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Les interventions intensives peuvent être associées à des changements visibles pendant ou immédiatement après l’expérience. Ces changements ne permettent…

Les interventions intensives peuvent être associées à des changements visibles pendant ou immédiatement après l’expérience. Ces changements ne permettent toutefois pas, à eux seuls, de conclure à l’existence d’une transformation durable. Cette Note CIRITS propose de déplacer l’unité d’analyse : du changement observé à la trajectoire du changement. Elle définit provisoirement une trajectoire transformationnelle comme l’évolution temporelle et contextuelle d’un ensemble de changements psychosociaux, relationnels, comportementaux ou identitaires, confrontés aux conditions de la vie ordinaire et à ses perturbations. Le cadre proposé articule cinq axes descriptifs : intensité, diversité, persistance, généralisation intercontextuelle et robustesse aux perturbations. Il formalise également les notions exploratoires d’écologie de consolidation, de latence transformationnelle, de réactivation et de réorganisation. L’originalité revendiquée ne réside pas dans chacun de ces phénomènes pris isolément, mais dans leur articulation au sein d’un cadre longitudinal commun destiné à produire des hypothèses comparables, falsifiables et progressivement testables.

Suppose a vector is observed through two families of linear measurements: a native family that we already control and a target family that we would like to control. How much of the target response is …

Suppose a vector is observed through two families of linear measurements: a native family that we already control and a target family that we would like to control. How much of the target response is left unexplained by the native measurements? We answer this question on finite-dimensional real or complex Hilbert spaces carrying a positive semidefinite energy. The answer is the adequacy residual, a generalized Schur complement whose quadratic form gives, for each combination of target measurements, the largest squared response at energy at most one that is invisible to the native family. We show that the residual is the smallest error left by any linear prediction of the target from the native measurements, that it vanishes exactly when the target factors through the native family, and that it obeys a Schur chain rule when measurements are added. We then give explicit, checkable conditions for three practical tasks: shrinking the residual by a fixed fraction with added measurements, deciding exactly which directions a restriction of the space must keep, and recovering an estimate after it has been transported to another space. Appendices treat sequences of such estimates. Energies may be singular, provided the measurements vanish on zero-energy vectors. The results are checked in Lean 4 with Mathlib.

Conditional mathematical arguments need careful bookkeeping: which obligations were incurred, which have been addressed, and which were addressed in a way that actually proves the target. Audited Oper…

Conditional mathematical arguments need careful bookkeeping: which obligations were incurred, which have been addressed, and which were addressed in a way that actually proves the target. Audited Operational Realisability (AOR) is a mathematical theory of this bookkeeping. An audit state consists of demanded records and stored certificates; each certificate carries a status, such as budgeted or blocked, and a payload whose meaning is fixed in advance. We separate three conclusions that are easily conflated. Inference closure registers every dependency of a demanded record, accounting requires a stored certificate for each demanded or dependency-reachable record, and delivery requires a certificate whose status an explicit consumer policy accepts as proving its target. A certificate recording that a budget is exceeded accounts for the obligation but does not deliver feasibility to a consumer who requires it. We prove that inference closure is a reflection that creates no evidence, in a category whose arrows preserve certificates exactly, and that this category has colimits in which every accepted certificate is already stored at some object of the diagram. For operations that add evidence, we prove finite completion under a decreasing rank and confluence under replayability, and we strictly separate accounting, recoverability, and uniform quantitative control. Retained evidence passes to increasing unions, and witnesses pass to limits under compactness and closed validity, provided an accepted payload is returned at the limit; without compactness they may escape. Two examples mark the boundary of accounting: a query system that pays its declared costs, is accounted, and identifies each hidden world along its exhaustive run, yet admits no finite-stage guarantee uniform over all worlds; and a Lipschitz target whose shrinking finite-prefix certificates, priced within a summable credit, are all accepted, with centers converging to the target. The stated structural results are formalized in Lean 4; the analytic estimates of applications remain explicit hypotheses.

A needle is a persistent obstruction: a direction in which a declared budget is violated, typically because the available measurements cannot see or explain the response in that direction. We give a l…

A needle is a persistent obstruction: a direction in which a declared budget is violated, typically because the available measurements cannot see or explain the response in that direction. We give a linear-algebraic account of when a richer family of measurements removes such an obstruction. Measurements are linear maps on a space carrying a positive semidefinite energy. The currency of a measurement family is the positive operator whose quadratic forms are its largest squared responses at energy at most one, and an obstruction is a direction in which a currency exceeds a declared budget. The first main theorem bounds the currency of a target family, after transport to a common space, by combining three separately proved estimates: a bound for the native measurements, a bound for the part of the target they cannot explain (the adequacy residual), and a transport estimate. The second main theorem works in infinite-dimensional Hilbert space. If the part of an object readout that changes sign under an involution is dominated by positive budgets whose traces tend to zero, then almost every object lies on the fixed locus of the involution. We then construct parts of the hypotheses of these theorems in useful cases: a Schur formula for minimum energy with its exact legality conditions, exhaustion by increasing Hilbert windows with explicitly paid cross terms, forward transfer along isometric bridges with errors tracked through chains, and a greedy selection of measurements from a finite dictionary that drives the residual to zero at a geometric rate when the dictionary satisfies a lower frame bound on the initially hidden directions. Examples from the Riemann hypothesis and the Navier–Stokes equations identify exactly which analytic estimates such applications would still have to prove; we prove neither conjecture. The principal abstract results are formalized in Lean 4.

Mazkur maqolada boshlang‘ich ta’lim tizimida mustaqil ta’lim shakllarini tashkil etishning pedagogik asoslari, ularning o‘quvchilar intellektual rivojlanishidagi o‘rni ha…

Mazkur maqolada boshlang‘ich ta’lim tizimida mustaqil ta’lim shakllarini tashkil etishning pedagogik asoslari, ularning o‘quvchilar intellektual rivojlanishidagi o‘rni hamda mustaqil ta’lim mexanizmlarini takomillashtirish masalalari yoritilgan. Shuningdek, mustaqil ta’lim jarayonida innovatsion pedagogik texnologiyalar, interfaol metodlar va raqamli vositalardan foydalanishning samaradorligi tahlil qilingan. Tadqiqot davomida boshlang‘ich sinf o‘quvchilarining mustaqil fikrlashini rivojlantirishga xizmat qiluvchi metodik yondashuvlar ishlab chiqilgan

Conventional scoring of matrix reasoning focuses on total accuracy, overlooking qualitative error patterns that may reflect distinct cognitive mechanisms, especially in psychiatric populations where t…

Conventional scoring of matrix reasoning focuses on total accuracy, overlooking qualitative error patterns that may reflect distinct cognitive mechanisms, especially in psychiatric populations where total scores often have limited discriminatory value. We examined performance in terms of accuracy and error patterns using MatriKS, a digital matrix reasoning task, in 430 individuals (28 with schizophrenia, 31 with bipolar disorder, and 371 healthy controls). Both clinical groups showed lower accuracy and more total errors than controls. To isolate qualitative differences, four conceptual error types were analyzed: repetition (R), wrong principle (WP), difference (D), and incomplete correlate (IC). Relative to controls, schizophrenia showed higher odds of D than R errors, though not robustly; bipolar disorder did not differ credibly on any error type. A direct comparison between the clinical groups revealed higher odds of D than R errors in patients with schizophrenia compared to bipolar ones but did not reach credibility; neither IC nor WP errors showed a credible diagnostic effect. A second model, restricted to the clinical sample, tested whether visual attention and verbal working memory can account for this pattern. Neither these measures, diagnosis, nor their interaction credibly predicted error type. Substantial unexplained heterogeneity emerged instead, driven more by individual differences for D errors and by item properties for IC and WP errors. Although preliminary, these findings offer an interesting support for qualitative error profiling alongside total-score metrics, though diagnostic specificity remains to be established in larger samples.

Batch upload of files from directory 'allium_validum_FSCR'.

We prove a conditional global regularity theorem for the three-dimensional incompressible Navier–Stokes equations with positive viscosity and real divergence-free initial data in Hγ0(ℝ3), γ0 > 5/2.…

We prove a conditional global regularity theorem for the three-dimensional incompressible Navier–Stokes equations with positive viscosity and real divergence-free initial data in Hγ0(ℝ3), γ0 > 5/2. The hypothesis, which we call a physical window closure, asks that on every time interval inside the lifespan of the strong solution, the size of each Littlewood–Paley shell in the Bradshaw–Grujic frequency window be certified by finite-dimensional data: a finite Fourier dictionary approximating the solution, independent test measurements, a Schur-complement residual bound, an amplitude bound, and a reconstruction error, all dominated by a single time profile with an integrable critical power. Linear algebra from the companion papers returns these certificates to bounds on the actual shells, so the closure makes the Bradshaw–Grujic critical dose finite on every such interval, and the Bradshaw–Grujic continuation criterion then rules out a finite maximal time. The hypothesis is therefore at least as strong as the finiteness of that dose: the theorem derives no new a priori bound from the equations, and it proves neither the closure nor global regularity. Its contribution is the exact certificate form and a checked composition. The return from certificates to the solution and the maximal-time contradiction are formalized in Lean 4, with the Bradshaw–Grujic criterion as an explicit input; the analytic bridges that make that criterion applicable to this solution are written proofs. A record-based route toward the closure, a family of no-go results for shortcuts, and a conditional analyticity branch are analyzed separately and are not used by the theorem.

Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and synchronizatio…

Deep Learning (DL)-based channel estimation has shown high accuracy and low latency in terrestrial 5G NR, but Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) introduce Doppler and synchronization impairments that may require NTN-specific architectures. We test whether High- Efficiency Learning-based channel Estimation using dual Neural Attention (HELENA), originally designed for terrestrial channels, remains effective after NTN retraining and suitable across high- performance and power-constrained inference platforms. Its un- changed architecture is trained on paired receiver-compensated (NTN-1) and residual-impaired (NTN-2) datasets and compared with eight terrestrial-origin models trained on the same NTN data and the NTN-specific MDELAN-SISO. HELENA achieves the lowest observed SNR-averaged NMSE among the DL estimators in both conditions, including 55.8–62.7% lower linear-scale NMSE than MDELAN-SISO. All DL models degrade in NTN-2, demonstrating the challenge posed by residual Doppler and its associated impairments. On an RTX PRO 4500, HELENA achieves 0.0595 ms 99th-percentile (P99) inference latency, 88.1% below the 0.5 ms budget, with lower energy than its closest attention-based competitors. On a 10 W Jetson Orin NX, it retains a favorable accuracy–energy trade-off, but no model meets the P99 budget. Thus, HELENA needs no NTN-specific redesign for the evaluated task, while embedded tail latency remains an open challenge.

Background: Traditional post-marketing drug monitoring framework metrics rely heavily upon passive, spontaneous clinical case notification configurations. These configurations systematically suffer fr…

Background: Traditional post-marketing drug monitoring framework metrics rely heavily upon passive, spontaneous clinical case notification configurations. These configurations systematically suffer from extensive reporting lag phases and a massive underreporting of actual consumer adverse drug reactions (ADRs). With the explosive expansion of global digital platforms, modern patient networks frequently document raw, unformatted clinical complaints online long before consulting a healthcare practitioner. Objective: This core research constructs, validates, and evaluates an enterprise cloud architecture prototype termed "Shreyas SmartPV Mobile" to automate case intake pipelines, deploy cognitive semantic extractors, map raw text onto official MedDRA hierarchies, and execute immediate regulatory eligibility checks. Methodology: A multi-layered low-code pipeline structure was engineered using Glide Apps and Softr for responsive frontend client interface extraction, Airtable as the primary secure relational cloud database storage, and Make.com as the background workflow synchronization integration orchestrator. Data streams are processed via a cognitive node driven by the Google Gemini Large Language Model (LLM) framework running specific clinical validation rules, multi-stage Named Entity Recognition (NER), and automated token dictionaries to extract suspect therapeutic drug classes cleanly from descriptive toxicological events. Results: Extensive experimental validation using standard chemical benchmark metrics (including Paracetamol, Aspirin, and Ibuprofen scenarios) verified 100% precision in parsing unstructured digital narratives, mapping baseline complaints to standard MedDRA Preferred Terms (PT) (e.g., matching 'red rashes on skin' to Erythematous Rash), and computing immediate validation states (Report Valid? = True) based on strict international safety compliance requirements. The system recorded an average automated triage execution velocity of less than 3.9 seconds per array. Conclusion: The validated automation workflow confirms that cloud-native cognitive processing pipelines cut manual data entry fatigue, optimize processing costs, and maximize patient-reported signal capture. This technical transition shifts drug safety management from passive tracking arrays into dynamic, real-time digital active safety listening ecosystems.