Flexible Approach Research Articles

Applying Iterative Student Feedback across Flipped Classroom and Flexible Teaching Approaches: Impact on Veterinary Students' Learning Experience.

No single teaching strategy supports all learning styles in veterinary science students. To facilitate more convenient and flexible teaching, learning, and revision, an innovative online digital learning platform-VetCloud-was developed to provide access to modularized programme content across courses to promote active, integrated learning. This study aimed to understand student perceptions regarding the enhancement of the student learning experience in a foundational course in gastrointestinal anatomy and physiology at The University of Queensland across two learning cycles, via applying iterative student feedback in transitioning a flipped classroom approach using VetCloud for the delivery of lecture content in 2022 to a flexible learning approach in 2023. By 2023, the use of VetCloud in the flexible learning approach improved students' work/study/life balance, reduced their stress levels, and enabled a more efficient use of their time when studying, compared to the flipped classroom approach in 2022. Surveying student perceptions was integral to maximizing their learning experience. Data clearly demonstrates that students will mix-and-match how they interact with available options provided via flexible delivery on an individualized basis. This teaching method offers veterinary educators an innovative and efficient approach to veterinary student education in anatomy and physiology while enhancing student well-being.

Negotiating North-South relations: The World Bank’s Grain Marketing Project in revolutionary Ethiopia, 1974–1978

This article explores the driving reasons that shaped the World Bank policy toward Africa in the 1970s through the lens of the relationship between the World Bank, western countries, and revolutionary Ethiopia from 1974 to 1978, during the negotiations for the Grain Marketing project (GMP). The trajectory of the GMP reveals that the World Bank’s attitude was shaped by external and internal factors as well. The organization operated under pressure of major Western shareholders and associated business groups demanding compensation for the nationalization of their assets. The continuation of negotiations and the final approval of the GMP, however, were primarily shaped by the demands of the Ethiopian government and the internal confrontation between the advocates of macro-economic orthodoxy and the supporters of a flexible approach to development. According to the latter, the relationship with Addis Ababa was a critical test for the World Bank’s legitimacy in sub-Saharan Africa at a time when demands for a New International Economic Order challenged established patterns of North-South relations.

Archimedean [formula omitted]-norm and [formula omitted]-conorm coupled q-rung orthopair fuzzy TOPSIS method for unknown criteria weighting information

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q-rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs’ weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t-norm and t-conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions.

(Invited) Dissolution of Organic Overlayers Modified Platinum Single Crystal Interfaces in Acidic Medium

Electrocatalysts design has been an important researching discipline in the filed of electrochemical energy conversion and storage, among which, surface (organic) modification has becoming an efficient, selective, and flexible approach in electrocatalysis study and applications. In the focus of direct modulation on electrocatalysis interfaces, the functionalization of metallic catalysts with organic ligands (i. e. functional organic molecule, ionomers, ionic liquids) has demonstrated its capacity on improving catalytic activity and selectivity in electrochemical reactions such as ORR, CO2RR, as well as oxidation processes. However the electrocatalysts stability behavior and surface degradation dynamics of such modified systems are still not sufficiently studied.To fully decipher the potentials of surface/interface modification in electrocatalysis, we focus our studies on the dissolution behavior of functional organics (i. e. melamine, immidazolium ionic liquids) modified model Pt (hkl) single crystal facets, in acidic medium. In regard of the excellent system sensitivity of our inductively coupled plasma mass spectrometer (ICP-MS, detection limit down to 0.1-1 ng/L), we employed a customized scanning flow cell (SFC) coupled on-line monitoring system to probe the potential-resolved surface metal dissolution dynamics. With designed electrochemical system configuration, we acquired results demonstrating the delicate stability behavior changes over transient and accumulative dissolution study on (modified) Pt facets. In combination of other state-of-art operando spectroscopy methods (electrochemical infrared reflection absorption spectroscopy, electrochemical scanning tunneling microscopy, Raman spectroscopy, etc.), we further comprehended the surface oxidation process, surface blockage/modification mechanisms on electrocatalysis interface. Our research conferred a model stability study approach on surface modification of electrodes and electrocatalysts, providing important insights into the dynamic interfacial interaction between functional modifier and electrocatalysts.

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA.

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

Could Mushrooms' Secondary Metabolites Ameliorate Alzheimer Disease? A Computational Flexible Docking Investigation.

Herein, we highlight the significance of molecular modeling approaches prior to in vitro and invivo studies; particularly, in diseases with no recognized treatments such as neurological abnormalities. Alzheimer disease is a neurodegenerative disorder that causes irreversible cognitive decline. Toxicity and ADMET studies were conducted using the Qikprop platform in Maestro software and Discovery Studio 2.0, respectively, to select the promising skeletons from more than 45 reviewed compounds isolated from mushrooms in the last decade. Using rigid and flexible molecular docking approaches such as induced fit docking (IFD) in the binding sites of β-secretase (BACE1) and acetylcholine esterase (ACHE), promising structures were screened through high precision molecular docking compared with standard drugs donepezil and (2E)-2-imino-3-methyl-5,5-diphenylimidazolidin-4-one (OKK) using Maestro and Cresset Flare platforms. Molecular interactions, binding distances, and RMSD values were measured to reveal key interactions at the binding sites of the two neurodegenerative enzymes. Analysis of IFD results revealed consistent bindings of dictyoquinazol A and gensetin I in the pocket of 4ey7 while inonophenol A, ganomycin, and fornicin fit quite well in 4dju demonstrating binding poses very close to native ligands at ACHE and BACE1. Respective key amino acid contacts manifested the least steric problems according to their Gibbs free binding energies, Glide XP scores, RMSD values, and molecular orientation respect to the key amino acids. Molecular dynamics simulations further confirmed our findings and prospected these compounds to show significant in vitro results in their future pharmacological studies.

Integration of patient and public involvement in a doctoral research study using the research cycle

BackgroundPatient and public involvement (PPI) in research is widely acknowledged as essential to achieving successful and impactful research. Despite this acknowledgement, there are limited reports on how to approach and apply meaningful PPI throughout the research cycle and how to address challenges for researchers such as doctoral students, particularly when undertaking research on sensitive topics. This paper provides insights and examples for researchers new to PPI, on the impact of active PPI and recommendations for building and developing a PPI group in a paediatric focused doctoral research study with bereaved parents and carers.MethodsPPI was informed by the research cycle. The GRIPP2 short-form checklist was used to report PPI. The research was funded by the National Institute for Health and Care Research.ResultsPPI enhanced the research through input into the study design, recruitment, co-design of the study website and branding; and ethics amendments to increase participation in response to the COVID-19 pandemic. The literature review was extended to incorporate a PPI consultation phase and members contributed to data analysis. A flexible approach enabled involvement to develop iteratively throughout the research study, resulting in changes being made to enhance the study design and outcomes.ConclusionThis paper contributes to the limited knowledge base on embedding PPI into a doctoral research study and within the paediatric setting specifically working in partnership with bereaved parents and carers. Employing an adaptive approach to meet individual PPI needs, building a trusting and respectful partnership, creating shared ownership and investment in the research, are essential components to successful PPI.

Euclidean-Distance-Preserved Feature Reduction for efficient person re-identification

Person Re-identification (Re-ID) aims to match person images across non-overlapping cameras. The existing approaches formulate this task as fine-grained representation learning with deep neural networks, which involves extracting image features using a deep convolutional network, followed by mapping the features into a discriminative space through another smaller network, in order to make full use of all possible cues. However, recent Re-ID methods that strive to capture every cue and make the space more discriminative have resulted in longer features, ranging from 1024 to 14336, leading to higher time (distance computation) and space (feature storage) complexities. There are two potential solutions: reduction-after-training methods (such as Principal Component Analysis and Linear Discriminant Analysis) and reduction-during-training methods (such as 1 × 1 Convolution). The former utilizes a statistical approach aiming for a global optimum but lacking end-to-end optimization of large data and deep neural networks. The latter lacks theoretical guarantees and may be vulnerable to training noise such as dataset noise or initialization seed.To address these limitations, we propose a method called Euclidean-Distance-Preserving Feature Reduction (EDPFR) that combines the strengths of both reduction-after-training and reduction-during-training methods. EDPFR first formulates the feature reduction process as a matrix decomposition and derives a condition to preserve the Euclidean distance between features, thus ensuring accuracy in theory. Furthermore, the method integrates the matrix decomposition process into a deep neural network to enable end-to-end optimization and batch training, while maintaining the theoretical guarantee. The result of the EDPFR is a reduction of the feature dimensions from fa and fb to fa′ and fb′, while preserving their Euclidean distance, i.e.L2(fa,fb)=L2(fa′,fb′). In addition to its Euclidean-Distance-Preserving capability, EDPFR also features a novel feature-level distillation loss. One of the main challenges in knowledge distillation is dimension mismatch. While previous distillation losses, usually project the mismatched features to matched class-level, spatial-level, or similarity-level spaces, this can result in a loss of information and decrease the flexibility and efficiency of distillation. Our proposed feature-level distillation leverages the benefits of the Euclidean-Distance-Preserving property and performs distillation directly in the feature space, resulting in a more flexible and efficient approach. Extensive on three Re-ID datasets, Market-1501, DukeMTMC-reID and MSMT demonstrate the effectiveness of our proposed Euclidean-Distance-Preserving Feature Reduction.

School District Leader Perspectives on Surveying Middle School Youth About Sexual Violence.

Schools are important contexts for preventing sexual violence (SV) among adolescents. Evaluating whether programming is effective requires surveying youth about SV experiences. However, school communities often have concerns about asking students, particularly those in middle school, about these experiences. This study sought to understand the types of concerns that school district leaders have related to surveying middle school students about SV and to identify ways to mitigate these concerns. We conducted semi-structured interviews with superintendents and school board members (n = 19) across Washington State and used inductive thematic analysis. Concerns regarding surveying students about SV centered around 3 main themes: community norms and misconceptions, parental/caregiver discomfort, and survey language and administration. Concerns were particularly salient for sixth-grade students. Suggestions for mitigating concerns included: providing clear motivation and reframing messaging to community members, involving parents and students in the survey process, and modifying survey language and administration. Researchers administering surveys to middle school students on sensitive topics including SV may face pushback and must consider flexible approaches to allow research and evaluation to be conducted.

Rethinking “On Just Terms”: Commonwealth v Yunupingu

Commonwealth v Yunupingu raises a number of constitutionally significant issues concerning the scope of the Commonwealth Parliament’s power under s 51(xxxi) of the Australian Constitution and its relationship to the territories power in s 122. One issue that is not directly raised by the questions before the High Court of Australia, but that will nevertheless inform the Court’s answers, concerns the meaning of ‘on just terms’. Is ‘on just terms’ a narrow and inflexible requirement to provide full market-based compensation? Or does it permit a broader and more flexible approach to determining the obligations of the Commonwealth and territory governments, which would allow for structural reparations and non-pecuniary remedies? We suggest that a broader and more flexible approach is generally to be preferred as a matter of both principle and prudence and that such an approach is especially relevant for the Court as it considers the consequences of its answers to the questions before it.

Formation And Implementation Of Digital Transformation The Companies: A Conceptual Aspect

The concept of formation and implementation of digital transformation for the development of the company is considered. The article analyzes the digital transformation of a company as a process by which digital technologies are introduced into the digital economy and business to achieve fundamental changes. It has been found that the digital transformation of a company during its formation leads to changes in three areas: customer service quality, operational processes, and business models. At the same time, the digital transformation process requires coordination across the organization and involves a change in business culture. The author outlines approaches to the company’s work that can improve the quality of customer service, stimulate employee innovation, and drive the company’s growth at a fundamental level. At the same time, the most important task of the company’s CEO is to stimulate digital transformation at all levels of the company: in processes and procedures; organizational structures; development of digital marketing and sales; offering digital services and digital business models, etc. The need to support an innovative culture that focuses on what new digital technologies should be introduced and how best to integrate them into everyday life is emphasized. At the same time, the software will help companies implement digital innovation strategies. The following stages are recommended for implementing digital transformation in a company: business transformation; developing own strategy and organizational changes; breaking down barriers to change; developing own partner ecosystem; using a flexible approach; and financing. It is determined that digital technologies, as the basis of digital business, have been integrated with business and have become more than just hardware or software. As digital technologies become more pervasive and companies move further along the path of digital transformation, digital strategy and business strategy will converge. All of this requires a change in the company’s business culture. It is proposed that in the formation and implementation of digital transformation, the possibility of integration into the European Statistical System (ESS) should be considered as a means of increasing the visibility of digital transformation.

The principle mutatis mutandis as an tool for changing judicial practice beyond the limits of reasonable discretion

The article is devoted to the analysis of the validity of changes in the practice of the Grand Chamber of the Supreme Court, which should be considered as a deviation from previously formulated legal positions regarding the effective way of protecting the law, using the mutatis mutandis principle. It is emphasized that scientists have the right to carry out a critical analysis of judicial practice in the context of its compliance/non-compliance with the doctrine. A distinction was drawn between the right to interpret laws by the court and the exercise of discretionary powers, in which the degree of discretion is assessed as unreasonable and excessive; the use of used lecture structures is argued. Attention is focused on the inadmissibility of the practical implementation of individual, arbitrarily selected elements of the scientific debate into judicial practice, in particular, concerning the principle of stare decisis and the justification with its help of the right to judicial rule-making - in the form of a precedent that de facto already exists in practice and is recognized by theory, but, being called precedent, is not identical with classical English precedent. The analysis was carried out according to three vectors corresponding to different periods and appropriated standards of judicial practice. The first is a change in the position from “a mistake in choosing an effective method of defense as an independent reason for refusing a claim” to “a refusal to resolve a dispute due to the ineffectiveness of the method of defense chosen by the plaintiff can actually be considered as a limitation of the right to access to court.” The second is the introduction of a flexible approach to understanding the principle of dispositiveness and moving away from an overly formal assessment of the “proper claim”, which “cannot be considered by the courts as an improper method of protection”. The third already marks the conclusion “about the interpretation” of the claim, mutatis mutandis - for the purpose of full protection of the right in one process. The conclusion states that the interpretation (interpreting) of the claim contradicts the principle of dispositiveness, and its flexible interpretation in favor of such an interpretation will violate the right of the other party, who will not be able to predict the result of such an interpretation when constructing and proving its own position. Courts’ excessive fascination with innovations - with the usurpation of the right to rulemaking and departure from the doctrine - is unlikely to contribute to justice. This is indirectly indicated by frequent deviations from previous legal positions, which do not contribute to legal certainty.

Graph Neural Network Enhanced Dual-Branch Network for lesion segmentation in ultrasound images

Ultrasound images are widely used in the detection of breast lesions, thyroid nodules, renal tumors and other medical conditions due to their cost-effectiveness and convenience. Automatic segmentation of lesions in ultrasound images is crucial for clinical diagnosis. U-shaped networks with encoder–decoder architectures and skip connections have achieved significant success in medical image segmentation. However, challenges arise in segmentation due to the inherent blurriness of ultrasound image boundaries and the irregular shapes and volumes of lesions. Despite the capability of transformers to capture global information, we posit that graph neural network (GNN) offers a more flexible approach to capturing irregular features. GNN constructs connections for each part, leveraging global information in the process. In this study, we propose GNN-Enhanced Dual-Branch Network (GED-Net), a robust framework for lesion segmentation in ultrasound images. The Network consists of an encoder with two branches: one for extracting local features using convolutional neural network (CNN) and the other for extracting global features using GNN, along with a fusion module and a decoder. We evaluated the model on four publicly available medical ultrasound datasets including BUSI, Dataset B, DDTI and OASBUD, and demonstrated superior performance compared to most classical U-Net networks and state-of-the-art U-shaped networks. The source code is available at https://github.com/Yakiw/GED-Net.

Antitrust Laws and the Future of Market Competition

Purpose: The general objective of the study was to investigate antitrust laws and the future of market competition. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to antitrust laws and the future of market competition. Preliminary empirical review revealed that while existing antitrust frameworks had been instrumental in promoting competition and preventing monopolistic practices, they faced significant challenges in the digital age. The rapid evolution of technology and the rise of digital platforms had created new competitive dynamics that traditional antitrust laws struggled to address. The dominance of a few tech giants led to market concentration, reducing the scope for competition and innovation. The study highlighted the inadequacies of current antitrust enforcement in dealing with digital platforms, data control, and network effects. Additionally, it emphasized the need for international cooperation and a balanced approach to antitrust enforcement that considered both competition and innovation to ensure that the benefits of the digital economy were widely shared. Unique Contribution to Theory, Practice and Policy: The Structure-Conduct-Performance (SCP) Paradigm, Contestable Market Theory and Innovation and Monopolistic Competition Theory may be used to anchor future studies on antitrust laws and the future of market competition. The study recommended updating theoretical frameworks to include factors like data control and network effects, and adopting more flexible and adaptive approaches in antitrust enforcement. It advocated for the creation of specialized regulatory units focused on digital markets, equipped with the necessary resources and expertise. The study also highlighted the importance of international cooperation in antitrust enforcement to address cross-border anti-competitive practices effectively. Furthermore, it suggested continuous dialogue with industry stakeholders to develop more informed policies and emphasized the role of consumer education in promoting competitive markets. These recommendations aimed to enhance antitrust laws and ensure robust market competition in the digital age.

Refining the narrative: An autoethnographic insight into academic identity construction and development in a Chinese doctoral student's journey in the United States

What seems to be overlooked in current higher education research is the perspective of individual doctoral students in applied linguistics and how they comprehend the academic training culture within U.S. neoliberal universities. Through an autoethnographic (AE) approach, this study investigated how I as a Chinese doctoral student experienced different stages of identity negotiation in my doctoral program. The findings, which integrated theories of academic identity, the third space, and neoliberalism, were primarily derived from my self-reflection data throughout the pandemic. The arguments center around the coexistence of multiple identities and an asynchronous process of identity construction, which were influenced and shaped by study-work balance, laissez-faire supervision, the digital third space, and the pressures from neoliberal norms. The study brings insights into theoretical models from an AE perspective and recommends that higher education stakeholders adopt a flexible approach to doctoral supervision and academic publishing. Autoethnography offered a first-person perspective for in-depth meaning-making, facilitating a comprehensive and nuanced understanding of one’s academic journey of becoming.

Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index

Grape quality is generally assessed by measuring individual indicators rather than utilizing a comprehensive metric. A flexible approach to aggregating multiple quality-related parameters of grapes is introduced in this study, and an aggregative index was proposed for the synthetical evaluation of grape quality. First, visible-near-infrared (Vis-NIR) spectral data of two grape varieties were obtained, and the physical and chemical quality indicators were measured as reference standards. The variation trends and correlations of the indicators were analyzed, and an aggregative quality indicator (AQI) was proposed using the min-max normalization and factor analysis methods. The maturity stages of grapes were categorized by analyzing the AQI values. The spectral data underwent preprocessing using multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), first-order derivative (FD), SG+FD and de-trending (DT) methods. Feature wavelengths were extracted using uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and the UVE-CARS algorithms. Three regression models for the AQI were established based on partial least squares regression (PLSR), support vector machine regression (SVR) and convolutional neural network (CNN). Results indicated that the PLSR combined with the FD+SG preprocessing and CARS-selected feature wavelengths achieved optimal performance for Cabernet Sauvignon grapes, with the coefficient of determination for the prediction set (Rp2)=0.972, for the calibration set (Rc2)=0.995, the root mean square error of prediction (RMSEP)=0.040 and residual predictive deviation (RPD)=6.064. In addition, for the Muscat Kyoho grape, CNN combined with DT preprocessed full-spectrum data yielded the best prediction for the AQI, with Rp2=0.989, Rc2=0.981, RMSEP=0.103 and RPD=9.482. This research demonstrated that the non-destructive prediction of grape aggregative qualities can be achieved through the combination of Vis-NIR spectroscopy and a chemometric analysis, offering a practical and comprehensive metric for determining grape maturity stages and optimal harvest times. This approach is particularly valuable for the wine industry, as it facilitates better decision-making and quality control.

Superhydrophobic quasi-periodic concave microlens array on fused silica prepared by spatially modulated picosecond laser parallel processing

Fused-silica-based microlens array (MLA) has excellent stability in the harsh environment. However, it is still a challenge to efficiently prepare superhydrophobic quasi-periodic MLA and keep the optical performance of the microlens. In this paper, we proposed spatial modulated picosecond laser parallel processing method to prepare a superhydrophobic quasi-periodic MLA on fused silica. By using a spatial light modulator, the picosecond laser was shaped to multi-focal spots and line-shape beam, respectively, which improved the fabrication efficiency. The line-shape beam was the key to induce micro-nanostructures on the smooth MLA and keep the optical performance of the microlens. The results showed that the surface roughness of the microlens region was less than 100 nm. Combing with shaping picosecond laser processing and chemical treatment, the superhydrophobic MLAs showed the excellent anti-fogging and self-cleaning properties. The average contact angle of the superhydrophobic MLAs was 162.3°. The superhydrophobic MLA showed low adhesion to water droplet (<10 μN) and low contact angle hysteresis (2.1° ± 0.7°). We believe shaping picosecond laser parallel processing method is a flexible and low-cost approach to prepare superhydrophobic micro-optical elements on fused silica.

Distributed avoidance-accommodating flexible performance approach for adaptive formation tracking of underactuated surface vehicles against moving obstacles

A novel strategy for formation tracking of multiple uncertain underactuated surface vehicles is introduced in this study, to maintain the prescribed formation performance in the presence of moving obstacles with unknown velocities. Unlike the existing methods for prescribed formation performance control, our key contribution is the development of distributed avoidance-accommodating flexible performance functions (FPFs). These functions prevent deviation from the prescribed performance bounds while navigating around moving obstacles, considering input saturation and model uncertainties. We design local adaptive formation controllers by using distributed posture errors and avoidance-accommodating FPFs. Auxiliary signals that facilitate adjustment of the posture errors and performance functions during obstacle avoidance are derived using a recursive design that ensures Lyapunov stability. Additionally, command filters for virtual control laws are redesigned as saturation-compensating filters to solve the input saturation problem. Consequently, the proposed controller ensures the prescribed distributed formation performance, even in scenarios involving moving obstacles and input saturation, a capability lacking in conventional prescribed performance-based formation controllers, which can become unstable in such scenarios. The theoretical findings of this study are validated through comparative simulations.

Tailoring oil blends for specific purposes: A study on nutritional and antioxidant properties of soybean oil mixed with corn, sunflower, and flaxseed oils

Soybean oil (SO), recognized for its nutrients and affordability, often oxidizes, leading to nutrient degradation. This study presents a methodology for tailor oil blends to meet specific requirements, using SO blended with corn oil (CO), sunflower oil (SFO), and flaxseed oil (FSO). We analyzed the physicochemical properties, nutrient composition, and antioxidant capacity of various blends. Pearson's correlation analysis was utilized to examine the interrelation between nutrient composition and antioxidant capacity. The principal component analysis identified suitable blend ratios based on the desired nutrient composition and antioxidant capacity. Blend F3 (60% SO, 15% CO, 16% SFO, 9% FSO) demonstrated superior qualities, including a higher α-tocopherol content (138.8 mg/kg), and enhanced antioxidant capacities (DPPH polar: 12 μmol TE/100g, ABTS: 67.3 μmol TE/100g, FRAP: 112 μmol TE/100g), alongside a lower acid value (0.15 mg/g) compared to other blends. We highlight the roles of squalene, γ-tocopherol, δ-tocopherol, and fatty acid composition in enhancing the antioxidant capacity of oil blends. Specifically, the blend labeled F3 emerged as a nutritionally superior and highly antioxidative option among soybean oil blends. This study provides a framework for tailoring oil blends to meet specific nutritional and antioxidative requirements, providing a more flexible and purpose-driven approach to oil blending.

The Impact of Cloud Computing on System Lifecycle Methodologies: an Innovative Approach to Scalability Flexibility

This research analyzes the impact of cloud computing on traditional system lifecycle methodologies and offers an innovative approach that leverages the advantages of cloud computing. Qualitative research was conducted with a literature study on conventional system lifecycle methodologies and cloud computing. The results show that cloud computing enables increased scalability and flexibility through a service-based approach, automation, and elastic management of resources. This research provides recommendations for implementing a hybrid lifecycle methodology that leverages the advantages of both cloud and traditional systems.This paper discusses the impact of cloud computing on system lifecycle methodologies. Cloud computing offers an innovative approach to system scalability and flexibility by allowing computing and storage resources to scale dynamically according to demand. Traditional system lifecycle methodologies are becoming less relevant for rapidly changing cloud environments, so they need to be adapted to respond to the scalability and flexibility capabilities offered by cloud computing.This paper evaluates the impact of the shift towards cloud computing on the methodologies used in the system development lifecycle. Cloud computing offers the ability to scale elastically according to demand, which opens up opportunities to adopt a more adaptable and flexible approach to system development. This paper discusses how traditional system lifecycle methodologies can be adapted to accommodate key cloud computing characteristics such as scalability, flexibility, and virtualization of computing resources. This paper reviews the implications of the move towards a cloud computing paradigm on the methodologies used in developing and maintaining software systems. The shift towards cloud-based environments offers the advantages of scalability and flexibility in dynamically managing system resources. However, these key characteristics of cloud computing pose challenges to traditional system lifecycle methodologies designed for on-premise environments. This paper discusses how system lifecycle methodologies can be adapted to better support system development in cloud computing environments.