bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal Positioning of Distributed Energy using Intelligent Hybrid Optimization
Authors: Paul Ward Ul Hijaz , Siddiqui Anwar Shahzad , Kirmani Sheeraz 
Published: August 2023

Abstract
The renewable based energy source integration into the smart grid has seen a rapid surge lately and so has the role of distributed energy. By reducing the power losses within distribution network, the appropriate location and size of the distributed energy or distributed generation sources could be found. In this paper, it has been proposed that where the distribution generating units should be placed. An intelligent optimization technique has been utilized to position different distribution generators in the best possible way. These advancements have both the advantages and disadvantages of their own. The research employs a novel optimization technique based on a hybrid of genetic algorithm and particle swarm optimization techniques. Placing the distribution generations properly lowers the power losses and improves the profile of the voltage in the distribution system.
CNN –based Spiral drawing classifier to detect Early-Stage Parkinson’s Disease
Authors: Khare Yash , Singh Pratima 
Published: August 2023

Abstract
Parkinson’s disease is a neurological condition wherein one experiences a loss in control of motor movements. The disease usually infects gradually but worsens over time. Parkinson’s often starts with a tremor in one hand. Slow movements, stiffness in the body, and loss of balance are some of the major symptoms of the disease. Damaged nerve cells present in the brain cause drops in the levels of dopamine in the body. It is very difficult to detect Early-Stage Parkinson’s Disease.
Researchers have now been able to detect Parkinson’s disease at an early stage (Perhaps at Stage 2 or Stage 3), but there have been no significant advancements in the detection of Parkinson’s at the initial stage. Analyzing the handwriting styles can be one of the initial symptoms of the disease. The dataset given by NIATS of the Federal University of Uberlandia was used in our research. In this study, we present a CNN model that can distinguish between spiral drawings made by patients with Parkinson’s disease and those made by people not suffering from Parkinson’s disease with high accuracy.
Functional Network of Neurocognitive Development in Correlations of BOLD Signals
Authors: Saurabh Sudhanshu , Gupta P. K. 
Published: August 2023

Abstract
Blood Oxygen Level Dependent (BOLD) fMRI is widely used for non-invasive brain mapping and neuroimaging. Despite the challenges, significant advancements have been made in empirically explaining several (BOLD) signal features. Before applying functional connectivity measures to assess the (BOLD) signal, preprocessing is required. Different stimuli produced by a regular periodic oscillation pulse and propagation loops were applied to generate the (BOLD) signal. The fundamentals of the brain and cognitive development are being highlighted through functional connectivity in a unique approach. In this work, we present an approach that analyzes variations in (BOLD) connectivity over time. Our approach emphasizes the temporal or spatial aspects however, simultaneous co-activation events may trigger (BOLD). Our findings show the measures to assess Functional Connectivity (FC) through Pearson’s correlation to describe FC. (BOLD) responses are used in a voxel-wise linear model with an estimation of the maximum likelihood to get the regressors’ correlated components.
Novel Particle Bee Optimisation based Framework for Fault Tolerance in Fog Environment
Authors: Sharma Pankaj , Gupta P K 
Published: August 2023

Abstract
Fog Computing characteristics have fascinated its services in many applicability areas such as Industrial IoT, health monitoring systems and high performance computing, weather forecasting, agronomy, etc. Here, data is captured using the various sensor nodes installed at different locations and further processed by a cloud-based server to obtain the desired results. Heterogeneity of fog nodes and end devices in fog computing architecture is a major feature that helps to utilize already existing computing resources, yet it complicates the managing process. Such a diversified environment must be considered when designing fog environments in terms of computing capabilities, power consumption, and connectivity. In this work An efficient Novel Particle Bee Optimization based Fault Tolerance framework has been projected that ensures the implementation of Fog computing in real time. The proposed framework handling live server faults and also diminishes latency and bandwidth usage of the network and improves Quality of Service (QoS). The achieved results for the projected technique represent the reduction of network bandwidth and reduction in overall cost.
Educational Software System For Teaching STEM To Visually Impaired People
Authors: Singh Pratima , Kapoor Ishita , Goyal Adarsh 
Published: August 2023

Abstract
‘Education for All’ is a farfetched dream for all Developing Countries. ‘Inclusive Education’ is still farther. Visually challenged individuals are still far from main steam Education. Their overall education is limited to humanity subjects only. They are not able to take up Science and Mathematics as a subject beyond middle school because of the lack of concepts, study material & sufficient teaching aid. The existing solution in Braille is a partial solution to this problem. Our research and resulting solutions enable them to develop the concepts & understanding of science & mathematics which help them study STEM subjects(Science, Technology, Engineering, Mathematics) beyond Middle School too. In the proposed solution, we have developed a Machine Learning-based Software –Hardware Integrated system that helps them understand middle level & higher concepts of Mathematics & Science. It is an integrated system that has a software application connected with hardware comprising features like ‘Concepts and Explanation’, ‘Study Material’, ‘and Practice and Test Yourself’. The whole application can be accessed using the TalkBack accessibility feature of the application. If a blind individual has to trace a Mathematical shape such as say ‘sinusoidal graph’, then the person will draw a graph in real-time on the digital screen attached to the hardware (along with real-time voice assistance for tracing the shape/graph). When the sketching of the shape is completed, the coordinates of the figure traced on the digital screen are mapped and those particular pins/buttons are raised on the hardware device. Thus, the blind individual can touch and understand the shape of the hardware board. Moreover, the figure sketched is further checked for its accuracy and the result is read aloud by the voice assistant embedded in the application which is based on Machine Learning Algorithms.
Novel low noise amplifier approach for deep brain stimulation
Authors: Sampath Kumar V , Upreti Kartik 
Published: August 2023

Abstract
This paper presents an analysis for a multi-stage Low Noise Amplifier (LNA) for application on deep brain stimulation. A low noise amplifier (LNA) with high gain, moderate bandwidth and reduced noise is designed and simulated using LT Spice and PTM BSIM4 CMOS models. The novel LNA circuit topology is proposed which uses a cascaded style to improve mid-band gain of an LNA. The proposed LNA achieves a gain margin which ranges between 60-67 (dB), 2X times; Phase Margin which ranges between 145 to 154 (deg), 3X times; Gain which ranges between 66 to 75 (dB), 2X times and bandwidth from kHz to MHz range. The current biasing circuit is used for enhancing stability and gain range. Also, optimal noise figure is achieved with the help of cascading input matching along with source degeneration technique.
The role of leading-edge serrations in controlling the flow over owls’ wing
Authors: Saussaman Tanner , Nafi Asif , Charland David , Ben-Gida Hadar , Gurka Roi 
Published: August 2023

Abstract
We studied the effects of leading-edge serrations on the flow dynamics developed over an owl wing model. Owls are predatory birds. Most owl species are nocturnal, with some active during the day. The nocturnal ones feature stealth capabilities that are partially attributed to their wing microfeatures. One of these microfeatures is small rigid combs (i.e. serrations) aligned at an angle with respect to the incoming flow located at the wings’ leading-edge region of the primaries. These serrations are essentially passive flow control devices that enhance some of the owls’ flight characteristics, such as aeroacoustics and, potentially, aerodynamics. We performed a comparative study between serrated and non-serrated owl wing models and investigated how the boundary layer over these wings changes in the presence of serrations over a range of angles of attack. Using particle image velocimetry, we measured the mean and turbulent flow characteristics and analyzed the flow patterns within the boundary layer region. Our experimental study suggests that leading-edge serrations modify the boundary layer over the wing at all angles of attack, but not in a similar manner. At low angles of attack (<20°), the serrations amplified the turbulence activity over the wing planform without causing any significant change in the mean flow. At 20° angle of attack, the serrations act to suppress existing turbulence conditions, presumably by causing an earlier separation closer to the leading-edge region, thus enabling the flow to reattach prior to shedding downstream into the wake. Following the pressure Hessian equation, turbulence suppression reduces the pressure fluctuations gradients. This reduction over the wing would weaken, to some extent, the scattering of aerodynamic noise in the near wake region.
An advanced review of climate change mitigation policies in Germany, France, and the Netherlands
Authors: Wang Di , Dong Liang , Mei Jianhao 
Published: August 2023

Abstract
Mitigation of climate change requires comprehensive policy arrangements. This article applies a systematic analysis framework comprising ‘vertical policy hierarchy—horizontal policy path—policy instruments’ with Germany, France, and the Netherlands as study cases, and first-hand policy and data from government websites collected, clustered, and matched. The study conducts a comparative analysis of the three countries’ systems, pathways, instruments, and their effectiveness in climate change mitigation. The findings indicate that, firstly, all three countries have relatively well-developed policy systems (laws, regulations, strategies, plans, and policy instruments) based on the six vertical policy hierarchy defined by government governance structure. Secondly, the three countries exhibit commonalities and disparities in seven sectors: energy, transport, buildings, industry, agriculture, forest, and waste. The commonalities stem from EU laws and directives, while disparities arise from resource endowments and emission structures. Thirdly, regarding policy instruments, the commonalities among the three countries are reflected in the dominance of Financial/Fiscal Mechanisms as the primary approach, the leadership position of Governance Mechanisms, the comprehensive coverage of Regulatory Reform, and the massive expenditure in the Direct investment. Individually, (1) the German Regulatory Reform primarily addresses energy resource transformation; France focuses on controlling the transport sector emissions; while the Netherlands commits to renewable energy generation. (2) Germany leads in terms of Commercialization Mechanisms. (3) Financial/Fiscal Mechanisms encompass all sectors, while Germany examplifies the transportation sector digitization, France’s provision of ecological housing loans, and the Netherlands’ support for sustainable agriculture. (4) France distinguishes itself with a forward-thinking approach towards Governance Mechanism including climate financial risks, ESG (Environmental, Social, and Governance) standards. Fourthly, the significant policy instruments analysis demonstrates that the climate governance of three countries incorporates not only direct or indirect efforts in emission reduction, but also considerations of institutional requirements, fairness, economic effectiveness, synergies, and transformative potential in policy considerations.
Comparing visitor motivation and demographics between an insect festival and science festivals
Authors: Pearson Gwen , Justus Emily 
Published: August 2023

AbstractBoth science festivals and insect festivals have increased in frequency in the last few decades. In order to compare insect festivals with science festivals, we modified a validated science festival visitor survey (EvalFest) for Purdue’s Bug Bowl, a long-running insect festival. Benchmarking against EvalFest and Collaboration for Ongoing Visitor Experience Studies (COVES) results, we found that Bug Bowl audiences were similar in having high levels of education, a high interest in science, and frequently visiting science venues. In fact, 23% of Bug Bowl survey takers indicated that they were actual scientists themselves. In contrast to EvalFest results, Bug Bowl had a higher rate of returning visitors than new visitors. Bug Bowl visitor motivations were primarily to have fun and bring their children for a learning experience, and they rated their experiences very positively. Ninety-one percent of Bug Bowl attendees surveyed lived in the state of Indiana. We discuss gaps in expected visitor demographics and possible applications for a shared common evaluation within a working group of insect festivals.
Individual and contextual risk factors for mortality in nursing home residents during the first wave of COVID-19 in France: a multilevel analysis of a nationwide cohort study
Authors: Rachas Antoine , Fontaine Roméo , Thomas Martine , Robine Jean-Marie , Gavazzi Gaëtan , Laurent Marie , Carcaillon-Bentata Laure , Canouï-Poitrine Florence 
Published: August 2023

Abstract

Background
Mortality amongst nursing home (NH) residents increased by 43% during the first wave of coronavirus disease 2019 (COVID-19). We estimated the ‘contextual effect’ on mortality, tried to explain it by NH characteristics and identified resident- and NH-level risk factors for mortality.

Methods
The contextual effect was measured for two cohorts of NH residents managed by the general scheme in metropolitan France (RESIDESMS data from 03/01/2020 to 05/31/2020 and 03/01/2019 to 05/31/2019) by the intraclass correlation coefficient (ICC) estimated from mixed-effects logistic regression.

Results
Amongst 385,300 residents (5,339 NHs) included in 2020 (median age 89 years, 25% men), 9.1% died, versus 6.7% of 379,926 residents (5,270 NHs) in 2019. In the empty model, the ICC was 9.3% in 2020 and 1.5% in 2019. Only the geographic location partially explained the heterogeneity observed in 2020 (ICC: 6.5% after adjustment). Associations with mortality were stronger in 2020 than in 2019 for male sex and diabetes and weaker for heart disease, chronic respiratory disease and residence &lt;6 months. Mortality was higher in 2020 (15.1%) than 2019 (6.3%) in NHs with at least one death with a mention of COVID-19 and more heterogeneous (ICC: 8.0%) than in the others (mortality: 6.7% in both years; ICC: 1.1%).

Conclusion
Our results suggest that the COVID-19 crisis had a heterogeneous impact on mortality in NH residents and that geographic location explain a part of the contextual effect, which appears to have had little influence on mortality in NHs not being affected by the virus.
Multimodal deep learning approaches for single-cell multi-omics data integration
Authors: Athaya Tasbiraha , Ripan Rony Chowdhury , Li Xiaoman , Hu Haiyan 
Published: August 2023

Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
Metformin has anti-inflammatory effects and induces immunometabolic reprogramming via multiple mechanisms in hidradenitis suppurativa
Authors: Petrasca Andreea , Hambly Roisin , Kearney Niamh , Smith Conor M , Pender Emily K , Mac Mahon Julie , O’Rourke Aoife M , Ismaiel Mohamed , Boland Patrick A , Almeida Jose P , Kennedy Czara , Zaborowski Alexandra , Murphy Siun , Winter Desmond , Kirby Brian , Fletcher Jean M 
Published: August 2023

Abstract

Background
Targeting immunometabolism has shown promise in treating autoimmune and inflammatory conditions. Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease involving painful lesions in apocrine gland-bearing skin. Therapeutic options for HS are limited and often ineffective; thus, there is a pressing need for improved treatments. To date, metabolic dysregulation has not been investigated in HS. As HS is highly inflammatory, we hypothesized that energy metabolism is dysregulated in these patients. Metformin, an antidiabetic drug, which is known to impact on cellular metabolic and signalling pathways, has been shown to have anti-inflammatory effects in cancer and arthritis. While metformin is not licensed for use in HS, patients with HS taking metformin show improved clinical symptoms.

Objective
To assess the effect and mechanism of action of metformin in HS.

Methods
To assess the effect of metformin in vivo, we compared the immune and metabolic profiles of peripheral blood mononuclear cells (PBMCs) of patients with HS taking metformin vs. those not taking metformin. To examine the effect of metformin treatment ex vivo, we employed a skin explant model on skin biopsies from patients with HS not taking metformin, which we cultured with metformin overnight. We used enzyme-linked immunosorbent assays, multiplex cytokine assays and quantitative real-time polymerase chain reaction (RT-PCR) to measure inflammatory markers, and Seahorse flux technology and quantitative RT-PCR to assess glucose metabolism.

Results
We showed that metabolic pathways are dysregulated in the PBMCs of patients with HS vs. healthy individuals. In metformin-treated patients, these metabolic pathways were restored and their PBMCs had reduced inflammatory markers following long-term metformin treatment. In the skin explant model, we found that overnight culture with metformin reduced inflammatory cytokines and chemokines and glycolytic genes in lesions and tracts of patients with HS. Using in vitro assays, we found that metformin may induce these changes via the NLR family pyrin domain containing 3 (NLRP3) inflammasome and the AMP-activated protein kinase (AMPK)–mammalian target of rapamycin (mTOR) pathway, which is linked to glycolysis and protein synthesis.

Conclusions
Our study provides insight into the mechanisms of action of metformin in HS. The anti-inflammatory effects of metformin support its use as a therapeutic agent in HS, while its effects on immunometabolism suggest that targeting metabolism is a promising therapeutic option in inflammatory diseases, including HS.
Enumeration Of Subtrees Of Two Families Of Self-Similar Networks Based On Novel Two-Forest Dual Transformations
Authors: Sun Daoqiang , Liu Hongbo , Yang Yu , Li Long , Zhang Heng , Fahad Asfand 
Published: August 2023

Abstract
As a structural topological index, the number of subtrees has great significance for the analysis and design of hybrid locally reliable networks. In this paper, with generating function and introducing a novel two-forest dual transformation technique, we solve the subtree enumerating problems of two representatives of the self-similar networks, such as the hierarchical lattice and $(u,v)$-flower networks. Moreover, by means of the circle weight transfer technique, two linear time algorithms of computing the subtree generation functions of these two families of networks are also proposed. The subtree density of two special cases for these self-similar networks is briefly discussed as an application.
Single-nuclear transcriptome profiling identifies persistent fibroblast activation in hypertrophic and failing human hearts of patients with longstanding disease
Authors: Kattih Badder , Boeckling Felicitas , Shumliakivska Mariana , Tombor Lukas , Rasper Tina , Schmitz Katja , Hoffmann Jedrzej , Nicin Luka , Abplanalp Wesley T , Carstens Daniel C , Arsalan Mani , Emrich Fabian , Holubec Tomas , Walther Thomas , Puntmann Valentina O , Nagel Eike , John David , Zeiher Andreas M , Dimmeler Stefanie 
Published: August 2023

Abstract

Aims
Cardiac fibrosis drives the progression of heart failure in ischaemic and hypertrophic cardiomyopathy. Therefore, the development of specific anti-fibrotic treatment regimens to counteract cardiac fibrosis is of high clinical relevance. Hence, this study examined the presence of persistent fibroblast activation during longstanding human heart disease at a single-cell resolution to identify putative therapeutic targets to counteract pathological cardiac fibrosis in patients.

Methods and results
We used single-nuclei RNA sequencing with human tissues from two samples of one healthy donor, and five hypertrophic and two failing hearts. Unsupervised sub-clustering of 7110 nuclei led to the identification of 7 distinct fibroblast clusters. De-convolution of cardiac fibroblast heterogeneity revealed a distinct population of human cardiac fibroblasts with a molecular signature of persistent fibroblast activation and a transcriptional switch towards a pro-fibrotic extra-cellular matrix composition in patients with established cardiac hypertrophy and heart failure. This sub-cluster was characterized by high expression of POSTN, RUNX1, CILP, and a target gene adipocyte enhancer-binding protein 1 (AEBP1) (all P &lt; 0.001). Strikingly, elevated circulating AEBP1 blood level were also detected in a validation cohort of patients with confirmed cardiac fibrosis and hypertrophic cardiomyopathy by cardiac magnetic resonance imaging (P &lt; 0.01). Since endogenous AEBP1 expression was increased in patients with established cardiac hypertrophy and heart failure, we assessed the functional consequence of siRNA-mediated AEBP1 silencing in human cardiac fibroblasts. Indeed, AEBP1 silencing reduced proliferation, migration, and fibroblast contractile capacity and α-SMA gene expression, which is a hallmark of fibroblast activation (all P &lt; 0.05). Mechanistically, the anti-fibrotic effects of AEBP1 silencing were linked to transforming growth factor-beta pathway modulation.

Conclusion
Together, this study identifies persistent fibroblast activation in patients with longstanding heart disease, which might be detected by circulating AEBP1 and therapeutically modulated by its targeted silencing in human cardiac fibroblasts.