Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give

Often there is no well drilling without problems. The solution lies in managing and evaluating these problems and developing strategies to manage and scale them. Non-productive time (NPT) is one of the main causes of delayed drilling operations. Many events or possibilities can lead to a halt in drilling operations or a marginal decrease in the advancement of drilling, this is called (NPT). Reducing NPT has an important impact on the total expenditure, time and cost are considered one of the most important success factors in the oil industry. In other words, steps must be taken to investigate and eliminate loss of time, that is, unproductive time in the drilling rig in order to save time and cost and reduce wasted time. The data of

Energy savings are very common in IoT sensor networks because IoT sensor nodes operate with their own limited battery. The data transmission in the IoT sensor nodes is very costly and consume much of the energy while the energy usage for data processing is considerably lower. There are several energy-saving strategies and principles, mainly dedicated to reducing the transmission of data. Therefore, with minimizing data transfers in IoT sensor networks, can conserve a considerable amount of energy. In this research, a Compression-Based Data Reduction (CBDR) technique was suggested which works in the level of IoT sensor nodes. The CBDR includes two stages of compression, a lossy SAX Quantization stage which reduces the dynamic range of the

This quasi-experimental study investigated generative AI (GenAI) tools—Copilot for chemistry and GitHub Copilot for mathematics—on academic achievement and sustainable professional development among 160 undergraduates (40 experimental/control per department) at the University of Baghdad’s Ibn Al-Haitham College of Education for Pure Sciences (2024–2025). Non-random assignment controlled for covariates. Pre/post validated tests (α ≥ .85; 15 MCQ + 5 essay items) measured outcomes. ANOVA revealed significant gains for experimental groups (p < .001, η2 = .41, Cohen’s d = 0.72 [95% CI: 0.45–0.98]). Chemistry excelled in affective domains; mathematics in cognitive/skills. Findings affirm GenAI’s domain-specific efficacy, prov

The purpose of this study is to examine the effect of human resource diversity management practices on achieving entrepreneurship in Jordanian public universities. To achieve the aims of the study, a well-designed questionnaire was used for collecting data. The population of the study was (7433) faculty members (including different ranks such as professors, associate professors, assistant professors and lecturers) in Jordanian public universities. The study sample was selected through the use of a random sample, the questionnaire is distributed to a sample (of 400 with the percentage of 5%) selected by using a random sampling (350) copies of the questionnaire were collected, reaching about (87.5%) out of the sum total of the dist

Some new norms need to be adapted due to COVID-19 pandemic period where people need to wear masks, wash their hands frequently, maintain social distancing, and avoid going out unless necessary. Therefore, educational institutions were closed to minimize the spread of COVID-19. As a result of this, online education was adapted to substitute face-to-face learning. Therefore, this study aimed to assess the Malaysian university students’ adaptation to the new norms, knowledge and practices toward COVID-19, besides, their attitudes toward online learning. A convenient sampling technique was used to recruit 500 Malaysian university students from January to February 2021 through social media. For data collection, all students

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra