Nutrition management is fundamental for children with chronic kidney disease (CKD). Fluid balance and low-protein and low-sodium diets are the more stressed fields from a nutritional point of view. At the same time, the role of micronutrients is often underestimated. Starting from the causes that could lead to potential micronutrient deficiencies in these patients, this review considers all micronutrients that could be administered in CKD to improve the prognosis of this disease. Full article

Procedural learning has been mainly tested through motor sequence learning tasks in children with neurodevelopmental disorders, especially with isolated Developmental Coordination Disorder (DCD) and Reading Disorder (RD). Studies on motor adaptation are scarcer and more controversial. This study aimed to compare the performance of children with isolated and associated DCD and RD in a graphomotor adaptation task. In total, 23 children with RD, 16 children with DCD, 19 children with DCD-RD, and 21 typically developing (TD) children wrote trigrams both in the conventional (from left to right) and opposite (from right to left) writing directions. The results show that movement speed and accuracy were more impacted by the adaptation condition (opposite writing direction) in children with neurodevelopmental disorders than TD children. Our results also reveal that children with RD have less difficulty adapting their movement than children with DCD. Children with DCD-RD had the most difficulty, and analysis of their performance suggests a cumulative effect of the two neurodevelopmental disorders in motor adaptation. Full article

Coordinated activation of sympathetic and respiratory nervous systems is crucial in responses to noxious stimuli such as intermittent hypoxia. Acute intermittent hypoxia (AIH) is a valuable model for studying obstructive sleep apnea (OSA) pathophysiology, and stimulation of breathing during AIH is known to elicit long-term changes in respiratory and sympathetic functions. The aim of this study was to record the renal sympathetic nerve activity (RSNA) and phrenic nerve activity (PNA) during the AIH protocol in rats exposed to monoanesthesia with sevoflurane or isoflurane. Adult male Sprague-Dawley rats (n = 24; weight: 280–360 g) were selected and randomly divided into three groups: two experimental groups (sevoflurane group, n = 6; isoflurane group, n = 6) and a control group (urethane group, n = 12). The AIH protocol was identical in all studied groups and consisted in delivering five 3 min-long hypoxic episodes (fraction of inspired oxygen, FiO₂ = 0.09), separated by 3 min recovery intervals at FiO₂ = 0.5. Volatile anesthetics, isoflurane and sevoflurane, blunted the RSNA response to AIH in comparison to urethane anesthesia. Additionally, the PNA response to acute intermittent hypoxia was preserved, indicating that the respiratory system might be more robust than the sympathetic system response during exposure to acute intermittent hypoxia. Full article

Online multi-object tracking (MOT) techniques are instrumental in monitoring workers’ positions and identities in construction settings. Traditional approaches, which employ deep neural networks (DNNs) for detection followed by body similarity matching, often overlook the significance of clear head features and stable head motions. This study presents a novel bidirectional tracking method that integrates intra-frame processing, which combines head and body analysis to minimize false positives and inter-frame matching to control ID assignment. By leveraging head information for enhanced body tracking, the method generates smoother trajectories with reduced ID errors. The proposed method achieved a state-of-the-art (SOTA) performance, with a multiple-object tracking accuracy (MOTA) of 95.191%, higher-order tracking accuracy (HOTA) of 78.884% and an identity switch (IDSW) count of 0, making it a strong baseline for future research. Full article

In recent years, blockchain technology has attracted substantial interest for its capability to transform supply chain management and finance. This paper employs evolutionary game theory to investigate the application of blockchain in mitigating financial risks within supply chains, taking into account the technology’s maturity and the risk preferences of financial institutions. By modeling interactions among financial institutions, small and medium enterprises (SMEs), and core enterprises within the accounts receivable financing framework, this study evaluates blockchain’s impact on their decision-making and its efficacy in risk reduction. Our findings suggest the transformative potential of blockchain in mitigating financial risks, solving information asymmetry, and enhancing collaboration between financial entities and SMEs. Additionally, we integrate smart contracts into supply chain finance, proposing pragmatic procedures for their deployment in real-world contexts. Via a detailed examination of blockchain’s maturity and financial institutions’ risk preferences, this research demonstrates the primary determinants of strategic decisions in supply chain finance and underscores how blockchain technology fosters system stability using risk mitigation. Our innovative contribution lies in the design of smart contracts for the ARF process, rooted in blockchain’s core attributes of security, transparency, and immutability, thereby ensuring efficient operation and cost reduction in supply chain finance. Full article

The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations. Full article

Here, we study the extension of p-trigonometric functions $\mathrm{sinp}$ and $\mathrm{cosp}$ family in complex domains and p-hyperbolic functions $\mathrm{sinhp}$ and the $\mathrm{coshp}$ family in hyperbolic complex domains. These functions satisfy analogous relations as their classical counterparts with some unknown properties. We show the relationship of these two classes of special functions viz. p-trigonometric and p-hyperbolic functions with imaginary arguments. We also show many properties and identities related to the analogy between these two groups of functions. Further, we extend the research bridging the concepts of hyperbolic and elliptical complex numbers to show the properties of logarithmic functions with complex arguments. Full article

This article investigates the Oldroyd fluid, which is widely used in industrial and engineering environments. When the Oldroyd fluid passes through a three-dimensional semi-infinite cylinder, the asymptotic properties of the solutions are established. On this basis, we also studied the continuous dependence of the viscosity coefficient. Full article

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to impact the world even three years after its outbreak. International border closures and health alerts severely affected the air transport industry, resulting in substantial financial losses. This study analyzes the global data on infected individuals alongside aircraft types, flight durations, and passenger flows. Using a Bayesian framework, we forecast the risk of infection during commercial flights and its potential spread across an air transport network. Our model allows us to explore the effect of mitigation measures, such as closing individual routes or airports, reducing aircraft occupancy, or restricting access for infected passengers, on disease propagation, while allowing the air industry to operate at near-normal levels. Our novel approach combines dynamic network modeling with discrete event simulation. A real-case study at major European hubs illustrates our methodology. Full article

The aim of this paper is to introduce a new type of two-dimensional convexity by using total-order relations. In the first part of this paper, we examine the Hyers–Ulam stability of two-dimensional convex mappings by using the sandwich theorem. Our next step involves the development of Hermite–Hadamard inequality, including its weighted and product forms, by using a novel type of fractional operator having non-singular kernels. Moreover, we develop several nontrivial examples and remarks to demonstrate the validity of our main results. Finally, we examine approximate convex mappings and have left an open problem regarding the best optimal constants for two-dimensional approximate convexity. Full article

In this paper, we develop a two-level supply chain model with low carbon emissions for defective deteriorating items under learning in fuzzy environment by using the double inspection process. Carbon emissions are a major issue for the environment and human life when they come from many sources like different kinds of factories, firms, and industries. The burning of diesel and petrol during the supply of items through transportation is also responsible for carbon emissions. When any company, firm, or industry supplies their items through a supply chain by using of transportation in the regular mode, then a lot of carbon units are emitted from the burning of petrol and diesel, etc., which affects the supply chain. Carbon emissions can be controlled by using different kinds of policies issued by the government of a country, and lots of companies have implemented these policies to control carbon emissions. When a seller delivers a demanded lot size to the buyer, as per demand, and the lot size has some defective items, as per consideration, the demand rate is uncertain in nature. The buyer inspects the received whole lot and divides it into two categories of defective and no defective deteriorating items, as well as immediately selling at different price. The fuzzy concept nullifies the uncertain nature of the demand rate. This paper covers two models, assuming two conditions of quality screening under learning in fuzzy environment: (i) the buyer shows the quality screening and (ii) the quality inspection becomes the seller’s responsibility. The carbon footprint from the transporting and warehousing the deteriorating items is also assumed. The aim of this study is to minimize the whole inventory cost for supply chains with respect to lot size and the number of orders per production cycle. Jointly optimizing the delivery lot size and number of orders per production cycle will minimize the whole fuzzy inventory cost for the supply chain and also reduce the carbon emissions. We take two numerical approaches with authentic data (from the literature reviews) for the justification of the proposed model 1 and model 2. Sensitivity observations, managerial insights, applications of these proposed models, and future scope are also included in this paper, which is more beneficial for firms, the industrial sector, and especially for online markets. The impact of the most effective parameters, like learning effect, fuzzy parameter, carbon emissions parameter, and inventory cost are shown in this study and had a positive effect on the total inventory cost for the supply chain. Full article

This article introduces a discrete-time fractional variable order over a SEIQR model, incorporated for COVID-19. Initially, we establish the well-possedness of solution. Further, the disease-free and the endemic equilibrium points are determined. Moreover, the local asymptotic stability of the model is analyzed. We develop a novel discrete fractional optimal control problem tailored for COVID-19, utilizing a discrete mathematical model featuring a variable order fractional derivative. Finally, we validate the reliability of these analytical findings through numerical simulations and offer insights from a biological perspective. Full article

The launch azimuth of the Naro Space Center is limited toward the south of the Korean peninsula, at 170 ± 10 degrees, suitable for the polar orbit, sun-synchronous orbit, and safety range issues. In this circumstance, one option to send a satellite into GEO is to perform a dog-leg maneuver during ascent, thus forming a medium-inclination orbit under such a restrictive condition. However, this option requires an immense amount of energy for the dog-leg maneuver, as well as a plane change maneuver. The only remaining option is to raise the apogee to the Moon, utilizing lunar gravity to lower the inclination to near zero and then returning to the vicinity of the Earth at an altitude of 35,786 km without maneuver. In order to design lunar-assisted GEO transfer, all feasible paths are defined, but questions remain about how seasonal variations affect all these potential paths. Therefore, this study aims to design and analyze all available trajectories for the year 2031 using a high-fidelity dynamic model, root-finding algorithm, and well-arranged initial conditions, focusing on the impact of seasonal trends. The simulation results indicate that cislunar free-return trajectories generally require less $\mathit{\Delta }V$ compared to circumlunar free-return trajectories, and circumlunar trajectories are minimally affected by lunisolar effects due to their relatively short return time of flight. Conversely, cislunar trajectories show seasonal variations, so spring and fall seasons require up to 20 m/s less $\mathit{\Delta }V$ than summer and winter seasons due to the relatively long time of return duration. Full article

: In this study spanning four decades, we explored the relationship between the Reserve Bank of India’s (RBI) interventions and the validity of Purchasing Power Parity (PPP) across two distinct exchange rate regimes: the fixed exchange rate regime (1975–1993) and the managed floating regime (1994–2015). Applying an error correction model (VECM), our analysis reveals that under the fixed exchange rate regime, the environment is conducive to PPP due to frequent interventions by the RBI. However, in the managed floating regime, selective interventions weaken the applicability of PPP. These findings align with prior research but also hint at the limitations of linear models in capturing the intricate dynamics of PPP when central banks are involved. Nonlinear models may hold the key to unraveling the relationship more effectively. Full article

The aerodynamic performance of wings degrades severely at low Reynolds number; lift often becomes non-linear, while drag increases significantly, caused by large extents of separation. Consequently, a non-conventional wing design approach is implemented to assess its ability to enhance performance. The design methodology is that of wing segmentation, where the wing is divided into spanwise panels that can be separated, thereby yielding small gaps between the panels. A moderate aspect ratio wing comprised of four separate wing panels was manufactured and wind tunnel tested through a Re range from 40,000 to 80,000. Force balance data and surface flow visualization were used to characterize performance. The results indicate that segmentation is effective in significantly augmenting efficiency at Reynolds numbers at which the fused wing (i.e., no gaps) shows large extents of open separation. Drag is greatly reduced, while lift is increased, and stall is delayed. The benefit of segmentation was noted to diminish at higher Re where the fused wing’s performance improves markedly. Wing segmentation could find application in micro-unmanned-aerial-vehicle and drone design. Further study would entail the effects of AR and the number of spanwise panels on performance. Full article

Buddleja cordata cell suspension cultures could be used as a tool for investigating the capabilities of this species to tolerate heavy metals (HMs) and for assessing the effects of HMs on the accumulation of phenolic compounds in this species. It grows in a wide range of habitats in Mexico, including ultramafic soils, and mobilizes some HMs in the soil. The mobilization of these HMs has been associated with phenolic substances. In addition, this species is used in Mexican traditional medicine. In the present study, a B. cordata cell suspension culture was grown for 18 days in a culture medium enriched with Cu (0.03–0.25 mM), Fe (0.25–1.5 mM), Mn (0.5–3.0 mM), or Zn (0.5–2.0 mM) to determine the effects of these HMs on growth and HM accumulation. We also assessed the effects of the HMs on phenolic compound accumulation after 1 and 18 days of HM exposure. Cells were able to grow at almost all tested HM concentrations and accumulated significant amounts of each HM. The highest accumulation levels were as follows: 1160 mg Cu kg⁻¹, 6845 mg Fe kg⁻¹, 3770 mg Mn kg⁻¹, and 6581 mg Zn kg⁻¹. Phenolic compound accumulation was affected by the HM exposure time and corresponded to each HM and its concentration. Future research should analyze whole plants to determine the capabilities of Buddleja cordata to accumulate abnormally high amounts of HM and to evaluate the physiological impact of changes in the accumulation of phenolic compounds. Full article

Social media platforms like Instagram have become a haven for online scams, employing various deceptive tactics to exploit unsuspecting users. This paper investigates advanced algorithmic approaches to combat this growing threat. We explore various machine learning models for scam profile detection on Instagram. Our methodology involves collecting a comprehensive dataset from a trusted source and meticulously preprocessing the data for analysis. We then evaluate the effectiveness of a suite of machine learning algorithms, including decision trees, logistic regression, SVMs, and other ensemble methods. Each model’s performance is measured using established metrics like accuracy, precision, recall, and F1-scores. Our findings indicate that ensemble methods, particularly random forest, XGBoost, and gradient boosting, outperform other models, achieving accuracy of 90%. The insights garnered from this study contribute significantly to the body of knowledge in social media forensics, offering practical implications for the development of automated tools to combat online deception. Full article

The application of Artificial Intelligence in Education (AIED) is experiencing widespread interest among students, educators, researchers, and policymakers. AIED is expected, among other things, to enhance learning environments in the higher education system. However, in line with the general trends, there are also increasing concerns about possible negative and collateral effects. The consequent social impact cannot be currently assessed in depth. Balancing benefits with social considerations according to a socio-technical approach is essential for harnessing the true power of AI in a responsible and trustworthy context. This study proposes a semi-systematic literature review of the available knowledge on the adoption of artificial intelligence (AI) in the higher education system. It presents a stakeholder-centric analysis to explore multiple perspectives, including pedagogical, managerial, technological, governmental, external, and social ones. The main goal is to identify and discuss major gaps and challenges in context, looking at the existing body of knowledge and momentum. AIED should encompass pedagogical, ethical, and social dimensions to be properly addressed. This review highlights a not-always-explicit socio-technical perspective. Additionally, this study reveals a significant lack of empirical systematic evaluation of added value and institutional readiness. Because of the broad scope of the study and the intense ongoing debate on the topic, an exhaustive identification of the current body of knowledge is probably unrealistic, so this study aims mainly to identify the mainstream and major trends by looking at the most recent contributions. Full article

Plant acyl-CoA-binding proteins (ACBPs) function in plant development and stress responses, with some ACBPs interacting with protein partners. This study tested the interaction between two Class II GmACBPs (Glycine max ACBPs) and seven kinases, using yeast two-hybrid (Y2H) assays and bimolecular fluorescence complementation (BiFC). The results revealed that both GmACBP3.1 and GmACBP4.1 interact with two soybean kinases, a mitogen-activated protein kinase MPK2, and a serine/threonine-protein kinase SAPK2, highlighting the significance of the ankyrin-repeat (ANK) domain in facilitating protein–protein interactions. Moreover, an in vitro kinase assay and subsequent Phos-tag SDS-PAGE determined that GmMPK2 and GmSAPK2 possess the ability to phosphorylate Class II GmACBPs. Additionally, the kinase-specific phosphosites for Class II GmACBPs were predicted using databases. The HDOCK server was also utilized to predict the binding models of Class II GmACBPs with these two kinases, and the results indicated that the affected residues were located in the ANK region of Class II GmACBPs in both docking models, aligning with the findings of the Y2H and BiFC experiments. This is the first report describing the interaction between Class II GmACBPs and kinases, suggesting that Class II GmACBPs have potential as phospho-proteins that impact signaling pathways. Full article

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions is challenging due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts in which an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal is implemented and assessed in a laboratory context, in which the model performance and QoS metrics are analyzed. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% accuracy. Also, the obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements. Full article

This article presents a third-order, feedforward, single-bit Delta-Sigma analog-to-digital modulator (DSM) based on an output swing-enhanced gain-boost inverter for low-voltage low-power applications such as wearable devices, mobile health, and the Internet of Things (IoTs). The proposed output swing-enhanced structure addresses the output-swing reduction in the conventional structure while achieving high DC gain and large output swing simultaneously. Implemented in a 180 nm CMOS process, the entire chip is comprised of a delta-sigma modulator, an oscillator, and a current reference. It achieves 86.1 dB peak SNR and 92 dB dynamic range (DR) with 1.95 kHz signal bandwidth. The whole chip dissipates 54.5 μW, leading to a 167.6 dB Schreier Figure of Merit (FoMs). Full article