Competitive Approach Research Articles

Competitiveness analysis of the South African avocado industry

ABSTRACT The South African avocado industry has experienced a major decline in its export performance which could be attributed to its overall competitiveness in the global export markets. This study seeks to analyse the competitiveness of the South African avocado industry based on the perceptions of the industry participants and other industry experts. The main purpose of this study is to provide a comprehensive statement on the competitiveness of this industry for strategic planning by agribusiness managers and other key stakeholders to improve the industry’s competitiveness. A five-step analytical framework previously used in similar studies is applied to measure and analyse the competitive performance of the South African avocado industry. Through engagements with industry participants and experts, 90 factors that influence the competitiveness of the South African avocado industry were identified. An analysis of these factors using Porter’s Determinants of Competitiveness approach supported by measurements of comparative advantage using trade measures such as the Revealed Comparative Advantage (RCA), Relative Trade Advantage (RTA), Revealed Symmetric Comparative Advantage (RSCA), Normalized Revealed Comparative Advantage (NRCA) and Global Competitiveness Index (GCI), this study was able to establish that the South African avocado industry is experiencing competitiveness challenges. Climate change, competition in the international markets, and various South African policies were identified as major constraints for the competitiveness of the South African avocado industry. Thirteen strategic actions that could be used to improve the competitiveness of this industry are recommended. The study mainly focused on the South African avocado industry; thus, generalisations should be cautious.

Semi-Supervised Subcategory Centroid Alignment-Based Scene Classification for High-Resolution Remote Sensing Images

It is usually hard to obtain adequate annotated data for delivering satisfactory scene classification results. Semi-supervised scene classification approaches can transfer the knowledge learned from previously annotated data to remote sensing images with scarce samples for satisfactory classification results. However, due to the differences between sensors, environments, seasons, and geographical locations, cross-domain remote sensing images exhibit feature distribution deviations. Therefore, semi-supervised scene classification methods may not achieve satisfactory classification accuracy. To address this problem, a novel semi-supervised subcategory centroid alignment (SSCA)-based scene classification approach is proposed. The SSCA framework is made up of two components, namely the rotation-robust convolutional feature extractor (RCFE) and the neighbor-based subcategory centroid alignment (NSCA). The RCFE aims to suppress the impact of rotation changes on remote sensing image representation, while the NSCA aims to decrease the impact of intra-category variety across domains on cross-domain scene classification. The SSCA algorithm and several competitive approaches are validated on two datasets to demonstrate its effectiveness. The results prove that the proposed SSCA approach performs better than most competitive approaches by no less than 2% overall accuracy.

Synthesis of UiO-66-NH2@PILs core-shell composites for CO2 conversion into cyclic carbonates via synergistic catalysis under solvent- and additive-free conditions

Metal-organic frameworks (MOFs) have been hybridized with other functional materials, such as ionic liquids, covalent organic frameworks (COFs), and metallic nanoclusters, to fabricate composite materials. These composites possess the structural characteristics of their individual components and exhibit synergistic properties. In this work, we demonstrate the synthesis of core-shell structure UiO-66-NH2@PILs-x composite materials by encapsulating MOFs within ionic polymers (PILs) with varying thicknesses of PILs shell. These hybrid materials serve as bifunctional catalysts for the conversion of CO2 into cyclic carbonates under solvent- and additive-free conditions through a synergistic catalytic effect facilitating the conversion of CO2 into cyclic carbonates under solvent- and additive-free conditions through synergistic catalytic effect, exhibiting superior catalytic performance and excellent recyclability. This MOFs-based composite material provides a competitive approach for CO2 catalytic conversion.

Questioning the West and Creating an Alternative: China’s and Türkiye’s Similar Approaches toward Africa

Several actors have turned their attention to Africa, and the continent is becoming a focal point for international relations. These actors have adopted various approaches to improve their relations with African countries. This study examines the alternative dimension of rising international competition on the continent. The main focus of the study is Türkiye’s and China’s alternative approaches to Africa. Accordingly, the study seeks to answer three interrelated questions: What are the differences between the traditional and alternative approaches? What are the main factors shaping China’s and Türkiye’s alternative approaches toward Africa? What are the similarities in Beijing’s and Ankara’s approaches regarding African countries in practice? As a result, this study argues that China and Türkiye present an alternative stance to the Western-centered approach for competition in Africa, despite some differences in their relations with continental countries.

PECULIARITIES OF THE FUNCTIONING OF DOMESTIC ELITE INSTITUTIONS OF HIGHER EDUCATION

The article provides a theoretical and practical analysis of the problem of the formation and development of elite higher education in Ukraine. In the context of the specified problem, the authors singled out: trends in the activity of educational institutions in the conditions of a full-scale invasion of Russia; factors of increased interest of researchers in studying the problem of elites, elite thinking, elite educational institutions; growth trends of private educational institutions in Europe and Ukraine.An analysis of different approaches to the interpretation of the concepts of "elite", "private school", "alternative educational institutions", "elite education", "elite university", etc. is offered. The authors proposed a description of different types of private schools, in particular, elitist, denominational, compensatory, conjunctural, national and alternative models. The article presents the classification of private schools (preschool, primary, day, non-formal, with moderate and high tuition fees, with historically developed traditions and modernity, etc.), and highlights their characteristic features.A comparative analysis of research and elite universities was partially carried out, and the characteristic features of a research university were singled out and characterized (the multi-functionality of the institution, the presence of a system of training specialists with a scientific degree, a wide selection of specialities and specializations, the formation of an intellectual environment around the university, a competitive and selective approach to the selection of students; the presence of corporate ethics, etc.). The article focuses on the specific characteristics of elite universities (autonomy, compactness, own traditions, small numbers, etc.), highlights the most significant factors of the emergence and development of elite higher education institutions in Ukraine (growing demand of the population for appropriate education, creation of an appropriate educational environment, expanded network of social provision of the most gifted students, etc.).

Enhanced Harmonic Partitioned Scheduling of Periodic Real-Time Tasks Based on Slack Analysis.

The adoption of multiprocessor platforms is growing commonplace in Internet of Things (IoT) applications to handle large volumes of sensor data while maintaining real-time performance at a reasonable cost and with low power consumption. Partitioned scheduling is a competitive approach to ensure the temporal constraints of real-time sensor data processing tasks on multiprocessor platforms. However, the problem of partitioning real-time sensor data processing tasks to individual processors is strongly NP-hard, making it crucial to develop efficient partitioning heuristics to achieve high real-time performance. This paper presents an enhanced harmonic partitioned multiprocessor scheduling method for periodic real-time sensor data processing tasks to improve system utilization over the state of the art. Specifically, we introduce a general harmonic index to effectively quantify the harmonicity of a periodic real-time task set. This index is derived by analyzing the variance between the worst-case slack time and the best-case slack time for the lowest-priority task in the task set. Leveraging this harmonic index, we propose two efficient partitioned scheduling methods to optimize the system utilization via strategically allocating the workload among processors by leveraging the task harmonic relationship. Experiments with randomly synthesized task sets demonstrate that our methods significantly surpass existing approaches in terms of schedulability.

Triggering dark showers with conditional dual auto-encoders

Abstract We present a family of conditional dual auto-encoders (CoDAEs) for generic and model-independent new physics searches at colliders. New physics signals, which arise from new types of particles and interactions, are considered in our study as anomalies causing deviations in data with respect to expected background events. In this work, we perform a normal-only anomaly detection, which employs only background samples, to search for manifestations of a dark version of strong force applying (variational) auto-encoders on raw detector images, which are large and highly sparse, without leveraging any physics-based pre-processing or strong assumption on the signals. The proposed CoDAE has a dual-encoder design, which is general and can learn an auxiliary yet compact latent space through spatial conditioning, showing a neat improvement over competitive physics-based baselines and related approaches, therefore also reducing the gap with fully supervised models. It is the first time an unsupervised model is shown to exhibit excellent discrimination against multiple dark shower models, illustrating the suitability of this method as an accurate, fast, model-independent algorithm to deploy, e.g. in the real-time event triggering systems of large hadron collider experiments such as ATLAS and CMS.

Multi-Label Conditional Generation From Pre-Trained Models.

Although modern generative models achieve excellent quality in a variety of tasks, they often lack the essential ability to generate examples with requested properties, such as the age of the person in the photo or the weight of the generated molecule. To overcome these limitations we propose PluGeN (Plugin Generative Network), a simple yet effective generative technique that can be used as a plugin for pre-trained generative models. The idea behind our approach is to transform the entangled latent representation using a flow-based module into a multi-dimensional space where the values of each attribute are modeled as an independent one-dimensional distribution. In consequence, PluGeN can generate new samples with desired attributes as well as manipulate labeled attributes of existing examples. Due to the disentangling of the latent representation, we are even able to generate samples with rare or unseen combinations of attributes in the dataset, such as a young person with gray hair, men with make-up, or women with beards. In contrast to competitive approaches, PluGeN can be trained on partially labeled data. We combined PluGeN with GAN and VAE models and applied it to conditional generation and manipulation of images, chemical molecule modeling and 3D point clouds generation.

A virtual case competition approach to bridging business acumen and biomedical graduate training.

A virtual case competition approach to bridging business acumen and biomedical graduate training.

Fabrication of rGO/BiOI photocathode and its catalytic performance in the degradation of 4-Fluoroaniline

Organic fluorine compounds are acute carcinogenic and mutagenic to humans. Photoelectrocatalysis (PEC) treatment is an innovative technology in the field of the removal of fluorine compounds, and thus current research focused on improving stability and catalytic ability of photoanode. In this study, it has been synthesized a rGO/BiOI photocathode for the efficient degradation of 4-Fluoroaniline (4-FA). The physical characterization and photoelectrochemical properties of the photocathode was determined. The results indicate that the PEC treatment with the rGO/BiOI photocathode was more efficient compared with individual processes. During the optimization experiments, the PEC treatment achieved 99.58 % and 72.12 % of 4-FA degradation and defluorination within 1 h. Cyclic stability experiments show that rGO/BiOI photocathode was efficient and stable, which reached 96.91 % and 67.64 % of 4-FA degradation and defluorination after five cycles. Mechanism analysis indicates that the PEC process was based on an electrochemical reaction and photo-induced processes. The degradation product of 4-FA was mainly 2,4-di-t-butylphenol, and trapping experiments indicates that h+ is the primary oxidizing species. Therefore, PEC treatment with rGO/BiOI photocathode is a competitive green approach to remove fluorine compounds pollutants and brings new insights into development of PEC treatment.

Adduct of Pesticide and Lignocellulosic Waste via Cleavable Silaketal Linkages for Agricultural Double Reduction.

The abuse of hazardous agrochemicals leads to excessive toxic agricultural emissions, posing a tremendous threat to the natural surroundings and human well-being. In practice, the amount of pesticides in protecting crops is often far less than that lost into the environment through evaporation and leaching. Minimizing the use of pesticides as well as improving their use efficiency has been included in the policy of "agricultural double reduction," besides replacing the chemical fertilizer with straw returning. Here, we establish a strategy for controlling pesticide release from the lignocellulosic waste based on the stimulus-responsive cleavage of silaketal linkages. Noting that the cleavage of the silaketal linkages relies heavily on the substituent groups on silicon atoms, this pesticide-releasing system has the advantages of predictable service life and less environmental pollution in a desired time window. Instead of lengthy laboratory synthesis, outdoor instant synthesis can be conveniently realized with the help of a photothermal heating apparatus. After utilization, both silaketal linkages and lignocellulosic residuals are eco-friendly and can be a source of nutrients for soil. Referring to agricultural double reduction, this type of pesticide formulation is coined as a competitive approach to minimize pesticide pollution along with straw returning.

A novel approach for automatic classification of macular degeneration OCT images

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are significant causes of blindness worldwide. The prevalence of these diseases is steadily increasing due to population aging. Therefore, early diagnosis and prevention are crucial for effective treatment. Classification of Macular Degeneration OCT Images is a widely used method for assessing retinal lesions. However, there are two main challenges in OCT image classification: incomplete image feature extraction and lack of prominence in important positional features. To address these challenges, we proposed a deep learning neural network model called MSA-Net, which incorporates our proposed multi-scale architecture and spatial attention mechanism. Our multi-scale architecture is based on depthwise separable convolution, which ensures comprehensive feature extraction from multiple scales while minimizing the growth of model parameters. The spatial attention mechanism is aim to highlight the important positional features in the images, which emphasizes the representation of macular region features in OCT images. We test MSA-NET on the NEH dataset and the UCSD dataset, performing three-class (CNV, DURSEN, and NORMAL) and four-class (CNV, DURSEN, DME, and NORMAL) classification tasks. On the NEH dataset, the accuracy, sensitivity, and specificity are 98.1%, 97.9%, and 98.0%, respectively. After fine-tuning on the UCSD dataset, the accuracy, sensitivity, and specificity are 96.7%, 96.7%, and 98.9%, respectively. Experimental results demonstrate the excellent classification performance and generalization ability of our model compared to previous models and recent well-known OCT classification models, establishing it as a highly competitive intelligence classification approach in the field of macular degeneration.

Coupling Cuprous Oxide with Upconversion Materials: A Holistic Approach to Efficient Solar Water Splitting

Photoelectrochemical (PEC) water splitting is a promising solution for harnessing solar radiation for hydrogen production. This technology combines sunlight capture and water electrolysis processes, resulting in the production of hydrogen and oxygen that can efficiently recombine in fuel cells. However, challenges in materials science still need resolution to establish PEC water splitting as a practical and competitive approach. Copper oxide semiconductors, particularly materials based on cuprous oxide (Cu2O), have attracted attention due to their abundant elemental availability, scalable synthesis methods, and inherent p-type characteristics. To improve the generated photocurrent of the photoelectrode system, photon upconversion (UC) materials can be implemented into water-splitting devices. TTA-based UC is particularly suitable for solar water splitting due to its efficient conversion at low photon intensity. This work highlights the potential application of TTA-based UC in solar-assisted water splitting and highlights the significance of photonic designs and nanointerface engineering to improve the light-harnessing properties of photoactive materials. To the best of our knowledge, our research group was the first to successfully implement the integration of photoactive materials for solar water splitting with an upconversion device based on the TTA mechanism. This strategy allows us to dramatically improve the light-harnessing properties of our photoelectrode by irradiating the photocatalyst from dual perspectives (front and back-side illumination). It has been demonstrated that Cu2O coupled with an upconverter (UC) outperforms bare Cu2O by 56% in terms of produced photocurrent density, namely bare Cu2O produces -0.32 mA·cm-2 at 0V (vs. RHE), whereas Cu2O/UC exhibits a photocurrent of -0.5 mA·cm-2. Figure 1

(Invited) Plasma Designing of Hybrid 2D Graphene: A Green Approach for Future Energy Materials

The global research interest in two-dimensional (2D) materials and their derivatives has increased significantly, driven by the requirements of application-oriented materials for different sectors, including energy storage. Among the various classes of material groups, 2D layered materials have emerged as a critical research area due to their high aspect ratio, open edge boundaries, high surface-to-volume ratios, and mechanical and electrical properties. Graphene is a widely researched 2D class material with an atomically thin structure, excellent electronic properties, very high intrinsic mobility and exceptional thermal and electrical conductivity1. Even though the research on the electronic properties of graphene is explored very well and in a matured state, the studies on non-electronic properties are still in the budding phase. One of the significant obstacles to extending the studies of graphene to different fields is the cost-effective and environmentally friendly large-scale synthesis and processing of high-quality graphene. Thus, a cutting-edge approach for probing graphene structures at the atomic scale is required to produce high-quality graphene for next-generation applications. As an alternative progressive technique, plasma-enabled methods have emerged as safe and green approaches to tailor different graphene structures at the nanoscale. During graphene production, plasma assembles the nanostructures from gaseous into a solid form or converts solid precursor to graphitic form 2,3. Besides, plasma techniques open up the possibility of tailoring the properties by crafting heteroatoms to the graphene lattice, which is beneficial for energy storage applications4. Therefore, this paper introduces the advantages of plasma-enabled techniques in designing graphene-based materials with different morphologies and orientations and tailoring them at the nanoscale using heteroatoms. Such plasma-assisted techniques ensure the high structural quality of graphene and controllability in the hybrid morphologies, making them frontrunner energy storage applications. Plasma also offers the production of graphene directly on any substrate surface, gives a new direction to designing binder-free electrodes for energy-related applications, and could be used as a competitive alternative approach to the widely known wet chemical procedures to design advanced graphene electrode materials for next-generation energy storage devices.Reference Geim, A. K. Graphene: Status and prospects. Science (1979) 324, 1530–1534 (2009).Dias, A. et al. N-Graphene-Metal-Oxide(Sulfide) hybrid Nanostructures: Single-step plasma-enabled approach for energy storage applications. Chemical Engineering Journal 430, 133153 (2022).Jagodar, A. et al. Growth of graphene nanowalls in low-temperature plasma: Experimental insight in initial growth and importance of wall conditioning. Appl Surf Sci 643, 158716 (2024).Santhosh, N.M. et al. N-Graphene Nanowalls via Plasma Nitrogen Incorporation and Substitution: The Experimental Evidence. Nanomicro Lett 12, 53 (2020).

IGU-Aug: Information-guided unsupervised augmentation and pixel-wise contrastive learning for medical image analysis.

Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel-wise contrastive learning mainly helps with pixel-wise dense prediction tasks. The counter-part to an instance in instance-level CL is a pixel, along with its neighboring context, in pixel-wise CL. Aiming to build better feature representation, there is a vast literature about designing instance augmentation strategies for instance-level CL; but there is little similar work on pixel augmentation for pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge this gap. We first classify a pixel into three categories, namely low-, medium-, and high-informative, based on the information quantity the pixel contains. We then adaptively design separate augmentation strategies for each category in terms of augmentation intensity and sampling ratio. Extensive experiments validate that our information-guided pixel augmentation strategy succeeds in encoding more discriminative representations and surpassing other competitive approaches in unsupervised local feature matching. Furthermore, our pretrained model improves the performance of both one-shot and fully supervised models. To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning. Code is available at https: //github.com/Curli-quan/IGU-Aug.

Comparative analysis of electrophoresis and interferometric optical detection method for molecular weight determination of proteins

Analytical detection methods play a pivotal role in scientific research, enabling the identification and quantification of specific analytes in various disciplines. This scientific report aims to compare two very different methodologies for determining the Molecular Mass (MM, also known as Molecular Weight, MW) of proteins: electrophoresis gel and the Interferometric Optical Detection Method (IODM). For this purpose, several proteins with different MM were selected.The electrophoresis technique was employed to validate the structure and MM of different parts or fragments of the Matrix Metallopeptidase 9 antibody (anti-MMP9), antibody against S100 calcium binding protein A6 (anti-S100A6) and Cystatin S4 antibody (anti-CST4) by examining the presence of bands with expected sizes. The IODM was applied to study the above-mentioned proteins (part of the antibodies) together with the protein G, as a reference to correlate the MM and protein sizes with the measured signal.We report the evidence of IODM as a competitive analytical approach for the determination of the MM of proteins for the first time. This innovative method allows for accurate MM determination using minimal sample volumes and concentrations, employing a simple experimental procedure that eliminates the requirement for protein denaturation.

Dynamic Valuation of Data in Bayesian Decision-Making: A Stackelberg Competition Approach

Dynamic Valuation of Data in Bayesian Decision-Making: A Stackelberg Competition Approach

A proposal for enhanced accountability in train station facilities management: The case of the commuter trains (KCI) in Jakarta, Indonesia

Background: The commuter trains (KCI), the commuter train system in Jakarta and its surrounding areas, faces significant challenges in station operation and maintenance, impacting the passenger experience. The current competitive tendering approach for procuring station facilities lacks a clear framework for accountability, resulting in inconsistent service delivery and passenger dissatisfaction. This paper proposes solutions to enhance accountability in train station facility management, aiming to improve the passenger experience. Method: By utilizing Moore's strategic triangle, the study identifies fundamental reasons for the accountability issues and examines the challenges posed by the restricted flexibility of the existing competitive tendering system. Potential solutions, including enhanced competitive tendering with performance-based negotiation, in-house production, and collaborative arrangements, are explored. Findings: The most feasible solution is recommended to address KCI's operational challenges. Despite KCI's popularity and extensive network, weak accountability, particularly in unresponsive management, disrupts the efficiency of train station facilities. Conclusion: This paper emphasizes the need for KCI to improve its accountability mechanisms to ensure safety, security, trustworthiness, convenience, ease of access, and equity for all passengers, ultimately enhancing the public value delivered by the commuter train system. Novelty/Originality of this study: The novelty of this research is developing a performance-based solution to improve accountability in KCI station facility management through enhanced competitive tendering and collaborative arrangements to enhance passenger experience and operational efficiency.

A peer-and self-group competitive behavior-based socio-inspired approach for household electricity conservation

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 house monthly energy consumption datasets are collected for Maharashtra state, along with a questionnaire-based survey that includes socio-demographic information, household appliances, family size, and some other parameters. After data collection, the pre-processing technique is applied for data normalization, and correlation technique-based key features are extracted. These features are used to classify different house categories based on consumption. A knowledge-based system is designed based on historical datasets for future energy consumption prediction and comparison with actual usage. These comparative studies provide a path for knowledgebase system design to generate monthly energy utilization reports for significant behavior changes for energy savings. Further, Linear Programming and Genetic Algorithms are used to optimize energy consumption for different household categories based on socio-demographic constraints. This will also benefit the consumers with an electricity bill evaluation range (i.e., normal, high, or very high) and find the energy conservation potential (kWh) as well as a cost-saving solution to solve real-world complex electricity conservation problem.

A Semi-Automatic Approach for Tree Crown Competition Indices Assessment From UAV LiDAR

Understanding the spatial heterogeneity of forest structure is crucial for comprehending ecosystem dynamics and promoting sustainable forest management. Unmanned aerial vehicle (UAV) LiDAR technology provides a promising method to capture detailed three-dimensional (3D) information about forest canopies, aiding in management and silvicultural practices. This study investigates the heterogeneity of forest structure in broadleaf forests using UAV LiDAR data, with a particular focus on tree crown features and their different information content compared to diameters. We explored a non-conventionally used method that emphasizes crown competition by employing a nearest neighbor selection technique based on metrics derived from UAV point cloud profiles at the tree level, rather than traditional DBH (diameter at breast height) spatial arrangement. About 300 vegetation elements within 10 plots collected in a managed Beech forest were used as reference data. We demonstrate that crown-based approaches, which are feasible with UAV LiDAR data at a reasonable cost and time, significantly enhances the understanding of forest heterogeneity, adding new information content for managers. Our findings underscore the utility of UAV LiDAR in characterizing the complexity and variability of forest structure at high resolution, offering valuable insights for carbon accounting and sustainable forest management.