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В статье проверяется распространённое допущение о том, что развитие цифровой инфраструктуры автоматически повышает уровень жизни населения. Данные по пяти странам Центральной Азии за 2018–2025 г…

В статье проверяется распространённое допущение о том, что развитие цифровой инфраструктуры автоматически повышает уровень жизни населения. Данные по пяти странам Центральной Азии за 2018–2025 годы это допущение опровергают. Казахстан — региональный лидер по цифровым рейтингам (индекс развития электронного правительства 0,9009, 24-е место в мире) — в 2025 году зафиксировал снижение реальных доходов населения на 2,7 %. Узбекистан при более скромной цифровой оснащённости (индекс 0,7999) сократил бедность с 14,0 % в 2022 году до 5,8 % в 2025 году, а неравенство доходов (коэффициент Джини) снизил с 0,35 до 0,33. Цель работы — объяснить это расхождение и предложить практический инструмент управления.

This study, conducted from May to July 2026, examined nursing practice compliance, service delivery challenges, and cultural competence among home care nurses in the Kingdom of Saudi Arabia and explor…

This study, conducted from May to July 2026, examined nursing practice compliance, service delivery challenges, and cultural competence among home care nurses in the Kingdom of Saudi Arabia and explored the mediating role of cultural competence in the relationship between service delivery challenges and nursing practice compliance. An explanatory sequential mixed-methods design was used. The quantitative phase involved 112 home care nurses who completed a validated questionnaire measuring nursing practice compliance, service delivery challenges, and cultural competence. Descriptive statistics, Mann–Whitney U, Kruskal–Wallis, Pearson product-moment correlation, and hierarchical regression mediation analysis were employed. The qualitative phase involved eight purposively selected nurses who provided written narratives describing their lived experiences. Quantitative findings showed very high nursing practice compliance (M = 4.29, SD = 1.13), high service delivery challenges (M = 3.94, SD = 0.87), and high cultural competence (M = 4.20, SD = 0.89). Service delivery challenges were significantly related to cultural competence (r = .766, p < .001) and cultural competence was significantly related to nursing practice compliance (r = .805, p < .001). In the mediation model, service delivery challenges significantly predicted compliance before cultural competence was introduced (β = .635, p < .001); after cultural competence was entered, its effect became nonsignificant (β = .045, p = .615), while cultural competence remained significant (β = .770, p < .001). Qualitative findings described strong professional commitment, environmental and communication challenges, cultural adaptation, adaptive strategies, and emotional resilience. The integrated findings provide a basis for a Multicultural Home Care Nursing Practice Program focused on sustaining compliance, strengthening cultural competence, improving communication, and supporting adaptive practice.

This is AI-generated research: the results, proofs and code were found and written by AI. Credit goes to all the humans whose work it builds on.We prove that the frog model on the rooted $d$-ary tree …

This is AI-generated research: the results, proofs and code were found and written by AI. Credit goes to all the humans whose work it builds on.We prove that the frog model on the rooted $d$-ary tree is recurrent for $d=3$ and transient for $d=4$, as conjectured by Hoffman, Johnson and Junge. One frog starts awake at the root and one sleeps at every other vertex; awake frogs perform independent simple random walks, waking the frogs they visit. For $d=4$ the mean number of visits to the root is at most $5.756$. The proof runs an exact recursion on the laws of the responses of subtrees, with a law given by a finite table that dominates its image, certified by two linear systems on $159786$ states. For $d=3$ a subtree of height $m\ge1778250$ entered by one frog sends back at least $(m+2)/5$ frogs on average; the proof combines a lower model, a certified chain of steps on finite tables of bounds, and an induction on the height. Both proofs are formalized in Lean 4.These are the cases $d=3$ and $d=4$ of Conjecture 2 of Hoffman, Johnson and Junge (Ann. Probab. 2017; arXiv:1404.6238).The recurrence for $d=3$, the transience for $d=4$ and the bound on the expected number of visits are proved in Lean 4 with Mathlib, with no hypothesis and only the standard axioms, and Comparator with nanoda checks the three theorems against their statements; the Lean kernel checks by evaluation the certificate for $d=4$ and the run, the certificate and the table for $d=3$.Code, Lean proofs and certificates: github.com/mt0-svg/frog-model-tree

Mazkur maqola o‘zbek tili milliy korpusida frazeologik birliklarni  teglash va ularni avtomatik lingvistik annotatsiya qilishning nazariy-metodik asoslarini ishlab chiqishga bag‘ishla…

Mazkur maqola o‘zbek tili milliy korpusida frazeologik birliklarni  teglash va ularni avtomatik lingvistik annotatsiya qilishning nazariy-metodik asoslarini ishlab chiqishga bag‘ishlanadi. Frazeologik birliklarning semantik jihatdan yaxlitligi, shuningdek, tarkibining o‘zgaruvchanligi sabab NLP tizimlarida oddiy so‘z birikmalaridan ularni ajratish biroz murakkab. Tadqiqotda tizimli lisoniy tahlil, korpus metodlari va zamonaviy neyron tarmoq modellari imkoniyatlarini qiyoslash metodologiyasidan foydalanish orqali frazeologizmlarning elektron bazasini shakllantirishda xalqaro tajribalar va ularning o‘zbek tili korpusiga tatbiq etilish imkoniyatlari ko‘rib chiqiladi.

Tezisda nazoratsiz o‘qitish usuliga kiruvchi K-means klasterlash algoritmini neyron tarmoq modeli sifatida qarash mumkinligiga asoslanadi. Ishning asosiy mazmunida K-means algoritmining sentroid…

Tezisda nazoratsiz o‘qitish usuliga kiruvchi K-means klasterlash algoritmini neyron tarmoq modeli sifatida qarash mumkinligiga asoslanadi. Ishning asosiy mazmunida K-means algoritmining sentroidlarni tayinlash mexanizmi hamda SOM (Self-Organizing Map) raqobatlashuvchi neyron qatlamining ekvivalentlik nazariyasini ko‘rib chiqish, algoritmning formal matematik ta’rifi va uni tasvir ma’lumotlarida sinash uchun mo‘ljallangan tadqiqot rejasi bayon qilinadi.

В статье рассматривается проблема автоматического обнаружения, классификации и индексирования афоризмов в текстовых корпусах. Задача анализируется на основе моделей обнаружения фрагментов текста, клас…

В статье рассматривается проблема автоматического обнаружения, классификации и индексирования афоризмов в текстовых корпусах. Задача анализируется на основе моделей обнаружения фрагментов текста, классификации предложений и ранжирования в рамках НЛП и интерпретируется как сложная задача типа «needle-in-the-haystack». Она основана на том, что для обнаружения афоризмов следует использовать комбинацию графико-пунктуационных, количественных, лексико-морфологических, синтаксических, семантических, прагматических и стилистических индикаторов; процесс представлен в виде четырехэтапного конвейера, состоящего из поиска кандидатов, оценки, разметки и ранжирования. Также рассматривается необходимая для узбекского языка инфраструктура предварительной обработки (нормализация, токенизация, лемматизация, POS-тегирование, синтаксический анализ зависимостей) на основе существующих лингвистических ресурсов (BBPOS, узбекский UD-корпус).

The deposit allows the reproduction of the statistical results reported. The data collection script is further available upon request.

In Europe, sugar production mainly relies on sugar beet as the primary input. The main by-products orresidues are produced during the manufacturing process are: molasses and pressed beet pulp. Thechar…

In Europe, sugar production mainly relies on sugar beet as the primary input. The main by-products orresidues are produced during the manufacturing process are: molasses and pressed beet pulp. Thecharacteristics of these residues, such as their water content, can vary depending on how they are usedsubsequently. For this data publication, several literature sources were consulted to identify the mostcommon residue types and compile a list of relevant residue factors. An average value was then calculatedfor these factors and multiplied by the amount of sugar beet consumed in the sugar plant. All plant potentialsare aggregated from NUTS-3 to NUTS-0 values for the EU27 countries. All literature used, as well as the dataand estimated numbers, are indicated in the biomass potential CSV. The dataset is based on desk research for primary data on sugar beet consumption or sugar production.Limitations arise due to the type of company data available. Not all companies publish their data or the yearto which it refers. Therefore, the year of the state of research was used as the reference year, but the actualyear of reference can be different. If data from several years was found, the most recent year was used. Allbiomass potentials are given in terms of fresh matter, since as this is the basis for the feedstock usercalculating further treatment steps or transportation costs. If several plants were in one NUTS entity, thevalues were aggregated. If no information on a given plant was found, its biomass potential is not reflectedin the NUTS entity and is indicated in the column with the header “COMPLETE”. Another limitation is thetiming of the sugar campaigns. Usually, a sugar campaign runs from September to January. Numbers areusually given for one year or as a daily number. This makes correct calculations more difficult. Therefore, theused year is always the start of the campaign, and an average campaign length for the EU was used tocalculate the yearly residue value, based on the most recent CEFS statistics. All the data on biomass potential is provided in the corresponding CSV file. A detailed overview of theavailable data and sources is provided in the metadata file for this dataset. The work was done within the project SURFsUp. The project is supported by the Circular Bio-based EuropeJoint Undertaking and its members under Grant Agreement No. 101157586 and UKRI Grant Agreement No.10115826.