bluuebunny/crossref_metadata_2025 huggingface dataset:
Barely Decidable Fragments of Planning
Authors: Dekker Maurice , Behnke Gregor
Published: October 2024

Both numeric planning and Hierarchical Task Network (HTN) planning are highly expressive planning formalisms – at the cost of being undecidable in general. For both formalisms, decidable fragments are known. Studying these restricted fragments has lead to valuable insights, which ultimately gave rise to the development of new efficient planning algorithms. We identify new decidable fragments of both numeric and HTN planning. For HTN planning, we introduce the fragments of one-hole-digging, initial, and final problems. The former restrict every task network to have at most one compound task, while the latter two restrict compound tasks to be order-minimal or order-maximal, respectively. For numeric planning, we introduce Positive Numeric Planning (PNP) where the value of numeric variables can only be non-negative. We determine the complexity of these fragments: they are Ackermann-complete – which is significantly more difficult than any prior known decidable fragment, but still barely decidable.
Scale-Adaptive Balancing of Exploration and Exploitation in Classical Planning
Authors: Wissow Stephen , Asai Masataro
Published: October 2024

Balancing exploration and exploitation has been an important problem in both adversarial games and automated planning. While it has been extensively analyzed in the Multi-Armed Bandit (MAB) literature, and the game community has achieved great success with MAB-based Monte Carlo Tree Search (MCTS) methods, the planning community has struggled to advance in this area. We describe how Upper Confidence Bound 1’s (UCB1’s) assumption of reward distributions with known bounded support shared among siblings (arms) is violated when MCTS/Trial-based Heuristic Tree Search (THTS) in previous work uses heuristic values of search nodes in classical planning problems as rewards. To address this issue, we propose a new Gaussian bandit, UCB1-Normal2, and analyze its regret bound. It is variance-aware like UCB1-Normal and UCB-V, but has a distinct advantage: it neither shares UCB-V’s assumption of known bounded support nor relies on UCB1-Normal’s conjectures on Student’s t and χ2 distributions. Our theoretical analysis predicts that UCB1-Normal2 will perform well when the estimated variance is accurate, which can be expected in deterministic, discrete, finite state-space search, as in classical planning. Our empirical evaluation confirms that MCTS combined with UCB1-Normal2 outperforms Greedy Best First Search (traditional baseline) as well as MCTS with other bandits.
Planning to be Healthy: Towards Personalized Medication Planning
Authors: Alon Lee-or , Weitman Hana , Shleyfman Alexander , Kaminka Gal A.
Published: October 2024

Personalized medication plans determine the selection, dosage, and administration schedule of medications, to achieve medical goals that are specific to the patient and to its individual health constraints. This paper introduces medication planning as a novel domain for artificial intelligence planning, using PDDL+. We evaluate the suggested representation via experiments based on data collected from medical studies conducted on mice and rats.
CODD: A Decision Diagram-Based Solver for Combinatorial Optimization
Authors: Michel Laurent , van Hoeve Willem-Jan
Published: October 2024

We introduce CODD, a system for solving combinatorial optimization problems using decision diagram technology. Problems are represented as state-based dynamic programming models using the CODD language specification. The model specification is used to automatically compile relaxed and restricted decision diagrams that are embedded inside a branch-and-bound search process. We introduce abstractions that allow us to generically implement the solver components while maintaining overall execution efficiency. We demonstrate the functionality of CODD on a variety of combinatorial optimization problems and compare its performance to other state-based solvers as well as integer programming and constraint programming solvers. CODD provides competitive results and can outperform the other solvers, sometimes by orders of magnitude.
Symmetry-Breaking Constraints for Directed Graphs
Authors: Rintanen Jussi , Feyzbakhsh Rankooh Masood
Published: October 2024

Finding a graph with given properties occurs as a sub-problem of many important problems in A.I. and other areas of computer science. Main approaches to solving such problems include automated reasoning and constraint satisfaction methods. These can often be substantially sped up by considering only a subset of graphs for each equivalence class of isomorphic graphs, motivating the use of symmetry-breaking constraints for graphs. We present a symmetry-breaking constraint for directed graphs, generalizing earlier works that have presented such constraints for undirected graphs without loops, and experimentally demonstrate their effectiveness.
Fact Probability Vector Based Goal Recognition
Authors: Wilken Nils , Cohausz Lea , Bartelt Christian , Stuckenschmidt Heiner
Published: October 2024

We present a new approach to goal recognition that involves comparing observed facts with their expected probabilities. These probabilities depend on a specified goal g and initial state s0. Our method maps these probabilities and observed facts into a real vector space to compute heuristic values for potential goals. These heuristic values estimate the likelihood of a given goal being the true objective of the observed agent. As obtaining exact expected probabilities for observed facts in an observation sequence is often practically infeasible, we propose and empirically validate a method for approximating these probabilities. Our empirical results show that the proposed approach offers improved goal recognition precision compared to state-of-the-art techniques while reducing computational complexity.
SAT-Based Approaches to Reasoning in Choice Logics
Authors: Lehtonen Tuomo , Niskanen Andreas , Järvisalo Matti
Published: October 2024

Representing and reasoning about preferences is a fundamental task in artificial intelligence. Various logic-based languages for representing preferences have been proposed. However, developing practical algorithms for reasoning in such logic-based languages remains a challenge due to high computational complexity. In this work, we develop practical algorithms based on Boolean satisfiability (SAT) for computing preferred models and for deciding preferred model entailment in qualitative and conjunctive choice logics QCL and CCL under the so-called minmax, lexicographic, and inclusion-based preference semantics. For each of the problem variants, we detail an algorithm which adheres to the computational complexity of the reasoning task, based on either maximum satisfiability (MaxSAT) or SAT with preferences (PrefSAT) solvers. We empirically evaluate our implementation of the algorithms, and show that our approach scales significantly better than a recently proposed answer set programming approach to computing preferred models.
Generating SROI− Ontologies via Knowledge Graph Query Embedding Learning
Authors: He Yunjie , Hernandez Daniel , Nayyeri Mojtaba , Xiong Bo , Zhu Yuqicheng , Kharlamov Evgeny , Staab Steffen
Published: October 2024

Query embedding approaches answer complex logical queries over incomplete knowledge graphs (KGs) by computing and operating on low-dimensional vector representations of entities, relations, and queries. However, current query embedding models heavily rely on excessively parameterized neural networks and cannot explain the knowledge learned from the graph. We propose a novel query embedding method, AConE, which explains the knowledge learned from the graph in the form of SROI− description logic axioms while being more parameter-efficient than most existing approaches. AConE associates queries to SROI− description logic concepts. Every SROI− concept is embedded as a cone in complex vector space, and each SROI− relation is embedded as a transformation that rotates and scales cones. We show theoretically that AConE can learn SROI− axioms, and defines an algebra whose operations correspond one-to-one to SROI− description logic concept constructs. Our empirical study on multiple query datasets shows that AConE achieves superior results over previous baselines with fewer parameters. Notably on the WN18RR dataset, AConE achieves significant improvement over baseline models. We provide comprehensive analyses showing that the capability to represent axioms positively impacts the results of query answering.
Learning Logic Programs by Finding Minimal Unsatisfiable Subprograms
Authors: Cropper Andrew , Hocquette Céline
Published: October 2024

The goal of inductive logic programming (ILP) is to search for a logic program that, with given background knowledge, generalises training examples. We introduce an ILP approach that identifies minimal unsatisfiable subprograms (MUSPs). We show that finding MUSPs allows us to efficiently and soundly prune the search space. Our experiments on multiple domains, including program synthesis and game playing, show that our approach can reduce learning times by 99%.
Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning
Authors: Zeng Runhao , Zhou Dingjie , Liang Qiwei , Liu Junlin , Li Hui , Huang Changxin , Li Jianqiang , Hu Xiping , Sun Fuchun
Published: October 2024

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running.
Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses
Authors: Lehoux-Lebacque Vassilissa , Silander Tomi , Loiodice Christelle , Lee Seungjoon , Wang Albert , Michel Sofia
Published: October 2024

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.
An Empirical Study of Grounding PPDDL Plans for AI-Driven Robots in Social Environment
Authors: Beraldo Gloria , Oddi Angelo , Rasconi Riccardo
Published: October 2024

Autonomous robots are agents that interact with the environment and perform tasks using their own abilities (i.e., skills) without continuous human intervention. However, in real-life scenarios, intelligent robots also need to discover the effects of their actions and understand how to save them for future use. This task appears time-consuming and very challenging, especially in a social environment populated by people who typically modify their behaviors based on the context and can dynamically impact the robot’s decision-making process. This paper aims to investigate the feasibility of autonomously creating an abstract representation of the domain knowledge from the data acquired during the robot’s exploration, inferring causal-effect relations between the executed actions, and learning context-aware symbols that describe the environment states at high level, ultimately producing a PDDL-based description of the domain. With this purpose, a new framework that relies on ROS, the standard de-facto in robotics, and ROSPlan has been developed to facilitate the transfer into several robotic platforms. Preliminary results suggest the possibility of describing the robot’s experience per option via context-based symbols that are consistently learned by the system from a few data samples.
Assisted Debate Builder with Large Language Models
Authors: Faugier Elliot , Armetta Frédéric , Bonifati Angela , Yun Bruno
Published: October 2024

We introduce ADBL2, an assisted debate builder tool. It is based on the capability of large language models to generalise and perform relation-based argument mining in a wide-variety of domains. It is the first open-source tool that leverages relation-based mining for (1) the verification of pre-established relations in a debate and (2) the assisted creation of new arguments by means of large language models. ADBL2 is highly modular and can work with any open-source large language models that are used as plugins. As a by-product, we also provide the first fine-tuned Mistral-7B large language model for relation-based argument mining, usable by ADBL2, which outperforms existing approaches for this task with an overall F1-score of 90.59% across all domains.
ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine
Authors: Cardellino Cristian , Collias Theo , Molinet Benjamin , Hain Erwan , Sun Wei , Agerri Rodrigo , Villata Serena , Cabrio Elena
Published: October 2024

The need for transparent AI systems in sensitive domains like medicine has become key. In this paper we present ANTIDOTE, a software suite proposing different tools for argumentation-driven explainable Artificial Intelligence for digital medicine. Our system offers the following functionalities: multilingual argumentative analysis for the medical domain, explanation extraction and generation of clinical diagnoses, multilingual large language models for the medical domain, and the first multilingual benchmark for medical question-answering. Experimental results demonstrate the efficacy of ANTIDOTE across different tasks, highlighting its potential as an asset in medical research and practice and fostering transparency, which is crucial for informed decision-making in healthcare.
A SAM Based Tool for Semi-Automatic Food Annotation
Authors: Abdur Rahman Lubnaa , Papathanail Ioannis , Brigato Lorenzo , Mougiakakou Stavroula
Published: October 2024

The advancement of artificial intelligence (AI) in food and nutrition research is hindered by a critical bottleneck: the lack of annotated food data. Despite the rise of highly efficient AI models designed for tasks such as food segmentation and classification, their practical application might necessitate proficiency in AI and machine learning principles, which can act as a challenge for non-AI experts in the field of nutritional sciences. Alternatively, it highlights the need to translate AI models into user-friendly tools that are accessible to all. To address this, we present a demo of a semi-automatic food image annotation tool leveraging the Segment Anything Model (SAM) kirillov2023segment??>. The tool enables prompt-based food segmentation via user interactions, promoting user engagement and allowing them to further categorise food items within meal images and specify weight/volume if necessary. Additionally, we release a fine-tuned version of SAM’s mask decoder, dubbed MealSAM, with the ViT-B backbone tailored specifically for food image segmentation. Our objective is not only to contribute to the field by encouraging participation, collaboration, and the gathering of more annotated food data but also to make AI technology available for a broader audience by translating AI into practical tools.
Interactive Example-Based Explanations to Improve Health Professionals’ Onboarding with AI for Human-AI Collaborative Decision Making
Authors: Lee Min Hun , Ng Renee Bao Xuan , Choo Silvana Xinyi , Thilarajah Shamala
Published: October 2024

A growing research explores the usage of AI explanations on user’s decision phases for human-AI collaborative decision-making. However, previous studies found the issues of overreliance on ‘wrong’ AI outputs. In this paper, we propose interactive example-based explanations to improve health professionals’ onboarding with AI for their better reliance on AI during AI-assisted decision-making. We implemented an AI-based decision support system that utilizes a neural network to assess the quality of post-stroke survivors’ exercises and interactive example-based explanations that systematically surface the nearest neighborhoods of a test/task sample from the training set of the AI model to assist users’ onboarding with the AI model. To investigate the effect of interactive example-based explanations, we conducted a study with domain experts, health professionals to evaluate their performance and reliance on AI. Our interactive example-based explanations during onboarding assisted health professionals in having a better reliance on AI and making a higher ratio of making ‘right’ decisions and a lower ratio of ‘wrong’ decisions than providing only feature-based explanations during the decision-support phase. Our study discusses new challenges of assisting user’s onboarding with AI for human-AI collaborative decision-making.
Enhancing Decision-Making in Energy Management Systems Through Action-Independent Dynamics Learning
Authors: Zangato Théo , Osmani Aomar , Alizadeh Pegah
Published: October 2024

Incorporating auxiliary objectives into Reinforcement Learning allows agents to acquire additional knowledge, thereby increasing their search for the optimal policy. This article presents the Model-Predictor Proximal Policy Optimization (MP-PPO) algorithm, which merges the concepts of various PPO variants with a Transformer probabilistic prediction module. This model capitalizes on the time dependence inherent in energy management systems, predicting future state transitions by learning to predict certain state characteristics. Notably, our algorithm seamlessly integrates this predictive capability into the Actor-Critic architecture, avoiding the need for an external model. Through experiments on real data, we demonstrate that integrating predictive capabilities for partial state prediction improves both the sample effectiveness and efficiency of the original PPO approach without requiring exterior prior information.
Predicting Solar Heat Production to Optimize Renewable Energy Usage
Authors: Boura Tatiana , Koliou Natalia , Meramveliotakis George , Konstantopoulos Stasinos , Kosmadakis George
Published: October 2024

Utilizing solar energy to meet space heating and domestic hot water demand is very efficient (in terms of environmental footprint as well as cost), but in order to ensure that user demand is entirely covered throughout the year needs to be complemented with auxiliary heating systems, typically boilers and heat pumps. Naturally, the optimal control of such a system depends on an accurate prediction of solar thermal production. Experimental testing and physics-based numerical models are used to find a collector’s performance curve — the mapping from solar radiation and other external conditions to heat production — but this curve changes over time once the collector is exposed to outdoor conditions. In order to deploy advanced control strategies in small domestic installations, we present an approach that uses machine learning to automatically construct and continuously adapt a model that predicts heat production. Our design is driven by the need to (a) construct and adapt models using supervision that can be extracted from low-cost instrumentation, avoiding extreme accuracy and reliability requirements; and (b) at inference time, use inputs that are typically provided in publicly available weather forecasts. Recent developments in attention-based machine learning, as well as careful adaptation of the training setup to the specifics of the task, have allowed us to design a machine learning-based solution that covers our requirements. We present positive empirical results for the predictive accuracy of our solution, and discuss the impact of these results on the end-to-end system.
License Plate Images Generation with Diffusion Models
Authors: Shpir Mariia , Shvai Nadiya , Nakib Amir
Published: October 2024

Despite the evident practical importance of license plate recognition (LPR), corresponding research is limited by the volume of publicly available datasets due to privacy regulations such as the General Data Protection Regulation (GDPR). To address this challenge, synthetic data generation has emerged as a promising approach. In this paper, we propose to synthesize realistic license plates (LPs) using diffusion models, inspired by recent advances in image and video generation. In our experiments a diffusion model was successfully trained on a Ukrainian LP dataset, and 1000 synthetic images were generated for detailed analysis. Through manual classification and annotation of the generated images, we performed a thorough study of the model output, such as success rate, character distributions, and type of failures. Our contributions include experimental validation of the efficacy of diffusion models for LP synthesis, along with insights into the characteristics of the generated data. Furthermore, we have prepared a synthetic dataset consisting of 10,000 LP images, publicly available at https://zenodo.org/doi/10.5281/zenodo.13342102. Conducted experiments empirically confirm the usefulness of synthetic data for the LPR task. Despite the initial performance gap between the model trained with real and synthetic data, the expansion of the training data set with pseudolabeled synthetic data leads to an improvement in LPR accuracy by 3% compared to baseline.
Automated Impact Echo Spectrum Anomaly Detection using U-Net Autoencoder
Authors: Liebert Artur , Dethof Fabian , Keßler Sylvia , Niggemann Oliver
Published: October 2024

Condition assessments are crucial in the field of civil engineering to avoid potential safety hazards and expensive repairs. For this, non-destructive testing methods like the Impact Echo method are used to detect defects like honeycombs inside of concrete without causing additional damage. This study introduces a four-step method and developed the U-Net Autoencoder (U-AE) to identify such defects from Impact Echo measurements. Our process involves preparing and learning from both simulated and real-world data to ensure an accurate anomaly detection. The key findings of this work demonstrate crucial aspects of data preparation and a significant performance enhancement achieved through pretraining with simulated data followed by fine-tuning with real-world datasets. Our approach effectively counteracts the information loss typically caused by dimension reduction, discovering an optimal balance in the latent space. Overall, this research introduces a novel architecture for detecting defects in concrete structures, marking a new advancement the practice of maintaining and inspecting civil infrastructure.
Graphite: A Graph-Based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation
Authors: Mishra Ashirbad , Dey Soumik , Zhao Jinyu , Wu Marshall , Li Binbin , Madduri Kamesh
Published: October 2024

Keyphrase Recommendation has been a pivotal problem in advertising and e-commerce where advertisers/sellers are recommended keyphrases (search queries) to bid on to increase their sales. It is a challenging task due to the plethora of items shown on online platforms and various possible queries that users search while showing varying interest in the displayed items. Moreover, query/keyphrase recommendations need to be made in real-time and in a resource-constrained environment. This problem can be framed as an Extreme Multi-label (XML) Short text classification by tagging the input text with keywords as labels. Traditional neural network models are either infeasible or have slower inference latency due to large label spaces. We present Graphite, a graph-based classifier model that provides real-time keyphrase recommendations that are on par with standard text classification models. Furthermore, it doesn’t utilize GPU resources, which can be limited in production environments. Due to its lightweight nature and smaller footprint, it can train on very large datasets, where state-of-the-art XML models fail due to extreme resource requirements. Graphite is deterministic, transparent, and intrinsically more interpretable than neural network-based models. We present a comprehensive analysis of our model’s performance across forty categories spanning eBay’s English-speaking sites.
Match’In - Pilot Project of an Algorithm-Based Decision Support System for Individualized Recommendations of Municipalities for the Integration of Refugees
Authors: Sauer Christian , Wenzel Carsten , Euler Katharina , Reinhold Sonja , Wuttke Frank , Oitmann Björn
Published: October 2024

For the successful integration of refugees, it is essential to place refugees in a municipality that best matches their personal needs and future plans. It is therefore important to individualize the choice of location with the highest possible match to personal attributes and needs of a refugee. In this paper, we describe the development of the Match’In system, a recommender system that recommends municipalities for the integration of refugees, based on the knowledge representing an individual refugee and his or her needs. We describe the knowledge elicitation and formalization process we developed and applied in the Match’In project. A participatory and multi-stage process was used to develop the algorithm-based decision support system for the individualized recommendation of municipalities for refugees. We detail on the decision and rational of using case-based reasoning (CBR) as the reasoning approach within the Match’In system. We then describe the specific capabilities of the use of structural CBR in terms of transparency of the knowledge model, with an emphasis on the detection and prevention of algorithmic bias. We further describe the reasoning technique and the possibilities to automatically derive explanations from these to justify the recommendation of specific municipalities by the Match’In system.
AI Personalized Interactive Fiction for Young Children
Authors: Lesner Jasmine , Murayama Luke , Guizar Tony , Phunjamaneechot Poom , Shapiro Daniel
Published: October 2024

This paper presents a novel high-fidelity prototype designed to enable young children to create personalized, interactive stories using advanced AI technologies. Our approach addresses a gap in existing AI-driven storytelling applications by focusing specifically on young children. The prototype allows children to select story topics using emojis and engages them with vivid multimedia elements including AI-generated illustrations, sounds, and music. Our goal is to foster critical thinking through choice-based narratives that allow children to influence story outcomes. Parents can review and provide feedback on AI-generated media to ensure age-appropriate content. The system integrates large language models (LLMs) for story generation with text-to-image, text-to-sound, and text-to-music AI models to deliver an engaging multimedia storytelling experience. The paper describes the prototype system in conjunction with a story generated by it. Our prototype demonstrates the potential of child-centric AI storytelling tools to positively impact children’s learning and development.
Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
Authors: Kovtun Elizaveta , Boeva Galina , Shulga Andrey , Zaytsev Alexey
Published: October 2024

Most user-related data can be represented as a sequence of events associated with a timestamp and a collection of categorical labels. For example, the purchased basket of goods and the time of buying fully characterize the event of the store visit. Anticipation of the label set for the future event called the problem of temporal sets prediction, holds significant value, especially in such high-stakes industries as finance and e-commerce. A fundamental challenge of this task is the joint consideration of the temporal nature of events and label relations within sets. The existing models fail to capture complex time and label dependencies due to ineffective representation of historical information initially. We aim to address this shortcoming by presenting the framework with a specific way to aggregate the observed information into time- and set structure-aware views prior to transferring it into main architecture blocks. Our strong emphasis on input arrangement facilitates the subsequent efficient learning of label interactions. The proposed model is called Label-Attention NETwork, or LANET. We conducted experiments on four different datasets and made a comparison with four established models, including SOTA, in this area. The experimental results suggest that LANET provides significantly better quality than any other model, achieving an improvement up to 65% in terms of weighted F1 metric compared to the closest competitor. Moreover, we contemplate causal relationships between labels in our work, as well as a thorough study of LANET components’ influence on performance. We provide an implementation of LANET to encourage its wider usage.
Rat hippocampus structures after experimental traumatic brain injury and GABA-benzodiazepine receptor complex modulators administration
Authors: Shemet Yaroslav , Likhodiievskyi Volodymyr
Published: October 2024

the global incidence of traumatic brain injury remains high and long-lasting consequences of such injury brings both medical and social burden on public health. Such comorbidity among others includes early and late post-traumatic epilepsy, post-traumatic stress disorder and depression and anxiety, cognitive impairments often require prolonged treatment. The incidence of such negative outcomes of traumatic brain injury is extremely high among deployed military personnel and war-affected civilians. All of the facts that mentioned above require the development of new strategies, including novel drug designing to provide complex treatment and overcome existing difficulties in rehabilitation after traumatic brain injury.  The aim of the study was to evaluate the effect of GABA-benzodiazepine receptor complex modulators on rat hippocampus zones after traumatic brain injury. The experiment was carried out on male Wistar rats. To obtain traumatic brain injury we used a modified weight drop model. Rats were divided to intact, placebo (received placebo treatment), MPTD-01 (treated with 4-(4’-Methoxyphenyl)-2,3,4,5-tetrahydro-1H-2,3-benzodiazepin-1-one; intraperitoneally, 5 mg/kg once a day) and BS 34-20 (treated with 6-(4-Methoxyphenyl)-7H-[1,2,4]triazolo[3,4-a][2,3]benzodiazepine; intraperitoneally, 5 mg/kg once a day) groups. The light microscopy with hematoxylin and eosin straining was performed on 3, 7, 14, 21 days after injury. Hippocampus CA1, CA3 zones and dentate gyrus were studied. Unlike the placebo group, the MPTD-01 and BS 34-20 experimental groups demonstrated a smoothing of the swelling phase and its reduction, a shift in the time phase of cellular reactions to a later time. In the BS 34-20 group, minimal edema and more complete restoration of the structure of the hippocampus was observed on the 21st day of the experiment. Even though the studied structures of the hippocampus were not directly traumatized during the performance of the TBI model, they also undergo remodeling according to the revealed patterns. Such remodeling consists in the sequential change of the swelling phase by the phase of cellular reactions followed by the recovery phase. As it was shown in current study, the use of MPTD-01 and BS 34-20 in rats after a moderate traumatic brain injury leads to a time shift and smoothing of the edema phase. The possible explanation of the studied drugs action is excitotoxicity reduction what results in less pronounced secondary alteration. Tested novel hydrogenated benzodiazepines can be considered as promising drugs for neurodegeneration prevention after mild traumatic brain injuries, but their effect on glia activation and other phases of inflammatory cascade both with neuron and gliocyte ultrastructure still need to be investigated.
Nutritional Profiling and Shelf-life Analysis of Developed Bar
Authors: Akansha Goyanar  , Neetu Singh  , Alkananda
Published: October 2024

Developing a nutritionally dense snack presents a significant challenge for consumers seeking healthier options. This study focuses on a specially crafted bar designed to meet the needs of consumers looking for both convenience and nutrition in their snacks. The bar is made up of two main components: carefully selected raw materials and a digestion mix containing concentrated sulfuric acid. Raw materials are sourced from reliable suppliers, and the bar is developed using cutting-edge laboratory facilities. This bar has the potential to be classified as a functional food, offering unique health benefits to consumers. The study's objective is to assess the nutritional composition and shelf life of the bar, ensuring its success in the market. The nutritional analysis of the bar reveals notable differences between Sample 1 and Sample 2. These differences indicate that Sample 1 may be appealing to those seeking a nutrient-rich snack, while Sample 2 may be more suitable for individuals looking for quick sources of energy.
O protagonismo do supremo tribunal federal em matéria tributária
Authors: Antunes Priscila de Fatima Cavalcante Bueno , Jesus Isabela Bonfá de
Published: October 2024

O Poder Judiciário, em especial o Supremo Tribunal Federal, tem desempenhado um papel de protagonismo na sociedade brasileira e suas decisões estão causando demasiada controvérsia, principalmente em matéria tributária. É certo que a disrupção do mundo moderno e a realidade brasileira exigem do juiz um papel ativo, então esse trabalho discorre sobre a atuação do Poder Judiciário e seus limites tendo como premissa as funções estabelecidas pela Constituição Federal, discorrendo ainda sobre algumas premissas do ativismo judicial e o consequencialismo.
Contabilidade: história, ensino e trajetória do curso de Ciências Contábeis na Universidade Estadual de Goiás – UEG
Authors: Barbosa Edna Alves , Oliveira Edvan Soares de
Published: October 2024

O presente trabalho desenvolve um estudo sobre Contabilidade. Para tanto, inicia com um retrospecto histórico de forma a caracterizar a trajetória e o campo de atuação da área. Amplia a discussão historiando atuação das Ciências Contábeis no Brasil e seu desenvolvimento como atividade de ensino e de profissionalização. Apresenta a trajetória da Universidade Estadual de Goiás – UEG, conhecida por sua história e tradição, e tem como objetivo formar estudantes com habilidades de liderança, pensamento crítico e solução de problemas para atuar em um mercado de trabalho inovador e competitivo. Essa discussão fornece elementos para analisar a implantação do curso de Ciências Contábeis na UEG, procurando demarcar sua importância para a cidade, Anápolis – Goiás, local que integra um dinâmico polo da economia regional. A fundamentação do trabalho, de caráter descritivo e qualitativo, deveu-se à consulta bibliográfica em livros, artigos e documentos institucionais, além da documentação do próprio Curso.
Parámetros de la demora de la gratificación en humanos
Authors:   Universidad Nacional Autónoma de México, Ávila Raúl , Ortega Brenda E. ,   Universidad Nacional Autónoma de México, Jardines Dalia K. ,   Universidad Nacional Autónoma de México
Published: October 2024

El procedimiento de demora de la gratifiación consiste en exponer a un sujeto a la elección entre una recompensa pequeña-inmediata y una grande-demorada. Después de la elección de esta última, durante el periodo de demora la recompensa pequeña-inmediata permanece disponible por lo que es posible cambiar la elección inicial (reversión de preferencias). La demostración con humanos consiste en un ensayo de elección único; en contraste, con otras especies se expone a los sujetos durante varias sesiones y ensayos de elección. Esta diferencia de procedimiento puede limitar la comparación de los resultados entre especies. Por lo tanto, se evaluó el efecto de exponer a humanos al procedimiento a más de una sesión y de un ensayo. En el Experimento 1, se expuso a 15 participantes a una prueba de demora de la gratifiación por computadora durante una sesión de 30 ensayos. En el Experimento 2, cinco adultos respondieron la misma prueba durante dos sesiones de 30 ensayos con cinco demoras diferentes. Se encontró que la reversión de preferencias ocurrió durante los primeros ensayos de la sesión y en los primeros 10 s del periodo de demora y este efecto fue parcialmente Modulado por la duración de la demora de la recompensa-grande.
Respuestas y consecuencias compartidas en la elección de contingencias de altruismo parcial
Authors:   Universidad Veracruzana, Pulido Lizbeth , Ribes Emilio ,   Universidad Veracruzana, López Iván ,   Universidad Veracruzana, Fuentes Víctor E. ,   Universidad Veracruzana, Reza Alejandra ,   Universidad Veracruzana
Published: October 2024

Se planeó un estudio para evaluar el efecto de las respuestas y las consecuencias compartidas o no compartidas (en sus cuatro combinaciones posibles) sobre la elección entre contingencias no compartidas o contingencias compartidas de altruismo parcial bajo dos condiciones de intercambio: intercambio de trabajo e intercambio de recursos y trabajo. La situación experimental consistió en la resolución de rompecabezas virtuales en la pantalla de dos computadoras interconectadas. Dieciséis estudiantes universitarios fueron distribuidos al azar en 8 díadas experimentales. En las condiciones experimentales, cada uno de los participantes de la díada pudo observar la ejecución de su compañero y colocar piezas tanto en su rompecabezas como en el de su compañero. Todas las díadas fueron ex-puestas a condiciones experimentales sucesivas de respuestas no compartidas y respuestas comparti-das. Los grupos difirieron entre sí en función de si las consecuencias por responder eran compartidas entre los participantes de cada díada (Grupo 1) o no compartidas (Grupo 2). Los resultados obtenidos muestran que la operación sincrónica de las respuestas y las consecuencias compartidas favorece la elección sistemática de contingencias compartidas de altruismo parcial. Los resultados son discutidos en términos de las relaciones interindividuales relativas al intercambio establecidas.
Phân lập một số hợp chất acid dicaffeoylquinic từ cao ethyl acetat của rễ Sài hồ nam (Pluchea pteropoda Hemsl.)
Authors: Nguyễn Thị Ánh Nguyệt , Thi Đại Thạnh , Nguyễn Tấn Phát , Lê Minh Tài , Lữ Thị Kim Chi
Published: October 2024

Đặt vấn đề: Sài hồ nam còn gọi là Lức (Pluchea pteropoda Hemsl., Asteraceae) là cây thuốc được dùng để chữa nóng sốt, cảm cúm, nhức đầu. Sài hồ nam thường mọc tự nhiên và phát triển ở môi trường sống ven biển. Các nghiên cứu hóa học của rễ Sài hồ nam chưa có nhiều hiện nay. Các tiêu chuẩn chất lượng của lá và rễ Sài hồ nam của Dược điển Việt Nam V còn khá đơn giản; chưa có phương pháp định tính và định lượng thành phần chính.

Mục tiêu: Mục tiêu của đề tài là chiết xuất, phân lập các thành phần chính của rễ Sài hồ nam nhằm phục vụ cho việc tiêu chuẩn hóa dược liệu.

Đối tượng - Phương pháp nghiên cứu: Từ 5 kg bột rễ Sài hồ nam được chiết ngấm kiệt với ethanol 96% và dịch chiết đậm đặc sau đó được lắc phân bố với dicloromethan, ethyl acetat và n-butanol. Phân lập các chất được thực hiện trên cao ethyl acetat (26,14 g) bằng các kĩ thuật sắc ký cột (VLC, Sephadex và sắc ký lỏng bán điều chế). Cấu trúc hóa học của các chất phân lập được xác định bằng phổ HRMS và NMR kết hợp so sánh với tài liệu tham khảo.

Kết quả: Ba hợp chất đã phân lập và xác định gồm (1) acid 3,4-dicaffeoylquinic (161,47 mg); (2) acid 3,5-dicaffeoylquinic (18,93 mg) và (3) acid 4,5-dicaffeoylquinic (87,86 mg).

Kết luận: Cả 3 hợp chất này lần đầu được phân lập ở loài Pluchea pteropoda.

Từ khoá: acid dicaffeoylquinic, Pluchea pteropoda, Sài hồ nam, Lức
Survival analysis of wildlife cameras exposed to theft
Authors: cardona laura , Brook Barry , Aandahl Zach , Buettel Jessie
Published: October 2024

Setting camera traps along roads is often necessary for ecological research, yet these locations expose cameras to theft leading to substantial data losses. Measures to minimise this risk include placing cameras away from human settlements. However, the effects of this and other measures on camera-trap theft risk are yet to be quantified. Here, we assessed the impact of gates on roads, the frequency of vehicle and human foot traffic, distance to the nearest town, and reduced visibility, on the risk of camera-trap theft, using a four-year, geographically extensive camera-trapping study in Tasmania, Australia. The large dataset covered 564 camera sites operating for 316,372 days (average of 561 camera days per unit), with 112 cumulative thefts. We used Bayesian survival modelling to determine the factors that best explained theft risk. Our results showed a high initial vulnerability to theft that gradually reduced over time, with significant predictors of reduced theft risk being: (i) road sites with lower frequencies of vehicle traffic, (ii) greater distance from the nearest town, (iii) where movement was curtailed by the presence of a gate, and (iv) a temporal trend that likely reflects a selective culling of ‘high exposure’ sites and increased efforts to hide camera units. The frequency of human foot traffic surprisingly did not significantly elevate theft risk. Our study provides important insights into the factors contributing to a higher risk of camera-trap theft on roads and offers a robust analytical framework to identify these factors for application in diverse social and ecological contexts.
Performance of TVWS-based LoRa Transmissions using Multi-Armed Bandit
Authors: R. Askhedkar Anjali , S. Chaudhari Bharat , A. Saeed Rashid , Alhumyani Hesham , Alenizi Abdullah
Published: October 2024

Low Power Wide Area Networks (LPWANs) support long-range communication that suits them for varied IoT applications such as asset tracking, environmental monitoring, agricultural management, work site monitoring, fleet management, and smart cities. Even with the large number of connected devices, LPWANs are more power efficient than traditional IoT networks. Long Range (LoRa) is a noteworthy LPWAN technology that employs the ISM band, which experiences congestion due to the growing IoT and smart network applications. LoRa networks can utilize the available TV white spaces to overcome the congestion problem. Performance enhancement of the LoRa network in terms of energy efficiency is a significant concern. This paper proposes, for the first time, Multi-Armed Bandit (MAB) to improve the performance of the LoRa network using TVWS. We have developed a novel algorithm, LoRaT-MAB, for TVWS-based LoRa, and results show that the mean rewards increase by about 12.5% over conventional LoRa and the energy consumption for TVWS-based LoRa using LoRaT-MAB decreases by about 11.7% over conventional LoRa. The findings are encouraging and provide a basis for further research on the TVWS-based LoRa and other IoT applications.
Comparative Study of Epithelial Tumor Development in General Hospitals in Iraq
Authors: Ali Thaer Kassim , AL-Kafaji Raid Razak Ali
Published: October 2024

Epithelial tumor is the most common cancer in Iraq.  The disease is frequently diagnosed in individuals who are more than 55 years old, both females and males.  Epithelial tumors are composed of different subtypes of surface epithelial tumors. The tumor is called after the component that predominates; however, the tissue has to establish more than 10% of the whole tumor. To evaluate the clinical and pathological parameters of epithelial tumor among Iraqi patients from 2010 to 2020. This retrospective study analyzed the clinical and pathological parameters of (1330) patients who were diagnosed with epithelial tumors from 2010 to 2020 in Iraq. The studied parameters comprised demographic factors like gender, age, tumor sites, and governorates. The Iraqi patients were highly significant at 55 years of age and showed a greater tendency to present at a later stage.  The most common epithelial tumors in the current study was squamous cell papilloma in the palate site, which showed the highest rate of occurrence that was more widespread among males for age among patients more than 55 years old. The significant differences in the clinical and tumor features of epithelial tumor among the Iraqi patients propose heterogeneity in the primary biology of the tumor, which is increased in Iraq by the problem of delayed diagnosis. The significant ethnic differences in epithelial tumor features recommend the rapid support of the national cancer control plan in Iraq as a main method for the administration of the disease.
A Educação Musical na Igreja Católica: uma investigação no Convento Franciscano São Boaventura, em Imigrante/RS
Authors: Giongo Leonardo , Rolim Wolffenbüttel Cristina
Published: October 2024

A presente pesquisa objetivou compreender como se estabeleceu no Convento Franciscano São Boaventura um ambiente de ensino e aprendizagem de música. Para tanto, a construção metodológica desta investigação possuiu caráter qualitativo em sua abordagem e a pesquisa documental como método. Como técnica para coleta dos dados, utilizou-se a pesquisa via internet, bem como a análise de conteúdo como técnica para a análise dos dados. O referencial teórico valeu-se de três conceitos: o primeiro deles, relativo às funções sociais da música na terceira idade da música; o segundo dispôs sobre o que significa formar-se musicalmente na Igreja Católica, enquanto o terceiro discorreu sobre o significado de ser formador musical na Igreja Católica. Assim, pôde-se concluir que a organização institucional da Igreja Católica, em que os espaços de formação, de estudo e de aprofundamento deveriam contemplar a preparação musical; a presença das dez funções sociais da música, por meio de diferentes manifestações; o intenso papel formador desempenhado pelo Professor Frei Franciscano Emílio Scheid; e um ensino de música direcionado não somente aos noviços e religiosos estabelecidos no Convento, mas também aos estudantes externos, são fatores que, entrelaçados, estabeleceram no Convento Franciscano São Boaventura um ambiente de ensino e aprendizagem de música, constituindo um fenômeno único que neste trabalho foi investigado.