bluuebunny/crossref_metadata_2025 huggingface dataset:
Clinical Impact of New Treatment Strategies for HER2-Positive Metastatic Breast Cancer Patients with Resistance to Classical Anti-HER Therapies
Authors: Tapia Marta , Hernando Cristina , Martínez María Teresa , Burgués Octavio , Tebar-Sánchez Cristina , Lameirinhas Ana , Ágreda-Roca Anna , Torres-Ruiz Sandra , Garrido-Cano Iris , Lluch Ana , Bermejo Begoña , Eroles Pilar
Published: September 2023

HER2-positive breast cancer accounts for 15–20% of all breast cancer cases. This subtype is characterized by an aggressive behavior and poor prognosis. Anti-HER2 therapies have considerably improved the natural course of the disease. Despite this, relapse still occurs in around 20% of patients due to primary or acquired treatment resistance, and metastasis remains an incurable disease. This article reviews the main mechanisms underlying resistance to anti-HER2 treatments, focusing on newer HER2-targeted therapies. The progress in anti-HER2 drugs includes the development of novel antibody–drug conjugates with improvements in the conjugation process and novel linkers and payloads. Moreover, trastuzumab deruxtecan has enhanced the efficacy of trastuzumab emtansine, and the new drug trastuzumab duocarmazine is currently undergoing clinical trials to assess its effect. The combination of anti-HER2 agents with other drugs is also being evaluated. The addition of immunotherapy checkpoint inhibitors shows some benefit in a subset of patients, indicating the need for useful biomarkers to properly stratify patients. Besides, CDK4/6 and tyrosine kinase inhibitors are also included in the design of new treatment strategies. Lapitinib, neratinib and tucatinib have been approved for HER2-positive metastasis patients, however clinical trials are currently ongoing to optimize combined strategies, to reduce toxicity, and to better define the useful setting. Clinical research should be strengthened along with the discovery and validation of new biomarkers, as well as a deeper understanding of drug resistance and action mechanisms.
Implementation of an Enhanced Recovery after Surgery Protocol in Advanced and Recurrent Rectal Cancer Patients after beyond Total Mesorectal Excision Surgery: A Feasibility Study
Authors: Nordkamp Stefi , Creemers Davy M. J. , Glazemakers Sofie , Ketelaers Stijn H. J. , Scholten Harm J. , van de Calseijde Silvie , Nieuwenhuijzen Grard A. P. , Tolenaar Jip L. , Crezee Hendi W. , Rutten Harm J. T. , Burger Jacobus W. A. , Bloemen Johanne G.
Published: September 2023

Introduction: The implementation of an Enhanced Recovery After Surgery (ERAS) protocol in patients with locally advanced rectal cancer (LARC) and locally recurrent rectal cancer (LRRC) has been deemed unfeasible until now because of the heterogeneity of this disease and low caseloads. Since evidence and experience with ERAS principles in colorectal cancer care are increasing, a modified ERAS protocol for this specific group has been developed. The aim of this study is to evaluate the implementation of a tailored ERAS protocol for patients with LARC or LRRC, requiring beyond total mesorectal excision (bTME) surgery. Methods: Patients who underwent a bTME for LARC or LRRC between October 2021 and December 2022 were prospectively studied. All patients were treated in accordance with the ERAS LARRC protocol, which consisted of 39 ERAS care elements specifically developed for patients with LARC and LRRC. One of the most important adaptations of this protocol was the anaesthesia procedure, which involved the use of total intravenous anaesthesia with intravenous (iv) lidocaine, iv methadone, and iv ketamine instead of epidural anaesthesia. The outcomes showed compliance with ERAS care elements, complications, length of stay, and functional recovery. A follow-up was performed at 30 and 90 days post-surgery. Results: Seventy-two patients were selected, all of whom underwent bTME for either LARC (54.2%) or LRRC (45.8%). Total compliance with the adjusted ERAS protocol was 73.6%. Major complications were present in 12 patients (16.7%), and the median length of hospital stay was 9 days (IQR 6.0–14.0). Patients who received multimodal anaesthesia (75.0%) stayed in the hospital for a median of 7.0 days (IQR 6.8–15.5). These patients received fewer opioids on the first three postoperative days than patients who received epidural analgesia (p < 0.001). Conclusions: The implementation of the ERAS LARRC protocol seemed successful according to its compliance rate of >70%. Its complication rate was substantially reduced in comparison with the literature. Multimodal anaesthesia is feasible in beyond TME surgery with promising effects on recovery after surgery.
Study of Helicobacter pylori Isolated from a High-Gastric-Cancer-Risk Population: Unveiling the Comprehensive Analysis of Virulence-Associated Genes including Secretion Systems, and Genome-Wide Association Study
Authors: Saruuljavkhlan Batsaikhan , Alfaray Ricky Indra , Oyuntsetseg Khasag , Gantuya Boldbaatar , Khangai Ayush , Renchinsengee Namsrai , Matsumoto Takashi , Akada Junko , Azzaya Dashdorj , Davaadorj Duger , Yamaoka Yoshio
Published: September 2023

Background: The prevalence of gastric cancer in Mongolia, in East Asia, remains the highest in the world. However, most Helicobacter pylori strains in Mongolia have a less virulent Western-type CagA. We aimed to determine how H. pylori genomic variation affected gastric diseases, especially gastric cancer, based on comprehensive genome analysis. Methods: We identified a set of 274 virulence-associated genes in H. pylori, including virulence factor and outer membrane protein (OMP) genes, the type four secretion system gene cluster, and 13 well-known virulence gene genotypes in 223 H. pylori strains and their associations with gastric cancer and other gastric diseases. We conducted a genome-wide association study on 158 H. pylori strains (15 gastric cancer and 143 non-gastric cancer strains). Results: Out of 274 genes, we found 13 genes were variable depending on disease outcome, especially iron regulating OMP genes. H. pylori strains from Mongolia were divided into two main subgroups: subgroup (Sg1) with high risk and Sg2 with low risk for gastric cancer. The general characteristics of Sg1 strains are that they possess more virulence genotype genes. We found nine non-synonymous single nucleotide polymorphisms in seven genes that are linked with gastric cancer strains. Conclusions: Highly virulent H. pylori strains may adapt through host-influenced genomic variations, potentially impacting gastric carcinogenesis.
Assessment of STAT4 Variants and Risk of Hepatocellular Carcinoma in Latin Americans and Europeans
Authors: Ayoub Alan , Anugwom Chimaobi M. , Prieto Jhon , Balderramo Domingo , Ferrer Javier Diaz , Mattos Angelo Z. , Arrese Marco , Carrera Enrique , Groothuismink Zwier M. A. , Oliveira Jeffrey , Boonstra Andre , Debes Jose D.
Published: September 2023

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide. The STAT4 rs7574865 genetic variant has been associated with an increased risk of developing HCC in Asian populations. However, this association has not been studied in Latin America and is poorly assessed in European populations. This case-control study investigated the association between STAT4 rs7574865 and HCC risk in these populations. We evaluated DNA samples from seven medical institutions across six Latin American countries and one Dutch institution in 1060 individuals (344 HCC and 716 controls). STAT4 rs7574865 SNP was genotyped using TaqMan-genotyping assay and analyzed using logistic regression. We found no significant association between the homozygous risk allele (G) of STAT4 and HCC development in either population, with odds ratios (OR) for GG versus TT of 0.85 (CI: 0.48–1.52, p = 0.58) and 0.81 (CI: 0.34–1.93, p = 0.67) for Latin Americans and Europeans respectively. No correlation was found between the risk allele and HCC based on underlying liver disease. However, we found that Latin Americans of European ancestry were more likely to carry the risk allele. Our results suggest that the STAT4 SNP rs7574865 does not influence the risk of developing HCC in Latin American or European populations, highlighting the importance of evaluating genetic risk factors in various ethnic groups and understanding the possible influence of ancestry on the genetic basis of disease.
A Fully Human IgE Specific for CD38 as a Potential Therapy for Multiple Myeloma
Authors: Candelaria Pierre V. , Nava Miguel , Daniels-Wells Tracy R. , Penichet Manuel L.
Published: September 2023

Multiple myeloma (MM) is an incurable malignancy of plasma cells and the second most common hematologic malignancy in the United States. Although antibodies in clinical cancer therapy are generally of the IgG class, antibodies of the IgE class have attractive properties as cancer therapeutics, such as their high affinity for Fc receptors (FcεRs), the low serum levels of endogenous IgE allowing for less competition for FcR occupancy, and the lack of inhibitory FcRs. Importantly, the FcεRs are expressed on immune cells that elicit antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cell-mediated phagocytosis (ADCP), and/or antigen presentation such as mast cells, eosinophils, macrophages, and dendritic cells. We now report the development of a fully human IgE targeting human CD38 as a potential MM therapy. We targeted CD38 given its high and uniform expression on MM cells. The novel anti-CD38 IgE, expressed in mammalian cells, is properly assembled and secreted, exhibits the correct molecular weight, binds antigen and the high affinity FcεRI, and induces degranulation of FcεRI expressing cells in vitro and also in vivo in transgenic BALB/c mice expressing human FcεRIα. Moreover, the anti-CD38 IgE induces ADCC and ADCP mediated by monocytes/macrophages against human MM cells (MM.1S). Importantly, the anti-CD38 IgE also prolongs survival in a preclinical disseminated xenograft mouse model using SCID-Beige mice and human MM.1S cells when administered with human peripheral blood mononuclear cells (PBMCs) as a source of monocyte effector cells. Our results suggest that anti-CD38 IgE may be effective in humans bearing MM and other malignancies expressing CD38.
TNF-α Preconditioning Improves the Therapeutic Efficacy of Mesenchymal Stem Cells in an Experimental Model of Atherosclerosis
Authors: Sekenova Aliya , Li Yelena , Issabekova Assel , Saparov Arman , Ogay Vyacheslav
Published: September 2023

Atherosclerosis (AS) is an inflammatory disease involving multiple factors in its initiation and development. In recent years, the potential application of mesenchymal stem cells (MSCs) for treating AS has been investigated. This study examined the effect of TNF-α preconditioning on MSCs’ therapeutic efficacy in treating AS in ApoE KO mice. TNF-α-treated MSCs were administered to high-fat diet-treated ApoE KO mice. Cytokine and serum lipid levels were measured before and after treatment. Cryosections of the atherosclerotic aorta were stained with Oil-Red-O, and the relative areas of atherosclerotic lesions were measured. The level of Tregs were increased in TNF-α-MSC-treated animals compared to the MSCs group. In addition, the systemic administration of TNF-α-MSCs to ApoE KO mice reduced the level of proinflammatory cytokines such as TNF-α and IFN-γ and increased the level of the immunosuppressive IL-10 in the blood serum. Total cholesterol and LDL levels were decreased, and HDL levels were increased in the TNF-α-MSCs group of ApoE KO mice. A histological analysis showed that TNF-α-MSCs decreased the size of the atherosclerotic lesion in the aorta of ApoE KO mice by 38%, although there was no significant difference when compared with untreated MSCs. Thus, our data demonstrate that TNF-α-MSCs are more effective at treating AS than untreated MSCs.
Silicon Carbide: Physics, Manufacturing, and Its Role in Large-Scale Vehicle Electrification
Authors: Di Giovanni Filippo
Published: September 2023

Silicon carbide is changing power electronics; it is enabling massive car electrification owing to its far more efficient operation with respect to mainstream silicon in a large variety of energy conversion systems like the main traction inverter of an electric vehicle (EV). Its superior performance depends upon unique properties such as lower switching and conduction losses, safer high-temperature operation and high-voltage capability. Starting briefly with a description of its physics, more detailed information is then given about some key manufacturing steps such as crystal growth and epitaxy. Afterwards, an overview of its inherent defects and how to mitigate them is presented. Finally, a typical EV’s propulsion inverter is shown, proving the technology’s effectiveness in meeting requirements for mass electrification. Foreword: In recent years, SiC has drawn the attention of a growing number of power electronics designers as the material has good prospects for reducing environmental impacts on a global basis. The goal of this paper, based on the author’s contribution to the introduction of the technology at STMicroelectronics, is to show the potential of silicon carbide in enabling massive car electrification. The company’s SiC MOSFETs, tailored to the automotive industry, are enabling visionary EV makers to pave the way for sustainable e-mobility. The intent of this paper is to describe, for a large crowd of readers, how SiC features can accelerate such a transition by quantifying the benefits they bring in terms of improved efficiency in an EV electric powertrain. The paper also has the ambition to highlight the material’s physics and to give an overview of its production processes, starting from the crystal growth for realizing substrates to the main epitaxy techniques. Some space has been devoted to the analysis of the main crystal defects not present in silicon and whose nature poses new challenges in terms of manufacturing yields and screening. Finally, some insights into the market evolution and on the transition to 200 mm wafers are given.
Repurposing the Strontium Ranelate Antiosteoporotic Drug as a New Outstanding Biocompatible Blue Dye
Authors: Sihn Luca M. , Bastos Erick L. , Nakamura Marcelo , Uchiyama Mayara K. , Toma Henrique E.
Published: September 2023

Blue dyes are relatively uncommon in nature, and a novel dithiophene dye (RanB) is reported in this paper. This dye is derived from an old anti-osteoporotic drug and is a metal ion complexing agent, displaying a planar molecular structure, with two sets of carboxylate, isonitrile, thiophene, and iminodiacetate groups. The blue color originates from a strong absorption peak at 648 nm, accompanied by an unusual fluorescence at 555 nm, with higher energy compared to the main absorption band. RanB forms complexes with lanthanoid ions through the iminodiacetate groups and serves as an effective sensitizer for Tb3+ ions, heightening their emission and improving their use as luminescent agents. Its photo-physical properties and the interaction with Tb3+ have been investigated using absorption spectroscopy, steady-state and time-resolved fluorescence spectroscopy, along with computational methods (ZINDO/S and DFT). The RanB toxicity in human umbilical vein endothelial cells has also been tested, showing a lack of toxicity, holding promising prospects for application as a luminescent and coloring agent in pharmaceuticals and food.
In-Silico Prediction of Mechanical Behaviour of Uniform Gyroid Scaffolds Affected by Its Design Parameters for Bone Tissue Engineering Applications
Authors: N. Musthafa Haja-Sherief , Walker Jason , Rahman Talal , Bjørkum Alvhild , Mustafa Kamal , Velauthapillai Dhayalan
Published: September 2023

Due to their excellent properties, triply periodic minimal surfaces (TPMS) have been applied to design scaffolds for bone tissue engineering applications. Predicting the mechanical response of bone scaffolds in different loading conditions is vital to designing scaffolds. The optimal mechanical properties can be achieved by tuning their geometrical parameters to mimic the mechanical properties of natural bone. In this study, we designed gyroid scaffolds of different user-specific pore and strut sizes using a combined TPMS and signed distance field (SDF) method to obtain varying architecture and porosities. The designed scaffolds were converted to various meshes such as surface, volume, and finite element (FE) volume meshes to create FE models with different boundary and loading conditions. The designed scaffolds under compressive loading were numerically evaluated using a finite element method (FEM) to predict and compare effective elastic moduli. The effective elastic moduli range from 0.05 GPa to 1.93 GPa was predicted for scaffolds of different architectures comparable to human trabecular bone. The results assert that the optimal mechanical properties of the scaffolds can be achieved by tuning their design and morphological parameters to match the mechanical properties of human bone.
Advances in the Development of Representation Learning and Its Innovations against COVID-19
Authors: Li Peng , Parvej Mosharaf Md , Zhang Chenghao , Guo Shufang , Zhang Jing
Published: September 2023

In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional and sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research topic. It involves the embedding of graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural-network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study provides guidance regarding future bioinformatics and biomedicine research methodologies.
Diencephalic Syndrome: Misleading Clinical Onset of Low-Grade Glioma
Authors: La Spina Milena , Caruso Manuela , Gulizia Carmela , Comella Mattia , Soma Rachele , Meli Mariaclaudia , Samperi Piera , Bertuna Gregoria , Di Cataldo Andrea , Russo Giovanna
Published: September 2023

Background: Diencephalic Syndrome is an atypical early manifestation of low-grade gliomas; so, it is important to detect it in patients that experience a failure to thrive despite adequate length growth and food intake. The purpose of this article is to focus attention on this rare but potentially dangerous cause of poor weight gain or stunting in childhood. Materials and Methods: We describe four patients with Diencephalic Syndrome and low-grade gliomas who were evaluated in our institution from January 2017 to December 2021. Case Description and Results: two patients presented with suspected malabsorption, and two presented with a suspected eating disorder. In all cases, neurological symptoms appeared late, explaining the reason for the diagnostic delay, which impacts negatively on prognosis and on quality of life. Currently, patients 1 and 2 have stable disease in second-line therapy, patient 3 has stable disease post end of second-line therapy, and patient 4 has stable disease in first-line therapy. Everyone is in psychophysical rehabilitation. Conclusions: A multidisciplinary evaluation is essential in order to make an early diagnosis and improve prognosis and quality of life.
Unexpected Links between Communities of a Freshwater–Cropland Mediterranean Metanetwork
Authors: González-Barrientos Javier , Ramos-Jiliberto Rodrigo , Aliste-Jara Lidia , Canelo-Araya Nahuel , Cattan Pedro E.
Published: September 2023

Cropland ecosystem functioning may be affected by human perturbations transmitted from adjacent ecosystems, such as freshwater systems. However, our limited knowledge of the ecological interactions within cropland–freshwater networks hinders projecting the consequences of anthropogenic pressures. We reviewed the information from freshwater and cropland communities in the Mediterranean zone of Chile and constructed an ecological metanetwork for characterizing the structure and projecting responses of this system facing environmental pressures. We performed a semi-qualitative analysis to identify the nodes that strongly influence other network components and (or) could significantly affect the transmission of effects within the system. Our analyses show that omnivore fish, amphibians, annual pollinated crops, and deciduous orchards are the most important groups. Although we expected that amphibious predators were the most important groups in transmitting perturbations between ecosystems, we also found groups of plants and pollinators performing this function. We stress that the fate of crop systems facing environmental disturbances should be assessed in a broader context rather than as an isolated system.
Effectiveness of Seed Traps for Assessing Seed Rain in Periurban Grasslands
Authors: Gros Clément , Bulot Adeline , Braud Ferréol , Aviron Stéphanie , Daniel Hervé
Published: September 2023

Landscape context plays an important role in plant community structuring, with selection pressure affecting dispersal ability. This is particularly true in cities, where land use heterogeneity and habitat fragmentation can affect plant dispersal patterns. Seed rain surveys are often used to study dispersal but involve a wide variety of methods and trap types and rarely address the urban context. This study aimed to (1) compare seed rain, especially of anemochorous seeds, in different spatial contexts in a periurban area in Angers (western France); and (2) compare seed rain captured using different trap types (funnel traps/sticky traps), trap heights, and shapes. Seven sites, each equipped with five replicates of funnel traps, were selected in a periurban area in the western part of Angers. Within one of these sites, ten types of traps (differing in trapping method, height, shape, degree of tilt, and area) were employed and their performance compared. The results show that trap height rather than trap type is responsible for differences in seed density and composition. Furthermore, the composition of collected seeds appears to be associated with surrounding land cover, in particular built areas, which has implications for urban ecology in terms of understanding the influence of landscape factors on plant dispersal.
Web-Based Malware Detection System Using Convolutional Neural Network
Authors: Alqahtani Ali , Azzony Sumayya , Alsharafi Leen , Alaseri Maha
Published: September 2023

In this article, we introduce a web-based malware detection system that leverages a deep-learning approach. Our primary objective is the development of a robust deep-learning model designed for classifying malware in executable files. In contrast to conventional malware detection systems, our approach relies on static detection techniques to unveil the true nature of files as either malicious or benign. Our method makes use of a one-dimensional convolutional neural network 1D-CNN due to the nature of the portable executable file. Significantly, static analysis aligns perfectly with our objectives, allowing us to uncover static features within the portable executable header. This choice holds particular significance given the potential risks associated with dynamic detection, often necessitating the setup of controlled environments, such as virtual machines, to mitigate dangers. Moreover, we seamlessly integrate this effective deep-learning method into a web-based system, rendering it accessible and user-friendly via a web interface. Empirical evidence showcases the efficiency of our proposed methods, as demonstrated in extensive comparisons with state-of-the-art models across three diverse datasets. Our results undeniably affirm the superiority of our approach, delivering a practical, dependable, and rapid mechanism for identifying malware within executable files.
Success of Xenografts in Alveolar Ridge Preservation Based on Histomorphometric Outcomes
Authors: Khan Rabia S. , Aslam Mohsin , Ucer Cemal , Wright Simon
Published: September 2023

Different xenograft approaches in alveolar ridge preservation (ARP) are essential to understand relative to their histomorphometric outcomes. Therefore, the aim of this study involved studying biomaterials of a xenograft nature that are used in ARP procedures, to compare the different approaches and evaluate their efficacy in relation to histomorphometric data. An electronic search was completed using the databases: Ovid (Medline), Google Scholar and Wiley Online Library, including a hand search for relevant articles and grey literature. Only randomised controlled trials, using xenograft biomaterials for alveolar ridge preservation procedures involving human studies, dated from 2010–2022 were included in the review. An initial search yielded 4918 articles, after application of the eligibility criteria, 18 studies were deemed eligible for inclusion in the systematic review. The two main xenograft groups found were of bovine origin and of porcine origin. The main histomorphometric outcomes evaluated included new bone percentage (N.B%) and residual graft percentage (R.G%). The mean N.B% for the bovine and porcine groups were 33.46% and 39.63% respectively and the mean R.G% for the bovine and porcine groups were 19.40% and 18.63% respectively. The current evidence suggests that the two main xenograft biomaterials used in ARP procedures after tooth extraction, which are of bovine and porcine origin, displayed effectiveness in producing new bone.
Digital Strategies to Enhance Cultural Heritage Routes: From Integrated Survey to Digital Twins of Different European Architectural Scenarios
Authors: Parrinello Sandro , Picchio Francesca
Published: September 2023

This paper focuses on a research project for the acquisition and post-production of digital data to create informative virtual representations and digital twins of different European Cultural Heritage sites. The goal was to establish a reliable database for a multi-scalar web platform, also accessible through extended reality (XR) tools. This initiative aims to support the promotion and management of cultural and historical monuments within the context of European Cultural Routes supported by the Council of Europe. The project involves different case studies spanning European geographic regions, such as the Upper Kama in Russia, the Valencian Routes of Jaime I in Spain, and the Gdańsk fortresses in Poland. The methodology employed in this effort primarily relies on integrated rapid survey techniques. Unmanned aerial vehicles (UAVs) and simultaneous localization and mapping (SLAM) technologies were used for data collection. These methods contribute to the creation of accurate 3D databases and models that transform the cultural routes into a digital format accessible via an informative platform. The actions presented in this paper are part of the European project “PROMETHEUS”, which is funded by the Horizon 2020 program of the European Union. The project involves collaboration between universities and enterprises, fostering inter-sectoral cooperation. Various techniques such as photographic archives, census analysis, and scan-to-BIM (building information modeling) processes are employed to develop this method further. In fact, the ultimate goal of the project is to establish a framework that can be replicated in other cultural contexts, enhancing the digital documentation and valorization of heritage sites.
Detection of Interaction Effects in a Nonparametric Concurrent Regression Model
Authors: Pan Rui , Wang Zhanfeng , Wu Yaohua
Published: September 2023

Many methods have been developed to study nonparametric function-on-function regression models. Nevertheless, there is a lack of model selection approach to the regression function as a functional function with functional covariate inputs. To study interaction effects among these functional covariates, in this article, we first construct a tensor product space of reproducing kernel Hilbert spaces and build an analysis of variance (ANOVA) decomposition of the tensor product space. We then use a model selection method with the L1 criterion to estimate the functional function with functional covariate inputs and detect interaction effects among the functional covariates. The proposed method is evaluated using simulations and stroke rehabilitation data.
What Is It like to Be a Brain Organoid? Phenomenal Consciousness in a Biological Neural Network
Authors: Montoya Ivanna , Montoya Daniel
Published: September 2023

It has been shown that three-dimensional self-assembled multicellular structures derived from human pluripotent stem cells show electrical activity similar to EEG. More recently, neurons were successfully embedded in digital game worlds. The biologically inspired neural network (BNN), expressing human cortical cells, was able to show internal modification and learn the task at hand (predicting the trajectory of a digital ball while moving a digital paddle). In other words, the system allowed to read motor information and write sensory data into cell cultures. In this article, we discuss Neural Correlates of Consciousness (NCC) theories, and their capacity to predict or even allow for consciousness in a BNN. We found that Information Integration Theory (IIT) is the only NCC that offers the possibility for a BNN to show consciousness, since the Φ value in the BNN is >0. In other words, the recording of real-time neural activity responding to environmental stimuli. IIT argues that any system capable of integrating information will have some degree of phenomenal consciousness. We argue that the pattern of activity appearing in the BNN, with increased density of sensory information leading to better performance, implies that the BNN could be conscious. This may have profound implications from a psychological, philosophical, and ethical perspective.
Nomophobia (No Mobile Phone Phobia) and Psychological Health Issues among Young Adult Students
Authors: Abdoli Nasrin , Sadeghi-Bahmani Dena , Salari Nader , Khodamoradi Mehdi , Farnia Vahid , Jahangiri Somayeh , Brühl Annette Beatrix , Dürsteler Kenneth M. , Stanga Zeno , Brand Serge
Published: September 2023

Background: Smart phone use has become a part of people’s everyday life. However, when the lack of using the smart phone to establish and maintain electronic communication is related to psychological distress, such a behavior may be considered a modern-age phobia, or nomophobia (no mobile phone phobia). The aims of the present study were to investigate among a sample of young adults the associations between scores for nomophobia and symptoms of depression, anxiety, stress, insomnia, and obsessive–compulsive disorders. Methods: A total of 537 students (mean age: 25.52 years; 42.3% females) participated in the study. They completed a booklet of self-rating questionnaires covering sociodemographic information and symptoms of nomophobia, depression, anxiety, stress, insomnia, and obsessive–compulsive disorders. Results: Higher scores for nomophobia were associated with higher scores for depression, anxiety, and stress, but not with scores for insomnia and obsessive–compulsive disorders. The regression model confirmed that symptoms of anxiety predicted nomophobia. Conclusions: The present results support the assumption that nomophobia appears to be a mood disturbance related to stronger associations with symptoms of anxiety and, to a lesser extent, with symptoms of depression and stress. By contrast, nomophobia appeared to be unrelated to insomnia and symptoms of obsessive–compulsive disorders.
Adaptive Fast-Terminal Neuro-Sliding Mode Control for Robot Manipulators with Unknown Dynamics and Disturbances
Authors: Ahsan Muhammad , Salah Mostafa M. , Saeed Ahmed
Published: September 2023

This paper presents a novel adaptive fast-terminal neuro-sliding mode control (AFTN-SMC) for a two-link robot manipulator with unknown dynamics and external disturbances. The proposed controller is chattering-free and adaptive to the time-varying system uncertainties. Furthermore, the radial base function neural network (RBFNN) is employed to approximate the unknown state dynamics. The simulations have been completed in MATLAB, which illustrates the successful implementation of the proposed controller. The results showcased the effectiveness of the AFTN-SMC in achieving accurate tracking and stability, even in the presence of uncertainties and parameter variations. The incorporation of the RBFNN in the controller proved to be a valuable tool for approximating the unknown dynamics, enabling accurate estimation and control of the manipulator’s behavior. The research presented in this paper contributes to the advancement in control techniques for robot manipulators in diverse industrial and automation applications.
A Graph-Theoretic Approach for Optimal Phasor Measurement Units Placement Using Binary Firefly Algorithm
Authors: Tshenyego Onkemetse , Samikannu Ravi , Mtengi Bokani , Mosalaosi Modisa , Sigwele Tshiamo
Published: September 2023

The pursuit of achieving total power network observability in smart grids using Phasor Measurement Units (PMUs) carries a significant promise of real-time Wide-Area Monitoring, Protection, and Control (WAMPAC). PMU applications eliminate periodical measurements, thereby increasing accuracy through a high sampling rate of the measured power systems quantities. The high costs of installation of PMUs for total power system observability presents a challenge in the implementation of PMUs. This is due to the expensive costs of PMU devices. This has led to a prominent optimal PMU placement (OPP) problem that researchers tirelessly aim to solve by ensuring a complete power network observability while using the least installed PMU devices possible. In this paper, a novel Binary Firefly Algorithm (BFA) based on the node degree centrality scores of each bus is proposed to minimize PMU installations. The BFA solves the OPP problem in consideration of the effect of Zero Injection Buses (ZIBs) under normal operation and single PMU outage (SPO). The robustness and efficiency of the proposed algorithm is tested on IEEE-approved test systems and visualized with a force-directed technique on an undirected power network graph. The proposed BFA yields the same but better optimal PMU numbers, obtained by existing meta-heuristic optimization techniques found in the literature for each of the IEEE test cases, as well as highlighting the cost–benefit of having a robust system against single PMU loss while considering the ZIB effect for an improved system measurement availability.
A Non-Invasive Circuit Breaker Arc Duration Measurement Method with Improved Robustness Based on Vibration–Sound Fusion and Convolutional Neural Network
Authors: Guo Ning , Whitmore Kevin , Cohen Morris , Beyah Raheem , Graber Lukas
Published: September 2023

Previous studies have shown that the contact wear estimation of circuit breakers can be based on the accumulative arc duration. However, one problem that remains unresolved is how to reliably measure the arc duration. Existing methods encounter difficulties in implementation and suffer from limited accuracy owing to the impact of the substation environment. To overcome these issues, this article presents a novel, non-invasive method for measuring arc duration that combines vibration–sound fusion and convolutional neural network. The proposed method demonstrates excellent performance, achieving errors below 0.1 ms under expected noise conditions and less than 1 ms in the presence of various forms of noise, transient interference, and even sensor failure. Its advantages include its ability to accurately measure arc duration and its robustness against noise and interference with unknown patterns and varying intensity as well as sensor failure. These features make it highly suitable for practical deployment in substation environments.
Mapping of Energy Community Development in Europe: State of the Art and Research Directions
Authors: Roberto Roberta , Ferruzzi Gabriella , Negro Viviana , Noussan Michel
Published: September 2023

Within the framework of defining a new energy paradigm to address climate change and other global challenges, the energy community model is gaining interest in several countries, especially in Europe. This article analyses the literature and experiences of organisational forms that fall under the definition of energy communities in a broad sense, in relation to their ability to bring improvements to the social, environmental and economic dimensions, and to ensure durability and replicability. The main elements that constitute a complete, albeit simplified, model of energy community are identified and analysed. The legislative and regulatory frameworks, technologies and social innovation frameworks, identified here as enabling elements, are discussed, as well as the elements of the energy community business models and the impacts generated at the environmental and energy, economic and social levels. The transformation potential of energy communities is confirmed as more than promising. However, in order to develop as a sustainable and replicable model capable of achieving social and environmental goals, as well as economic stability, further significant research and experimentation, following a cross-sectoral and multidisciplinary approach and strong political leadership, are needed.
A Current Control Method for Grid-Connected Inverters
Authors: Zhang Wenping , Wang Yiming , Xu Po , Li Donghui , Liu Baosong
Published: September 2023

It is simple to implement conventional current control with a proportional integral (PI) controller. However, system stability and dynamic performance are not perfect, particularly when operating under unfavorable conditions. In this paper, an improved control method is proposed by introducing a compensation unit. The compensation unit can effectively compensate the system’s phase around the crossover frequency, greatly enhancing the system’s phase margin and stability. It is also capable of handling weak-grid conditions. In this paper, the concept of the proposed compensation unit is explained first. Then, the corresponding mathematical model for the current control loop is built, and system bode diagrams for the conventional and proposed methods are compared. Furthermore, the effect of the parameters for the compensation unit is investigated, and an optimization method is proposed to determine optimal parameters. In addition, to handle weak-grid conditions, the proposed scheme is expanded by including the compensation unit in the grid’s feed-forward loop. Finally, an experimental platform is constructed, and the experimental results are presented to validate the proposed method.
Rock Typing Approaches for Effective Complex Carbonate Reservoir Characterization
Authors: Krivoshchekov Sergey , Kochnev Alexander , Kozyrev Nikita , Botalov Andrey , Kochneva Olga , Ozhgibesov Evgeny
Published: September 2023

For highly heterogeneous complex carbonate reef reservoirs, rock typing with respect to depositional conditions, secondary processes, and permeability and porosity relationships is a useful tool to improve reservoir characterization, modeling, prediction of reservoir volume properties, and estimation of reserves. A review of various rock typing methods has been carried out. The basic methods of rock typing were applied to a carbonate reservoir as an example. The advantages and disadvantages of the presented methods are described. A rock typing method based on a combination of hydraulic flow units and the R35 method is proposed. Clustering methods for rock typing are used. The optimum clustering method is identified, and for each rock type, the permeability–porosity relationships are built and proposed for use in the geomodelling stage.
Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image
Authors: Cui Wenbo , Peng Xiangang , Yang Jinhao , Yuan Haoliang , Lai Loi Lei
Published: September 2023

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China.
SPICE-Aided Models of Magnetic Elements—A Critical Review
Authors: Górecki Krzysztof , Detka Kalina
Published: September 2023

This article analyzes the problem of modeling the properties of such magnetic elements as inductors, coupled inductors, and transformers using the SPICE software (version 17.2). Both the classical models of magnetic elements, built in this software, and the models implemented in the form of subcircuits are described. In particular, attention was paid to the possibility of taking into account the non-linearity of the characteristics of the considered elements and mutual couplings between electrical, magnetic, and thermal quantities. Using the results of thermographic measurements, the need to take into account the differences in temperature values between the individual windings and the core of inductors and transformers was justified. Selected models of the considered elements given in the literature are briefly characterized. The network structures of the electrothermal models of the considered elements elaborated at Gdynia Maritime University are presented. The results of calculations and measurements illustrating the correctness of the described models and their prac-tical usefulness for the elements of different structures are presented and discussed.
Research on Piston Dynamics and Engine Performances of a Free-Piston Engine Linear Generator Coupling with Various Rebound Devices
Authors: Guo Chendong , Wang Yahui , Tong Liang , Feng Huihua , Zuo Zhengxing , Jia Boru
Published: September 2023

Free-piston engine linear generators (FPELGs) are an innovative linear power device that exhibits the distinctive dynamics of pistons and performance of free-piston engines. Furthermore, the single-cylinder/single-piston FPELG structure type has more advantages than other FPELG structure types, including a straightforward structure and ease of control. However, when coupled with various rebound devices, the operational characteristics and piston and engine performance of single-cylinder/single-piston FPELGs are quite different. Therefore, this paper aims to quantitatively compare the dynamics of the piston and engine performance of single-cylinder/single-piston FPELGs coupled with various types of rebound device. The results indicate that when the equivalent stiffness of the gas spring is greater than that of the mechanical spring, the operating frequency of the piston of the FPELG coupled with a gas spring will be higher than that when coupled with a mechanical spring. During the compression stroke, the piston velocity of a FPELG coupled with a mechanical spring changes linearly, while the piston velocity of a FPELG coupled with a gas spring changes nonlinearly. FPELGs coupled with gas springs have shorter compression and expansion durations compared to those coupled with mechanical springs. In addition, the indicated powers of FPELGs coupled with ideal gas springs and mechanical springs are 1.5 kW and 1.3 kW, respectively. However, due to leakage, the thermal efficiency of a FPELG coupled with an actual gas spring is reduced by approximately 2.5% compared with the FPELG coupled with the ideal gas spring. Furthermore, the operation frequency of the piston is positively correlated with the stiffness of the mechanical spring. In addition, as the stiffness of the mechanical spring increases, the combustion process of the engine becomes close to an isovolumetric process. The changes in piston dynamics and engine performance when increasing the initial gas pressure of the gas spring are similar to those when increasing the stiffness of the mechanical spring.
Application of Microbial Fuel Cell Technology in Potato Processing Industry
Authors: Toczyłowska-Mamińska Renata , Mamiński Mariusz Ł.
Published: September 2023

The potato processing industry is among the biggest water-consuming industries, using an average of 17 L of water per 1 kg of processed product. Taking into account that the potato is the fourth-most-important non-cereal food crop with a global production of 376 million tons a year, this branch is a large wastewater producer. Potato-processing wastewater is highly loaded and thus difficult to treat through conventional methods, especially when a low energetic input for environmental benignancy is required. In this review, it was shown that microbial fuel cells (MFCs) are an excellent technology for sustainable potato wastewater treatment. MFCs allow for potato wastewater COD removal with efficiencies as high as 99%, which is accompanied by electricity production that may reach 3.7 W/m2. Thus, the recently published research reviewed in this paper indicates that simultaneous power production and removal of chemical oxygen demand make MFCs superior to conventional treatment methods. Encouraging results and the unique advantages of MFC technology, like significant water and energy use reduction, give a promising perspective on potato-processing wastewater treatments.