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Found 6374 publications
Flower fertilization optimization algorithm with application to adaptive controllers
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
This article presents the Flower Fertilization Optimization Algorithm (FFO), a novel bio-inspired optimization technique inspired by the natural fertilization process of flowering plants. The FFO emulates the behavior of pollen grains navigating through the search space to fertilize ovules, effectively balancing exploration and exploitation mechanisms. The developed FFO is theoretically introduced through the article and rigorously evaluated on a diverse set of 32 benchmark optimization problems, encompassing unimodal, multimodal, and fixed-dimension functions. The algorithm consistently outperformed 14 state-of-the-art metaheuristic algorithms, demonstrating superior accuracy, convergence speed, and robustness across all test cases. Also, exploitation, exploration, and parameter sensitivity analyses were performed to have a comprehensive understanding of the new algorithm. Additionally, FFO was applied to optimize the parameters of a Proportional-Integral-Derivative (PID) controller for magnetic train positioning—a complex and nonlinear control challenge. The FFO efficiently fine-tuned the PID gains, enhancing system stability, precise positioning, and improved response times. The successful implementation underscores the algorithm’s versatility and effectiveness in handling real-world engineering problems. The positive outcomes from extensive benchmarking and practical application show the FFO’s potential as a powerful optimization tool. In applying multi-objective PID controller parameter optimization, FFO demonstrated superior performance with a sum of mean errors of 190.563, outperforming particle swarm optimization (250.075) and dynamic differential annealed optimization (219.629). These results indicate FFO’s ability to achieve precise and reliable PID tuning for control systems. Furthermore, FFO achieved competitive results on large-scale optimization problems, demonstrating its scalability and robustness.
Open Access: Yes
Qualitative analysis of the sustainability of local attachment and identity based on in-depth interviews conducted in two scattered farmstead settlements
Publication Name: Discover Sustainability
Publication Date: 2025-12-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
Based on qualitative research, the paper focuses on the transformation of the traditional lifestyle of scattered farmsteads in recent decades, primarily driven by economic shifts and demographic changes. The challenging conditions of agriculture and farming, and the rise of alternative economic activities, such as tourism, recreation, and small-scale business, have reshaped these rural settlements, contributing to a different social landscape. Besides, another important trend is the influx of newcomers, including professionals, entrepreneurs, and pensioners, who adopt alternative, often non-agricultural lifestyles while maintaining a strong sense of place attachment and identity. In contrast, younger generations of native inhabitants tend to migrate elsewhere due to limited prospects, better education and job opportunities, leading to a generational shift in the population base. This study investigates two farmstead settlements exemplifying the tendencies, highlighting how sustainability is increasingly supported not by the continuation of traditional practices, but by the emergence of new lifestyle patterns such as agritourism and rural commuting. The process of change reflects a transition in the meaning and practice of rural living, where sustainability is redefined by changing forms of identity and attachment supporting the survival of farmsteads in a postmodern world.
Open Access: Yes
Is digitalization necessary for e-commerce adoption at small and medium-sized enterprises? The pandemic effects
Publication Name: Entrepreneurial Business and Economics Review
Publication Date: 2025-12-01
Volume: 13
Issue: 4
Page Range: 209-230
Description:
Objective: The study aimed to conduct a comparative analysis of e-commerce adoption in small and me-dium-sized enterprises (SMEs) of Pakistan and Bangladesh in the period following the COVID-19 pandemic. This study aims to observe in detail the significance of e-commerce adoption for small and medium sized enterprises, their abilities to evolve consumer preferences and to dissect the challenges that hinder in the way of e-commerce adoption in Bangladesh and Pakistan. The key focus of this study was to find out how digitisation acts as a moderator to overcome challenges. Research Design & Methods: We based the study on a quantitative design using a structured questionnaire to collect data from 500 SMEs with 250 respondents from each country, Bangladesh and Pakistan. We collected the study sample through a stratified random sampling from key industrial cities in both countries. We analysed the data of the study with SmartPLS version 4.0 to explore the relationship between external factors, need-bead factors, organisational factors, technological factors and e-commerce adoption with digitisation as a moderator. Findings: This study has also examined that market related demands and the access to the technology has significant positive impact on digitisation in both countries. However, company’s resources and cultural factors that are related to the organisational factors have negative impact on digitisation in Bangladesh and Pakistan. The multi-group analysis composed to find the distinguishing factors among two countries, found the impact of all the factors on digitisation. Implications & Recommendations: We observed a crucial role of digitisation in different fields to adopt e-Commerce in SMEs of Pakistan and Bangladesh. This study has been conducted in the time of COVID-19 pandemic, suggests that it should be the top priority of the policymakers to focus more on digitisation to enrich the country’s economic and digital infrastructure. The partnership programs can help both countries improve digitisation on modern standards. Findings suggests combined strategies to boost economic stability of both countries. However, this study has some limitations and future suggestions to entertain the role of artificial intelligence (AI) to examine the adoption of e-Commerce in both countries. Contribution & Value Added: This study has provided a qualified analysis of the drivers of e-commerce adoption in SMEs of Pakistan and Bangladesh in the specific context of the post-COVID-19 pandemic, with digitisation as a moderator. It offers novel insight into the different types of challenges faced by SMEs in both countries and in the transformation of digitisation in the South Asian region.
Open Access: Yes
Assessing the texture profile and optimizing the temperature and soaking time for the rehydration of hot air-dried Auricularia auricula-judae mushrooms
Publication Name: Discover Food
Publication Date: 2025-12-01
Volume: 5
Issue: 1
Page Range: Unknown
Description:
Rehydrating dried jelly ear mushrooms allows them to take on the original shape, and texture, but no thorough study has been done to date to determine the ideal rehydration parameters. The study aimed to optimize the rehydration conditions of the hot-air-dried jelly ear mushroom, to achieve the most similar stock to the fresh mushroom. To achieve this, the mushrooms dried to a constant weight at 40 °C were soaked in water that had been heated to 20–100 °C for 10–70 min. The mushrooms were weighed and examined the texture profile to determine the rehydration %, hardness, gumminess, chewiness, springiness, and cohesiveness at each tested temperature and soaking time. The fresh mushroom used as a control had a moisture content of 95.39 m/m%, hardness of 847.40, springiness of 0.70, gumminess of 562.04, chewiness of 423.98 N/m2, and cohesiveness of 0.66 J/m3. These results were compared to the rehydrated mushroom samples texture profile test results, and it was found that the dried mushrooms recovered nearly the same texture as the fresh mushrooms with a 20-minute soak at 40 °C. As consumers prefer rehydrated products to be similar to fresh products in terms of texture and enjoyment value, it is crucial to determine the ideal rehydration parameters. However, each drying method and temperature has a different effect on the texture and water absorption capacity of the mushrooms, so the mentioned results are only achieved with the described parameters.
Open Access: Yes
Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. The proposed approach utilizes a multi-objective reward model to adaptively balance energy consumption, task success rate, and network lifetime. Extensive simulation results demonstrate that ETORL-UAV outperforms five state-of-the-art methods Meta-RL, g-MAPPO, Backscatter Optimization, Hierarchical Optimization, and Game Theory based Pricing achieving up to 9.3 % higher task offloading success, 18.75 % improvement in network lifetime, and 27 % reduction in energy consumption. These results validate the framework's scalability, reliability, and practical applicability for real-world disaster-response WSN deployments. • Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs • Leverages PPO-based reinforcement learning and a multi-objective reward model • Demonstrates superior performance over five benchmark approaches in disaster-response simulations
Open Access: Yes
Integration of MULTIMOORA algorithm combined with circular q-rung orthopair fuzzy information for optimizing player positioning
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The following paper presents a new analytical framework for the optimization of player positioning, a methodology with significant practical implications. The method implements the multi-objective optimization by ratio analysis with full multiplicative form (MULTIMOORA) in a decision-making context in which several non-commensurable performance variables have to be combined. The application of Dombi operationalizes the framework by prioritizing weighted aggregation operators coupled with circular q-rung orthopair fuzzy sets (Cq-ROFSs). The Cq-ROFSs allow multidimensional representation of uncertainty, and allow dynamic actions upon the fuzzy parameter q, such that both intuitionistic fuzzy sets and Pythagorean fuzzy sets are subsets. Two Dombi prioritized operators on Cq-ROFSs are thereby devised a Cq-ROFSs Dombi prioritized weighted averaging operator (Cq-ROFSDPWA) and a Cq-ROFSs Dombi prioritized weighted geometric operator (Cq-ROFSDPWG). Results from empirical experiments are reported that demonstrate the performance of the resulting methodology, highlighting its practical relevance. The fundamental properties of these operators are also examined. The proposed aggregation operators are applied within the MULTIMOORA technique to assess their effectiveness. Numerical examples demonstrate that the methods yield logical and consistent results across different decision-making scenarios. Comparative analyses further highlight the advantages of the Cq-ROFSDPWA and Cq-ROFSDPWG operators over existing approaches.
Open Access: Yes
Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
For effective and early diagnosis of diseases such as leukemia and anemia, accurate classification and interpretation of peripheral blood cells are critical. A novel hybrid deep learning model is proposed in this study for multi-class blood cell classification, called Spectral-Aware CNN with Learnable Spectral Biorthogonal Downsampling Units (LSBDUs) and Depthwise Separable Convolutions. The model replaces conventional pooling layers with wavelet-inspired LSBDUs for improved feature retention. This results in reduced computational overhead through efficient separable convolutions. The research used a balanced dataset of 17,092 images across eight blood cell classes. The techniques, such as stratified data splitting, advanced augmentation, and label smoothing, are included in the training pipeline for improving generalizability. As a result, the model achieves 99.18 % of overall classification accuracy with superior class-wise performance. • Replaces pooling layers with spectral-aware LSBDU blocks for better feature preservation. • Integrates Depthwise Separable Convolutions to reduce parameter count and training cost. • Demonstrates superior generalization across all classes without overfitting.
Open Access: Yes
Health and well-being surveys in higher education: a scoping review
Publication Name: International Journal of Educational Research Open
Publication Date: 2025-12-01
Volume: 9
Issue: Unknown
Page Range: Unknown
Description:
Objective: Higher education institutions increasingly recognize student and staff well-being as critical to institutional success. This scoping review examines existing university health and well-being surveys to support the development of a standardized assessment framework for informed decision-making. Methods: The review follows PRISMA-ScR guidelines. The protocol was registered in the Open Science Framework. A total of 237 full-text articles were systematically reviewed and analyzed using a predefined structured framework. Results: The review identified and detailed the key features of existing surveys, including the topics covered, the measurement instruments employed, and other methodological characteristics. A classification system was developed to categorize questionnaires, and a hierarchical model was established to link relevant surveys to corresponding themes. Conclusions: To the best of our knowledge, this is the first scoping review of health and well-being surveys conducted among university members. The findings provide valuable insights for improving future survey designs and advancing comprehensive well-being assessments in higher education.
Open Access: Yes
Exploring public discourse on green hydrogen via YouTube comments: A comparative sentiment analysis using VADER and ChatGPT
Publication Name: Economic Analysis and Policy
Publication Date: 2025-12-01
Volume: 88
Issue: Unknown
Page Range: 2012-2030
Description:
This study investigates public attitudes toward green hydrogen (GH) by analyzing YouTube comments through sentiment analysis and topic modelling. Unlike previous research that situates hydrogen within broader climate or energy debates and focuses on platforms such as Twitter or Bilibili, this work examines GH as a standalone topic and leverages YouTube's longer, context-rich comments to capture richer public discourse. Comments were collected via the YouTube API (Application Programming Interface) from a curated set of videos and analyzed for sentiment using both the rule-based VADER (Valence Aware Dictionary and sEntiment Reasoner) and the generative model ChatGPT-3.5, enabling a qualitative comparison of their performance. Latent Dirichlet Allocation (LDA) was then applied to identify major discussion themes, which were subsequently linked to sentiment trends. The results indicate that ChatGPT-3.5 outperforms VADER in interpreting sarcasm, slang, emoticons, and mixed sentiments. Topic modelling revealed eight key themes, including skepticism about institutional barriers and costs, optimism regarding GH's role in hard-to-decarbonize sectors, comparisons with nuclear energy and electric vehicles, and concerns about environmental and technical challenges. Overall, the study enhances understanding of online public discourse on GH by demonstrating how advanced sentiment analysis tools, combined with topic modelling, can generate deeper insights to inform strategies that better integrate public perceptions with the economic and policy conditions of GH deployment.
Open Access: Yes
