AIJR Proceedings is an established scholarly proceedings series for publishing peer-reviewed conference papers. It provides conferences with publication under a recognized ISSN series, while assigning an ISBN and DOI to each volume and an individual DOI to every published paper. Metadata for all published proceedings volumes and individual papers are registered with CrossRef, while published papers are discoverable through Google Scholar.
Influence of Fracture Characteristics and the Use of Network Connectivity Index on Synthetic Rock Mass Strength Characterisation of Moderately Jointed Granite in Hong Kong
Studies by Elmo & Stead (2010) and Stavrou et al. (2019) demonstrated that rock mass strength can be quantitatively characterised and directly linked to fracture characteristics. However, to date, rock mass classification and strength characterisation processes remain largely inductive, relying on empirical methods. It has been identified that this process is influenced by subjectivity and engineering judgement. With the rise of computing power and numerical modelling tools, engineers tend to apply and extend the use of rock mass classification system to determine rock mass strength and parameters in more advanced analyses. Typically, assessment of rock mass strength is undertaken by correlating different rock mass classification systems with the Hoek- Brown criterion for engineering and design purposes. Fracture characteristics; in terms of intensity and trace lengths, are rarely analysed in detail. This study addresses this gap by evaluating the influence of fracture characteristics on rock mass strength. The focus is on the assessment of unconfined compressive strength (UCS) of moderately jointed granite, based on actual tunnel face mapping records. Virtual large scale UCS tests on Synthetic Rock Masses (SRM), which consist of Voronoi Grain-Based Models (GBM) and explicit fractures, are undertaken to determine the rock mass strengths. The results indicate that fracture characteristics poses significant influence on the SRM strengths and demonstrate that rock mass strength characterisation can be directly assessed through the Network Connectivity Index (NCI, Elmo et al. (2021), which considers explicit assessment of fracture intensity & trace lengths. Through clear correlations between rock mass strength and NCI values, this paper demonstrates that NCI provides a more robust and objective alternative to the commonly used Q'-GSI correlations in deriving rock mass strength. The findings demonstrate the importance of integrating detailed fracture assessment into rock mass classification and mapping processes, to improve reliability of rock mass strength and parameters prediction for engineering purposes.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
Structural and Third Order Nonlinear Optical Properties of PbS:La Thin Films
The present study examines the third order NLO and electrochemical properties of spray deposited lanthanum doped PbS (PbS:La) thin films. FCC crystallization was confirmed by XRD patterns along the plane (2 0 0). The crystallite size of PbS increases with La doping concentration and ranges from 25 to 34 nm.PbS:La thin films demonstrate self-defocusing due to temperature effects caused by absorption of single photons or free carriers. As a result of the low cationic field strength and high polarizability of La3+ ions, PbS exhibits a higher nonlinear refractive index. The nonlinear susceptibility and refractive index were found to be in the range 3.12 x 10-6 to 5.23 x 10-6esu and 2.65 x 10-9 to 3.52 x 10-9 cm2/W, respectively. The PbS:La thin films improve the structural properties and therefore, it becomes suitable candidature for the applications in nonlinear optical devices.
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc/4.0
A Surfactant Role on The Polyethylene Glycol (PEG) Nanoparticles by Sol-Gel Method
This study investigates the role of surfactants in the synthesis of polyethylene glycol (PEG) nanoparticles via the sol-gel method. Surfactants significantly influence nanoparticle size, distribution, and stability by acting as stabilizing and templating agents. By varying surfactant type and concentration, improved colloidal stability, reduced agglomeration, and enhanced dispersibility were achieved. These findings highlight the potential of surfactant-assisted PEG nanoparticles for applications in drug delivery and biomedical engineering, offering precise control over nanoparticle properties.
Carica Papaya Seed Ash (CPS) a Bio-waste Based Heterogeneous Catalyst for Sustainable Synthesis of Benzoxazole Derivatives
We have introduced thermally and chemically stable metal free carica papaya seed ash (CPS) from a bio-waste, heterogeneous catalyst for organic synthesis. Moreover, the main advantages of this CPS catalyst like simple procedure, reusability and lower cost. The CPS catalyst physical and chemical properties were confirmed by scanning electron microscopy, X-ray diffraction studies, and EDX analysis. In continuation, we have synthesized of benzoxazole derivatives from aminophenol and aldehydes by using CPS catalyst. The synthesized compounds were confirmed by FT-IR, 1H NMR and 13C NMR techniques.
Investigation on Synthesis, Physical, Structural and Spectroscopic Features of Pr3+:Yb3+ co-doped Alkali Borate Glasses for Photonic Applications
The incorporation of small amounts of dopants in borate glasses is found to change properties of the host matrix and it could change drastically with the type of dopant and its concentration. In the current analysis, Pr3+:Yb3+ co doped alkali borate glasses are prepared by melt quenching technique. The ionic nature of the bonds present in the glass is explored by calculating the bonding parameter values from the absorption spectra. The orange color emission is observed through the luminescence spectra which denotes the utility of prepared glasses in various photonic applications.
Green Extract Mediated Synthesis of Cerium Nitrate Hexahydrate by Using Banana Rim for the Evaluation of Antibacterial Efficacy
This work deals with the systematic study of structural, optical and antibacterial study of CeNO3 and green extract of banana rim. Natural plant extracts and cerium nitrate CeNO3 have attracted attention because of their possible synergistic effects in biological applications. This study examines the antibacterial, wound-healing, and antioxidant qualities of a green plant extract in conjunction with CeNO3. These substances stabilize cerium ions when coupled with CeNO3, which may lessen cytotoxicity while increasing the bioavailability and effectiveness of cerium ions. These results demonstrate the potential of CeNO3 in conjunction with green extracts to create cutting-edge wound care products and antioxidant treatments. To clarify the underlying mechanisms and improve the formulation for therapeutic applications, more research is necessary. Furthermore, the combination shows enhanced antimicrobial activity in tests against common pathogenic bacteria (E. coli and S. aureus), which may make it appropriate for use as a wound dressing.
Synthesis, Structure, Crystal Growth and Characterization of an Organic Nonlinear Optical Single Crystal: L - Alanine L - Mandelic Acid
L - alanine L - Mandelic acid [LALMA] single crystal was grown by slow evaporation technique at room temperature. The lattice parameters were assessed from single crystal X - ray diffraction analysis. The single crystal X - ray diffraction study proclaimed that the LALMA crystal crystallizes in monoclinic system and powder XRD analysis confirms the crystalline nature. FT - IR and FT - Raman spectral studies were carried out to confirm functional groups present in the crystal. Optical properties were analyzed using UV - Vis - NIR spectrum. Its lower cut off wavelength is 265 nm. The optical band gap of the crystal was calculated as 3.5eV. The photoluminescence spectrum exhibited two peaks (572 nm and 582 nm) due to the donation of protons from carboxylic acid to amino group.
Fluorescent Carbon Dots via a Green Synthesis Approach
Carbon Dots (CDs) are best alternative to heavy metal-based quantum dots and fluorescent dyes. The horizon of green carbon dots is developing swiftly, but it faces several challenges like ensuring the durability of their properties. Herein, we discuss the green synthesis of carbon dots from papaya extract using hydrothermal method. The physical, chemical and optical properties of the synthesised CDs is critically analysed using several methods like UV spectroscopy, photoluminescence, high resolution transmission electron microscopy and Fourier transform infrared spectroscopy. The development of cost effective stable and sustainable green CDs with tuneable properties has a wide range of applications include physics, chemistry, biology, nanotechnology, energy and other interdisciplinary areas.
An Investigation on the Synthesis and Characterization of Copper Oxide Doped Nano Particles
Cobalt-doped copper oxide (CuO) nanoparticles, synthesized by hydrothermal method, exhibit enhanced photocatalytic, magnetic, and electrochemical properties, making for energy storage, catalysis, and gas sensing applications. The characterization confirmed successful cobalt doping nanoparticles and material properties. X-ray diffraction (XRD) analysis confirmed the crystalline structure, while field-emission scanning electron microscopy (FESEM) revealed uniform particle morphology and consistent dopant distribution. Optical properties were assessed using UV-Vis diffuse reflectance spectroscopy bandgap energy and Fourier-transform infrared (FTIR) spectroscopy identified characteristic functional groups. These results highlight the potential of cobalt-doped CuO as a multifunctional material for advanced applications.
Crop Production Prediction Using Ensemble Neural Network Model
This study presents a pioneering methodology for predicting and classifying crop production, leveraging an Ensembled Neural Network Model (ENNM) that integrates the Squeeze Net and Efficient Net architectures. By amalgamating the strengths of these models, our approach aims to significantly enhance the accuracy and efficiency of crop production forecasting. Squeeze Net’s lightweight design and Efficient Net’s superior performance collectively contribute to a robust framework capable of tackling the complexities of agricultural data. Through extensive experimentation on a diverse crop production dataset, we demonstrate the efficacy of our proposed ENNM, achieving an outstanding accuracy rate of 98.5%. It enhances the model's adaptability to diverse agricultural environments and cropping systems. Our findings signify a significant step forward in harnessing advanced machine learning (ML) techniques for addressing real-world challenges in agriculture, with implications for improving crop yield forecasts, optimizing resource allocation, and bolstering food security efforts on a global scale.
Machine Learning Approaches for Predicting Autism in Children: A Comparison of AdaBoost and Other Algorithms
This study presents a Autism Spectrum Disorder (ASD) in children is a neurodevelopmental disorder characterized by challenges in social interactions, communication, and behavior. Early identification and diagnosis of ASD, especially between the ages of 20 and 60 months, are essential for effective treatment. Without early detection, intervention becomes significantly more difficult. While a variety of machine learning (ML) techniques have been employed to predict ASD, the accuracy of predictions for younger age groups is still limited. This study investigates the use of three machine learning algorithms—Support Vector Machine (SVM), Random Forest, and AdaBoost—to predict and identify autism in children. The AdaBoost classifier, which integrates multiple weak learners to form a more robust classifier, is proposed as the primary approach. To assess the performance of these algorithms, key metrics such as accuracy, precision, F-score, and the confusion matrix are computed. The algorithm with the highest accuracy is then utilized to forecast autism in children.
Quantum–Driven Insights: A Medical Expert System for Insomnia Prediction Secured by Quantum Cryptography
Quantum computing is protected by quantum cryptography, it offers a novel medical expert system for predicting sleeplessness. In order to increase predicted accuracy and address the substantial negative effects of insomnia on health, it uses sophisticated quantum machine learning algorithms to evaluate a variety of patient data, including lifestyle, psychological profiles, and sleep habits. It highlights how the system outperforms conventional techniques in terms of accuracy and efficiency, enabling the early identification of at-risk patients and facilitating individualized treatment plans. Future developments in medical diagnostics and individualized care are made possible by the research, which highlights the revolutionary potential of quantum technology in the treatment of insomnia and other healthcare applications.
Advanced Machine Learning Techniques for Intrusion Detection System Development
Intrusion Detection Systems (IDS) play a critical role in safeguarding digital infrastructures against evolving cyber threats. This research explores the integration of advanced machine learning (ML) techniques to enhance the detection accuracy and adaptability of IDS. Traditional IDS methodologies often struggle with scalability, false positives, and identifying novel attack patterns. To address these challenges, this study leverages advanced ML algorithms, including deep learning architectures, ensemble models, and anomaly detection techniques, for effective threat identification. A comprehensive dataset is utilized to evaluate system performance, ensuring robust benchmarking across various metrics such as accuracy, precision, recall, and F1-score. Furthermore, feature selection and dimensionality reduction methods are employed to optimize computational efficiency while maintaining predictive performance. The results demonstrate that advanced ML techniques significantly improve intrusion detection capabilities compared to conventional methods, particularly in detecting zero-day attacks and minimizing false positives. This paper concludes with a discussion on the implications of integrating advanced ML models into real-world IDS applications and outlines future directions for research in this domain.
Withdrawn: Predictive Malicious Identification and Avoidance Using Real-Time Quantum-Enhanced AI
This paper has been withdrawn as the authors failed to send the revised manuscript.
Big Data in Healthcare: Catalyzing Innovation in Personalized Medicine and Predictive Analytics
The integration of big data analytics in healthcare is driving a paradigm shift towards precision medicine and predictive analytics, revolutionizing how care is delivered and diseases are managed. This paper explores the transformative potential of big data in catalyzing innovation in personalized medicine by leveraging genomic, clinical, and environmental datasets to tailor treatments to individual patients. Additionally, it examines the role of predictive analytics in early disease detection, risk stratification, and optimizing operational efficiencies in healthcare systems. Through case studies and a review of recent advancements, the paper highlights the applications of big data in enhancing patient outcomes, reducing healthcare costs, and fostering innovation in drug discovery and public health initiatives. Despite its immense promise, the integration of big data into healthcare presents challenges related to data security, interoperability, and ethical considerations. Addressing these issues through robust frameworks and technological innovations is critical to realizing its full potential.
Withdrawn: The Critical Need for Cybersecurity in Data Science: Protecting Data, Models, and Insights
Perfecting Structured Pattern Mining by Text Data through Decision Tree and Bitmap indicator Integration
This paper proposes Bitmap indicator- grounded Decision Tree (BIDT) fashion to estimate the rigidity of data in threat operations. Originally, the Bitmap indicator- grounded Decision Tree bracket is used to induce the knowledge tree which helps to ameliorate scalability. A bitmap indicator in BIDT is used to pierce large databases effectively. Rather of using a list of row ids in the BIDT bitmap indicator, it uses the primary crucial value in a table which is numbered in sequence with each crucial value (array of bytes). The BIDT algorithm along with the bit-wise logical operations AND applied through the select query in SQL. The BIDT fashion with the logical ‘AND’ driver which helps to combine queries and produce applicable result. Query results help to make a decision tree and to estimate the rigidity of colorful operations. The population frequenters are attained by simply counting the total number of “1” in the bitmaps constructed on the trait to prognosticate and estimate certain attributes like threat factor rate with the help of main table in decision tree for the root node. The algorithm is applied on colorful attributes similar as nonsupervisory false positive rate, threat rate and threat identification time.
M-Polynomial and Entropy Measures for Graphene Structures
A topological index (TI) is a real number that defines the relationship between a chemical structure and its properties and remains invariant under graph isomorphism.TIs defined for chemical structures are capable of predicting physical properties, chemical reactivity and biological activity. Several kinds of TIs have been defined and studied for different molecular structures.Graphene is the thinnest material known to man and is also extremely strong while being a good conductor of heat and electricity. With such unique features, graphene and its derivatives have found commercial uses and have also fascinated theoretical chemists. In this article, the neighbourhood sum degree-based M-polynomialand entropy measures have been computed for graphenestructures. The proper analytical expressions for these indices are derived. The obtained results will enable theoretical chemists to study these exciting structures further from a structural perspective.
Inverse Majority Neighborhood Number for Tensor Product of Path and Cycle
The tensor product of \( G \) and \( H \) is the graph, denoted as \( G \otimes H \), whose vertex set is \( V(G) \otimes V(H) \), and for which vertices \( (g,h) \) and \( (g',h') \) are adjacent if \( g g' \in E(G) \) and \( h h' \in E(H) \). In this article, the inverse majority neighborhood number \( n_i^{-1} (\chi) \) of \( G \) for the tensor product of path and cycle are determined. Also, some bounds for \( \chi = P_n \otimes C_l \) are obtained.
Solution of Fuzzy Linear Equation Using Cramer’s Rule
Many real-time engineering systems are too complex to be defined in precise terms and imprecision is often involved. Linear system of equations with uncertainty parameters plays a significant role in the areas of economics, finance, engineering, control system, and so on. To analyze such situation, fuzzy information is required. In this paper deals in solving fuzzy system of linear equations. There are many non-fuzzy classical methods to solve linear equations but, in this paper, solving fuzzy linear equation and solving linear equation using Cramer’s rule are discussed.
A Rule-Based Framework for Predicting Rainfall Using Decision Trees and Fuzzy Petri Nets
In a crucial component of meteorology is weather prediction, and precise forecasting has a big impact on everyday life, agriculture, and disaster relief. Based on past meteorological data, this study investigates the possibility of data mining approaches for forecasting rainfall and other weather conditions. We apply the C4.5 decision tree algorithm to a dataset of 2,500 weather observations in order to categorize meteorological conditions and find trends that affect the likelihood of rainfall. We use fuzzy Petri nets to handle the inherent uncertainty in meteorological data, enabling a more adaptable, sophisticated understanding of probabilistic weather patterns. Our combined strategy provides a well-balanced framework that makes use of the C4.5 algorithm's strong classification capabilities while improving the model's dependability and interpretability via fuzzy logic. According to preliminary findings, this hybrid strategy offers a viable way to increase the precision of weather forecasts. By demonstrating how data mining and fuzzy algorithms can efficiently support short-term weather forecasting, this study advances the area of data-driven meteorology.
A Mathematical Model for the Effects of Critical Care in Infancy on Cortisol Levels in Subsequent Childhood and Teen Years using Exponential-Exponential Distribution
Real-world events are typically described using statistical distributions. Their theory is widely studied and used in a variety of practical situations due to the usefulness of these statistical distributions. The Exponential-Exponential distribution is a novel probability distribution that is proposed in this work. This study sought to determine how cortisol levels in children and adolescence were impacted by intensive care treatment and early exposure to stress and pain. Neither the general hospitalization group nor the intensive-care unit subgroups differed significantly from the control group in terms of cortisol waking reaction, diurnal decline, or stress reactivity.
On $(i,j)$ ${\boldsymbol{\beta}}^{*}$ - Closed Sets in Bitopological Spaces
In this paper, we introduce and study the concept of a new class of closed sets called $(i,j)$ ${\boldsymbol{\beta}}^{*}$ - closed sets (briefly ${\boldsymbol{\beta}}^{*}$ - closed set) in Bitopological spaces. Also, we investigate some of their properties.
Upper Square Free Detour Number of Graphs
In this article we introduce the minimal square free detour sets and investigate the upper square free detour number of a graph. A square free detour set of vertices in a graph is called a minimal square free detour set if no proper subset is a square free detour set of the graph<!-- [if gte msEquation 12]><m:oMath><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>.</m:r></m:oMath><![endif]--> <span lang="EN-US" style="font-size: 11.0pt; line-height: 115%; font-family: 'Aptos',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Aptos; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> The upper square free detour number is the maximum order of its minimal square free detour set of the graph. We also determine the upper square free detour number of standard graphs that supports robotics path planning and biological network analysis for efficient navigation.
An $L(2, 1)$-labeling of a graph $G(V, E)$ is a function $f : V(G) \rightarrow \{0, 1, 2, \ldots\}$ is a set of non- negative integers such that $|f(x) - f(y)| \geq 2$ if $d(x, y) = 1$ and $|f(x) - f(y)| \geq 1$ if $d(x, y) = 2$, where $d(x, y)$ is the distance between the vertices $x$ and $y$ in the graph $G$. $L(2,1)$-labeling number, represented as $\lambda_{2,1}$ is the smallest number $k$ such that the $L(2,1)$-labeling exists with a maximum label $k$. In this manuscript, we have determined the bounds for $L(2, 1)$-labeling number of Melem structure and Cyclen-Pyrene Dendrimers.
Graph Energy Value of Alkenes Using Laplacian Minimum Majority Domination
The Laplacian Minimum Majority Domination energy value of alkenes has been examined in this article. Only one covalent bond exists between carbon atoms in alkanes, also known as saturated hydrocarbons. In an alkane, every carbon atom has sp3 hybrid orbitals and is joined to four additional atoms, all of which are either hydrogen or carbon. Diagrams are frequently used to conveniently describe chemical structures, with atoms occupying vertices and chemical bonds relating to the edges. The most important information about the chemical characteristics of molecules is passed down to this image. The chemical characteristics of molecules have also been observed to have a strong correlation with the theoretical graph invariant.
Conceptual Framework on Digital Leadership, Knowledge Sharing, and Emotional Intelligence in Cambodian Schools
This conceptual paper examines the interplay between Digital Leadership, Knowledge Sharing, and Emotional Intelligence, with Cultural Sensitivity and Capacity Building as contextual enablers, in the context of Cambodian secondary schools undergoing digital transformation. Drawing on recent literature from 2018–2025, the paper proposes a framework where Digital Leadership and Emotional Intelligence independently foster Knowledge Sharing, while Emotional Intelligence also moderates the Digital Leadership–Knowledge Sharing relationship by enhancing trust, motivation, and openness. Cultural Sensitivity and Capacity Building are positioned as cross-cutting factors that strengthen these relationships by addressing cultural norms, infrastructural disparities, and professional development needs. The framework responds to Cambodia’s leadership gap in digital readiness by emphasizing both technology-oriented and human-centric leadership capabilities. The proposed model contributes to theory by integrating socio-emotional and contextual dimensions into digital leadership research and offers practical guidance for school leaders and policy-makers seeking to cultivate collaborative, innovative educational environments. This conceptualisation invites future empirical studies to validate the relationships and mechanisms proposed.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
SmartBox: An IoT-Enabled Locker System for Secure and Efficient Package Handling
Cambodia’s e-commerce is growing rapidly, but last-mile delivery remains inefficient. Issues such as missed deliveries, limited courier availability, poor coordination, and lack of real-time updates continue to affect customer satisfaction and reduce trust in online shopping. Despite increasing digital adoption, the country’s logistics infrastructure still faces challenges in meeting delivery demands. This paper presents SmartBox, an IoT‑enabled locker that improves last‑mile delivery by adding flexibility, reliability, and control. Instead of relying on traditional door-to-door delivery, SmartBox enables customers to receive packages at designated lockers. The system features real-time pickup notifications and PIN-based access to ensure security and convenience. SmartBox was developed using Telegram and the python-telegram-bot library for user interaction, Laravel and MySQL for backend development, and tools such as AWS, Cloudflare, ApexCharts.js, GitHub, and Gmail SMTP for deployment and communication. Locker control is managed by an ESP32 microcontroller, integrated via the MQTT protocol for remote activation. Testing showed all twelve features worked reliably across browsers and mobile devices. A UTAUT survey of 14 users showed high performance and behavioral‑intention scores. The system is scalable, cost-effective, and suitable for deployment in universities, offices, and residential areas.
Automatic Cambodian License Plate Recognition Using Deep Learning With Robust Skew Handling
This study addresses the challenges of Automatic License Plate Recognition (ALPR) in Cambodia, with a specific focus on accurately detecting and recognizing license plates at varying skew angles. While recent advances in deep learning have improved ALPR systems, the unique characteristics of Cambodian license plates including dual script formats and distinct layout types combined with skewed orientations present significant technical challenges that current solutions do not adequately address. The research employs YOLOv8 instance segmentation for license plate detection, segmentation and type classification, followed by perspective correction using Canny Edge Detection, Douglas-Peucker algorithm, and Homography Transformation to rectify plate skew. Text zone detection utilizes separate YOLOv8 models for Type 1 and Type 2 plates to localize Latin script and plate number, with EasyOCR performing final character recognition enhanced through preprocessing and post-processing optimization. The study utilizes a purpose-built dataset of 1,200 real-world Cambodian license plate images systematically distributed across four skew angle categories ranging from 0° to 60° and two plate types. Performance evaluation demonstrates exceptional results with YOLOv8m-seg achieving 99.5% mAP@70 for detection, a 26.9% improvement over previous benchmarks, while maintaining 100% recall across all skew categories. The perspective correction and EasyOCR integration delivered substantial recognition improvements, with character recall exceeding the previous study by margins ranging from 1.2% to 32.6%, particularly achieving 30% exact match rate improvements for severely skewed plates (45-60°). The findings establish the first systematic skew-handling methodology for Cambodian license plates and demonstrate robust performance across varying skew conditions, providing a methodological foundation for advancing Cambodian ALPR research beyond previous limitations in handling skewed plates.
Green Cloud Computing: A Technical and Policy Framework for the AI Era
The global adoption of artificial intelligence is causing an unsustainable increase in the energy consumption of data centers, threatening to reverse a decade of efficiency gains. While legacy green computing strategies held power consumption steady through the 2010s, the unique, high-intensity nature of Al workloads renders these measures insufficient. Projections now indicate that data center electricity demand could triple by 2030, consuming a significant share of global electricity. This paper provides a framework for this challenge by reassessing legacy measures and examining four critical domains: advanced thermal management, security and data sovereignty, AI as a tool for energy optimization, and the economic and policy landscape. The analysis concludes that the exponential scaling of AI, which pushes server rack power densities from 15 kW to over 100 kW, requires a paradigm shift toward new infrastructure like liquid cooling and on-site clean power. Actionable recommendations are provided for industry and policymakers to ensure the AI revolution can proceed sustainably.