Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on Improved Particle Swarm Optimization Algorithm in Portfolio Optimization Problem

The PSO algorithm suffers from a singular population and a propensity to settle on a local optimal solution. We propose CPSO algorithm to enhance the diversity of the particle population and prevent local optima. The algorithm modifies the initialization process to ensure higher diversity and ergodicity of particles within the pre-stage. It employs non-linearly varying inertia weights and acceleration coefficients and introduces the Cauchy variational operator to increase population diversity and prevent particles from becoming trapped in local optima. According to the experimental findings, it shows that the CPSO algorithm has improved precision in terms of convergence and a faster rate of convergence for optimization problems of high complexity. It can achieve higher Sharpe ratios in the portfolio optimization problems.
Opus: University of Bath Online Publication Store Alpha-synuclein Is a Cellular Ferrireductase

This version is made available in accordance with publisher policies. Please cite only the published version using the reference above. Abstract a-synuclein (aS) is a cellular protein mostly known for the association of its aggregated forms with a variety of diseases that include Parkinson's disease and Dementia with Lewy Bodies. While the role of aS in disease is well documented there is currently no agreement on the physiological function of the normal isoform of the protein. Here we provide strong evidence that aS is a cellular ferrireductase, responsible for reducing iron (III) to bio available iron (II). The recombinant form of the protein has a V Max of 2.72 nmols/min/mg and K m 23 mM. This activity is also evident in lysates from neuronal cell lines overexpressing aS. This activity is dependent on copper bound to aS as a cofactor and NADH as an electron donor. Overexpression of a-synuclein by cells significantly increases the percentage of iron (II) in cells. The common disease mutations associated with increased susceptibility to PD show differences in activity or iron (II) levels. This discovery may well provide new therapeutic targets for PD and Lewy body dementias.
Short-Term Wind Speed Prediction Using GMDH Neural Networks: Binalood Wind Farm Case Study

Smart grid is remarkably recognized by the dominant role of renewable sources of energy. Wind energy is considered as one of the main sources of renewable energy and has the most contribution in global energy generation among other renewable energy over the globe. However, erratic and non-dispatchable nature of wind power generation is recognized as a cumbersome and sophisticated issue for the main players of power industry. Hence, wind power prediction is of great prominence, with the wind speed as its most influential factor. GMDH neural network model could capture the non-linearity and complex features of wind speed time series. In this article, short-term wind speed prediction of Binalood wind farm is conducted applying GMDH neural network model and its performance is analyzed. The results indicate the competency of this prediction method to deal with the complex nature of wind speed time series.
Broadband Millimeter Wave MIMO Antennas at 28 GHz with Low Mutual Coupling Using Frequency Selective Surface Wall

Multiple input multiple out (MIMO) gains a lot of attention in the current and future generation (5G/6G) wireless communication. This paper presents two new designs of 2-element and 4-element MIMO antennas with low mutual coupling at millimeter wave regime. The two MIMO antennas are optimized to operate over 5G new radio band n261 (27.5-28.35 GHz). The substrate-integrated waveguide (SIW) based cavity-backed slot antenna with a gap-coupled parasitic patch is used for the MIMO element. A new dual-layer frequency selective surface (FSS) is proposed to suppress the space wave coupling between the antennas. The FSS exhibits wideband second-order bandstop filter response, which reflects the RF signal between 25.99 to 30.06 GHz with $\left|S_{21}\right|\lt -20 \mathrm{~dB}$. The FSS is placed vertically between the antennas to obtain isolation of > 49.9 dB and > 24.4 dB for the 2-element and 4-element MIMOs at the center frequency. When all the elements are excited with equal amplitude and equal phase, the designed MIMOs show a peak realized gain and efficiency of 9.89 dBi and 97.1 % for 2-element and 10.84 and 94 % for 4-element. Both MIMO arrays exhibit a low cross-polarization <−12.44 dB and <−18.5 dB and good diversity performances.
p 16 INK 4 a Modulates p 53 in Primary Human Mammary Epithelial Cells

p16 (p16) and p53 are tumor suppressor genes that are inactivated during carcinogenesis in many tumors. Here we show that p16 gene activity inversely modulates p53 status and function in primary human mammary epithelial cells. Reduced levels of p16 protein stabilize p53 protein through inhibition of proteolytic degradation, and this increase in p53 protein levels enhances the cellular response to radiation, represses proliferation, and transcriptionally activates downstream targets. Stabilization of p53 is mediated through the retinoblastoma/E2F/p14/murine double minute-2 pathway. However, we have observed that p16 does not modulate p53 in fibroblasts, indicating a possible cell type–specific regulation of this pathway. (Cancer Res 2006; 66(21): 10325-31)
M2S2: A Policy Transfer Framework for Deep Reinforcement Learning

Deep reinforcement learning (DRL) has greatly promoted the progress of investigations in robotic motion planning. However, when faced with different scenarios, the path obtained from each training for a specific scenario is unique, which means that the DRL needs additional resources to relearn and adapt the policy to the new scenario. Inspired by the mentor-student relationship, a multi-mentor-single-student (M2S2) framework is proposed in this paper to enhance the policy generalization performance among similar task scenarios. It promotes the learning efficiency of the student network by adding networks that have been trained in similar scenarios as the mentor networks in the training, where weight parameters are assigned to balance the relationship among all the networks. The experimental results show that M2S2 has good feasibility and superior performance for policy transfer of DRL among similar scenarios.
Sentence Embedding with Named Entities for Conversation Fingerprint

Social Network Analysis (SNA) and its subdomain, Organisational Network Analysis (ONA) can strongly benefit from Natural Language Processing techniques as they enable semantic analysis of interactions between employees leading to various applications such as knowledge classification, knowledge recommendation, visualisation of knowledge dissemination or informal community identification and management. In particular, sentence embedding technics such as SBERT or Word2Vec enable comparison of similarities between such interactions. But these techniques may lack precision in some cases as semantic similarities may come from working procedure similarities. In this paper, we study and evaluate the impact of using named entities in sentence embedding to generate conversation fingerprints and improve the ability to differentiate or relate conversations and provide greater conversation classification granularity as well as better visualisation of knowledge dissemination.
Utilising the YOLOv3 Algorithm for the Student Posture Recognition System in Classroom Settings

Effective student-teacher interaction helps transfer knowledge, clarify concepts, and create a conductive learning environment. The effectiveness of the interaction can be seen through students’ behaviour and various factors, such as pos- ture and gestures in the classroom. However, educators face significant difficulties in tracking each student’s performance and behaviour during class. Therefore, this study focuses on student posture recognition in classroom settings, which is essential for monitoring student behaviour and engagement during lectures. The proposed system utilises the YOLOv3 machine learning model for real-time detection. A dataset of student postures was collected from Google Images, and the data was used to train a deep neural network. The model was then tested on classroom images and compared to manual annotations. The results showed that the model can accurately recognise student postures with high precision, recall, F1-score, and mean average precision (mAP), achieving an average precision of 88%, recall of 89%, F1-score of 88%, and mAP of 95.20%. The real-time processing capability of YOLOv3 allows for immediate posture detection during lectures in a classroom; this may help educators monitor student behaviour and engagement.
Essential Hypertension and Histocompatibility Antigens A Linkage Study

&NA; It is well established that genetic and environmental factors are involved in the etiology of essential hypertension. The presence of genes predisposing to essential hypertension in the human leukocyte antigen (HLA) complex is controversial because studies of an association between HLA antigens and essential hypertension have failed to yield consistent results. Our aim in the present study was to further investigate this issue through the method of linkage analysis. Analysis of 96 hypertensive siblings distributed in 31 families indicated a significant distortion (p=0.0009) of the normal segregation pattern of inheritance of HLA haplotypes. Thus, our data indicate that at least one of the genes responsible for genetic predisposition to essential hypertension is located very near or within the HLA complex. (Hypertension 1989;14:604‐609)
Mathematics of Arrays Computations for Embedded Systems Applications

In this paper, the Mathematic of Arrays (MoA) approach to solving complex multi-dimensional array computations using array indexing is considered with reference in its implementation in code targeting embedded systems applications using software programmed microcontrollers. The array computations, memory access, and communications requirements are considered with application to different software programming/scripting languages. The ideas can then be extended to realizations as digital logic circuit descriptions using Hardware Description Languages (HDLs). With reference to a range of tensor products for rank 2 tensors, the MoA derived algorithm is presented in different code formats.
Prediction of Traffic Flow Density and Velocity Based on Kalman Filter Fusion of The Non-Local Gas-Kinetic Model and Convolution Neural Networks-Long-Short Term Memory Model

The integrated perception, planning, and control of intelligent transportation system has become a research hotspot in academia. In this context, the safe running of an intelligent vehicle highly depends on the accurate prediction of its driving environment and working conditions. Traffic flow parameters represent the macro characteristics of an intelligent vehicle driving environment. Accurate prediction of traffic flow parameters is beneficial to improve the accuracy of trajectory prediction of vehicles in the surrounding environment. As two kinds of traffic flow parameter prediction models: the macroscopic traffic flow model and the data-based learning model have their own characteristics. To fully take advantage of these two models, our work proposes a study of the macroscopic traffic flow model based on a nonlocal gas-kinetic (GKT) and a deep learning model based on convolutional neural networks-long short term memory. Real data sets US-101 and PeMS are used respectively to predict traffic flow density and velocity. Finally, the Kalman filter is employed to fuse the results of the two models. The experiment shows that the prediction accuracy of traffic flow density and velocity can be improved as compared with the Macroscopic traffic flow model and the data-based learning model.
Well Placement Optimization under Uncertainty with CMA-ES Using the Neighborhood

In the well placement problem, as well as in other field development optimization problems, geological uncertainty is a key source of risk affecting the viability of field development projects. Well placement problems under geological uncertainty are formulated as optimization problems in which the objective function is evaluated using a reservoir simulator on a number of possible geological realizations. In this paper, we present a new approach to handle geological uncertainty for the well placement problem with a reduced number of reservoir simulations. The proposed approach uses already simulated well configurations in the neighborhood of each well configuration for the objective function evaluation. We use thus only one single reservoir simulation performed on a randomly chosen realization together with the neighborhood to estimate the objective function instead of using multiple simulations on multiple realizations. This approach is combined with the stochastic optimizer CMA-ES. The proposed approach is shown on the benchmark reservoir case PUNQ-S3 to be able to capture the geological uncertainty using a smaller number of reservoir simulations. This approach is compared to the reference approach using all the possible realizations for each well configuration, and shown to be able to reduce significantly the number of reservoir simulations (around 80%).
Energy Efficiency of 5G Massive MIMO Base Stations in Dense Urban Environments

Emerging 5G networks feature lower latency, higher capacity, and increased bandwidth compared to 4G and have far reaching impacts on several applications such as transportation and Industry 4.0. However, energy efficiency constitutes the Achilles’ heel of 5G technology. This paper analyses key energy efficiency metrics for 5G networks with focus on the Base Station applying massive MIMO technology in a dense urban scenario. More specifically, the impact of data throughput on energy efficiency of 5G networks is investigated through two energy efficiency metrics, namely the mobile network data energy efficiency metric, EEDT, and the mobile network coverage energy efficiency metric, EECA.
UAV Swarm Communication Reliability Prediction using Machine Learning

Communication reliability is a key factor in enabling unmanned aerial vehicles (UAVs) to be deployed in many applications. Unlike conventional ground networks, UAV networks present unique challenges, experiencing diverse fading models and specific network traffic patterns. Many existing studies employing machine learning techniques often overlook the complete understanding of UAV communication reliability, which is defined not just by the probability of receiving packets correctly but also receiving them within a latency constraint. This research investigates employing a Bayesian network (BN) classification model and a logistic regression (LR) model as conditional probability estimators to not only predict UAV swarm communication reliability but also identify the most probable modes of communication failure. The models were trained utilizing a dataset generated through comprehensive network simulations reflecting different UAV network scenarios. The study shows that the trained BN model accurately predicts and classifies UAV swarm communication reliability, along with the most likely communication failure mode in various network scenarios. The trained BN model is useful for ensuring communication reliability during UAV mission planning and for maintaining the required level of communication reliability during UAV deployment.
Proposing Novel Recurrent Neural Network Architectures for Infant Cry Detection in Domestic Context

Infants must be monitored around the clock because they are at risk for various health issues and cannot communicate their needs properly. Though several infant monitoring systems exist to monitor infants, most of these did not explicitly focus on detecting infant cries, which is infants' primary form of communication. Automatic detection of an infant cry in a domestic context is essential in such systems. It is also crucial for researchers who study the relationship between infant cry patterns and various health parameters. This research proposes novel recurrent neural network architectures for automatically detecting infant cries in a domestic context that will achieve better performance and provide a more convenient way for parents to monitor their infants. To attain these objectives, four recurrent neural network architectures were built and trained with a dataset containing infants' recordings and various noises available in a domestic context. The models were also evaluated in terms of accuracy, precision, recall, and f1-score, As an outcome, the trained models were found to achieve more than 88 % on all the performance parameters, which proves the satisfactory performances of the developed models.
A Modular Four-Port DC-DC Converter for Battery Management Systems

A novel modular four-port dc-dc converter (FPC) is proposed in this paper, which is capable of integrating the photo-voltaic (PV), voltage balancing and charging of multiple batteries using a single-inductor in a modular converter structure for the battery management system (BMS). The proposed topology is competent to work under single-input single-output (SISO), dual-input single-output (DISO), and single-input dual-output (SIDO) modes based on the PV, battery, and load availability. These operating modes are analyzed under various conditions of the sources (PV and battery) and load, and the steady-state analysis is performed for the proposed FPC to obtain the output voltage expressions. Finally, the simulation results are presented using the LTspice simulator under various operating modes to verify the working of the proposed modular FPC.
Redefining Anti-Forensics in Digital Image Operation: Exploring the Power of Dual-Domain Generative Adversarial Networks

Millimeter-wave (mmWave) radar may be used in a variety of imaginative ways to monitor and observe several things at the same time. However, problems with the radar itself are possible. It is challenging to improve mmWave radar detection while maintaining reliable data categorization. Building a reliable model that is consistent with tracking and sensing goals is a significant barrier to realising the full potential of mmWave sensing. Because radar frames are associated with major events, manually annotating mmWave data takes a lot of time and frequently requires topic expertise. This article lays the groundwork for training mmWave radar by utilising a camera as a labelling and monitoring tool. During the research phase, a preliminary drawing of the framework was constructed. The proposed strategy is evaluated in contrast to other methods that strive to achieve the same goals. Experiments in a variety of real-world settings have verified the conceptual framework. The mmWave technology is used in the multi-object tracking system that arises from the stated architectural design. This technology differs from others in that it can discriminate between sprinting, tripping, and simply strolling. The results of the experiment show that guided tagging from a camera may be utilised to train a radar model consistently. This trained model consistently demonstrates great classification accuracy over a wide range of instances, not just the ones it was trained on. This report lays the groundwork for further research into hybrid monitoring setups. It specifically supports the construction of mmWave classification models by tackling labelling and training issues.
Challenging YOLO and Faster RCNN in Snowy Conditions: UAV Nordic Vehicle Dataset (NVD) as an Example

In the world of autonomous systems and aerial surveillance, the quest to efficiently detect vehicles in diverse environmental conditions has emerged as a pivotal challenge. While these technologies have made significant advancements in the identification of objects under ordinary circumstances, the complexities introduced by snow-laden landscapes present a unique set of hurdles. The deployment of unmanned aerial vehicles (UAVs) equipped with state-of-the-art detectors in snowy regions has become an area of intense research, as it holds promise for various applications, from search and rescue operations to efficient transportation management. This paper explores the complexities that surface when it comes to identifying vehicles within snowy landscapes through the utilization of drones. It delves into the intricacies of this state-ofthe-art undertaking, offering insights into potential future directions to tackle these challenges for the unique demands of such environments. The research aims to apply the conventional procedures typically used to enhance the performance of stateof-the-art (STOA) detectors such as YOLO and faster RCNN. This is done to underscore that adhering to traditional approaches may not suffice to achieve the desired level of efficiency and accuracy when viewed from an industrial standpoint. The code and the dataset will be available at https://nvd.ltu-ai.dev/
Undocumented Debug Interface HDT of Modern AMD CPUs

The paper discusses the implementation of the IJTAG network in the TAP controller of the modern AMD CPUs, related to the implementation of the CPU debug unlock procedure and the unlock procedure itself. During the research, reverse engineering of protocol implementation was carried out, and the structure of the hardware and software that implements the functionality of the Hardware Debug Tool (HDT) debugging interface for the AMD EPYC 7313 CPU was restored. The logic of organizing access to the TAP controller in the JTAG network based on the IEEE 1687 standard, which implements the exchange interface with the security coprocessor, was analyzed. The paper analyzed the protocol for unlocking the debugging capabilities of an undeclared hardware interface and proposed an algorithm for checking whether the debugging mode is blocked.
Bandwidth Enhancement of Circular Patch Antenna Through Reflective Excitation Technique

With the advancement in wireless communication technology towards 5G and higher generations, researchers have invested much efforts towards attaining high bandwidth communication. Bandwidth of a circular patch antenna and its overall performance has been enhanced using a novel reflective technique proposed in this paper. By exciting the patch antenna reflectively through a metallic post connected to an ungrounded coplanar waveguide (CPW) line terminated with a pair of vias attached to the line by a metallic pad, another resonance is generated near the fundamental mode, significantly improving impedance bandwidth. The bandwidth of the proposed radiator is enhanced by 387.7% when compared with that of a probefed circular patch antenna (from 6.61% to 30.11%) using the proposed technique. A radiation efficiency >90% was attained for the antenna structure mentioned in this work. The progress of the proposed design from a conventional circular patch to that of a bandwidth enhanced patch is also discussed.
On Some Properties of the Space 2*w of Binary-Digit Sequences

In this article I deal with some computationally interesting properties of the space 2 ∗ω of finite and infinite binary sequences and of its subspace 2 of maximal points usually taken with spectral or Stone topology. First, I define 2∗ω in terms of a metric-induced quasi-uniformity Ud and then based on the more extended notion of strong inclusion relations I prove the completeness of the quasi-uniform space 2 ∗ω as a syntopological space and the coincidence of its quasi-uniform completion with the usual completeness of it as a uniform space; the latter is proved on the grounds of certain results by P. Sünderhauf in [15]. Next, taking the union 2 <ω of the spaces 2 n of finite binary sequences, for all n ∈ ω, each one taken with the patch of Alexandroff topology I prove that its one-point (Alexandroff) compactification is a retract of the Cantor space 2 ω and its base of clopen sets is a projective Boolean algebra with a unique regular open completion. This provides a new approach to the compactness of space 2 ω and has to do with some general results on the one-point compactification of injective Boolean spaces in [5].