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Gestational Diabetes Mellitus (GDM) is characterized as any degree of glucose intolerance that manifests during pregnancy and typically resolves postpartum. Diagnosis is commonly made through an Oral …

Gestational Diabetes Mellitus (GDM) is characterized as any degree of glucose intolerance that manifests during pregnancy and typically resolves postpartum. Diagnosis is commonly made through an Oral Glucose Tolerance Test (OGTT); however, there are notable inconsistencies in diagnostic criteria and treatment thresholds both nationally and internationally. The onset of GDM generally occurs in the late second or early third trimester, with potential complications including fetal macrosomia, which may lead to difficult labor, and neonatal hypoglycemia due to excess insulin production in the infant’s pancreas. Furthermore, certain studies suggest that GDM may delay fetal brain development, resulting in long-term neurological impairments. Although artificial intelligence (AI) models have only recently emerged as a potential solution in various medical fields, their application continues to raise concerns, particularly regarding the use and storage of personal data. Machine Learning (ML), a subset of AI, utilizes multivariate classification methods, also known as supervised pattern recognition approaches, which are designed to identify patterns and correlations among multiple variables to categorize them into specific groups or classes. In this context, we are describing here .Thus, artificial intelligence has not yet demonstrated its full clinical potential in the management of gestational diabetes mellitus (GDM). Current applications remain limited in their impact on improving patient outcomes, largely due to methodological heterogeneity and the early stage of implementation. Still, AI has shown considerable promise in the domain of predictive modeling, particularly through its ability to process large volumes of clinical and biochemical data. This analytical capacity enables the identification of complex patterns and correlations that can support the accurate early prediction of GDM, potentially allowing for earlier intervention and personalized care strategies. Future research should focus on validating these predictive models in larger, diverse populations and integrating them into clinical workflows under appropriate regulatory and ethical frameworks.

The rapid pace of technological innovation has reshaped entrepreneurial dynamicsworldwide with digitalization and artificial intelligence becoming key drivers of change. Whileadvanced economies are co…

The rapid pace of technological innovation has reshaped entrepreneurial dynamicsworldwide with digitalization and artificial intelligence becoming key drivers of change. Whileadvanced economies are considered leaders of technological breakthroughs, emerging economiesare gaining more and more prominence in implementing digital transformation by refiningentrepreneurial strategies under conditions of resource scarcity and institutional constraints. Thisarticle investigates the close link and interplay between entrepreneurship, AI, and digitalizationthrough a multiple case study of three technologically driven Romanian companies operating indistinct sectors. Our research employs a qualitative and exploratory approach consisting ofinterviews, questionnaires, and digital branding analysis. This mixed methodology assists inrevealing the multifaceted nature of these economic transformations in an emerging market. Thestudy also examines how entrepreneurs adopt technological tools, adapt business models, andconfront emerging challenges. Research findings suggest that digitalization is not perceived as anoptional asset but rather as a necessity for business survival, visibility, and scalability. Despitedisplaying contrasting approaches to leadership and strategic planning, all three Romanianentrepreneurs leveraged digital tools to enhance efficiency and customer engagement, but stillfaced multiple barriers such as limited resources, disruptive technological change, and dependenceon external platforms. To sum up, the study contributes to the existing body of research byhighlighting the need of further investigating the growing role of emerging economies in globaldigital entrepreneurship, while outlining structural challenges that continue to obstruct theirtransformative potential.

simscape2matpower is a MATLAB/Simscape toolbox that converts Simscape electrical network models into MATPOWER case structures. It enables interoperability between Simulink/Simscape and MATPOWER for lo…

simscape2matpower is a MATLAB/Simscape toolbox that converts Simscape electrical network models into MATPOWER case structures. It enables interoperability between Simulink/Simscape and MATPOWER for load-flow, optimal power flow, and power system simulation studies.

Room compensation aims to improve the accuracy of loudspeaker reproduction in reverberant environments. Traditional methods, however, are limited to improving only spectral (timbral) and temporal accu…

Room compensation aims to improve the accuracy of loudspeaker reproduction in reverberant environments. Traditional methods, however, are limited to improving only spectral (timbral) and temporal accuracy, neglecting the spatial accuracy of loudspeaker reproduction. Proposed is a method that compensates for both spectral and spatial properties of loudspeaker reproduction, by adding energy to the perceived reverberant sound field in a frequency-selective manner using a delayed secondary supporting source. This approach allows for the modification of the direct to reverberant ratio as a function of frequency, altering spatial and spectral reproduction. The proposed method is perceptually evaluated, demonstrating its ability to alter the perception of a primary loudspeaker without the listener perceiving the supporting source. The results show that the proposed method performs comparably to a well-established commercial room compensation algorithm and has several advantages over traditional room compensation methods.