(1) Background: The goal of our study was to identify SNPs, metabolic pathways (KEGG), and gene ontology (GO) terms significantly associated with calving and workability; (2) Methods: Based on the EuroGenomics reference data set, we analyzed direct (DCE) and maternal (MCE) calving ease, direct (DSB), and maternal (MSB) stillbirth, milking speed (MSP), and temperament (TEM). We estimated SNP effects using a multi-SNP mixed-model. Further, SNP positions were mapped to genes, and GO terms/KEGG pathways of the corresponding genes were assigned. The estimation of GO term/KEGG pathway effects was based on a mixed-model using SNP effects as dependent variables; (3) Results: The number of significant SNPs comprised 59 for DCE, 25 for DSB and MSP, 17 for MCE and MSB, and 7 for TEM. Significant KEGG pathways were found for MSB (2), TEM (2), and MSP (1), while 11 GO terms were significant for MSP, 10 for DCE, 8 for DSB and TEM, 5 for MCE, and 3 for MSB.; (4) Conclusions: From the perspective of a better understanding of the genomic background of the phenotypes, traits with low heritabilities suggest that the focus should be moved from single genes to metabolic pathways or gene ontologies significant for the phenotype.

In this paper, we present a framework that automatically labels Latent Dirichlet Allocation (LDA) generated topics using sentiment and aspect terms from COVID-19 tweets to help the end-users by minimizing the cognitive overhead of identifying key topics labels. Social media platforms especially Twitter are considered as one of the most influential sources of information for providing public opinion related to a critical situation like the COVID-19 pandemic. LDA is a popular topic modelling algorithm that extracts hidden themes of documents without assigning a specific label. Thus automatic labelling of LDA-generated topics from COVID-19 tweets is a great challenge instead of following the manual labelling approach to get an overview of wider public opinion. To overcome this problem, in this paper, we propose a framework named \texttt{SATLabel} that effectively identifies significant topic labels using \textit{top unigrams features of sentiment terms and aspect terms clusters from LDA generated topics} of COVID-19 related tweets to uncover various issues related to the COVID-19 pandemic. The experimental results show that our methodology is more effective, simpler, and traces better topic labels compare to the manual topic labelling approach.

Poly(3,4-ethylenedioxythiophene) (PEDOT) with a dopant of polystyrene sulfonate (PSS), namely PEDOT:PSS, has been mostly used as counter electrode to give a high performance of dye-sensitized solar cells (DSSCs). Recently, PEDOT doped by carrageenan (PEDOT:Carrageenan) was introduced as a new material to be applied on DSSC as electrolyte. PEDOT:Carrageenan has a similar synthesis process as PEDOT:PSS owing to their similar ester sulphate (-SO3H) groups in both PSS and carrageenan. This review provides an overview of the different roles between PEDOT:PSS as counter electrode and PEDOT:Carrageenan as electrolyte for DSSC applications. The synthesis process and characteristics of PEDOT:PSS and PEDOT:Carrageenan were also described in this review. In conclusion, we found that PEDOT:PSS as counter electrode has the main roles of transferring electrons from the external circuit and accelerating the reduction reaction of redox coupled with its high electrical conductivity and high electrocatalytic activity. As electrolyte, PEDOT:Carrageenan has not shown the main role as an electron transfer from counter electrode to regenerate the dye-sensitized at the oxidized state due to its low ionic conductivity. Therefore, PEDOT:Carrageenan still obtained a low performance of DSSCs. Additionally, the future perspective and challenges of using PEDOT:Carrageenan as both electrolyte and counter electrode are described in detail.

In this research paper, we implement the theory of the primorial function, to develop the Supremum and Infimum bounds for the sum of (log(p))². There are, however, considerable computational difficulties related to these bounds. Therefore, from a pragmatic point of view, a set of Upper and Lower bounds had been developed to bypass this issue. Despite the increased estimation error, the Upper and Lower bounds are still considered sufficiently accurate, while facilitating an easy and fast computation of the estimate of the sum.

The first appearance of viruses can date back to the late last century. As an effective form of malware, viruses reside in the permanent storage of target hosts. Before a virus can execute, that must load into memory from the persistent storage included in the associated file. Due to the reliable destructive power of viruses, many mechanisms have been developed to defend computer systems against these hazardous threats. Antivirus software is one of the most famous and popular among these mechanisms. Most antivirus software uses static analysis (signature-based) technology on files stored in permanent storage, such as hard disks or USB flashes, to detect viruses hidden in files. Fileless malware was developed to enhance the survivability of malware by circumventing detection. Fileless malware only exists in the target hosts’ memory, not files. Antivirus software cannot even access the fileless malware code, much less analyze it, since it may be performed in memory directly without needing to load it from a disk. As a result, it is difficult for an antivirus engine to defend a system against fileless malware attacks. This paper proposes a kernel-based solution called Check-on-Execution (CoE) to detect fileless malware on a Linux system. When a program is going to execute a piece of code in a writable and executable memory area of a process, CoE suspends the code execution first. Coe retrieves the code from memory, packs the code with an ELF header to create an ELF file, and uses VirusTotal to check the file to prevent a Linux system from executing fileless malware. Experimental results show that CoE noticeably enhances the ability of a Linux system to defend itself again fileless malware. CoE is also suitable for protecting a system from shell code injection attacks, such as buffer and heap overflow attacks. It is capable of handling even packed malware. But in this paper, we only focus on fileless malware.

Considerable efforts have been devoted to modifying gravity, which aim to elucidate the possible existence or nature of dark matter and dark energy, achieve a better description of observation data, verify theoretical restrictions in the strong curvature regime and to formulate quantum gravity. In addition, despite the enormous success of the quantum field theory, the framework requires the so-called renormalization techniques and breaks down at high energies. Recently, the Planck Legacy 2018 release has confirmed the presence of an enhanced lensing amplitude in the cosmic microwave background power spectra, which prefers a positively curved early Universe with a confidence level greater than 99%. This study considers the implied positive curvature of the early Universe as the curvature of ‘the background or the 4D conformal bulk’ and distinguishes it from the localized curvature that is induced into the bulk by the presence of comparably smaller celestial objects that are regarded as ‘4D relativistic cloud-worlds’. To consider the interaction between the bulk and cloud-worlds, this paper presents interaction field equations in terms of the brane-world modified gravity counting for the bulk conformal curvature and the boundary terms, which could remove the singularities and satisfy a conformal invariance theory. Similarly, the quantum fields and clouds are regarded as ‘4D relativistic branes’ embedded in vacuum energy of a field strength reliant on the background curvature due to gravity. A visualization of the evolution of the 4D relativistic cloud-worlds over the conformal space-time of the 4D bulk is presented.

Human trafficking is the third most lucrative form of trafficking in the world (just behind drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered around 745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox’s Bazar. In this regard the media confirmed that over a thousand Rohingya people particularly women and girls were victims of human traffickers. This research aims to explore the underlying cause of human trafficking (HT) during emergency response and seeks how the knowledge and capacity of the refugee, local administration and law enforcement agencies in Bangladesh can be improved in promoting counter trafficking (CT) and safe migration processes. In order to achieve the objectives, this research starts with reviewing acts, rules, policies and action plans of the Government of Bangladesh on the HT, CT and safe migration processes. Then, a case study method has been applied to present ongoing the CT and safe migration programs of a NGO called Young Power in Social Action (YPSA) that received funding and technical support from the International Organization of Migration (IOM) for this purpose, the research in this paper will later evaluate the effectiveness of the program through conducting key informant interviews (KII) and focus group discussion (FGDs) with beneficiary and non-beneficiary participants from refugees, law-enforcing agencies (LEA) and non-governmental organizations’ (NGOs) in Teknaf, and Ukhyia sub-districts in Bangladesh. Thus, this research identified program level strengths and weaknesses in relation to the CT, and safe migration process and provide key directions how they can be improved.

The purpose of this article is to demonstrate the ability of bibliometric data from the Lens platform to identify relevant bibliometric/scientometric issues based on the construction of a network of key terms co-occurrence and their clustering. The advantages of using the Lens platform for bibliometric analysis are briefly demonstrated. Key terms were selected on the basis of n-grams and noun phrases. VOSviewer, Scimago Graphica, and Sifaka text mining application were used as analytical tools. Analysis of the Lens metadata on bibliometrics/scientometrics showed their predominant use in the political, social, and medical fields of research.

Occasionally, officials of the world’s regulatory agencies embark upon attempts at bringing previously unregulated physical systems under regulation by them. Each such attempt raises an epistemological issue. At issue is whether enough information about the outcomes of the events of the future, given the outcomes of the events of the present, will be in the hands of a would-be regulator for this regulator to regulate effectually. If present, this information is provided by runs of a model of the physical system that is slated for regulation. Ideally, this model makes an argument that draws its conclusion from the evidence presented to it. If so, this argument is of the form of a predictive inference. However, the process by which an argument draws its conclusion from the evidence may go awry. This happens, for example, when the axiom of probability theory called unit measure is falsified by a conclusion that is drawn from the evidence by this argument.A method is derived from first principles for determination of whether unit measure is satisfied or falsified by an argument made by a model, given that this argument may attach unusual meanings to statistical terms. This method is used in a study of whether unit measure is satisfied or falsified by the arguments that are made by a pair of models. Both models are in active use by regulatory agencies around the world. Under neither argument do runs of the model provide an official of a regulatory agency with the information gain aka mutual information that he or she would need to regulate effectually. Under both arguments, attachment of unusual meanings to statistical terms creates the illusion that such an official can and does regulate effectually.

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have experienced a significant surge in both civilian and military applications, owing to their versatility and cost-effectiveness. However, a negligent use or the operation with harmful intent of these aircraft poses a potential risk to public safety and the privacy of individuals. In response to this threat, counter-drone systems have been developed in recent years, including technologies to detect, position, and neutralize these aircraft. This paper presents a review of different existing commercial counter-drone systems, the technologies behind detection, tracking, and classification, as well as soft and hard kill mitigation.

The rampant misuse of drones poses a serious threat to national security and human life. Currently, the CNN method has been widely used in drone detection. However, there is a challenge that traditional CNN cannot cope with, which is small drone targets often have reduced amplitude or even lost features in infrared images. This paper proposes a Progressive Feature Fusion Network (PFFNet) that gradually increases the response amplitude of the target in the deep network. The Feature Selection Model (FSM) is designed to improve the utilisation of the output coding graph and enhance the feature representation of the target in the network. A lightweight segmentation head is also designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm can achieve low duration and high accuracy in drone target detection. On the public dataset, the IoU is improved by 2.53% and the detection time is reduced by 81.03%.

Multirotors are gaining great importance in the layout of innovative and more agile mobility. In this framework, a possible solution to developing an aircraft complying with the stringent size requirements characterizing this type of application can be a coaxial rotor configuration. To exploit the several possibilities linked to coaxial rotors, a scaled experimental model is designed to evaluate the performances of the counter-rotating propellers system, concerning the distance between the two propellers. Both thrust and noise are considered as parameters of interest. Two brushless motors are deployed whereas the propellers' angular velocity, in terms of round per minute (rpm), is controlled by an external control system. Tests are conducted on both single isolated propellers as well as on the counter-rotating system: the two propellers and their respective motors have been characterized concerning the thrust. Furthermore, a comparison with a numerical model is performed. Noise evaluation on the single propeller has shown a motor contribution prevalence at a low rpm regime (1140-1500 rpm) and a propeller prevalence for angular velocities higher than 1860 rpm. By varying the distances between the propellers a sensitivity analysis is performed with the aim of identifying the optimum configuration taking into account both noise and thrust performances.

This paper presents a comprehensive analysis of the SEIRS COVID-19 pandemic model with saturated incidence rate. By modifying the existing model and proposing a new simplified version, we investigate the dynamics of disease transmission, including the impact of saturation terms on disease spread. Through stability analysis at disease-free and endemic equilibria, we aim to enhance understanding of the spread of COVID-19 and inform effective control strategies. Drawing on mathematical models and epidemiological insights, this study contributes to the ongoing efforts to combat the global pandemic.

Background: Long COVID, characterized by a persistent symptom spectrum following SARS-CoV-2 infection, poses significant health, social, and economic challenges. This review aims to consolidate knowledge on its epidemiology, clinical features, and underlying mechanisms to guide global responses; Methods: We conducted a systematic literature review, analyzing peer-reviewed articles and reports to gather comprehensive data on long COVID’s epidemiology, symptomatology, and management approaches; Results: Our analysis revealed a wide array of long COVID symptoms and risk factors, with notable demographic variability. The current understanding of its pathophysiology suggests a multifactorial origin yet remains partially understood. Emerging diagnostic criteria and potential therapeutic strategies were identified, highlighting advancements in long COVID management.; Conclusion: This review highlights the multifaceted nature of long COVID, revealing a broad spectrum of symptoms, diverse risk factors, and the complex interplay of physiological mechanisms underpinning the condition. Long COVID symptoms and disorders will continue to weigh on healthcare systems in years to come. Ad-dressing long COVID requires a holistic management strategy that integrates clinical care, social support, and policy initiatives. The findings underscore the need for increased international cooperation in research and health planning to address the complex challenges of long COVID. There is a call for continued refinement of diagnostic and treatment modalities, emphasizing a multidisciplinary approach to manage the ongoing and evolving impacts of the condition.

This study assessed the impact of counter-mapping on university students' perception and knowledge of their cities, focusing on cultural diversity, urban dynamics, and social challenges. Using a quasi-experimental design with pretests and posttests in the province of Alicante, Spain, changes in the understanding of urban geography, everyday life, and cultural diversity were analyzed among 54 Geography Didactics students. Likert scale surveys and open-ended questions were employed, complemented by statistical and qualitative analyses, to measure knowledge and perceptions before and after the counter-mapping project. The results indicate that although quantitative correlations did not show statistically significant significance, qualitative analysis revealed significant and profound learning. Participants uncovered the hidden layers of their urban environments and gained a better understanding of the complexities and challenges of their cities. The project promoted the development of critical skills such as analytical thinking, active research, and effective communication, proving to be a valuable pedagogical tool that surpasses the limitations of traditional teaching methods and encourages active and committed citizenship. This inclusive and multidimensional approach enriches the diversity of experiences and knowledge, significantly contributing to the teaching of urban subjects, community engagement, and social responsibility, laying the groundwork for proper tourist, cultural, social, or urban planning of city spaces.

The diffusion coefficient (D) is a key parameter characterizing gas transport in coal seams. Usually, the D is calculated from the desorption curve of particle coal, which cannot accurately reflect the diffusion characteristics under the stress constraint conditions of in-situ coal seams. In this paper, based on Fick's law of counter diffusion, different metamorphic deformed coals of medium and high coal rank are taken as the research object. The change laws of D under different confining pressure, gas pressure, and temperature conditions are tested and analyzed, and the influencing mechanisms on the D are discussed. The results show that the D of different metamorphic deformed coals decreases exponentially with the increase of confining pressure, and increases exponentially with the increase of gas pressure and temperature. There is a limit diffusion coefficient. The influence of confining pressure on the D is essentially determined by the change of effective stress, and the D has a negative effect of effective stress similar to permeability. The effect of gas pressure on the D involves two mechanisms, namely, mechanical and adsorption effects, which are jointly restricted by effective stress and coal particle shrinkage and expansion deformation. The effect of temperature on the D is mainly achieved by changing the root mean square speed and average free path of gas molecules. Under the same temperature and pressure conditions, the D increases first and then decreases with the increase of deformation degree, and the D of fragmented coal is the largest. Under similar deformation conditions, the D of high-rank anthracite is greater than that of medium-rank fat coal. It is considered that porosity is the key factor affecting the change of D in different metamorphic deformed coals.

In the wake of the novel coronavirus, SARS-CoV-19, the world has undergone a critical situation in which grave threats to global public health emerged. Among human populations across the planet, travel restraints, border enforcement measures, quarantine, and isolation provisions were implemented in an attempt to control and limit the spread of the contagion. Decisions on how to implement and enforce various control policies should be determined based on available real-world evidence and theoretical prediction. In this study, we propose a novel exportation- importation epidemic model associated with the quarantine and hospitalized-isolation policies by considering the two-body system: a source country of a contagious disease and a neighboring country that is initially disease-free. The model is first applied to the original COVID-19 data in China, Italy, and the Republic of Korea (ROK) and observed through consistent fitting results with equivalent goodness-of-fit. Then, the data are estimated per the fitting parameters. Driven by these parametric settings and considering the normalized population, the numerical analysis, and epidemiological exploration, this work further elucidates the substantial impact of quarantine policies, healthcare facilities, and the public counter-compliance effect.

Large scale coherent structures in atmospheric boundary layer (ABL) are known to contribute to the power generation in wind farms. In the current paper, we perform a detailed analysis of the large scale structures in a finite sized wind turbine canopy using modal analysis from three dimensional proper orthogonal decomposition (POD). While POD analysis sheds light on the large scale coherent modes and scaling laws of the eigenspectra, we also observe a slow convergence of the spectral trends with the available number of snapshots. Since the finite sized array is periodic in the spanwise direction, we propose to adapt a novel approach of performing POD analysis of the spanwise/lateral Fourier transformed velocity snapshots instead of the snapshots themselves. This methodology not only helps in decoupling the length scales in the spanwise and the streamwise direction when studying the energetic coherent modes, but also provides a detailed guidance towards understanding the convergence of the eigenspectra. In particular, the Fourier-POD eigenspectra helps us illustrate if the dominant scaling laws observed in 3D POD are actually contributed by the laterally wider or thinner structures and provide more detailed insight on the structures themselves. We use the database from our previous large eddy simulation (LES) studies on finite-sized wind farms which uses wall-modeled LES for modeling the Atmospheric boundary layer laws, and actuator lines for the turbine blades. Understanding the behaviour of such structures would not only help better assess reduced order models (ROM) for forecasting the flow and power generation but would also play a vital role in improving the decision making abilities in wind farm optimization algorithms in future. Additionally, this study also provides guidance for better understanding the POD analysis in the turbulence and wind farm community.

This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.

The world of science is drowned in a wealth of information. How to make sense of this wealth of published articles, blog posts and abstracts has become an important challenge given the importance of science to different aspects of societal function. At the crux of the issue lies the increasing trend where scientific discovery informs decision making at the societal level. One example, is the elucidation of the ozone hole to the promulgation of the Montreal Protocol in 1987, and documenting increasing atmospheric carbon dioxide concentration led to climate action and signing of the Paris Agreement in 2015. Hence, understanding a research field becomes an important need for many decision makers across different sectors of society. But, the scientific literature is cryptic and esoteric, and presents a significant barrier to comprehension. One approach to ameliorate the problem is statistical machine reading, which provides the critical capability of identifying key concepts that underpins a research field. Such important concepts help provide an incision point to gain further understanding of the field and initiating further conversation about the field. This work sought to validate the concept of whether applying statistical machine reading to a body of literature comprising short blog posts and abstracts of published articles help in understanding the field of metabolic engineering. One important angle pursued in this research is whether the tabulated list of terms and phrases identified by statistical machine reading could be creatively analyzed to gain a deeper understanding of the research field. For example, the most frequently occurring terms and phrases could describe key concepts of the research field. Moving down in frequency occurrence would be terms and phrases that describe methodologies and approaches of the field. Finally, less frequently occurring terms and phrases may be tools and resources used in the research field. Results validated the utility of statistical machine reading in identifying important terms and phrases associated with the research field. But the small dataset of blog posts and abstracts used in this study severely hampered the identification of most of the key concepts of metabolic engineering, which is a fairly broad field of research. Overall, statistical machine reading shows utility in identifying terms and phrases that could describe a field. However, the level of understanding is closely tied in to the breadth and depth of reading material available, which meant that the methodology is data intensive in nature. Future use of supercomputing or quantum computing could help alleviate constraints of computational capacity, and help tackle the exponential rise in computational complexity as the size of the reading material for machine reading expands.

The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology ( SBO). The SBO terms enable modelers to explicitly define and unambiguously describe model entities, including their roles and characteristics. Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks. Availability: The open-source project SBOannotator is freely available under the terms of LGPL version 3.0 from https://github.com/draeger-lab/SBOannotator/.

The radial distribution networks are designed for unidirectional power flows and are passive in nature. However, with the penetration of Distributed Generation (DG), the power flow becomes bidirectional and the network becomes active. The integration of DGs into distribution network creates many issues with: system stability, protection coordination, power quality, islanding, proper placement and sizing etc. Among these issues, the two most significant are optimal sizing and placement of DGs and their protection coordination in utility network. The proper coordination of relays with high penetration of DGs placed at optimal location increases the availability and reliability of the network during abnormal operating conditions.This research addresses most of the available methods for efficient sizing and placement of DGs in distribution system (numerical, analytical and heuristic) as well as the developed protection coordination techniques for utility networks in the presence of DGs (Artificial Intelligence (AI), adaptive and non-adaptive, multi-agent, hybrid). This paper indicates the possible research gaps and highlights the applications possibilities and methods’ limitations in the area of DGs.

Digital-Radio-Frequency-Memory (DRFM) has emerged as an advanced technique to achieve diverse jamming signals, due to its capability of intercepting waveforms within a short time. Multiple-Input Multiple-Output (MIMO) radars can transmit agile orthogonal waveform sets for different pulses to combat the DRFM-based jamming, where any two groups of waveform sets are also orthogonal. In order to design multiple groups of orthogonal waveform sets at the same time, this article formulates a group orthogonal waveforms optimal design model, whose objec-tive function is the weighted sum of the intra- and inter-group orthogonal performances metrics. To solve this optimization problem, this article proposes a group orthogonal waveforms design algorithm. Based on a primal-dual type method and proper relaxations, the proposed algorithm transforms the original problem into a series of simple sub-problems. Numerical results demonstrate that the proposed algorithm has the ability to balance the intra- and inter-group orthogonal performances by adjusting the weighting factors, which is not available using other state-of-the-art orthogonal waveform design algorithms.

DEXTER (Detection of EXplosives and firearms to counter TERrorism) is a project funded by NATO’s Science for Peace and Security (SPS) program with the goal of developing an integrated system capable of remotely and accurately detecting explosives and firearms in public places without impeding the flow of pedestrians. The active microwave component of the DEXTER project is referred to as MIC (Microwave Imaging Curtain), supported by the French SGDSN (General Secretariat of Defense and National Security), and utilizes a radar system capable of generating 3D images in real time to address non-checkpoint detection of explosives and fire-arms. The project, led by the ONERA (France), is based on a radar imaging system developed by the Fraunhofer FHR institute, using a MIMO architecture with an Ultra Wide-Band waveform. Although high-resolution 3D microwave imaging is already being used in expensive body scanners to detect firearms concealed under clothing, MIC's innovative approach lies in utilizing a high-resolution 3D imaging device that can detect larger dangerous objects carried by moving individuals at a longer range, in addition to providing discrete detection in pedestrian flow. Automatic detection and classification of these dangerous objects is carried out on 3D radar im-ages using a deep-learning network. This paper will outline the project's objectives and constraints, as well as the design, architecture, and performance of the final system. Additionally, it will present real-time imaging results ob-tained during a live demonstration in a relevant environment.

Electrode is a key component in a microbial electrolysis cell (MEC) and it needs significant improvement for practical implementation of MEC. For effective development of electrode technology, accurate and reproducible analytical methods are very important. Linear sweep voltammetry (LSV) is an essential analytical method for evaluating electrode performance; however, it has not been firmly established yet in the MEC field. In this study, biological brush (BB), abiotic brush (AB), Pt wire (PtW), stainless steel wire (SSW) and mesh (SSM)) were tested to explore the most suitable counter electrode in different medium conditions. Coefficient of variation (CV) for Imax of LSV were comparatively analyzed. In BB-anode LSV, SSW (0.48%) and SSM (2.17%) showed higher reproducibility as a counter electrode. In SSM-cathode LSV, BB (1.76%) and PtW (2.01%) produced more reproducible results. In the Ni-AC-SSM-cathode LSV, PtW (3.54%) and BB (8.81%) produced more reproducible result. It shows electrode used in the operation is an appropriate counter electrode in the acetate-added condition. However, in the absence of acetate, PtW (1.24%) and BB (1.71%) produced more reproducible results in SSM cathode and PtW (0.61%) and SSW (1.21%) did in the Ni-AC-SSM-cathode, showing PtW is an appropriate counter electrode. These results also shows that PtW is an appropriate counter electrode in cathode LSV.

The article is aimed at a brief comparison and analysis of the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors in the Author Keywords in Web of Science have been revealed. Therefore, a more detailed analysis was conducted by comparing different types of key terms for IEEE Xplore and Scopus platforms only. I used IEEE Access journal metadata as indexed on both platforms. The sample match for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more types of key terms, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides a more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, has been used to compare co-occurrence of terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Using the IEEE conference proceedings bibliometric data, I illustrated the importance of context in estimating the rate of growth of publishing activity on a topic of interest.

Premenstrual syndrome is characterized by pain and symptoms affecting the women´s quality of life. We aimed to evaluate the safety and effectiveness of an oral fixed dose combination containing naproxen 220 mg + paracetamol 300 mg + pamabrom 25 mg tablet (Analgen Fem®) during the routine clinical practice. A prospective, open-label, multicenter, observational post-marketing study was conducted from December 2017 to December 2019 including 270 women older than 18 years. Primary endpoint was the proportion of adverse events, meanwhile the pain intensity, premenstrual syndrome symptoms, and patient proportion with a pain score reduction by at least 50% were secondary endpoints. The mean age of population was 28.9 ± 8.8 years. Among the 270 women, 8 (3%) experienced 10 adverse events. These were headache (5/8), gastritis (2/8) dyspepsia (1/8), diarrhea (1/8) and nausea (1/8). The investigator discontinued the study medication in 3/8 patients because of adverse events. The median of differences between the pain intensity at baseline and at last observation (-4.5, 95%CI; -5, -4) demonstrated that naproxen 220 mg + paracetamol 300 mg + pamabrom 25 mg fixed dose combination reduced pain intensity (p<0.001). In addition, the proportion of patients with pain reduction by at least 50% at the end of the treatment was 70.7%. The study demonstrated that this drug represents a safe and tolerated treatment, reducing pain intensity and premenstrual symptoms.

Virtual Histology- Intravascular Ultrasound (VH-IVUS) image is an available method for visualizing plaque component to detect thin cap fibroatheroma. Nevertheless, this imaging modality has considerable limitations to extract the plaque component features and identifying the TCFA plaque. The aim of this paper is to improve the identification of TCFA using fusion of IVUS and VH-IVUS images. In order to generate the automatic technique for reducing the human interaction, a new method namely Active Contour based Plaque Border Detection (ACPB) is proposed. In order to perform the pixel wise classification, hybrid of K-means algorithm with Particle Swarm Optimization and Plaque based Minimum Euclidean Distance (KMPSO-PMED) method is presented to classify the plaque region as well. Moreover, to obtain more significant information of imaging modalities, fusion of two different images consisting of VH-IVUS and IVUS is performed. Therefore, geometric features are extracted from the plaque region and combine with IVUS features. Furthermore, different group of plaque features are divided by means of the histopathological studies. SVM classifiers is applied to detect the TCFA and non-TCFA plaques. The proposed method is applied on 566 in-vivo IVUS and their matching VH-IVUS images obtained from 9 patients. The best result of SVM illustrates the accuracy rates of 99.41% for classification of TCFA plaque. The results prove that the highest accuracy is achieved by integrated features of IVUS and VH-IVUS images.

We assessed the use of Evidence-Based Practice (EBP) among pharmacists working in community pharmacies in France, and the factors linked to this practice. During 3 months in 2018 an online survey was sent to over 7,000 active pharmacists, and posted on pharmacist social media sites. The 40-item questionnaire described four clinical cases reflecting typical situations (conventional med-icine and Complementary and Alternative Medicine) encountered daily by community pharma-cists. Multiple-choice responses were proposed and scored according to whether they reflected EBP. A high total score indicated behavior in line with EBP. Univariate and multivariate analyses were used to evaluate factors that might explain adherence to EPB (pharmacy’s characteristics, the pharmacist’s status, mode of continuing education and sources of information). 595 pharmacists completed the questionnaire. The majority relied on pharmaceutical industry and other biased and/or non-evidence-based sources, particularly concerning information on homeopathic products. The consultation of independent reviews, health agency recommendations, and peer-reviewed scientific journals, was associated with evidence-based decisions. In contrast, reliance on phar-maceutical industry documents, personal experience and informal handbooks was linked to lower EBP scores. The level of EBP use by French community pharmacists needs to be improved to ensure that good quality scientifically-based advice is given to customers.

Background: Although rarely indicated, antibiotics are commonly used for acute diarrhoea in China. We conducted a randomized, double blind exploratory clinical trial of loperamide, berberine and turmeric for treatment of acute diarrhoea. Methods: Adults with acute uncomplicated diarrhoea were randomized to 4 groups: (A) loperamide; (B) loperamide and berberine; (C) loperamide and turmeric; (D) loperamide, berberine and turmeric. All participants were given rescue ciprofloxacin for use after 48 hours if symptoms worsened or were unimproved. Primary endpoints were feasibility and ciprofloxacin use during the 2 week follow-up period. Semi-structured interviews were conducted following recruitment. Results: Only 21.5% (278/1295) of patients screened were deemed eligible, and 49% (136/278) of these consented and entered into the final analysis. Most participants had mild symptoms, because most patients with moderate or severe symptoms wanted to be given antibiotics. Follow-up was good (94% at 2 weeks). Only two participants used rescue antibiotics compared to 65% of acute diarrhoea patients in the hospital during the recruitment period. The median symptom duration was: 14 hours in group B (IQR 10-22), 16 hours in group D (IQR 10-22), 18 hours in group A (IQR 10-33), 20 hours in group C (IQR 16-54). Re-consultation rates were low. There were no serious treatment-related adverse events. Most interviewed participants said the treatment was effective. Conclusion: Although recruitment was challenging because of widespread expectations for antibiotics, patients with mild diarrhoea accepted to try an alternative. This therapy requires further evaluation in a fully powered, randomised controlled trial among a broader sample.

This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.

The shallow water equations are widely applied for the simulation of flow routing in rivers and floodplains, as well as for flood inundation mapping. From a mathematical point of view, they are a hyperbolic system of nonlinear partial differential equations, whose numerical integration is sometimes computationally burdensome. For this reason, the interest of many researchers has been focused on the study of simplified forms of the original set of equations, which requires less computational effort. One of the most commonly applied simplifications consists in neglecting the inertial terms, which changes the hyperbolic model to a parabolic one. The effects of such a choice on the outputs of the simulations of flooding events are controversial and an important topic of debate. In the present paper, two numerical models, recently proposed for the solution of the complete and zero-inertia forms of the shallow waters equations, are applied to several unsteady flow routing scenarios. We simulate synthetic and laboratory studies, starting from very simple geometries and moving towards complex topographies. Analyzing the role of the terms in the momentum equations, we try to understand the effect, on the computed results, of neglecting the inertial terms in the zero-inertia formulation. We analyze the computational costs.

Drones are being used more and more frequently and in various ways to attack people and critical infrastructures, which increases their level of danger and makes early detection and risk assessment even more difficult. There is no ultimate drone sensor. Rather, drones can only be effectively detected, if suitable sensor technologies are integrated within a system. Among the best suited sensorics for this purpose are radar and optical sensors, because the advantage of the radar, given by the long detection range, can be well combined with the additional visual-based information gained from a camera in the mid- and short-range, where usually the radar fails detecting micro drones. In this paper, a modular drone detection and assistance system MODEAS is presented, which can be used to automatically detect and track drones within a range up to some kilometers on the sky, but also in complex urban environments, where buildings and vegetation offer a perfect background for camouflage. The system provides an early detection of flying objects as potential drones, when those are not at all or only as small dots visible for the human eye. Main strengths of the system are the robust AI detection to differentiate such an object against clutter (birds, clouds) and its further classification by model and payload recognition, the latter aspect being considered as an indication of dangerousness. Moreover, once a drone is detected, the system follows it automatically, precisely and smoothly with a high-resolution pan-tilt-camera, measuring the distance to the sensor station with a laser rangefinder, compute the geocoordinates and visualize them live on a map. To improve the precision of the detection, a multisensorial fusion of all sensor detections is performed. Furthermore, alarms may be generated when a drone enters a restricted area.

I extend earlier work that derived a Born rule for nuclear-decay cloud chamber tracks (Mott problem) without invoking quantum measurement axioms. The key ingredient there was the identification of sites at which the cross section for ionization by a passing charged particle is singular at ionization threshold. This singularity arose from a Penning-like process involving molecular polarization in sub-critical vapor clusters. In the present paper, I propose a simple experiment to determine if similar ionization physics takes place in the mica window of a Geiger counter and drives the collimation of spherically-symmetric wavefunctions outside the counter tube into ion tracks inside. The experiment measures the count rate from a radioactive point source as a function of source-window separation. I have performed a proof-of-concept of this experiment; results are consistent with the singular-ionization picture. I consider implications for the Stern-Gerlach experiment and superconducting qubits. I detail further work required to flesh out these results more rigorously.

In this study we propose a semi-parametric, parsimonious Value at Risk forecasting model, based on quantile regression and machine learning methods, combined with readily available market prices of option contracts from the over-the-counter foreign exchange rate interbank market. We aim at improving existing methods for VaR prediction of currency investments using machine learning. We employ two different methods - ensemble methods and neural networks. Explanatory variables are implied volatilities with plausible economic interpretation. The forward-looking nature of the model, achieved by the application of implied volatilities as risk factors, ensures that new information is rapidly reflected in Value at Risk estimates. To the best of our knowledge, this paper is the first to utilize information in the volatility surface, combined with machine learning and quantile regression, for VaR prediction of currency investments. The proposed ensemble models achieve good estimates across all quantiles. The light gra-dient-boosting machine model and the categorical boosting model both yield estimates which are better than, or equal to, those of the benchmark model. The neural network models are in general quite unstable.

A protocol was designed for plasmid curing using a novel counter-selectable marker, named pylSZK-pylT, in Escherichia coli. The pylSZK-pylT marker consists of the archaeal pyrrolysyl-tRNA synthetase (PylRS) and its cognate tRNA (tRNApyl) with modification, and incorporates an unnatural amino acid (Uaa), Nε-benzyloxycarbonyl-l-lysine (ZK), at a sense codon in ribosomally synthesized proteins, resulting in bacterial growth inhibition or killing. Plasmid curing is performed by exerting toxicity on pylSZK-pylT located on the target plasmid, and selecting only proliferative bacteria. All tested bacteria obtained using this protocol had lost the target plasmid (64/64), suggesting that plasmid curing was successful. Next, we attempted to exchange plasmids with the identical replication origin and an antibiotic resistance gene without plasmid curing using a modified protocol, assuming substitution of plasmids complementing genomic essential genes. All randomly selected bacteria after screening had only the substitute plasmid and no target plasmid (25/25), suggesting that plasmid exchange was also accomplished. Counter-selectable markers based on PylRS-tRNApyl, such as pylSZK-pylT, may be scalable in application due to their independence from the host genotype, applicability to a wide range of species, and high tunability due to the freedom of choice of target codons and Uaa’s to be incorporated.

JBIR-99 is a secondary metabolite of marine fungi that has been shown to possess strong antibiotic activity. An efficient approach using a combination of size exclusion chromatography with a Sephadex LH-20 and high-speed counter-current chromatography (HSCCC) has been successfully developed for the isolation and purification of a polyketide from the solid-state fermentation of Meyerozyma guilliermondii. The active compound was isolated with purity >95% by HSCCC using an optimized solvent system composed of petroleum ether–ethyl acetate– 95% ethanol–water (5:3:5:3, v/v/v/v) after size exclusion chromatography. This compound was successfully purified in the quantity of 68 mg from 120 mg of the crude extract. The structure of JBIR-99 was elucidated and assigned by 1D, 2D NMR spectroscopic, and positive HRESITOFMS. Moreover, the relative configuration of compound JBIR-99, displaying a quite complex multi-ring structure, is determined by X-ray crystallography for the first time. The purification method developed for JBIR-99 will facilitate the further investigation and development of this antibiotic agent as a lead compound. Furthermore, it is suggested that the combination of size exclusion chromatography and HSCCC could be more widely applied for the isolation and purification of polyketides from marine fungi.

Functional gastro-intestinal disorders (FGID) including irritable bowel syndrome (IBS) are frequently handled by self-management with over the counter (OTC) products such as hyoscine butylbromide (HBB), alone or in combination with paracetamol, and natural products such as peppermint oil. To obtain real-world information, we have performed an anonymous pharmacy-based patient survey among 1686 users of HBB, HBB + paracetamol and peppermint oil. Based on the distinct but overlapping indications for the three OTC products, multiple logistic regression was applied to compare them in users reporting gastrointestinal cramps and pain, bloating, flatulence, or IBS as cardinal symptom. All three treatments reduced symptoms and associated impairments of work/daily chores, leisure activities, and sleep by approximately 50%. Based on the four cardinal symptoms and the four dependent continuous variables of interest (change of intensity of symptoms and of the three impairment domains) a total of 16 logistic regression models were applied. HBB, HBB + paracetamol, and peppermint oil had similar reported overall effectiveness in those models. Gender, age, baseline symptom severity and impairment in one of three domains had small and inconsistent effects on perceived treatment success. We provide evidence that HBB, HBB + paracetamol, and peppermint oil have comparable effectiveness in their approved indications under real-world conditions in an OTC setting.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (Model Predictive Control) framework explicitly including a generic flexibility mechanism which is easy to particularise to specific strategies such as Demand Response, Flexible Production and Energy Efficiency Services. The proposed framework is benchmarked with other non-optimal control configurations to better show the advantages it provides. The work of this paper is completed by the implementation of a generic use case which aims to further clarify the use of the framework and thus, to ease its adoption by other researchers in their specific flexibility mechanisms applications.

In our previous papers [1,3], using only the concepts of the zero-point energy and quantized space–times, all the fields including gravity were explained. However, the previous papers had the following limitations: First, the concept of the quantized space-time must be experimentally confirmed. Second, we should clarify the meaning of the quantized Einstein’s gravity equation, which is derived in [1]. Moreover, in another paper [2], we succeeded in describing the neutrinos’ self-energy and their oscillations. However, this paper assumes the rest energy of 3-leptons in advance, which is why we needed to uncover the reason why leptons have 3-generations. As mentioned, using the concepts of the zero-point energy and quantized space–times, we derived the quantized Einstein’s gravity equation in our previous paper [1]. The paper provides an analytical solution of this equalized Einstein’s equation, which implies the conservation of angular momentum in terms of quantized space–times. Employing this solution and without the standard big bang model, a unique form of acceleration equation for the acceleration-expansion universe is derived. Moreover, the temperature of the cosmic microwave background (CMB) emission is also obtained. Further, this solution results in an analytical (not numerical) derivation of the gravity wave. Moreover, based on the configuration of quantized space–times in terms of both electric and magnetic fields, we analytically attempted to calculate every equation in terms of electromagnetic and gravity fields, using the solution of the quantized Einstein’s gravity equation. As a result of this theory, first the calculated acceleration and temperature of CMB emission agree with the measurements. Furthermore, the analytical solution of the quantized Einstein’s gravity equation resulted in all the laws of electromagnetic and gravity fields in addition to the analytically derived gravity wave, which agrees well with the recent measurements. Moreover, the calculations of the energies in the basic configuration of the quantized space–times resulted in all 3-leptons’ rest energies. Considering this basic configuration is uniformly distributed everywhere in the universe, we can conclude that τ-particles or static magnetic field energy derived from the basic configuration of the quantized space–times is the identity of dark energy, which also distributes uniformly in the universe.

The food system, the most important driver of planetary transformation, is in a deep crisis. Therefore, seeking a sustainable and socially-fair transition pathway becomes an issue of utmost priority for our own survival. The consideration of food as a commodity, a social construct that played a central role in driving this crisis, remains the uncontested narrative to lead the different transition pathways what seems rather contradictory. By exploring the normative values in the transition landscape, this paper seeks to understand how relevant is the hegemonic narrative of “food as commodity” and its alternative of “food as commons” to determine transition trajectories and food policy beliefs. Applying the Multi-level Perspective framework and developing the ill-studied “agency in transition”, this research enquired food-related professionals that belong to an online community of practice (N=95) on valuation of food dimensions and agency in food transitions to check whether the valuation of food is relevant to explain personal stances in transition. Results suggest the socially-constructed view of food as commodity is positively correlated to the gradual reforming attitude, whereas food as commons is positively correlated to the counter-hegemonic transformers regardless the self-defined position in the transition landscape (regime or niches). At personal level, there are multiple loci of resistance with counter-hegemonic attitudes in varied institutions of the regime and the innovative niches, many of them holding this discourse of food as commons. Conversely, alter-hegemonic attitudes are not positively correlated to this alternative discourse and they may inadvertently or purportedly reinforce the ‘‘neoliberal narrative’’. Food as commons, a different narrative whose rationale is explained in the paper, seems to be a relevant framework that could enrich the multiple transformative constituencies that challenge the industrial food system and therefore facilitate the convergence of movements that reject the commodification of food.

The antimicrobial properties of the new strains of microorganisms isolated from natural sources of various ecological niches in the Moscow Region and the Republic of Tatarstan were studied. An antifungal activity of isolates was established in a test culture of toxin-producing microscopic fungi that cause various plant diseases: Fusarium oxysporum, Aspergillus flavus, Penicillium spp. and Candida albicans. Of the 46 studied microorganisms, 4 isolates (Bacillus subtilis, Propionibacterium freudenreichii, Lactobacillus plantarum and Streptomyces spp.) showed an ability to produce biologically active metabolites with a pronounced antimicrobial potential. This is promising in the growth and protection of the Scots pine, pedunculate oak and small-leaved linden forest crops from diseases caused by fungi Lophodermium pinastri Chev. and Microsphaera alphitoides stimulation. Based on selected isolates, a Bacterial product LRV composition has been developed. A Scots pine, pedunculate oak and small-leaved linden seedlings single and double foliar treatment with Bacterial product LRV at a concentration of 10 ml/l led to an increase in the growth of an above-ground part by 31.8, 51.9 and 25.4 %, respectively, and an underground part – by 25.0, 37.2 and 25.7 %, respectively, compared with the control. The weight of seedling at the end of the study exceeded the control variant by an average of 26.0, 44.0 and 78.0 %, respectively. A use of a plant protection Bacterial product LRV did not have a significant effect on a powdery mildew and Schütte disease damage to forest crops. A Biogeosystem Technique methodology has been developed to improve a long-term forest growing.

In this paper, dual-rotor counter-rotating (CR) configurations of vertical axis wind turbines (VAWT) are briefly inspected and divided into three types. This investigation was focused on one of these types – the CR-VAWT with co-axial rotors, in which two equal rotors are placed on the same shaft, displaced from each other along it and rotate in opposite directions. For this CR-VAWT with three-blade H-Darrieus rotors, the properties of the design in terms of aerodynamics, mechanical transmission and electric generator, as well as control system are analyzed. A new direct-driven dual-rotor (DR) permanent magnet synchronous generator (PMSG) was proposed, in which two built-in low-power PM electric machines have been added. They perform two functions – start-up and overclocking of the rotors to the angular velocity at which the lifting force of the blades is generated and stabilizing the CR-VAWT work as wind gusts act on the two rotors. Detailed in this paper is the evaluation of aerodynamic performance of the CR-VAWT via 3D computational fluid dynamics (CFD) simulations. The evaluation was conducted using the CONVERGE CFD software with the inclusion of the actuator line model for the rotor aerodynamics, which significantly reduces the computational effort. Obtained results show that both rotors, while they rotate in opposite directions, had a positive impact on each other: the power coefficients of the upper and lower rotors in the CR-VAWT increased by 5.5% and 13.3% respectively compared to the single-rotor VAWT at the optimal distance between the rotors of 0.3 from a rotor height.

Applying a procedure similar to that of E.S. Bring, by using a 4^th degree Tschirnhaus transformation, it was possible to transform the Bring-Jerrard normal quintic (BJQ) equation into a De Moivre form (DMQ), so that it could be solved by radicals. The general solution by radicals of the De Moivre equations of any degree is presented. By the same procedure the BJSx (normal sextic) equation was taken to another one without the 2^nd, 4^th and 6^th terms which was transformed into a cubic (solvable) equation. By applying a 6^th degree Tschirnhaus transformation to the BJSp (normal septic) equation its binormal (without the 2^nd, 3^rd, 4^th and 5^th terms) form was obtained.

Applying a procedure similar to that of E.S. Bring, by using a 4^th degree Tschirnhaus transformation, it was possible to transform the Bring-Jerrard normal quintic (BJQ) equation into a De Moivre form (DMQ), so that it could be solved by radicals. The general solution by radicals of the De Moivre equations of any degree is presented. By the same procedure the BJSx (normal sextic) equation was taken to another one without the 2^nd, 4^th and 6^th terms which was transformed into a cubic (solvable) equation. By applying a 6^th degree Tschirnhaus transformation to the BJSp (normal septic) equation its binormal (without the 2^nd, 3^rd, 4^th and 5^th terms) form was obtained.

Applying a procedure similar to that of E.S. Bring, by using a 4^th degree Tschirnhaus transformation, it was possible to transform the Bring-Jerrard normal quintic (BJQ) equation into a De Moivre form (DMQ), so that it could be solved by radicals. The general solution by radicals of the De Moivre equations of any degree is presented. By the same procedure the BJSx (normal sextic) equation was taken to another one without the 2^nd, 4^th and 6^th terms which was transformed into a cubic (solvable) equation. By applying a 6^th degree Tschirnhaus transformation to the BJSp (normal septic) equation its binormal (without the 2^nd, 3^rd, 4^th and 5^th terms) form was obtained.

Applying a procedure similar to that of E.S. Bring, by using a 4^th degree Tschirnhaus transformation, it was possible to transform the Bring-Jerrard normal quintic (BJQ) equation into a De Moivre form (DMQ), so that it could be solved by radicals. The general solution by radicals of the De Moivre equations of any degree is presented. By the same procedure the BJSx (normal sextic) equation was taken to another one without the 2^nd, 4^th and 6^th terms which was transformed into a cubic (solvable) equation. By applying a 6^th degree Tschirnhaus transformation to the BJSp (normal septic) equation its Bring-Jerrard binormal (without the 2^nd, 3^rd, 4^th and 5^th terms) form was obtained.