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Microwave sensors offer appealing features such as susceptibility, quick response, and non-invasiveness, making them valuable tools for highly accurate measurements of material characterisation. A wide range of techniques, including cavity waveguide, planar transmission line, cavity waveguide perturbation, open-ended coaxial probe, and free-space transmission, have been employed to characterise materials that are essential for their costeffectiveness, ease of manufacturing, high sensitivity, good quality factor (Q-factor), and compact size, allowing them to be applied to different material types. Among the microwave sensor types, the substrate-integrated waveguide (SIW) has emerged as a promising technology in order to characterise materials in an efficient manner. This paper presents a review of the current state and potential opportunities of SIW microwave sensors in the characterisation of dielectric materials. It provides insights into various design principles, techniques, and applications of SIW microwave sensors across different sectors, highlighting their advantages and limitations compared to conventional waveguide-based sensors. Furthermore, the paper summarises several fabrication methods that can be implemented for SIW microwave sensors to enable the production of efficient and reliable sensors. Additionally, the future directions provided in this paper aim to contribute to the ongoing development and optimisation of SIW-based microwave sensors for accurate and efficient dielectric material characterisation. Overall, this review article serves as a beneficial resource for new researchers seeking to understand the role of SIW microwave sensors in material characterisation. It outlines the current status, opportunities, and potential advancements of SIW sensors, shedding light on their significance and potential impact in the field of material characterisation. [ABSTRACT FROM AUTHOR]

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement's structural condition. The aim is to be able to predict whether a pavement section's structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement's structural condition. [ABSTRACT FROM AUTHOR]

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. The model examined anomalies, commonly referred to as botnets, in a cluster of IoT devices attempting to connect to a network. Essentially, this paper exhibited the use of transport layer data (User Datagram Protocol - UDP) generated through IoT devices. An intelligent novel model comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed for botnet detection in IoT devices. Various machine learning algorithms were also implemented upon the processed data for comparative analysis. The experimental results of the proposed model generated state-of-the-art results for three different datasets, achieving up to 99.99% accuracy effectively with the lowest prediction time of 0.12 seconds without overfitting. The significance of this study lies in detecting botnets in IoT devices effectively and efficiently under all circumstances by utilizing ICA with Random Forest Classifier, which is a simple machine learning algorithm. [ABSTRACT FROM AUTHOR]

Solder joints can experience fatigue cracking at the interface between the solder and substrate during thermal cycling due to the difference in thermal expansion between joined components. In order to strengthen the interfacial region and prevent cracking, two Cu pad surface treatments were studied on Sn-3Ag-0.5Cu solder, and on matte Sn plate and Ni plate coatings. The resulting intermetallic microstructures were characterized using scanning electron microscopy and energy-dispersive spectroscopy. Mechanical testing was performed using nano-indentation on the solder joints. The matte Sn plate on Cu sample formed an uneven distribution of Ag3Sn particles and a planar Cu6Sn5 interfacial intermetallic. The Ni-plated sample formed a needle-like Ni-rich interfacial intermetallic and uniform dispersion of Ag3Sn particles. The rough intermetallic compound (IMC)/solder interface and even IMC particle distribution cause the Ni-plated sample to experience reduced damage under board-level thermomechanical cycling because the interfacial structure reduces the Sn matrix recrystallization that contributes to fatigue cracking. In contrast, the matte Sn-plated sample exhibited an Ag3Sn particle-free zone adjacent to a planar Cu6Sn5 IMC layer which allows for rapid Sn recrystallization and fatigue crack propagation. [ABSTRACT FROM AUTHOR]

Cholesterol granuloma is a rare benign tumour and is described as inflammatory granulation that occurs in response to the deposition of cholesterol crystals. It is found most commonly in the paranasal sinuses or temporal bones, but there are also rare reports of their occurrence in the peritoneum, parotid gland, lymph nodes, thyroglossal duct, kidney, liver, and spleen. Involvement of the ribs has rarely been described previously. We report a case of cholesterol granuloma involving chest wall in second intercostals space of a 74-year-old male who presented with a slowly growing lesion of the anterior aspect of right side of the chest. [ABSTRACT FROM AUTHOR]

In this letter, the performance of a 25-Gb/s $1 \times 2$ multimode interference (MMI) coupler-based monolithic differential phase shift keying (DPSK) receiver integrated with Germanium (Ge) on silicon-on-insulator (SOI) photodetector (PD) is presented. Theoretical analysis suggests that the device exhibits more uniform extinction ratio over the C-band compared with a DPSK demodulator based on 2\times 2$ MMI coupler. Electrical scattering parameters (S-parameters) investigated through component level simulation comparing the fabrication tolerance shows power penalty of 1.2 and 5 dB for unbalanced and balanced detection DPSK receiver, respectively. The SOI 1\times 2$ MMI coupler-based unbalanced DPSK receiver has a sensitivity of 0.41 dBm at a bit error rate of without post-electrical amplification. The PD integrated single-end detection-based receiver has a total footprint area of 0.39 mm ^{2} . [ABSTRACT FROM PUBLISHER]

In the original article, there is an error in Fig. 1. Following is the corrected Fig. 1. [ABSTRACT FROM AUTHOR]