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This study conducted a comparative analysis to characterize crude polysaccharides from seven selected strains of Auricularia cornea in China, including commercial, hybrid, and wild domesticated strains. The obtained crude polysaccharide samples were analyzed using electron microscope-energy dispersive X-ray (SEM-EDX), Fourier transform infrared (FTIR) spectroscopy, and for their monosaccharide composition. The results revealed that crude polysaccharide samples were mainly composed of 85.9% mannose and 12.3% glucose. Among the strains tested, Ac1 and Ac33 exhibited significantly higher total polysaccharide content (215.9 ± 2.4 mg/g and 200.3 ± 1.5 mg/g, respectively) and phenolic content (40.2 ± 4.0 mg GAE/g and 44.2 ± 2.7 mg GAE/g, respectively). Additionally, Ac33 and Ac24 showed the highest levels of DPPH radical scavenging and ABTS scavenging ability among the strains tested. Overall, the results suggest that the crude polysaccharides from A. cornea have the potential as a natural source of antioxidant and antimicrobial agents and could be developed into functional nutritional foods. [ABSTRACT FROM AUTHOR]

In this study, the co-pyrolysis of Thar lignite coal and waste plastic was investigated, exploring the effects of different parameters such as the coal to waste plastic blending ratio and temperature. The oil obtained from pyrolysis was analyzed for GCV, viscosity, flash point and for chemical composition through GC-MS and FTIR techniques. The results revealed that a blend of 20 % coal and 80 % waste plastic yielded 67 % pyrolysis oil with a GCV of 42 MJ/kg. It was found that adding waste plastics to coal enhanced both the yield and quality of pyrolysis oil. Physical parameters including viscosity and GCV were found to be highly promising and comparable to regular Petro diesel. According to FTIR, presence of significant quantities of aliphatic and aromatic linkages along with carbonyl and hydroxyl functional groups were observed. Similarly, GCMS analysis showed the presence of C 7 to C 20 carbon fractions in pyro-oil of optimum sample. Char residue 21 % was also obtained from the results with carbon contents up to 68 % and less than 1 % sulfur at 80:20 blending ratio of WP and coal. This char residue could be especially useful for use in steam generation or cement industries as clean fuel. • Synergetic effect between waste plastic with Thar lignite coal in co-pyrolysis process was investigated. • The co-feeding process improved the quality and quantity of pyrolysis oil. • Blending waste plastic with lignite coal enhance aromatic contents. • Lignite coal-derived oil can be upgraded via co-pyrolysis with waste plastic. [ABSTRACT FROM AUTHOR]

With increased penetration of the converter dominated renewables, the overall inertial response and frequency stability of the power grid has been reduced. Low inertia contributes to maximum frequency deviations with higher rate of change of frequency that undermines the reliability of the power system. In the case of type-4 grid connected wind turbines, the kinetic energy of the rotor cannot be directly used to mitigate frequency excursions in the AC grid since fully-rated converters decouple the grid from the turbine rotor. Nonetheless, by designing an adequate control system for full converters, emulated inertia of the rotor can be propagated to the grid. In this paper, we present inertia emulation control strategy based on second order differential equation analogous to the swing equation of classical synchronous machines employed in machine side converter of the permanent magnet synchronous generator wind turbines. The proposed control allows the management of virtual inertia, damping and reactance. The performance of the proposed controller is verified through time-domain simulations and eigenvalue analysis performed in MATLAB Simulink. • The proposed control emulates SMs' behaviour and renders inertia support to the grid. • Unlike typical inertia controllers, proposed scheme does not rely on derivative term. • Enables power oscillation damping without affecting the steady state of the frequency. • Offers better RoCoF mitigation, and nadir improvement over PD-based controllers. • Its features include simple control design, easily scalable, efficient, and reliable. [ABSTRACT FROM AUTHOR]

Molecular perovskites are a class of materials that have attracted considerable attention in recent years due to their unique physical characteristics, structural properties, and diverse applications. These materials are also known as Hybrid Organic-Inorganic Perovskite (HOIP) and are denoted by the general formula ABX 3. HOIP materials feature an organic or inorganic molecular cation (A), a metal or molecular cation (B), and a molecular anionic bridging ligand (X). The perovskite structure comprises BX 6 octahedra that share corners, forming a three-dimensional network. The remarkable properties of molecular perovskites arise from their intrinsic polarization, resulting from the presence of polar organic cations in hybrid perovskites that possess a constant electric dipole moment. The alignment of these dipoles plays a crucial role in the properties of molecular perovskites. Consequently, this polarization can be harnessed to capture ambient mechanical energy and convert it into electrical energy. This review paper provides a comprehensive overview of molecular perovskites, focusing on their physical characteristics, synthesis methods, and multifunctional applications, particularly in piezoelectric nanogenerators. Furthermore, it addresses the challenges and opportunities associated with boosting their piezoelectric performance and integrating them with nanogenerator technology and flexible devices. Recent advances and breakthroughs in molecular perovskite-based nanogenerators are highlighted, including lead-free, metal-free, double perovskite materials, etc. Finally, the paper proposes future directions and perspectives for further research and innovation in this dynamic field. [Display omitted] • Review of HOIP materials, synthesis, and multifunctional applications, emphasizing piezoelectric nanogenerators. • Advancements in lead-free, metal-free, and double perovskite materials and their state-of-the-art nanogenerator performance. • Future perspectives of hybrid organic inorganic perovskite (HOIP) materials and their energy harvesting applications [ABSTRACT FROM AUTHOR]

Opioids are the mainstay therapy in burned adults. Little data in the pediatric burn population exists that elucidates opioid prescribing practices. The primary purpose of this report is to quantify opioid and non-opioid analgesic use in pediatric burn patients admitted to a tertiary referral burn center. A retrospective audit of hospital charts and discharge records for patients <18 years old from March 2016 to March 2017 was performed. Opioid amounts were converted to either oral morphine miligram equivalents (MME) or oral MME per day and subsequently adjusted for age in kilograms (kg). Of the 226 patients, 223 (98.7%) were administered an opioid during admission. The median total opioid amount administered during admission was 0.4 (IQR: 0.3-0.6) mg oral MME per kilogram per day. Anecdotally, doses above 1 mg/kg/day are considered high risk for opioid tolerance. The median total opioid amount prescribed upon discharge was high at 3.9 (IQR: 2.3, 5.6) mg of oral MME per kilogram. Hydrocodone (96.0%) was the most common opioid administered, followed by morphine (88.1%). The most commonly prescribed discharge opioid was hydrocodone (95.4%). Non-opoioid analgesia during admission was used in 112 patients (49.6%). This study provides novel insight into the opioid practices at a tertiary burn center for pediatric patients, with our analysis showcasing high usage of opioids during admission and discharge for burn analgesia. It emphasizes the need to expand beyond opioids for burn analgesia and the importance of promoting non-opioid, multimodal analgesia in the pediatric burn population. [ABSTRACT FROM AUTHOR]

The ubiquitous sensors at the core network of Internet of Things (IoT) have accelerated the realization of micro/nano systems that necessitate not only the development of self-powered sensors but also requires a reliable wireless communication mode. Here, a self-powered multi-broadcasting wireless sensing system based on an all-in-one triboelectric nanogenerator (TENG) is reported, which can simultaneously power up a custom-made wireless node and act as a sensor. The whole device and the system are encapsulated in a unique compact structure, in which the top segment consists of a spring-assisted multifunctional TENG (MTENG) and, the bottom section contains an integrated energy management and Bluetooth transmitter module. Following the structural and material modifications, the top unit of the MTENG is used to detect vibration frequency and amplitude by correlation with the TENG output voltage. The device has been employed to successfully detect the running frequency of a linear shaker and various generated signals. Owing to the nanostructured material surfaces, the device yields an output current of 300 μA and a power density of 4 W/m2 with the normal hand pressing. Moreover, the developed portable MTENG is also assessed to remotely monitor automobile engine vibration and is expected to result in a myriad of applications. Image 1 • A novel all-in-one triboelectric nanogenerator enabling multi-broadcasting wireless monitoring system. • Integration of organic/inorganic nanostructured materials leads to higher energy conversion efficiency and reliable wireless monitoring. • The unique compact and portable device design incorporate an optimized TENG sensor and TENG harvester together with integrated energy management, storage and RF transmission module to detect vibration characteristics in a wide range of frequencies. • A very promising nanogenerator in designing a self-powered structural health monitoring system in an automobile engine, jet engine, etc. due to mechanical robustness, high output, repeatability, and long-term reliability. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suggested approach BC-AI is utilized to determine the prediction of breast cancer using computer vision. • A data-driven computer-aided diagnostic (CAD) method for identifying patients as malignant, non-cancerous, or neither. • Feature extraction using the GLCM and HOG-based approach. • Self-constructing ensemble learning fuzzy algorithm (S-ELFA) blends a fuzzy methodology with an advanced neural network for optimum breast cancer illness detection and diagnosis. Breast cancer is a leading cause of mortality among women. Early detection will increase the chances of successful treatment and minimize the death rate. Even though many studies have been conducted to detect breast cancer, medical experts still face difficulty distinguishing between malignant and benign tumors. Hence, a technique enabling medical practitioners to effectively identify breast cancer was developed in this study. A computer-aided diagnostic (CAD) tool is used for classifying and diagnosing patients. The input images are pre-processed at the initial stage and a algorithm based on histogram of oriented gradients (HOG) and gray level co-occurrences matrix (GLCM) is applied to extract key features from pre-processed images. Then, shuffle shepherded optimization (SSO) selects the best features from the extracted features. Finally, the proposed self-constructing ensemble learning fuzzy algorithm (S-ELFA) identifies benign and malignant tumors. ROC, sensitivity, specificity, precision, and accuracy metrics were used to evaluate the developed method. [ABSTRACT FROM AUTHOR]

Heel pain is 1 of the most common presentations to the foot surgeon, and its causes are multifactorial. Baxter's neuropathy is caused by an impingement of the inferior calcaneal nerve and has been reported to be responsible for up to 20% of heel pain. The diagnostic imaging features are striking, with inflammation or atrophy of the abductor digiti minimi muscle. Multiple studies have found that the prevalence of this finding is much greater than initially thought. However, it is more unusual to find bilateral and symmetrical features. The possible causes of this condition lie along the course of the inferior calcaneal nerve. Management is focused on treating the underlying condition, with conservative therapy and steroid injection as the mainstay. Refractory cases may require surgical release. We present the case of a 56-year-old female presenting with bilateral foot pain. Imaging reveals symmetrical abductor digiti minimi atrophy associated with bilateral plantar fasciitis. These appearances are well demonstrated on both magnetic resonance imaging and ultrasound. [ABSTRACT FROM AUTHOR]

Abstract Recent reports demonstrate that hybrid energy harvesting devices can efficiently convert ubiquitously available but mostly unexploited ambient energies (e.g., mechanical, chemical, thermal, solar) into usable power that can potentially support a new generation of self-powered electronic systems. In this paper, we present a hybrid organic/inorganic nanogenerator on shim substrates, which integrates both piezoelectric and triboelectric components based on inorganic p-n junction ZnO nanostructures and nanostructured organic polytetrafluoroethylene (PTFE) film, respectively. In this design, individual components can be operated independently or concurrently. Moreover, when operated concurrently, component performance is mutually enhanced, enabling more efficient conversion of mechanical energy into electrical energy in a single press-and-release cycle. When triggered with 25 Hz frequency and 1 G acceleration of external force, the piezoelectric nanogenerator (PENG) component generates a peak-to-peak output voltage of 34.8 V, which is ∼3 times higher than its output when it acts alone. Similarly, the triboelectric nanogenerator (TENG) component generates a peak-to-peak output voltage of 356 V under the same conditions, which is higher than its initial output of 280 V when acting alone. The nanogenerator unit produces an average peak output voltage of 186 V, current density of 10.02 µA/cm2, and average peak power density of 1.864 mW/cm2 when operated in the hybrid configuration. The device can even produce an average peak-to-peak voltage of ~160 V from normal hand movement when placed under a wristband fitness tracker, and ~580 V from human walking when placed within the walker's shoe. The device has been demonstrated to charge commercial capacitors up to a few volts within several seconds. Graphical abstract fx1 Highlights • A novel piezo-tribo hybrid energy harvester utilizing organic/inorganic nanostructured materials. • Mutual performance enhancement due to a common electrode leads to higher output performance. • Piezoelectric and triboelectric components operate simultaneously and thus lead to improved energy conversion efficiency. • A very promising nanogenerator for application in self-powered micro-/nano systems due to mechanical robustness, high output, repeatability, and long-term reliability. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a Convolutional Neural Network (CNN) based approach for the classification and prediction of various types of in-plane and through-the-thickness delamination in smart composite laminates using low-frequency structural vibration outputs. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates, and their structural vibration responses are obtained in the time domain. Short Time Fourier Transform (STFT) is employed to transform the transient responses into two-dimensional spectral frame representation. A convolutional neural network is incorporated to distinguish between the damaged and undamaged states, as well as various types of damage of the laminated composites, by automatically extracting discriminative features from the vibration-based spectrograms. The CNN showed a classification accuracy of 90.1% on one healthy and 12 delaminated cases. The study of the confusion matrix of CNN provided further insights into the physics of the problem. The predictive performance of a pre-trained CNN classifier was also evaluated on unseen cases of delamination, and physically consistent results were obtained. [ABSTRACT FROM AUTHOR]

Abstract Very few qualitative studies focusing on teaching and learning practices at the tertiary level have emerged from Pakistan. The present study, which applied a qualitative approach, examined those contextual and institutional factors that have a direct link with teaching/learning practices at the tertiary level in Pakistan. While exploring the said phenomenon, the study exclusively focused on three factors: 1) reading material, 2) course syllabi, and 3) teaching practices. The study noted that although there was a consensus among the university students and instructors regarding the satisfactory level of teaching and learning practices, some fundamental elements related to teaching and learning, were either ignored or missing. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes the synergetic integration of system identification and artificial intelligence for the detection and assessment of delamination damages in smart composite laminates. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates on the basis of improved layerwise theory, higher order electric potential field and finite element method. A discriminative feature space is constructed for the healthy and delaminated structures via system identification from their structural vibration responses. The discriminative features are used for the training and cross-validation of various supervised machine learning classifiers and an optimal classifier is identified. The optimal classifier is employed to make predictions on unseen test delamination cases, and its predictions are validated via a dimensionality reduction tool. The obtained results show that the proposed technique could be employed as a reliable tool for nondestructive evaluation of smart composite laminates. [ABSTRACT FROM AUTHOR]

Abstract Polyindole–tin (IV) molybdophosphate (PIn-SMP), an organic-inorganic nanocomposite ion exchanger, was synthesized by a sol-gel process and exhibited excellent ion exchange capacity (2.1 meq g−1). A heterogeneous ion exchange membrane of PIn-SMP (ion exchange capacity 0.95 meq g−1) was also prepared by a solution casting method. The materials were characterized by various instrumental methods, such as scanning electron microscopy, transmission electron microscopy, Fourier transform IR spectroscopy, and thermogravimetric analysis. PIn-SMP exhibits high electrical conductivity (6.1 × 10−2 S/cm) and is stable up to 120 °C under ambient conditions. A Cu2+-selective membrane electrode was fabricated, and its linear working range (1.8 × 10−7–1.0 × 10−1 M), response time (20 s), Nernstian slope (24.07 mV dec−1), and working pH range (4–7) were calculated. Moreover, it was used as an indicator electrode in the potentiometric titration of Cu2+. Graphical abstract Image 1 Highlights • Polyindole–tin (IV) molybdophosphate nanocomposite ion exchanger (IEC-2.1 meq g−1), was synthesized by a sol-gel process. • The nanocomposite displayed high DC electrical conductivity (6.1 × 10−2 S cm−1) at room temperature. • A heterogeneous ion exchange membrane (IEC-0.95 meq g−1) was prepared by a solution casting method. • The materials were characterized by various instrumental methods, such as SEM, TEM, FT-IR, and TGA analysis. • A Cu2+-selective membrane electrode was fabricated and exhibited a wide linear response range (1.8 × 10−7–1.0 × 10−1 M). [ABSTRACT FROM AUTHOR]

Currently, comfort analyses of buildings equipped with thermoelectric air cooling or heating systems mainly focus on when occupants are in a wakeful state. In this study, both objective and subjective analyses of the sleeping behavior for fifteen (15) healthy occupants were conducted by exposing the occupants to two sleeping environments (i.e., test room equipped with the thermoelectric air duct cooling system (TE-AD) and naturally ventilated test room (NH)). The result shows that there were significant variations in the sleep satisfaction level in the test room with TE-AD and NH. Occupants felt more comfortable (5) and a slightly cooler thermal environment (3) while sleeping in the test room equipped with the TE-AD system. Their body movements, heart rate and sleeping stages shift from non-rapid eye movement (NREM) to rapid eye movement (REM) and then to the waking stage (WS), was less in test room with the TE-AD system as compared to NH. The occupants gave slightly hot (5) for indoor climatic ratings in NH room and felt a slightly uncomfortable (3) while sleeping. The PMV and PPD analyses showed that occupants were very sensitive to climatic conditions around bed and with slightly change in temperature (1.2 ± 0.4 °C) results in the shifting of sleeping stages. For the TE-AD room, the average occupant sleep onset latency was 19 ± 0.5 min, which is 20 ± 0.4 min lesser than NH room. [ABSTRACT FROM AUTHOR]

In demand side management (DSM), scheduling of appliances based on consumer-defined priorities is an important task performed by a home energy management controller (HEMC). This paper presents a DSM technique that is capable of controlling loads within a smart home considering time-varying appliances’ priorities. An evolutionary accretive comfort algorithm (EACA) is developed based on four postulations that allow time-varying priorities to be quantified in time and device-based features. Based on the input data considering the appliances’ power ratings, its time of use, and absolute comfort derived from priorities, the EACA is able to generate an optimal energy consumption pattern which would give maximum satisfaction at a predetermined user budget. A cost per unit comfort index ( χ ) which relates the consumer expenditure to the achievable comfort is also demonstrated. To test the applicability of the proposed EACA, three budget scenarios of $1.5/day, $2.0/day and $2.5/day are performed. The results of each of the scenarios using EACA are compared to five base cases in which the appliances are randomly used. The simulation results revealed that the proposed EACA obtained an optimal absolute comfort with reduced cost per unit comfort values for all three scenarios within the budget limits. [ABSTRACT FROM AUTHOR]

Wise phosphorus (P) management is paramount to ensure food and environmental security and prolong the life of P resources. Long-term experiments under winter wheat–summer maize (with irrigation) and winter wheat–summer fallow (rain-fed) cropping systems were undertaken to investigate P efficiency, soil P availability and P downward movement in the deep profile of a loess soil in China. The four nutrient management regimes in each experiment were: a control without any nutrient input; P and K combined (PK); N, P and K combined (NPK), and organic manure (M) combined with NPK (MNPK). The P efficiency ranged from 20 to 54% under the double cropping system, and from 13 to 33% in the single cropping system over the 25 years studied, and the highest P efficiency was in the NPK treatment in both systems. Application of P significantly increased soil total-P, Olsen-P and CaCl 2 -P over the years, especially in the MNPK treatment. Two segment regression analyses indicated that Olsen-P was about 14 mg kg −1 or 16 mg kg −1 for optimal crop yield under double cropping or single cropping. In addition, Olsen-P content above 37 mg kg −1 or 57 mg kg −1 led to a significant increase in CaCl 2 -P content under double cropping or single cropping, which might indicate risk of P leaching. After 24 years of P applications, total P accumulated at various depths depending on treatment under double cropping, but there was no accumulation under single cropping. While Olsen-P leached down to a depth of 300 cm in the MNPK treatments under both cropping systems, and higher concentrations were recorded in the deeper layers in single cropping than in double cropping systems. The Olsen-P leached to a shallower depth in the PK and NPK treatments under single cropping compared to double cropping. We conclude that P supply in excess of the crop’s requirement (e.g. MNPK) or an unbalanced nutrient supply (e.g. PK) resulted in not only low P efficiency and massive accumulation of P in the topsoil but also leaching out of root zone under both irrigated and rain-fed conditions in the loess soil. Thus, P fertilization recommendations need to be adjusted based on changes in soil P over time. [ABSTRACT FROM AUTHOR]

System identification is an inverse algorithm of developing/improving mathematical representation of a physical system from input-output responses. This paper assessed a mathematical model of smart composite laminate identified with spurious output data due to sensor debonding failure. Improved layerwise theory and higher-order electric potential field were incorporated to develop electromechanically coupled finite element based mathematical model of the smart composite laminate with or without sensor debonding failure. The input-output data of the developed model were fed into the direct system identification algorithm to identify the state-space model of the smart structure. The developed theory was numerically implemented on a 16-layer cross-ply laminate with surface bonded piezoelectric sensor and actuator. Results showed that system identification algorithm misapprehended the true dynamic behavior of the smart structure in the presence of sensor debonding failure. In addition, principal component analysis was used to detect the presence and severity of partial sensor debonding in the identified state-space model. [ABSTRACT FROM AUTHOR]

As substantive components of intelligent structures, piezoelectric sensors need to perform their intended function without any spuriousness. In this work, a mathematical model has been developed to characterize the performance degradation of a piezoelectric sensor due to its debonding from a smart composite laminate. The electromechanically coupled governing equation of motion is developed by incorporating improved layerwise theory and higher electric potential field along with the finite element method and Extended Hamilton's principle. The problem governing equation is solved by Newmark time-integration algorithm to assess the performance of a piezoelectric sensor in the presence of partial debonding at the edge and inner side of the sensor. The partially debonded piezoelectric sensor is investigated in frequency domain via power spectral density of the sensor output. The degradative performance of the partially debonded sensor is generalized for random loadings via basic signal statistics. The numerical results show that the developed model recover the presence and extent of partial debonding between the piezoelectric sensor and the host laminate. [ABSTRACT FROM AUTHOR]

Forensic odontology plays a vital role in the identification and age estimation of unknown deceased individuals. The purpose of this study is to estimate the chronological age from Cone-Beam Computed Tomography (CBCT) images by measuring the buccal alveolar bone level (ABL) to the cemento-enamel junction and to investigate the possibility of employing the age-related structural changes of teeth as studied by Gustafson. In addition, this study will determine the forensic reliability of employing CBCT images as a technique for dental age estimation. A total of 284 CBCT images of Malays and Chinese patients (150 females and 134 males), aged from 20 years and above were selected, measured and stages of age-related changes were recorded using the i-CAT Vision software. Lower first premolars of both left and right side of the jaw were chosen and the characteristics described by Gustafson, namely attrition, secondary dentine formation and periodontal recession were evaluated. Linear regression analysis was performed for the buccal bone level and the R values obtained were 0.85 and 0.82 for left and right side respectively. Gustafson's characteristics were analysed using multiple regression analysis with chronological age as the dependent variable. The results of the analysis showed R values ranged from 0.44 to 0.62. Therefore it can be safely concluded that the buccal bone level highly correlated with the chronological age and is consequently the most suitable age-related characteristic for forensic age estimation. [ABSTRACT FROM AUTHOR]

Background: Limited data exist on cryoablation of atrial fibrillation (Cryo-AF) using the newly available third-generation (Arctic Front Advance-Short Tip [AFA-ST]) cryoballoon.Objective: In this multicenter study, we evaluated the safety and efficacy of Cryo-AF using the AFA-ST vs the second-generation (Arctic Front Advance [AFA]) cryoballoon.Methods: We examined the procedural safety and efficacy and the short- and long-term clinical outcomes associated with a first-time Cryo-AF performed in 355 consecutive patients (254/355 [72%] with paroxysmal AF), using either the AFA-ST (n = 102) or the AFA (n = 253) cryoballoon catheters.Results: Acute isolation was achieved in 99.6% of all pulmonary veins (PVs) (AFA-ST: 100% vs AFA: 99.4%; P = .920). Time to pulmonary vein isolation was recorded in 89.2% of PVs using AFA-ST vs 60.2% using AFA (P < .001). PVs targeted using AFA-ST required fewer applications (1.6 ± 0.8 vs 1.7 ± 0.8; P = .023), whereas there were no differences in the balloon nadir temperature (AFA-ST: -47.0°C ± 7.3°C vs AFA: -47.5°C ± 7.8°C; P = .120) or thaw time (AFA-ST: 41 ± 24 seconds vs AFA: 44 ± 28 seconds; P = .056). However, AFA-ST was associated with shorter left atrial dwell time (43 ± 5 minutes vs 53 ± 16 minutes; P < .001) and procedure time (71 ± 11 minutes vs 89 ± 25 minutes; P < .001). Furthermore, Cryo-AF using AFA-ST was completed more frequently by "single-shot" PV ablation (27.4% vs 20.2%; P = .031). Persistent phrenic nerve palsy (AFA-ST: 0% vs AFA: 0.8%; P = .507) and procedure-related adverse events (AFA-ST: 1.0% vs AFA: 1.6%; P = .554) were similar, as was the freedom from recurrent atrial arrhythmias at 10 months of follow-up (AFA-ST: 81.8% vs AFA: 79.9%; P = .658).Conclusion: Cryo-AF using the AFA-ST cryoballoon offers an enhanced ability to assess time to pulmonary vein isolation, allowing for fewer cryoapplications and shorter left atrial dwell time and procedure time. Consequently, this allowed for procedural completion more frequently using a "single-shot" PV ablation with equivalent safety and efficacy. [ABSTRACT FROM AUTHOR]

Dedifferentiation, that is, the acquisition of stem cell-like state, commonly induced by stress ( e.g. , protoplasting), is characterized by open chromatin conformation, a chromatin state that could lead to activation of transposable elements (TEs). Here, we studied the activation of the Arabidopsis class II TE Tag1, in which two copies, situated close to each other (near genes) on chromosome 1 are found in Landsberg erecta (Ler) but not in Columbia (Col). We first transformed protoplasts with a construct in which a truncated Tag1 (ΔTag1 non-autonomous) blocks the expression of a reporter gene AtMBD5-GFP and found a relatively high ectopic excision of ΔTag1 accompanied by expression of AtMBD5-GFP in protoplasts derived from Ler compared to Col; further increase was observed in ddm1 ( decrease in DNA methylation1 ) protoplasts (Ler background). Ectopic excision was associated with transcription of the endogenous Tag1 and changes in histone H3 methylation at the promoter region. Focusing on the endogenous Tag1 elements we found low level of excision in Ler protoplasts, which was slightly and strongly enhanced in ddm1 and cmt3 ( chromomethylase3 ) protoplasts, respectively, concomitantly with reduction in Tag1 gene body (GB) CHG methylation and increased Tag1 transcription; strong activation of Tag1 was also observed in cmt3 leaves. Notably, in cmt3 , but not in ddm1 , Tag1 elements were excised out from their original sites and transposed elsewhere in the genome. Our results suggest that dedifferentiation is associated with Tag1 activation and that CMT3 rather than DDM1 plays a central role in restraining Tag1 activation via inducing GB CHG methylation. [ABSTRACT FROM AUTHOR]

The growth and implementation of biofuels and bioenergy conversion technologies play an important part in the production of sustainable and renewable energy resources in the upcoming years. Recycling sources from waste could efficiently ease the risk of world source strain. The waste classification was a good resolution for separating the waste from the recycled objects. It is inefficient and expensive to rely solely on manual classification of garbage and recycling sources. Convolutional neural networks (CNNs) have lately been used to classify recyclable waste, and this is the primary way for recycling the waste. This study presents a recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) model for bioenergy production. RWC-EPODL model focuses on recycling waste materials recognition and classification. When it comes to detecting and classifying trash, the RWC-EPODL model uses two stages. At the initial stage, the RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. In addition, Bayesian optimization (BO) algorithm is applied as hyperparameter optimizer of the AX-RetinaNet model. Following the EPO algorithm with a stacked auto-encoder (SAE) model, the EPO algorithm is used to fine-tune the parameters of the SAE technique for trash classification. The RWC-EPODL model's experimental validation is examined through a number of studies. The RWC-EPODL approach has a 98.96 percent success rate. The comparative result analysis reported the better performance of the RWC-EPODL model over recent approaches. [Display omitted] • Novel recycling waste classification using emperor penguin optimizer with deep learning (RWC-EPODL) for bioenergy production • The presented RWC-EPODL model majorly focuses on the recognition and classification of recycling waste materials. • The proposed RWC-EPODL model uses AX-RetinaNet model for the recognition of waste objects. • The proposed model employs the EPO algorithm with stacked auto-encoder (SAE) model for waste classification. • To demonstrate the improved outcomes of the RWC-EPODL model, a series of experiments has been conducted to test the model. [ABSTRACT FROM AUTHOR]

Lately, lead-free flexible piezoelectric nanogenerators (PENGs) have drawn much attention because of the threat posed by lead (Pb)-based piezoelectric materials to the environment. Here, we reported an organic-inorganic hybrid perovskites (OIHP) PENG, which is a combination of lead-free formamidinium tin (Sn) halide perovskite (CH(NH 2) 2 SnBr 3 (FASnBr 3)) nanoparticles (NPs) and polydimethylsiloxane (PDMS) polymer matrix. By using piezoelectric force microscopy (PFM) measurements, we unveil the excellent piezoelectric properties of the FASnBr 3 NPs with a high piezoelectric charge coefficient (d 33) of ~ 50 pm/V. Due to the outstanding flexibility and uniform distribution properties, the device demonstrated a maximum piezoelectric peak to peak output voltage of 94.5 Vp-p, peak to peak current of 19.1 μAp-p, and output power density of 18.95 μW/cm2 with a tiny force of 4.2 N; these characteristics substantially outperform a number of the state-of-the-art halide perovskite based PENGs (Table S1). Given their high electromechanical energy conversion efficiency, the electrical energy produced from the PENGs was used to power a Bluetooth-capable system on chip (SoC) to build an entirely self-powered radio frequency (RF) communication system. For the first time, we established a self-powered RF wireless communication between nanogenerator and smart electronic devices which is solely based on a lead-free PENG. It is anticipated that the fabricated FASnBr 3 @PDMS nanocomposite PENG not only possesses outstanding performance and reliability but also serves as a stepping-stone towards achieving self-powered Internet of Things (IoT) devices built using environment-friendly perovskite piezoelectric materials. [Display omitted] • For the first time, we unveil the excellent piezoelectric properties of FASnBr 3 nanoparticles (NPs) with a d 33 of 5050 pm/V, for Lead-free PENG. • The PENG demonstrated an output voltage of 94.594.5 V p-p , current of 19.1 μA p-p , and output power density of 18.95 μW/cm2 with a tiny force of 4.24.2 N. • For the first time successful integration of lead-free perovskite PENG in self-powered wireless communication. [ABSTRACT FROM AUTHOR]

The present study was aimed at determining the efficacy of rock phosphate (RP) 3% loaded in a green coconut shell, chicken manure, and vegetable waste to make green coconut-modified biochar (GMB), chicken manure modified-biochar (CMB), and vegetable waste-modified biochar (VMB) in the fixation of Cr, Pb, Cu, Zn, Ni, and Cd in Sharafi goth and Malir polluted soils. The impact of RP impregnated with organic waste material to produce modified biochars (MBs) on stabilizing PTEs from polluted soils and reducing their uptake by mustard plant has not yet been thoroughly investigated. All modified BCs in 0.5, 1, and 2% doses were used to stabilize Cr, Pb, Cu, Zn, Ni, and Cd in two polluted soils and to reduce their uptake by the mustard plant. The obtained results revealed that the maximum mustard fresh biomass was 17.8% higher with GMB 1% in Sharafi goth polluted soil and 25% higher with VMB 0.5% in Malir polluted soil than in the control treatment. After applying modified BCs, immobilization of Cr, Pb, Cu, Ni, and Cd was observed in both soils and it reduced the uptake of these elements by mustard plants. On the other hand, although Zn mobilization increased by 0.38% for CMB 0.5% and by 5.9% for VMB 0.5% in Sharafi goth polluted soil, as well as by 3.15% for GMB 1%, 6.34% for GMB 2%, and 4.78% for VMB 0.5% in Malir polluted soil, this was due to changes in soil pH and OM. It was found that GMB 1%, CMB 0.5%, and VMB 0.5% have the potential to increase Zn uptake by mustard, while VMB 2% can reduce the element uptake by the plant. Redundancy analysis showed that soil chemical parameters were negatively correlated with PTEs in both soils and reduced their uptake by mustard. The present study revealed that MBs can stabilize PTEs in industrial and wastewater soils polluted with multiple metals and reduce their uptake by plants. [Display omitted] • Impact of rock phosphate-modified biochars on the fixation of PTEs in two soils was investigated. • Modified biochars potentially stabilize PTEs in both polluted soils. • Modified biochars clearly reduced the uptake of PTEs by mustard plant. • Changes in fresh and dry biomasses of mustard were observed after applying modified biochars. • Long-term ex-situ trials must be conducted to assess the potential of different modified biochars. [ABSTRACT FROM AUTHOR]

Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods. [Display omitted] • Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. • RODL-HMSEP technique aims for predicting the sorption performance of HMs of various biochar features. • The RODL-HMSEP model uses density-based clustering (DBSCAN) technique for simulating the features of metal adsorption data. • Deep belief network (DBN) model performs the next phase of data clustering. • RODL-HMSEP technique ensured promising performance on the prediction of sorption efficiency onto biochar over other methods. [ABSTRACT FROM AUTHOR]

Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250. [Display omitted] • A novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) method. • The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. • The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals. • The proposed model employs AOA based hyperparameter tuning process to tune the hyperparameter included in the MABLSTM model. • To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. [ABSTRACT FROM AUTHOR]

In recent times, due to the growing global population and increased food demand, smart agriculture is becoming more vital. In this context, Internet of Things (IoT) technologies have emerged as a significant pathway to innovative agricultural techniques. Due to their low capacity, these IoT nodes have faced energy limits and complicated routing methods. As a result, in the sphere of IoT-based agriculture, transmitting data failure, energy consumption, network lifetime reduction, and delay occur. To overcome this problem, this study proposes a novel combination of optimized intelligent smart irrigation systems to improve the energy management performance of the system. Here, the optimal cluster head formation and selection is performed by Hierarchy Shuffled Shepherd Clustering (HSSC) method. Also, the finest energy regulation and routing path are provided by the proposed Emperor Penguin Jellyfish Optimizer (EPJO) method. The simulation of this work is performed on Network Simulator-2 (NS2) software. The simulation consequences from the proposed method are validated and compared with the conventional methods. Thus, the proposed approach results demonstrate that the developed model has much lesser energy consumption and improved network lifetime as compared to the traditional works. [Display omitted] [ABSTRACT FROM AUTHOR]

For a city in transition not only is there a need for planners to provide accountability of development and growth continuum but also ensure a pattern for objective spatial description network. Orientation towards ideal cognition and provision of a framework for accountability is vital. Interpretation of the structure of mould forming the physical environment made up of people and space with time playing an integral contextually relevant catalyst is essential to evolve experiential knowledge about the urban stature. The planned or self evolved urban grammar owing a perceptual sense of order with accountability for continuum could often be treated as the base order with perpetual indicators defining the edges of physical realm associated with a developing conurbation. The study provides insight into new urbanisation trends prevailing in India and approaches propagated by individuals, organisations and statutory bodies for realizing a better built habitat. A theoretical ideation: Critical Proposition Theory, a decisive framework to ensure progress in the right direction has been derived, supported by graphical - visual illustrations with cognitive reasoning. It is apparently evident, how these cities are envisioned with time would determine the practicability of subsistence for future generations. [ABSTRACT FROM AUTHOR]

This study investigates existing users' attitudes towards public transport from two perspectives. First, the effects of accessibility to various destinations and ease of access to terminals on public transport users' attitudes are determined. Second, the contribution of social norm, as an information source, in the formation of users' attitudes is assessed. A user-preference survey was undertaken in Auckland, New Zealand, at two terminals. Data were analyzed using ordinal and logistic regression models. Findings showed that residential density and quality of the built environment, particularly safety, have an effect on the number of pedestrians who access a terminal. Accessibility to various destinations, "reaching work/education," and "reaching other suburbs" in both data sets were statistically significant for existing users' satisfaction with the current system. The findings also show that negative experiences of others have an adverse effect on existing users' intentions to continue ridership. Overall, the results showed that to retain existing patronage, the ease of access to terminals and connectivity to various destinations need to be of a high standard. [ABSTRACT FROM AUTHOR]

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Triboelectric nanogenerators (TENGs) are promising energy harvesting devices for powering next generation wearable electronics. TENGs performance are largely determined by the triboelectric effect between the tribonegative and tribopositive layers. To date, fluorine-containing petroleum-based polymers, such as polytetrafluoroethylene (PTFE), remain the most popular choice as tribonegative layer due to their high tribonegativity against various materials during frictional contact. We report for the first time a natural wood-derived lignocellulosic nanofibrils (LCNF) tribolayer that could replace fluorine-containing petroleum-based polymers as a tribonegative material for TENGs. The high tribonegativity was due to the presence of natural lignin on the surface of LCNF and LCNF's nanofibril morphology. The LCNF nanopaper-based TENGs produced significantly higher voltage (~160%) and current (~120%) than TENGs with PTFE as the tribonegative material when paired with various polymeric/metallic tribolayers. Furthermore, assembling LCNF nanopaper as the tribonegative layers into a cascade TENG generated an output sufficient for powering a wireless communication node, capable of sending a radio-frequency signal to a smartphone every 3 min. This study demonstrates the excellent promises of using LCNF to make high-performance and more environmentally friendly wireless self-powered electronics; and thus pinpoints a new approach for fabricating sustainable triboelectric nanogenerators using natural lignocellulosic materials instead of conventional fluorine-containing petroleum-based polymers as tribonegative layers. [Display omitted] • We explore the effect of natural lignin bound on the cellulosic nanofibrils on the triboelectric properties of the nanopapers. • We introduce the first natural material as a superior alternative to tribonegative fluorine polymers for TENGs. • A cascade LCNF nanopaper-based TENGs was capable of powering energy-intensive wireless communication nodes. • LCNF energy-harvesting capability and reliability toward sustainable self-powered IoT devices was demonstrated. [ABSTRACT FROM AUTHOR]