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关键词： Upper-Division Undergraduate   Physical Chemistry   Laboratory Instruction   Inquiry-Based/Discovery Learning   Alcohols   Aldehydes/Ketones   Enzymes   Kinetics   Cultural Relevance  

摘要： This lab updates the study of acetaldehyde oxidation in vitro using the enzyme aldehyde dehydrogenase (ALDH) by presenting the study in a guided inquiry approach that is culturally relevant. Acetaldehyde is a toxic compound that builds up in the liver following the consumption of alcohol, which must be metabolized. We used a spectrophotometer to follow the kinetics of this reaction, determine how substrate concentrations alter the rate of this enzymatic reaction, and screen potential enzyme inhibitors. Students are guided through questions that explore comparatively uncomplicated kinetic and thermodynamic theory that supports the reaction of enzyme catalysis. This inquiry approach was aided by three-dimensional structural models of the enzyme that were explored within the laboratory sessions, enhancing the arcane lab procedures and developing student skills in experimental design. Students can draw from their everyday life experiences based on their cultural norms and knowledge to choose experimental conditions within a provided list of options, such as the alcohol or the reaction inhibitor selection. This was accompanied by written reports and finalized by oral presentations to the class.

关键词： Quantum entanglement   Routing   Quantum computing   Photonics   Optical switches   Repeaters   Throughput   Qubit   Optimization   Computer science   Quantum networks   entanglement routing   EPS assignment   all-optical switching  

摘要： Entanglement routing plays a vital role in distributed quantum computing and quantum networks. Previous works on entanglement routing have addressed several design challenges due to limited quantum resources, failures to establish entanglement, and decoherence of established entanglement, but considered neither the limitations imposed by having a limited number of entangled photon sources (EPSes), nor the benefits of using all-optical (or quantum) switching to distribute entangled photons. In this paper, we first explore the problem of jointly optimizing entanglement routing and EPS assignment, assuming no all-optical switching capability. In other words, a pair of entangled photons generated by one EPS can be distributed to two neighboring quantum nodes. We then relax the above assumption so as to allow a pair of entangled photons generated by one EPS to be distributed to non-adjacent nodes using all-optical switching. We propose two corresponding solutions, namely, entanglement routing and EPS assignment (or ERSA), and ERSAwith all-optical switching-based distribution (or ERSA $+$ D) that aim to maximize the number of entanglement connections between the given set of source-destination (SD) pairs while avoiding starvation and achieving fairness among the SD pairs. In order to obtain efficient solutions in a large discrete solution space in a timely manner, we first formulate each optimization problem as an Integer Linear Programming (ILP), and then propose efficient algorithms to derive near-optimal solutions based on relaxation, Lagrangian decomposition, duality iteration, and rounding techniques. Extensive simulations show that ERSA $+$ Dcan increase network throughput by up to 1652% and 113%, respectively, thanks to EPS assignment optimization, and all-optical switching based entanglement distribution.

关键词： Health sciences   Natural sciences   computer science  

摘要： In the field of steady-state visual evoked potential (SSVEP), stimulus paradigms are regularly arranged or mimic the style of a keyboard with the same size. However, stimulation paradigms have important effects on the performance of SSVEP systems, which correlate with the electroencephalogram (EEG) signal amplitude and recognition accuracy. This paper provides MP dataset that was acquired using a 12-target BCI speller. MP dataset contains 9-channel EEG signals from the occipital region of 24 subjects under 5 stimulation paradigms with different stimulus sizes and arrangements. Stimuli were encoded using joint frequency and phase modulation (JFPM) method. Subjects completed an offline prompted spelling task using a speller under 5 paradigms. Each experiment contains 8 blocks, and each block contains 12 trials. Designers can use this dataset to test the performance of algorithms considering "stimulus size"and "stimulus arrangement". EEG data showed SSVEP features through amplitude-frequency analysis. FBCCA and TRCA confirmed its suitability.

关键词： Topology  

摘要： Can outreach inspire and lead to research and vice versa? In this work, we introduce our approach to the gamification of research in mathematics and computer science through three illustrative examples. We discuss our primary motivations and provide insights into what makes our proposed gamification effective for three research topics in discrete and computational geometry and topology: (1) DominatriX, an art gallery problem involving polyominoes with rooks and queens;(2) Cubical Sliding Puzzles, an exploration of the discrete configuration spaces of sliding puzzles on the d-cube with topological obstructions;and (3) The Fence Challenge, a participatory isoperimetric problem based on polyforms. Additionally, we report on the collaborative development of the game Le Carré du Diable, inspired by The Fence Challenge and created during the workshop Let's talk about outreach!, held in October 2022 in Les Diablerets, Switzerland. All of our outreach encounters and creations are designed and curated with an inclusive culture and a strong commitment to welcoming the most diverse audience *** Codes 00A08, 00A09, 68R05, 68Q17, 05B45, 05B50, 97P20, 97N70, 97K10, 97U10, 97U60, 97U70, 97U80 © 2024, CC BY-NC-ND.

关键词： Internet of Things   Scalability   Faces   Routing   Software defined networking   Security   Computer science   Information-centric networking (ICN)   Internet of Things (IoT)   pending interest table (PIT)   software-defined networking (SDN)   type-length-value (TLV)  

摘要： The rapid expansion of the Internet of Things (IoT) introduces critical challenges in scalability, mobility, and security, particularly in large-scale deployments. While information-centric networking (ICN) addresses these by enhancing content mobility, multipath support, and edge-embedded caching with inherent security features, it faces limitations in handling large heterogeneous environments due to its in-network caching and content-based forwarding strategies. Software-defined networking (SDN) complements ICN by employing a centralized controller to intelligently orchestrate content caching and forwarding, yet struggles with the efficient allocation and acquisition of content across expansive IoT systems. In response to these challenges, we propose CIC-SIoT, a novel information-centric SDN (IC-SDN) solution, designed to optimize the ICN-IoT framework. Our solution incorporates specialized algorithms for controllers, consumers, producers, and ICN nodes. These algorithms improve content forwarding decisions by moving beyond the traditional reliance on the forwarding information base (FIB) and instead utilizing the pending interest table (PIT) to efficiently manage and distribute content. Validated through ndnSIM and MATLAB simulations, CIC-SIoT achieves substantial performance enhancements, including an 80% increase in throughput, a 34% reduction in latency, and a 25% savings in bandwidth. Additionally, it reduces packet loss by 67% and communication overhead by 66%, compared to existing solutions. These results underscore the framework's ability to significantly improve the efficiency and scalability of content distribution in IoT environments, highlighting its robustness and adaptability in addressing the complex dynamics of modern networked systems.

关键词： technology   journal   science   computer   volume   contents  

关键词： Functional programming  

摘要： Computer scientists Jeannette Wing and Simon Peyton Jones have catalyzed a pivotal discussion on the need to introduce computing in K-12 mandatory education. In Wing’s own words, computing represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use. The crux of this educational endeavor lies in its execution. This paper reports on the efforts of the Ensico association to implement such aims in Portugal. Starting with pilot projects in a few schools in 2020, it is currently working with 4500 students, 35 schools and 100 school teachers. The main aim is to gain enough experience and knowledge to eventually define a comprehensive syllabus for teaching computing as a mandatory subject throughout the basic and secondary levels of the Portuguese educational system. A structured framework for integrating computational thinking into K-12 education is proposed, with a particular emphasis on mathematical modeling and the functional programming paradigm. This approach is chosen for its potential to promote analytical and problem-solving skills of computational thinking aligned with the core background on maths and science. © 2024, CC BY-NC-SA.

关键词： Computer science   Social sciences   Research methodology social sciences  

摘要： On social media platforms, priority users (e.g., verified profiles on X) are users whose posts are promoted by recommendation algorithms. However, their influence on opinion dynamics, in particular polarization and echo chamber formation, is not well understood. Through computational modeling, we investigate this influence in a stylized setting. By introducing priority user accounts, we find that prioritization can mitigate polarization. However, by incorporating stubborn user behavior, we find that the results change and that priority accounts can exacerbate the formation of echo chambers. In other words, a minority of extremist ideologues can trigger a transition from consensus to polarization. Our study suggests careful monitoring of platform prioritization policies to prevent potential misuse of users with enhanced influence.

关键词： Earth sciences   Computer science   Engineering  

摘要： Urban flooding significantly impacts city planning and resident safety. Traditional flood risk models, divided into physical and data-driven types, face challenges like data requirements and limited scalability. To overcome these, this study developed a model combining graph convolutional network (GCN) and spiking neural network (SNN), enabling the extraction of both spatial and temporal features from diverse data sources. We built a comprehensive flood risk dataset by integrating social media reports with weather and geographical data from six Chinese cities. The proposed Graph SNN model demonstrated superior performance compared to GCN and LSTM models, achieving high accuracy (85.3%), precision (0.811), recall (0.832), and F1 score (0.821). It also exhibited higher energy efficiency, making it scalable for real-time flood prediction in various urban environments. This research advances flood risk assessment by efficiently processing heterogeneous data while reducing energy consumption, offering a sustainable solution for urban flood management.

关键词： Power generation   Electricity   Industries   Market research   Forecasting   Guidelines   Bayes methods   Artificial neural networks   Deep learning   Computer science  

摘要： Electricity demand continues to exceed supply in many sub-Saharan countries like South Africa, and frequent plant failures further reduce energy availability. To address this issue, it is essential to proactively predict plant failures and inform decisions on when to plan for outages. Given a myriad of prediction techniques, this study systematically analyzed various literature to provide a collective view of prediction approaches, their use cases, and context. Following the PRISMA guideline, relevant literature was searched using the Scopus database, and retrieved from the corresponding publisher sites. The selected studies focused on predicting the unplanned capability loss factor or the availability of power plants within the electricity industry domain. A thematic analysis was performed to identify emerging patterns related to current knowledge. Results revealed that prediction studies focus more on predicting availability than failure in coal-fired plants. The prediction horizon is mainly short-term, mostly in renewable plant. Artificial neural network, Bayesian analysis, and fuzzy rules are the prevalent technique found in most studies. Scholars and researchers can benefit from this study as it provided a simplified summary of power plant prediction techniques in a consolidated view.