Agricultural waste-based modified biochars differentially affected the soil properties, growth, and nutrient accumulation by maize (Zea mays L.) plants.

Biochar (BC) is an organic compound formed by the pyrolysis of organic wastes. Application of BCs as soil amendments has many benefits including carbon sequestration, enhanced soil fertility and sustainable agriculture production. In the present study, we acidified the different BCs prepared from rice straw, rice husk, wheat straw, cotton stalk, poultry manure, sugarcane press mud and vegetable waste; following which, we applied them in a series of pot experiments. Comparisons were made between acidified and non- acidified BCs for their effects on seed germination, soil properties (EC, pH) nutrient contents (P, K, Na) and organic matter. The treatments comprised of a control, and all above-described BCs (acidified as well as non-acidified) applied to soil at the rate of 1% (w/w). The maize crop was selected as a test crop. The results showed that acidified poultry manure BC significantly improved germination percentage, shoot length, and biomass of maize seedlings as compared to other BCs and their respective control plants. However, acidified BCs caused a significant decrease in nutrient contents (P, K, Na) of soil,maize seedlings, and the soil organic matter contents as compared to non- acidified BCs. But when compared with control treatments, all BCs treatments (acidified and non-acidified) delivered higher levels of nutrients and organic matter contents. It was concluded that none of the BCs (acidified and non-acidified) had caused negative effect on soil conditions and growth of maize. In addition, the acidification of BC prior to its application to alkaline soils might had altered soil chemistry and delivered better maize growth. Moving forward, more research is needed to understand the long-term effects of modified BCs on nutrient dynamics in different soils. In addition, the possible effects of BC application timings, application rates, particle size, and crop species have to be evaluated systemtically.

Effects of biochar types on seed germination, growth, chlorophyll contents, grain yield, sodium, and potassium uptake by wheat (Triticum aestivum L.) under salt stress.

Soil salinity is a significant challenge in agriculture, particularly in arid and semi-arid regions such as Pakistan, leading to soil degradation and reduced crop yields. The present study assessed the impact of different salinity levels (0, 25, and 50 mmol NaCl) and biochar treatments (control, wheat-straw biochar, rice-husk biochar, and sawdust biochar applied @ 1% w/w) on the germination and growth performance of wheat. Two experiments: a germination study and a pot experiment (grown up to maturity), were performed. The results showed that NaCl-stress negatively impacted the germination parameters, grain, and straw yield, and agronomic and soil parameters. Biochar treatments restored these parameters compared to control (no biochar), but the effects were inconsistent across NaCl levels. Among the different biochars, wheat-straw biochar performed better than rice-husk and sawdust-derived biochar regarding germination and agronomic parameters. Biochar application notably increased soil pHs and electrical conductivity (ECe). Imposing NaCl stress reduced K concentrations in the wheat shoot and grains with concomitant higher Na concentrations in both parts. Parameters like foliar chlorophyll content (a, b, and total), stomatal and sub-stomatal conductance, and transpiration rate were also positively influenced by biochar addition. The study confirmed that biochar, particularly wheat-straw biochar, effectively mitigated the adverse effects of soil salinity, enhancing both soil quality and wheat growth. The study highlighted that biochar application can minimize the negative effects of salinity stress on wheat. Specifically, the types and dosages of biochar have to be optimized for different salinity levels under field conditions.

Osteoarthriris year in review 2023: genetics, genomics, and epigenetics.

OBJECTIVE: To elucidate the scientific advances made in the last 12 months within the realm of osteoarthritis genetics, genomics, and epigenetics. This review paper highlights major research publications that enhance our current understanding of the role of genetics, genomics, and epigenetics in osteoarthritis. METHODS: A systematic literature search was conducted on pubmed.ncbi.nlm.nih.gov on "March 17, 2023", using the following keywords: "osteoarthritis" in combination with any of these terms: "genetic(s)", "mutation(s)", "genomic(s)", "epigenetic(s)", "DNA methylation", "noncoding RNA", "lncRNA", "circular RNA", "microRNA", "transcriptomic(s)", "RNA sequencing", "single cell RNA sequencing", or "single nucleus RNA sequencing". The selection comprised original research articles published in the English language between the OARSI congresses of 2022 and 2023. RESULTS: A total of 2178 research articles were identified, which subsequently reduced to 67 unique articles relevant to the field. Current trends in osteoarthritis genetics research involve meta-analyses of various cohorts to explore the impact of gene variants on osteoarthritis-related outcomes, such as pain. Early developmental changes within the joint were also found to influence osteoarthritis through genetic variations. Researchers also prioritize testing the mechanisms and functions of miRNAs, circRNAs, and lncRNAs. Potential drug targets began to emerge; however, independent validation studies are lacking. Single cell RNA sequencing studies revealed unique immune cell populations in the knee; however, no study reported single nucleus RNA sequencing analysis. CONCLUSIONS: This review focused on recent advances in the above-mentioned themes within the field of osteoarthritis. These advances improve our understanding of the disease's complexity and guide us toward functional assessments of genetic/epigenetic outcomes and toward their translational and clinical applications.

Three decades of advancements in osteoarthritis research: insights from transcriptomic, proteomic, and metabolomic studies.

OBJECTIVE: Osteoarthritis (OA) is a complex disease involving contributions from both local joint tissues and systemic sources. Patient characteristics, encompassing sociodemographic and clinical variables, are intricately linked with OA rendering its understanding challenging. Technological advancements have allowed for a comprehensive analysis of transcripts, proteomes and metabolomes in OA tissues/fluids through omic analyses. The objective of this review is to highlight the advancements achieved by omic studies in enhancing our understanding of OA pathogenesis over the last three decades. DESIGN: We conducted an extensive literature search focusing on transcriptomics, proteomics and metabolomics within the context of OA. Specifically, we explore how these technologies have identified individual transcripts, proteins, and metabolites, as well as distinctive endotype signatures from various body tissues or fluids of OA patients, including insights at the single-cell level, to advance our understanding of this highly complex disease. RESULTS: Omic studies reveal the description of numerous individual molecules and molecular patterns within OA-associated tissues and fluids. This includes the identification of specific cell (sub)types and associated pathways that contribute to disease mechanisms. However, there remains a necessity to further advance these technologies to delineate the spatial organization of cellular subtypes and molecular patterns within OA-afflicted tissues. CONCLUSIONS: Leveraging a multi-omics approach that integrates datasets from diverse molecular detection technologies, combined with patients' clinical and sociodemographic features, and molecular and regulatory networks, holds promise for identifying unique patient endophenotypes. This holistic approach can illuminate the heterogeneity among OA patients and, in turn, facilitate the development of tailored therapeutic interventions.

Evolution and advancements in genomics and epigenomics in OA research: How far we have come.

OBJECTIVE: Osteoarthritis (OA) is the most prevalent musculoskeletal disease affecting articulating joint tissues, resulting in local and systemic changes that contribute to increased pain and reduced function. Diverse technological advancements have culminated in the advent of high throughput "omic" technologies, enabling identification of comprehensive changes in molecular mediators associated with the disease. Amongst these technologies, genomics and epigenomics - including methylomics and miRNomics, have emerged as important tools to aid our biological understanding of disease. DESIGN: In this narrative review, we selected articles discussing advancements and applications of these technologies to OA biology and pathology. We discuss how genomics, deoxyribonucleic acid (DNA) methylomics, and miRNomics have uncovered disease-related molecular markers in the local and systemic tissues or fluids of OA patients. RESULTS: Genomics investigations into the genetic links of OA, including using genome-wide association studies, have evolved to identify 100+ genetic susceptibility markers of OA. Epigenomic investigations of gene methylation status have identified the importance of methylation to OA-related catabolic gene expression. Furthermore, miRNomic studies have identified key microRNA signatures in various tissues and fluids related to OA disease. CONCLUSIONS: Sharing of standardized, well-annotated omic datasets in curated repositories will be key to enhancing statistical power to detect smaller and targetable changes in the biological signatures underlying OA pathogenesis. Additionally, continued technological developments and analysis methods, including using computational molecular and regulatory networks, are likely to facilitate improved detection of disease-relevant targets, in-turn, supporting precision medicine approaches and new treatment strategies for OA.

Angiogenic protein profiling, phytochemical screening and in silico anti-cancer targets validation of stem, leaves, fruit, and seeds of Calotropis procera in human liver and breast cancer cell lines.

The main focus of anticancer drug discovery is on developing medications that are gentle on normal cells and should have the ability to target multiple anti-cancer pathways. Liver cancer is becoming a worldwide epidemic due to the highest occurring and reoccurring rate in some countries. Calotropis procera is a xerophytic herbal plant growing wildly in Saudi Arabia. Due to its anti-angiogenic and anticancer capabilities, "C. procera" is a viable option for developing innovative anticancer medicines. However, no study has been done previously, to discover angiogenic and anti-cancer targets which are regulated by C. procera in liver cancer. In this study, leaves, stems, flowers, and seeds of C. procera were used to prepare crude extracts and were fractionated into four solvents of diverse polarities. These bioactivity-guided solvent fractions helped to identify useful compounds with minimal side effects. The phytoconstituents present in the leaves and stem were identified by GC-MS. In silico studies were done to predict the anti-cancer targets by major bioactive constituents present in leaves and stem extracts. A human angiogenesis antibody array was performed to profile novel angiogenic targets. The results from this study showed that C. procera extracts are an ideal anti-cancer remedy with minimum toxicity to normal cells as revealed by zebrafish in vivo toxicity screening assays. The novel antiangiogenic and anticancer targets identified in this study could be explored to design medication against liver cancer.

Assessment of Digital Dentistry Knowledge and Practices Among Dental Students at King Faisal University, Saudi Arabia.

BACKGROUND Digital dental (DD) technologies need to be developed for dental use because of the prominent position that these technologies have recently acquired. This 21-item online questionnaire-based study aimed to assess the understanding of DD methods in 120 undergraduate dental students at King Faisal University, Saudi Arabia (SA). MATERIAL AND METHODS Electronic surveys were sent to 123 dental students at different study phases (basic, preclinical, and clinical). Dental students were requested to answer the questionnaires in accordance with their knowledge, observations, particular experiences, and DD practice. Data were analyzed using descriptive statistics, which involved numerical values and ratios. Then, the associations among study level, knowledge, practice, and study phases were analyzed using chi-square and Fisher's exact tests. RESULTS Completed questionnaires were returned by 120 students. The chi-square test showed significant differences in relation to questions "Do you have any previous knowledge of DD?", "The field that uses DD the most is…", "Does DD provide more precise results than conventional dentistry?", and "Do you know about CAD/CAM?", with P values of 0.006, 0.000, 0.018, and 0.002, respectively. Students at clinical phase exhibited significantly higher levels of knowledge than those at the preclinical phase (P<0.01). With regard to DD practice, 73.3% of students expressed a negative viewpoint, 82.5% stated that DD is essential for the future, and half said that practicing DD will result in improvements in patient satisfaction, time consumed, and level of predictability. CONCLUSIONS Students at basic, preclinical, and clinical phases had good knowledge on DD and were motivated to practice it in future in their workplaces.

Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework.

Detecting pipeline leaks is an essential factor in maintaining the integrity of fluid transport systems. This paper introduces an advanced deep learning framework that uses continuous wavelet transform (CWT) images for precise detection of such leaks. Transforming acoustic signals from pipelines under various conditions into CWT scalograms, followed by signal processing by non-local means and adaptive histogram equalization, results in new enhanced leak-induced scalograms (ELIS) that capture detailed energy fluctuations across time-frequency scales. The fundamental approach takes advantage of a deep belief network (DBN) fine-tuned with a genetic algorithm (GA) and unified with a least squares support vector machine (LSSVM) to improve feature extraction and classification accuracy. The DBN-GA framework precisely extracts informative features, while the LSSVM classifier precisely distinguishes between leaky and non-leak conditions. By concentrating solely on the advanced capabilities of ELIS processed through an optimized DBN-GA-LSSVM model, this research achieves high detection accuracy and reliability, making a significant contribution to pipeline monitoring and maintenance. This innovative approach to capturing complex signal patterns can be applied to real-time leak detection and critical infrastructure safety in several industrial applications.

Translation and psychometric properties of the Arabic version of the ageism scale for dental students (ASDS-Ar): A multi-institutional validation.

BACKGROUND AND OBJECTIVE: Ageism represents an important barrier to high-quality healthcare for older adults. The present study sought to translate and validate the Arabic version of the Ageism Scale for Dental Students (ASDS-Arabic). MATERIALS AND METHODS: The 27-item ASDS tool was translated from English into Arabic following recommended cross-sectional forward and backward translation guidelines. The translated version was subjected to the content validity ratio (CVR) and sent to dental students in 21 institutes from 10 different Arab countries. Principal components analysis (PCA) was used to assess the dimensionality of the scale, and Cronbach's alpha was used to determine internal consistency reliability. The discriminant validity of the scale was assessed using the independent t-test. Confirmatory factor analysis (CFA) was also undertaken. RESULTS: Based on CVR, three items were removed. The 24-item Arabic version was completed by 3284 dental students. PCA and CFA retained 17 items in six components, explaining 50.3% of the total variance, with acceptable reliability, validity and discrimination. The first component "Adherence of older patients with dental treatment and instructions," included four items with a Cronbach α of 0.64 and scored 4.3 ± 0.8. The second component "Feasibility of the treatment plan," included three items with a Cronbach α of 0.66 and scored from 2.6 ± 1.2 to 2.9 ± 1.1. The third component "Cost of and responsibility for the dental treatment" included four items with a Cronbach α of 0.47 and scored 4.4 ± 0.8 to 4.5 ± 0.8. The fourth component "Medical history of older patients" included two items with a Cronbach α of 0.70 and scored 4.0 ± 1.0 to 4.1 ± 1.0. The fifth Component "Feeling towards older patients" included two items with a Cronbach α of 0.672 and scored 2.6 ± 1.2 to 2.0 ± 1.4. The sixth Component "Confidence and experience in treating older patients" included two items with a Cronbach α of 0.33 and scored 4.4 ± 1 to 4.6 ± 1. CONCLUSION: This preliminary validation of the ASDS-Ar resulted in a new 17-item scale with six components with acceptable validity, reliability and discrimination.

Bacillus safensis filtrate-based ZnO nanoparticles control black heart rot disease of apricot fruits by maintaining its soluble sugars and carotenoids.

Black heart rot is a serious disease of apricot and it has been reported to be caused by Alternaria solani, around the world. The present research was designed to control this disastrous disease using zinc oxide nanoparticles (b-ZnO NPs). These NPs were synthesized in the filtrate of a useful bacterium (Bacillus safensis) and applied to control black heart rot of apricot. After synthesis, the reduction of b-ZnO NPs was confirmed by UV-visible spectroscopy, at 330 nm. Fourier transform infrared (FTIR) spectra ensured the presence of multiple functional groups (alcohols, phenols, carboxylic acids, nitro compounds and amines) on the surface of b-ZnO NPs. X-Ray diffraction (XRD) analysis elucidated their average size (18 nm) while scanning electron microscopy (SEM) micrograph described the spherical shape of b-ZnO NPs. The synthesized b-ZnO NPs were applied in four different concentrations (0.25 mg/ml, 0.50 mg/ml, 0.75 mg/ml, 1.0 mg/ml) under both in vitro and in vivo conditions. These NPs were very efficient in inhibiting mycelial growth (85.1%) of A. solani at 0.75 mg/ml concentration of NPs, in vitro. Same concentration also performed best, in vivo, and significantly reduced disease incidence (by 67%) on self-inoculated apricot fruit. Apart from this, application of b-ZnO NPs helped apricot fruit to maintain its quality under fungal-stress conditions. The decay of apricot fruit was reduced and they maintained greater firmness and higher weight. Moreover, b-ZnO NPs treated fruits controlled black heart rot disease by maintaining higher contents of ascorbic acid, soluble sugars and carotenoids. These b-ZnO NPs were produced in powder form for their easy carriage to the farmers' fields.

Concurrent application of bacterial-mediated and mycosynthesized ZnO nanofungicides to maintain high ascorbic acid and delay postharvest decay of apricot.

Quality of apricot fruit is affected by different biotic stresses during growth, harvesting and storage. Due to fungal attack, huge losses of its quality and quantity are observed. The present research was designed for the diagnoses and management of postharvest rot disease of apricot. Infected apricot fruit were collected, and the causative agent was identified as A. tubingensis. To control this disease, both bacterial-mediated nanoparticles (b-ZnO NPs) and mycosynthesized nanoparticles (f-ZnO NPs) were used. Herein, biomass filtrates of one selected fungus (Trichoderma harzianum) and one bacterium (Bacillus safensis) were used to reduce zinc acetate into ZnO NPs. The physiochemical and morphological characters of both types of NPs were determined. UV-vis spectroscopy displayed absorption peaks of f-ZnO NPs and b-ZnO NPs at 310-380 nm, respectively, indicating successful reduction of Zinc acetate by the metabolites of both fungus and bacteria. Fourier transform infrared (FTIR) determined the presence of organic compounds like amines, aromatics, alkenes and alkyl halides, on both types of NPs, while X-ray diffraction (XRD) confirmed nano-size of f-ZnO NPs (30 nm) and b-ZnO NPs (35 nm). Scanning electron microscopy showed flower-crystalline shape for b-ZnO NPs and spherical-crystalline shape for f-ZnO NPs. Both NPs showed variable antifungal activities at four different concentrations (0.25, 0.50, 0.75 and 1.00 mg/ml). Diseases control and postharvest changes in apricot fruit were analyzed for 15 days. Among all treatments, 0.50 mg/ml concentration of f-ZnO NPs and 0.75 mg/ml concentration of b-ZnO NPs exhibited the strongest antifungal activity. Comparatively, f-ZnO NPs performed slightly better than b-ZnO NPs. Application of both NPs reduced fruit decay and weight, maintained higher ascorbic acid contents, sustained titratable acidity, and preserved firmness of diseased fruit. Our results suggest that microbial synthesized ZnO NPs can efficiently control fruit rot, extend shelf life, and preserve the quality of apricot.

Association of maternal hypertension and diabetes with variants of the NKX2-5, LEFTY1 and LEFTY2 genes in children with congenital heart defects: a case-control study from Pakistani Population.

BACKGROUND: Globally, congenital heart defect (CHD) is the most common congenital malformation, responsible for higher morbidity and mortality in the pediatric population. It is a complex multifactorial disease influenced by gene-environment and gene-gene interactions. The current study was the first attempt to study these polymorphisms in common clinical phenotypes of CHD in Pakistan and the association between maternal hypertension and diabetes with single nucleotide polymorphisms (SNPs) in children. METHODS: A total of 376 subjects were recruited in this current case-control study. Six variants from three genes were analyzed by cost-effective multiplex PCR and genotyped by minisequencing. Statistical analysis was done by GraphPad prism and Haploview. The association of SNPs and CHD was determined using logistic regression. RESULTS: The risk allele frequency was higher in cases as compared to healthy subjects, but the results were not significant for rs703752. However, stratification analysis suggested that rs703752 was significantly associated with the tetralogy of Fallot. The rs2295418 was significantly associated with maternal hypertension (OR = 16.41, p = 0.003), while a weak association was present between maternal diabetes and rs360057 (p = 0.08). CONCLUSION: In conclusion, variants in transcriptional and signaling genes were associated with Pakistani pediatric CHD patients that showed varied susceptibility between different clinical phenotypes of CHD. In addition, this study was the first report regarding the significant association between maternal hypertension and the LEFTY2 gene variant.

Phytochemical profiling, anti-hyperglycemic, antifungal, and radicals scavenging potential of crude extracts of Athyrium asplenioides- an in-vitro approach.

This research was aimed to evaluate the phytochemical profile, antifungal, anti-hyperglycemic, as well as antioxidant activity competence of different extracts of Athyrium asplenioides through in-vitro approach. The A. asplenioides crude methanol extract contained considerable quantity of pharmaceutically precious phytochemicals (saponins, tannins, quinones, flavonoid, phenols, steroid, and terpenoids) than others (acetone, ethyl acetate, and chloroform). Interestingly, the crude methanol extract showed remarkable antifungal activity against Candida species (C. krusei: 19.3 ± 2 mm > C. tropicalis: 18.4 ± 1 mm > C. albicans: 16.5 ± 1 mm > C. parapsilosis: 15.5 ± 2 mm > C. glabrate: 13.5 ± 2 mm > C. auris: 7.6 ± 1 mm) at a concentration of 20 mg mL-1. The crude methanol extract also showed remarkable anti-hyperglycemic activity on concentration basis. Surprisingly, remarkable free radicals scavenging potential against DPPH (76.38%) and ABTS (76.28%) free radicals at a concentration of 20 mg mL-1. According to the findings, the A. asplenioides crude methanol extract contains pharmaceutically valuable phytochemicals and may be useful for drug discovery.

A study on the role of surface functional groups of metakaolin in the removal of methylene blue: Characterization, kinetics, modeling and RSM optimization.

In this study, thermally activated kaolinite clay is explored as a suitable material for dye removal applications, which gave rise to highly reactive silica species in a broad range of aluminosilicate clusters. Multinuclear NMR studies described it as a short-range network in which Al sites in IV, V, and VI are coordinated, and Si is present mainly as Si(Q4(1Al)). Critical parameters for methylene blue (MB) were determined by the Placket Burman Design (PBD) as initial dye concentration, contact time, adsorbent dosage, pH and size. The % of MB removal studied after optimizing the parameters by central composite design (CCD), based on Response Surface Methodology, was found to be 90%. The adsorption kinetics and thermodynamics were systematically studied and reported by fitting them into different models. The maximum removal of the dye reached 97.8 mg/g according to the Freundlich isotherm, accomplished through chemisorption, following a pseudo-second-order reaction and the process is thermodynamically spontaneous and endothermic. The line spectrum of X-ray photoelectron spectroscopy (XPS) shows the participation of Si, Al, O, Ca and Na of Metakaolin (AK) and nitrogen of MB in the adsorption process. The appropriate stabilization of the N atom of the chromophore on the Si and Al atom in AK resulting from the ionic interaction on the surface is established from an increase in the binding energy of Al and Si. A single bridging oxygen signal at 532.32eVcorresponding to AK after dye adsorption tends to form siloanol/aluminol, and their interaction is lowered to 531.58eV. Regeneration of adsorbent after thermal treatment without loss of efficiency proved.

Different feedstocks of biochar affected the bioavailability and uptake of heavy metals by wheat (Triticum aestivum L.) plants grown in metal contaminated soil.

Heavy metals (HMs) contamination of agricultural soils is an emerging food safety challenge at world level. Therefore, as a possible treatment for the remediation of a HMs contaminated soil (sewage water irrigation for 20-years), the impact of biochar (BC) was investigated on the uptake of HMs by wheat (Triticum aestivum L.) plants. The BC was produced from seven different feedstocks (cotton stalks (CSBC), rice straw (RSBC), poultry manure (PMBC), lawn grass (LGBC), vegetable peels (VPBC), maize straw (MSBC), and rice husks (RHBC)). Each BC was applied at 1.25% (dry weight basis, w/w) in contaminated soil and a control was maintained without BC addition and wheat was grown in potted soil and harvested at maturity. Results revealed that the properties of different biochars regulated their effects on soil nutrient and HMs mobility and uptake by plants. The maximum plant phosphorous and potassium uptake and translocation to grain (173.4% and 341%, respectively) was found in RSBC treatment over control. The RHBC, PMBC, and MSBC treatments showed a maximum decrease in grain Cd concentration (32.9%, 33.8%, and 34.1%, respectively) compared to the control. The grain Pb (-41% to -51%, with no significant differences among different treatments) and Ni (-63%) concentrations were also reduced significantly following BC treatments compared to control. The daily intake and health risk index of Cd were significantly decreased due to PMBC (-28.1% and -33.8%, respectively), and MSBC (-28.3% and -34.1%, respectively) treatment over control. The BC treatments significantly increased the translocation factor of Cd in the order of VPBC (52.1%) > LGBC (25.4%) > CSBC (13.6%) > RSBC (12.1%) compared to control. The study demonstrated that the effects of BC on metal uptake in plants varied with feedstocks and suitable BC can be further exploited for the rehabilitation of contaminated soils and thereby ensuring food safety.

Effect of titanium dioxide nanoparticles and co-composted biochar on growth and Cd uptake by wheat plants: A field study.

Cadmium (Cd) is a common toxic trace element found in agricultural soils which is mainly due to anthropogenic activities. Cadmium posed a significant risk to humans all around the world due to its cancer-causing ability. The current study demonstrated the effects of soil-applied biochar (BC) and foliar-applied titanium dioxide nanoparticles (TiO2 NPs) (at a rate of 0.5% and 75 mg/L respectively) alone or in combination on growth and Cd accumulation in wheat plants under field experiment. Soil applied BC and foliar TiO2 NPs, as well as BC coupled with TiO2 NPs, reduced Cd contents in grains by 32%, 47%, and 79%, than control respectively. The usage of NPs and BC boosted the plant height as well as chlorophyll contents by lowering oxidative injury and changing selected antioxidant enzyme activities in leaves than control plants. The combined use of NPs and BC prevented excess Cd accumulation in grains over the critical level (0.2 mg/kg) for cereals. The health risk index (HRI) due to Cd was reduced by 79% by co-composted BC + TiO2 NPs treatment than control. Although, HRI was lower than one for all treatments but this may exceed the limit if grains obtained from such field consumed over long periods. In conclusion, TiO2 NPs and BC amendments can be implemented in fields across the globe where excess Cd is present in soils. Additional studies on the use of such approaches in more precise experimental settings are needed in order to address this environmental problem at larger scale.

Urea molasses mineral block under various feeding systems improved nutrient digestibility, productive performance and blood biochemical profile of Yaks.

BACKGROUND: The study aimed to investigate the effect of urea molasses mineral blocks (UMMB) on nutrient digestibility, productive performance and blood biochemical profile of indigenous yaks under various feeding systems. A total of sixteen yaks were randomly divided into four groups (n = 4 animal per group) and offered the, following feeding systems: (A) stall feeding, (B), urea molasses mineral block (UMMB) + stall feeding, (C) yard feeding and (D) UMMB + yard feeding. Trial lasted for 40 days. RESULTS: Results showed that nutrients intake (g) and nutrient digestibility (%) of dry matter (DM), organic matter (OM), crude protein (CP), ether extract (EE) and crude fiber (CF) were significantly higher (p < 0.05) in stall and yard feeding groups with UMMB licking. Blood zinc, cobalt, hemoglobin (Hb), red blood cell (RBC), glucose and serum glutamate private transaminase (SGPT) significantly (p < 0.05) increased in stall and yard feeding with UMMB licking. Milk yield, Ca and monounsaturated fatty acid except milk composition improved significantly (p < 0.05) in stall and yard feeding groups with UMMB licking. CONCLUSION: It was concluded that feeding of UMMB improved utilization of low-quality roughages and best results were obtained from stall and yard feedings with UMMB licking as compared to other groups.

Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN.

This paper presents a novel framework for classifying ongoing conditions in centrifugal pumps based on signal processing and deep learning techniques. First, vibration signals are acquired from the centrifugal pump. The acquired vibration signals are heavily affected by macrostructural vibration noise. To overcome the influence of noise, pre-processing techniques are employed on the vibration signal, and a fault-specific frequency band is chosen. The Stockwell transform (S-transform) is then applied to this band, yielding S-transform scalograms that depict energy fluctuations across different frequencies and time scales, represented by color intensity variations. Nevertheless, the accuracy of these scalograms can be compromised by the presence of interference noise. To address this concern, an additional step involving the Sobel filter is applied to the S-transform scalograms, resulting in the generation of novel SobelEdge scalograms. These SobelEdge scalograms aim to enhance the clarity and discriminative features of fault-related information while minimizing the impact of interference noise. The novel scalograms heighten energy variation in the S-transform scalograms by detecting the edges where color intensities change. These new scalograms are then provided to a convolutional neural network (CNN) for the fault classification of centrifugal pumps. The centrifugal pump fault classification capability of the proposed method outperformed state-of-the-art reference methods.

A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection.

A hybrid deep learning approach was designed that combines deep learning with enhanced short-time Fourier transform (STFT) spectrograms and continuous wavelet transform (CWT) scalograms for pipeline leak detection. Such detection plays a crucial role in ensuring the safety and integrity of fluid transportation systems. The proposed model leverages the power of STFT and CWT to enhance detection capabilities. The pipeline's acoustic emission signals during normal and leak operating conditions undergo transformation using STFT and CWT, creating scalograms representing energy variations across time-frequency scales. To improve the signal quality and eliminate noise, Sobel and wavelet denoising filters are applied to the scalograms. These filtered scalograms are then fed into convolutional neural networks, extracting informative features that harness the distinct characteristics captured by both STFT and CWT. For enhanced computational efficiency and discriminatory power, principal component analysis is employed to reduce the feature space dimensionality. Subsequently, pipeline leaks are accurately detected and classified by categorizing the reduced dimensional features using t-distributed stochastic neighbor embedding and artificial neural networks. The hybrid approach achieves high accuracy and reliability in leak detection, demonstrating its effectiveness in capturing both spectral and temporal details. This research significantly contributes to pipeline monitoring and maintenance and offers a promising solution for real-time leak detection in diverse industrial applications.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.