Afeka College of Engineering

Operating electronic systems in space environments presents significant challenges due to continuous exposure to cosmic, solar, and trapped radiation, which can induce catastrophic single-event effects. This paper introduces a novel nonintrusive mitigation apparatus designed to protect high-end commercial off-the-shelf electronics in space. The apparatus incorporates an array of real-time particle detectors coupled with a mitigation algorithm. Upon identifying potentially harmful particles, the system power cycles affected electronics, preempting permanent damage. The apparatus was evaluated using GEANT4 simulations, which were compared with empirical data from the "COTS-Capsule" experiment aboard the International Space Station, demonstrating strong agreement. Key results indicate that the system achieves a 95% detection accuracy with a power cycle rate of once every seven hours per square centimeter of sensitive electronics. The COTS-Capsule represents a cost-effective, flexible solution for integrating modern, high-end, non-space-qualified electronics into a variety of space missions, addressing critical challenges in the new-space era.

Physician–patient diagnostic interactions often rely on incomplete, uncertain, and noisy symptom descriptions expressed in non-specialist language. To reach an accurate diagnosis efficiently, physicians employ adaptive sequences of focused questions in which each inquiry depends on prior patient responses. Similar requirements apply to AI assistants engaged in clinical dialogue, which must strategically select questions to elicit diagnostic information efficiently. While several datasets and benchmarks address clinical reasoning, few focus on evaluating the strategic inquiry process itself. To address this gap, we introduce Q4Dx (Question-Driven Diagnosis), a benchmark for assessing goal-directed diagnostic questioning. Q4Dx consists of synthetically generated patient cases derived from curated symptom–disease knowledge. Each case is instantiated at multiple information levels (100%, 80%, and 50% symptom exposure) to evaluate performance under varying informational constraints. We simulate patient–physician interactions using GPT-4.1 and GPT-4o-mini agents to generate clinician questions, patient responses, and intermediate diagnostic hypotheses. Performance is evaluated using Zero-shot Diagnostic Accuracy (ZDA), Mean Questions to Correct Diagnosis (MQD), and Inquire Sequence Efficiency (ISE), which measures convergence toward the correct diagnosis. Q4Dx provides a reusable framework for benchmarking large language models in strategic clinical inquiry and supports future research on AI-assisted diagnostic training. The dataset and benchmark are publicly available at: https://github.com/MaiWert/MedQDx.

Objective. Outpatient notes carry follow-up instructions pairing actions with future times ("MRI brain in two weeks"). Extracting (action, date) pairs supports scheduling and audit, but generative extractors miss the date because linking and arithmetic are implicit in decoding. We test a hybrid neural-symbolic pipeline against direct generation. Methods. We define TestSpecification and TimeSpecification entities and a ScheduledFor relation. BioBERT feeds BIO tagging and a biaffine linker; entities are canonicalized via a 28-action ontology and times normalized to day offsets deterministically. We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits (18 train, 6 OOV-test) against zero-shot GPT-4o-mini and LoRA-fine-tuned LLaMA-3 8B with note-level bootstrap 95

Collisions between over-height vehicles and low-clearance bridges cause infrastructure damage and pose safety risks. Existing detection systems rely primarily on optical sensors, which suffer from performance degradation in adverse weather conditions. This paper presents an alternative approach based on a 94 GHz millimeter-wave radar that achieves velocity-independent height measurement. The proposed technique exploits the ratio of Doppler shifts from two scattering centers on a vehicle, specifically the roof and the wheel-road interface. This ratio depends only on the measurement geometry, as the unknown vehicle velocity cancels algebraically, enabling direct height computation without speed measurement. The paper provides a closed-form height estimation model, analyzes the trade-off between frequency resolution and geometric constancy during integration, and presents experimental validation using a scaled laboratory testbed. An optical tracking system is used solely for ground-truth validation in the laboratory and is not required for operational deployment. Results across six test cases with heights ranging from 20 cm to 46 cm demonstrate an average absolute error of 0.60 cm and relative errors below 3.3 percent. A scaling analysis for representative full-scale geometries indicates that at highway speeds of 80 km/h, integration times in the millisecond range (approximately 3-18 ms for representative 20-50 m measurement standoff) are feasible; warning distance can be extended independently by upstream radar placement. The expected advantage in fog, rain, and dust is based on established W-band propagation characteristics; dedicated adverse-weather and full field validation (including multipath, clutter, and multi-vehicle scenarios) remain future work.

The singularity level near a bi-material interface crack is crucial for failure prediction. Unlike a crack in a homogeneous material with a fixed eigenvalue of 1/2, interface crack eigenvalues are complex. Their fixed real part is 1/2, while the stochastic imaginary part depends explicitly on the material properties, governing the stress field’s oscillatory nature. Consequently, deterministic approximations may underestimate the severity of the singularity. This paper presents a stochastic approximation of the eigenvalue’s imaginary component for a bi-material interface crack. Both materials are isotropic, involving stochastic Young’s modulus and stochastic Poisson’s ratio, yielding four independent random variables. The generalized Polynomial Chaos (gPC) method is used to construct a multidimensional polynomial expansion via tensor products of orthogonal bases that are dictated by the material distributions. The deterministic coefficients are derived from evaluations at selected quadrature points. The methodology is demonstrated and directly validated on a benchmark problem of a crack between bonded half-planes, utilizing its exact closed-form analytical solution. The gPC approximation converges rapidly as the polynomial order increases, achieving relative differences below 1% in all statistical moments compared to the exact analytical solution, using only 4% of the computational effort typically required for such statistical characterization These findings underscore that accounting for material stochasticity is essential for accurate failure criteria.