Architectural Design Research Articles

Design of efficient binary multiplier architecture using hybrid compressor with FPGA implementation

In signal processing applications, the multipliers are essential component of arithmetic functional units in many applications, like digital signal processors, image/video processing, Machine Learning, Cryptography and Arithmetic & Logical units (ALU). In recent years, Profuse multipliers are there. In that, Vedic multiplier is one of the high-performance multiplications and it is used to signal/image processing applications. In order to ameliorate the performance of this multiplier further, by proposed a novel multiplier using hybrid compressor. The proposed hybrid compressor-based multiplier is designed and implemented in Field programmable Gate Array (FPGA—spartan 6). The synthesis result shows that the speed of proposed hybrid compressor-based multiplier gets improved as compared to Array multiplier (35.83%), Wallace tree multiplier (34.58%), Vedic Multiplier based on Carry look ahead adder (CLA) (28.49%), Vedic Multiplier based on Ripple carry adder (RCA) (20.65%), Booth Multiplication (21.65%) and Vedic Multiplication based on Han-Carlson Adder (HCA) (20.10%) and Hybrid multiplier using Carry Select Adder (CSELA) (17.81%) and Hybrid Vedic Multiplier (7.15%).

Multiscale architecture design of 3D printed biodegradable Zn-based porous scaffolds for immunomodulatory osteogenesis

Reconciling the dilemma between rapid degradation and overdose toxicity is challenging in biodegradable materials when shifting from bulk to porous materials. Here, we achieve significant bone ingrowth into Zn-based porous scaffolds with 90% porosity via osteoinmunomodulation. At microscale, an alloy incorporating 0.8 wt% Li is employed to create a eutectoid lamellar structure featuring the LiZn4 and Zn phases. This microstructure optimally balances high strength with immunomodulation effects. At mesoscale, surface pattern with nanoscale roughness facilitates filopodia formation and macrophage spreading. At macroscale, the isotropic minimal surface G unit exhibits a proper degradation rate with more uniform feature compared to the anisotropic BCC unit. In vivo, the G scaffold demonstrates a heightened efficiency in promoting macrophage polarization toward an anti-inflammatory phenotype, subsequently leading to significantly elevated osteogenic markers, increased collagen deposition, and enhanced new bone formation. In vitro, transcriptomic analysis reveals the activation of JAK/STAT pathways in macrophages via up regulating the expression of Il-4, Il-10, subsequently promoting osteogenesis.

Architectural design and epidemic prevalence: Insights from Hong Kong's fifth wave

Understanding spatiotemporal patterns of COVID-19 transmissions and their relationships with the built environment is crucial for effective pandemic prevention. While existing research has primarily focused on the locations inhabited or visited by COVID-19 cases, limited attention has been given to the spatiotemporal pattern of transmission locations and their associations with architectural design forms. This study addresses this research gap by examining the spatiotemporal distribution patterns of COVID-19 transmission locations and their associations with residential building forms in high density context of Hong Kong during the fifth wave of the outbreak. Employing information on n = 319 residential buildings with a restriction-testing declaration, we report that attributes of building blocks, especially design forms, and housing type were associated with transmission rates. Our findings revealed that public housing were more vulnerable to COVID-19 pandemic relative to private housing block. Relative to the L-shaped building form, Y- and X-shaped building forms had the lowest COVID-19 prevalence, adjusting for other factors. The study results provide valuable insights for urban designers, health authorities and policymakers regarding keys attributes of building forms and types that influence the transmission dynamics of COVID-19, exemplifying the role architectural design has on pandemic prevention. Evidence generated from this study can be tested in other high density contexts as we prepare to risk-proof our cities from future pandemics.

Improving the computational efficiency and flexibility of FPGA-based CNN accelerator through loop optimization

The convolution operation consists of three-dimensional multiply-accumulate (MAC) operations within four loops, leading to a large design space to be optimized. However, prior research did not thoroughly investigate the loop optimization operations, which led to the development of accelerators that employed inefficient parallel computing architectures and hence consumed unnecessary resources. This study addresses the limitations of existing FPGA-based Convolutional Neural Network (CNN) accelerators in terms of computational efficiency and flexibility by proposing a novel scalable accelerator architecture. We first define a design space that includes loop optimization operations such as loop tiling, loop interchange, and loop unrolling. Based on this, we explore a more efficient dataflow and accelerator architecture through a quantitative analysis of the trade-off between accelerator performance and hardware costs. Then, this paper demonstrates exploring the optimal loop optimization strategy within the design space to guide the design of accelerator architectures, advancing towards the optimal solutions for accelerator performance. The effectiveness of the suggested acceleration architecture is confirmed by implementing VGG-16, ResNet-50, and ResNet-152 on Xilinx ZCU102 and Xilinx ZCU111 FPGAs. The achieved peak throughputs for the networks are 721.48 GOPS, 546.98 GOPS, and 664.66 GOPS, demonstrating outstanding performance, efficient resource usage, and flexibility.

Blockchain-Enhanced Sensor-as-a-Service (SEaaS) in IoT: Leveraging Blockchain for Efficient and Secure Sensing Data Transactions

As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT’s Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model’s potential for efficient and secure data-sharing in IoT.

Using Building Orientation to Promote Sustainability

Maximizing energy efficiency and overall performance is paramount in sustainable architectural design. Building orientation is proving to be a cornerstone of strategy in achieving these goals. This paper explores the critical role of building orientation in promoting sustainability in the built environment. It delves into the complex relationship between the layout and placement of structures and their impact on energy consumption, natural lighting and occupant comfort. The orientation of the building is carefully designed to interact harmoniously with the elements of the environment, especially the path of the sun and the prevailing winds. By strategically arranging structures, architects and designers can use natural resources to minimize energy demands and optimize building performance. For example, the right orientation can harness solar energy for passive heating and cooling, reducing reliance on mechanical systems and reducing carbon emissions. In addition, the orientation of the building deeply affects the distribution of natural light in the space. By strategically orienting openings such as windows and skylights, designers can maximize daylight penetration, reduce the need for artificial lighting and increase visual comfort for occupants, thoughtful building placement can mitigate glare and solar heat gain, promoting a more comfortable indoor environment while minimizing reliance on energy-intensive cooling systems.

Home among right angles: cube living

This article focuses on the dwellings of the Villa in Beroun in the Czech Republic, designed by HŠH Architects, and Buckminster Fuller’s Dymaxion, with architectural designs inspired by games, toys and creative play. The architectural designs inspired by the stacking blocks Lego®, K’nex® and Cidori® are explored and compared to GC Prostho by Kengo Kuma. The environmental implications of housing designs with malleable layouts, which reduce the need for constant changing and moving of houses throughout occupant lifetimes, are explored. The melding of the house and the game as a basis for rethinking the home space, which allows for rearranging walls and renewable capsular compartments, are analysed through the unsuccessful Nakagin Capsule Tower case study. The Villa in Beroun was dissected using gestalt psychology in order to unpack how the human mind reads its enveloping, malleable design. Due to its unique approach to black steel, concrete and glass material, the Villa in Beroun was analysed against new brutalist definitions.

A framework for eradicating women discrimination in architectural design firms in Egypt and GCC countries

PurposeThis research aims to develop and validate a framework for eradicating women’s discrimination in architectural design firms (ADFs) in Egypt and propose strategies for prospective adoption in the Gulf Cooperation Council (GCC) countries to increase the transformational impact of women on the architectural and urban developments of the Gulf region.Design/methodology/approachA combined qualitative and quantitative research approach was designed to accomplish four objectives. Firstly, to investigate the nature of business in ADFs, women’s discrimination and policies for gender equity (PGE) in architecture. Secondly, to analyse three case studies to validate the identified causes and impacts of women’s discrimination and examine the role of PGE towards eradicating women’s discrimination in ADFs. Thirdly, to conduct a survey questionnaire with a representative sample of ADFs to investigate their perception of women’s discrimination and the correlation between PGE towards eradicating women’s discrimination in ADFs. Based on the above, the research developed and validated a framework to eradicate women’s discrimination in ADFs in Egypt and suggested strategies for potential application in the GCC countries to increase the transformational role of women in the architectural and urban developments of the Gulf region.FindingsThrough literature review and case studies, the research identified and validated the causes and impacts of women’s discrimination as well as PGE in Architecture. The findings of data analysis showed that the highest-ranked causes of women discrimination in ADFs were “customs and beliefs” followed by “Underestimating women’s work” and “Recruiting males more than females”. Moreover, the highest-ranked impacts of women’s discrimination were “career shift and leaving the profession” followed by “decrease in creativity and innovation” and “decrease in economic growth”. Furthermore, the Spearman correlation test showed that while some PGE were correlated, others were not. These findings helped in the development of the proposed framework and proposed strategies.Practical implicationsThis research presents a practical framework to eliminate women’s discrimination in ADFs in Egypt based on the findings of literature review, case studies and survey questionnaire. The framework explained in a workable way the objectives of the framework functions; activities; tools and techniques; involved personnel and needed resources as well as enforcing mechanisms. It was validated by a representative sample of ADFs in Egypt to ensure its practicability and viability and suggested strategies for implementation in the GCC countries to increase women’s role in architectural and urban development.Originality/valueThe research identified and validated the causes and impacts of women’s discrimination as well as PGE in Architecture. Moreover, the research investigated the perception of women’s discrimination and the correlation between PGE through conducting a survey questionnaire with a representative of ADFs in Egypt. The research tackled a topic that received scant attention in construction literature, especially in Egypt and the GCC countries. The research developed and validated a framework to eradicate women’s discrimination in ADFs in Egypt. It represents a synthesis that is novel and creative in thought and adds value to the knowledge in a manner that has not previously occurred. The research suggested several strategies for the prospective implementation of the framework in the GCC countries as an approach to enhancing the transformational role of women in the architectural and urban developments of the Gulf region.

Employability skills in architecture and design: toward bridging the gap between academia and practice for Saudi women employees

PurposeThe role of women in the architecture and design industry is aligned with Saudi Arabia’s Vision 2030, which aims to increase the participation of women in the Saudi construction sector. Saudi female graduates from architectural and design schools are currently working in most of the mega projects underway in Riyadh; however, they face certain challenges at the beginning of their working lives due to the gap between the skills acquired during an architectural and design education and those needed for employability.Design/methodology/approachThis study evaluates the employability skills of Saudi female cooperative education (COOP) students and graduates from architectural schools in Riyadh. A quantitative approach is taken, and two questionnaires are administered: one for female COOP students and graduates from the college of architecture and design in one of the private universities in Riyadh and the other for employers from different architectural firms and governmental organizations in Riyadh. Descriptive and inferential statistics were used to analyze the research data.FindingsThe research findings suggest that practical experiences and on-site observation activities should be provided, and sustainability solutions and applications should be incorporated into the curriculum to bring architecture education closer to the architectural profession and highlight the role of universities in supporting graduates' skills development.Research limitations/implicationsIt is worth mentioning that the results of this study were based on the survey distributed to architecture and interior female students in a private university in Saudi Arabia. The number of students surveyed in this study is not large enough to generalize the survey results to the entire population of architecture and interior design students in the kingdom and in the world. The study will be extended to include more participants from male and female students in other universities in Saudi Arabia to investigate the gaps in employability skills in the field of architecture and interior design in the Kingdom.Practical implicationsThe study has practical and policy implications for further development and upcoming challenges as the labor market seems to improve and innovate every day. The study suggests providing practical experiences and onsite observation activities and incorporating sustainability solutions and applications into the curriculum.Originality/valueThe originality of this study is that it focuses on the importance of some employability skills, such as research and budgeting aspects, Sustainability solutions and applications for students that have not been mentioned in the previous studies.

Harnessing Deep Learning Methods for Detecting Different Retinal Diseases: A Multi-Categorical Classification Methodology

Medical image classification plays a vital part in identifying and detecting diseases. Vision impairment affects 2.2 billion individuals globally, with cataracts, glaucoma, and diabetic retinopathy as major contributors. Timely diagnosis, crucial for effective treatment, often relies on imaging like color fundus photography. This study tackles multi-class classification challenges in retinal diseases using MobileNetV2. Traditional CNN models struggle with accuracy and efficiency, prompting the exploration of lightweight architectures. Leveraging MobileNetV2's efficiency, the aim is to improve diagnosis using a comprehensive ocular disease dataset. By integrating deep learning with conventional methods, growing challenges in ophthalmological analysis are addressed. The research underscores the importance of collaborative efforts in dataset curation, architecture design, and model interpretability to advance the multi-class classification of retinal diseases.

Floor plan reconstruction from indoor 3D point clouds using iterative RANSAC line segmentation

Indoor Floor Plans (IFPs) are crucial in many fields, such as architectural design, BIM generation, indoor navigation, as well as reliability analysis and safety assessment in civil engineering. The traditional manual creation of IFPs is labor-intensive and time-consuming, hence the growing interest in their automatic generation. To automate IFP creation and ensure their consistency with original indoor layouts, a method for generating IFPs from indoor 3D point clouds is proposed. This method uses a complete point cloud from an indoor scene as the sole input and produces a corresponding vectorized IFP. It first identifies wall points from the 3D point cloud, and projects them onto a 2D plane. An iterative RANSAC-based clustering algorithm is then used to detect and segment line segments from the 2D point cloud. Finally, we restore the topological structure of the discrete line segment set and outputs a vectorized IFP composed of continuous line segments. When tested on two different scale indoor point cloud datasets, this new method achieved over 90 % of accuracy in line segment reconstruction and 97 % of IoU in IFPs reconstruction, proving its effectiveness in non-Manhattan indoor scenes. Compared with other representative methods, Floor-sp and ASIP, this new method offers more detail and higher accuracy.

Enhancing electrochemiluminescence intensity through emission layer control

Electrochemiluminescence (ECL), an intriguing luminescent phenomenon induced by electrochemical stimulation, has evolved from studying electron transfer reactions to a powerful analytical method and imaging technique. ECL can be generated through annihilation or co-reactant methods, with recent advancements integrating it into imaging devices for diverse applications. This review traces the evolution of ECL from its early applications to recent developments in imaging technology. Notably, the utilization of charge-coupled devices (CCD) and electron multiplying charge-coupled devices (EMCCD) in ECL microscopy has revolutionized imaging capabilities, making it a cost-effective option for point-of-care testing. The review explores the heterogeneous ECL mechanism, emphasizing its limitations and challenges in visualizing objects away from the electrode surface. The synergy between ECL and microscopy is highlighted, showcasing its diverse applications and contributions to understanding ECL mechanisms and improving its use in biological contexts. Finally, the review encapsulates innovative approaches in material design, surface modification, and electrode architecture, providing a comprehensive overview of strategies to control the active layer of the ECL and advance ECL microscopy.

Improving Speech Recognition with Convolutional Neural Networks

This project explores advanced techniques in speech recognition, focusing on emotion identification using Convolutional Neural Networks for improved accuracy and real-time processing efficiency. Emotion recognition from speech signals plays a crucial role in various applications, including human-computer interaction, customer service, mental health monitoring, and entertainment. This project proposes an innovative approach to emotion recognition using Convolutional Neural Networks (CNNs) applied to speech data. By leveraging advanced deep learning techniques, the proposed system aims to accurately identify and classify emotions conveyed through vocal expressions. The project begins with a comprehensive review of existing literature on emotion recognition and speech processing, identifying key challenges and opportunities in the field. Building upon prior research, the project introduces a novel CNN architecture optimized for emotion recognition tasks. This architecture is designed to extract relevant features from speech signals and capture subtle nuances indicative of different emotional states. One of the distinguishing features of the proposed approach is its multi-modal integration, which combines information from both audio and visual modalities to enhance emotion recognition accuracy. In addition to analysing speech signals, the system incorporates visual cues such as facial expressions and gestures, providing a more comprehensive understanding of the speaker's emotional state. Real-time processing efficiency is prioritized in the design of the system, ensuring prompt and responsive emotion recognition in interactive applications. Optimization techniques such as model quantization and lightweight architecture design are employed to minimize computational overhead while maintaining high accuracy. To address the variability and subjectivity of emotional expression, the system incorporates user-specific adaptation mechanisms. Through continuous learning and feedback integration, the system dynamically adapts to individual speakers' speech patterns and emotional characteristics, enhancing its ability to accurately recognize emotions in diverse contexts. The project also explores ensemble learning strategies to improve robustness and generalization performance. By combining predictions from multiple CNN models trained on diverse datasets, the system achieves greater resilience to variations in emotional expression and environmental factors. Ethical considerations, including privacy protection and responsible data handling, are integral aspects of the project's design and implementation. Measures are implemented to ensure the ethical collection, storage, and usage of speech data, safeguarding user privacy and maintaining trust in the system. Overall, the proposed system represents a significant advancement in emotion recognition technology, offering a sophisticated and versatile solution for accurately identifying emotions from speech signals. By leveraging deep learning techniques, multi-modal integration, real-time processing optimization, user-specific adaptation, and ensemble learning, the system demonstrates promising potential for various practical applications requiring robust and context-aware emotion recognition capabilities. Keywords: Speech Recognition, Emotion Identification, Convolutional Neural Networks (CNNs), Real-time Processing, Multi-modal Integration, User-specific Adaptation, Ensemble Learning, Deep Learning, Emotional Expression, Ethical Data Handling

Artificial Intelligence, Lymphoid Neoplasms, and Prediction of MYC, BCL2, and BCL6 Gene Expression Using a Pan-Cancer Panel in Diffuse Large B-Cell Lymphoma

Background: Artificial intelligence in medicine is a field that is rapidly evolving. Machine learning and deep learning are used to improve disease identification and diagnosis, personalize disease treatment, analyze medical images, evaluate clinical trials, and speed drug development. Methods: First, relevant aspects of AI are revised in a comprehensive manner, including the classification of hematopoietic neoplasms, types of AI, applications in medicine and hematological neoplasia, generative pre-trained transformers (GPTs), and the architecture and interpretation of feedforward neural net-works (multilayer perceptron). Second, a series of 233 diffuse large B-cell lymphoma (DLBCL) patients treated with rituximab-CHOP from the Lymphoma/Leukemia Molecular Profiling Project (LLMPP) was analyzed. Results: Using conventional statistics, the high expression of MYC and BCL2 was associated with poor survival, but high BCL6 was associated with a favorable overall survival of the patients. Then, a neural network predicted MYC, BCL2, and BCL6 with high accuracy using a pan-cancer panel of 758 genes of immuno-oncology and translational research that includes clinically relevant actionable genes and pathways. A comparable analysis was performed using gene set enrichment analysis (GSEA). Conclusions: The mathematical way in which neural networks reach conclusions has been considered a black box, but a careful understanding and evaluation of the architectural design allows us to interpret the results logically. In diffuse large B-cell lymphoma, neural networks are a plausible data analysis approach.

Expanding the applications of the wear-resistant titanium aluminum nitride thin-film to include temperature sensing

This study investigates an approach to temperature sensing by integrating Titanium Aluminum Nitride (TiAlN), originally engineered for wear and corrosion applications, as a temperature sensor within a multilayered thin film system. A nitride multilayer system was developed by physical vapor deposition (PVD) using a single four-target magnetron sputtering chamber; intermediate vacuum interruption steps were employed for masking procedures. The multilayer architecture design aimed to provide the sensor layer with mechanical protection and electrical shielding. Structural and electrical characterization of the TiAlN single layer revealed semiconductor behavior and stable electrical resistance up to 750 °C, with minimal signal stabilization requirements. Despite the higher Al content, the TiAlN temperature sensor exhibited a cubic crystal structure characterized by diffuse nanolayers, resulting from a two-fold rotational deposition and target configuration. A detailed examination of the multilayer system cross-section containing the TiAlN sensor was conducted using scanning transmission electron microscopy (STEM). The analysis revealed its columnar morphology with the presence of typical PVD growth defects, including voids and droplets. While the presence of these defects may impact the electrical characteristics of the sensor, the selected experimental conditions effectively maintained the structural integrity of the multilayer system despite the vacuum interruptions caused by masking procedures. Validation experiments confirmed the functionality of the multilayer system for temperature measurements up to 400 °C. The signal acquisition system addressed room temperature resistance variations and low sensitivity (thermistor coefficient ∼100 K), resulting in a measured error of approximately 6%. This study demonstrates promising results of TiAlN as a temperature sensor within a multilayered system, expanding its range of potential applications.

Exploring The Power Of Virtual Reality Technology And Image Processing In Tourism: The Architecture Of Quirino Laquatsa

Tourism industry have experienced major income losses and a spike in unemployment due to the pandemic. The need for technology have arisen to be able to help cope with challenges of the tourism industry of both cities and provinces. Technological advancements drive private and public sectors to adapt, with virtual reality gaining rapid popularity in tourist-oriented cities. Quirino province, the smallest in its region, requires recognition on social media and technology to advance its tourism industry. The province has to cope up with what the technology has to offer to its tourism management.
 The study's main goal is to create a virtual tourism architecture as close as possible to a particular place or tourist destination in the province. This may be used in the development of the Quirino Laquatsa and give visitors a taste of what is expected of the place or tourist site has to offer. This study used the Design Thinking Methodology mixed with Feature Driven Development. The data collection was done through the empathy phase.
 The proposed system architecture design promotes the development of a virtual tourism with the aid of virtual reality. It can help address the problems and issues in the tourism management of the province. This can eventually lead into the development of a web-based virtual tourism site to become a medium for sustainable tourism development.

Perancangan Enterprise Arsitektur Menggunakan TOGAF ADM Di Bea Cukai Tanjung Pandan pada Aplikasi SIMPORA

The web-based SIMPORA Customs Tanjung Pandan application is an efficient solution for optimizing annual budgeting business processes. Customers can easily get the latest information about the services provided in real-time. So it is hoped that it can improve performance significantly. To develop it, several data collection techniques were used, namely by conducting in-depth interviews, making observations and documenting them. Meanwhile, the research methodology is TOGAF ADM 9.1. This method is known to be the best method that can develop architecture in a structured and reliable manner. Several types of diagrams that help in designing this system are Value Chain, BPMN, Context Diagram, Use Case, and Class Diagram. It is hoped that this enterprise architecture design will enable budgeting to be monitored and improved immediately by stakeholders. The result of this research is an enterprise architecture design to support business processes at Tanjung Pandan Customs and Excise. It is hoped that various budgeting errors and design inaccuracies can be reduced and eliminated with this application, so that it can support the optimization of this institution's business processes.

MMSeaIce: a collection of techniques for improving sea ice mapping with a multi-task model

Abstract. The AutoICE challenge, organized by multiple national and international agencies, seeks to advance the development of near-real-time sea ice products with improved spatial resolution, broader spatial and temporal coverage, and enhanced consistency. In this paper, we present a detailed description of our solutions and experimental results for the challenge. We have implemented an automated sea ice mapping pipeline based on a multi-task U-Net architecture, capable of predicting sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). The AI4Arctic dataset, which includes synthetic aperture radar (SAR) imagery, ancillary data, and ice-chart-derived label maps, is utilized for model training and evaluation. Among the submissions from over 30 teams worldwide, our team achieved the highest combined score of 86.3 %, as well as the highest scores on SIC (92.0 %) and SOD (88.6 %). Notably, the result analysis and ablation studies demonstrate that instead of model architecture design, a collection of strategies/techniques we employed led to substantial enhancement in accuracy, efficiency, and robustness within the realm of deep-learning-based sea ice mapping. Those techniques include input SAR variable downscaling, input feature selection, spatial–temporal encoding, and the choice of loss functions. By highlighting the various techniques employed and their impacts, we aim to underscore the scientific advancements achieved in our methodology.

Study on the Tensile and Shear Performances of Fully Precast Partially Composite Floor Slab Joints

This study explored the tensile and shear characteristics of fully prefabricated partially composite floor slab joints through the design and testing of two tensile specimens, three steel–concrete specimens, and three concrete–concrete shear specimens. These tests aimed to evaluate how various connection designs influence the joints’ load-bearing capabilities and failure patterns. The findings revealed that the tensile specimens predominantly showed bond failures at the interface of the precast and cast-in-place layers, accompanied by rebar pull-out. Incorporating reinforcing bars or sleeves was found to potentially increase their ultimate load-bearing capacity by about 20%. The shear failures in the steel–concrete specimens were primarily due to interactions between the steel beam and adjacent composite slab, whereas the concrete–concrete specimens mostly underwent local crushing at the load application point and failure at the bonding interface. These observations affirmed the accuracy of the existing methods for calculating tensile and shear strengths, offering vital insights for the architectural design and construction of such floor joints.

An Examination of Machine Learning-Based Outlier Identification from Mobile Phone Tracks

In this paper, two machine learning algorithms—local outlier factor (LOF) and density-based spatial clustering of applications with noise (DBSCAN)—that are used to identify outliers in the context of a continuous framework for point of interest (PoI) detection are analyzed. The mobile trajectories of users are continuously and almost instantaneously loaded into this system. These frameworks are still in their infancy, but they are already essential for large-scale sensing deployments, such as Smart City planning deployments, where the anonymous individual mobile user trajectories can be valuable to improve urban planning. There are two contributions made by this paper. First, the functional design of the entire PoI detection architecture is provided by the study. Second, the study evaluates the effectiveness.