This document outlines the advancements in AI- accelerated frame generation utilizing Neural Processing Units (NPU) in mobile devices. The integration of NPU technology enhances the processing efficiency of mobile graphics, enabling real-time frame generation that significantly improves video and image quality. By leveraging specialized hardware designed for AI computations, the system reduces latency and optimizes power consumption, making it ideal for demanding applications such as gaming and augmented reality. This paper discusses the underlying architecture of NPUs, their role in accelerating frame generation, and the potential impacts on user experience in mobile environments. The findings illustrate how NPU-driven solutions can transform mobile graphics, offering a more immersive and responsive experience while efficiently managing resources.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

Context: Noise in the work environment, in all types of productive activities, represents a hazard and has not really been valued in its real dimension. Little has been seen that stakeholders have determined the urgency of managing noise control programs. Therefore, losses resulting from medical treatment and absenteeism, represented in health care and social services, result in hidden work-related costs that directly affect the gross domestic product in any country.

Method: This article compiles different case studies from around the world. The studies were divided for review into general studies on the effects of workforce noise and then particularized according to the effects of industrial noise on workers’ health. At a control level, the assessment and measurement of noise is defined through the use of tools such as noise maps and their respective derivations, in addition to spatial databases.

Results: According to the collection of information and its analysis, we observe that in the medium term, the economies will be diminished in an important percentage due to the consequences generated by the exposure to noise. Specific information can be found in the development of the article.

Conclusions: The data provided by the case studies point to the need for Colombia, a country that is no stranger to this phenomenon, and which additionally has the great disadvantage of not having significant studies in the field of noise analysis, should strengthen studies based on spatial data as a mechanism for measurement and control.

Financing: Fundación universitaria Los Libertadores.

The regulation of compressor extraction and energy storage can improve the performance of gas turbine energy system. In order to make the gas turbine system match the external load more flexibly and efficiently, a gas turbine cogeneration system with solar energy coupling compressor outlet extraction and energy storage is proposed. By establishing the variable condition mathematical model of air turbine, waste heat boiler and solar collector, we use Thermoflex software to establish the variable condition model of gas turbine compressor outlet extraction, and analyze the variable condition of the coupling system to study the changes of thermal parameters of the system in the energy storage, energy release and operation cycle. Taking the hourly load of a hotel in South China as an example, this paper analyzes the case of the cogeneration system of solar energy coupling compressor outlet extraction and energy storage, and compares it with the benchmark cogeneration system. The results show that taking a typical day as a cycle, the primary energy utilization rate of the system designed in this paper is 3.2% higher than that of the traditional cogeneration system, and the efficiency is 2.4% higher.

This study examines how the framing of organizational gender-equity policies shapes support among Generation Z employees. Drawing on performativity (Butler, 1990) and intersectionality (Crenshaw, 1991), we conceptualize framing as mediating how Gen Z employees perceive equity initiatives. Using a mixed-methods design, we combine survey data from 4,861 Gen Z respondents in 30 countries with directed content analysis of four HR policy documents (coded for equity vs enforcement, identity recognition, and youth engagement). Results reveal a gender gap: Gen Z women strongly endorse inclusive equity measures, consistent with evidence that women show stronger support for equality policies, whereas Gen Z men are more skeptical of policies framed as exclusive or punitive – mirroring polls finding many Gen Z men say equality efforts have gone too far. These findings suggest that performative policy framing activates social identities differently by gender and that intersectional policy language affects reception. Practically, we recommend framing equity initiatives in terms of shared fairness and collective benefit, using transparent rationale and inclusive identity language. Gen Zers expect fair pay, inclusive policies, and transparency, so HR communications should emphasize fairness and allyship to enhance legitimacy and support among this cohort.