Cluster-based routing in Mobile Ad-Hoc Networks is considered one of the convenient methods of routing. The existence of Cluster Head (CH) in a group of nodes for data forwarding improves the performance of routing in terms of routing overhead and power consumption. In this paper, a clustering algorithm using hierarchical routing for mobile ad hoc networks is proposed. In the proposed method, first, clusters are formed by the FLOC clustering method with low overlapping. Then, according to the most important network criteria, including hop count, noise ratio, distance, remaining energy, and link expiration time, head clusters are selected based on the weighting algorithm. To transfer data in the proposed method, two types of communication are used, e.g., inter-cluster communication and intra-cluster communication in which the shortest path to the target cluster is selected. The proposed P-METHOD technique is based on hierarchy and clustering as well as using a weighted algorithm that reduces delay in data transmission on the network. Moreover, P-METHOD is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the MANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), delay, residual energy, and lifetime.

OSI model used to be common network model for years. In the case of ad hoc networks with dynamic topology and difficult radio communications conditions, gradual departure is happening from the classical kind of OSI network model with a clear delineation of layers (physical, channel, network, transport, application) to the cross-layer approach. The layers of the network model in ad hoc networks strongly influence each other. Thus, the cross-layer approach can improve the performance of an ad hoc network by jointly developing protocols using interaction and collaborative optimisation of multiple layers. The existing cross-layer methods classification is too complicated, because it’s based on the whole manifold of network model layers combinations, regardless their importance. In this work, we review ad hoc networks cross-layer methods, propose the new useful classification of cross-layer methods, and show future research directions in ad hoc networks cross-layer methods development. The proposed classification can help to simplify the goal-oriented cross-layer protocol development.

Opportunistic Routing is a promising paradigm that has been proposed for efficient and reliable transfer of data packets in mobile ad hoc networks. This routing strategy takes advantage of the broadcasting nature of the wireless medium to increase the number of probable forwarding devices and improves the reliability of data transfer in the network. Opportunistic Routing utilizes the reception of the same broadcasted packet at multiple devices in the network and selects one best forwarder dynamically from the set of multiple receivers. A number of opportunistic routing protocols have been proposed over these years for effective data delivery in ad hoc networks. But as the speed and mobility of devices increases in the network, performances of the existing opportunistic routing protocols degrade considerably, leading to reduced Quality of Service and poor transmission efficiency. The exceptional potential of opportunistic routing is thus underutilized. In this research we introduce Optimized Opportunistic Routing (OOR) strategy that guarantee excellent Quality of Service and high transmission efficiency to the latest applications using opportunistic routing for communication in highly dynamic ad hoc networks. Simulation results show that our method achieve significant performance improvements compared to all other existing opportunistic routing protocols in highly dynamic mobile ad hoc networks.

This article explores the implementation of high accuracy GPS denied Ad-Hoc localization. There is little research on Ad-Hock UWB-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node’s field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration Ad-Hoc Localization System (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF fuses the robot’s odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts to its neighbors its estimated position to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile Ad-Hoc localization network.

This paper initiates the study of the mathematical aspects of the ad-hoc Lanzhou index. If G is a graph with the vertex set {x1,⋯,xn}, then the ad-hoc Lanzhou index of G is defined by Lz¯(G)=∑i=1ndi(n−1−di)2, where di represents the degree of the vertex xi. Several identities for the ad-hoc Lanzhou index, involving some existing topological indices are established. The problems of finding graphs with the extremum values of the ad-hoc Lanzhou index from the following sets of graphs are also attacked: (i) set of all connected ξ-cyclic graphs of a fixed order, (ii) set of all connected molecular ξ-cyclic graphs of a fixed order, (iii) set of all graphs of a fixed order, (iv) set of all connected molecular graphs of a fixed order.

Vehicular ad-hoc network (VANET) is a type of mobile network which is used for establishing connection between vehicles (M2M) and also between vehicles and nearby stationary equipment which are often road-side equipment. The main target of VANET is to provide security and convenience for the passengers. In order to achieve this goal, a special electronic device called OBU (On-Board Unit) is embedded in each vehicle which makes the connection between vehicles and between the vehicles and the road-side equipment possible. In this paper, the Location-Aware Clustering in Vehicular Ad-hoc Networks (LAC-VANET) is proposed. We try to achieve the main and major goal in VANET networks, i.e. fast propagation of security and urgent messages in ITS systems, using clustering and selecting the best cluster head based on Fuzzy logic such that the cluster head can transfer important information such as the obstacles and accidents detected on the road with a suitable speed and without creating a large traffic load in the vehicle network in order to notify other vehicles and prevent the danger and vehicle accidents. Moreover, LAC-VANET method is evaluated here via extensive simulations carried out in NS-2. The simulation results indicate that the VANET network performance metrics are improved in terms of average throughput, Packet Delivery Ratio (PDR), end to end delay, and packet loss rate.

It is a challenging issue to provide a secure and conditional anonymous authentication scheme in vehicle ad hoc networks(VANETs) with low storage space and computational cost. In 2008, Lu et al. [8] proposed an conditional privacy preservation scheme called ECPP protocol. The ECPP protocol provides conditional privacy preservation to vehicles in VANETs, that is, on one hand vehicles can achieve anonymous authentication in the network, on the other hand allow to be traced and revoked if necessary. However, ECPP scheme suffers from large storage and high computational cost. In our scheme, an improved protocol based on the concept of ECPP protocol has been proposed, which uses minimal interaction steps, little storage space and less computation overhead to achieve more efficiency conditional privacy preservation(MECPP) scheme in VANETs.

Mobility models are used to evaluated the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility which is usually used for performance evaluation of ad-hoc mobile network. Mobile nodes have the dynamic mobility in the ad hoc network so the mobility model plays an important role to evaluate the protocol performance.In this article, we developed modify random waypoint mobility (MRWM) model based on random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence on the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) condition. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end to end delay and normalized routing overhead. The obtained results revealed that proposed modify random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realist.

Forming a clustered network structure has been proposed as a solution to increase network performance, scalability, stability and manageability in an ad hoc network. A good clustering algorithm aims to select cluster heads among available nodes so that a number of specific constraints are satisfied; thus the cluster head selection problem is a multiobjective optimization problem. This paper proposes an algorithm on the basis of ant colony optimization (ACO) to be used to solve this problem. The proposed algorithm is a simple, one hop cluster formation algorithm, to form a clustered structure with the minimum number of clusters. The centralized ACO-based clustering algorithm is evaluated and compared with other clustering algorithms in ad hoc networks in terms of cluster density.

In scenarios such as natural disasters and military strike, it is common for unmanned aerial vehicles (UAVs) to form groups to execute reconnaissance and surveillance. To ensure the effectiveness of UAV communications, repeated resource acquisition issues and transmission mechanism design need to be addressed urgently. In this paper, we build an information interaction scenario in a Flying Ad-hoc network (FANET). The data transmission problem with the goal of throughput maximization is modeled as a coalition game framework. Then, a novel mechanism of coalition selection and data transmission based on group-buying is investigated. Since large-scale UAVs will generate high transmission overhead due to the overlapping resource requirements, we propose a resource allocation optimization method based on distributed data content. Comparing existing works, a data transmission and coalition formation mechanism is designed. Then the system model is classified into graph game and coalition formation game. Through the design of the utility function, we prove that both games have stable solutions. We also prove the convergence of the proposed approach with coalition order and Pareto order. Binary log-linear learning based coalition selection algorithm (BLL-CSA) is proposed to explore the stable coalition partition of system model. Simulation results show that the proposed data transmission and coalition formation mechanism can achieve higher data throughput than the other contrast algorithms.

Opportunistic routing uses the broadcasting nature of the wireless medium to increase the number of potential forwarding nodes in the network. This, in turn improves the delivery rate and reliability of data transmission in the network. Compared to all previous classes of protocols, opportunistic routing offers numerous advantages which is exploited by the latest applications for efficient communication and resource sharing in dynamic ad hoc networks. The objective of this research work is to review and classify all the major opportunistic routing protocols proposed for dynamic ad hoc networks. Further the issues and challenges with each of these existing protocol is discussed and future research directions are put forward.

Opportunistic routing has emerged as one of the prominent solutions for efficient data delivery in ad hoc networks especially in highly dynamic scenarios. Opportunistic routing uses the broadcasting nature of the wireless medium to increase the number of potential forwarding nodes in the network, thereby improving the delivery rate. But most of the proposed opportunistic routing protocols suffer from increased duplicate data forwarding at the intermediate nodes in the network which leads to increase in traffic and reduced efficiency. We propose a novel method which uses acknowledgements for controlling the data forwarding by intermediate nodes and thereby eliminating most of the duplicate forwarding in the network. Simulation results show that our proposed method is much more efficient that all the major existing opportunistic protocols.

A low-cost machine learning (ML) algorithm is proposed and discussed for spatial tracking of unknown, correlated signals in localized, ad-hoc wireless sensor networks. Each sensor is modeled as one neuron and a selected subset of these neurons are called to identify the spatial signal. The algorithm is implemented in two phases of spatial modeling and spatial tracking. The spatial signal is modeled using its M iso-contour lines at levels {ℓj}j=1M and those sensors that their sensor observations are in Δ margin of any of these levels report their sensor observations to the fusion center (FC) for spatial signal reconstruction. In spatial modeling phase, the number of these contour lines, their levels and a proper Δ are identified. In this phase, the algorithm may either use adaptive-weight stochastic gradient or scaled stochastic gradient method to select a proper Δ. Additive white Gaussian noise (AWGN) with zero mean is assumed along with the sensor observations. To reduce the observation noise’s effect, each sensor applies moving average filter on its observation to drastically reduce the effect of noise. The modeling performance, the cost and the convergence of the algorithm are discussed based on extensive computer simulations and reasoning. The algorithm is proposed for environmental monitoring. In this paper, the percentage of the communication attempts of wireless sensors is assumed as cost. Performance evaluation results show that the proposed spatial tracking approach is low cost and can model the spatial signal over time with the same performance as that of spatial modeling.

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social internet of vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into an intra-cluster and an inter-cluster emergency as well as general safety message dissemination technique. In a vehicle to infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.

The era of Internet of Things (IoT) has begun to evolve and with this the devices around us are getting more and more connected. Vehicular Ad-hoc NETworks (VANETs) is one of the applications of IoT. VANET allow vehicles within these networks to communicate effectively with each another. VANETs can provide an extensive range of applications that support and enhance passenger safety and comfort. It is important that VANETs are applied within a safe and reliable network topology; however, the challenging nature of reaching reliable and trustworthy agreement in such distributed systems is one of the most important issues in designing a fault-tolerant system. Therefore, protocols are required so that systems can still be correctly executed, reaching agreement on the same values in a distributed system, even if certain components in the system fail. In this study, the agreement problem is revisited in a VANET with multiple damages. The proposed protocol allows all fault-free nodes (vehicles) to reach agreement with minimal rounds of message exchanges, and tolerates the maximal number of allowable faulty components in the VANET.

The demand for improved service in mobile networks is always increasing. That means signal transmission with higher capacity is required. To achieve that goal a combined optical and wireless link using double polarization multiplexing has been developed. A high quality signal transmission is achieved by applying high polarization extinction ratio. The new concept is validated by experiments varying the bit rate and fiber length. A signal with 12 Gbit/s bit rate was transmitted over a 25 km long combined link with about 1.10-8 bit error rate. That result is much better than the already published data which were measured on links transmitting information with 2.5 Gbit/s bit rate. The polarization multiplexing technique is very practical for capacity enhancement in a specific section of an existing optical fiber by inserting a new link into it without deploying a new fiber.

Vehicular ad hoc networks (VANET) are a subset of mobile ad hoc networks used to communicate between vehicles and infrastructure. During vehicular congestion communication, nodes compete to acquire channels, causing the channels to become congested. The congestion on the vehicle network results in increased delay and packet loss, resulting in reduced VANET performance. To address this problem, we developed a load-aware and priority adaptive traffic congestion control method in vehicular ad hoc networks (VANETs). The proposed scheme identifies less-congested road segments based on the network's load and reduces traffic congestion by suggesting other routes between nearby roadside units (RSUs). This research aims to improve the efficiency of the vehicular environment by utilizing the movement of vehicles with (RSUs) and sharing the traffic load between them. Simulation results demonstrate the effectiveness of the proposed protocol in reducing congestion and enhancing the overall performance of VANETs. To validate the proposed algorithm, we have implemented and tested the proposed algorithm using Network Simulator 3 (NS3) for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication scenarios and computed the performance of the algorithm on different parameters of the network. The simulation result of the proposed load aware and priority adaptive traffic congestion control method in VANET improved the packet delivery ratio, packet lost ratio, and end-to-end delay by 96%, 4.1%, and 1102 milliseconds, when compared to TDCCA value of 92%, 5.7%, and 1154 milliseconds, respectively

The purpose of this article is to simulate data transmission and calculate traffic parameters in SAGIN air segment for which Ad Hoc network of flying drones is considered as a model. Traffic modeling is based on the manet-routing-compare example from the ns3 simulator library, which has been supplemented with the code for calculation packet losses, throughput/goodput, and message transmission delays. The program allowed considering drones movement at both low and high speeds from 3.6 km/h to 72 km/h. The dependences of traffic losses on data transmission power, transaction sizes and data transmission rate are obtained and analyzed. The distribution of the average effective arrival rate λ and the throughput/goodput for drones has been studied. Comparing traffic characteristics in models with different numbers of drones allows judging how the required quality of service can be achieved by choosing the right transmission parameters.

As split learning (SL) becomes popular for addressing data privacy and resource limitation issues in edge model training process, it is crucial to pre-validate whether the extensive use of wireless and computing resources in SL can achieve the expected training latency and convergence. While digital twin network (DTN) can potentially estimate network strategy performance in pre-validation environments, they are still in their infancy for SL tasks, facing challenges like unknown non-i.i.d. data distributions, inaccurate channel states, and misreport resource availability across devices. To address these challenges, this paper proposes a TransNeural algorithm for DTN pre-validation environment to estimate SL latency and convergence. First, the TransNeural algorithm integrates transformers to efficiently model data similarities between different devices, considering different data distributions and device participate sequence greatly influence SL training convergence. Second, it leverages neural network to automatically establish the complex relationships between SL latency and convergence with data distributions, wireless and computing resources, dataset sizes, and training iterations. Deviations in user reports are also accounted for in the estimation process. Simulations show that the TransNeural algorithm improves latency estimation accuracy by 13.44% and convergence estimation accuracy by 116.74% compared to traditional equation-based methods.

Introduction: Lack of certainty or knowledge in health professions generates uncertainty, affecting clinical practice in decision-making (diagnostic or therapeutic), intervention design and therapeutic communication. In nursing, the lack of information to predict prognosis and outcome of interventions generates uncertainty: recognizing and managing it is essential for individualized, safe, and effective care. Objective: to assess uncertainty and the perception of a debriefing within the same shift ("intrathoracic pass") as a proposal for improvement. Method: ad-hoc survey of nursing professionals and Auxiliary Nursing Care Technicians (ANC). Variables: age, work experience, PRU scale (Physisicians' Reactions to Uncertainty) and additional question on intrathoracic pass. Results: Sample: 61 nurses and 10 TCAE (90.14 % female, 9.86 % male; mean age 35.18, standard deviation SD=13.04). Mean professional experience 11.74 years (SD=12.21); 56.33% had ≤4 years of experience. Origin: 49.30% Intensive Care Units (ICU), 11.27% Hospitalization, 7.04% Emergency, 1.41% Primary Care and 30.99% Other Services. The mean level of uncertainty was 27.99 points (max. possible 75; SD=7.50), with stress (mean=25.55, max. possible: 40; SD=5.24) and anxiety (mean=15.80, max. possible: 25; SD=3.05) as major manifestations. The association uncertainty-age (r=0.339; p=0.004) and uncertainty-experience (r=0.391; p=0.001) correlated negatively (the older and more experienced, the less uncertainty), with no significant differences between degrees or services. 91.55% stated that the intra-day pass was useful to reduce uncertainty during the working day. Conclusion: Uncertainty is frequent in nursing, and implementing the intrashift pass is perceived as a competent strategy.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

Use of MS Excel as a platform for teaching simple iterative solution of partial differential equations runs into issues of reliability of the computation because of the large number of calculations required. This article documents the problem and an empirical solution. The cause of this unreliability was found by detailed examination of the numerical results from three simultaneous iterations. Computers of several types from different manufacturers were found to fail occasionally in updating variables in the numerical iteration. A simple work-around is suggested and was tested on several platforms.

Various dietary supplements have been shown to extend the life span of Drosophila melanogaster, including several that promote autophagy, such as rapamycin and spermidine. The goal of the study presented here was to test numerous additional potential anti-aging supplements, primarily inhibitors of the target of rapamycin (TOR) and/or phosphatidylinositol 3-kinase (PI3K). Using a single, comparatively long-lived y w test strain, screening was performed in male flies supplemented either throughout adulthood or in a few cases beginning in middle or late adult life, with concentrations spanning 4-6 orders of magnitude in most cases. Supplementation with PP242 and deferiprone, an iron chelator, beginning in late adult life had no positive effect on life span. Lifelong supplementation with Ku-0063794, LY294002, PX-866-17OH, Torin2 and WYE-28 had no effect at any dose. Rapamycin, spermidine and wortmannin all had significant life-shortening effects at the highest doses tested. AZD8055, PI-103 hydrochloride and WYE-132 yielded slight beneficial effects at 1-2 doses, but only 100 nM AZD8055 was confirmed to have a minor (1.3%) effect in a replicate experiment, which was encompassed by other control groups within the same study. These compounds had no effect on fly fecundity (egg-laying) or fertility (development of progeny to adulthood), but equivalent doses of rapamycin abolished fertility. The solvent DMSO had no significant effect on life span at the concentrations used to solubilize most compounds in the fly medium, but it drastically curtailed both survival and fertility at higher concentrations. 2-hydroxypropyl-β-cyclodextrin also failed to extend the life span when provided throughout adulthood or beginning in mid-adult life. Collectively, the results suggest that inhibition of the TOR/PI3K pathway and autophagy through dietary intervention is not a straightforward anti-aging strategy in Drosophila, and that further extension of life is difficult in comparatively long-lived flies.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Background/Purpose: Multimorbidity of malaria, anaemia, and malnutrition (MAMM) is a condition in which an individual cohabits with two or more of these health outcomes and is becoming an emergent public health concern in sub-Saharan African countries. The independent associations of a child’s demographic variables and socioeconomic disparities with a child’s health outcomes have been established in literature. However, the effects of the intersection of these factors on MAMM, while accounting for other covariates have not been studied. Therefore, this study aimed to determine how children’s sex, age and socioeconomic status interact to explain the variations in MAMM among children aged 6-59 months in Nigeria. Methods: Data from the 2018 Nigeria Demographic and Health Survey and the 2018 National Human Development Report (NHDR) were used. The study includes weighted samples of 10,184 children aged 6-59 months in Nigeria. A three-level multilevel mixed effect ordinal logistic regression model such that individual characteristics at level 1 are nested in communities at level 2 and nested in states at level 3 was used. Subsequently, predictive probability charts and average adjusted probability tables were used to interpret the intersectional effects. Five models were created in this scenario. Model 1 is the interaction between the child's sex and wealth status; model 2, child's sex and age; model 3 is between child's age and wealth status; model 4 has the three 2-way interactions of child's sex, age, and household wealth status, and model 5 includes model 4 and the 3-way interactions between a child's sex, age, and wealth quintiles. Results: The prevalence of children with ‘none of the three diseases’ outcomes was 17.3% (1767/10,184), while, 34.4% (3499/10,184) had ‘only one of the diseases’, and 48.3% (4,918/10,184) cohabit with ‘two or more’ of MAMM. However, in the multivariate analyses, model 3 was the best fit compared with other models, so, that the two-way interaction effects of a child's age and wealth status are significant predictors in the model. Children aged 36-47 months living in the poorest households have the probability of 0.11, 0.18, and 0.32 of contracting MAMM above the probability of children of the same age and live in the middle, more prosperous and richest households, respectively, while all other covariates are held constant. Thus, the variation in MAMM over a child's age differs depending on the household wealth quintile. In other words, as the children grow older, variations in MAMM become more evident between the rich and the poor household. Conclusion: Therefore, it is recommended that policies that are geared toward economic redistribution will help bridged the disparities observed in multiple diseases among children aged 6-59 months in Nigeria

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperparameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed the advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Mobile payment has become increasingly popular due to the widespread use of smartphones and their applications. However, its adoption in African countries has been limited, despite its potential to simplify our lives. This study aims to enhance our understanding of the factors that affect the acceptance of mobile payment in Nigeria. To achieve this, the paper explores the impact of "network externalities" in addition to traditional technology acceptance factors. The study hypothesizes that the key drivers of mobile payment acceptance are performance expectancy, effort expectancy, social influence, trust, and network externality. The research findings suggest that while traditional drivers still play a role in customers' willingness to adopt mobile payment, network externalities have the strongest impact. Although the results did not support the influence of effort expectancy, the paper provides recommendations for future research.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

In contrast to physical sustainable measures carried out in homes, such as insulation, the installation of a Home Energy Management System (HEMS) has no direct and immediate energy-saving effect. A HEMS gives insight into resident behaviour regarding energy use. When this is linked to the appropriate feedback, the resident is in a position to change his or her behaviour. This should result in reduced gas and/or electricity consumption. The aim of our study is to contribute towards the effective use of home energy management systems (HEMS) by identifying types of homeowners in relation to the use of HEMS. The research methods used were a literature review and the Q-method. A survey using the Q-method was conducted among 39 owners of single-family homes in various Rotterdam neighbourhoods. In order to find shared views among respondents, a principal component analysis (PCA) was performed. Five different types of homeowner could be distinguished: the optimists, the privacy-conscious, the technicians, the sceptics, and the indifferent. Their opinions vary as regards the added value of a HEMS, what characteristics a HEMS should have, how much confidence they have in the energy-saving effect of such systems, and their views on the privacy and safety of HEMS. The target group classification can be used as input for a way in which local stakeholders, e.g. a municipality, can offer HEMS that is in line with the wishes of the homeowner.

Wireless sensor networks (WSNs) are a critical research area with numerous practical applications. WSNs are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As WSNs advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

In this paper, we are proposing a bio-inspired location management (LM) technique for personal communication system (PSC). It is based on artificial immune system (AIS), with self-adaptation and self-updates attributes in order to perform the location management, and work helps to achieve the better quality of service (QoS) and quality of experience (QoE) for the mobile users. Here, we are suggesting a modified mobile switching center (MSC) architecture, and an adaptive self-modified location management procedure. The proposed mobile switching centre architecture has an advantage of rule-based and fact-based system to store the rules and fact related to location management procedure, and it shows the intelligent behavior of system. The mobile switching centre calculates the best method for location management and rule-base system trigged the rules to perform the techniques. The system stores the result (techniques for location management) in fact-base system for future use. The efficiency and effectiveness of the proposed techniques been analyzed, and it observed that the proposed system has 45-50% improvement in performance over the current location management techniques. Here, we are taking the performance parameters such as signaling cost, database update cost, overhead measurement, mobility management cost.

The fifth Generation (5G) mobile networks use millimeter Waves (mmWaves) to offer giga bit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn cause too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity and avert unnecessary HO, we propose a HO scheme based on Jump Markov Linear System (JMLS) and Deep Reinforcement Learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behaviour by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduces training sample size. Thus, the JMLS-DRL platform formulates intelligent and versatile HO policies for 5G. Results show our proposed prediction scheme about target link behavior post HO to be highly reliable. The scheme also averts unnecessary HOs thus ably supports longer dew time.

(1) Background: LUS has emerged as a simple, rapid and non-invasive method for the dynamic assessment of pulmonary congestion, a major prognostic factor and a therapeutic target in AHF. (2) Methods: In the single-center prospective observational study, 42 patients hospitalized for AHF in the post-emergency polyvalent medicine department of CHU Grenoble were successively included between May 2021 and July 2022. Clinical examination, LUS and electrolyte panel results were collected daily. Vital status was assessed 30 days after the last LUS. The primary endpoint was the evolution of the number of B-lines according to the dose of diuretic administered ; (3) Results: We performed 188 LUS. Our patients were elderly (85.8 [SD 8.1]) and comorbid. The median number of B-lines decreased from 17 at admission to 7 in the middle of the hospital stay, then stagnated until discharge. Simultaneously, the median dose of diuretics, initially stable at a maximum of 40 mg/d IV, decreased to 20mg/d IV by the end of hospital stay. Patients with CKD had a higher mean number of B-lines at entry (24.2 [SD 11.6]) compared to those without CKD (8.2 [SD 8.8]). However, the evolution of B-lines number was independent of creatinine levels. 30-day all-causes mortality was significantly related to the number of B-lines at discharge (15.2 [SD 10.1] in deceased patients versus 3.9 [SD 5.4] in living patients, p

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment but has also posed significant challenges to network security, especially the physical layer security challenges,which is crucial for the reliable transmission of messages interconnected devices and sensors. Focus on the enhancing the physical layer security in the IIoT communication, this paper proposed a mechanism of Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate device is known, while eavesdropper on the random distribution of device locations, forming the SSOP model related to device locations from the perspective of insecure regions (ISR) and secure regions (SR), and deduce the closed-form expression for its upper bound. Subsequently under the constraint of SSOP conditions, establish an optimization model with the objective of system throughput maximization based on Spatial Secrecy Outage Probability(SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in the MATLAB, we verified the accuracy of the definition and derived upper bound of SSOP. The experimental results also show that SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. These performance are crucial for understanding and optimizing the security performance of IIoT communication systems.

Private Set Intersection Cardinality(PSI-CA) is a cryptographic method in secure multi-party computation that allows entities to identify cardinality of the intersection without revealing their private data.Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances.In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI-CA protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI-CA protocols to these imbalances is crucial.This paper explores unbalanced scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities.This paper, based on the concept of commutative encryption, introduces Cuckoo filter, cloud computing technology, homomorphic encryption, among other technologies, to construct three novel solutions for unbalanced Private Set Intersection Cardinality (PSI-CA): an unbalanced PSI-CA protocol based on Cuckoo filter, an unbalanced PSI-CA protocol based on single cloud assistance, and an unbalanced PSI-CA protocol based on dual cloud assistance. Depending on performance and security requirements, different protocols can be employed for various applications.

Class attendance in institutions of higher learning plays a major role in the success of students and as such putting in measures to curb absenteeism of students would go a long way to help improve their grades. This research sought to design a ‘Bring Your Own Device’ System which will be very suitable for the post-COVID era as well as the fast-growing university community. The research results in a mobile-based attendance infrastructure used by students to prove their presence for class sessions. Flutter, a Software Development Kit (SDK) was used to program the mobile platform and Firebase was used for both the authentication and database. This system also comes in as an improvement on most existing systems as it functions on both Android and iOS. After the testing phase, the models achieved high accuracy and the proposed design has proven to have great potential. This solution stands beyond a research model as a ready-to-use platform.

Alzheimer’s is a degenerative brain condition brought on by intricate brain changes after cell injury. The prevalence of Alzheimer’s disease is highest in those over 65. Presently, AD is detected by manual examination of a patient’s Magnetic Resonance Imaging (MRI) scan and neuropsychological assessments. Due to its subpar accuracy and performance, solutions based on artificial intelligence (AI) have not been made more widely available. This paper proposes a deep Convolutional Neural Network (CNN) with the pre-trained MobileNet-V2 architecture as the model’s base for feature extraction and reducing the parameter size and training time. The proposed model accurately classifies various early stages of AD. The proposed approach visualizes the class activation maps in the form of a heat map on brain MRI scans via the Grad-CAM algorithm that lifts the black-box nature of the deep model. To handle the imbalance problem of the AD data set acquired from Kaggle, the oversampling algorithm SMOTE interpolates new images to even the class samples. The proposed multi-class classification deep model is named Bio-AD and evaluated using different quality metrics. The Bio-AD model has the following values for evaluation metrics: 97.92%, 99.87%, 97.87%, 98%, 98%, and 0.0646 for accuracy, Area Under the Curve (AUC), F1-score, Precision, Recall, and loss respectively. The proposed Bio-AD model is compared with DenseNet-169, VGG-19, and InceptionResNet-V2 using different quality metrics and outperformed in comparison with all other models using the AD data set.

FY-3E WindRAD (Fengyun-3E Wind Radar) is a dual-frequency rotating fan-beam scatterometer. Its data characteristics, NOC (NWP Ocean Calibration), and wind retrieval performance are investigated in this paper. The radar view geometry diversity varies across the swath, with maximum diversity in the sweet swaths and limited diversity in the outer and nadir swaths. When NOC backscatter calibration coefficients are computed as a function of incidence angle only (NOCint), a smooth correction is found. However, when relative antenna azimuth angle is included (NOCant), it appears that the corrections as a function of relative azimuth angle vary harmonically and substantially for a specific incidence angle. NOCant corrections yield a better fit of the measurements to the GMF (Geophysical Model Function). Hence NOCant is applied for the wind retrieval analysis from Ku-band, C-band. An extra engineering correction of 0.15dB and 0.2dB is applied on Ku-band and C-band backscatter values, respectively, to reduce the wind speed bias without increasing the standard deviation. Overall, NOCant is the best option for both channels. Also, the instrument backscatter data stability over time is good and the retrieved winds can fulfill operational requirements.

The purpose of our study is to research Peter’s memory ad catacumbas. According to the Depositio Martyrum ‒ a document of the late Emperor Constantine ‒, there was no memory of the first St Peter’s Basilica on the Vatican Hill. We start with a critical analysis on the Roman Basilicae attributed to Emperor Constantine in the Liber Pontificalis, then we deepen the search of Peter’s memory in the catacombs of the SS Marcellino and Pietro (ad Duos Lauros), also known as Tor Pignattara. Indeed, the Basilica and Mausoleum built in this cemeterial area are the only buildings attributable, with certainty, to Emperor Constantine, who wished to be buried in the Mausoleum, close to an apostle. Besides some striking archeological finds on Peter’s memory already discovered near a particular cubicle in these catacombs, a geometrical and mathematical study of the unusual architectonic characteristics of the Basilica and Mausoleum of Tor Pignattara shows that the buildings were part of a single architectonic plan, very likely designed for coding data useful to locate Peter’s burial site unambiguously, in the area of the cubicle mentioned.

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in edge computing mobile device using the intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an inprovement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

Alzheimer's Disease (AD) and Parkinson's Disease (PD) are two neurodegenerative diseases posing a significant disease burden due to their increasing prevalence and socio-economic cost. Traditional therapeutic approaches for these diseases exist but provide limited symptomatic relief without addressing the underlying pathologies. This review examines the potential of immunotherapy, specifically monoclonal antibodies (mAbs), as disease-modifying treatments for AD and PD. We analyze the pathological mechanisms of AD and PD, focusing on the roles of amyloid-beta (Aβ), tau (τ), and alpha-synuclein (α-syn) proteins. We discuss the latest advancements in mAbs therapies targeting these proteins, evaluating their efficacy in clinical trials and preclinical studies. We also explore the challenges faced in translating these therapies from bench to bedside, including issues related to safety, specificity, and clinical trial design. Additionally, we highlight future directions for research, emphasizing the need for combination therapies, improved biomarkers, and personalized treatment strategies. This review aims to provide insights into the current state and future potential of antibody-based immunotherapy in modifying the course of AD and PD, ultimately improving patient outcomes and quality of life.

In wheeled vehicles, there exists a challenge concerning the alignment of the front wheels during turns. When both wheels are turned to the same extent, the inner wheel experiences lateral slippage on the road surface, resulting in reduced steering efficiency. Such problems can be solved by using the Ackermann principle. There are two approaches: through the steering mechanism or by employing autonomous wheel control. From a technical standpoint, it is achievable to synthesize a steering mechanism that fulfills the Ackermann condition, ensuring as accurately as possible for any given turning radius. This research paper focuses on synthesizing a steering mechanism, specifically a two-circuit linkage that links the left and right wheels. The structure and kinematics of this mechanism are being investigated. Following the principles of Ackermann, an objective function for the kinematic synthesis of the mechanism has been formulated. The synthesis process of the mechanism is conducted using the analytical method of interpolation in four parameters. A dedicated method has been developed, and kinematic analysis of the mechanism using the synthesized parameters has been performed. The simulation results have demonstrated high accuracy in achieving the desired implementation of the Ackermann condition.

Private Set Intersection (PSI) is a cryptographic method in secure multi-party computation that allows entities to identify common elements in their datasets without revealing their private data. Traditional approaches assume similar-sized datasets and equal computational power, overlooking practical imbalances. In real-world applications, dataset sizes and computational capacities often vary, particularly in the Internet of Things and mobile scenarios where device limitations restrict computational types. Traditional PSI protocols are inefficient here, as computational and communication complexities correlate with the size of larger datasets. Thus, adapting PSI protocols to these imbalances is crucial. This paper explores unbalanced PSI scenarios where one party (the receiver) has a relatively small dataset and limited computational power, while the other party (the sender) has a large amount of data and strong computational capabilities. It introduces three innovative solutions for unbalanced PSI: unbalanced PSI protocol based on Cuckoo filters, unbalanced PSI protocol based on single cloud assistance, and unbalanced PSI protocol based on dual cloud assistance, each addressing the shortcomings of the previous solution. These protocols primarily optimize database computational load, storage capacity, protocol execution time, and security. Depending on performance and security needs, different protocols can be employed for applications such as private contact discovery.

A model of a single-server queuing-inventory system (QIS) with catastrophes and a finite waiting room for consumer customers (c-customers) has been developed. When a catastrophe occurs, all inventory in the system's warehouse is destroyed, but c-customers in the system are still waiting for restocking. In addition to c-customers, negative customers (n-customers) are also taken into account, which displaces one c-customers (if any). The policy (?, ?) is used to replenish stocks. If upon arrival of the c-customer the inventory level is zero, then according to Bernoulli’s scheme this c-customer is either lost or joining the queue. The mathematical model of the studied QIS is constructed in the form of a two-dimensional continuous-time Markov chain (2D CTMC). Exact and approximate methods for calculating the steady-state probabilities of constructed 2D CTMCs are developed and closed-form expressions are derived for calculating the performance measures. The results of numerical experiments are presented, demonstrating the high accuracy of the developed approximate formulas, as well as the behavior of performance indicators depending on the reorder point. In addition, an optimization problem is solved to obtain the optimal reorder point to minimize the expected total cost.

Mobile health technologies enable continuous, quantitative assessment of mobility and gait in real-world environments, facilitating early diagnosis of gait disorders, disease progression monitoring, and prediction of adverse events like falls. Traditionally, mobile gait assessment predominantly relied on body-fixed sensors positioned at the feet or lower trunk. Here, we investigate the potential of an algorithm utilizing an ear-worn motion sensor for spatiotemporal segmentation of gait patterns. We collected 3D acceleration profiles from the ear-worn sensor during varied walking speeds in 53 healthy adults. Temporal convolutional networks were trained to detect stepping sequences and predict spatial relations between steps. The resulting algorithm, mEar, accurately detects initial and final ground contacts (F1 score of 99% and 91%, respectively). It enables determination of temporal and spatial gait cycle characteristics (among others stride time and stride length) with good to excellent validity at a precision sufficient to monitor clinically relevant changes in walking speed, stride-to-stride variability, and side asymmetry. This study highlights the ear as a viable site for monitoring gait and proposes its potential integration with in-ear vital sign monitoring. Such integration offers a practical approach to comprehensive health monitoring and telemedical applications, by integrating multiple sensors in a single anatomical location.

Many systems in the manufacturing industry have spatial distribution characteristics, which correlate with both time and space. Such systems are known as distributed parameter systems (DPS). Due to the spatiotemporal coupling characteristics, the modeling of such systems is quite complex. The paper presents a new approach for three-dimensional fuzzy modeling using Simultaneous Perturbation Stochastic Approximation (SPSA) for nonlinear DPS. The Affinity Propagation clustering approach is utilized to dynamically determine the optimal number of fuzzy rules and construct a collection of preceding components for three-dimensional fuzzy models. Fourier space base functions are used in the resulting components of three-dimensional fuzzy models, and their parameters are learned by the SPSA algorithm. The proposed three-dimensional fuzzy modeling technique was utilized on a conventional DPS within the semiconductor manufacturing industry, with the simulation experiments confirming its efficacy.

Obesity is a risk factor for the development of gestational diabetes mellitus (GDM). However, the most optimal type of nutritional intervention to prevent GDM in high-risk women is not clearly defined. This study investigates if nutritional treatment based on the Mediterranean diet (MedDiet) before 12th gestational week (GW) in women at high risk due to a body mass index (BMI)≥25kg/m2, reduces the rate of GDM and metabolic syndrome (MetS) at 3 years postpartum. We performed a post-hoc analysis of the San Carlos Gestational Prevention Study. A total of 735 women with BMI≥25kg/m2 were evaluated between 2015-2018, 246 in standard diet control group (CG) and 489 in MedDiet intervention group (IG). The rate of GDM was significantly lower in IG compared to CG (25.1%vs31.7%;p=0.037). Postnatal follow-up was completed by 141 women in CG (57%) and 312 women in IG (64%). At 3 years postpartum, we observed a reduction (relative risk 95% confidence interval) in the rates of impaired fasting glucose (IFG) (0.51 (0.28-0.92);p=0.019), obesity (0.51 (0.28-0.92),p=0.041), waist circumference (WC)≥89.5cm (0.54 (0.31-0.94);p=0.022) and MetS (0.56 (0.33-0.94);p=0.003). MedDiet reduces the rate of GDM and postpartum MetS in women with BMI)≥25kg/m2, suggesting that its implementation should be routinely recommended from the first GWs.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASS). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML) based algorithms and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCC) by using real-time data to optimise their operations, enhance safety and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity aspects must be considered and properly addressed to prevent such complex systems from being tampered with. In this paper, we propose an approach to secure communication channels between the SCCs and the vessels using SECOM standard and the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly detection. The proposed approach is validated against a real implementation of the SCC deployed at Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

The association of the APOE4 (vs APOE3) isoform with an increased risk of Alzheimer’s Disease (AD) is unequivocal, but the underlying mechanisms remain incompletely elu-cidated. A prevailing hypothesis incriminates the impaired ability of APOE4 to clear neurotoxic amyloid-β peptides (Aβ) from the brain as the main mechanism linking apolipoprotein isoform to disease aetiology. APOE protein mediates lipid transport both within the brain and from the brain to the periphery, suggesting that lipids may be potential co-factors in APOE4-associated physiopathology. The present study reveals several alterations in pathways of lipid homeostasis in the brains of mice expressing the human APOE4 versus APOE3 isoform. Carriers of APOE4 had deficient cholesterol turnover, an imbalance in the ratio of specific classes of phospholipids, lower levels of phosphatidylethanolamines bearing poly-unsaturated fatty acids and an overall eleva-tion in levels of monounsaturated fatty acids. These modifications in lipid homeostasis were related with increased production of Aβ peptides as well as augmented levels of tau and phosphorylated tau in primary neuronal cultures. This suite of AP-OE4-associated anomalies in lipid homeostasis and neurotoxic protein levels may be related to the accrued risk for AD in APOE4 carriers and provides novel insights into potential strategies for therapeutic intervention.

The historical residential area of Kōm ad–Dikka in Alexandria has experienced subsequent morphological transformation since the ancient era until present. Each historical period had a physical impact on the city’s urban structure that in turn struggled to survive its successive one with its different urban conception. However, the sinuous streets of this area, which probably date back to the late Egyptian Medieval period, are characterized as the only surviving organic fabric intra–muros that was not altered during the Egyptian Modern period. This paper elaborately investigated the chronological history of the historical residential area since the ancient era until the mid—twentieth century. Based on in–depth investigation of historical maps and memoirs, it revealed the possible reasons behind its extant sinuous urban form and postulated reconstructions of its urban morphology through sequential phases.

This paper introduces a three-layer system, proposing a comprehensive model of granular mixture transport over a mobile sloped bed in a steady flow. This system, consisting of the bottom, contact and upper zones, provides complete, continuous sediment velocity and concentration vertical profiles. The model considers gravity’s effect on sediment transport in the bottom (dense) layer, a crucial factor often overlooked in previous studies. The induced movement in the mobile sublayer of the dense layer causes an increase in velocity at the upper boundary of this layer, significantly affecting the vertical distributions of velocity and concentration above this layer. We described the interactions between fractions and an intensive sediment mixing and sorting process in the contact layer. The proposed shear variation, with an increase in its maximum value at the bed, adds to the novelty of our approach. The data sets used for the model’s validation cover various conditions, including slopes, grain diameters, densities and grain mobility conditions, from incipient motion to a fully mobilised bed. This extensive validation process instils confidence in the theoretical description and its applicability to real-world scenarios.

As most Korean OTT subscribers use mobile devices as their primary medium for watching OTT video content, mobile network traffic is soaring. However, the existing build-out of mobile communication technology – such as deploying more base stations or expanding the network bandwidth – is struggling to keep pace with the enormous increased demands of mobile network traffic. Accordingly, wireless device-to-device (D2D) caching networks are considered one of the most reliable alternatives for reducing the overload from mobile network traffic caused by streaming video content online. However, wireless D2D caching networks have pros and cons, like any other technology. Thus, this study aims to examine the practical decision-making process of mobile OTT service users in response to the application of wireless D2D caching networks on their mobile devices. An online survey of 94 respondents was conducted in South Korea during 19–23 May 2022; the data obtained were analyzed using the analytic hierarchy process (AHP) approach. The study finds that it was the risks, rather than the situational conditions and benefits, that mostly affected mobile OTT service users’ behavioral intentions and strategies. Streaming video content using mobile data or Wi-Fi was perceived as the most viable option. Mobile OTT service users were also willing to take the leap of shifts towards downloading video content in advance rather than adopting wireless D2D caching networks on a mobile device. However, mobile OTT service users’ decisions are heavily dependent on situational conditions related to use of mobile OTT services that make the quality of service consumption different. As one of the first to examine the responses of mobile OTT service users, this study provides meaningful implications for a timely and sensitive issue.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims at dissecting the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic methods can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. Ethical considerations related to patient privacy, biases, and lack of reliability and generalizability are the main issues. Furthermore, there is a need for high-quality real-word validation as well as the development of standardized protocols and policy rules to guide the implementation of these technologies in diverse clinical settings.

Cancer is second leading cause of death, worldwide. Lung cancer is the leading cause of cancer-related mortality. Plant-based therapeutics and herbal medicine have played a vital role in the development of several anti-cancerous agents, and has been used to reduce the severe side effects of chemotherapy as well. Since the anti-lung cancer properties of the plant Medicago. orbicularis are not explored yet, we identified its phytochemical composition and investigated the anti-oxidant, anti-hemolytic, and anti-cancerous properties of extracts of this plant in A549 human lung adenocarcinoma cells. Results show that all parts of M. orbicularis (stems, leaves, and fruits) exhibit remarkable anti-oxidant and hemolytic activities. In addition, all extracts showed a dose-dependent anti-cancerous cytotoxic activity against A549 cells; with fruit extracts being the most potent. This cytotoxic effect could be related, at least partly, to the induction of apoptosis, where M. orbicularis fruit extracts activated Caspase-3 and PARPP-1, and reduced the ratio of anti-apoptotic BCL-2/ pro-apoptotic BAX, thereby promoting cellular death. Furthermore, the use of M. orbicularis, in combination with a conventional chemotherapeutic agent, cisplatin, was assessed. Indeed, combination of cisplatin and M. orbicularis fruit extracts was more cytotoxic and induced more aggregation of A549 cells than either treatment alone. GC-MS analysis and total polyphenol and flavonoid content determination indicated that M. orbicularis is rich in compounds that have anti-cancerous effects. M. orbicularis may be a potential source of anti-cancerous agents to manage progression of lung cancer and its resistance to therapy.

In plants, water flows are the major driving force behind the growth and play a crucial role in the life cycle. To study hydrodynamics, methods based on tracking small particles inside water flows occupy a special place. Due to these tools, it is possible to get information about the dynamics of the spatial distribution of the fluxes characteristics. In this paper, using contrast-enhanced MRI, we have shown that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of xylem bundles of maize stem nodes and internodes. Supplementing MRI data, a high-precision visualization of xylem vessels by laser scanning microscopy was used to reveal structural and dimensional characteristics of the stem vascular system. In addition, we proposed the concept of using the prototype "Y-type xylem vascular bundles" as a model of the elementary connection of vessels within the vascular system. A Reynolds number can match the microchannel model with the real xylem vessels.

Phylostratigraphic analysis is a way to look anew on phylogenetic data in the evolutionary aspect. It allows counting the evolutionary age based on the analysis of genes, their orthologs and finding the last common ancestor. We performed phylostratigraphic analysis of Arabidopsis thaliana genes associated with several types of abiotic stresses (heat, cold, water-related, light, osmotic, salt, and oxidative) determined by the Gene Ontology annotation. Comparison of the distributions of ages of genes associated with stresses of different type has shown the heat stress to involve older genes while the light stress – younger genes. At the same time, all types of stress are characterized by a significantly higher proportion of old genes (common to all eukaryotes) compared to the whole set of A.thaliana genes. This can be explained by the involvement of basic molecular processes in plant cells into the stress response. Reconstruction and graphical analysis of the gene network of the heat stress educed several clusters associated with different response functions. Some of these clusters contain only ancient genes. The results obtained show that the phylostratigraphic analysis reveals the fundamental features of the organization of gene networks and their evolution.

For multi-sensor integrated systems, such as a mobile mapping system (MMS), data fusion at sensor-level, i.e., the 2D-3D registration between optical camera and LiDAR, is a prerequisite for higher level fusion and further applications. This paper proposes a line-based registration method for panoramic images and LiDAR point cloud collected by a MMS. We first introduce the system configuration and specification, including the coordinate systems of the MMS, the 3D LiDAR scanners, and the two panoramic camera models. We then establish the line-based transformation model for panoramic camera. Finally, the proposed registration method is evaluated for two types of camera models by visual inspection and quantitative comparison. The results demonstrate that the line-based registration method can significantly improve the alignment of the panoramic image and LiDAR datasets under either the ideal spherical or the rigorous panoramic camera model, though the latter is more reliable.

ACL instability poses a significant challenge in traumatology and orthopedic medicine, often requiring accurate diagnosis for appropriate treatment. While the pivot-shift test offers a crucial means of assessment, its reliance on subjective interpretation underscores the need for supplementary imaging studies. This study aims to address this limitation by developing a Bayesian classification algorithm tailored for integration into a mobile application. Using the built-in inertial sensors of smartphones, this new approach aims to dynamically evaluate rotational stability during knee examinations. Orthopedic specialists conducted knee evaluations on 52 subjects, with subsequent analysis revealing interesting insights. Intraobserver and interobserver analyses, as measured by ICC, demonstrated strong agreement both in terms of timing between maneuvers (ICC = 0.94) and signal amplitude (ICC = 0.71-0.66). Notably, the Bayesian algorithm successfully classified 95% of joint hypermobility cases, with an additional 7 cases of hyperlaxity identified by the Pivot-Shift Meter (PSM). These findings highlight the practicality and effectiveness of implementing a Bayesian classification algorithm within a mobile application for assessing and categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

Hereditary cancer syndromes caused by germline mutations account for 5-10% of all cancers. The finding of a genetic mutation could have far-reaching consequences for pharmaceutical therapy, personalized prevention strategies, and cascade testing. According to the National Comprehensive Cancer Network’s (NCCN) and the Italian Association of Medical Oncology (AIOM) guidelines, unaffected family members should be tested only if the affected one is unavailable. This article explores whether germline genetic testing may be offered to high-risk families for hereditary cancer even if a living affected relative is missing. A retrospective study was carried out on 103 healthy subjects tested from 2017 to 2023. We enrolled all subjects with at least two first or second-degree relatives affected by breast, ovarian, pancreatic, gastric, prostate, or colorectal cancer. All subjects were tested by Next Generation Sequencing (NGS) multi-gene panel of 27 cancer-associated genes. In the study population, 5 (about 5%) pathogenic/likely pathogenic variants (PVs/LPVs) were found while 40 (42%) had a Variant of Uncertain Significance (VUS). This study highlights the importance of genetic testing for individuals with a strong family history of hereditary malignancies. This approach would allow women who tested positive to receive tailored treatment and prevention strategies based on their personal mutation status.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

This paper explores innovative approaches to determining eigenvalues and eigenvectors through the combination of neural networks and traditional numerical methods. Traditional methods like the Newton-Raphson and Durand-Kerner algorithms are proven effective but often require good initial approximations and can be sensitive to the choice of starting points. In this study, we propose a hybrid approach that uses neural networks to generate better initial approximations, which are then iteratively refined using traditional methods. By combining the pattern recognition power of neural networks with the adaptability of traditional numerical methods, we demonstrate faster convergence and higher accuracy in determining eigenvalues and eigenvectors. Experimental results on various polynomials of different degrees and characteristics show that our hybrid approach outperforms individual methods, providing a more robust and efficient solution.

The Active Distribution Networks (ADNs) are Multi-Input Multi-Output (MIMO) systems with coupled dynamics which cause interactions among the control loops of the Distributed Energy Resources (DERs). This undesired effect lead to performance degradation in the voltage control. To mitigate the effects of these unavoidable coupling, the present paper proposes a systematic design procedure based on the analysis of the interaction's sources. In details, each DER is equipped with a double loop PI to control the active and reactive powers output by the Voltage Source Converter (VSC) which connects the DER to the network's node. Furthermore, to guarantee Ancillary Services (ASs), the two loops are coupled by a simple mechanism of cooperation of the active power to voltage regulation realized by a filtered droop law. To achieve voltage regulation with reduced loop interactions, the PI parameters and the filter's pulse are designed according to a two sequential steps procedure based on the (Internal Model Control) IMC technique. Simulation studies are finally presented to demonstrate that the proposed design method achieves both reduction of the loop interaction and robust voltage control in presence of model parameter uncertainty in the MIMO model.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Mobile devices (MD) are used by mobile cloud computing (MCC) customers users because of their 11 portability and robust connectivity and the ability to house and operate third-party applications 12 (apps). However, the apps installed on a MD may pose data security risks to the MD owner and to 13 other MCC users, especially when the requested permissions include access to sensitive data (e.g., 14 user’s location and contacts). Calculating the risk score of an app or quantifying its potential 15 harmfulness based on user input or on data gathered while the app is actually running may not 16 provide reliable and sufficiently accurate results to avoid harmful consequences. This study 17 develops and evaluates a risk assessment framework for Android-based MDs that does not depend 18 on user input or on actual app behaviour. Rather, an app risk evaluator assigns a risk category to 19 reach resident app based on the app’s classification (benign or malicious) and the app’s risk score. 20 The app classifier (a trained machine learning model) considers the permissions and the intents 21 requested by the app. The apps risk score is calculated by a probabilistic function based on the 22 app’s use of a set of selected dangerous permissions. The results from the testing of an instance of 23 the framework on a MD with real-life resident apps indicated that the proposed security solution 24 was effective and feasible.

Federated learning (FL) has emerged as a crucial technology in today’s data-centric technological environment, enabling decentralized machine learning and safeguarding user privacy. This study introduces “Federated Operations (FedOps) Mobile,” a novel FL framework optimized for the dynamic and heterogeneous ecosystem of mobile devices. FedOps Mobile enhances traditional FL approaches for mobile devices by integrating real-time operational control and advanced on-device training capabilities using TensorFlow Lite and CoreML, addressing critical challenges in scalability, efficiency, and system heterogeneity. Our approach utilizes a wide range of devices, facilitated by intelligent client-selection mechanisms. This mechanism evaluates the capabilities and readiness of multiple devices per client to ensure fair and efficient network participation. The framework also utilizes remote device control for seamless task management and sustained engagement, enabling continuous learning without compromising the user experience. We conducted extensive experiments to validate the framework’s performance, focusing on three core aspects: operational efficiency, model personalization, and resource optimization in multi-device environments. The results demonstrate that the proposed method is effective for efficient client selection, energy consumption, and model optimization.

Suicidal behaviour remains difficult to predict and prevent, even for experienced mental health care professionals. The known distal risk factors for suicidal behaviour are not sufficiently specific to fully understand the complex dynamic processes that precede a suicide attempt. Real-time mobile monitoring data can be used to analyse proximal risk mechanisms within the suicidal process. At the same time smartphone-based safety planning and self-monitoring may enhance a patient’s self-management skills thereby increasing their capacity to respond to a suicidal crisis and to become more aware of crisis symptoms. The current paper describes the theoretical and conceptual rationale for the CASPAR study which applies an innovative approach to the study of suicidal processes. It uses basic science approaches to inform the implementation of an innovative suicide prevention intervention. We aim to develop and implement mobile safety plan in conjunction with real-time monitoring in order to both directly implement suicide prevention interventions and to study the ongoing dynamics of individual suicidal behaviour by applying network analysis.

Image stitching is a crucial aspect of image processing. However, factors like perspective and environment often lead to irregular shapes in stitched images. Cropping or completion methods typically result in substantial loss of information. This paper proposes a method for rectifying irregularly images into rectangles using deformable meshes and residual networks. The method utilizes a convolutional neural network to quantify rigid structures of images. Choosing the most suitable mesh structure based on the extraction results, offering options such as triangular, rectangular, and hexagonal. Subsequently, the irregularly image, predefined mesh structure, and predicted mesh structure are input into a wide residual neural network for regression. The loss function comprises local and global, aimed at minimizing the loss of image information within the mesh and global target. This method not only significantly reduces information loss during rectification but also adapting to different images with various rigid structures. Validation on the DIR-D dataset shows this method outperforms state-of-the-art methods in image rectification.

Global warming and the associated increasing frequency of heatwaves are on the rise leading to numerous health complications. Diabetic patients are more vulnerable to heat exposure than healthy individuals, owing to their impaired sweating ability, however, little is known about how heat exposure may impact the function of skin cells, commonly keratinocytes. Our recent report provided the first proof-of-concept revealing that the metabolic composition of sweat EVs is associated with metabolic changes in healthy individuals and type 2 diabetes patients. Given this and the shared cellular proprieties of eccrine sweat glands with keratinocytes, we questioned whether the metabolic changes in sweat EVs may mirror the responses of keratinocytes upon heat exposure. To this, we exposed HaCat, which were maintained in low (mimicking healthy condition) and high glucose (mimicking diabetes condition) to acute (90 min), chronic (48 h), and repetitive heat exposure at 40 ⁰C. Through conducting a set of experiments including a targeted metabolomics approach, Greiss assay, western blotting analysis, and electron microscopy studies. The data demonstrate that heat exposure results in substantial changes of metabolite levels belonging to amino acids, pyrimidine metabolism, and glycolysis pathways in high glucose maintained HaCat cells when compared to those noted in low glucose condition. Exploring the molecular mechanisms through which heat exposure may impact the metabolic composition in keratinocytes, our discoveries highlight nitric oxide (NO) as a novel biomarker that the level is correlated with heat duration and frequency. Furthermore, data reveal that the HSP-70 protein levels and mitochondrial morphology are consequently altered. This study provides evidence of shared metabolic alterations in keratinocytes and sweat EVs collected from clinical study and proposes, for the first time, NO as a novel biomarker for monitoring changes in health and diabetes upon heat exposure.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

The paper addresses the energy consumption minimization problem in wireless powered communication networks (WPCNs) and proposes a DT-SWIPT-assisted time allocation scheme, named DaTA, such that the wireless station can share the remaining energy after transmission to the HAP to those who have not transmitted to the HAP to minimize the energy consumption of the network. In addition to proposing a new frame structure, the paper also considers the SNR (Signal-to-Noise) constraint to guarantee that the HAP can successfully receive data from wireless stations. In the paper, the energy consumption minimization problem is formulated as a nonlinear programming model. We employ the SQP (Sequential Quadratic Programming) algorithm to figure out the optimal solution. Moreover, a heuristic method is proposed as well to obtain a near-optimal solution in a shorter time. Simulation results showed that the proposed scheme can have better performance than the related work in terms of energy consumption and energy efficiency.

Crop rotation diversification using legumes has been advocated as one of the solutions to improve the resilience of the crop system to various environmental stresses and the use of nitrogen resources. However, when forage legumes or grass-legumes are ploughed, there is a high risk of nitrate leaching, especially in sandy soils. The aim of the study was to determine the effect strip tillage intercropping management on soil mineral nitrogen (SMN), water-extractable organic carbon (WEOC) content and winter wheat (WW, Triticum aestivum L.) grain yield compared to forage legume and WW monocropping with conventional tillage. In intercropping was used winter wheat - black medick (Medicago lupulina L.) WW+BM, winter wheat - white clover (Trifolium repens L.) WW+WC, and winter wheat - Egyptian clover (Trifolium alexandrinum L.) WW+EC. Research was repeated in two experiments. The results showed that after strip tillage and WW sowing less or similar SMN content cud be detected, as in conventional tillage and WW + forage legumes intercropping. WW grain yield in intercrops decreased, compared to the grasses monocultures that were ploughed before WW sowing. The highest amount of WEOC was in intercropping soil growing WW+WC or in all fields (except WW+EC) after applying strip tillage. During the research period, the amounts of mobile humic substances (HSs) and mobile humic acids (HAs) changed analogously. Their content increased substantially in fields with WC and EC, regardless of whether the grasses were ploughed or grown with WW.

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

Graph Neural Networks (GNNs) are powerful models capable of managing intricate connections in non-Euclidean data, such as social networks, physical systems, chemical structures, and communication networks. Despite their effectiveness, the large-scale and complex nature of graph data demands substantial computational resources and high performance during both training and inference stages, presenting significant challenges, particularly in the context of embedded systems. Recent studies on GNNs have investigated both software and hardware solutions to enhance computational efficiency. Earlier studies on Deep Neural Networks (DNNs) have indicated that methods like reconfigurable hardware and quantization are beneficial in addressing these issues. Unlike DNN research, studies on efficient computational methods for GNNs are less developed and require more exploration. This survey reviews the latest developments in quantization and FPGA-based acceleration for GNNs, showcasing the capabilities of reconfigurable systems (often FPGAs) to offer customized solutions in environments marked by significant sparsity and the necessity for dynamic load management. It also emphasizes the role of quantization in reducing both computational and memory demands through the use of fixed-point arithmetic and streamlined vector formats. This paper concentrates on low-power, resource-limited devices over general hardware accelerators and reviews research applicable to embedded systems. Additionally, it provides a detailed discussion of potential research gaps, foundational knowledge, obstacles, and prospective future directions.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.

The discovery of the quantum tunnelling effect---the transmission of particles through a high potential barrier---was one of the most impressive achievements of quantum mechanics made in the 1920s. Responding to the contemporary challenges, I introduce a novel deep neural network (DNN) architecture that processes information using the effect of quantum tunnelling. I demonstrate the ability of the quantum tunnelling DNN (QT-DNN) to recognise optical illusions like a human. Hardware implementation of QT-DNN is expected to result in an inexpensive and energy-efficient neuromorphic chip suitable for applications in autonomous vehicles. The optical illusions recognition tests developed in this paper should lay foundations for cognitive benchmarking tasks for AI systems of the future, benefiting the fields of sociophysics and behavioural science.

The Fruit Harvest Helper, a mobile application developed by Northwest Nazarene University’s (NNU) Robotics Vision Lab, aims to assist farmers in estimating fruit yield for apple orchards. Currently, farmers manually estimate the fruit yield for an orchard, which is a laborious task. Fruit Harvest Helper seeks to simplify their process. While prior research efforts at NNU concentrated on developing an iOS app for blossom detection, this current research aims to adapt that smart farming application for apple detection across multiple platforms, iOS and Android. The old and new applications were designed with an intuitive user interface that is easy for farmers to use, allowing for quick image selection and processing. Unlike before, the adapted app utilizes a color ratio-based image segmentation algorithm implemented in OpenCV C++ to detect apples in apple tree images selected for processing. The results of testing the algorithm with a dataset of images indicate an 8.52% Mean Absolute Percentage Error (MAPE) and a Pearson correlation coefficient of 0.6 between detected and actual apples on the trees. These findings were obtained by evaluating the images from both the east and west sides of the trees, which was the best method to reduce the error of this algorithm. Although the Fruit Harvest Helper shows promise, there are many opportunities for improvement. These opportunities include exploring alternative machine-learning approaches for apple detection, conducting real-world testing without any human assistance, and expanding the app to detect various types of fruit. The Fruit Harvest Helper mobile application is among the many mobile applications contributing to precision agriculture, nearing readiness for farmers to use in yield monitoring and farm management.

The rapid development and integration of Autonomous Mobile Robots (AMRs) have 1 revolutionized industries by enhancing automation capabilities. A critical challenge in this evolution 2 is achieving effective navigation and obstacle avoidance, which are essential for deploying AMRs 3 seamlessly in varied environments. This paper presents a detailed exploration of AMR navigation 4 and obstacle avoidance advancements through the application of reinforcement learning, specifically 5 focusing on small-scale Sri Lankan manufacturing facilities. The study demonstrates the effectiveness 6 of Q-learning in managing dynamic obstacles within a factory environment. The AMR successfully 7 avoided obstacles in 36 out of 50 test runs, achieving a 72% success rate, and maintained an average 8 distance of 12 cm from each obstacle, underscoring the algorithm’s precision in maintaining safe nav- 9 igation paths while dynamically adapting to environmental changes. The continuous monitoring by 10 ultrasonic sensors, combined with iterative learning, enabled the robot to refine its decision-making 11 process and efficiently navigate through the environment. This paper also provides a comprehensive 12 examination of conventional methods, tracing their historical development and assessing their role in 13 addressing real-world challenges. The results highlight the significant improvements brought by rein- 14 forcement learning, particularly when integrated with sensor fusion and motor control technologies, 15 to enhance navigation and dynamic object collision avoidance. The study involves developing an 16 AMR prototype, refining algorithms, and assessing system performance in controlled environments. 17 By automating material transportation and addressing operational constraints in manufacturing 18 settings that predominantly rely on manual labor, this paper advances the practical deployment of 19 AMRs. Additionally, it discusses ethical considerations, potential limitations, and the importance 20 of real-world validation, offering valuable insights for the future development and integration of 21 autonomous mobile robotics in dynamic industrial environments.

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

The outcomes of the 2015 Paris Agreement triggered a number of climate impact assessments, such as for floods and droughts, to focus on future time frames corresponding to the years of reaching specific levels of global warming. Yet, the links between the timing of the warming levels and the corresponding greenhouse gas concentration pathways to reach them, remain poorly understood. To address this gap, we compare projected changes of annual mean, extreme high and extreme low river discharges in Europe at 1.5°C and 2°C under scenarios RCP8.5 and RCP4.5 from an ensemble of Regional Climate Model (RCM) simulations. The statistical significance of the difference between the two scenarios for both warming levels is then evaluated. Results show that in the majority of Europe (>95% of the surface area for the annual mean discharge, >98% for high and low extremes), the changes projected in the two pathways are statistically indistinguishable. These results suggest that in studies of changes at specific warming levels the projections of the two pathways can be merged into a single ensemble without major loss of information. With regard to the uncertainty of the unified ensemble, findings show that the projected changes of annual mean, extreme high and extreme low river discharge are statistically significant in large portions of Europe.

Suicide and suicidal behavior are major public health concerns and affect 3-9% of the population worldwide. Despite growing evidence, there are still few effective interventions available to reduce suicide risk. In this article, we describe theoretical models of suicide ideation and behavior and propose to examine the possible effectiveness of a new and innovative preventive strategy. A model of suicidal intrusion (mental imagery related to suicide, also referred to as suicidal flash-forwards) is presented describing one of the assumed mechanisms in the etiology of suicide and the mechanism of therapeutic change. We provide a brief rationale for an Eye Movement Dual Task (EMDT) treatment for suicidal intrusions describing techniques that can be used to target these suicidal mental images and thoughts to reduce overall behavior. Based on the available empirical evidence for the mechanisms of suicidal intrusions, this approach appears to be a promising new treatment to prevent suicidal behavior as it potentially targets one of the linking pins between suicidal ideation and suicidal actions.

The Atlantic Canada Together Enhancing Acute Stroke Treatment (ACTEAST) project is a Modified Quality Improvement Collaborative (mQIC) designed to improve ischemic stroke treatment rates and time-based efficiency in Atlantic Canada. This study specifically evaluates the implementation of the mQIC in Nova Scotia using qualitative methods. The mQIC spanned six months and included two Learning Sessions, multiple webinars, and a per-site virtual visit. Each session was followed by an action planning period to guide implementation efforts over the following 2-4 months. The Consolidated Framework for Implementation Research (CFIR) was utilized to develop a pre-tested, semi-structured interview guide, which aimed to uncover barriers and facilitators to mQIC implementation. Interviews were conducted with 14 healthcare professionals, generating 454 references that were coded into 28 CFIR constructs. Notably, 84% of these references were positively framed as facilitators. Despite this, significant barriers were identified, including resource availability, competing priorities, communication challenges, and difficulties engaging key stakeholders. Some barriers were particularly prominent during specific phases of implementation. The study findings offer valuable insights into the implementation of quality improvement initiatives in stroke care. They underscore the importance of recognizing and addressing context-specific barriers, while also leveraging identified facilitators to drive successful implementation and ultimately improve patient outcomes.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.

In this digital era, laundry applications come as an innovative solution to meet the laundry needs of modern people who often have busy schedules and limited time. This research aims to make a significant contribution to our broader understanding of how digital innovations can revolutionize mundane tasks. This research uses the Waterfall model, which is one of the methods in the Software Development Life Cycle (SDLC). The development process begins with the Requirement Analysis stage, where application requirements and specifications are collected and thoroughly analyzed to ensure that the developed application will meet user expectations. After that, the System Design stage is conducted, where the application architecture, user interface design, and database structure are designed in detail to ensure conformity with the previously identified needs.The next stage is Implementation, where program code is written and application features are developed according to the design that has been made. The final stage is Integration and Testing, where all application components are combined and tested thoroughly to detect and fix any errors or bugs that may exist. The results of this study produced a web-based shuttle laundry service application using the Laravel framework called "All Fresh Laundry". This application can order clothes washing services according to its membership, where there are 3 types of membership based on price and of course have different benefits. In addition, the testing phase of this application using black box testing shows that all menu features function properly according to their functions. Each component of the "All Fresh Laundry" app was thoroughly tested to ensure that no errors or malfunctions occurred. The results of this testing make it clear that every feature, from online ordering to laundry status tracking, works well and meets the specifications that have been set, so that this application can provide an optimal and reliable user experience.

Cashless payments are so common and popular that people are gradually not taking cash out of the house. But people still need to carry some cash and a couple of debit cards, which increases the risk of people having their cash or cards lost or stolen. The mobile payment method based on scanning codes solves the problem that you don't need to carry cash or cards when you go out, but you still need to enter the APP to scan the code to complete the payment. This report proposes a POS system with NFC technology, enabling people to complete transactions with new payment methods and reducing carrying other payment tools. Therefore, this paper explores the understanding of the proposed system and improves its functionality. The project aims to facilitate quickness for users to complete transactions, that is, near-field communication, using POS systems with received signals for transactions. Data is collected via triangulated research methods from various respondents across Malaysia with statistics and analysis on the proposed solution. The contactless NFC POS solution allows users to interact with the system via smartphone host card emulation application technology to complete transactions and allow people to reduce carrying other payment tools.

Nowadays the dynamism induced by the constant change of strategic decisions on the markets produces an extra difficulty in the management of an organization. The strategic decisions made by managers can become obsolete in a short period of time. One of the major difficulties in managing a commercial organization is to predict, with some precision degree, the impact some strategic decisions have on the financial results. Business Intelligence (BI) is an area widely used to help managers to make strategic decisions. But the methods used, behind the scenes, to achieve the conclusions are kept secret by BI company-based services. Modelling the environment can be a good option to study the impact of an action, provided by a strategic decision, in a real environment. A good model should give an approximate result of an action applied to a previous state of the environment. Artificial Neural Networks (ANNs) are proven to be excellent in modeling environments with some level of data noise. The same strategic action can have different results in different organizations. A tool that allows an evaluation of a strategic action applied to an environment should have a major importance in the management scope. Modeling the environment should save time and money for the organization to improve the performance of the strategic plan. If one evaluates the state of the environment after a strategic action being applied, it can be possible to mitigate the risk of failure of the strategic plan. As we will be able to verify, it is possible to use ANNs to model strategic environments that should allow the prediction of sales and operating results by the used strategies with some precision, although the level of success will be directly related with the user’s necessity.

The SOC (State of Charge) in Lithium-ion battery packs refers to determining a battery's current energy level or capacity at a given time. SOC is important for many battery-operated systems since it shows how much energy is available. In the earlier stage SOC is calculated by different techniques such as Coulomb counting, open-circuit voltage, Kalman filters, Data-Driven Approaches, and Real-Times Updates so these all methods are used to calculate the SOC Estimation in battery packs. In recent years, these old methods have had some errors and accuracy malfunctions in SOC estimation in battery packs. The SOC estimation errors occur on Two-Wheeler, Three-Wheeler, and four-wheeler of Electric Vehicles (EV). To Overcome this problem in current years the AI&ML Algorithms Methods are applied everywhere related to Accuracy, Solutions & All. To increase the accuracy of SOC Estimations we are going to use Machine Learning Algorithms to improve the SOC in Machine Learning there are various methods such as supervised, unsupervised, and reinforcement. Of these three methods, one of the best methods is to select the Algorithms is apply to the Battery packs to get accurate SOC Estimations. In Machine Learning, we select the Supervised Learning Algorithms. Currently, the project will focus on the use of a Supervised Machine Learning Algorithm which includes an Extra Trees Regressor, Bagging Regressor, K-Neighbors Regressor, Decision Tree Regressor, and Random Forest Regressor for SOC Estimation. In this, we will develop algorithms applied to Lithium-ion Battery Packs to accurately the SOC estimates. In conclusion, the algorithms we will develop by using Machine Learning are compared with the algorithms and analytical Models.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.