Automatic differentiation of uncertainties: an interval computational differentiation for first and higher derivatives with implementation.

Acquiring reliable knowledge amidst uncertainty is a topical issue of modern science. Interval mathematics has proved to be of central importance in coping with uncertainty and imprecision. Algorithmic differentiation, being superior to both numeric and symbolic differentiation, is nowadays one of the most celebrated techniques in the field of computational mathematics.

Analgesia and spread of erector spinae plane block in breast cancer surgeries: a randomized controlled trial.

Background: To evaluate the analgesic efficacy and spread of variable volumes of local anesthetics (LA) in Erector spinae plane block (ESPB).

Methods: Sixty patients aged between 18 and 50 years with an ASA I-II and scheduled for breast cancer surgery were randomized to receive either ESPB with 20 ml 0.25% bupivacaine (Standard volume ESPB), or with 40 ml 0.

Smart Cities after COVID-19: Building a conceptual framework through a multidisciplinary perspective.

This study provides theoretical grounds for planning smart cities using multidisciplinary approaches, offering insightful suggestions to researchers and policy- and decision-makers. Its main purpose is to contribute to the debate on the new connotations of the smart city paradigm in the context of the COVID-19 pandemic. It will emphasize how the Internet of Things and related technologies will collaborate to develop an antivirus-built environment against future pandemics.

Primary thyroid chondrosarcoma: a case report of an extremely rare malignancy.

Introduction: There are different types of malignant tumors that can affect the thyroid gland where differentiated thyroid carcinomas (papillary and follicular) are the most common representing nearly 90% of cases. Non-epithelial malignancies were also reported to affect the thyroid gland particularly lymphomas and sarcomas that were reported in literature to range from 0.01 to 1.