STCANet is a spatio-temporal coupled attention deep network model for predicting ocean currents, which consists of a spatio- temporal module and an attention module , forming a complex nested/coupled system. Understanding and optimizing the STCANet model is challenging due to the complexity of the underlying modules influencing model predictions.In this work we present STCANetViz, a web- based visual analysis tool for understanding and optimizing STCANet models. We proposed an Inter-Module Interdependencies Computing Algorithm (IMDCA), which computes module specific interdependency metrics derived from module interactions to optimize the STCANet model. A modular inter-dependency graph is then designed for examining and analyzing the behavior of the model.The graph consists of a network layer visualization (for module-level exploration) and a heatmap-scatterplot (for module output analysis). We implement a scalable visualization technique incorporating cloud storage for dynamically extracting, seamlessly visualizing and analyzing the high-dimensional ensemble data generated by the STCANet model on ensemble force-directed diagrams (EFDs).
Nested / Coupled models, Scalability, Ensemble data, Visual Analytics.
Sarthak Pattnaik1, Natasya Liew1, Ali Ozcan Kures1, Kathleen Park2, and Eugene Pinsky1, 1Department of Computer Science, Metropolitan College, Boston University 1010 Commonwealth Avenue, Boston, MA 02215, 2Department of Administrative Sciences, Metropolitan College, Boston University 1010 Commonwealth Avenue, Boston, MA 02215, United States of America
Supply Chain Management (SCM) stands at the forefront of modern business practices, serving as the backbone of efficient operations in a globalized and interconnected world. This paper delves into the intricate web of activities that define SCM and explores the transformative impact of technology on optimizing the flow of goods, services, information, and finances across organizations. The evolution of SCM from a logistics-centric function to a multi-functional corporate endeavor underscores the complexity and criticality of managing the end-to-end supply chain processes. By integrating technology into SCM practices, organizations can streamline operations, enhance collaboration, and drive competitive advantage in today’s dynamic marketplace. Technology plays a pivotal role in reshaping traditional supply chain paradigms, offering innovative solutions to longstanding challenges and unlocking new opportunities for growth and efficiency. From advanced data analytics to artificial intelligence (AI) tools, organizations can harness the power of technology to gain real-time insights, forecast demand accurately, and optimize inventory levels. The advent of digital supply chain platforms has revolutionized information sharing and external collaboration, enabling organizations to adapt swiftly to changing market dynamics and customer preferences. Furthermore, technology facilitates the optimization of logistics and transportation processes, leading to reduced fuel consumption, lower carbon emissions, and enhanced sustainability in supply chain operations.
Supply Chain Management, Customer Satisfaction, Customer Loyalty, Innovation, Machine Learning, Predictive Models.
Asharful Islam and Chuan Li, Department of Computer Science, Sichuan University, Chengdu, China
Optimizing the delivery of products from a central depot to multiple retail locations presents a multifaceted challenge, especially when considering factors such as minimizing costs while ensuring product availability for customers. Traditional approaches to this problem often rely on heuristic methods or mathematical optimization techniques. However, these approaches may struggle to adapt to dynamic real-world scenarios with complex, evolving conditions. This study pioneers the application of Proximal Policy Optimization (PPO), a state-of-the-art reinforcement learning algorithm, to the domain of product transportation and inventory management. By creating a custom simulation environment, “ProductTransportEnv,” we delve into the complexities of supply chain logistics, demonstrating the significant potential of reinforcement learning to transform operational efficiencies. The “ProductTransportEnv” mimics real-world logistics scenarios, allowing for a detailed exploration of transportation routes, inventory levels, and demand fluctuations, providing a rigorous testing ground for the PPO algorithm.
Openai Gym-environment, “ProductTransportEve,” PPO, RL, DQN, Inventory management.
Razafindrakoto Fanoina Ny Riana1, 2, 1doctoral School of Process Engineering for Agricultural and Food Industrial Systems, University of Antananarivo, 2antananarivo Higher Polytechnic School, University of Antananarivo
The Big Island is one of the few countries in the world that boasts a specific biological megadiversity in terms of flora and fauna. With its exceptional biological diversity, we were guided to focus our study on the leaves of Melicope madagascariensis, belonging to the Rutaceae family, an endemic species, to discover its medicinal properties. The Rue family (Rutaceae) includes more than 125 species across 09 genera in Madagascar, most of which are woody shrubs and trees. The flowers are generally showy and fragrant, and many species have attractive aromatic foliage. Extensive research into the biological activities of the essential oil from the leaves of this plant has been carried out in the rainforest of Madagascars east coast. The essential oil of Melicope madagascariensis has a very specific chemotype. Various GPC and TLC analyses were carried out. The majority of essential oils contain (Z)-β- ocimene (12.7%) and (E)-β-ocimene (25.7%) belonging to the monoterpene family. The synergy of these various bioactive molecules, namely α-pinene (8.56 %); methyl chavicol (6.25 %); trans-piperitol (7.42 %); (E)-β-caryophyllene (3.21 %), gives it several biological activities and an aromatic profile that is unusual for Madagascar. The synergy of its bioactive molecules helps to eliminate free radicals and fungal germs. However, analyses of its toxicity have produced some interesting results. Therefore, the notion of the duality of "efficacity and toxicity" needs to be taken into account, as all active substances are potentially toxic. This is why a more in-depth toxicity study was carried out, using various in vitro tests, namely larval activity and acute toxicity, to determine the lethal dose (LD50) performed on Swiss Musculus L mice. Given its biological properties, the use of the essential oil of Melicope madagascariensis leaves offers a vast opportunity for scientific exploration.
Melicope madagascariensis, Endemic species, Essential oil, Biological activity, Toxicity.
Raji .A , AL- Awadi (Ph.D) Erciyes University,Turkey
The experiment was carried out on the farm of sayeed. Habib Al-Khatib in the city of Numaniyah, 45 km west of the city of Kut, during the 2024 agricultural season. To study 6 types of organic waste: cows, sheep, and poultry, then a mixture of cow and sheep waste, a mixture of cow and poultry waste, and a mixture of poultry and sheep waste, in an amount of 2 t.ha-1 for each of them, in addition to the comparison sample F0, were given the following symbols, respectively, F1,F2,F3, (F1+F2), (F1+F3), (F2+F3), to know the effect of these fertilizers on the growth and production of Maize plants and soil properties. The land was plowed, ground, and mixed, then the waste was added to the soil with the surface layer 2 t.ha-1, then the corn grains were planted and irrigation began until the end of the experiment. Agricultural operations and crop service were carried out according to the recommendations followed in the region. Soil, plant and yield measurements were taken at the end of the growing season and included. The research results showed that the level (F 2 + F3) of organic Fertilizer from sheep waste mixed with poultry waste resulted in a significant increase in plant height, root length, Ear length, leaf area, weight of 500 grains, yield, and biological yield compared to other levels of fertilizer and organic matter in the soil, while the PH and EC of the soil did not give a significant difference.
Maize ,Poultry, Fertilizer, Organic waste, Al-Khatib.
Xia Wu, The Rockefeller University, New York, US
This study comprehensively examines GATA2 expression across 17 cancer types to explore its role in cancer prognosis and immune response. GATA2 showed variable expression in different cancer cell lines and was generally overexpressed in malignant tumors, significantly correlating with patient survival and tumor immune infiltration levels. Analysis revealed associations between GATA2, tumor-infiltrating immune cell markers, and immune response scores. Enrichment analysis identified relevant biological terms and pathways linked to GATA2. The findings suggest GATA2s critical involvement in cancer development and its influence on immune cell infiltration, highlighting its potential as a therapeutic target in cancer immunotherapy. This research supports GATA2s importance as a prognostic biomarker and suggests opportunities for novel targeted cancer treatments.
pan-cancer, prognostic biomarker, immune infiltration, GATA2, TME.
Alexander Kirnasov, Head of Quant Dev in Znamenka Capital
We introduce a new approach of Reinforcement Learning Application for High Frequency Trading called Quantum Reinforcement Learning as our agent learns to react on ‘quantum’ individual events in Limit Order Book – single Limit Order Book updates and single trades (and optionally single Orders if provided by Exchange). We claim that such level of learning granularity allows our agent to find optimal trading strategies by on-line modeling of Market Microstructure with a maximum rate and precision.
Issac Hsu, Aalto University, Finland
This paper examines the sustainability challenges posed by the increased movement of people and objects, driven by advancements in transportation technologies. Through a comprehensive review of literature and mobility data analysis, we explore the environmental impacts of heightened transportation activities, including greenhouse gas emissions, air and noise pollution, and habitat disruption. The study identifies multifaceted solutions to address these issues at urban, interurban, and global scales, emphasizing the role of electric and hydrogen-fuel-cell vehicles, public transportation systems, and smart traffic management. Additionally, the paper reflects on the authors travel behaviors, highlighting the significance of individual choices in promoting sustainability. The challenges and methodologies for analyzing mobility data from GNSS devices, mobile phone operators, and social media platforms are discussed, providing insights into the complexities of data privacy, accuracy, and representativeness.
Spatial Mobility, Sustainability, Greenhouse Gas Emissions, Urban Transportation, Data Analysis, Personal Travel Behavior.
Asia Mumtaz1, Ahmed Shukri Mohd Noor1, Saba Farzeen23, 1Faculty of Ocean Engineering Technology and Informatics, University Malaysia Terengganu, Kuala Terengganu, 21300, Malaysia, 2Department of Computer Science, University of Sialkot, Sialkot, 51310, Pakistan, 3Department of Computer Science, University of Sialkot, Sialkot, 51310, Pakistan
In modern agriculture, accurate crop yield prediction is crucial for optimizing food quality, production, and profitability while minimizing costs. This research focuses on developing a yield prediction system for enhancing vegetable production, by leveraging IoT-based sensors. The study collects comprehensive soil data, including temperature, humidity, soil moisture, light intensity, pH, and internet-sourced weather data. Machine learning algorithms are employed to analyze this dataset and accurately forecast crop yield. The systems precise and timely predictions empower farmers to make informed decisions on irrigation, fertilizer WebApp, and pest control, ultimately leading to improved vegetable production. Implementing IoT-based sensors in Sialkot showcased significant improvements, resulting in higher profitability for farmers. With higher accuracy achieved through the Random Forest model, compared to the Support Vector Machine and Decision Trees, this research provides foundational basis for developing yield prediction systems in agriculture, offering solutions for global farmers to enhance crop management practices sustainably and efficiently.
ML, IoT, Random Forest, SVM, Decision tree.
Zerihun Olana Asefa and Admas Abtew, Jimma University, Department of Information Technology
News recommendation schemes utilize features of the news itself and information about users to suggest and recommend relevant news items to the users towards the interest they have. However, the effectiveness of the existing news recommendation scheme is limited in the occurrence of new user cold start problems. Therefore, we designed a news recommender system using hybrid approaches to address new user cold start problems to ease and suggest more related news articles for new users. To achieve the objective mentioned above, user demographic data with a hybrid recommendation system that contains the scheme of both content-based and collaborative filtering approaches is proposed. To evaluate the effectiveness of the proposed model, an extensive experiment is conducted using a dataset of news articles with user rating value and user demographic data. The performance of the proposed model is done by two ways of experiment. So, the performance of the proposed model performs around 68.05% of Precision, 42.46% of Recall and 52.1% of the average F1 score for the experiment based on individual user similarity in the system. And also performs around 93.75% of precision, 40.25% of recall and 56.31% F1-score for the similarity of users based on the similarity of users within the same category which is better than the first experiment.
news recommendation system; cold start problem; hybrid approach; demographic information; new users; popular news.
Copyright © BIOS 2024