Future Technology

Emergency action plan for Haditha dam failure scenario, Al-Anbar, Iraq

Yasmeen Hamid — 2025-03-03

Dams are essential structures that regulate and manage water for human activities such as irrigation, power generation, flood control, and water supply. However, building and operating dams involve inherent risks that can lead to catastrophic consequences in case of failure, such failures can threaten the environment and populations downstream. Haditha Dam, Al-Anbar Governate, Iraq has been chosen as a case study due to its unique geological conditions (existence of limestone formations prone to karstification) and susceptibility to terrorist attacks. In this research, the risk factor for Haditha Dam is categorized as extremely high risk, with a Total Risk Factor (TRF) of 36. An emergency action plan that includes three possible failure scenarios has been proposed. Based on the flood maps, there is an urgent need for evacuation planning and the designation of safe and unsafe zones in the cities downstream of Haditha Dam to mitigate the consequences of a potential failure of the Dam. This plan aims to address immediate flood inundation, minimize loss of life, and manage the damage that could occur to infrastructure. As part of the emergency response strategy, an evacuation program has been proposed to protect lives and reduce the impact on affected populations.

Hybrid boost-cuk converter with bat-chicken swarm-optimized PI controller for photovoltaic grid systems

S. Hema — 2025-03-26

Recently, the reduction of greenhouse gas emissions and fuel consumption has been attended to by adopting the Photovoltaic (PV) system. Due to their intermittent nature, energy generated by PV systems is unpredictable for microgrid operation. Therefore, in this research, a novel hybrid Boost-Cuk converter is developed to efficiently increase the low voltage received from the PV system. Subsequently, the Bat-Chicken swarm optimized Proportional Integral (PI) controller is exploited to adjust the PI controller's parameters. Furthermore, the intermittency and instability of PV systems are addressed by adding a Battery Energy Storage System (BESS) to the microgrid to provide a steady and continuous power supply. This output is delivered to the grid via a Three Phase Voltage Source Inverter (3ϕ VSI), and the grid synchronization is accomplished with the aid of a PI controller. In order to validate the efficacy of the developed work, it is executed using the MATLAB/Simulink tool and compared with traditional topologies. The outcomes reveal that the developed research attains an efficacy of 93%, ensuring effective grid synchronization.

Investigation and optimization of process parameters in the electrical discharge machining process for Inconel 660 using response surface methodology

Kunal Singh — 2025-03-28

Based on its exceptional mechanical and thermal qualities, Inconel 660 is a high-performance superalloy that is frequently used in marine and aerospace engineering. However, attaining the ideal material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR) is severely hampered by its low machinability. This study uses Response Surface Methodology (RSM) based on the Box-Behnken Design (BBD) to examine the impacts of different process parameters in Electrical Discharge Machining (EDM) of Inconel 660. Statistical models were created to forecast performance results, and experimental trials were carried out to optimize machining parameters. The results show that whereas pulse-off time primarily affects SR, current and pulse-on time have a considerable impact on MRR and TWR. The adjusted parameters offer improved machining performance by decreasing electrode wear and enhancing surface morphology. These insights allow Inconel 660 and related superalloys to be machined more effectively.

Reframing the early-stage design process of residential buildings based on an energy-efficient, designerly decision support system (DDSS)

Mohamed Faisal Al-Kazee — 2025-04-14

The literature emphasizes the role of the early-stage design process, particularly early design decisions related to mid-rise residential buildings. On the other hand, the futuristic concepts of high-performance architecture represent a paradigm shift that requires a data-conscious approach to climate change mitigation. This research adopts a designer approach to address the complex and ill-defined sci-tech problems within the architectural field. The study aims to develop a framework for a user-friendly, data-driven Designerly Decision Support System (DDSS) to categorize and automate the architectural design process, with a particular focus on the early design stage. The methodology is based on in-depth structured interviews with architects to identify and classify influential parameters in the early design stages. These parameters were extracted to construct a metamodel. Subsequently, sensitivity analysis was employed to investigate the background of key performance metrics and the relationships among them. The research calculates the energy loads of nine mid-rise residential building patterns in Tehran using Energy Plus software. Based on the quantitative results, three representative patterns—1) high-consumption, 2) low-consumption, and 3) mid-rise—were selected for further sensitivity analysis. The findings indicate that a reference database can be created to comprehensively guide designers working on mid-rise residential patterns. This database can also serve as a resource for revising urban planning guidelines with energy metrics in mind. Additionally, the north and south Window-to-Wall Ratios (WWRs) are identified as the most significant design parameters, directly and interactively influencing heating, cooling, and lighting functions.

An innovative ZFFinch-MLPNet architecture for improving cyber intrusion prediction efficiency and accuracy

Dinesh Kumar Budagam — 2025-04-16

Intrusion Detection System (IDS) is one of the most significant security elements in today’s information technology-related organizations. For overcoming intrusion detection difficulties, Deep Learning (DL) has shown a significant contribution in recent times. An innovative IDS that merges the Zebra- Falcon Finch algorithm with a Multi-Layer Perceptron Recurrent Neural Network (ZFFinch-MLPNet) classifier is developed in this research. To assure data compatibility and integrity, the developed work initiates with data preprocessing comprising data inspection, handling missing values, and label encoding. Then, to recognize the structure of data, the Exploratory Data Analysis (EDA) combines correlation and visualization analysis. To enhance the intrusion detection efficacy, a Recursive Feature Elimination (RFE) is utilized to select the appropriate features. Finally, the MLPRNN classification approach with the Zebra-Falcon Finch algorithm offers flexibility, opposing overfitting, and improved accuracy. Also, for detecting network anomalies, this work addresses the developed approach’s outcomes in Python software and compares it with modern approaches. It is confirmed that the developed approach detects distinct types of network intrusions and attains better performance in identification with an accuracy of 96.20%, MCC of 92.33%, and ROC of 0.99.

Agenda setting theory in the digital media age: a comprehensive and critical literature review

Safran Safar Almakaty — 2025-04-25

This thorough literature study looks at how Agenda Setting Theory (AST) has developed in the digital media era over the last two decades (2004-2024). From its beginnings in McCombs and Shaw's work, the study tracks AST's evolution across three levels: issue salience transfer, attribute agenda setting, and the more recent Network Agenda Setting model. It examines how digital media's qualities- fragmentation, interactivity, algorithmic curation, and decentralized gatekeeping- have challenged and altered conventional agenda-setting mechanisms. Based on about 40 studies, the analysis concludes that although agenda-setting impacts remain online, they function in a more complicated, networked manner with a broader spectrum of players affecting public agendas. The article investigates digital platforms' empirical data, the rise of new agenda-setting players outside conventional media, and issues including audience fragmentation and false information. AST is still shown to be relevant, but major adjustments are needed to grasp the several aspects of agenda creation completely in today's mixed media environment.

Reconstructing pharmaceutical service competency framework: development of AI-informed competency indicators and localized practices in China

Yuqiu Wang — 2025-05-07

This study introduces an innovative method for reconstructing pharmaceutical service competency frameworks. The approach integrates artificial intelligence technologies with localization practices specific to the Chinese context. Employing a mixed-methods sequential exploratory design, we analyzed six major international competency frameworks using natural language processing and machine learning techniques to extract 4,782 unique competency statements, which were subsequently classified with 91.4% accuracy into relevant domains. The resulting preliminary integrated framework—comprising 5 domains, 24 competencies, and 103 behavioral indicators—underwent localization through a modified Delphi process involving 32 pharmaceutical stakeholders and verification via a national survey of 456 pharmacists across 18 Chinese provinces. Implementation across diverse healthcare settings resulted in significant improvements in service quality metrics, including a 23.7% reduction in medication errors (p<0.01) and an 18.6% increase in patient satisfaction. Cross-setting analysis revealed variable adaptability, with implementation feasibility scores ranging from 4.7/5 in tertiary hospitals to 3.2/5 in rural community pharmacies. Four critical success factors for effective framework adoption were identified: institutional leadership engagement, integration with existing quality systems, phased implementation, and dedicated training resources. The framework's distinctive features include competencies addressing the integration of traditional Chinese medicine with modern pharmacy practice and a modular structure enabling context-specific adaptation while maintaining core standards. This research contributes to bridging the gap between global standards and local realities in pharmaceutical competency development, demonstrating the potential of AI-informed approaches to enhance framework relevance, efficiency, and effectiveness across diverse healthcare contexts.

PNASFH-Net: Parallel NAS forward harmonic Network for colon cancer detection using CT images

V T Ram Pavan Kumar M — 2025-05-05

Colon cancer is a leading cause of cancer-related deaths worldwide, and early detection is vital to reduce mortality rates. While Deep Learning (DL) models are commonly used for colon cancer detection, they often require large datasets and are time-consuming. To address these challenges, a new model, Parallel Neural Architecture Search Forward Harmonic Network (PNASFH-Net), has been developed. PNASFH-Net begins by preprocessing colon images through Adaptive Median Filtering (AMF) to remove noise. It then segments the affected colon region using Pyramid Non-local U-Net (PNU-Net), optimized by the Remora Shuffled Shepherd Optimization Algorithm (RSSOA)—a hybrid algorithm combining the Remora Optimization Algorithm (ROA) and Shuffled Shepherd Optimization Algorithm (SSOA) for improved segmentation accuracy. Next, features from the segmented images are extracted and analyzed by PNASFH-Net, which combines Harmonic Analysis, Neural Architecture Search Network (NASNet), and Parallel Convolutional Neural Network (PCNN) for accurate detection. Experimental results show that PNASFH-Net achieves 98.345% accuracy, 98.512% specificity, and 98.679% sensitivity, demonstrating its potential for precise and early colon cancer detection.

Computational linguistic processing for evaluating policy effectiveness: textual analysis of China-Korea continuing education regulations

Tongle Li — 2025-05-10

This study employs computational linguistic methods to compare continuing education regulatory frameworks in Korea and China via systematic text comparison. Applying algorithmic methods like thematic decomposition, sentiment analysis, and semantic correlation measures to the government reports of the two countries, we develop an innovative cross-cultural assessment framework. The analytical process integrates entity extraction, vector-based semantic mapping, and quantitative content mining in order to identify regulatory patterns and efficacy signals within policy documents from 2010 to 2023. Empirical results show notable divergence in administrative priorities, discursive frameworks, and governance styles, with Chinese regulations showing centralized coordination characteristics in contrast to Korea's market-responsive institutions. The research adds to the policy analysis literature by demonstrating computational methodologies' ability to identify obscured administrative priorities and operational nuances outside of conventional analytical grasp. The contribution enhances computational policy studies through the creation of replicable, unbiased processes for comparative cross-country regulation, inferring useful implications for administrators and researchers constructing streamlined continuing education models. The research confirms computational linguistics as an effective means of evidence-based policy analysis in multilingual settings.

Cascade CNN: a two-stage segmentation framework for efficient and accurate brain tumor segmentation in multi-modal MRI

Mangalapalli Vamsikrishna — 2025-05-12

The region of a brain tumor is critical in gliomas diagnosis and treatment, which involves multi-modal MRI segmentation. While segmentation models like U-Net and nnU-Net do exist, they aren't effective in dealing with small tumor structures or with limited computational resources in general. To address these drawbacks, we propose a Cascade CNN (C-CNN) Model. C-CNN is a two-stage model that consists of two processes: coarse segmentation and refined segmentation. CoarseNet is the first process roughly segments the tumor and localizes the Region of Interest (ROI). This is succeeded by RefineNet, which does thorough multi-class segmentation on the cropped ROI, dividing the image into edema, Whole Tumor(WT), tumor core (TC), and enhancing tumor (ET). Our sequential training and multi-modal (T1, T1ce, T2, FLAIR) MRI inputs to the model reduce false positives and improve segmentation accuracy. We implemented our approach on the BraTS 2023 dataset and achieved the following Dice scores: 89.1% for WT, 83.2% for TC, 78.3% for ET, which bested single-stage models' results. Adaptive cropping further allows for lower computational costs, enabling the algorithm to be implemented in real-time clinical settings.