Competitive Approach Research Articles

Analysis of Water Absorption on the Young’s Modulus and Damping of Sponge Gourd (Luffa cylindrica) Composites

ABSTRACT Composites reinforced with lignocellulosic fibers are attracting attention for use in several applications. This fact is directly linked to the advantages of using renewable lignocellulosic fibers. However, due to their cellulose-based structure, lignocellulosic fibers easily absorb water, which can affect the behavior of the composites. Surface treatments are used to reduce the hydrophilicity of fibers, but common surface treatments use chemicals, compromising the green approach of using lignocellulosic fibers. This work compares the water absorption behavior of sponge gourd-epoxy matrix composites with untreated and surface treated fibers. The effect of these treatments on the variation of the Young’s modulus and damping factor of the composites during the immersion time in distilled and salt water was also evaluated using the nondestructive impulse excitation test. In addition to the commonly used surface treatments (mercerization and acetylation), hornification has also been used to reduce the use of chemicals. The results obtained show that hornification is a competitive approach to reduce the water absorption of composites, being an interesting alternative to chemical surface treatments. A relationship was established between the amount of water absorbed and the variation in Young’s modulus and damping factor. Both properties stabilized when the water absorption saturation value was reached.

Innovative digital marketing strategies for SMEs: Driving competitive advantage and sustainable growth

In the rapidly evolving digital landscape, small and medium-sized enterprises (SMEs) must adopt innovative digital marketing strategies to drive competitive advantage and sustainable growth. This review explores the critical components and benefits of leveraging digital marketing for SMEs, emphasizing how tailored strategies can enhance market presence, customer engagement, and business performance. SMEs face unique challenges in resource allocation and market competition, necessitating cost-effective and impactful marketing approaches. Innovative digital marketing strategies, including search engine optimization (SEO), social media marketing, content marketing, email campaigns, and data-driven analytics, provide SMEs with the tools to compete effectively against larger enterprises. By utilizing these strategies, SMEs can achieve greater visibility, attract a broader audience, and convert prospects into loyal customers. SEO and content marketing play a pivotal role in enhancing online visibility and organic traffic. By producing high-quality, relevant content and optimizing it for search engines, SMEs can improve their search rankings and attract targeted audiences. Social media platforms offer powerful channels for engaging with customers, building brand awareness, and fostering community. Through targeted advertisements and influencer partnerships, SMEs can reach specific demographics and drive engagement. Email marketing remains a cost-efficient strategy for maintaining customer relationships and promoting products or services. Personalized email campaigns can nurture leads, encourage repeat business, and build customer loyalty. Additionally, data-driven analytics enable SMEs to measure campaign performance, understand customer behavior, and make informed decisions to refine marketing efforts. The integration of innovative technologies such as artificial intelligence (AI) and machine learning further enhances digital marketing capabilities. AI-driven tools can automate tasks, provide insights into customer preferences, and optimize marketing strategies in real-time. For instance, chatbots can improve customer service, while predictive analytics can forecast trends and personalize marketing messages. In conclusion, innovative digital marketing strategies are essential for SMEs seeking to drive competitive advantage and sustainable growth. By embracing SEO, social media marketing, content marketing, email campaigns, and data-driven analytics, SMEs can maximize their marketing impact, enhance customer engagement, and achieve long-term business success. Leveraging these strategies allows SMEs to remain agile, responsive, and competitive in the dynamic digital marketplace. Keywords: Innovative, Digital Marketing, SMEs, Sustainable Growth, Competitive Advantage.

Facing the solid waste of cotton straw and plastic mulch film mixture in China: Centralized or decentralized pyrolysis facility?

The widespread use of plastic mulch film (PMF) has led to significant environmental pollution, with PMF residues dispersed and mixed with straw and soil, posing challenges for recycling. Here, we proposed the mobile pyrolysis facility for the cotton straw and mulch film mixture (CMM) to mitigate the collection, storage, and transportation costs, while the application of co-pyrolysis technology for CMM conversion could improve the added value of products. Additionally, centralized combustion power generation and centralized pyrolysis systems were also established to evaluate and compare their sustainability from economic and environmental perspectives. Results showed that mobile pyrolysis has better economic performance than the centralized scenarios, due to its high internal rate of return (31 %) and significant net present value (29.21 M USD). Meanwhile, the mobile pyrolysis facility achieved a GWP of −1.298 kgCO2-eq/kg, reducing emissions by 70.79 % and 38.82 % compared to the two centralized scenarios. In conclusion, mobile pyrolysis technology provides a promising solution for PMF residue recycling because of its economically competitive approach with a lower carbon footprint.

Multimodal lab-on-a-tube detection of AFB1 based on fluorescence and nanozyme activity of DNA templated silver nanocluster: Enhanced sensitivity through G-quadruplex formation

Aflatoxin B1 (AFB1) is the potent carcinogenic toxin secreted by fungi with widespread distribution around the world as the food contaminant. In the present study, a novel strategy has been developed for sensitive detection of AFB1 based on multiplex aptamer mediated probe and ExoIII enzyme activity. Inspired by generated fluorescence signal by DNA templated silver nanocluster (AgNCs), a specific aptasensor was designed to provide efficient interaction with AFB1 and subsequent amplify the released heterostructure included G-quadruplex after Exo-assisted activity for AgNCs fabrication. This strategy triggered repeated recognition-sensing cycle, leading to output colorimetric and fluorometric response signal. The integration of G-quadruplex in released probe, amplified the intrinsic low quantum yield and fluorescence of synthesized AgNCs and also enhanced its nanozyme activity on TMB and H2O2 substrate for colorimetric detection in single tube reaction. The developed AFB1 sensing assay showed LOD of 2.5 pM in colorimetric mode and had more sensitive LOD of 8.5 fM in fluorescence analysis. Smartphone assisted assay was also applied successfully as competitive detection approach. The proposed strategy was applicable in real sample and showed reliable results. Overall, this platform takes advantages of sensitive fluorometric analysis and nanozyme assisted colorimetric method which make it promising in practical application.

Management in Education: Competitiveness and Above-Professional Approach

To study the whole range of problems that should be resolved in the process of innovation management in education it is necessary to realize that teaching is flexible, supple and requires continuous research. The goal of the article is to corroborate rightness of theoretical vectors by permanently changing practice. Theoretical and methodological foundation of the step-by-step research presented in the article is formed by principles of economic and comparative analysis, methods of induction and deduction and finally the method of archive search. The authors proposed an innovation approach to achievements in the field of education and showed the efficiency of Russian experience of well-known economists and experts in economics, psychology and sociology.

Sustainability in the Czech Republic: From a Green Growth Laggard to a Degrowth Hotspot

The Czech Republic is a notorious laggard in green transition policies. This begs the question of how stable the current socio-political setting is and whether it can resist deeper sustainability transitions in the long run. The paper combines institutional literature with sustainability transition research to describe the current situation and outline possible future developments in terms of the economic discourse. It shows that the reluctance towards the green transition may be caused not only by the strong position of incumbents but also by the limited relevance of the green competitiveness approach for the country’s situation. Based on recent developments and existing vulnerabilities, the paper identifies the possible strengths of the more radical approach to sustainability entailed in degrowth. Rather than a pure hegemony of one of the niche paradigms, however, it proposes as likely a pluriversal pathway combining elements of both in a patchwork manner.

Multi-response optimization in laser processing technologies by applying desirability function approach and response surface methodology based on grey relation analysis

Laser processing technologies are among the leading industry technologies for efficient and economical processing of a wide spectrum of engineering materials. The determination of optimal parameter settings with respect to multiple and opposite process performances is of great practical importance in laser processing technologies. This paper discusses, analyzes, and compares two main approaches for multi-response optimization (MRO) in engineering, that is, desirability function approach (DFA) and grey relation analysis (GRA), while solving five case studies, covering different laser processing technologies, such as cutting, drilling, welding, micro-channeling, and cladding. In each case study the MRO solutions obtained with two competitive approaches were assessed using the relative target deviation (RTD) criterion. Analysis of the obtained MRO solutions and comparative analysis with results from previous studies indicate that the integrated RSM-GRA provide similar and comparable solutions with the hybrid Taguchi method and GRA, while DFA proved to be a better and more promising approach for solving MRO in laser processing technologies. Some specific features, limitations, and possibilities of MRO approaches were also discussed.

Efficient semi-supervised clustering with pairwise constraint propagation for multivariate time series

Semi-supervised clustering is an effective method, which improves the clustering performance based on pairwise constraints. However, state-of-the-art methods suffer from two issues: 1) due to the high dimensionality and multiple variables of multivariate time series (MTS) data, the competitive similarity approach DTW is time consuming on large-scale MTS data. 2) Traditional semi-supervised clustering methods could not make full use of pairwise constraints information, which affects clustering performance. To deal with these issues, we propose an efficient semi-supervised clustering with pairwise constraint propagation for MTS data. First, two approximate distance measure methods are designed based on dynamic time warping (DTW) from the perspectives of boundary and dimension reduction, which greatly improve the efficiency of clustering without sacrificing its accuracy. Then, a graph-based clustering method with pairwise constraints propagation (GCPCP) is advanced on multivariate time series data. In GCPCP, the pairwise constraint propagation matrix and the affinity matrix are jointly optimized to exploit the dependence between them, which finally improves the clustering performance. Experimental results on 12 multivariate time series datasets show the effectiveness and efficiency of our proposed method.

Rethinking the legal test for excessive pricing: insights from the landmark UK CMA v Pfizer/Flynn Case and its legal implications

Abstract The legal treatment of excessive pricing in the pharmaceutical sector has been a topic of intense debate. This article examines the UK Competition and Market Authority (CMA) approach in the Pfizer/Flynn case and the subsequent appeal. It explores the implications of the Courts’ findings on the CMA’s latest investigations. The article criticizes the UK Courts for imposing unnecessarily high burden on the CMA, which will likely impose additional burdens on future investigators. The analysis also suggests that the cost-plus test conducted by the CMA is a very advanced methodology that can provide different benchmarks not only for assessing the excessiveness but also for assessing the unfairness under the 2-fold United Brands test.

Factor analysis of changes in labor force parameters

Subject. This article examines changes in the parameters of the economically active population that meet the needs of the dynamic growth of the domestic economy and its international competitiveness. Objectives. The article aims to analyze and assess the factors of change in the number of economically active population of Russia and make a forecast for the period up to 2036. Methods. For the study, we used statistical and factor analyses, and a competitive edge approach. Results. Based on the consideration of the cause-and-effect relationships between economic development and population growth, quantitative and qualitative parameters of the labor force, as well as the prerequisites and factors of their change, the article presents a forecast of the dynamics of the economically active population, taking into account the reduction in natural growth and aging of the population, the impact of pension reform and other factors, and an assessment of the potential for increasing the labor force due to the inclusion of new territories in Russia. Conclusions. Demographic problems, exacerbated by the military confrontation and systemic conflict with the countries of the collective West, significantly limit the possibilities of economic growth in Russia. However, the territorial and pension effects in the medium term make it possible to compensate for this negative impact.

Deep Heterogeneous AutoML Trend Prediction Model for Algorithmic Trading in the USD/COP Colombian FX Market Through Limit Order Book (LOB)

This study presents a novel and competitive approach for algorithmic trading in the Colombian US dollar inter-bank market (SET-FX). At the core of this strategy is an advanced predictive model, developed using the Tree-based Pipeline Optimization Tool (TPOT). TPOT, an automated machine learning platform based on strongly-typed genetic programming, incorporates the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This multi-objective evolutionary algorithm is instrumental in identifying machine learning models that strike an optimal balance between high accuracy and low complexity, thereby advancing the field of predictive modeling in financial markets.

Robust Semi-Supervised 3D Medical Image Segmentation With Diverse Joint-Task Learning and Decoupled Inter-Student Learning.

Semi-supervised segmentation is highly significant in 3D medical image segmentation. The typical solutions adopt a teacher-student dual-model architecture, and they constrain the two models' decision consistency on the same segmentation task. However, the scarcity of medical samples can lower the diversity of tasks, reducing the effectiveness of consistency constraint. The issue can further worsen as the weights of the models gradually become synchronized. In this work, we have proposed to construct diverse joint-tasks using masked image modelling for enhancing the reliability of the consistency constraint, and develop a novel architecture consisting of a single teacher but multiple students to enjoy the additional knowledge decoupled from the synchronized weights. Specifically, the teacher and student models 'see' varied randomly-masked versions of an input, and are trained to segment the same targets but reconstruct different missing regions concurrently. Such joint-task of segmentation and reconstruction can have the two learners capture related but complementary features to derive instructive knowledge when constraining their consistency. Moreover, two extra students join the original one to perform an inter-student learning. The three students share the same encoding but different decoding designs, and learn decoupled knowledge by constraining their mutual consistencies, preventing themselves from suboptimally converging to the biased predictions of the dictatorial teacher. Experimental on four medical datasets show that our approach performs better than six mainstream semi-supervised methods. Particularly, our approach achieves at least 0.61% and 0.36% higher Dice and Jaccard values, respectively, than the most competitive approach on our in-house dataset. The code will be released at https://github.com/zxmboshi/DDL.

Camelina Intercropping with Pulses a Sustainable Approach for Land Competition between Food and Non-Food Crops

With increasing global attention toward the need for mitigating climate change, the transition to sustainable energy sources has become an essential priority. Introducing alternative oilseed crops, such as camelina (Camelina sativa L.), into intercropping systems with staple food crops can mitigate ILUC (indirect land use change) and their negative impact on biofuel production. The present study compared camelina + field pea intercropping (ICw + IP, winter sowing) and camelina + lentil intercropping (ICs + IL, spring sowing) with their respective single crops regarding weed control, soil coverage, yields, and camelina seed quality (1000-seed weight, oil, and fatty acid composition). The comparison between different cropping systems was conducted using a one-way ANOVA. Both intercropping improved weed control at an early stage but no differences in soil coverage were found. Camelina seed yield was negatively affected by the presence of peas, whereas the pulse was unaffected. Conversely, camelina seed yield was not affected when intercropped with lentils while lentils reduced their yield in the intercropping. Furthermore, when camelina was intercropped with lentils, a significant increase was reported in 1000-seed weight and α -linolenic acid (C18:3) compared with the sole-camelina. However, both intercropping systems had a land equivalent ratio (LER, based on total seed yield at maturity) higher than one. Defining the best combination of crops and the optimal sowing and harvesting settings remain key to increasing the adoption of intercropping systems by farmers.

An innovative indirect competitive immunosorbent approach for precise detetion aflatoxin B 1 based on high-affinity IgG coupled CuO-anchored Fe 3O 4 nanozymes

An innovative indirect competitive immunosorbent approach for precise detetion aflatoxin B ₁ based on high-affinity IgG coupled CuO-anchored Fe ₃O ₄ nanozymes

Aerobic composting characteristics of corn straw and pig manure under dynamic aeration

ABSTRACT The conventional aeration method is compulsorily continuous ventilation or aeration at equal intervals, and a uniform aeration rate does not vary during composting. A dynamic on-demand aeration approach based on the diverse oxygen consumption of microorganisms at different composting stages could solve the problems of insufficient oxygen supply or excessive aeration. This study aims to design an aerobic composting system with dynamic aeration, investigate the effects of dynamic aeration on the temperature rise and physicochemical characteristics during the aerobic composting of corn straw and pig manure, and optimise the control parameters of oxygen concentration. Higher temperatures and longer high-temperature durations were achieved under dynamic aeration, thereby accelerating the decomposition of organic compounds. Dynamic aeration effectively reduced the aeration frequency, the convective latent heat and moisture losses, and the power consumption in the middle and later stages of composting. The dynamic aeration regulated according to the oxygen concentration of 14%–17% in the exhaust was optimum. Under the optimal conditions, the period above 50 ℃ lasted 8.5 days, and the highest temperature, organic matter removal, and seed germination index reached 65.82 ℃, 37.59%, and 74.59%, respectively. The power consumption was decreased by 33.58% compared to the traditional intermittent aeration. Dynamic aeration would be a competitive approach for improving aerobic composting characteristics and reducing the power consumption and the hot exhaust gas emissions, especially in the cooling maturation stage, which was of great significance for realising the low-cost production of composting at scale and promoting the blossom of the organic fertiliser industry.

Modelling competitive adsorption of organic micropollutants onto powdered activated carbon in continuous stirred tank reactors for advanced wastewater treatment

This work investigates the validation and application of a competitive model approach for full-scale wastewater treatment plants (WWTP) with external recirculation of partially loaded powdered activated carbon (PAC) for removal of organic micropollutants (OMP). It is based on the ideal adsorbed solution theory (IAST) for multisolute mixtures combined with calibration of fictive organic components and correction of single-solute model parameters for OMP by use of the tracer model (TRM). Adsorption kinetics are represented by a pseudo first order reaction (PFO) and compared to mass transfer calculated with the homogenous surface diffusion model (HSDM). Model validation with operational data from two different WWTPs showed a strong dependency of model results on the batch sample quality used for model calibration. In contrast, the kinetic approach is of less importance for predicting full-scale OMP removal with long PAC sludge retention times. Further model application demonstrated that external PAC recirculation significantly improves the OMP removal with regard to both adsorption capacity and compensation of competitive effects of Dissolved Organic Carbon (DOC).

Super Fe3+ competing ability, high biocompatibility, and mild antibacterial carbon dots for food preservation

Food spoilage caused by bacteria led to about 600 million people suffering from foodborne diseases in 2020, and the diseases kill approximately 400,000 people annually. Traditional food preservatives, antibiotics, and nanomaterials have problems such as high dosage, bacterial resistance, and low biocompatibility. In this study, mild antibacterial carbon dots (TM-CDs) with Fe3+ nutritional competitiveness were prepared. TM-CDs specifically chelated Fe3+ and formed Fe-O. The maximum chelating capacity of TM-CDs for Fe3+ was 154.56 mg mL−1. TM-CDs exhibited a much stronger chelating ability for Fe3+ than traditional iron chelators and even siderophores secreted by Pseudomonas fragi. The minimum inhibitory concentration of TM-CDs against Pseudomonas fragi was 0.25 mg mL−1. The antibacterial mechanism of TM-CDs was the existence of Fe3+ competition between TM-CDs and siderophores secreted by Pseudomonas fragi, which ultimately led to the death of Pseudomonas fragi due to iron deficiency. Notably, no obvious cytotoxicity of TM CDs was observed. In addition, the experimental results of freshness showed that TM-CDs effectively prolonged the shelf-life of salmon by 3–4 d. Therefore, this study provides insight into developing a competitive approach to depriving bacteria of nutrients for antibacterial purposes, with potential applications in seafood storage and preservation.

When algebra makes you smile: Playful engagement with early algebraic practices

BackgroundThe typical competitive and results-driven approach to school mathematics has traditionally been conceived as devoid of play, joy, and positive affect. AimsIn this paper we address the following questions: What markers of positive affect are observed while students are doing early algebra? Specifically, how are students’ markers of joy related to early algebraic practices? What are the characteristics of playful stances to learning early algebra that are observed when children express positive epistemic affect? SampleWe analyze three cases in which elementary school students engaged in tasks from an early algebra classroom teaching experiment. MethodsDrawing from two theoretical frameworks, epistemic affect and early algebra, we conducted microgenetic analyses of lesson transcripts to identify markers of joy and early algebraic practices. We conducted frequency analyses to determine their co-occurrence. ResultsOur results indicate that children expressed joy while engaging in early algebraic practices, evidence of positive epistemic affect. We describe the aspects of each of the cases we present in terms of prior literature on playful stances to learning to further bolster our claims about the relationship between joy and engagement with the early algebraic practices. ConclusionsWe conclude that mathematical learning environments should include open opportunities for students to engage with mathematical content, with multiple entry points and ways to respond. We also conclude that early algebraic practices provide opportunities for playfully engagement and positive epistemic affect.

Multi-class boosting for the analysis of multiple incomplete views on microbiome data

BackgroundMicrobiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed to ML methods can be subject to different sampling, sequencing and preprocessing techniques. Each different choice in the pipeline can lead to a different view (i.e., feature set) of the same individuals, that classical (single-view) ML approaches may fail to simultaneously consider. Moreover, some views may be incomplete, i.e., some individuals may be missing in some views, possibly due to the absence of some measurements or to the fact that some features are not available/applicable for all the individuals. Multi-view learning methods can represent a possible solution to consider multiple feature sets for the same individuals, but most existing multi-view learning methods are limited to binary classification tasks or cannot work with incomplete views.ResultsWe propose irBoost.SH, an extension of the multi-view boosting algorithm rBoost.SH, based on multi-armed bandits. irBoost.SH solves multi-class classification tasks and can analyze incomplete views. At each iteration, it identifies one winning view using adversarial multi-armed bandits and uses its predictions to update a shared instance weight distribution in a learning process based on boosting. In our experiments, performed on 5 multi-view microbiome datasets, the model learned by irBoost.SH always outperforms the best model learned from a single view, its closest competitor rBoost.SH, and the model learned by a multi-view approach based on feature concatenation, reaching an improvement of 11.8% of the F1-score in the prediction of the Autism Spectrum disorder and of 114% in the prediction of the Colorectal Cancer disease.ConclusionsThe proposed method irBoost.SH exhibited outstanding performances in our experiments, also compared to competitor approaches. The obtained results confirm that irBoost.SH can fruitfully be adopted for the analysis of microbiome data, due to its capability to simultaneously exploit multiple feature sets obtained through different sequencing and preprocessing pipelines.

Linking methodologies to assess climate impacts and circular economy strategies along supply chains

This paper introduces a generic hybrid methodology for assessing economic and environmental impacts along interconnected supply chain markets, considering both the material/product level and the market mechanisms that govern resource allocation. The methodology is illustrated by considering a competitive market and benevolent social planner variant of a supply chain equilibrium (SCE) model, applied to the specific case of single-use and reusable plastic bottles in Belgium. The benevolent social planner optimises a societal welfare function that includes all relevant revenues and costs along the plastic bottle supply chains – including external environmental damage costs. These costs comprise climate damage and are incorporated into the SCE model by combining life cycle assessment data with economic data. The main advantage of this model setup is that it better represents policy reality because it allows to study the effectiveness and efficiency of the simultaneous implementation of multiple circular strategies, their interactions, and possible rebound effects. The findings indicate that when all climate costs are internalised simultaneously, the competitive market solution approaches that of the benevolent social planner. Yet, when policymakers have fewer instruments at their disposal in relation to the number of environmental externalities, the welfare impact of competitive market solutions is limited or even negative. Moreover, key circular economy practices such as material intensity reduction and green design are particularly lagging behind compared to their welfare-optimising levels.