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The Internet of Things (IoT) is a promising technology for sensing and monitoring the environment to reduce disaster impact. Energy is one of the major concerns for IoT devices, as sensors used in IoT devices are battery-operated. Thus, it is important to reduce energy consumption, especially during data transmission in disaster-prone situations. Clustering-based communication helps reduce a node's energy decay during data transmission and enhances network lifetime. Many hybrid combination algorithms have been proposed for clustering and routing protocols to improve network lifetime in disaster scenarios. However, the performance of these protocols varies widely based on the underlying network configuration and the optimisation parameters considered. In this research, we used the clustering parameters most relevant to disaster scenarios, such as the node's residual energy, distance to sink, and network coverage. We then proposed the bio-inspired hybrid BOA-PSO algorithm, where the Butterfly Optimisation Algorithm (BOA) is used for clustering and Particle Swarm Optimisation (PSO) is used for the routing protocol. The performance of the proposed algorithm was compared with that of various benchmark protocols: LEACH, DEEC, PSO, PSO-GA, and PSO-HAS. Residual energy, network throughput, and network lifetime were considered performance metrics. The simulation results demonstrate that the proposed algorithm effectively conserves residual energy, achieving more than a 17% improvement for short-range scenarios and a 10% improvement for long-range scenarios. In terms of throughput, the proposed method delivers a 60% performance enhancement compared to LEACH, a 53% enhancement compared to DEEC, and a 37% enhancement compared to PSO. Additionally, the proposed method results in a 60% reduction in packet drops compared to LEACH and DEEC, and a 30% reduction compared to PSO. It increases network lifetime by 10–20% compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Deepfake has become an emerging technology affecting cyber-security with its illegal applications in recent years. Most deepfake detectors utilize CNN-based models such as the Xception Network to distinguish real or fake media; however, their performance on cross-datasets is not ideal because they suffer from over-fitting in the current stage. Therefore, this paper proposed a spatial consistency learning method to relieve this issue in three aspects. Firstly, we increased the selections of data augmentation methods to 5, which is more than our previous study's data augmentation methods. Specifically, we captured several equal video frames of one video and randomly selected five different data augmentations to obtain different data views to enrich the input variety. Secondly, we chose Swin Transformer as the feature extractor instead of a CNN-based backbone, which means that our approach did not utilize it for downstream tasks, and could encode these data using an end-to-end Swin Transformer, aiming to learn the correlation between different image patches. Finally, this was combined with consistency learning in our study, and consistency learning was able to determine more data relationships than supervised classification. We explored the consistency of video frames' features by calculating their cosine distance and applied traditional cross-entropy loss to regulate this classification loss. Extensive in-dataset and cross-dataset experiments demonstrated that Swin-Fake could produce relatively good results on some open-source deepfake datasets, including FaceForensics++, DFDC, Celeb-DF and FaceShifter. By comparing our model with several benchmark models, our approach shows relatively strong robustness in detecting deepfake media. [ABSTRACT FROM AUTHOR]

Recently, baby healthcare monitoring systems have emerged as a key research area. However, numerous research studies have been conducted on babies and their state of either awakening or sleeping. But there is a lack of comprehensive and systematic review in this area of research. The existing research considers mostly the children's monitoring vital health parameters, in turn, giving relaxation to the parents in caring of their babies. Still, the fundamental practical issues related to daily life are missing. As a result, the safety of babies against suffocation and falling hazards for employed parents or mothers involved in the workforce has been overlooked. Only a few studies exist that cover the age range of 0 months to 4 years, with further focus on the age range of 6 months to 4 years. In response to these gaps in previous research and existing technologies, this review work proposes a vision for a baby safety system that utilizes various sensors and technologies like machine learning and image processing to ensure a safer environment for babies. [ABSTRACT FROM AUTHOR]

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

Localization using sensor network has attracted much attention for its comparable low-cost and potential use with monitoring and targeting purposes in real and hostile application scenarios. Currently, there are many available approaches to locate persons/things based of GPS and RFID technologies. However, in some application scenario, e.g., disaster rescue application, such localization devices may be damaged and may not provide the location information of the survivors. The main goal of this paper is to design and develop a robust localization technique for human existence detection in case of disasters such as earthquake or fire. In this paper, we propose a 3-D localization technique based on the hop-count data collected from sensor anchors to estimate the location of the activated sensor mote in 3-D coordination. Our algorithm incorporates two salient features, grid-based output and event-triggering mechanism, to guarantee both improve accuracy and power efficiency. Simulation results indicate that the proposed algorithm can improve the localization precision of the human existence and work well in real environment.

Introduction: Advancing approaches to locally invasive pelvic malignancy creates a large tissue defect resulting in perineal wound complications, dehiscence, and perineal hernia. Use of reconstructive flaps such as vertical rectus abdominus myocutaneous (VRAM) flap, gracilis, anterolateral thigh and gluteal flaps have been utilised in our institution to address perineal closure. The authors compared outcomes using different flap techniques along with primary perineal closure in advanced pelvic oncological resection. Methods: A prospectively maintained database of patients undergoing advanced pelvic oncological resection in a single tertiary hospital was retrospectively analysed. This study included consecutive patients between 2014 and 2021 according to the Strengthening The Reporting of Cohort Studies in Surgery (STROCSS) criteria. Primary outcome measures were the frequency of postoperative perineal complications between primary closure, VRAM, gluteal and thigh (anterolateral thigh and gracilis) reconstruction. Results: One hundred twenty-two patients underwent advanced pelvic resection with perineal closure. Of these, 40 patients underwent extra-levator abdominoperineal resection, and 70 patients underwent pelvic exenteration. Sixty-four patients received reconstructive flap closure, which included VRAM (22), gluteal (21) and thigh flaps (19). Perineal infection and dehiscence rates were low. Infection rates were lower in the flap group despite a higher rate of radiotherapy (P< 0.050). Reoperation rates were infrequent (< 10%) but specific for each flap, such as donor-site hernia following VRAM and flap dehiscence after thigh flap reconstruction. Conclusions: In patients who are at high risk of postoperative perineal infections, reconstructive flap closure offers acceptable outcomes. VRAM, gluteal and thigh flaps offer comparable outcomes and can be tailored to the individual patient. [ABSTRACT FROM AUTHOR]

Background: Decision-making in the management of patients with retroperitoneal sarcoma is complex and requires input from a number of different specialists. The aim of this study was to evaluate the levels of agreement in terms of resectability, treatment allocation, and organs proposed to be resected across different retroperitoneal sarcoma multidisciplinary team meetings. Methods: The CT scans and clinical information of 21 anonymized retroperitoneal sarcoma patients were sent to all of the retroperitoneal sarcoma multidisciplinary team meetings in Great Britain, which were asked to give an opinion about resectability, treatment allocation, and organs proposed to be resected. The main outcome was inter-centre reliability, which was quantified using overall agreement, as well as the chance-corrected Krippendorff's alpha statistic. Based on the latter, the level of agreement was classified as: 'slight' (0.00-0.20), 'fair' (0.21-0.40), 'moderate' (0.41-0.60), 'substantial' (0.61-0.80), or 'near-perfect' (>0.80). Results: Twenty-one patients were reviewed at 12 retroperitoneal sarcoma multidisciplinary team meetings, giving a total of 252 assessments for analysis. Consistency between centres was only 'slight' to 'fair', with rates of overall agreement and Krippendorff's alpha statistics of 85.4 per cent (211 of 247) and 0.37 (95 per cent c.i. 0.11 to 0.57) for resectability; 80.4 per cent (201 of 250) and 0.39 (95 per cent c.i. 0.33 to 0.45) for treatment allocation; and 53.0 per cent (131 of 247) and 0.20 (95 per cent c.i. 0.17 to 0.23) for the organs proposed to be resected. Depending on the centre that they had attended, 12 of 21 patients could either have been deemed resectable or unresectable, and 10 of 21 could have received either potentially curative or palliative treatment. Conclusions: Inter-centre agreement between retroperitoneal sarcoma multidisciplinary team meetings was low. Multidisciplinary team meetings may not provide the same standard of care for patients with retroperitoneal sarcoma across Great Britain. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) environment monitoring and smart city applications present challenges for maintaining network connectivity when, for example, dynamic events occur. Such applications can benefit from recent technologies such as software-defined networks (SDNs) and network virtualization to support network flexibility and offer validation for a physical network. This paper aims to present a testbed-based, software-defined wireless sensor network (SDWSN) for IoT applications with a focus on promoting the approach of virtual network testing and analysis prior to physical network implementation to monitor and repair any network failures. Herein, physical network implementation employing hardware boards such as Texas Instruments CC2538 (TI CC2538) and TI CC1352R sensor nodes is presented and designed based on virtual WSN- based clustering for stationary and dynamic networks use cases. The key performance indicators such as evaluating node (such as a gateway node to the Internet) connection capability based on packet drop and energy consumption virtually and physically are discussed. According to the test findings, the proposed software-defined physical network benefited from "prior-to-implementation" analysis via virtualization, as the performance of both virtual and physical networks is comparable. [ABSTRACT FROM AUTHOR]

Globally, the increases in vehicle numbers, traffic congestion, and road accidents are serious issues. Autonomous vehicles (AVs) traveling in platoons provide innovative solutions for efficient traffic flow management, especially for congestion mitigation, thus reducing accidents. In recent years, platoon-based driving, also known as vehicle platoon, has emerged as an extensive research area. Vehicle platooning reduces travel time and increases road capacity by reducing the safety distance between vehicles. For connected and automated vehicles, cooperative adaptive cruise control (CACC) systems and platoon management systems play a significant role. Platoon vehicles can maintain a closer safety distance due to CACC systems, which are based on vehicle status data obtained through vehicular communications. This paper proposes an adaptive traffic flow and collision avoidance approach for vehicular platoons based on CACC. The proposed approach considers the creation and evolution of platoons to govern the traffic flow during congestion and avoid collisions in uncertain situations. Different obstructing scenarios are identified during travel, and solutions to these challenging situations are proposed. The merge and join maneuvers are performed to help the platoon's steady movement. The simulation results show a significant improvement in traffic flow due to the mitigation of congestion using platooning, minimizing travel time, and avoiding collisions. [ABSTRACT FROM AUTHOR]

A Software Defined Vehicular Network (SDVN) is a new paradigm that enhances programmability and flexibility in Vehicular Adhoc Networks (VANETs). There exist different architectures for SDVNs based on the degree of control of the control plane. However, in vehicular communication literature, we find that there is no proper mechanism to collect data. Therefore, we propose a novel data collection methodology for the hybrid SDVN architecture by modeling it as an Integer Quadratic Programming (IQP) problem. The IQP model optimally selects broadcasting nodes and agent (unicasting) nodes from a given vehicular network instance with the objective of minimizing the number of agents, communication delay, communication cost, total payload, and total overhead. Due to the dynamic network topology, finding a new solution to the optimization is frequently required in order to avoid node isolation and redundant data transmission. Therefore, we propose a systematic way to collect data and make optimization decisions by inspecting the heterogeneous normalized network link entropy. The proposed optimization model for data collection for the hybrid SDVN architecture yields a 75.5% lower communication cost and 32.7% lower end-to-end latency in large vehicular networks compared to the data collection in the centralized SDVN architecture while collecting 99.9% of the data available in the vehicular network under optimized settings. [ABSTRACT FROM AUTHOR]

This study assessed the acceptability and feasibility of a question prompt list (QPL) to facilitate informed treatment decision-making in men with suspected localised prostate cancer, which involves values-based choices between options with similar efficacy but different side effects. The QPL was developed through iterative consultation with consumers, clinicians and researchers. Acceptability was assessed using study-specific questions regarding QPL satisfaction and usefulness and qualitative interviews. Feasibility was determined via the proportion of men given the QPL according to medical records and the completion of standardised measures of decisional outcomes. Quantitative data were analysed using descriptive and univariate statistics. Qualitative data were thematically analysed. Fifty-two men consented; 34 provided data for analysis. The QPL recipients reported moderate–high content satisfaction (70.6%) and perceived usefulness in guiding appointments when receiving biopsy results (64.7%). Two main qualitative themes also indicated the QPL acceptability: (1) the freedom to ask—acceptable timing, flexible usage and usefulness of the QPL, and (2) satisfaction with the QPL content. However, only 18.4% of eligible men received the QPL, indicating limited feasibility. The QPL is safe and acceptable, but further research is needed regarding how to facilitate the uptake of the question prompt list in clinical practice. [ABSTRACT FROM AUTHOR]

This article discusses a case of proximal penile fracture in a man in his 30s. The initial examination did not recognize the fracture, but an ultrasound performed later suggested a possible fracture. Magnetic resonance imaging (MRI) was then used to confirm the fracture and determine its precise location. The patient underwent urgent operative management using a perineal approach, which provided efficient access to the root of the penis. The article highlights the superior diagnostic accuracy of MRI for proximal fractures and the effectiveness of the perineal surgical approach for repair. [Extracted from the article]

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies. [ABSTRACT FROM AUTHOR]

The opportunistic fog radio access network (OF-RAN) expands its offloading computation capacity on-demand by establishing virtual fog access points (v-FAPs), comprising user devices with idle resources recruited opportunistically to execute the offloaded tasks in a distributed manner. OF-RAN is attractive for providing computation offloading services to resource-limited Internet-of-Things (IoT) devices from vertical industrial applications such as smart transportation, tourism, mobile healthcare, and public safety. However, the current OF-RAN design is lacking a trusted and distributed mechanism for automating its processes such as v-FAP formation and service execution. Motivated by the recent emergence of blockchain, with smart contracts as an enabler of trusted and distributed systems, we propose an automated mechanism for OF-RAN processes using smart contracts. To demonstrate how our smart-contract-based automation for OF-RAN could apply in real life, a federated deep learning (DL) use-case where a resource-limited client offloads the resource-intensive training of its DL model to a v-FAP is implemented and evaluated. The results validate the DL and blockchain performances of the proposed smart-contract-enabled OF-RAN. The appropriate setting of process parameters to meet the often competing requirements is also demonstrated. [ABSTRACT FROM AUTHOR]