The Higher Colleges of Technology (HCT; Arabic: كليّات التقنيّة العليا) was established in 1988 and is the largest applied, higher education institution in the United Arab Emirates (UAE). During the 2019–2020 academic year, there were 14,246 female and 6,744 male students enrolled at 16 campuses and six academic divisions throughout the country. More than 67,000 UAE nationals are graduates of the institution.The HCT has formal alliances with a number of international tertiary education and training institutions, and corporate partnerships with local and multinational companies. Some programs have international accreditation: for example, the HCT's Bachelor of Education degree was developed with, and is certified by the University of Melbourne.The CERT (Centre of Excellence for Applied Research and Training) is the commercial arm of the Higher Colleges of Technology, developing and providing education, training and applied technology for public and private sector clients, since 1996.The HCT Chancellor is H.E. Nasser Bin Thani Juma Al Hamli, UAE Minister of Human Resources & Emiratization. Dr Tayeb A. Kamali was appointed as the Vice Chancellor in June 2005. Mohammed Omran Al Shamsi was made President of the Higher Colleges in 2013, an appointment that carried Ministerial rank. On March 17, 2015 Abdullatif M. Al Shamsi was appointed as HCT Vice Chancellor, by Federal Decree. He is now the President & CEO of HCT.E.
Supercritical carbon dioxide (SC-CO₂) extraction has emerged as a promising green technology for the recovery of high-value compounds from microalgae, including lipids, omega-3 fatty acids, carotenoids, and antioxidants. This review critically evaluates recent advances in thermodynamic optimization, kinetic modelling, response surface methodology (RSM), and process integration strategies. Operating pressures typically range from 200 to 400 bar, with extraction performance strongly influenced by adsorption-controlled mass transfer, moisture content (up to 23 wt
ContextFood security is highly reliant on agricultural activity to drive the world economy. However, this activity is in great danger due to climatic changes and improper use of irrigation techniques. Consequently, the lives of numerous individuals worldwide are in jeopardy. In light, this paper investigates the promise of smart irrigation systems based on new technology.ObjectiveTo meet the growing demand for water in agriculture, this study presents an intelligent irrigation system that uses cutting-edge technologies of (1) cloud computing, (2) embedded systems, and (3) Internet-of-Things (IoT). The main objective is to demonstrate how this innovative strategy can effectively manage water resources, supporting food security through cutting-edge agricultural technology.MethodsThis paper proposes a smart irrigation system based on cutting-edge technologies like the embedded system, Internet of Things (IoT), and cloud computing as a groundbreaking strategy to improve food security through the implementation of advanced agricultural technology. This system supervises real-time monitoring of crucial environmental factors such as (1) moisture, (2) humidity, (3) temperature, and (4) water levels, in smart agriculture practices. In addition, this system employs the latest sensors, including the module (DHT22), water level sensor, and moisture sensors, which are connected to the widely used embedded system (ESP32). The system uses the ThingSpeak cloud and ThingView app to enable wireless communication between the device and the farm owner, enhancing their interaction. The automated control of the two water pumps is based on the readings of various environmental factors. Moreover, this will also present a mathematical-driven function known as linear interpolation to calibrate the water level sensor in percentage. This system was created using the V-model software development approach.Results and conclusionFarmers can access comprehensive farm data from anywhere in the world as the sensor data is transmitted in real-time to both the ThingSpeak cloud and the ThingView. This capability allows for more precise crop irrigation and increased production. The study's findings demonstrate a striking 70% reduction in water consumption for soil irrigation when utilizing the proposed smart irrigation system. This paper underscores the significant promise of smart irrigation systems, driven by IoT, embedded systems, and cloud computing, to conserve water resources and advance food security.SignificanceThis article proposes an innovative solution that reduces soil irrigation water consumption by 70% compared to traditional methods. It explores how smart irrigation can improve the sustainability of agriculture and positively influence food security.
Abstract Fused Deposition Modeling (FDM) slicers typically apply uniform process parameters across entire parts, leading to inefficient material usage and limited adaptation to local geometric or functional requirements. This work presents a post-slicing G-code optimization framework that operates directly at the instruction level without modifying the original CAD model or re-running the slicer. The framework integrates three modules: (1) curvature-aware adaptive layer height control, (2) function-specific reinforcement for load-bearing regions, and (3) AI-based weak-zone detection using unsupervised clustering. Physically correct extrusion is enforced through incremental ΔE handling in relative mode (M83). The method is validated through three case studies: a quadcopter frame, an ASTM D638 tensile test specimen, and a complex mounting bracket, all printed using a Creality Ender-3 printer (0.4 mm nozzle, Nylon filament) with baseline G-code generated by Ultimaker Cura 5.0. Compared to the original toolpaths, the optimized G-code preserves geometric fidelity (RMS deviation < 0.05 mm) while reducing effective material extrusion by approximately 22–25% across the three geometries. Print time increased by 7–9% due to feedrate smoothing and localized reinforcement. These results suggest that instruction-level, post-slicing optimization can improve material efficiency for functional FDM components across diverse geometries, though further validation across additional printers, materials, and part complexities is needed to establish full generalizability.
Volatile memory contains valuable forensic evidence, but retrieving relevant strings at forensic scale is difficult because memory-extracted corpora are large, noisy, fragmented, multilingual, and often relevant without sharing exact lexical forms. Conventional keyword and regular-expression workflows remain effective when the expected wording is known, but they are limited for synonymic, cross-lingual, or corrupted evidence. This paper investigates a hybrid semantic–lexical retrieval strategy for scalable evidence discovery over unstructured memory strings. The study uses a two-phase design. Phase I introduces a controlled benchmark spanning 10 forensic concepts, 10 retrieval categories, and four languages to evaluate exact, semantic, cross-lingual, and corruption-tolerant retrieval. Dense multilingual embedding models perform best for semantic and cross-lingual retrieval, whereas fuzzy lexical matching is strongest for corrupted-string recovery. Phase II transfers the learned thresholds into a forensic-scale corpus of 4.45 million memory-derived strings. The strongest dense models reduce the candidate review space by 98.66–99.24
Artificial intelligence systems increasingly operate in high-risk domains where regulatory frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001 impose explicit evidence and accountability requirements. However, existing engineering practice remains largely manual, retrospective, and decoupled from operational pipelines, resulting in inconsistent provenance, limited reproducibility, and inadequate clause-level traceability. This paper introduces Governance as Evidence for AI Pipelines (GEAP), a pipeline-native governance framework that expresses regulatory and organizational policies as machine-interpretable Governance as Code rules. GEAP integrates governance directly into a unified SDLC–MLOps execution spine by enforcing promotion decisions at five gates—Data, Training, Validation, Release, and Operations—each of which emits signed, content-addressed artifacts into a tamper-evident Evidence Backbone. These artifacts are assembled into a per-run Conformity Bundle, from which the proposed Clause-to-Artifact Traceability mechanism deterministically renders clause coverage across multiple regulatory regimes without manual crosswalks or duplicated documentation. The framework further introduces quantitative governance metrics that measure adequacy, completeness, stability, and evidence hygiene. A detailed synthetic case study of an intensive-care sepsis early-warning system demonstrates GEAP’s ability to standardize promotion control, detect policy violations, and produce replayable, audit-ready compliance manifests in a high-risk clinical context. The results show that governance can operate as a deterministic, reproducible, and verifiable pipeline property rather than an external documentation exercise, enabling more disciplined, transparent, and accountable AI deployment practices.
