption and CO2 emissions. A low-risk strategy of diverting flights only if there is no fuel penalty, thereby avoiding additional long-lived CO2 emissions, would reduce contrail EF by 20.0% [17.4, 23.0%]. In the longer term, widespread use of new engine combustor technology, which reduces BC particle emissions, could achieve a 68.8% [45.2, 82.1%] reduction in the contrail EF. A combination of both interventions could reduce the contrail EF by 91.8% [88.6, 95.8%].

Ultra-structure alteration via enhanced silicon uptake in arsenic stressed rice cultivars under intermittent irrigation practices in Bengal delta basin.

The study implements a periodical intermittent water cycle during rice cultivation providing insight potential in minimizing soil bio-available arsenic. Soil As concentrations were 34 ±â€¯0.49 and 72.03 ±â€¯0.54 mg kg-1 As respectively in two selected fields with rice cultivars gosai and satabdi, in comparison to 42.26 ±â€¯0.37 and 83.69 ±â€¯0.48 mg kg-1 in continuously flooded field soil, determined through ICP-MS. The study found higher translocation of silicon from soil to rice plant parts under intermittent irrigation having pH range of 7.6-9.4 and greater availability of soil organic content that in turn release more labile silicon from soil to aqueous phase for plant accumulation. This increased uptake of silicon strengthens rice shoots, nodes and leaf xylem-phloem integrity compared to conventional continuously flooded rice cultivation approach, suppressing the arsenic translocation, as observed under FE-SEM real-time imaging. Fresh plants were analysed for bioaccumulation and translocation factors of arsenic and silicon to justify the enhanced silicon uptake under proposed practice. Plant stress regulator enzymes viz. malondialdehyde (MDA), total protein, superoxide dismutase (SOD), guaiacol peroxidase (GPX) and ascorbate peroxidase (APX) from both conditions and found to be better in intermittent method over conventional practice with higher productivity.

An assessment of arsenic hazard in groundwater-soil-rice system in two villages of Nadia district, West Bengal, India.

The present study measured arsenic (As) concentrations in soil, groundwater and rice grain samples in two villages, Sarapur and Chinili, under Chakdaha block, Nadia district, West Bengal, India. This study also included a survey of the two villages to understand the knowledge among villagers about the As problem. Soil and groundwater samples were collected from fields in two villages while rice grain samples were collected from villagers' houses. The results revealed the presence of As in higher concentrations than the maximum permissible limit of As in drinking water (10 µg L-1 and 50 µg L-1 by WHO and Indian standard, respectively) in groundwater [124.50 ± 1.11 µg L-1 (Sarapur) and 138.20 ± 1.34 µg L-1 (Chinili)]. The level of As in soil was found to range from 47.7 ± 0.14 to 49.3 ± 0.19 mg Kg-1 in Sarapur and from 57.5 ± 0.25 to 62.5 ± 0.44 mg Kg-1 in Chinili which are also higher than European Union maximum acceptable limit in agricultural soil (i.e. 20 mg Kg-1). The analysis of As in rice grains of five varieties, collected from residents of two villages, showed the presence of higher than recommended safe level of As in rice by FAO/WHO (0.2 mg Kg-1). The As concentration order was Gosai (0.95 ± 0.044 mg kg-1), Satabdi (0.79 ± 0.038 mg kg-1), Banskathi (0.60 ± 0.026 mg kg-1), Kunti (0.47 ± 0.018 mg kg-1) and Ranjit (0.29 ± 0.021 mg kg-1). Importantly, Gosai and Satabdi were the most popular varieties being consumed by local people. The data of consumption of rice per day in the survey was used for the measurement of average daily dose and hazard quotient. It was seen that the As hazard was negatively correlated to the age of residents. Therefore, children and toddlers were at higher risk of As exposure than elderly people. In addition, people with skin related As toxicity symptoms were also cited in the two villages. The study emphasized the severity of As problem in remote areas of West Bengal, India where people consume As tainted rice due to lack of awareness about the As problem and associated health issues.

Vermiremediation of metal(loid)s via Eichornia crassipes phytomass extraction: A sustainable technique for plant amelioration.

Eichhornia crassipes (water hyacinth), imparts deficiency of soluble arsenic and other toxic metal (loid)s through rhizofiltration and phytoaccumulation. Without proper management strategy, this phytoremediation of metal (loid)s might fail and get reverted back to the environment, contaminating the nearby water bodies. This study, focused on bio-conversion of phytoremediating hyacinths, spiked with 100 times and greater arsenic, lead and cadmium concentrations than the average water contamination, ranging in 58.81 ±â€¯0.394, 16.74 ±â€¯0.367, 12.18 ±â€¯0.153 mg Kg-1arsenic, 18.95 ±â€¯0.212, 9.53 ±â€¯0.054, 6.83 ±â€¯0.306 mg kg-1 lead and 2.79 ±â€¯0.033, 1.39 ±â€¯0.025, 0.92 ±â€¯0.045 mg kg-1 cadmium, respectively in root, shoot and leaves, proving it's phytoaccumulation capacity. Next, these hyacinths has been used as a source of organic supplement for preparing vermicompost using Eisenia fetida following analysis of total metal content and sequential extraction. Control soil was having 134.69 ±â€¯2.47 mg kg-1 arsenic in compare to 44.6 ±â€¯0.91 mg kg-1 at premature stage of compost to 23.9 ±â€¯1.55 mg kg-1 at mature compost indicating sustainable fate of phytoremediated vermicompost. This vermiremediation of arsenic and other toxic elements, restricted the bioavailability of soil pollutants. Furthermore, processed compost amended as organic fertilizer, growing chickpea, coriander, tomato and chilli plant, resulted in negligible metal(loid)s in treated samples, enhancing also plant's growth and production.

High-pressure phase transition of alkali metal-transition metal deuteride Li2PdD2.

A combined theoretical and experimental study of lithium palladium deuteride (Li2PdD2) subjected to pressures up to 50 GPa reveals one structural phase transition near 10 GPa, detected by synchrotron powder x-ray diffraction, and metadynamics simulations. The ambient-pressure tetragonal phase of Li2PdD2 transforms into a monoclinic C2/m phase that is distinct from all known structures of alkali metal-transition metal hydrides/deuterides. The structure of the high-pressure phase was characterized using ab initio computational techniques and from refinement of the powder x-ray diffraction data. In the high-pressure phase, the PdD2 complexes lose molecular integrity and are fused to extended [PdD2]∞ chains. The discovered phase transition and new structure are relevant to the possible hydrogen storage application of Li2PdD2 and alkali metal-transition metal hydrides in general.