bluuebunny/crossref_metadata_2025 huggingface dataset:
DiverTEAM: An Effective Evolutionary Algorithm for Diversified Top-k (Weight) Clique Search Problems
Authors: Xue Jinghui , Zheng Jiongzhi , He Kun , Li Chu-Min , Liu Yanli
Published: October 2024

In many real-world problems and applications, finding only a single element, even though the best, among all possible candidates, cannot fully meet the requirements. We may wish to have a collection where each individual is not only outstanding but also distinctive. Diversified Top-k (DTk) problems are a kind of combinatorial optimization problem for finding such a promising collection of multiple sub-structures, such as subgraphs like cliques and social communities. In this paper, we address two representative DTk problems, DTk Clique search (DTkC) and DTk Weight Clique search (DTkWC), and propose a novel and effective algorithm called Diversified Top-k Evolutionary AlgorithM (DiverTEAM) for the two problems. DiverTEAM consists of a local search algorithm, which focuses on generating high-quality and diverse individuals and sub-structures, and a genetic algorithm that makes individuals work as a team and converge to (near-)optima efficiently. Extensive experiments show the excellent and robust performance of DiverTEAM across various benchmarks of DTkC and DTkWC.
A Faster Branching Algorithm for the Maximum k-Defective Clique Problem
Authors: Luo Chunyu , Zhou Yi , Wang Zhengren , Xiao Mingyu
Published: October 2024

A k-defective clique of an undirected graph G is a subset of its vertices that induces a nearly complete graph with a maximum of k missing edges. The maximum k-defective clique problem, which asks for the largest k-defective clique from the given graph, is important in many applications, such as social and biological network analysis. In the paper, we propose a new branching algorithm that takes advantage of the structural properties of the k-defective clique and uses the efficient maximum clique algorithm as a subroutine. As a result, the algorithm has a better asymptotic running time than the existing ones. We also investigate upper-bounding techniques and propose a new upper bound utilizing the conflict relationship between vertex pairs. Because the conflict relationship is common in many graph problems, we believe that this technique can be potentially generalized. Finally, experiments show that our algorithm outperforms state-of-the-art solvers on a wide range of open benchmarks. Our source code, as well as the experiment data, is open source and available https://github.com/cy-Luo000/Maximum-k-Defective-Clique.git.
Canonical Decision Diagrams Modulo Theories
Authors: Michelutti Massimo , Masina Gabriele , Spallitta Giuseppe , Sebastiani Roberto
Published: October 2024

Decision diagrams (DDs) are powerful tools to represent effectively propositional formulas, which are largely used in many domains, in particular in formal verification and in knowledge compilation. Some forms of DDs (e.g., OBDDs, SDDs) are canonical, that is, (under given conditions on the atom list) they univocally represent equivalence classes of formulas. Given the limited expressiveness of propositional logic, a few attempts to leverage DDs to SMT level have been presented in the literature. Unfortunately, these techniques still suffer from some limitations: most procedures are theory-specific; some produce theory DDs (T-DDs) which do not univocally represent T-valid formulas or T-inconsistent formulas; none of these techniques provably produces theory-canonical T-DDs, which (under given conditions on the T-atom list) univocally represent T-equivalence classes of formulas. Also, these procedures are not easy to implement, and very few implementations are actually available. In this paper, we present a novel very-general technique to leverage DDs to SMT level, which has several advantages: it is very easy to implement on top of an AllSMT solver and a DD package, which are used as black boxes; it works for every form of DDs and every theory, or combination thereof, supported by the AllSMT solver; it produces theory-canonical T-DDs if the propositional DD is canonical. We have implemented a prototype tool for both T-OBDDs and T-SDDs on top of OBDD and SDD packages and the MathSAT SMT solver. Some preliminary empirical evaluation supports the effectiveness of the approach.
Less is More: Efficient Brain-Inspired Learning for Autonomous Driving Trajectory Prediction
Authors: Liao Haicheng , Li Yongkang , Li Zhenning , Wang Chengyue , Li Guofa , Tian Chunlin , Bian Zilin , Zhu Kaiqun , Cui Zhiyong , Hu Jia
Published: October 2024

Accurately and safely predicting the trajectories of surrounding vehicles is essential for fully realizing autonomous driving (AD). This paper presents the Human-Like Trajectory Prediction model (HLTP++), which emulates human cognitive processes to improve trajectory prediction in AD. HLTP++ incorporates a novel teacher-student knowledge distillation framework. The “teacher” model, equipped with an adaptive visual sector, mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. On the other hand, the “student” model focuses on real-time interaction and human decision-making, drawing parallels to the human memory storage mechanism. Furthermore, we improve the model’s efficiency by introducing a new Fourier Adaptive Spike Neural Network (FA-SNN), allowing for faster and more precise predictions with fewer parameters. Evaluated using the NGSIM, HighD, and MoCAD benchmarks, HLTP++ demonstrates superior performance compared to existing models, which reduces the predicted trajectory error with over 11% on the NGSIM dataset and 25% on the HighD datasets. Moreover, HLTP++ demonstrates strong adaptability in challenging environments with incomplete input data. This marks a significant stride in the journey towards fully AD systems.
A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements
Authors: Tosello Elisa , Valentini Alessandro , Micheli Andrea
Published: October 2024

Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community as it significantly enhances robot’s autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner’s search space to prune the task planner’s exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements.
Learning Uncertainty Tubes via Recurrent Neural Networks for Planning Robust Robot Motions
Authors: Wasiela Simon , Ait Bouhsain Smail , Cognetti Marco , Cortés Juan , Siméon Thierry
Published: October 2024

Taking into account the effects of parameter uncertainties in the robot model is crucial to the robustness of motion generation. One approach to address this issue is to compute ‘uncertainty tubes’ enveloping the robot state for any combination of parameters within a given range, and to use these tubes to robustly check for collisions within a motion planning algorithm. However, computing these tubes for complex dynamical systems can be too computationally expensive due to the need to solve and integrate potentially numerous nonlinear ordinary differential equations (ODEs) associated with robot dynamics. To overcome this limitation, we propose a GRU-based architecture that provides fast and accurate estimation of these uncertainty tubes. We demonstrate that GRUs achieve the best compromise between prediction accuracy, prediction time, and network size compared to basic RNNs and LSTMs, justifying our choice. Finally, we showcase the efficiency of the learning process within a motion planning framework for an aerial vehicle.
Planning for Human-Robot Collaboration Scenarios with Heterogeneous Costs and Durations
Authors: Izquierdo-Badiola Silvia , Canal Gerard , Alenyà Guillem , Rizzo Carlos , Coles Andrew
Published: October 2024

This paper looks at human-robot collaboration (HRC) scenarios, in particular where the durations and costs of the actions are heterogeneous between agents, reflecting the agents’ capabilities as well as environmental constraints. We explore the use of temporal PDDL planning as a means of finding over-arching task plans for such collaborative scenarios, and apply suitable heuristics and search algorithms to improve the extent to which plans can be found that are sensitive to combined duration and cost metrics. An evaluation in a kitchen scenario shows our approach is effective, finding cost-effective task plans compared to those from existing planners, and a hand-crafted baseline.
InsideOut: Unifying Emotional LLMs to Foster Empathy
Authors: Mozikov Mikhail , Severin Nikita , Glushanina Maria , Baklashkin Mikhail , Savchenko Andrey , Makarov Ilya
Published: October 2024

This paper introduces InsideOut, an original innovative framework that augments the emotional intelligence of Large Language Models (LLMs). Motivated by the cartoon, InsideOut is designed around a net of specialized agents, each dedicated to one of Ekman’s fundamental emotions. These agents collaboratively refine responses sensitive to the emotional context of interactions. Our assessments, conducted using EmpatheticDialogues and involving models like GPT-4 and GigaChat, indicate substantial improvements in identifying human emotions and generating empathetic responses. These improvements are most evident in situations with apparent valence-arousal differences. InsideOut offers a promising avenue for evolving AI into more perceptive and human-centric communicators.
UCCIX: Irish-eXcellence Large Language Model
Authors: Tran Khanh-Tung , O’Sullivan Barry , Nguyen Hoang D.
Published: October 2024

The development of Large Language Models (LLMs) has predominantly focused on high-resource languages, leaving extremely low-resource languages like Irish with limited representation. This work presents UCCIX, a pioneering effort on the development of an open-source Irish-based LLM. We propose a novel framework for continued pre-training of LLMs specifically adapted for extremely low-resource languages, requiring only a fraction of the textual data typically needed for training LLMs according to scaling laws. Our model, based on Llama 2-13B [23], outperforms much larger models on Irish language tasks with up to 12% performance improvement, showcasing the effectiveness and efficiency of our approach. We also contribute comprehensive Irish benchmarking datasets, including IrishQA, a question-answering dataset, and Irish version of MT-bench [28]. These datasets enable rigorous evaluation and facilitate future research in Irish LLM systems. Our work aims to preserve and promote the Irish language, knowledge, and culture of Ireland in the digital era while providing a framework for adapting LLMs to other indigenous languages.
Entity Matching Across Small Networks Using Node Attributes
Authors: Ahmadi Zahra , Zhang Zijian , Nguyen Hoang H. , Burdisso Sergio , Madikeri Srikanth , Motlicek Petr , Dikici Erinc , Backfried Gerhard , Kovac Marek , Maly Květoslav , Kudenko Daniel
Published: October 2024

Entity matching, also known as user identity linkage, is a critical task in data integration. While established techniques primarily focus on large-scale networks, there are several applications where small networks pose challenges due to limited training data and sparsity. This study addresses entity matching in the field of criminology, where small networks are common and the number of known matching nodes is restricted. To support this research, we exploit a multimodal dataset, collected as part of a security-related project, consisting of an intercepted telephone calls network (i.e., ROXSD data) and a network of social forum interactions (i.e., ROXHOOD data) collected in a simulated environment, although following real investigation scenario. To improve accuracy and efficiency, we propose a novel approach for entity matching across these two small networks using node attributes. Existing techniques often merely focus on topology consistency between two networks and overlook valuable information, such as network node attributes, making them vulnerable to structural changes. Inspired by the remarkable success of deep learning, we present UGC-DeepLink, an end-to-end semi-supervised learning framework that leverages user-generated content. UGC-DeepLink encodes network nodes into vector representations, capturing both local and global network structures to align anchor nodes using deep neural networks. A dual learning paradigm and the policy gradient method transfer knowledge and update the linkage. Additionally, node attributes, such as call contents and forum exchanged texts, enhance the ranking of matching nodes. Experimental results on ROXSD and ROXHOOD demonstrate that UGC-DeepLink surpasses baselines and state-of-the-art methods in terms of identity-match ranking. The code and dataset are available at https://github.com/erichoang/UGC-DeepLink.
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Authors: Liu Zhiwei , Liu Boyang , Thompson Paul , Yang Kailai , Ananiadou Sophia
Published: October 2024

The internet has brought both benefits and harms to society. A prime example of the latter is misinformation, including conspiracy theories, which flood the web. Recent advances in natural language processing, particularly the emergence of large language models (LLMs), have improved the prospects of accurate misinformation detection. However, most LLM-based approaches to conspiracy theory detection focus only on binary classification and fail to account for the important relationship between misinformation and affective features (i.e., sentiment and emotions). Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories. These tasks include not only conspiracy theory detection, but also classification of theory type and detection of related discussion (e.g., opinions towards theories). ConspEmoLLM is fine-tuned based on an emotion-oriented LLM using our novel ConDID dataset, which includes five tasks to support LLM instruction tuning and evaluation. We demonstrate that when applied to these tasks, ConspEmoLLM largely outperforms several open-source general domain LLMs and ChatGPT, as well as an LLM that has been fine-tuned using ConDID, but which does not use affective features. ConspEmoLLM can be easily applied to identify and classify conspiracy-related text in the real world. The work has been released at https://github.com/lzw108/ConspEmoLLM/.
Enhancing Manufacturing Knowledge Access with LLMs and Context-Aware Prompting
Authors: Monka Sebastian , Grangel-González Irlan , Schmid Stefan , Halilaj Lavdim , Rickart Marc , Rudolph Oliver , Dias Rui
Published: October 2024

Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into the SPARQL format, thus bridging the gap between user-friendly interfaces and the sophisticated architecture of KGs. The challenge remains in adequately informing LLMs about the relevant context and structure of domain-specific KGs, e.g., in manufacturing, to improve the accuracy of generated queries. In this paper, we evaluate multiple strategies that use LLMs as mediators to facilitate information retrieval from KGs. We focus on the manufacturing domain, particularly on the Bosch Line Information System KG and the I40 Core Information Model. In our evaluation, we compare various approaches for feeding relevant context from the KG to the LLM and analyze their proficiency in transforming real-world questions into SPARQL queries. Our findings show that LLMs can significantly improve their performance on generating correct and complete queries when provided only the adequate context of the KG schema. Such context-aware prompting techniques help LLMs to focus on the relevant parts of the ontology and reduce the risk of hallucination. We anticipate that the proposed techniques help LLMs to democratize access to complex data repositories and empower informed decision-making in manufacturing settings.
Estimating the Time of Arrival of a Shipment with Machine Learning
Authors: Gibellini Francesco , van Gool Bart , Firat Murat , Bromuri Stefano
Published: October 2024

Effectively estimating the arrival day of a shipment is an important capability for an express courier company to ensure both customer satisfaction and internal operations efficiency. This paper studies predicting the estimated time of arrival (ETA) of a package, shortly denoted by PETAP, under a collaboration with an industrial partner. Our approach employs machine learning (ML) techniques: CatBoost, multi-layer perceptrons (MLPs) with categorical embeddings, and Transformer neural networks, to predict delivery dates based on shipment locations and status. Challenges such as complex inter-modal networks and high-cardinality categorical features are addressed. Our paper contributes to the literature by formalizing the PETAP problem in the context of express shipping: the proposed models outperform the current business baseline accuracy by more than 25%. In our experimentation with a dataset including millions of data-points we propose a tabular vs sequence to sequence approach observing the superiority of the former. Future research directions include explicit modeling of transportation networks and exploring alternative ML approaches for improved prediction accuracy.
Federated Online Learning for Heavy Hitter Detection
Authors: Silva Paula , Vinagre João , Gama João
Published: October 2024

Effective anomaly detection in telecommunication networks is essential for securing digital transactions and supporting the sustainability of our global information ecosystem. However, the volume of data in such high-speed distributed environments imposes strict latency and scalability requirements on anomaly detection systems. This study focuses on distributed heavy hitter detection in telephone networks – a critical component of network traffic analysis and fraud detection. We propose a federated version of the Lossy Counting algorithm and compare it to its centralized version. Our experimental results reveal that the federated approach can detect considerably more unique heavy hitters than the centralized method while enhancing privacy. Furthermore, Federated Lossy Counting does not need a large amount of centralized processing power since it can leverage the networked infrastructure with minimal impact on bandwidth and computing power.
Frisbees and Dogs: Domain Adaptation for Object Detection with Limited Labels in Rugby Data
Authors: Connors Will , Rushe Ellen , Ventresque Anthony
Published: October 2024

Object detection often struggles when applied to low-resource, domain-specific datasets. This challenge is exacerbated when dealing with sports-related data such as rugby, where fast-paced gameplay and tackles result in frequent instances of motion blur and occlusion, representing a substantial domain-shift from widely available pre-trained models. Given the high cost of manual labelling, we seek to determine whether we can minimise the number examples needed for fine-tuning by identifying implausible label classifications made by pre-trained object detection models. We do this using a coarse-grained labelling approach in the absence of detailed ground truth bounding boxes, allowing us to determine whether a label is implausible within the context of a rugby pitch. This is done to maximize the information provided by each example used for fine-tuning with the goal of minimizing the number of examples needed. Our results show that using pool-based, single-step uncertainty sampling to select examples from a subset of frames with implausible labels improves the model performance. More specifically, we show that fine-tuning on frames with the lowest confidence scores first can lead to greater performance after roughly 30 examples.
Editorial
Authors: Editorial office
Published: October 2024

Dear Readers,we apologize that you did not receive the tfi numbers of our last year International relations magazine. This situation occurred because the completely new leadership of the Institute of International Affairs relations, which today is headed by an important world-renowned political scientist, professor dr. ing. Jifí Valenta (member of the prestigious organization Council on Foreign Relations, for many years also worked for the US Department of the Navy and today works as the director of the Institute for the Study of the Soviet Union and Eastern Europe and a professor of political science at the University of Miami's School of International Affairs, adviser Minister of Foreign Affairs CSFR), decided not to follow up onprevious editions of this magazine.
Reporte de vigilancia tecnológica
Authors:   Centro de Recursos para el Aprendizaje e Investigación  (CRAI)
Published: October 2024

La innovación educativa en el contexto universitario corresponde a toda iniciativa o proyecto intencionados que busca cambiar, mejorar o resolver algún aspecto de los procesos educativos que ofrece la institución en relación con la experiencia de aprendizaje de su estudiantado y que, por lo tanto, se transforma en un proceso de gestión institucional. Estos aspectos comprenden no solo a factores sobre las estrategias de enseñanza, si no que aborda dimensiones más amplias como la implementación de entornos para el aprendizaje y las mismas condiciones internas que se generan para incentivar la innovación en la propia institución. En este sentido, se pude innovar educativamente en todo aquello que impacta en el aprendizaje del estudiantado. En este primer reporte de vigilancia tecnológica, prácticas y recursos para la innovación docente, se presenta la entrevista realizada al Dr. Andrew Pitchford, PhD en Sociología Educacional de la University of Gloucestershire, Reino Unido. Actualmente es el director del Centro de Innovación Educativa y Pedagógica en la University of Westminster, quien presenta su experiencia en innovación educativa y las estrategias que han implementado para lograr un aprendizaje significativo, prácticas de investigación comprometida con la vida, entre otros temas.
Enhancing Reliability in Distributed Systems: A Comprehensive Approach to Telemetry and Monitoring
Authors: Liyakathali Patan
Published: October 2024

This comprehensive article explores the implementation of telemetry and monitoring in distributed systems, addressing the challenges and opportunities in managing complex, scalable architectures. The article begins by examining the fundamental concepts of telemetry, including data collection methodologies, transmission protocols, and analysis techniques. It then provides an in-depth comparison of popular telemetry tools such as AWS CloudWatch and Prometheus, discussing their features, capabilities, and integration challenges. The article delves into effective monitoring strategies, emphasizing the importance of key performance indicators (KPIs) in distributed environments and the implementation of comprehensive solutions including real-time dashboards, proactive alert systems, and automated remediation techniques. Furthermore, the study investigates emerging trends in AI-driven monitoring and predictive analytics, highlighting their potential to revolutionize system observability. The article also addresses critical challenges facing the field, including scalability concerns in large-scale systems, privacy, and security considerations in telemetry data collection, and the need for balance between monitoring overhead and system performance. By synthesizing current practices with future directions, this article provides valuable insights for practitioners and researchers alike, contributing to the ongoing evolution of telemetry and monitoring practices in the rapidly advancing landscape of distributed computing.
Enhancing Agro Packaging Textiles : Innovations and Functional Formulations for Improved Agricultural Productivity and Sustainability
Authors: Sharan Shetty
Published: October 2024

One third of the Agro production is lost due to improper handling, storage and microbial contamination as well as environmental effects. The Agricultural sector has crucial contribution to the Nation’s economy so it is imperative to take proactive steps to minimize the losses and functional formulations in finishing or as additives can prove as an active game changer in the Agro Packaging Industry. By using essential oil and functional formulations consisting of Anti-microbial, UV repellent and Anti-rodent properties would be the most effective for the Agro-packaging because of being susceptible to such environment.
Microsphere as a Novel Drug Delivery System : A Review
Authors: Poonam Ankush Jadhav  , Komal Hiraman Hake  , Kalyani Bhagwat Jadhav  , Sheetal Rajesh Ghorad
Published: October 2024

Microspheres play a very important role as particulate drug delivery system because of their small size and other efficient properties. Microspheres have been proved to be a suitable bridge to scale the distance over to formulate an effective dosage form, to simulate controlled drug release. Microspheres are characteristically free flowing solid powders, which consist of proteins or synthetic polymer, which are biodegradable in nature[1]. Microsphere size ranges Between 1 to 1000 um . It can be administered By oral, Parentral ,Transdermal, ophthalmic, nasal ,colonal routes etc. Microspheres received much attention not only for prolonged release, but also for targeting of anticancer drugs. In future by combining various other strategies, microspheres will find the central place in novel drug delivery, particularly in diseased cell sorting, diagnostics, gene & genetic materials, safe, targeted and effective in vivo delivery and supplements as miniature versions of diseased organ and tissues in the body[2]
ЗАХИСТ ДОВКІЛЛЯ В КОНТЕКСТІ ПРОТИДІЇ ЗБРОЙНІЙ АГРСІЇ РОСІЇ: КРИМІНОЛОГІЧНИЙ ВИМІР
Authors: ШЕВЧУК Т.А.
Published: October 2024

Повномасштабне вторгнення рф являє собою суттєву загрозу для екологічної безпеки України, як елементу національної безпеки у внутрішньодержавному та міжнародному її вимірах. Сучасна екологічна ситуація в Україні фактично є кризовою саме через активні воєнні дії та пов’язаний із цим негативний, згубний вплив на природнє середовище. Внаслідок збройної агресії рф шкода завдається землі, ґрунтам, атмосферному повітрю, земельному фонду України, водними ресурсам, надрам, лісовому фонду, об’єктам природно заповідного фонду, через що довкілля можна вважати жертвою збройного конфлікту. На підставі результатів аналізу наукової літератури, судової практики, положень нормативно-правових актів у сфері охорони довкілля обґрунтовано необхідність: адаптації кримінального законодавства України до загроз безпековому стану довкілля в контексті повномасштабної воєнної агресії росії проти України; реалізація заходів із забезпечення захисту та відновлення порушених екологічних прав та інтересів населення в Україні в умовах війни; забезпечення фіксації фактів вчинення кримінальних правопорушень проти довкілля в результаті російської агресії на тимчасово окупованих територіях України; оцінки негативного впливу на довкілля в результаті повномасштабного російського вторгнення та прогнозування наслідків війни в середньо та довгостроковій перспективі; забезпечення правильної фіксації злочинів проти довкілля у відповідності до вимог міжнародних судових інституцій з метою встановлення справедливості та притягнення до відповідальності за екологічну шкоду, спричинену воєнними діями, та забезпечення включення збитків довкіллю до міжнародного реєстру збитків.
ПАРАДИГМА ФІНАНСОВИХ РОЗСЛІДУВАНЬ ПІД ЧАС ДОСУДОВОГО РОЗСЛІДУВАННЯ ТЕРОРИСТИЧНИХ КРИМІНАЛЬНИХ ПРАВОПОРУШЕНЬ
Authors: СОКОЛОВСЬКИЙ М.В.
Published: October 2024

Сучасна практика протидії транскордонній терористичній діяльності неможлива без активного використання знань у фінансовій сфері. Алгоритмізація процедури проведення фінансових розслідувань з метою виявлення терористичних кримінальних правопорушень є усталеною та ефективною компетенцією, що забезпечує своєчасне викриття та припинення цього різновиду злочинів, а також дозволяє запобігти їхвчиненню. Проте на сьогодні цей функціонал фактично знаходиться поза межами правового регулювання, оскільки не регламентований вітчизняним законодавством і належним чином не унормований на рівні відомчих (підзаконних) правових актів. В статті досліджуються механізми проведення фінансових розслідувань під час документування терористичних кримінальних правопорушень. Розкрита сутність фінансових розслідувань, у тому числі паралельних; узагальнені та систематизовані форми проведення фінансових розслідувань. На підставі аналізу фінансових документів різних терористичних організацій та звітів FATF виділені основні напрями використання фінансових ресурсів для терористичної діяльності. Визначена структура та повноваження державних відомств, які сприяють правоохоронним органам у здійсненні фінансових розслідувань; досліджена система міжнародних організацій, діяльність яких спрямована на забезпечення виявлення фінансових порушень. У роботі наведено зарубіжний досвід проведення спеціалізованих розслідувань задля можливості його застосування у вітчизняній практиці. Виділені та обґрунтовані переваги проведення фінансових розслідувань під час досудового розслідування терористичних кримінальних правопорушень. Зроблено акцент на недосконалості механізму фінансових розслідувань, оскільки поза увагою законодавця залишились, зокрема, питання щодо визначення поняття «фінансове розслідування», а також кримінальних правопорушень, в ході розслідування яких потрібне обов’язкове застосування методів проведення фінансових розслідувань, у тому числі паралельних. Автором запропоновані відповідні зміни до тематичного законодавства України, що мають на меті усунути наявну правову невизначеність. Це, у свою чергу, дозволить правоохоронній системі України успішно виконати вимоги FATF та інтегруватись у правове середовище розвинутих країн світу.
БОЛГАРИСТИКА ЯК МІЖДИСЦИПЛІНАРНА ПЛАТФОРМА ДОСЛІДЖЕНЬ ПРОБЛЕМ ПРОТИДІЇ ЗЛОЧИННОСТІ: КОГНІТИВНИЙ ПОНДЕМОНІУМ
Authors: КРІЦАК І.В.
Published: October 2024

Стаття презентує результати опрацювання кількох сотень наукових та науково-популярних публікацій різноманітної тематичної спрямованості з коренем слова «болгар» у назві, що розміщені у бібліотеці ім. В. І. Вернадського. Зібраний унікальний науково-фактичний матеріал, а також визначені напрями й рівні співробітництва болгарського та українського народів, які у скрутний час завжди допомагають один одному впродовж більш ніж тисячолітньої давності. Акцентована увага на окремих наукових статтях українських авторів, які пропонується детально опрацювати для збагачення своїх знань з болгаристики. Методологічні, гносеологічні можливості останньої, як міждисциплінарної пізнавальної платформи, формують перспективи дослідження проблем злочинності з позицій колективного безсвідомого, втіленого у категоріях етнічності, ментальності, духовно-культурних станів народів, націй. Лише таким чином можна побачити весь детермінаційний комплекс зла/злочинності та відшукати шляхи виходу з багатьох криміногенних станів у спрямованості добра. Ця стаття – лише невелика, проте щира спроба, намагання пізнати болгаристику, представити науковій спільноті цей конструкт, описати його змістовні компоненти.
ВПЛИВ ІНДУСТРІЇ 5.0 НА СТАЛИЙ РОЗВИТОК ЕКОНОМІКИ
Authors: Нечипорук Л.В. , Кочергіна О.
Published: October 2024

Визначено, що Індустрія 5.0 характеризується інтеграцією передових технологій, таких як штучний інтелект, робототехніка, 3D-друк та інтернет речей, має значний потенціал для зменшення екологічного впливу, оптимізації використання ресурсів і покращення соціально-економічних показників. Зазначено, що завдяки впровадженню циркулярної економіки та інноваційних виробничих процесів підприємства можуть знижувати витрати та підвищувати свою конкурентоспроможність на глобальному ринку. Окрім того, Індустрія 5.0 сприяє створенню нових робочих місць, вимагаючи при цьому нових навичок і компетенцій від працівників. Встановлено основні виклики, такі як регуляторні питання та необхідність підготовки кадрів. Визнано, що наукова спільнота відіграє важливу роль у впровадженні Індустрії 5.0, оскільки її дослідження можуть сприяти розвитку нових технологій і моделей управління. Уточнено, що співпраця між науковими установами та бізнесом повинна бути спрямована на вирішення практичних завдань, що виникають у процесі інтеграції інновацій. Спільна робота урядів, бізнесу та наукової спільноти дозволить забезпечити сталість і інноваційність в економіці, сприяючи не лише економічному зростанню, а й соціальному добробуту. Цей інтегрований підхід стане запорукою успішної адаптації до нових викликів та можливостей, які відкриває Індустрія 5.0.
Efeito de três tipos de conteúdos de feedback no desempenho em tarefa
Authors:   Universidade Positivo, de Moraes Rebeca Bueno , Gusso Helder Lima ,   Universidade Federal de Santa Catarina
Published: October 2024

O uso de feedbacks é um recurso popular nas organizações, embora não sejam claros os efeitos das variáveis que os constituem. O objetivo desta pesquisa foi avaliar o efeito de diferentes conteúdos do feedback no desempenho em tarefas. Foi realizado um experimento utilizando delineamento expe-rimental de linha de base múltipla entre sujeitos com 12 universitários. Foi utilizada uma atividade de reconhecimento de emoções em expressões faciais em um software de escolha de acordo com o modelo. Os conteúdos de feedbacks “Percentual de acertos”, “Comparação de percentual de acertos com tentativas anteriores” e “Pontuação” foram apresentados em gráficos e em mensagem textual. Foram analisadas as variações de desempenho entre grupos, intra-sujeitos e de seus relatos verbais. Os resultados indicam aumento da média de desempenho em todos os participantes após a inserção de qualquer um dos tipos de feedback. O feedback do tipo “Percentual de acertos” foi o que produziu maior variação no desempenho. O feedback “Pontuação” produziu maior instabilidade no desem-penho dos participantes entre as rodadas. Desse modo, observou-se que os resultados fortalecem a relevância do uso de feedbacks para a promoção de desempenho em tarefas, mas não evidenciam diferenças claras no desempenho a partir de diferentes conteúdos de feedback.
Urban bumblebees diversify their foraging strategy to maintain nutrient intake
Authors: Selva Simonetta , Moretti Marco , Ruedenauer Fabian , Keller Alexander , Fournier Bertrand , Leonhardt Sara , Eggenberger Helen , Casanelles Abella Joan
Published: October 2024

Anthropogenic ecosystems can alter individual functions and ecological processes such as resource use and species interactions. While variability on morphological traits involved in diet and resource use has been observed between urban and non-urban populations of pollinators, the consequences on the dietary and pollen transportation patterns remain poorly understood. Here, we investigate the variability in the diet breadth of rural and urban individuals of two bumblebee species and the consequences for nutrient intake and pollen transportation. We show that urban bumblebees exhibit a broader diet breadth than their rural counterparts, driven by the enhanced floral diversity in cities. However, we found that the nutrient intake remained similar across urban and rural ecosystems. Finally, we found distinct pollen transportation between urban and rural individuals. Our findings highlight the importance of considering complementary facets of species’ diet and interactions when assessing the effects of anthropogenic ecosystems.
Miniaturized Ultra-Wide Band (UWB) Delta-Stub Based 4-Way Power Divider with Enhanced Isolation For Internet of Things
(IoT) and Next-Generation Wireless Systems
Authors: Karimbu Vallappil Arshad , A. Khawaja Bilal , M Alenezi Abdulmajeed
Published: October 2024

This paper proposes a novel wideband 1-to-4-way Wilkinson power divider (WPD) designed for operation in the ultra- wideband (UWB) frequency band. The proposed power divider operates in the frequency range of 3-8GHz. The design process started with conventional WPD design and evolved based on three stages of a 1-to-2 way power divider, incorporating 100Ω resistors and two delta-stubs for each stage. Delta-stubs are introduced in the WPD design for enhanced operational bandwidth and impedance- matching characteristics. The proposed power divider is designed using an FR4 substrate with a thickness of 0.76mm and εr of 4.3, respectively. The proposed design exhibits equal power split at all ports, good insertion-loss of approximately -6 ± 2dB, and return- loss of below -10 dB over the operating frequency band. Moreover, good impedance-matching, and isolation performance have been obtained over the desired frequency band. The proposed WPD was initially modeled mathematically and then designed and optimized using CST microwave studio (CST-MWS). The proposed design can be used in the next-generation UWB-Internet of Things (UWB-IoT) antenna array systems.
Efficacy of Continued Fraction Expansion technique in the approximation of fractional order systems
Authors: Shrivastava Nitisha , Baliyan Arjun
Published: October 2024

At a macroscopic level derivatives and integrals are the usual mathematical tools to model real time processes and to perform the basic control and signal processing actions. However, the analysis, design, synthesis and implementation of fractional order differentiator and integrator is a difficult task because of its irrational behaviour. Therefore, for mathematical evaluation of any fractional order system, conversion to its approximate integer order equivalent is essential. In this paper approximated integer order models of fractional differentiator and integrator are developed using the continued fraction expansion technique. A continued fraction is an expression obtained through an iterative process. For any iteration to terminate, a finite numerical value is assigned, which in this paper is equal to the number of frequency points within the desired frequency band. It includes both the lower and upper limit values. A set of coefficients are obtained by finding the gains of the fractional term at respective frequencies and thereby applying the recursive formula. The coefficients thus obtained are substituted in the expression of continued fraction which results in a polynomial function of finite order. The developed models can be directly applied for analysis and realization of fractional order systems. The models are developed for fractional terms 0.1 to 0.9 in steps of 0.1, and also for 0.25 and 0.75. A detailed discussion on the sensitivity analysis is presented, which includes the influence of variable parameters on the accuracy and length of the order. Simulations have been performed in MATLAB. A comparison with both, the ideal values and also with existing methods is performed and tabulated to validate the correctness of the developed models both in terms of accuracy and integer order of the model. It shows that the Matsuda method yield very good results both in terms of magnitude and phase. And, is most suitable for linear phase circuits. Also, the proposed models can be directly used for the realization of customized fractional order Proportional Integral (PI), Proportional-Derivative (PD) and PID controllers. To establish the correctness of CFE based technique for hardware realization, the integer order approximated model of one-tenth and seven-tenth differentiator is decomposed to obtain the circuit parameters resistor (R) and capacitor (C). Then its implementation in OrCAD Capture CIS is performed. It can be seen that the results of realization closely match the actual response.
Translator’s Earnestness and Conveyance: Eva Lüdi Kong’s Translation of Chinese Classics
Authors: Ye Mengze
Published: October 2024

Translators play a very important role in the exchanges between Chinese and foreign civilizations and in the translation of Chinese culture to foreign countries. The Swiss sinologist Eva Lüdi Kong devotes herself to translating Chinese classics into German and makes outstanding contributions to the dissemination of Chinese culture in the German-speaking world. This study takes the course of Eva Lüdi Kong’s translation of Xiyou Ji and Qianzi Wen as a clue and analyzes her translation activities from three aspects, namely, “what to translate,” “how to translate,” and “how to promote,” to explore her love, earnestness, and conveyance in translating Chinese classic works.
PENGEMBANGAN MODUL PRAKTIKUM ADMINSITRASI SERVER MENGGUNAKAN LINUX DEBIAN 10 PADA KELAS 11 TKJ DEVELOPMENT OF SERVER ADMINISTRATION PRACTICUM MODULE
Authors: Firman Firman , Astuti Ayu Dwi , Matahari Matahari
Published: October 2024

Pada SMK Muhammadiyah Aimas Kabupaten Sorong khususnya pada kelas 11 TKJ memiliki permasalahan pada bahan ajar praktikum Administrasi server yang digunakan belum disesuaikan dengan kurikulum dan kurang menarik serta masih menggunakan debian versi lama yaitu debian 8. Hal ini yang menyebabkan peneliti mengembangkan bahan ajar berupa modul praktikum administrasi server menggunakan linux debian 10 dan layak digunakan pada peserta didik kelas 11 TKJ SMK Muhammadiyah Aimas. Penelitian ini menggunakan metode penelitian dan pengembangan (Research & Development) dengan model pengembangan ADDIE. Hasil penilaian kelayakan modul praktikum pada penelitian ini yaitu berdasarkan validasi ahli media diperoleh jumlah skor persentase adalah 96,3% dengan kriteria “Sangat Layak”, hasil validasi ahli materi diperoleh jumlah skor persentase adalah 96% dengan kriteria “Sangat Layak. Kemudian untuk hasil uji coba kelompok kecil yang memperoleh jumlah presentase keseluruhan adalah 81,8% dengan kriteria “Sangat Layak”, sedangkan untuk uji coba kelompok besar memperoleh jumlah presentase keseluruhan adalah 90,7%. Hasil dari penelitian ini yaitu Modul Praktikum Administrasi Server Menggunakan Linux Debian 10 sangat layak digunakan peserta didik kelas 11 TKJ SMK Muhammadiyah dalam kegiatan pembelajaran praktikum administrasi server.
Pada SMK Muhammadiyah Aimas Kabupaten Sorong khususnya pada kelas 11 TKJ memiliki permasalahan pada bahan ajar praktikum Administrasi server yang digunakan belum disesuaikan dengan kurikulum dan kurang menarik serta masih menggunakan debian versi lama yaitu debian 8. Hal ini yang menyebabkan peneliti mengembangkan bahan ajar berupa modul praktikum administrasi server menggunakan linux debian 10 dan layak digunakan pada peserta didik kelas 11 TKJ SMK Muhammadiyah Aimas. Penelitian ini menggunakan metode penelitian dan pengembangan (Research & Development) dengan model pengembangan ADDIE. Hasil penilaian kelayakan modul praktikum pada penelitian ini yaitu berdasarkan validasi ahli media diperoleh jumlah skor persentase adalah 96,3% dengan kriteria “Sangat Layak”, hasil validasi ahli materi diperoleh jumlah skor persentase adalah 96% dengan kriteria “Sangat Layak. Kemudian untuk hasil uji coba kelompok kecil yang memperoleh jumlah presentase keseluruhan adalah 81,8% dengan kriteria “Sangat Layak”, sedangkan untuk uji coba kelompok besar memperoleh jumlah presentase keseluruhan adalah 90,7%. Hasil dari penelitian ini yaitu Modul Praktikum Administrasi Server Menggunakan Linux Debian 10 sangat layak digunakan peserta didik kelas 11 TKJ SMK Muhammadiyah dalam kegiatan pembelajaran praktikum administrasi server.
PERANCANGAN USER INTERFACE SISTEM PENILAIAN ANGKA KREDIT DENGAN METODE AGILE-SCRUM
Authors: Tandra Rudy , Ramli Ramli , Irwan Dede
Published: October 2024

Angka kredit merupakan tolak ukur yang dipergunakan dosen untuk memperoleh kenaikan jabatan akademik. Proses kenaikan jabatan akademik asisten ahli dan lektor di Politeknik Negeri Pontianak masih menggunakan cara konvensional dalam pemberkasan. Namun, proses tersebut sudah menggunakan borang spreadsheet dalam merekap dan menghitung angka kredit seorang dosen. Seiring berjalannya mekanisme tersebut, ditemukan sejumlah persoalan yang menghambat proses penilaian angka kredit seperti kekeliruan pengisian borang spreadsheet secara mandiri oleh dosen sebagai akibat disamakan dengan perhitungan Beban Kinerja Dosen (BKD), kekeliruan penempatan capaian kinerja pada aspek-aspek penilaian angka kredit, perubahan format borang spreadsheet yang dilakukan secara sepihak oleh dosen, proses validasi hasil entri dosen yang membutuhkan waktu lebih lama sebagai akibat dari adanya berkas lampiran yang tidak lengkap ataupun tidak disusun secara sistematis/berurutan dan perbedaan pemahaman asesor terhadap pedoman operasional penilaian angka kredit yang berakibat pada kekeliruan asesmen. Beranjak dari permasalahan tersebut, penggunaan aplikasi atau sistem berbantuan komputer yang dirancang secara khusus seperti sistem penilaian angka kredit diharapkan dapat mengubah proses penilaian yang berjalan selama ini. Perancangan sistem penilaian angka kredit tidak lagi dimulai dari sisi backend mengingat adanya web service pada Sistem Informasi Terintegrasi (SISTER) perguruan tinggi yang dapat digunakan. Perancangan sistem penilaian angka kredit lebih difokuskan pada sisi frontend, yaitu user interface berdasarkan pedoman Google Material Design dan manajemen perancangan sistem menggunakan metode Scrum. Hasil perancangan mengalami keterlambatan dalam penyelesaian namun, hasil pengujian sistem dengan System Usability Scale (SUS) menunjukkan kegunaan sistem berada pada skala mutu cukup baik.
Bile Acids – Friend or Foe? A Review of Pathological Significance and Therapeutic Potential
Authors: Foster Thomas , Lim Patrick , Ionescu Corina Mihaela , Jones Melissa , Wagle Susbin Raj , Kovacevic Bozica , Wong Elaine YM , Mooranian Armin , Al-Salami Hani
Published: October 2024

Bile acids have significant therapeutic and pathological interest in diagnostic and research-based investigations. This review describes the key processes involved in bile acid synthesis from cholesterol in addition to the conversion from primary to secondary bile acids. The normal physiological properties of bile acids are described, with particular focus on bile acids’ role as signalling molecules and their intra- and extra-hepatic circulation. The role of bile acids in pathology is also discussed, with particular emphasis on the role of bile acids in intrahepatic cholestasis of pregnancy. Measurement of bile acid concentration in the clinical laboratory is discussed in detail, including current methods of quantification. Finally, the role of exogenous bile acids is discussed to highlight how bile acids are utilised therapeutically and how they are being used in research settings for the delivery of various therapeutics. Overall, this review presents the dichotomy of bile acids as a pathological feature and a therapeutic interest in research.