This paper examines whether students are entering the generative AI era with sufficiently strong educational foundations, focusing on the relationship between learning environments and changes in ICT related career aspirations across countries. The analysis uses country-level data from PISA 2018 and 2022, combining indicators of student autonomy, digital skills and teacher support. A mixed-method approach is applied, including descriptive statistics, regression analysis, clustering, latent representation learning (using Variational Autoencoder-VAE), discriminant analysis and probabilistic modeling to capture both observable and latent dimensions of educational readiness. Unlike prior research that treats learning loss, digital skills and career expectations separately, our analysis integrates them within a comparative longitudinal framework. It shifts the focus from short-term post-pandemic effects to the structural capacity of education systems to prepare students for digital and AI-driven labor markets. Results show a global but uneven increase in ICT career aspirations. Digital skills emerge as the strongest and most consistent predictor, while teacher support plays a complement
We perform an updated global analysis of the known and unknown parameters of the standard $3ν$ framework as of 2025. The known oscillation parameters include three mixing angles $(θ_{12},\,θ_{23},\,θ_{13})$ and two squared mass gaps, chosen as $δm^2=m^2_2-m^2_1>0$ and $Δm^2=m^2_3-{\textstyle\frac{1}{2}}(m^2_1+m^2_2)$, where $α=\mathrm{sign}(Δm^2)$ distinguishes normal ordering (NO, $α=+1$) from inverted ordering (IO, $α=-1$). With respect to our previous 2021 update, the combination of oscillation data leads to appreciably reduced uncertainties for $θ_{23}$, $θ_{13}$ and $|Δm^2|$. In particular, $|Δm^2|$ is the first $3ν$ parameter to enter the domain of subpercent precision (0.8\% at $1σ$). We underline some issues about systematics, that might affect this error estimate. Concerning oscillation unknowns, we find a relatively weak preference for NO versus IO (at $2.2σ$), for CP violation versus conservation in NO (1.3$σ$) and for the first $θ_{23}$ octant versus the second in NO ($1.1σ$). We discuss the status and qualitative prospects of the mass ordering hint in the plane $(δm^2,\,Δm^2_{ee})$, where $Δm^2_{ee}=|Δm^2|+{\textstyle\frac{1}{2}}α(\cos^2θ_{12}-\sin^2θ_{12})δm^2$, to
While the quantum realm seems hidden, it can also reach examples of infinite energy, especially when a part of space is roughly removed until it disappears, possibly forever. Since it follows that Nothing can enter a region where the space is missing, the quantum realm, as seen now in affine quantization, will automatically come to help everything else by creating colossal `quantum walls' that will ensure that everything stays out of all black holes. In this article, we show that the expanded quantum realm allows Nothing to ever fall into a black hole.
The Replay Protected Memory Block (RPMB) in modern storage systems provides a secure area where data integrity is ensured by authentication. This block is used in digital devices to store pivotal information that must be safeguarded against modification by potential attackers. This paper targets the authentication scheme of the RPMB in three different eMMCs from a major manufacturer. A glitch was injected by sending an electromagnetic pulse to the target chip. RPMB authentication was successfully glitched and the information stored in two target eMMCs was overwritten with arbitrary data, without affecting the integrity of other data.
On 24 September 2023, the Origins, Spectral Interpretation, Resource Identification, and Security Regolith Explorer (OSIRIS-REx) Sample Return Capsule entered the Earth's atmosphere after successfully collecting samples from an asteroid. The known trajectory and timing of this return provided a rare opportunity to strategically instrument sites to record geophysical signals produced by the capsule as it traveled at hypersonic speeds through the atmosphere. We deployed two optical-fiber distributed acoustic sensing (DAS) interrogators to sample over 12 km of surface-draped, fiber-optic cables along with six co-located seismometer-infrasound sensor pairs, spread across two sites near Eureka, NV. This campaign-style rapid deployment is the first reported recording of a sample return capsule entry with any distributed fiber optic sensing technology. The DAS interrogators recorded an impulsive arrival with an extended coda which had features that were similar to recordings from both the seismometers and infrasound sensors. While the signal-to-noise of the DAS data was lower than the seismic-infrasound data, the extremely dense spacing of fiber-optic sensors allowed for more phases to be
When an open tube of small diameter touches a bubble of a larger diameter, the bubble spontaneously shrinks and pushes a soap film in the tube. We characterize the dynamics for different bubble sizes and number of soap films in the tube. We rationalize this observation from a mechanical force balance involving the Laplace pressure of the bubble and the viscous force from the advancing soap lamellae in the tube. We propose a numerical resolution of this model, and an analytical solution in an asymptotic regime. These predictions are then compared to the experiments. The emptying duration is primarily affected by the initial bubble to tube diameter ratio and by the number of soap films in the tube.
Taxonomy is formulated as directed acyclic concepts graphs or trees that support many downstream tasks. Many new coming concepts need to be added to an existing taxonomy. The traditional taxonomy expansion task aims only at finding the best position for new coming concepts in the existing taxonomy. However, they have two drawbacks when being applied to the real-scenarios. The previous methods suffer from low-efficiency since they waste much time when most of the new coming concepts are indeed noisy concepts. They also suffer from low-effectiveness since they collect training samples only from the existing taxonomy, which limits the ability of the model to mine more hypernym-hyponym relationships among real concepts. This paper proposes a pluggable framework called Generative Adversarial Network for Taxonomy Entering Evaluation (GANTEE) to alleviate these drawbacks. A generative adversarial network is designed in this framework by discriminative models to alleviate the first drawback and the generative model to alleviate the second drawback. Two discriminators are used in GANTEE to provide long-term and short-term rewards, respectively. Moreover, to further improve the efficiency, p
HD 166620 was recently identified as a Maunder Minimum candidate based on nearly 50 years of Ca II H & K activity data from Mount Wilson and Keck-HIRES (Baum et al. 2022). These data showed clear cyclic behavior on a 17-year timescale during the Mount Wilson survey that became flat when picked up later with Keck-HIRES planet-search observations. Unfortunately, the transition between these two data sets -- and therefore the transition into the candidate Maunder Minimum phase -- contained little to no data. Here we present additional Mount Wilson data not present in Baum et al. (2022) along with photometry over a nearly 30-year baseline that definitively trace the transition from cyclic activity to a prolonged phase of flat activity. We present this as conclusive evidence of the star entering a grand magnetic minimum and therefore the first true Maunder Minimum analog. We further show that neither the overall brightness nor the chromospheric activity level (as measured by S$_{\mathrm{HK}}$) is significantly lower during the grand magnetic minimum than its activity cycle minimum, implying that anomalously low mean or instantaneous activity levels are not a good diagnostic or crite
Countries tend to diversify their exports by entering products that are related to their current exports. Yet this average behavior is not representative of every diversification path. In this paper, we introduce a method to identify periods when countries enter unrelated products. We analyze the economic diversification paths of 93 countries between 1965 and 2014 and find that countries enter unrelated products in only about 7.2% of all observations. We find that countries enter more unrelated products when they are at an intermediate level of economic development, and when they have higher levels of human capital. Finally, we ask whether countries entering more unrelated products grow faster than those entering only related products. The data shows that countries that enter more unrelated activities experience a small but significant increase in future economic growth, compared to countries with a similar level of income, human capital, capital stock per worker, and economic complexity.
Given the potential for illicit nuclear material being used for terrorism, most ports now inspect a large number of goods entering national borders for radioactive cargo. The U.S. Department of Homeland Security is moving toward one hundred percent inspection of all containers entering the U.S. at various ports of entry for nuclear material. We propose a Bayesian classification approach for the real-time data collected by the inline Polyvinyl Toluene radiation portal monitors. We study the computational and asymptotic properties of the proposed method and demonstrate its efficacy in simulations. Given data available to the authorities, it should be feasible to implement this approach in practice.
In this paper, we address the vehicle scheduling problem for improving passenger safety in bus rapid transit systems. Our focus is on passengers waiting at street stops to enter terminal stations. To enhance their safety, we minimize deviations from the proposed timetable, thereby minimizing passengers' initial waiting time. We formulate an optimization problem considering the position, speed deviation, and passenger count at each stop, solved using dynamic programming. Numerical simulations validate the effectiveness of our approach in enhancing passenger safety. Our work is the first attempt to minimize waiting time for improved safety and the first to utilize position tracking for departure time matching.
The main purpose of this paper is to formalize the modelling process, analysis and mathematical definition of corruption when entering into a contract between principal agent and producers. The formulation of the problem and the definition of concepts for the general case are considered. For definiteness, all calculations and formulas are given for the case of three producers, one principal agent and one intermediary. Economic analysis of corruption allowed building a mathematical model of interaction between agents. Financial resources distribution problem in a contract with a corrupted intermediary is considered.Then proposed conditions for corruption emergence and its possible consequences. Optimal non-corruption schemes of financial resources distribution in a contract are formed, when principal agent's choice is limited first only by asymmetrical information and then also by external influences.Numerical examples suggesting optimal corruption-free agents' behaviour are presented.
Hysteresis is a general phenomenon regularly observed in measurements of various materials properties such as magnetism, elasticity, capillary pressure, adsorption, battery voltage etc. Usually, the hysteretic behaviour is an intrinsic property that cannot be avoided or circumvented in dynamic operation of the system. Here we show, however, that at least as regards the hysteretic behaviour of phase-separating battery materials, one can enter (deeply) into the hysteretic loop in specific, yet realistic, transient operating conditions. Within the hysteretic loop a (significant) portion of particle population resides in an intraparticle phase separated state. Interestingly, the transition to the more conventional interparticle phase separation state found outside the hysteretic loop is very slow. Further, we establish a direct interrelation between the intraparticle phase separated electrode state and altered electric response of the electrode, which significantly impacts DC and AC characteristics of the battery. The experimental evidence of entering the hysteretic loop and the resulting altered response of the battery are explained based on thermodynamic reasoning, advanced modelling
Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language denoising and token recovery. We propose DiHAL, a geometry-guided diffusion-transformer hybrid that asks where diffusion should enter a pretrained transformer. DiHAL scores layers with geometry-based proxies, selects a diffusion-friendly hidden-state interface, and replaces the lower transformer prefix with a diffusion bridge while retaining the upper layers and original LM head. By reconstructing the selected-layer hidden state rather than tokens, DiHAL avoids direct continuous-to-discrete recovery. Experiments on 8B-scale backbones show that the geometry score predicts effective shallow insertion layers under a fixed bridge-training protocol and that hidden-state recovery improves over continuous diffusion baselines in a diagnostic comparison matching the diffusion/recovery training budget. These results suggest that hidden-state geometry helps identify where diffusion-based replacement is feasible inside pretrained language models.
Prediction markets are starting to look less like crowd polls and more like electronic markets. The central question is therefore no longer only whether these markets forecast well, but what happens when institutional liquidity enters: do spreads tighten, does price discovery improve, and do those gains actually reach the traders who are slowest to react when information arrives? This paper offers a research design for answering that question. It defines a broad market-quality lens, separates the main channels through which institutional liquidity enters, and maps the identification problems that arise in live venue data. It also uses a synthetic microstructure laboratory as a proof of concept for the measurement pipeline. The main lesson of the synthetic exercise is deliberately narrow. Market-maker coverage, liquidity incentives, and automation do not have to work through the same channel; average liquidity gains do not have to translate into equal gains for all traders; and the sharpest welfare losses are most likely to appear in shock states, when slower takers receive the least pass-through of tighter quoted markets. The synthetic results are useful because they stress-test th
In 2011, Friedmann [F 7] claimed to have proved that pathological linear programs existed for which the Simplex method using Zadeh's least-entered rule [Z 14] would take an exponential number of pivots. In 2019, Disser and Hopp [DH 5] argued that there were errors in Friedmann's 2011 construction. In 2020, Disser, Friedmann, and Hopp [DFH 3,4] again contended that the least-entered rule was exponential. We show that their arguments contain multiple flaws. In other words, the worst-case behavior of the least-entered rule has not been established. Neither [F 7] nor [DFH 3,4] provides pathological linear programs that can be tested. Instead, the authors contend that their pathological linear programs are of the form (P) as shown on page 12 of [DFH 3]. The authors contend that the constraints of (P) ensure that the probability of entering a vertex u is equal to the probability of exiting u. In fact, we note that the authors' constraints (P) are flawed in at least three ways: a) they require the probability of exiting u to exceed the probability of entering u, b) they require the probability of exiting some nodes to exceed 1, and c) they overlook flows from decision nodes to decision no
Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets under dynamic metabolic demands. We introduce DeepEN, a reinforcement learning (RL) framework for personalized EN optimization using electronic health record data. Methods: DeepEN was trained on over 11,000 ICU patients from MIMIC-IV to generate 4-hourly, patient-specific caloric, protein, and fluid targets. The state representation incorporated demographics, comorbidities, vital signs, laboratory values, and recent interventions. A physiologically aligned reward framework balanced biomarker stability with long-term survival. Policy learning employed a dueling double deep Q-network with Conservative Q-Learning regularization to enable safe offline training. Results: DeepEN achieved the highest estimated policy value ($V^π= 9.48$) and the lowest calibrated mortality (18.8 +/- 1.0%), representing a 4.0 percentage-point absolute reduction compared with clinician practice (22.8%). The policy also demonstrated superior metabolic stability, achieving the highest proportion of glucose, phosphate, and sodium values
In this paper, we present ENTER, an interpretable Video Question Answering (VideoQA) system based on event graphs. Event graphs convert videos into graphical representations, where video events form the nodes and event-event relationships (temporal/causal/hierarchical) form the edges. This structured representation offers many benefits: 1) Interpretable VideoQA via generated code that parses event-graph; 2) Incorporation of contextual visual information in the reasoning process (code generation) via event graphs; 3) Robust VideoQA via Hierarchical Iterative Update of the event graphs. Existing interpretable VideoQA systems are often top-down, disregarding low-level visual information in the reasoning plan generation, and are brittle. While bottom-up approaches produce responses from visual data, they lack interpretability. Experimental results on NExT-QA, IntentQA, and EgoSchema demonstrate that not only does our method outperform existing top-down approaches while obtaining competitive performance against bottom-up approaches, but more importantly, offers superior interpretability and explainability in the reasoning process.
Neural tissues of the central nervous system are among the softest and most fragile in the human body, protected from mechanical perturbation by the skull and the spine. In contrast, the enteric nervous system is embedded in a compliant, contractile tissue and subject to chronic, high-magnitude mechanical stress. Do neurons and glia of the enteric nervous system display specific mechanical properties to withstand these forces? Using nano-indentation combined with immunohistochemistry and second harmonic generation imaging of collagen, we discovered that enteric ganglia in adult mice are an order of magnitude more resistant to deformation than brain tissue. We found that glia-rich regions in ganglia have a similar stiffness to neuron-rich regions and to the surrounding smooth muscle, of ~3 kPa at 3 $μ$m indentation depth and of ~7 kPa at 8 $μ$m depth. Differences in the adhesion strength of the different tissue layers to the glass indenter were scarce. The collagen shell surrounding ganglia and inter-ganglionic fibers may play a key role in strengthening the enteric nervous system to resist the manifold mechanical challenges it faces.
Background. Women bring unique problem-solving skills to software development, often favoring a holistic approach and attention to detail. In software testing, precision and attention to detail are essential as professionals explore system functionalities to identify defects. Recognizing the alignment between these skills and women's strengths can derive strategies for enhancing diversity in software engineering. Goal. This study investigates the motivations behind women choosing careers in software testing, aiming to provide insights into their reasons for entering and remaining in the field. Method. This study used a cross-sectional survey methodology following established software engineering guidelines, collecting data from women in software testing to explore their motivations, experiences, and perspectives. Findings. The findings reveal that women enter software testing due to increased entry-level job opportunities, work-life balance, and even fewer gender stereotypes. Their motivations to stay include the impact of delivering high-quality software, continuous learning opportunities, and the challenges the activities bring to them. However, inclusiveness and career developme