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The established method of synthesising tetraaza macrocyclic ligand by introducing a transition metal ion as a templating agent has demonstrated difficulties in the decomplexation process. Therefore, this study examined the potential of forming the ligand before converting them into transition metal complex and further explored the characteristic of Pd 2+ tetraaza macrocyclic complex through density functional theory (DFT) calculation. The results show that the cation successfully fits in the macrocyclic ligand as a result of the electron transfer process.

Nitrogen is the main limiting nutrient after carbon, hydrogen and oxygen for photosynthetic process, phyto-hormonal, proteomic changes and growth-development of plants to complete its lifecycle. Excessive and inefficient use of N fertilizer results in enhanced crop production costs and atmospheric pollution. Atmospheric nitrogen (71%) in the molecular form is not available for the plants. For world’s sustainable food production and atmospheric benefits, there is an urgent need to up-grade nitrogen use efficiency in agricultural farming system. The nitrogen use efficiency is the product of nitrogen uptake efficiency and nitrogen utilization efficiency, it varies from 30.2 to 53.2%. Nitrogen losses are too high, due to excess amount, low plant population, poor application methods etc., which can go up to 70% of total available nitrogen. These losses can be minimized up to 15–30% by adopting improved agronomic approaches such as optimal dosage of nitrogen, application of N by using canopy sensors, maintaining plant population, drip fertigation and legume based intercropping. A few transgenic studies have shown improvement in nitrogen uptake and even increase in biomass. Nitrate reductase, nitrite reductase, glutamine synthetase, glutamine oxoglutarate aminotransferase and asparagine synthetase enzyme have a great role in nitrogen metabolism. However, further studies on carbon–nitrogen metabolism and molecular changes at omic levels are required by using “whole genome sequencing technology” to improve nitrogen use efficiency. This review focus on nitrogen use efficiency that is the major concern of modern days to save economic resources without sacrificing farm yield as well as safety of global environment, i.e. greenhouse gas emissions, ammonium volatilization and nitrate leaching.

Prenatal and postnatal skeletal muscle development in ruminants is coordinated by interactions between genetic, nutritional, epigenetic, and endocrine factors. This review focuses on the influence of maternal nutrition during gestation on fetal myogenesis, satellite cell dynamics, and myogenic regulatory factors expression, including MYF5, MYOD1, and MYOG. Studies in sheep and cattle indicate that nutrient restriction or overnutrition alters muscle fiber number, the cross-sectional area, and the transcriptional regulation of myogenic genes in offspring. Postnatally, muscle hypertrophy is primarily mediated by satellite cells, which are activated via PAX7, MYOD, and MYF5, and regulated through mechanisms such as CARM1-induced chromatin remodeling and miR-31-mediated mRNA expression. Hormonal signaling via the GH–IGF1 axis and thyroid hormones further modulate satellite cell proliferation and protein accretion. Genetic variants, such as myostatin mutations in Texel sheep and Belgian Blue cattle, enhance muscle mass but may compromise reproductive efficiency. Nutritional interventions, including the plane of nutrition, supplementation strategies, and environmental stressors such as heat and stocking density, significantly influence muscle fiber composition and carcass traits. This review provides a comprehensive overview of skeletal muscle programming in ruminants, tracing the developmental trajectory from progenitor cell differentiation to postnatal growth and maturation. These insights underscore the need for integrated approaches combining maternal diet optimization, molecular breeding, and precision livestock management to enhance muscle growth, meat quality, and production sustainability in ruminant systems.

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

Ferruginous deposits are iron-rich sediments or sedimentary rocks found in various sizes, shapes, and compositions within sedimentary strata in different depositional settings. This study investigates the characteristics, distribution, and origin of ferruginous deposits found in the Late Ordovician glaciogenic Sarah Formation and surrounding deposits in central Saudi Arabia. Several types of ferruginous deposits have been identified through field observations and laboratory investigations, including thin-section petrography, geochemical, surface, and bulk mineralogical analyses, and computed tomography scans. The identified ferruginous deposits include solid and rinded concretions, pipes, layers, ferricretes, liesegang bands, and fracture infills. They were associated with the periglacial and proglacial facies of the Sarah Formation. For instance, ferruginous deformed layers were mainly observed in subglacial facies, while rinded concretions occurred in bleached glaciofluvial facies. Ferruginous deposits were also found in the uppermost parts of non-glacial facies, such as the shallow marine Quwarah Member of the Qasim Formation and the braided deltaic Sajir Member of the Saq Formation. Compositionally, goethite was the dominant iron oxide mineral in all ferruginous deposits, and it is mostly distributed as cement, filling pore spaces. In comparison to ferruginous deposits reported in different depositional settings on Earth and Mars, the studied ferruginous deposits in an ancient glaciogenic setting exhibit different mineralogical characteristics. Specifically, the studied solid concretions are less abundant and primarily amalgamated, while the rinded concretions appear to be more mature than those reported in other depositional environments. This study suggests that the weathered basement rocks of the Arabian Shield were the primary source of iron. The iron-bearing rocks were eroded and transported by Hirnantian glaciation and deglaciation processes.

The global transition towards clean energy sources is becoming essential to reduce reliance on conventional fuels and mitigate carbon emissions. In the future, the clean energy storage landscape, green hydrogen, and green ammonia (powered by renewable energy sources) are emerging as key players. This study explores the prospectives and feasibility of producing and storing offshore green hydrogen and green ammonia. The potential power output of Hornsea one and Hornsea two winds farms in the United Kingdom was calculated using real wind data. The usable electricity from the Hornsea one wind farm was 5.83 TWh/year, and from the Hornsea two wind farm, it was 6.44 TWh/year, harnessed to three different scenarios for the production and storage of green ammonia and green hydrogen. Scenario 1 fulfil the requirement of green hydrogen storage for flexible ammonia production but consumes more energy for green hydrogen compression. Scenario 2 does not offer any hydrogen storage which is not favourable in terms of flexibility and market demand. Scenario 3 offers both, a direct routed supply of produced hydrogen for green ammonia synthesis and a storage facility for green hydrogen storage. Detailed mathematical calculations and sensitivity analysis was performed based on the total energy available to find out the energy storage capacity in terms of the mass of green hydrogen and green ammonia produced. Sensitivity analysis in the case of scenario 3 was conducted to determine the optimal percentage of green hydrogen going to the storage facility. Based on the cost evaluation of three different presented scenarios, the levelized cost of hydrogen (LCOH) is between USD 5.30 and 5.97/kg, and the levelized cost of ammonia (LCOA) is between USD 984.16 and USD 1197.11/tonne. These prices are lower compared to the current UK market. The study finds scenario 3 as the most appropriate way in terms of compression energy savings, flexibility for the production and storage capacity that depends upon the supply and demand of these green fuels in the market, and a feasible amount of green hydrogen storage.

As the crypto-asset ecosystem matures, the use of high-frequency data has become increasingly common in decentralized finance literature. Using bibliometric analysis, we characterize the existing cryptocurrency literature that employs high-frequency data. We highlighted the most influential authors, articles, and journals based on 189 articles from the Scopus database from 2015 to 2022. This approach enables us to identify emerging trends and research hotspots with the aid of co-citation and cartographic analyses. It shows knowledge expansion through authors’ collaboration in cryptocurrency research with co-authorship analysis. We identify four major streams of research: (i) return prediction and measurement of cryptocurrency volatility, (ii) (in)efficiency of cryptocurrencies, (iii) price dynamics and bubbles in cryptocurrencies, and (iv) the diversification, safe haven, and hedging properties of Bitcoin. We conclude that highly traded cryptocurrencies’ investment features and economic outcomes are analyzed predominantly on a tick-by-tick basis. This study also provides recommendations for future studies.

The thermophysical features of Casson fluid flow caused by a nonlinear permeable stretchable surface are assessed in the present study. The computational model of Casson fluid is used to define viscoelasticity, which is quantified rheologically in the momentum equation. Exothermic chemical reactions, heat absorption/generation, magnetic field and nonlinear volumetric thermal/mass expansion over the stretched surface are also considered. The proposed model equations are lessened by the similarity transformation to the dimensionless system of ODEs. The obtained set of differential equations are numerically computed through parametric continuation approach. The results are displayed and discussed via figures and tables. The outcomes of the proposed problem are compared to the existing literature and bvp4c package for the validity and accuracy purposes. It has been perceived that the energy and mass transition rate of Casson fluid increased with the flourishing trend of heat source parameter and chemical reaction respectively. Casson fluid velocity can be elevated by the rising effect of thermal, mass Grashof number and nonlinear thermal convection.

Background: Pediatric thyroid carcinoma, the most common endocrine malignancy among children, displays a notably higher incidence rate of 16% compared to 5% in adults. This disease often presents as advanced illness in pediatric cases, yet there\\'s a paucity of data on its clinical features in this demographic. Our study, a first in North Sumatera, Indonesia, aims to fill this gap by examining the clinical characteristics of pediatric thyroid carcinoma.Objective: This research endeavors to provide comprehensive data on the demographic and clinical profiles of pediatric patients with thyroid nodules treated at our institution. Methods: A retrospective descriptive study was conducted from 2018-2021, encompassing 35 pediatric patients (under 18 years) who underwent partial or total thyroidectomy for thyroid nodules. We reviewed and analyzed patient demographics and pathology results, presenting these as frequencies and percentages. Results: Of the patients, 85.7% (30 patients) were female, and 14.3% (5 patients) were male, with an average age of 15.66 years (ranging from 10 to over 16 years). The age distribution showed 60% (21 patients) between 16-18 years, 22.9% (8 patients) between 6-11 years, and 17.1% (6 patients) between 12-15 years. Diagnoses included Colloid Goitres (42.9%), Follicular Adenoma (22.9%), Papillary Thyroid Carcinoma (20%), Hashimoto Thyroiditis and Graves\\' disease (5.7% each), and Follicular Neoplasm (2.9%). Conclusion: The study period witnessed a steady prevalence of pediatric thyroid nodules necessitating thyroidectomy. This underscores the critical need for early detection of thyroid nodules in children for malignancy screening. Timely diagnosis is paramount for favorable outcomes and prognoses in pediatric thyroid conditions.

In the modern age of data enrichment, it has become necessary to incorporate adaptive inference processes into survey-based estimation systems in order to achieve efficient and consistent population summaries. In this work, a new type of data-adaptive approach to the prospective estimation of central tendency under stratified random sampling (StRS) frameworks is presented. The suggested structure takes advantage of the auxiliary information based on locally tuned, non-parametric smoothing plans that dynamically adapt to a heterogeneity of sampled and unsampled domains. The estimator wisely reacts to an intricate pattern of the data, ensured by the application of variable bandwidth functions, stratified weighting plans, which ensure resilience to model misspecification and outlier effects. Substantial Monte Carlo simulations and two empirical studies, i.e., solar radiation data and fish market data, are performed to confirm its performance in a variety of bandwidth and sample size settings. The findings have consistently shown that the suggested adaptive inference mechanism is significantly more precise and stable than traditional estimators, not only when auxiliary expectations are known, but also when they have to be estimated. This study brings into play a flexible, design-conscious framework that connects model-driven estimation with design-driven survey inference, which is of importance in contemporary information-gathering settings of informational diversity and enrichment.