Miracle-dz/UniFAR-FTUs huggingface dataset:
Metaheuristic Techniques based Optimal Placement and Sizing of Multiple Distributed Generations in Radial Distribution System

In this paper, Meta-heuristic techniques were employed for optimal placement and sizing of multiple Distributed Generations (DGs) in a Radial Distribution System (RDS) to mitigate the losses and voltage deviations. In this study, Genetic Algorithm (GA), Shuffled Frog Leap Algorithm (SFLA) and Jaya Algorithm (JAYA) were applied on a standard IEEE 33 bus test system. It was observed from the test results simulated under MATLAB environment that Jaya Algorithm outperformed GA and SFLA for optimal placement and sizing of multiple DGs to mitigate the losses and voltage deviations.
User Fairness Optimization for Multi-UAV-Aided NOMA Networks: A Location-Aware Perspective

In the blind areas of current fifth generation (5G) networks, e.g. the remote areas, unmanned aerial vehicles (UAVs) can be used to provide on-demand connectivity. To efficiently serve the sparsely distributed users in these areas, non-orthogonal multiple access (NOMA) could be adopted to exploit the user distinguish ability in the power domain. In this paper, we consider a NOMA-based multi-UAV-aided network, where a swarm of coordinated UAVs transmit messages to unevenly distributed users through a virtual multiple-input-multiple-output (MIMO) channel. We formulate a power allocation problem to maximize the minimum user rate to assure fairness in the transmission. Different from existing studies, we use only the large-scale channel state information (CSI) in the transmission design, which characterizes the basic channel feature, and can be obtained using the location information of UAVs/users. By leveraging the random matrix theory and successive convex optimization tools, we propose an iterative algorithm to solve the problem after a series of problem transformation. Simulation results show that the proposed power allocation scheme outperforms existing methods, which shows the potential of multi-UAV-aided NOMA communications for coverage enhancement in remote areas.
Hierarchical Federated Learning over HetNets enabled by Wireless Energy Transfer

Training centralized machine learning (ML) models becomes infeasible in wireless networks due to the increasing number of internet of things (IoT) and mobile devices and the prevalence of the learning algorithms to adapt tasks in dynamic situations with heterogeneous networks (HetNets) and battery limited devices. Hierarchical federated learning (HFL) has been proposed as a promising learning that can preserve the data privacy of the wireless devices, tackle the communication bottlenecks in wireless networks, and improve the energy effi-ciency. We propose a novel energy-efficient HFL framework for HetNets with massive multiple-input multiple-output (MIMO) wireless backhaul enabled by wireless energy transfer (WET). We formulate a joint energy management and device association optimization problem in HFL over HetNets subject to maximal divergence constraints. Next, an optimal solution is developed, but with high complexity. To reduce the complexity, a heuristic algorithm for HFL over HetNets with energy, channel quality, and accuracy constraints, is developed in order to minimize the grid energy consumption cost and preserve the value of loss function, which captures the HFL performance. Simulation results show the efficiency of the proposed resource management approach in the HFL context in terms of grid power consumption cost and training loss.
Near-Optimal User Clustering and Power Control for Uplink MISO-NOMA Networks

This paper investigates the user clustering and power control in the uplink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) networks. A joint optimization problem is formulated to minimize the system transmit power. The original optimization problem is prohibitively complicated when the number of users is large. Alternatively, a two-step user clustering and power control algorithm is proposed. First, an im-proved K-means algorithm is proposed for user clustering, where the clustering metric considers both channel gain and channel correlation among users to reduce the intra- and inter-cluster interference. With this basis, the optimal cluster number and cluster centers are dynamically obtained by the semi-orthogonal user selection (SUS) algorithm. Further, the closed-form expression of the optimal intra-cluster power control is derived, and the resulting inter-cluster power control problem is solved iteratively. Simulation results show that the proposed scheme achieves the near-optimal performance in terms of power consumption and energy efficiency with low computational complexity.
A Learning Based Branch-and-Bound Algorithm for Single-Group Multicast Beamforming

Consider the single-group multicast beamforming problem in wireless systems, where a multi-antenna transmitter has a common message intended to a group of users. The problem is known be non-convex and NP-hard except in very few special cases. Traditional optimization-based algorithms either obtain sub-optimal solutions or take exponential complexity to reach the optimum. In this paper, we propose a learning based branch-and-bound (LBB) algorithm to find a near-optimal solution of the quality-of-service (QoS)-constrained multicast beamforming problem with affordable computational complexity. LBB regards the search procedure of the argument-cut based branch-and-bound (AC-BB) algorithm as a sequential decision problem in a binary tree and learns the optimal pruning policy via supervised learning. We propose to apply ensemble learning to train multiple classifiers and then combine them to achieve better performance. To tackle the imbalanced issue, we propose an undersampling-supervised learning method to sample several balanced subsets and train a classifier on each of them. We also show that the computational complexity of LBB is determined by the depth of the binary tree and give its expression in the worst case. Numerical results show that LBB runs significantly faster than AC-BB while achieving nearly the same optimal performance.
Low-Complexity Grouped Symbol-Level Precoding for MU-MISO Systems

Symbol-level precoding (SLP), which can convert the harmful multi-user interference (MUI) into beneficial signals, can significantly improve symbol error rate (SER) performance in multi-user communication systems. While enjoying symbolic gain, however, the complicated non-linear symbol-by-symbol SLP design suffers high computational complexity exponential with the number of users, which is unaffordable in realistic systems. In this paper, we propose a novel low-complexity grouped SLP (G-SLP) approach and develop an efficient design algorithm for a typical max-min fairness problem. This practical G-SLP strategy divides all users into several groups. SLP is utilized for the users within each group to convert intra-group MUI into constructive interference, meanwhile the inter-group MUI is also suppressed. In particular, we first use Lagrangian and Karush-Kuhn-Tucker (KKT) conditions to simplify the G-SLP design problem and then propose an iterative majorization-minimization (MM) based algorithm to solve it. Simulation results illustrate that the proposed G-SLP strategy dramatically reduces the computational complexity without causing significant performance loss compared with the traditional SLP scheme.
Particle Swarm Optimization based Task Offloading in Vehicular Edge Computing

Recent development in the field of Internet-of-Things (IoT) enhancing various safety and non-safety services of Vehicular Ad-Hoc networks (VANET). These applications execute on cloud or Mobile Edge server (MES) due to resource restriction of IoT devices. MES is limited in-network because of higher installation costs. Hence, some applications are unable to get service within the deadline. To resolve this, a new evaluation has been introduced named Vehicular Edge Computing (VEC) which utilizes idle vehicles as an edge server to fulfill upcoming demands of IoT. The challenge in VEC is to offload the task to the appropriate vehicle according to available resources, position and speed in order to improve resource utilization and makespan. Hence in this paper, we have proposed an offloading mechanism that offloads tasks using Particle Swarm optimization (PSO). Here the PSO assigns the best fit task to the vehicle according to position and speed of the vehicle for completing the computation within time. Simulation using OMNET++, Veins, and Sumo, shows that the proposed PSO-based offloading improves makespan, resource utilization and offloading ratio around 28.1%, 17.43% and 47.75% compared to Branch and Bound offloading technique.
Data Privacy Protection based on Feature Dilution in Cloud Services

Machine learning as a service (MLaaS) brings many benefits to people's daily life. However, the service mode of MLaaS will increase the risk of users' privacy leakage. Existing works focusing on privacy-preserving based on encryption, differential privacy, and distributed framework require high computing resources or cannot be applied in MLaaS. In this paper, we propose feature dilution (FD), a noise-based desensitization algorithm to remove sensitive information in raw data. In particular, FD continuously adds raw data features to the random noise until it meets the minimum amount for an effective query, and we call this noise weak-feature noise (WFN). By fine-tuning the MLaaS architecture, we have realized that users can utilize WFN to get normal services without exposing their local private data. Meanwhile, noise addition technology is introduced by us to reduce the risk of privacy leakage caused by “weak features”. Extensive experiments have demonstrated that users can use FD to obtain effective services without exposing their private data. Finally, we conducted practical tests on weak-feature noises and found that these noises are difficult to use by malicious service providers.
ICE: Intelligent Caching at the Edge

The unprecedented growth of mobile data traffic brings unique challenges for network bandwidth and server resources to meet the diverse QoE (Quality of Experience). Caching becomes a promising way to alleviate these issues by storing a subset of data at the network edge, for which caching policy becomes critical. To this end, various caching schemes have been put forward, however, these schemes are either not intelligent lacking the ability of self-learning and self-decision-making, or inefficient with low data hit rate. Based on these observations, in this paper, we propose a novel Intelligent Caching framework at the Edge, named ICE, via deep reinforcement learning to capture certain valued information of the requested data. Notably, in our approach, the popularity of the data to be cached will be explored and considered. A Markov decision model is further developed to determine whether the data should be cached. The evaluation shows that ICE greatly improves the hit rate in comparison with the state-of-the-art approaches, and reduces the energy consumption for data transmission. Furthermore, based on ICE, the users' QoE is greatly improved. In conclusion, both theoretical analysis and experimental results prove the effectiveness and high performance of ICE compared with conventional strategies.
Evolution of Fertilization-Related Genes Provides Insights Into Reproductive Health in Natural Ascrotal Mammals

Cryptorchidism is the failure of one or both testes to descend into the bottom of the scrotum. This common congenital malformation in humans and domestic animals is the best characterized risk factor for abnormal sperm functions and infertility. However, current treatment approaches for cryptorchidism do not ensure paternity in all cases. Some lineages of mammals (such as elephants and cetaceans) have natural ascrotal testes (i.e., undescended or incompletely descended testes) and normal sperm motility and fertility, providing an opportunity to understand the genetic basis of cryptorchidism. In this study, we showed that genes associated with sperm motility and competition/fertility in ascrotal mammals experienced frequent, strong selective pressure. The fixation of specific amino acids and positive selection in ascrotal mammals could affect the physicochemical properties and functions of fertilization-related proteins. In a comparison between mammals with undescended testes and incompletely descended testes, discrepancies in genes showing evidence for adaptive evolution and in functional enrichment suggested that multiple molecular mechanisms contribute to the maintenance of fertility in the challenging testicular environment. Our findings revealed substantial heterogeneity in the divergence of fertilization-related genes between natural scrotal and ascrotal mammals and provide insight into molecular mechanisms underlying normal sperm motility and competition in natural ascrotal mammals. We provide a detailed theoretical basis for understanding the pathology of cryptorchidism from a molecular evolutionary perspective. This study may contribute to the establishment of diagnostic and therapeutic targets for sperm motility and fertility disorders due to congenital cryptorchidism in humans and domestic animals. Graphical Abstract Evolution of fertilization-related genes provides insights into reproductive health in natural ascrotal mammals. The fixation of specific amino acids and selection on sperm motility and competition-related genes contributed to normal fertility in ascrotal mammals. The silhouettes are reproduced from FLATICON (https://www.flaticon.com/).
Effects of Excess Iron on the Retina: Insights From Clinical Cases and Animal Models of Iron Disorders

Iron plays an important role in a wide range of metabolic pathways that are important for neuronal health. Excessive levels of iron, however, can promote toxicity and cell death. An example of an iron overload disorder is hemochromatosis (HH) which is a genetic disorder of iron metabolism in which the body’s ability to regulate iron absorption is altered, resulting in iron build-up and injury in several organs. The retina was traditionally assumed to be protected from high levels of systemic iron overload by the blood-retina barrier. However, recent data shows that expression of genes that are associated with HH can disrupt retinal iron metabolism. Thus, the effects of iron overload on the retina have become an area of research interest, as excessively high levels of iron are implicated in several retinal disorders, most notably age–related macular degeneration. This review is an effort to highlight risk factors for excessive levels of systemic iron build-up in the retina and its potential impact on the eye health. Information is integrated across clinical and preclinical animal studies to provide insights into the effects of systemic iron loading on the retina.
Genetically Predicted Causality of 28 Gut Microbiome Families and Type 2 Diabetes Mellitus Risk

Mounting evidence indicates that gut microbiome may be involved in the pathogenesis of type 2 diabetes mellitus (T2DM). However, there is no consensus on whether there is a causal link between gut microbiome and T2DM risk. In the present study, the Mendelian randomization (MR) analysis was performed to investigate whether gut microbiome was causally linked to T2DM risk. The single nucleotide polymorphisms (SNPs) that were significantly related to exposure from published available genome-wide association study (GWAS) were selected as instrumental variables (IVs). The robust methods including inverse variance weighting (IVW), MR Egger, and weighted median were conducted to infer the causal links. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) and MR-Egger regression were used to test whether there was horizontal pleiotropy and identify outlier SNPs. The estimates of IVW suggested that Streptococcaceae (odds ratio (OR) = 1.17, 95% confidence interval (CI), 1.04–1.31, p = 0.009) was associated with higher risk of T2DM in European population. In Asian population, the MR IVW estimates revealed that there was a causal link between Acidaminococcaceae and T2DM risk (OR = 1.17, 95% CI, 1.04–1.31, p = 0.008). There was no evidence of notable heterogeneity and horizontal pleiotropy. However, after false discovery rate (FDR) correction, the causal link between gut microbiome and T2DM was absent (FDR, p > 0.05). In summary, using genetic instruments, this study does not find evidence of association between the 28 gut microbiome families and T2DM risk. However, Streptococcaceae and Acidaminococcaceae may have a borderline positive correlation with T2DM risk.
Cardiovascular Pulsatility Increases in Visual Cortex Before Blood Oxygen Level Dependent Response During Stimulus

The physiological pulsations that drive tissue fluid homeostasis are not well characterized during brain activation. Therefore, we used fast magnetic resonance encephalography (MREG) fMRI to measure full band (0–5 Hz) blood oxygen level-dependent (BOLDFB) signals during a dynamic visual task in 23 subjects. This revealed brain activity in the very low frequency (BOLDVLF) as well as in cardiac and respiratory bands. The cardiovascular hemodynamic envelope (CHe) signal correlated significantly with the visual BOLDVLF response, considered as an independent signal source in the V1-V2 visual cortices. The CHe preceded the canonical BOLDVLF response by an average of 1.3 (± 2.2) s. Physiologically, the observed CHe signal could mark increased regional cardiovascular pulsatility following vasodilation.
An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism.
Developmental Origins of Metaflammation; A Bridge to the Future Between the DOHaD Theory and Evolutionary Biology

Metabolic syndrome refers to obesity-associated metabolic disorders that increase the risk of type 2 diabetes, coronary diseases, stroke, and other disabilities. Environmental imbalance during the early developmental period affects health and increases susceptibility to non-communicable diseases, including metabolic syndrome, in later life; therefore, the Developmental Origins of Health and Disease (DOHaD) theory was established. According to the DOHaD theory, the hypothesis of the energy-saving ‘Thrifty Phenotype’ in undernourished fetuses is one of the well-accepted schemes as a risk of developing metabolic syndrome. This phenotype is evolutionarily advantageous for survival of the fittest in a hangry environment after birth, a strong selection pressure, but increases the risk of developing metabolic syndrome under an obesogenic diet according to the ‘Mismatch’ hypothesis. Increasing evidences support that chronic inflammation pathophysiologically connects obesity to metabolic disorders in metabolic syndrome, leading to the concept of ‘Metaflammation’. ‘Metaflammation’ in humans is proposed to originate from the evolutionary conservation of crosstalk between immune and metabolic pathways; however, few studies have investigated the contribution of evolutionary maladaptation to the pathophysiology of ‘Metaflammation’. Therefore, it is promising to investigate ‘Metaflammation’ from the viewpoint of selective advantages and its ‘Mismatch’ to an unexpected environment in contemporary lifestyles, in consideration of the principal concept of evolutionarily conserved nutrient sensing and immune signaling systems.
Proteomic Analysis of the Alterations in Follicular Fluid Proteins During Oocyte Maturation in Humans

Many components in ovarian follicles (follicular fluid, cumulus cells, granular cells, etc.) dynamically change during folliculogenesis and play a positive or negative role in oocyte maturation. Infertile women who underwent intracytoplasmic sperm injection (ICSI) treatment in the reproductive medicine centre of Hangzhou Women’s Hospital between October 2018 and October 2021 were included. The ovarian follicular fluid and cumulus cells of diminished ovarian response (DOR) patients and control subjects with medical records of clinical data were collected. In total, 31 differentially expressed proteins, including 10 upregulated proteins (>1.50-fold, P<0.05) and 21 downregulated proteins (<0.67-fold, P<0.05), were identified in mature vs. immature oocytes by iTRAQ labelling coupled with 2D LC-MS/MS. GO analysis revealed that ‘cell population proliferation’ was the most diverse enrichment trend between up/downregulated proteins, while phagosome process and the PI3K-Akt signaling pathway were the two most significant pathways revealed by KEGG enrichment classification. Human prostatic acid phosphatase (PAP, ACPP) and CD5 antigen-like (CD5L) were two proteins verified by ELISA to be differentially expressed between MII and Gv oocytes (P<0.0001 and P<0.0001, respectively). Further measurement found significantly lower level of ACPP in follicular fluids and cumulus cells of DOR patients (P=0.028 and P=0.004, respectively), as an indicator of oocyte quality. Otherwise, CD5L level is upregulated in follicular fluid of DOR patients (P<0.0001). Our study provided experimental data to establish the objective indicator of oocyte maturation in the microenvironment of ovarian follicles, and also provided new insight into the measurement of oocyte quality.