Current Issue : January-March Volume : 2026 Issue Number : 1 Articles : 5 Articles
The adoption of artificial intelligence has risen, yet research on its impact on innovation processes between actual businesses remains sparse. This research fills the present gap by investigating ten workers from a tech startup who utilize artificial intelligence tools in operational and creative activities. The paper analyzes business-related AI functionality through a qualitative analysis of ten tech start-up employees. The examination reveals that AI produces significant enhancements in problem resolution by executing mundane actions while analyzing large datasets to deliver data-driven suggestions to users. The interview respondents mentioned that AI’s role in diminishing supply chains is 15%, while allowing AI to manage customer service without employee engagement in 80% of interactions. The implementation costs, along with data dependency and occasional contextual blindness in AI systems, represented some of the problems in this system. Analysis demonstrated that AI tools enable the development of innovative concepts and challenge established viewpoints, prompting participants to create a gamified loyalty system and dynamic content planning. Participants in the study emphasized the need for human involvement to refine AI-based insights, recognizing how human imagination complements AI capabilities effectively. The work enhances academic discussions about AI-related problem-solving and creativity while offering specific business-related recommendations for implementation. The recommendations begin with establishing initial experimental programs, while providing support for employee’s skills development, and fostering strong alliances between technical AI personnel and professional subject matter experts. Research topics focused on AI application fields and the anticipated impacts on company decision-making, as well as the ethical ramifications, need further exploration. This research confirms the revolutionary potential of artificial intelligence systems for problem-solving methods, but requires proper execution, along with human supervision, to fully realize their advantages....
The paper outlines the hybrid framework of spin hydrodynamics, combining classical kinetic theory with the Israel–Stewart method of introducing dissipation. The local equilibrium expressions for the baryon current, the energy–momentum tensor, and the spin tensor of particles with spin 1/2 following the Fermi–Dirac statistics are obtained and compared with the earlier derived versions where the Boltzmann approximation was used. The expressions in the two cases are found to have the same form, but the coefficients are shown to be governed by different functions. The relative differences between the tensor coefficients in the Fermi–Dirac and Boltzmann cases are found to grow exponentially with the baryon chemical potential. In the proposed formalism, nonequilibrium processes are studied including mathematically possible dissipative corrections. Standard conservation laws are applied, and the condition of positive entropy production is shown to allow for the transfer between the spin and orbital parts of angular momentum....
This empirical pilot study explored the use of wearable eye-tracking technology to gain objective insights into interpersonal interactions, particularly in healthcare provider training. Traditional methods of understanding these interactions rely on subjective observations, but wearable tech offers a more precise, multimodal approach. This multidisciplinary study integrated counseling perspectives on therapeutic alliance with an empirically motivated wearable framework informed by prior research in clinical psychology. The aims of the study were to describe the complex data that can be achieved with wearable technology and to test our primary hypothesis that the therapeutic alliance in clinical training interactions is associated with certain behaviors consistent with stronger interpersonal engagement. One key finding was that a single multimodal feature predicted discrepancies in client versus therapist working alliance ratings (b = −4.29, 95% CI [−8.12, −0.38]), suggesting clients may have perceived highly structured interactions as less personal than therapists did. Multimodal features were more strongly associated with therapist rated working alliance, whereas linguistic analysis better captured client rated working alliance. The preliminary findings support the utility of multimodal approaches to capture clinical interactions. This technology provides valuable context for developing actionable insights without burdening instructors or learners. Findings from this study will motivate data-driven methods for providing actionable feedback to clinical trainees....
In this study, a novel synthesis method for bandpass filters is proposed. The method relies on Richard’s transform and avoids approximations in circuit realizations. Thus, proper frequency responses are obtained for bandpass filters with bandwidths ranging from narrow to wide. In the presented approach, a method for removing the input and output inverters/transformers is proposed and is used to show how classic parallel-coupled resonator filters can be designed using the proposed method. Also, a degree of freedom is introduced that allows the overall impedance level of the fabricated filter to be tuned, which is used to tune the frequency response of the filter to the theoretical one. Both narrowband and wideband solutions in terms of impedance inverter realization are discussed in the paper. The theoretical investigations are confirmed by an experimental realization of two bandpass filters with parallel-coupled shorted resonators....
Rocket launches generate infrasound signatures that have been detected at great distances. Due to the sparsity of the networks that have made these detections, however, most signals are detected tens of minutes to hours after the rocket launch. In this work, a method of near-real-time detection of rocket launches using data from a network of smartphones located 10–70 km from launch sites is presented. A machine learning model is trained and tested on the open-access Aggregated Smartphone Timeseries of Rocket-generated Acoustics (ASTRA), Smartphone High-explosive Audio Recordings Dataset (SHAReD), and ESC-50 datasets, resulting in a final accuracy of 97% and a false positive rate of <1%. The performance and behavior of the model are summarized, and its suitability for persistent monitoring applications is discussed....
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