Nidhal Ben Khedher, Zia Ullah, Md Mahbub Alam
et al.
The novelty of current research is to investigate exponential thermal density and thermal conductivity effect on heat-mass transmission in second-grade magneto nanofluid along stretching surface with thermal-solutal slip boundary conditions. The well define similarity transformation is applied on the couple of partial differential equations (PDEs) to transform into ordinary differential equations (ODEs). The Newton-Raphson and central difference technique is used to linearize these equations. Using MATLAB software technique, the results in graphical and numerical form are explored. The impact of various variables on fluid velocities, fluid temperatures and nanoparticle concentrations profiles are found graphically and numerically including, variable thermal conductivity, magnetic field, second-grade nanofluid factor, thermal slip, and concentration slip. The value of Nusselt number, Sherwood number and skin friction are found numerically for Prandtl number, density index, and thermophoretic factor. For asymptotic numerical outcomes, the following range of parameters such 0.1≤n≤1.4, 0.1≤Nt≤1.2, 0.1≤Nb≤2.0, 0.1≤ξ≤6.0, 0.1≤λ≤14.0, 0.1≤λ1≤8.0, 0.1≤α≤0.8, 0.1≤Pr≤10.0, 0.1≤Le≤3.5, 0.1≤Bt≤4.0, and 0.1≤Bc≤5.0 is used. It found that the higher value of thermal conductivity and thermal-concentration slip increases the velocity profile while magnetic field decreased it. Mass transmission and Nusselt rate are increased with increasing magnetic field. The maximum Nusselt number is explored for higher thermophoresis and density parameter. The noticeable amplitude in fluid velocity profiles is observed with thermal conductivity, temperature-concentration slip and buoyancy force effects under lower fluid density.
As foundation models grow rapidly in capability and deployment, evaluating their scientific understanding becomes increasingly critical. Existing science benchmarks have made progress towards broad Range, wide Reach, and high Rigor, yet they often face two major challenges: data leakage risks that compromise benchmarking validity, and evaluation inefficiency due to large-scale testing. To address these issues, we introduce the Ever-Evolving Science Exam (EESE), a dynamic benchmark designed to reliably assess scientific capabilities in foundation models. Our approach consists of two components: 1) a non-public EESE-Pool with over 100K expertly constructed science instances (question-answer pairs) across 5 disciplines and 500+ subfields, built through a multi-stage pipeline ensuring Range, Reach, and Rigor, 2) a periodically updated 500-instance subset EESE, sampled and validated to enable leakage-resilient, low-overhead evaluations. Experiments on 32 open- and closed-source models demonstrate that EESE effectively differentiates the strengths and weaknesses of models in scientific fields and cognitive dimensions. Overall, EESE provides a robust, scalable, and forward-compatible solution for science benchmark design, offering a realistic measure of how well foundation models handle science questions. The project page is at: https://github.com/aiben-ch/EESE.
Jiaxin Pei, Dustin Wright, Isabelle Augenstein
et al.
Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.
Accurately calibrating the center-of-mass (CoM) offset between the spacecraft (SC) and the inertial sensor test mass (TM) is crucial for space-based gravitational-wave (GW) antennas, such as LISA and Taiji. Current calibration methods require additional spacecraft maneuvers that disrupt science data continuity and inter-satellite links, compromising the coherence of gravitational wave signals. Here, we present a maneuver-free calibration scheme that directly estimates the CoM offset vector using only standard science-mode measurements from inertial sensors, interferometers, and differential wavefront sensors. By embedding the CoM offset induced coupling acceleration as an extended state in a model-based adaptive Kalman filter, we achieve estimation accuracy of 0.01-1.5 mm across all axes with a maximum error below 1%. This approach enables continuous, high-precision calibration during nominal observation runs, ensuring continuous and coherent gravitational wave data collection while maintaining the required precision, and also facilitating advanced DFACS functions such as performance evaluations and fault diagnosis. For LISA-like missions, where data continuity is paramount for detecting faint gravitational wave signals, this method will enhance scientific output and reliability.
Emilio Porcu, Roy El Moukari, Laurent Najman
et al.
This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific goals. The data scientist is an abstract entity that comes from the logical organization of data agents with their actions. Data scientists face challenges that are defined according to the missions. We define specific discipline-induced data science, which in turn allows for the definition of pan-data science, a natural ecosystem that integrates specific disciplines with the essential data science. We semantically split the essential data science into computational, and foundational. By formalizing this ecosystemic view, we contribute a general-purpose, fusion-oriented architecture for integrating heterogeneous knowledge, agents, and workflows-relevant to a wide range of disciplines and high-impact applications.
У статті запропонований методологічний підхід до вирішення задачі обґрунтування обрису системи боротьби з безпілотними літальними апаратами стратегічного, оперативного і тактичного призначення. Суть цієї задачі полягає у визначенні складу, структури та сукупності показників основних функціональних властивостей (спроможностей) системи, при яких вона повинна забезпечувати з потрібною ефективністю відбиття ударів малопомітних та малошвидкісних цілей в умовах їх масованого застосування. Для вирішення цієї задачі система повинна створюватися на основі оптимального поєднання пасивних і активних засобів розвідки, радіоелектронного подавлення та вогневого ураження різних класів. Показано, що загроза масового застосування малорозмірних безпілотних літальних апаратів потребує перегляду існуючих положень щодо організації протиповітряної оборони об’єктів і угруповань військ та критеріїв застосування існуючих і перспективних засобів розвідки і вогневого ураження. В основу методологічного підходу до обґрунтування обрису системи боротьби з безпілотними літальними апаратами повинна бути покладена багатокрокова процедура генерації і аналізу альтернативних варіантів її якісно-кількісного складу. Показано, що обґрунтування обрису системи боротьби з безпілотними літальними апаратами повинно проводитися з застосуванням методів імітаційного математичного моделювання бойових дій, для чого повинен бути сформований сценарій виконання розрахункових бойових задач в процесі відбиття нальотів безпілотних літальних апаратів різного призначення.
У цій статті проаналізовано різні класи і типи існуючих безпілотних літальних апаратів. Обговорюються численні переваги безпілотних літальних апаратів. Безпілотні літальні апарати (БПЛА) все частіше використовуються в різних секторах, включаючи військову сферу, логістику. Навігація є однією з найважливіших систем БПЛА, що забезпечує автономне пересування та виконання завдань. Актуальність теми полягає в тому, що вона спрямована на вирішення важливої проблеми при проектуванні та експлуатації БПЛА: підвищення точності визначення навігаційних параметрів навігаційним обладнанням БПЛА за рахунок комплексної обробки навігаційної інформації від інерціальної навігації та супутникових навігаційних систем.
Recent advances in hand gesture recognition have produced more natural and intuitive methods of controlling unmanned aerial vehicles (UAVs). However, in unknown and cluttered environments, UAV motion planning requires the assistance of hand gesture interaction in complex flight tasks, which remains a significant challenge. In this paper, a novel framework based on hand gesture interaction is proposed, to support efficient and robust UAV flight. A cascading structure, which includes Gaussian Native Bayes (GNB) and Random Forest (RF), was designed, to classify hand gestures based on the Six Degrees of Freedom (6DoF) inertial measurement units (IMUs) of the data glove. The hand gestures were mapped onto UAV’s flight commands, which corresponded to the direction of the UAV flight.The experimental results, which tested the 10 evaluated hand gestures, revealed the high accuracy of online hand gesture recognition under asynchronous detection (92%), and relatively low latency for interaction (average recognition time of 7.5 ms; average total time of 3 s).The average time of the UAV’s complex flight task was about 8 s shorter than that of the synchronous hand gesture detection and recognition. The proposed framework was validated as efficient and robust, with extensive benchmark comparisons in various complex real-world environments.
The jamming resource allocation problem of the aircraft formation cooperatively jamming netted radar system is investigated. An adaptive allocation strategy based on dynamic adaptive discrete cuckoo search algorithm (DADCS) is proposed, whose core is to adjust allocation scheme of limited jamming resource of aircraft formation in real time to maintain the best jamming effectiveness against netted radar system. Firstly, considering the information fusion rules and different working modes of the netted radar system, a two-factor jamming effectiveness evaluation function is constructed, detection probability and aiming probability are adopted to characterize jamming effectiveness against netted radar system in searching and tracking mode, respectively. Then a nonconvex optimization model for cooperatively jamming netted radar system is established. Finally, a dynamic adaptive discrete cuckoo search algorithm (DADCS) is constructed by improving path update strategies and introducing a global learning mechanism, and a three-step solution method is proposed subsequently. Simulation results are provided to demonstrate the advantages of the proposed optimization strategy and the effectiveness of the improved algorithm.
W dniu 3 czerwca 2022 r. w Akademii Sztuki Wojennej odbyła się konferencja naukowa poświęcona wyzwaniom geostrategicznym polskiego przemysłu obronnego. Wydarzenie to zorganizowane zostało przez Wydział Zarządzania i Dowodzenia, Akademii Sztuki Wojennej oraz Wydział Bezpieczeństwa, Logistyki i Zarządzania, Wojskowej Akademii Technicznej.
Data Science is a complex and evolving field, but most agree that it can be defined as a combination of expertise drawn from three broad areascomputer science and technology, math and statistics, and domain knowledge -- with the purpose of extracting knowledge and value from data. Beyond this, the field is often defined as a series of practical activities ranging from the cleaning and wrangling of data, to its analysis and use to infer models, to the visual and rhetorical representation of results to stakeholders and decision-makers. This essay proposes a model of data science that goes beyond laundry-list definitions to get at the specific nature of data science and help distinguish it from adjacent fields such as computer science and statistics. We define data science as an interdisciplinary field comprising four broad areas of expertise: value, design, systems, and analytics. A fifth area, practice, integrates the other four in specific contexts of domain knowledge. We call this the 4+1 model of data science. Together, these areas belong to every data science project, even if they are often unconnected and siloed in the academy.
Adelmo Eloy, Thomas Palmeira Ferraz, Fellip Silva Alves
et al.
Science and Engineering fairs offer K-12 students opportunities to engage with authentic STEM practices. Particularly, students are given the chance to experience authentic and open inquiry processes, by defining which themes, questions and approaches will guide their scientific endeavors. In this study, we analyzed data from over 5,000 projects presented at a nationwide science fair in Brazil over the past 20 years using topic modeling to identify the main topics that have driven students' inquiry and design. Our analysis identified a broad range of topics being explored, with significant variations over time, region, and school setting. We argue those results and proposed methodology can not only support further research in the context of science fairs, but also inform instruction and design of contexts-specific resources to support students in open inquiry experiences in different settings.
Muhamed Suceska, B. Stimac Tumara, Vinko Skrlec
et al.
An adverse effect resulting from explosive mine blasts is the production of toxic nitrogen oxides (NO and NO2) and carbon monoxide (CO). The empirical measurements of the concentration of toxic gases showed that it depends not only on the composition of an explosive and properties of its ingredients but also on several other parameters, such as volume of blasting chamber, explosive charge mass and design, confinement characteristics, surrounding atmosphere, etc. That explains why measured concentrations of toxic gases reported in literature significantly differ.In this paper, we discuss the possibility of theoretical prediction of the concentration of toxic gases by thermochemical equilibrium calculation applying two models: ideal detonation model and deflagration model. It can be demonstrated that thermochemical calculations can provide a good estimation of the measured concentrations and reproduce experimentally obtained effects of additives on the production of toxic gases. It was also found that the ideal detonation model applies to heavily confined explosive charges, while the deflagration model is more suitable for low detonation velocity explosives with light confinement.
Elmedina Avdibasic, Amanzholova Saule Toksanovna, Benjamin Durakovic
Cybersecurity is an important topic for Industry 4.0, which will face a lot of non-traditional challenges before it can be fully implemented to help society. The objective of the study is to recognize recent cybersecurity trends, newly occurring threats and challenges as well as their potential solutions. The articles reviewed in the paper are found through science direct, Scopus, Semantic scholar and google scholar. After reviewing them, ideas from articles were grouped together to show how different articles had similar thoughts. Through the analysis of 70 articles, it was found that cybersecurity still needs a lot of improvement in order to efficiently implement Industrial Internet of Things (IIoT), especially since many articles underline the need for security-by-design approach, followed by regular updating. Key challenges are lack of awareness and security experts, increased cybercriminal and the fact that the chain is as only as strong as its weakest point. Some of the most important solutions include incorporating security into design, stronger encryption, regular updates to patch vulnerabilities and good prevention and detection mechanisms. Once cybersecurity challenges are overcome, Industry 4.0 will be able to bloom to its full extent.
The Cold War is not an event, it seems, but a process that continues nowadays, too. In this scholarly article, we defend the thesis that the asymmetric end of the Cold War left a lasting memory on Russia‘s foreign policy. In developing this thesis,
we proceed as follows: firstly, we will outline the realist counterargument to our thesis which suggests that no lessons can be learned from the ending of the Cold War because the bipolar struggle itself and the period after were continuations of the
constant struggle for power between states in which historical narratives and ideas have no part to play. Secondly, building on the existing constructivist perspective, we will show that understanding the different ideational frameworks that developed
in Washington and Moscow are paramount to understanding the deterioration of Russia’s relations with the West in the past decade. Thirdly, we will show how the narratives that developed within Russia about the ending of the Cold War and Russia’s
place in the world are critical to understanding the potential for change in the domestic system.
К. С. Васюта, В. В. Слободянюк, О. Б. Танцюра
et al.
В статті викладено аналіз методів формування активних імітуючих перешкод за допомогою технології сурогатних даних (ТСД) з метою створення перешкод по каналу вимірювання дальності (зриву супроводження) або створення їх необхідної кількості для перевантаження радіолокаційної станції (РЛС), тобто, у загальному випадку, для подавлення та зриву їх роботи. Розглянуто два алгоритми формування сурогатних даних, а саме: ATS (attractor trajectory surrogates) та AATFT (amplitude adjusted truncated Fourier transform surrogates). Дані алгоритми дозволяють формувати “сурогатні” реалізації по прийнятому сигналу від радіоелектронних засобів противника (РЕЗ) з подальшим внесенням хибної інформації та з наступним їх випромінюванням в якості активних імітаційних перешкод. Використання ТСД дозволяє зберігати спектральні, кореляційні та динамічні властивості прийнятих сигналів. Побудовано математичні моделі та проаналізовано “сурогатні” реалізації для фазокодоманіпульованого (ФКМ) сигналу з маніпуляцією фази 13-елементним кодом Баркера. З метою зменшення впливу шуму спостереження побудовано ансамбль “сурогатних” реалізацій з подальшим його усередненням. Проаналізовано якість формування за критеріями середньоквадратичної похибки та коефіцієнта кореляції Пірсона між ФКМ сигналом та результатом усереднення його реалізацій. Надана чисельна оцінка близькості образів у псевдофазовому просторі результату усереднення сурогатних реалізацій до ФКМ сигналу. На підставі аналізу обрано найкращий алгоритм формування сурогатних даних для подальшого застосування в якості метода формування імітаційних перешкод.
Data and Science has stood out in the generation of results, whether in the projects of the scientific domain or business domain. CERN Project, Scientific Institutes, companies like Walmart, Google, Apple, among others, need data to present their results and make predictions in the competitive data world. Data and Science are words that together culminated in a globally recognized term called Data Science. Data Science is in its initial phase, possibly being part of formal sciences and also being presented as part of applied sciences, capable of generating value and supporting decision making. Data Science considers science and, consequently, the scientific method to promote decision making through data intelligence. In many cases, the application of the method (or part of it) is considered in Data Science projects in scientific domain (social sciences, bioinformatics, geospatial projects) or business domain (finance, logistic, retail), among others. In this sense, this article addresses the perspectives of Data Science as a multidisciplinary area, considering science and the scientific method, and its formal structure which integrate Statistics, Computer Science, and Business Science, also taking into account Artificial Intelligence, emphasizing Machine Learning, among others. The article also deals with the perspective of applied Data Science, since Data Science is used for generating value through scientific and business projects. Data Science persona is also discussed in the article, concerning the education of Data Science professionals and its corresponding profiles, since its projection changes the field of data in the world.
Abstract Background Postoperative care has been evolving since the concept of enhanced recovery after surgery (ERAS) was introduced in China. This study aimed to evaluate the effects of early ambulation within 24 h after unilateral total knee arthroplasty (TKA) on postoperative rehabilitation and costs in a Chinese population. Methods This cohort study of patients with knee osteoarthritis who had undergone TKA at 24 large teaching hospitals between January 2014 and November 2016 involved 2687 patients who began ambulating within 24 h (Group A) and 3761 who began ambulating later than 24 h (Group B). The outcome measurements, such as length of stay (LOS), total hospitalization costs, dynamic pain level, knee flexion range of motion (ROM), results of the 12-Item Short Form Survey (SF-12), incidence of thromboembolic events and other complications, were recorded and compared. Results The early ambulation group (Group A) had a shorter LOS and lower hospitalization costs and pain levels than the late ambulation group (Group B). There was a favorable effect in enhancing ROM for patients in Group A compared with patients in Group B. In Group A, patients had significantly higher postoperative SF-12 scores than those in Group B. The incidence of deep venous thrombosis (DVT) and pulmonary infection was significantly lower in Group A than in Group B. The incidence of pulmonary embolism (PE) and other complications did not differ between the two groups. Conclusion Early ambulation within 24 h after TKA was associated with reduced LOS, improved knee function, lower hospitalization costs and lower incidence of DVT and pulmonary infection in the Chinese population.
Статтю присвячено розробленню методичного підходу до обґрунтування вимог до оперативності роботи органів управління (ОУ) перспективної автоматизованої системи управління військами (АСУВ). Відомо, що одним із пріоритетних завдань оборонної реформи є створення на основі сучасних інформаційних технологій єдиної автоматизованої системи управління Збройними Силами України (ЄАСУ ЗС України) як основи системи управління силами оборони держави. Але на теперішній час її стан є незадовільним унаслідок недосконалості існуючого методичного апарату з обґрунтування вимог до перспективних АСУВ, як її складових. У першу чергу це стосується вимог до оперативності роботи ОУ. Використання наявного методичного апарату дозволяє обґрунтувати за допомогою часткових показників функціонування ОУ орієнтовні (прогнозовані) значення показника ймовірності своєчасного виконання завдань ОУ протягом певного часу щодо управління військами, який визначено основним показником оперативності роботи ОУ. При цьому не враховуються особливості та можливості ОУ щодо виконання своєї головної роботи під час підготовки та застосування військ – роботи з інформацією, яка циркулює на етапах циклу управління військами. Саме обсяги інформації, з якими необхідно службовим особам ОУ виконати певні дії (отримати, обробити, узагальнити, надіслати), визначають тривалість кожного етапу та циклу управління військами у цілому. Для досягнення мети статті її автором розроблено методичний підхід, який дозволяє обґрунтувати з позиції кібернетики та дослідження операцій послідовний порядок моделювання процесу автоматизованого управління у перспективній АСУВ типу C4ISR. Даний підхід є основою для розроблення у подальшому відповідного методу обґрунтування вимог до оперативності роботи ОУ, а також формулювання висновків і рекомендацій щодо досягнення її необхідного рівня.