"Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law". In accordance with the aforementioned declaration on Free Access to Law by Legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques, should be employed in the context of legal information retrieval, as to increase user satisfaction. We address diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard data set from the Common Law domain that we subjectively annotated with relevance judgments for this purpose. Our results i) reveal that users receive broader insights across the results they get from a legal information retrieval system, ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification and iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials databases has fundamentally expanded the choices of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit ZT quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principles calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principles calculations are carried out for narrow-bandgap semiconductors which exhibiting potential thermoelectric application. The present framework of calculations mainly includes deformation potential module, electrical transport performance module, mechanical and thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing searching and viewing the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Background and Objectives: Cancer as the second leading cause of death in the United States poses a huge healthcare burden. Barriers to access to advanced therapies influence the outcome of cancer treatment. In this study, we examined whether insurance types affect the quality of cancer clinical care regarding access to an important monoclonal antibody, ramucirumab. Materials and Methods: Data for 13,340 cancer patients with Purchased or Medicaid insurance from the All of Us Database were collected for this study. The Chi-square test of proportions was employed to determine the significance of patient cohort characteristics and ramucirumab usage between Purchased and Medicaid insurance groups. The independent t-test was utilized to assess the influences of other determinants on insurance types. Results: Cancer patients who are African American, with lower socioeconomic status, or with lower educational attainment are more likely to be insured by Medicaid. Analysis of survey questions demonstrated the relationship between income and education level with insurance type as Medicaid cancer patients were less likely to receive primary care and specialist physician access and more likely to request lower cost medications. In addition, those with Medicaid insurance were identified to have poorer access to expensive therapeutics like ramucirumab compared to those with Purchased insurance.  Conclusions: The inequities of the US healthcare system are observed for cancer patient care; access to physicians and therapeutics are highly varied and dependent on insurance types. Socioeconomic status determines insurance type, which unfortunately generates a significant impact on cancer treatment and disease outcome.

In the discussion on environmental policies, the notion of eco-efficiency is often used. Eco-efficiency is defined as the delivery of products and services with competitive values, while reducing the ecological impacts and satisfying human needs. In an environment of great competitiveness in which the companies operate, the improvements in the productive process is one of the differentiating factors for guaranteeing a strong competition. Also it is essential rationalizing energy consumption and natural resources. For this it is necessary to understand the main operations and dynamics of the company. This work presents an empirical study of a Portuguese company in the industrial sector. The problematic here presented is based on the company’s growing concern to reduce the amount of scrap produced. From the literature research that was performed, a strong dependence relation between the different variables in the extrusion process was found. The main objective of this work is to model the aluminium extrusion process, with the aim of minimizing the production of scrap. For this several variables involved in the process are taking into account. Using statistical techniques, in particular multiple linear regressions, it was possible to identify the importance of the variables under study for the scrap production.

With the growing of university chemistry experiment projects, scientific research personnel, specialized equipment, laboratory accident is increasing yearly. And accident data lacks a safety platform to store related information and cannot be guaranteed for efficient conditional sharing. To solve these problems, we designed a laboratory accident system to store, share related data and predict risk level. In this paper, we manually collected chemistry laboratory accidents by python software and class assignments, then analyses risk factor variables using Spsspro, finally established a prediction model using Stata. We intended to register laboratory related data into proposed chemistry accident system based on data ownership safety architecture. The chemistry accident system can break data barriers using confirmation and authorization key technology to trace non-tampered data source in real time when emergency accident happens. Meanwhile, our proposed system can use our designed accident risk model to predict risk level of any experiment project. It can also recommend appropriate safety education models.

Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from a large variety of published catalogues and databases. We describe the new features in organization of the database, integration of new catalogues and implementation of new possibilities available to users. The development of the BDB index-catalogue, Identification List of Binaries, is discussed. This star catalogue provides cross-referencing between most popular catalogues of binary stars. We describe ideas and methods for reliable cross-identification of different entities (systems, pairs, components) in binary and multiple stellar systems.

Molecular markers, including Simple Sequence Repeat (SSR), Single Nucleotide Polymorphism (SNP), and Intron Length Polymorphism (ILP), are widely utilized in crop improvement and population genetics studies. However, these marker resources remain insufficient for Musa species. In this study, we developed genome-wide SSR, SNP, and ILP markers from Musa and its sister species, creating a comprehensive molecular marker repository for the improvement of Musa species. This database contains 2115474 SSR, 63588 SNP, and 91547 ILP markers developed from thirteen Musa species and two of its relative species. We found that 77% of the SSR loci are suitable for marker development; 38% of SNP markers originated from the genic region, and transition mutations (C↔T; A↔G) were more frequent than transversion. The database is freely accessible and follows a ‘three-tier architecture,’ organizing marker information in MySQL tables. It has a user-friendly interface, written in JavaScript, PHP, and HTML code. Users can employ flexible search parameters, including marker location in the chromosome, transferability, polymorphism, and functional annotation, among others. These distinctive features distinguish the Musa Marker Database (MMdb) from existing marker databases by offering a novel approach that is tailored to the precise needs of the Musa research community. Despite being an in silico method, searching for markers based on various attributes holds promise for Musa research. These markers serve various purposes, including germplasm characterization, gene discovery, population structure analysis, and QTL mapping.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

The COST Action ‘PED-EU-NET’, JPI Urban Europe, IEA EBC Annex 83 and EERA JP Smart Cities can be considered as the main promoters and pioneer initiatives of the research on Positive Energy Districts (PEDs), that collaborated to develop a tool called 'PED Database'. The tool aims to map, filter, sort, and compare PED experiences, providing a balanced overview of the technological and non-technological solutions adopted in different PED projects and case studies around Europe and beyond. The paper aims to present the collaborative step-by-step process leading to the PED DB implementation, describing the adopted methodology, the current results and potentials of the online version platform and introducing its future possible developments towards a more user-friendly and stakeholders-tailored tool.

(1) Background: National health system databases represent one of the most obvious sources of information about epidemiology of adverse drug reactions including drug-induced allergy and anaphylaxis. (2) Methods: Retrospective analysis of data from national database of pharmacovigilance in the Russian Federation (analyzed period 02.04.2019 - 21.06.2023) was performed. The prevalence of anaphylactic reactions (ARs) was determined, the structure of drugs involved was estimated together with patients’ characteristics. (3) Results: ARs were reported in 8.3% of drug-induced allergic reactions (2304/27,727), mean age of patients was 48.2 ± 15.8 years, 53.2% were females. Main causative groups of drugs were antibiotics, ABs (44.62%), local anesthetics, (19.97%), and cyclooxygenase (COX)-inhibitors (10.07%). Fatal ARs was reported in 9.5% (218/2304), mean age 48.0 ± 16.7 years, 56.2% females. Pediatric population accounted for 5.8% (133/2304), mean age 11.8± 4.5 years, 51.9% females. Elderly population accounted for 2.8% (65/2304), mean age 73.0±5.3 years, 43.5% (27/65) females. ABs were the leading causative groups of ARs in the elderly (40%), children (42.86%), and among fatal cases (50%). (4) Conclusions: ARs accounted for 8.3% of all drug-induced allergic reactions, and ABs were the most common causative agents. Females predominated in all groups except elderly patients.

This research deals with the possibility for construction of the database on the braking friction coefficient for actual roads from the viewpoint of traffic safety especially for automated driving such as level 4 or higher. In an automated driving such levels, the controller needs to control the vehicle, but the road surface condition, especially the road friction coefficient on wet roads, snowy or icy roads, changes greatly, and in some cases, changes by almost one order. Therefore, it is necessary for the controller to constantly collect environment information such as the road friction coefficients and prepare for emergencies such as obstacle avoidance. However, at present, the measurement of the road friction coefficients is not systemically performed, and a method for accurately measuring has not been established. In order to improve this situation, this study examines a method for continuously measurement for the road friction characteristics such as μ-s characteristics.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic presented a new challenge in modern medicine. The development of vaccines was followed by massive population vaccinations. A few reports on post-vaccination allergic reactions have made patients and medical personnel uneasy as to anti-COVID-19 vaccines’ allergising potential. Most of the studies in this area to date have been small, and some that were based on global databases skipped most of the allergic diseases and concentrated only on anaphylaxis. We aimed to analyse the incidence of serious allergic reactions based on the EudraVigilance (EV) database, regardless of the reported symptoms and allergy mechanism. Methods: The total number of administrated vaccine doses was extracted on the 5th October 2023 from Vaccine Tracker and included all administrations since vaccinations began in the European Economic Area (EEA). Data on serious allergic reactions to COVID-19 vaccines was extracted from the EudraVigilance database with the same time point. The code names of 147 allergic symptoms or diseases were used. Results: The frequency of serious allergic reactions per 100,000 administered vaccine doses was 1.53 for Comirnaty, 2.16 for Spikevax, 88.6 for Vaxzevria, 2.11 for Janssen, 7.9 for Novavax, 13.3 for VidPrevtyn Beta and 3.1 for Valneva. The most prevalent reported reactions were oedema (0.46) and anaphylaxis (0.40). Only 6% of these reactions were delayed hypersensitivity-oriented. Conclusions: The overall frequency of potential serious allergic reactions to COVID-19 is very rare. Therefore, COVID-19 vaccines seem to be safe for human use. The lowest frequency of allergic reaction was observed for Comirnaty and the highest for Vaxzevria.

This study investigates the suitability of the timestamp approach algorithm for predicting blood pressure (BP) using wearable and comfortable devices, showcasing its accuracy even with low-frequency sampled signals. Developed within the SINTEC European project, the algorithm utilizes Electrocardiogram (ECG) and Photoplethysmogram (PPG) sensors, demonstrating promising results in both laboratory and hospital settings. The research emphasizes the algorithm’s potential to transform continuous and non-invasive BP monitoring through wearable technology, offering frequent measurements with minimal discomfort. While the study shows promise, further exploration is needed, focusing on optimizing machine learning coefficients for long-term monitoring applications and addressing potential changes over time. Technological improvements, including the implementation of edge computing algorithms, hold the key to enhancing BP accuracy, denoising techniques, and integrating alarm systems for future wearable solutions. This study emphasizes the adaptability of the algorithm for diverse healthcare environments, paving the way for broader applications in convenient and reliable BP monitoring.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

Background: To prioritize the development of antiviral compounds, it is necessary to compare their relative preclinical activity and clinical efficacy. Methods: We reviewed in vitro, animal model, and clinical studies of candidate anti-coronavirus compounds and placed extracted data in an online relational database. Results: As of July 2020, the Coronavirus Antiviral Research Database (CoV-RDB; covdb.stanford.edu) contained >2,400 cell culture, entry assay and biochemical experiments, 240 animal model studies, and 56 clinical studies from >300 published papers. SARS-CoV-2, SARS-CoV, and MERS-CoV account for approximately 85% of the data. Approximately 75% of experiments involved compounds with a known or likely mechanism of action, including receptor binding inhibitors and monoclonal antibodies (20%); viral protease inhibitors (18%); polymerase inhibitors (9%); interferons (8%); fusion inhibitors (8%); host endosomal trafficking inhibitors (7%); and host protease inhibitors (5%). For 724 compounds with a known or likely mechanism, 95 (13%) are licensed in the US for other indications, 72 (10%) are licensed outside the US or are in human trials, and 557 (77%) are pre-clinical investigational compounds. Conclusion: CoV-RDB facilitates comparisons between different candidate antiviral compounds, thereby helping scientists, clinical investigators, public health officials, and funding agencies prioritize the most promising compounds and repurposed drugs for further development.

Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks – low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations. As a result, macrocyclic peptides represent attractive drug candidates. Moreover, many drugs are macrocycles that originated from natural product scaffolds, suggesting that nature offers solutions to the challenges faced by AMPs. In this review, we explore natural cyclic peptides from the DBAASP database. DBAASP is a comprehensive repository of data on antimicrobial/cytotoxic activities and structures of peptides. We analyze the data on small (≤25 AA) ribosomal and non-ribosomal cyclic peptides from DBAASP according to their amino acid composition, bonds used for cyclization, targets they act on, and mechanisms of action. This analysis will enhance our understanding of the small cyclic peptides that nature has provided to defend living organisms.

At present, diversified and highly concurrent businesses in the Internet industry often require heterogeneous databases formed by multiple databases to meet the needs. This report introduces database kernel SG-ColBase we developed. After achieving read and write concurrency control, data rollback, atomic log writing, and downtime data redo to ensure complete transaction support. The parallelism of database kernel execution is extended through field level locks and snapshot reads. Use the Bloom filter, resource cache pool, memory pool, skip list, non blocking log cache, and asynchronous data writing mechanism to improve the overall execution efficiency of the system. In terms of data storage, column storage, logical key and LSM-tree are introduced. While improving the data compression ratio and reducing data gaps, all disk data operations are written in incremental order. With the characteristics of asynchronous batch operation, the data writing speed is greatly improved. Thanks to the continuous feature of vertical data brought by column storage, the disk scanning brought by vertical traversal is reduced, which is a qualitative leap in efficiency compared with traditional relational databases in the big data analysis scenario. SG-ColBase can reduce the use of heterogeneous databases in business and improve R&D efficiency.

The dark proteome as we define it, is the part of the proteome where 3D structure has not been observed either by homology modeling or by experimental characterization in the protein universe. From the 550.116 proteins available in Swiss-Prot (as of July 2016) 43.2% of the Eukarya universe and 49.2% of the Virus universe are part of the dark proteome. In Bacteria and Archaea, the percentage of the dark proteome presence is significantly less, with 12.6% and 13.3% respectively. In this work, we present the map of the dark proteome in Human and in other model organisms. The most significant result is that around 40%- 50% of the proteome of these organisms are still in the dark, where the higher percentages belong to higher eukaryotes (mouse and human organisms). Due to the amount of darkness present in the human organism being more than 50%, deeper studies were made, including the identification of ‘dark’ genes that are responsible for the production of the so-called dark proteins, as well as, the identification of the ‘dark’ organs where dark proteins are over represented, namely heart, cervical mucosa and natural killer cells. This is a step forward in the direction of the human dark proteome.

Opportunistic Routing is a promising paradigm that has been proposed for efficient and reliable transfer of data packets in mobile ad hoc networks. This routing strategy takes advantage of the broadcasting nature of the wireless medium to increase the number of probable forwarding devices and improves the reliability of data transfer in the network. Opportunistic Routing utilizes the reception of the same broadcasted packet at multiple devices in the network and selects one best forwarder dynamically from the set of multiple receivers. A number of opportunistic routing protocols have been proposed over these years for effective data delivery in ad hoc networks. But as the speed and mobility of devices increases in the network, performances of the existing opportunistic routing protocols degrade considerably, leading to reduced Quality of Service and poor transmission efficiency. The exceptional potential of opportunistic routing is thus underutilized. In this research we introduce Optimized Opportunistic Routing (OOR) strategy that guarantee excellent Quality of Service and high transmission efficiency to the latest applications using opportunistic routing for communication in highly dynamic ad hoc networks. Simulation results show that our method achieve significant performance improvements compared to all other existing opportunistic routing protocols in highly dynamic mobile ad hoc networks.

Eccentric punching shear can occur in concrete slab-column connections when the connection is subjected to shear and unbalanced moments. Typically, this situation results in edge and corner columns and is thus a common practical case. However, most punching experiments in the literature are concentric punching shear. This paper presents a developed database of eighty-eight experiments of flat slabs under eccentric punching shear, including a summary of the testing procedure of each reference and a description of the slab specimens. Additionally, a linear finite element analysis of all the specimens is included to determine the relevant sectional shear forces and moments. Finally, the ultimate shear stresses from the database experiments are compared to the shear capacities determined with ACI 318-19, Eurocode 2 NEN-EN 1992-1-1:2005, and the Model Code 2010. The comparison shows that the Model Code 2010 is the most precise in the predictions with an average tested over predicted ratio of 0.96 and a coefficient of variation of 27.96%. It can be concluded that this study represents the inconsistencies of the currently used design methods and the lack of experimental information.

Hospital readmission and length of stay prediction provide info to manage hospitals’ bed capacity and the number of required staff, especially during pandemics. We present a hybrid deep model called Genetic Algorithm-Optimized Convolutional Neural Network (GAOCNN) with a unique preprocessing method to predict hospital readmission and the length of stay in patients having various conditions. GAOCNN uses one-dimensional convolutional layers to predict hospital readmission and length of stay. The parameters of the layers are optimized using a genetic algorithm. To show the performance of the proposed model in patients with various conditions, we evaluate the model under three healthcare datasets; the Diabetes 130-US hospitals dataset, the COVID-19 dataset, and the MIMIC-III dataset. The diabetes 130-US hospitals dataset has information on both readmission and the length of stay, while COVID-19 and MIMIC-III datasets just include information on the length of stay. Experimental results show that the proposed model’s accuracy for hospital readmission is 97.2% for diabetic patients. Also, the accuracy of the length of stay prediction is 89%, 99.4%, and 94.1% for diabetic, COVID-19, and ICU patients, respectively. These results confirm the superiority of the proposed model compared to existing methods. Our findings offer a platform for managing healthcare funds and resources for patients with various diseases.

As operations in Cislunar space continue to expand, the sustainability of operations in orbital environments beyond GEO has become a critical concern. The Cislunar region is known to have a higher non-linear chaotic component in the dynamics compared to the low-Earth environment. This research is focused on producing simulations to model debris trajectories and dispersion from spacecraft mishaps across various identified key Cislunar orbits. By varying initial conditions, simulations of explosions at different locations of the orbits are carried out and the debris propagation is analyzed. This research emphasizes the creation of a robust debris propagation scheme and the development of a comprehensive database to address the computational costs associated with debris propagation. The resulting codes and databases documenting the historical evolution of debris are published as open-source resources, aiming to aid in solving the Cislunar space debris and traffic problem and in forecasting future needs and policies.

This review study explores and analyzes distributed database management systems (DDBMS), focusing on the important role of transparency methods in improving the functioning and interaction between users and systems. Transparency in DDBMS refers to several important factors, such as data, replication, performance, scaling, and network transparency. Each one of these components plays a role in concealing the intricate operations that underlie distributed systems from the end user. This not only speeds interactions but also enhances the reliability of the system. The article highlights the criticality of data independence as well as replication transparency in particular. Data independence enables adaptable modifications to the system's architecture and alterations to be made without causing any disruption to user activities. On the other hand, replication transparency ensures that the data remains consistently accurate across distributed nodes, even in the absence of user awareness. Moreover, the article investigates the progression of DDBMS through the incorporation of cutting-edge technologies such as quantum computation and artificial intelligence, which are anticipated to significantly augment the capabilities of data processing. The text proposes potential avenues for further investigation, emphasizing how these advancements may enhance the fundamental resilience of database management systems (DBMS) and expand their applicability in intricate, data-rich settings.

The Brassica family contains several economically important crops, including rapeseed (Brassica napus, 2n = 38, AACC), the second largest source of seed oil and protein meal worldwide. However, research in rapeseed is hampered because it is complicated and time-consuming for researchers to access different types of expression data. We therefore developed the Brassica Expression Database (BrassicaEDB, https://biodb.swu.edu.cn/brassica/) for the research community. We conducted RNA sequencing (RNA-Seq) of 103 tissues from rapeseed cultivar ZhongShuang11 (ZS11) at seven developmental stages (seed germination, seedling, bolting, initial flowering, full-bloom, podding, and maturation). We determined the expression patterns of 101,040 genes via FPKM analysis and displayed the results using the eFP browser. We also analyzed transcriptome data for rapeseed from 70 BioProjects in the SRA database and obtained three types of expression level data (FPKM, TPM, and read counts). We used this information to develop the BrassicaEDB, including eFP, Treatment, Coexpression, and SRA Project modules based on gene expression profiles and Gene Feature, qPCR Primer, and BLAST modules based on gene sequences. The BrassicaEDB provides comprehensive gene expression profile information and a user-friendly visualization interface for Brassica crop researchers. Using this database, researchers can quickly retrieve the expression level data for target genes in different tissues and in response to different treatments to elucidate gene functions and explore the biology of rapeseed at the transcriptome level.

Compound databases of natural products have a major impact on drug discovery projects and other areas of research. The number of databases in the public domain with compounds from natural origin is increasing. Several countries have initiatives in place to construct and maintain compound databases that are representative of their diversity. Examples are Brazil, France, Panama and recently Vietnam. Herein, we discuss the first version of BIOFACQUIM, a novel compound database with natural products isolated and characterized in Mexico. We discuss its construction, curation, and a complete chemoinformatic characterization of the content and coverage in chemical space. It is reported the profile of physicochemical properties, scaffold content, and diversity, as well as structural diversity based on molecular fingerprints. BIOFACQUIM is freely available.

Track fasteners play a pivotal role in infrastructure inspection for high-speed rail. Yet, images taken by drones often capture shadows cast by electrical towers flanking the high-speed rail tracks. These shadows can hinder the visibility of the track fasteners, thereby impacting detection efficiency and accuracy considerably. The present paper introduces an end-to-end shadow removal algorithm, rooted in generative adversarial network training. The comprehensive network framework is segmented into three sub-networks: pseudo-mask generation, shadow removal, and result refinement. We have integrated a Fourier convolutional residual module to bolster the feature extraction capability of the generator network. This integration ensures the network retains a global receptive field, even in its more superficial layers. By employing an overall weighted loss function, we enhance the quality of the images produced without shadows. Further, a perceptual loss function has been incorporated to retain the structural information of objects, setting the stage for subsequent defect detection. Our results highlight that Pse-ShadowNet adeptly eradicates fastener shadows while maintaining vital visual features, including object position, structure, texture, edges, and other key visual elements. Consequently, the reconstructed images are detailed and showcase superior image quality.

Qualitative and quantitative values estimation is a crucial aspect of social data science and plays a vital role in social and psychological research. Numerous methods of personal values measurement such as Schwartz Value Survey (SVS) and its re-modified versions have been proposed and invented, but many are challenged with one or more limitations. In this article, an alternative approach in a new paradigm of values measurement called Kabiru’s Value Survey (KVS) was proposed to measure the relative degree of importance or significance attached to particular value-actions of an individual(s) in a given population over a period. The methodological innovations in KVS unlike other existing methods of human value measurement include the use of habitually reported practices associated with a particular value-action in place of judging the series of Schwartz hypothetical statements, the use of multiple dependent variables rather than a single coded hypothetical statement. In terms of data analysis, a new approach of multivariate compression is introduced which merges the chain of multiple dependent variables into one coded scale. Psychometric result interpretation is also another innovative aspect of KVS methodology. Finally, the proposed methodology however solved some of the criticized limitations of the other methods of human value measurement.

Medicinal plants are generally defined as rare herbals with potent medicinal activities that can be used as an alternative treatment for diseases. Recent studies exploring novel medicine developments, originating from folk-medicinal practices challenges this notion and suggests that both the circumference of the term medicinal plant and their potential application covers a substantially extensive verse than previously suggested. While medicinal plants are not limited to the borders of any country, Bangladesh and its south-east Asian neighbors do boast a huge collection of potent medicinal plants with considerable folk-medicine history compared to most other countries of the world. MPDB 2.0 is the continuation of MPDB 1.0, it serves as both a data repertoire for medicinal of Bangladesh and a user-friendly interface for researchers, health practitioners, drug developers, and students who wish to study the various medicinal & nutritive plants scattered around Bangladesh and the underlying phytochemicals contributing to their efficacy in folk medicine. While in developing MPDB 2.0 human diseases have been highly focused upon, the information in this database is not limited in its application for human diseases or diseases only, as many of the plants indexed here can serve in developing biofuel or bioremediation technologies or nutritive diets or cosmetics, etc. MPDB 2.0 comprises a collection of more than five hundred medicinal plants from Bangladesh along with a record of their corresponding scientific, family, and local names together with their utilized parts, information regarding ailments, active compounds, and PubMed ID of related publications.

In the era of large data, the cloud is increasingly used as a computing environment, necessitating the development of cloud-compatible pipelines that can provide uniform analysis across disparate biological datasets. The WDL Analysis Research Pipelines (WARP) repository is a GitHub repository of open-source, cloud-optimized WDL workflows for biological data processing that are semantically versioned, tested, and documented. A companion repository, WARP-Tools, hosts Docker containers and custom tools used in WARP workflows.

A highly efficient reduction process of Cr (VI) with biochar was conducted in this paper. The results showed that nearly 100% Cr (VI) was reduced at selected reaction conditions: the dosage of biochar at m (C)/m(Cr) = 3.0, reaction temperature of 90 ℃, reaction time at 60 min and concentration of H2SO4 of 20 g/L, respectively. The reduction kinetics analysis demonstrated that the reduction of Cr (VI) fitted well with the pseudo-first-order model and the apparent activation energy was calculated to 40.24 kJ/mol. Response surface methodology confirmed that all the experimental parameters had positive effect on the reduction of Cr (VI). The influence of each parameter on the reduction process followed the order: dosage of biochar> Concentration of H2SO4 > Reaction Temperature > Reaction Time. This paper provided a versatile strategy for treatment of wastewater containing Cr (VI) and showed a bright tomorrow for wastewater treatment.

Abstract: Insecure and portable devices in the smart city’s Internet of Things (IoT) network are increasing at an incredible rate. Various distributed and centralized platforms against cyber-attacks have been implemented in recent years, but these platforms are inefficient due to their constrained levels of storage, high energy consumption, the central point of failure, underutilized resources, high latency, and etc. In addition, the current architecture confronts the problems of scalability, flexibility, complexity, monitoring, managing & collecting of IoT data, and defend against cyber-threats. To address these issues, the author presents distributed and decentralized Blockchain-Software Defined Networking (SDN) based energy-optimized architecture for IoT in smart cities. Thus, SDN continuous observing, controlling, managing IoT devices activities and detect possible attacks in the network; Blockchain provides adequate security & privacy against cyber-attacks, reduces the central point of failure issues; Network Function Virtualization (NFV) are used to saving energy, load balancing, as well as increasing the lifetime of the entire network. Also, we introduce a Cluster Head Selection (CHS) algorithm to reduce the energy consumption in the presented model. Finally, we analyze the performance using various parameters (e.g. throughput, response time, gas consumption, communication overhead) and demonstrating the result that provides higher throughput, lower response time, lower gas consumption than existing works for smart cities.

We demonstrate how a rigorous mathematical approach can be used to extract a current CMB temperature of 2.7276 ±0.0723 K from the 580 type Ia supernova data points in the Union2 database. Using redshift formula: z = Sqrt(Rh/R_t) − 1, incorporated into our new CMB redshift prediction redshift formula, we find that the above current CMB temperature value is a remarkably good match for this particular data set. In addition, we can alternatively start out with the measured current CMB temperature and extract the Hubble constant from all 580 supernovae. We then get an estimated Hubble constant of H0 = 66.8711+0.0019 km/s/Mpc when using the CMB temperature from the recent Dhal et al study [1] in combination with redshifts from all 580 supernovae. Furthermore, we get an estimated Hubble constant value of H_0 = 66.8943+0.0287 km/s/Mpc when using the CMB temperature measured by Fixsen [2]. In summary, our simple model essentially perfectly matches all 580 type Ia supernova redshifts while extracting H_0 or T_0 values. The Hubble constant we extract by this method fits very well with the Hubble constant extracted from the CMB by other studies. We believe that our new approach likely solves the Hubble tension problem, at least inside R_h = ct cosmology.

Our main goal is here to make a comparative analysis between the well-known MOND theory and a more recent model called κ-model. An additional connection, between the κ-model and two other novel MOND-type theories: Newtonian Fractional-Dimension Gravity (NFDG) and Refracted Gravity (RG), is likewise presented. All these models are built to overtake the DM paradigm, or at least to strongly reduce the dark matter content. Whereas they rely on different formalisms, however, all four seem to suggest that the universal parameter, a0, appearing in MOND theory could intrinsically be correlated to either the sole baryonic mean mass density (RG and κ-model) and/or to the dimension of the object under consideration (NFDG and κ-model). We could then confer to the parameter a0 a more flexible status of multiscale parameter, as required to explain the dynamics together in galaxies and in galaxy clusters. Eventually, the conformal gravity theory (CFT) also seems to have some remote link with the κ-model, even though the first one is an extension of general relativity, and the second one is Newtonian in essence. The κ-model has been tested on a small sample of spiral galaxies and in galaxy clusters. Now we test this model on a large sample of galaxies issued from the SPARC database.

We tested the possibility of using “adverse events count” (AEC) as a drug-risk indicator and side-effect severity indicator. Data from 3938 adverse event (AE) reports for COVID-19 vaccines (Comirnaty, Moderna, Vaxzevria, and Janssen) and 6869 AE reports for other medicines were collected from the Bulgarian Drug Agency database (01.01.2018–31.03.2022). AEC was modeled with zero-adjusted negative binomial (ZANBI) and zero-adjusted Poisson inverse Gaussian (ZAPIG) regression models, which account for zero absence and overdispersion. The models’ fit was checked with residual diagnostic plots and parametric correspondence to normality. Explanatory variables were: age, sex, sequence number (order of submission), severity of AE, and vaccine type. Average AEC was higher in severe vs. non-severe AE, and in females vs. males; it decreased with age and was lower in Comirnaty than other vaccines. Variability of AEC for COVID-19 data decreased with sequence number in severe AE and increased in non-severe AE. Full ZANBI models had greater sensitivity than parametric ZANBI or ZAPIG models. Results showed correlation between AEC and AE severity, suggesting AEC as a simple and reliable measure of drug-risk and side-effect severity for clinicians and regulators, especially for COVID-19 vaccines.

The COVID-19 pandemic has posed an unprecedented challenge to healthcare and the available solutions are unsatisfactory. Classical homeopathy may have a role to play in alleviating this burden. Covid cases treated with homeopathy was curated with the intention to provide basic information for further studies. The results are promising although far from being definitive. 367 patients considered were for statistical analysis, the mean age of the participants was 42.75 years, and males and females were 166 and 201 respectively. The mean follow-up period was 6.5 (SD 5.3) days, with a median of 1 homeopathic remedy used per case. 192 patients were diagnosed by RT–PCR, 111 by the WHO clinical criteria and 64 via retrospective antibodies. According to the WHO criteria, 255 were confirmed cases, 61 were probable cases, and 51 were suspected cases. It was seen that 73.8% of covid patients improved under homeopathic treatment, even those among severe disease 78.6%. Correlational analyses showed that presence of fever was associated with more likelihood of improvement and increasing age and a greater number of homeopathic remedies required in a case were associated negatively with improvement. However, it was seen that severe cases were more likely to improve under homeopathic treatment.

Nuclear pollution is an urgent environmental issue as a consequence of rapid industrialization and nuclear accidents in the past. Remediation of nuclear polluted sites using microbial vital activity (bioremediation) is a promising approach to recover contaminated areas in an environmentally friendly and cost-saving way. At the same time, the number of known bacterial and archaeal species able to withstand extremely high doses of ionizing radiation is steadily growing every year, together with growing knowledge about mechanisms of radioresistance. This opens up new opportunities for developing new biotechnological solutions. However, these data are often not systemized and it can be difficult to access. Here, we present the Determinants of Radioresistance Database, or DetR DB (http://extremebiolab.com/detr-db/), gathering a comprehensive catalog of radioresistant microbes and their molecular and genetic determinants of enhanced ionizing radiation tolerance. The database provides search tools including taxonomy, common gene name, and BLAST. DetR DB will be a useful tool for research community by facilitating the extraction of the necessary information to help further analysis of radiation-resistant mechanisms.

Around the World, the number of compound databases of natural products in the public domain is rising. This is in line with the increasing synergistic combination of natural product research and chemoinformatics. Towards this global endeavor, countries in Latin America are assembling, curating, and analyzing the contents and diversity of natural products available in their geographical regions. In this manuscript we collect and analyze the efforts that countries in Latin America have made so far to build natural product databases. We further encourage the scientific community in particular in Latin America, to continue their efforts to building quality natural product databases and, whenever possible, to make them publicly accessible. It is proposed that all compound collections could be assembled into a unified resource called LANaPD: Latin America Natural Products Database. Opportunities and challenges to build, distribute, and maintain LANaPD are also discussed

The human proteome is composed from diverse and heterogeneous gene products/proteoforms. Previously, we have been discussing the main technical aspects in developing for inventory of human proteoforms that would be visually attractive, clear, and easy to search (Naryzhny S. J. Proteomics 2018, S1874-3919(18) 30220-3). Here, we present our first draft of the database of proteoforms that is based on this discussion. The database principles and structure are described. The database is called “2DE-pattern” as it contains multiple isoform-centric patterns of proteoforms separated according to 2DE principles.

In wireless sensor networks (WSNs) deployed in smart agriculture systems, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a novel method for localizing unknown sensor nodes in WSN-based smart agriculture systems using range measurements. The proposed method utilizes the Levenberg-Marquardt optimization algorithm to solve a nonlinear least squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the approach, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes, effectively enhancing the overall performance of the WSN-based smart agriculture system. Furthermore, the method demonstrates robustness against measurement noise and scalability for large-scale networks. The practical implications of this research extend beyond smart agriculture systems, as the proposed method can be applied to various WSN applications where precise node localization is essential.

The escalating reliance on revolutionary online web services has introduced heightened security risks, with persistent challenges posed by phishing despite extensive security measures. Traditional phishing systems, reliant on machine learning and manual features, struggle with evolving tactics. Recent advances in deep learning offer promising avenues for tackling novel phishing challenges and malicious URLs. This paper introduces a two-phase stack generalized model named AntiPhishStack, designed to detect phishing sites. The model leverages the learning of URLs and character-level TF-IDF features symmetrically, enhancing its ability to combat emerging phishing threats. In Phase I, features are trained on a base machine learning classifier, employing K-fold cross-validation for robust mean prediction. Phase II employs a two-layered stacked-based LSTM network with five adaptive optimizers for dynamic compilation, ensuring premier prediction on these features. Additionally, the symmetrical predictions from both phases are optimized and integrated to train a meta-XGBoost classifier, contributing to a final robust prediction. This work's significance lies in advancing phishing detection with AntiPhishStack, operating without prior phishing-specific feature knowledge. Experimental validation on two benchmark datasets, comprising benign and phishing or malicious URLs, demonstrates the model's exceptional performance, achieving a notable 96.04% accuracy. This research adds value to the ongoing discourse on symmetry and asymmetry in information security and provides a forward-thinking solution for enhancing network security in the face of evolving cyber threats.

According to the development needs of marine seismic station networking, it is necessary to develop a marine seismometer with a networking function to realize quasi-real-time and large-scale long-term observation of undersea seismic signals. This can lay a solid foundation for marine seismic research and research on earth tectonics, activities, and processes. Based on the advantages and disadvantages of the existing observation equipment, this study proposed to develop a new type of the Autonomous Glide Marine Seismometer(AGMS) that can be applied to the global sea area, focusing on the design and study of the main structure of the carrier of the AGMS. The ultimate strength of the ellipsoidal pressure-resistant spherical shell is investigated by using the finite element analysis method. A reasonable finite element analysis model is proposed through comparing the finite element parameter results with the theoretical formulas and relevant specifications. Finally, the design process and strength checking of the pressure-resistant shell of the AGMS are established. These relevant results can also provide a reference for the design of other deep-sea pressure-resistant chambers.

Regulatory T cells (Tregs) are major drivers behind immunosuppressive mechanisms and present a major hurdle for cancer therapy. Tregs are characterized by high expression of CD25, which is a potentially valuable target for Treg depletion to alleviate immune suppression. The preclinical anti-CD25 (αCD25) antibody, clone PC-61, has met with modest anti-tumor activity, due to its capacity to clear Tregs from circulation and lymph nodes but not those that reside in the tumor. Optimization of the Fc domain of this antibody clone has been shown to enhance intratumoral Treg depletion capacity. Here, we generated a stable cell line that produces optimized recombinant Treg depleting antibodies. A genome engineering strategy in which CRISPR-Cas9 was combined with homology directed repair (CRISPR-HDR) was utilized to optimize the Fc domain of the hybridoma PC-61 for effector functions by switching it from the original rat IgG1 to a mouse IgG2a isotype. In a syngeneic tumor mouse model the resulting αCD25-m2a antibody mediated effective depletion of tumor resident Tregs leading to a high effector T cell (Teff) to Treg ratio. Moreover, combination of the αCD25-m2a with αPD-L1 treatment augmented tumor eradication in mice, demonstrating the potential for αCD25 as a cancer immunotherapy.

The aim of this articles is to investigate the entropy optimization in unsteady MHD flow Darcy-Forchheimer nanofluids towards a stretchable sheet. The surface we tend to think about is porous and stretchy under acceleration. Flow occurs due to the stretching of the surface. Four distinct types of aqueous nanostructures are taken in this examination where copper oxide ( ), copper ( ), titanium dioxide ( ) and aluminum oxide ( ) are the nanoparticles. Irreversibility analysis are discussed through second law of thermodynamics. The expression of energy is mathematically designed and discussed according to heat generation / absorption, dissipation, thermal radiation, and joule heating. The nonlinear PDE (partial differential conditions) is first changed to ODE (normal differential conditions) through appropriate similarity variables. Here we used the numerically embedded solution technique to develop a numerical result for the obtained nonlinear flow expression. Influence of various flow parameter velocity temperature distribution and entropy generation are discussed. Reduction occurs in velocity profile for larger porosity and magnetic parameters. An enhancement in entropy generation and temperature distribution is seen for Brinkman number. An opposite effect is noticed in velocity and temperature through solid volume friction.

The combination of wet-lab experimental data on multi-site combinatorial mutations and machine learning is an innovative method in protein engineering. In this study, we present an improved innovative sequence–activity relationship (innov'SAR) methodology based on novel descriptors and digital signal processing (DSP) to construct a predictive model. In this improved approach, 21 experimental (R)-selective amine transaminases from Aspergillus terreus (AT-ATA) were used as an input to predict higher thermostability than that predicted using the existing data. We successfully improved the determination coefficient (R2) of the model from 0.66 to 0.92. In addition, root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were estimated and conformation analysis based on molecular dynamics simulations was performed to verify the enhanced thermal stability of the screened mutants. The improved innov'SAR algorithm enhanced the predictive accuracy, suggesting a method for modifying the stability of AT-ATA, which may help in directed evolutionary screening and open up new avenues for protein engineering.

Background: All Payer Claims Databases (APCD) are a rich source of health information, however, race and ethnicity (R&E) data are largely missing. Bayesian Improved Surname Geocoding (BISG) is a common R&E imputation method, yet, validation of BISG in APCDs is lacking. We used the BISG to impute missing R&E in the Oregon APCD. Methods: BISG imputed R&E for Asian Pacific Islanders (API), Blacks, Hispanics and Whites were contrasted to the gold standard (vital statistics) and sensitivity and specificity improvements were assessed. Logistic regression examined whether missing R&E was random across patient characteristics. Results: Among 85,857 individuals in the study, 32.1% (n=27,594) had missing R&E. Missing R&E was not randomly distributed. There were higher odds of missingness among males, Whites, those age 65 and older, and commercially insured individuals. Differences in the percent missing were also found by co-morbid conditions and mortality causes. Imputing the missing R&E with BISG method improved the sensitivity to identify White, Black, API, and Hispanics. Conclusions: APCDs can benefit from enhancing missing R&E with BISG imputation to perform more robust population-health level analyses and identify inequities according to R&E without losing power or dropping non-random records with missing R&E data.

The AtomDB project provides models of X-ray and EUV emitting astrophysical spectra for optically thin, hot plasma. We present the new software package, PyAtomDB, which now underpins the entire project, providing access to the underlying database, collisional radiative model calculations, and spectrum generation for a range of models. PyAtomDB is easily extensible allowing users to build new tools and models for use in analysis packages such as XSPEC. We present two of these, the kappa and ACX models for non-Maxwellian and Charge-Exchange plasmas respectively. In addition, PyAtomDB allows full open access to the apec code which underlies all of the AtomDB spectra and has enabled development of a module for estimating the sensitivity of emission lines and diagnostic line ratios to uncertainties in the underlying atomic data. We present these publicly available tools and results for several X-ray diagnostics of Fe L-shell ions and He-like ions as examples.

The emission of [O I] lines in the coma of Comet 67P/Churyumov-Gerasimenko during the Rosetta mission have been explained by electron impact dissociation of water rather than the process of photodissociation. This is the direct evidence for the role of electron induced processing has been seen on such a body. Analysis of other emission features is handicapped by a lack of detailed knowledge of electron impact cross sections which highlights the need for a broad range of electron scattering data from the molecular systems detected on the comet. In this paper we present an overview of the needs for electron scattering data relevant for the understanding of observations in coma, the tenuous atmosphere and on the surface of 67P/Churyumov-Gerasimenko during the Rosetta mission. The relevant observations for elucidating the role of electrons come from optical spectra, particle analysis using the ion and electron sensors and mass spectrometry measurements. To model these processes electron impact data should be collated and reviewed in an electron scattering database and an example is given in the BEAMD, which is a part of a larger consortium of Virtual Atomic and Molecular Data Centre – VAMDC.

Lavander distillation leads to huge quantities of solid waste produced yearly. This waste is usually discarded resulting in serious environmental issues. However, it still contains residual essential oil and other bioactive compounds. This research reports on the development and comparison of optimized solid-liquid separation methods, i.e. microwave (MAE) and ultrasound assisted extraction (UAE) of phenolic compounds from lavender distillation waste. Optimal pretreatment conditions, such as waste moisture content and particle size were also determined. The extracts were spray or freeze-dried and the resulting powders were characterized for their physicochemical properties. The majority of the original phenolic compounds in lavender were found in the leachate fraction after distillation (61%), while 43% in the solid waste. Drying of the solid waste before extraction affected the process efficiency. UAE led to higher phenolic content and antioxidant properties compared to MAE. Drying (spray or freeze) the extracts did not significantly affect their phenolic content, while the use of maltodextrin as a drying agent improved the drying process yield especially in the freeze-drying method. It is concluded that valorization of lavender distillation wastes can be achieved via an integrated process consisting of a green extraction method and a consequent drying process that result in a stable bioactive powder.

In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Lightning discharges in electric power networks generate voltage and current surges that are propagated through the electrical network causing damage and shutdowns in the electrical system. To protect the system against these phenomena, surge arresters are very effective and widely used by electrical utilities in their electric grids. This paper presents a methodology for optimized surge arrester allocation based on genetic algorithm (GA), creating a simulation environment in the software ATP (Alternative Transients Program) to implement the proposed methodology. The optimized allocation procedure is based on a fitness function that minimizes the cost of surge arresters and maximizes the number of protected equipment. To carry out this optimized arrester allocation procedure using ATP may demand too much processing time when running large distribution grids. To overcome this difficulty a procedure is proposed to obtain an overvoltage severity description of the grid and select the most critical electric nodes for the incidence of lightning discharges, in the GA allocation procedure. The case study is applied to the IEEE 123-bus electrical feeder to demonstrate the effectiveness of the proposed methodology.

Multi-criteria decision analysis (MCDA), one of the prevalent branches of operations research, aims to design mathematical and computational tools for selecting the best alternative among several choices with respect to specific criteria. In the cloud, MCDA based online brokers uses customer specified criteria to rank different service providers. However, subjected to limited domain knowledge, the customer may exclude relevant or include irrelevant criterion, which could result in suboptimal ranking of service providers. To deal with such misspecification, this research proposes a model, which uses notion of factor analysis from the domain of unsupervised machine learning. The model is evaluated using two quality-of-service (QoS) based datasets. The first dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The second dataset i.e., feedback from servers, was generated from cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The simulation runs in a stable cloud environment i.e. when uncertainty is low, shows that online broker (equipped with the proposed model) produces optimized ranking of service providers as compared to other brokers. This is due the fact that proposed model assigns priorities to criteria objectively (using machine learning) rather than using priorities based on subjective judgments of the customer. This research will benefit potential cloud customers that view insufficient domain knowledge as a limiting factor for acquisition of web services in the cloud.

VITEK MS PRIME (bioMerieux), a newly developed Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) system, alongside VITEK PICKME Pen (PICKME), offers easy sample preparation for bacteria and yeast. VITEK MS PRIME also offers two software platforms for filamentous fungi: the IVD database and the RUO database. Our study evaluated its identification agreement on 320 clinical isolates of bacteria and yeasts, comparing PICKME and traditional wooden toothpick sampling techniques against ASTA Mi-croIDSys Elite results. Additionally, we assessed the IVD (v3.2) and SARAMIS (v4.16) RUO data-bases on 289 filamentous fungi against molecular sequencing. The concordant rates for spe-cies-level identification of bacteria and yeasts were about 89.4% (286/320) between the PICKME and wooden toothpick, about 83.4-85.3% between VITEK MS PRIME and ASTA MicroIDSys Elite. Retesting with PICKME improved concordance to 91.9%. For filamentous fungi, species-level identification reached 71.3% with the IVD database and 85.8% with RUO, which significantly enhanced basidiomycetes' identification from 35.3% to 100%. Some strains in the IVD database, like Aspergillus vesicolor, Exophiala xenobiotica, and Nannizzia gypsea failed to be identified. VITEK MS PRIME with PICKME offers reliable and efficient microorganism identification. For fila-mentous fungi, combining the use of the RUO database can be beneficial, especially for basidi-omycetes.

Lily database online genomic resource composed of Korean Lily Germplasm Collection, Transcriptome Sequences, molecular markers, transcription factors (TF) and DEGs data. A total of ~0.23 gb of RNA-sequences data were mined for gene identification, marker development and gene expression analysis. As a result, 47,863 SSR, 20,929 SNP and 1213 COS-marker were developed. A total of 1327 TF genes were identified and characterized. This is the first unique, user-friendly, genomic resource database for Lilium species. It is a relational database based on a ‘three-tier architecture’ that catalogs all the information in MySQL table and a user-friendly query interface and data visualization page developed using JavaScript, PHP and HTML code. The search parameters are highly flexible, user can be retrieved data by using either single or multiple search parameters. Data present in this database can be used for germplasm characterization, gene discovery, population structure analysis, QTL mapping and accelerating the lily variety improvements. This database can be accessed through this link: http://www.genomicsres.org/lilidb/.

Clustering algorithms for multi-database mining (MDM) rely on computing $(n^2-n)/2$ pairwise similarities between $n$ multiple databases to generate and evaluate $m\in[1, (n^2-n)/2]$ candidate clusterings in order to select the ideal partitioning which optimizes a predefined goodness measure. However, when these pairwise similarities are distributed around the mean value, the clustering algorithm becomes indecisive when choosing what database pairs are considered eligible to be grouped together. Consequently, a trivial result is produced by putting all the $n$ databases in one cluster or by returning $n$ singleton clusters. To tackle the latter problem, we propose a learning algorithm to reduce the fuzziness in the similarity matrix by minimizing a weighted binary entropy loss function via gradient descent and back-propagation. As a result, the learned model will improve the certainty of the clustering algorithm by correctly identifying the optimal database clusters. Additionally, in contrast to gradient-based clustering algorithms which are sensitive to the choice of the learning rate and require more iterations to converge, we propose a learning-rate-free algorithm to assess the candidate clusterings generated on the fly in a fewer upper-bounded iterations. Through a series of experiments on multiple database samples, we show that our algorithm outperforms the existing clustering algorithms for MDM.

This paper reassesses the detrimental effect on fatigue performance due to thicker sections based on extensive fatigue strength test database, taken from research program worldwide over the past half of a century in offshore oil &amp; gas and renewable industry. The data entries in the database have been evaluated to ensure its data integrity. Statistical analyses on these S-N data are performed with or without the thickness correction at different exposure level to corrosive environment, in order to re-evaluate the suitability of current standards in regard to the thickness effect. The study has concentrated on T-joint, transverse butt welded joint and tubular joint as these are the most commonly used joint types in the offshore wind industry. The analysis indicates general agreement of fatigue strength with the thickness effects in current standard for in air conditions but great conservatism for corrosive environment.

Introduction: A large number of COVID-19 publications has created a need to collect all research-related material in practical and reliable centralized databases. The aim of this study was to evaluate the functionality and quality of the compiled World Health Organisation COVID-19 database and compare it to Pubmed and Scopus. Methods: Article metadata for COVID-19 articles and articles on 8 specific topics related to COVID-19 was exported from the WHO global research database, Scopus and Pubmed. The analysis was conducted in R to investigate the number and overlapping of the articles between the databases and the missingness of values in the metadata. Results: The WHO database contains the largest number of COVID-19 related articles overall but retrieved the same number of articles on 8 specific topics as Scopus and Pubmed. Despite having the smallest number of exclusive articles overall, the highest number of exclusive articles on specific COVID-19 related topics was retrieved from the Scopus database. Further investigation revealed that PubMed and Scopus have more comprehensive structure than the WHO database, and less missing values in the categories searched by the information retrieval systems. Discussion: This study suggests that the WHO COVID-19 database, even though it is compiled from multiple databases, has a very simple and limited structure, and significant problems with data quality. As a consequence, relying on this database as a source of articles for systematic reviews or bibliometric analyses is undesirable.

Africa spans the hemispheres from temperate region to temperate region, has a long history of hominin evolution and yet has a relatively poorly understood Quaternary climate history. Speleothems, as archives of terrestrial hydroclimate variability, can help reveal this history, and here we review the progress made to date, with a focus on the first version of the Speleothem Isotopes Synthesis & Analysis (SISAL) database. The geology of Africa has limited development of large karst regions to four areas - the northern and eastern coasts bordering the Mediterranean and the Indian Ocean, west Africa and southern Africa. Exploitation of the speleothem palaeoclimate archives in these regions is uneven, with long histories of research in South Africa and Morocco but no investigations elsewhere e.g. West Africa. Consequently, the evidence of past climate change reviewed here is irregularly sampled in both time and space. Nevertheless, we show evidence of migration of the monsoon belt, with enhanced rainfall during interglacials observed in northeast Africa, southern Arabia and the northern part of southern Africa. Evidence from East Africa also indicates significant centennial scale rainfall variability. In northwestern and southern Africa precession and eccentricity influence speleothem growth, largely through changing synoptic storm activity.

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modeling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using the Neo4j graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

Accurately predicting drug-drug interactions (DDIs) is crucial for preventing adverse drug events in clinical settings. However, existing methodologies often rely on complex models built from diverse data sources, posing challenges in computational drug discovery. To address the need for precise computational methods in predicting unknown DDIs, this study introduces a novel Deep Neural Network (DNN)-based approach. By leveraging a wide range of drug-related information, including substructure, targets, side effects, pathways, and indications, our method calculates multiple drug similarities. These similarities are then synthesized using a nonlinear fusion method to extract high-level features. Subsequently, a tailored neural network is deployed for interaction prediction. Comparative evaluation against three prominent machine learning classifiers—Extreme Gradient Boosting (XGBoost), Adaptive Gradient Boosting (AdaBoost), and Light Gradient-Boosting Machine (LGBM)—using three benchmark datasets demonstrates the superior performance of DNN. It achieves outstanding accuracy, precision, recall, and F1-score metrics, all reaching 94.6% in cross-validation. Additionally, case studies involving numerous drug pairs confirm the reliability of DNN in accurately predicting unknown DDIs. These findings underscore DNN as a potent and reliable method for DDI prediction, with promising implications for drug discovery and healthcare applications.

This study demonstrates the efficacy of structured pruning and architectural fine-tuning on the YOLOv7-tiny model for eye state detection, emphasizing optimization for real-time applications. Structured pruning significantly reduced the model's complexity and storage size while maintaining high detection accuracy, as evidenced by stable precision, recall, and [email protected] metrics across iterations. Further fine-tuning adjusted the model's width and depth, optimizing efficiency and processing speed without compromising performance. These optimizations yielded YOLOv7-tiny variants that are both computationally efficient and accurate, suitable for resource-constrained environments. The findings highlight the critical role of model optimization in deploying effective neural networks for specific detection tasks in real-time scenarios.

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system's response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing impressive performance. These important results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.

In order to assess the degradation state of rolling bearings and accurately grasp the remaining life information of rolling bearings, a bearing remaining life prediction method based on the grey wolf optimization algorithm to improve the BP neural network model is proposed. The method consists of two steps, firstly, using the craggy value and root mean square value to determine the first failure time of rolling bearings so as to approximate the input features, and secondly, using the Grey Wolf optimization algorithm to optimize the BP neural network to construct the degradation model of the bearings through machine learning. The method reduces the prediction error compared with conventional techniques, indicating that the method can effectively simulate the bearing degradation process and predict the remaining useful life (RUL) of the bearing.

The paper addresses the ongoing and continuous interest in photovoltaic energy systems (PESs). In this context, the study focuses on an isolated photovoltaic system with hybrid bat-tery-supercapacitor storage (HBSS). The integration of supercapacitors (SCs) in this system is of particular importance because of their high specific power density. In photovoltaic (PV) systems, multi-storage systems use two or more energy storage technologies to enhance system perfor-mance and flexibility. When batteries and supercapacitors are combined in a PV system, their benefits are maximized and offer a more reliable, efficient, and cost-effective energy storage op-tion. In addition, effective multi-storage power management in a PV system needs a solid grasp of the energy storage technologies, load power demand profiles, and the whole system architecture. In this work, battery-supercapacitor storage system (HBSS) is established by combining batteries and supercapacitors. The primary objective is to devise a novel management algorithm that ef-fectively controls the different power sources. The algorithm's purpose is to regulate the charge and discharge cycles of the HBSS, ensuring that the state of charge (SOC) of both batteries and supercapacitors remains within the desired range. The proposed management algorithm is de-signed to be simple, efficient, and light on computational resources. It efficiently handles the energy flow within the HBSS, optimizing the usage of both batteries and supercapacitors based on real-time conditions and energy demands. By maintaining the SOC of these energy storage components within the specified limits, the proposed method ensures their longevity and max-imizes their performance. Simulation results obtained from applying the management strategy are found to be satisfactory. These results show that the proposed algorithm maintains the SOC of batteries and supercapacitors within the desired range, leading to improved energy management and enhanced system efficiency.

The growing integration of renewable energy sources (RES) into the power system causes congestion to occur more frequently. In order to reduce congestion in the short term and to make the utilization of the power system more efficient in the long term, power flow control (PFC) in the transmission system has been proposed. However, exemplary studies show that congestion will increase also in the distribution system if the transmission system is expanded. For this reason, the potential of PFC to reduce congestion in a model of a real 110 kV distribution system is investigated. Several Unified Power Flow Controller (UPFC) devices are optimized in terms of their number and placement in the power system, their size, control parameters, and costs, by using a Parallel Tempering approach as well as a greedy algorithm. Two optimization variants are considered, one reducing the number of degrees of freedom by integrating system knowledge while the other is not. We find that near a critical grid state and disregarding costs, PFC can reduce congestion significantly (99.13 %). When costs of the UPFCs are taken into account, PFC can reduce congestion by 73.2 %. A basic economic analysis of the costs reveals that the usage of UPFCs is profitable. Furthermore, it is found that the reduction of the solution space of the optimization problem leads to better results faster and that, contrary to expectations, the optimization problem is simple to solve. The developed methods allow not only for the determination of the optimal use of UPFCs to minimize congestion, but also to estimate their profitability.

Platelet polyphosphate (polyP) can be conveniently quantified by a recent methodological breakthrough using 4′,6-diamidino-2-phenylindole (DAPI). However, the preservation of these biological samples has not yet been standardized. In a preliminary study, possible protocols were screened, and passed protocols were further tested in this study. Pure-platelet-rich plasma (P-PRP) samples and washed platelet suspensions were prepared using blood obtained from non-smoking, healthy male donors and fixed with ThromboFix for 24 h at 4 °C. Mass polyP levels were determined using a fluorometer at wavelengths of 425 and 525 nm. Platelet polyP levels were normalized to platelet counts. Statistical analyses were performed using non-parametric tests. In platelet suspension maintained in fixed conditions at 4 °C (control), platelet polyP levels significantly decreased by 20% at and after 7 days. In contrast, platelet polyP levels in both P-PRP and washed platelet suspensions were maintained without significant reduction for up to 6 weeks by removing ThromboFix after fixation and subsequent freezing in pure water at −80 °C. Owing to the low specificity of DAPI binding and the wavelengths used, fluorometric polyP quantification is often interfered with. Our validated protocols will enable long-term preservation and high-throughput polyP quantification and could be applied to relatively large cohort studies.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

Ustilago maydis, member of the Ustilaginaceae family, is a promising host for the production of several metabolites including itaconic acid. This dicarboxylate has great potential as a bio-based building block in the polymer industry, and is of special interest for pharmaceutical applications. Several itaconate overproducing Ustilago strains have been generated by metabolic and morphology engineering. This yielded stabilized unicellular morphology through fuz7 deletion, reduction of by-product formation through deletion of genes responsible for itaconate oxidation and (glyco)lipid production, and the overexpression of the regulator of the itaconate cluster ria1 and the mitochondrial tricarboxylate transporter encoded by mttA from Aspergillus terreus. In this study, itaconate production was further optimized by consolidating these different optimizations into one strain. The combined modifications resulted in itaconic acid production at theoretical maximal yield, which was achieved under biotechnologically relevant fed-batch fermentations with continuous feed.

Accurate and fast traffic flow forecasting is vital in intelligent transportation system because many of the advanced features in intelligent transportation systems are based on it. However, existing methods have poor performance regarding accuracy and computational efficiency in long-term traffic flow forecasting under big data. Hence, we propose an improved Long short-term memory (LSTM) Network and its cluster computing implementation in this paper to address the above challenge. We propose a singular point probability LSTM (SDLSTM) algorithm. The method discards the units of the network according to the singular point probability during the training process and amends the SDLSTM by Autoregressive Integrated Moving Average Model (ARIMA) to achieve the accurate prediction of 24-hour traffic flow data. Furthermore, the paper designs a scheme for implementing this method through cluster computing to shorten the calculation time and improve the system's operating speed. Theoretical analysis and experimental results show that SDLSTM gains a higher accuracy rate and better stability in the long-term traffic flow forecasting compared with previous methods.

We report recent enhancements of the online atomic database at the Ohio State University, NORAD-Atomic-Data, that provides various parameters for radiative and collisional atomic processes dominant in astrophysical plasma. NORAD stands for Nahar Osu RADiative. The database belongs to the data sources, especially for the latest works, of the international collaborations of the Opacity Project and the Iron Project. The contents of the database are calculated values for energies, oscillator strengths, radiative decay rates, lifetimes, cross sections for photoionization, electron-ion recombination cross sections and recombination rate coefficients. We have recently expanded NORAD-Atomic-Data with several enhancements over those reported earlier. They are as follows. i) We continue to add energy levels, transition parameters, cross sections for atoms and ions with their publications. ii) Recently added radiative atomic data contains significant amount of transition data for photo-absorption spectral features corresponding to x-ray resonance fluorescence effect, prominent wavelength regions of bio-signature elements, such as phosphorus ions, and emission bumps of heavy elements, such as of lanthanides, that may be created in a kilonova event. We are including iii) collisional data for electron-impact-excitation, iv) experimental data for energies and oscillator strengths for line formation, v) experimental cross sections for photoionization that can be applied for benchmarking and other applications, and vi) introduction of a web-based interactive feature to calculate spectral line ratios at various plasma temperature and density diagnostics, starting with our recently published data for P II. We presented summary description of theoretical backgrounds for the computed data in the earlier paper. With introduction of experimental results, we present summary description of measurement of high resolution photoionization cross sections at Advanced Light Source of LBNL synchrotron set-up and briefly discuss other set-ups. These additions should make NORAD-Atomic-Data more versatile for various applications. For brevity, we provide information on the extensions and avoid repetition of data description of the original paper.

Cultural Tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists the tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for Riyadh for peak visitor time. Primarily, the map data and cultural event dataset are processed for location, such as grouping with Kriging Interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes are processed with word embedding. Meanwhile, the Social Network Sites (SNS) data like reviews and news are extracted with an external Application Programming Interface (API). The review data is processed with keyword extraction and word embedding, whereas the news data is processed with score value estimation. Lastly, the data are fused corresponding to a historical site and given to the Multi-Quadratic-Long Short Term Memory (MQ-LSTM) Recommendation System (RS); also, the recommended result with the map is stored in a database. Lastly, the database security is maintained with Locality Sensitive Hashing (LSH); moreover, by attaining higher performance values, the proposed model is experimentally verified.

Type III interferon (IFN-λs) exhibit potent antiviral activity and immunomodulatory effects in specific cells. Nucleotide fragements of bovine ifn-λ (boifn-λ) gene was synthetized after codon optimization. The boifn-λ gene was then amplified by splicing by overlap extension PCR (SOE PCR), resulting in serendipitous acquisition of the mutated boIFN-λ3V18M. Recombinant plasmids pPICZαA-boIFN-λ3/λ3V18M were constructed and the corresponding proteins were expressed in Pichia pastoris with high-level extracellular soluble form. Dominant expression strains of boIFN-λ3/λ3V18M were selected by Western blot and ELISA, cultured on a large scale, and the recombinant proteins purified by ammonium sulphate precipitation and ion exchange chromatography, yielding 1.5g/L and 0.3 g/L, with 85% and 92% purity, respectively. The antiviral activities of boIFN-λ3/λ3V18M exceeded 106 U/mg, were neutralized with IFN-λ3 polyclonal antibodies, were susceptible to trypsin, and retained stability within defined pH and temperature ranges. Furthermore, boIFN-λ3/λ3V18M exerted antiproliferative effects on MDBK cells without cytotoxicity at 104 U/mL. Overall, boIFN-λ3 and boIFN-λ3V18M did not differ substantially in biological activity, except for reduced glycosylation of the latter. Development of boIFN-λ3 and comparative evaluation with the mutant provide theoretical insights into antiviral mechanisms of boIFN-λs and provide material for therapeutic development.

Canola is the major oilseed crop of Canada. The de-oiled material is an important by-product due to its rich phenolic profile and high protein content. This co-processing stream from canola is primarily utilized as animal feed but represents an invaluable source of nutraceuticals. Microwave-assisted solvent extraction (MAE), as a green extraction method, has received considerable attention in recent times. The ease of use and application of many solvents at the same time makes the MAE one of the best methods for studying multiple solvents at the same time. The formation of canolol, from sinapine and sinapic acid, is primarily dependant on temperature which favors the decarboxylation reaction. Hence, MAE using green extractants can be used to enhance the yield of canolol. This study examined the effects of different pre-treatment temperature-time combinations of 140, 150, 160, and 170℃ for 5, 10, 15, 20 and 30 minutes on the extraction of canolol and other canola endogenous phenolic compounds. Three antioxidant assays assessed the antioxidant activity of the different extracts obtained by MAE confirming the microwave as a novel and versatile instrument for enhancing the yield of canolol. Improvements in the antioxidant activity of the different extracts further established the efficacy of the current method for isolating important natural phenolic derivatives for utilization by the nutraceutical industry.

Ice covering on overhead transmission lines would cause damage to transmission system and long-term power outage. Among various de-icing devices, modular multilevel converter (MMC) based DC de-icer (MMC-DDI) is recognized as a promising solution due to its excellent technical performance. Its principle feasibility has been well studied, but few literature discuss its economy or hardware optimization, thus the designed MMC-DDI for high voltage transmission lines is usually too large and too expensive for engineering applications. To fill this gap, this paper presents a quantitative analysis on the converter characteristics of MMC-DDI, and calculates the minimal converter rating and its influencing factors. It reals that, for a given de-icing requirement, the converter rating varies greatly with its AC-side voltage. Then an optimization configuration is proposed to reduce the converter rating and improve its economy. The proposed configuration is verified in a MMC-DDI for a 500kV transmission line as a case study. The result shows, in the case of outputting same de-icing characteristics, the optimized converter rating is reduced from 151 MVA to 68 MVA, and total cost of MMC-DDI is reduced by 48%. This analysis and conclusion are conductive to the optimized design of multilevel DC de-icer, then to its engineering application.

The pro-protein convertase FURIN (PCSK3) is implicated in a wide range of normal and pathological biological processes such as infectious disease, cancer and cardiovascular disease. Previously, we performed a systemic inhibition of FURIN in a mouse model of atherosclerosis and demonstrated significant plaque reduction and alterations in macrophage function. To understand the cellular mechanisms affected by FURIN inhibition in myeloid cells, we optimized a CRISPR-mediated gene deletion protocol for successfully deriving hemizygous (HZ) and nullizygous (NZ) FURIN knockout clones in U937 monocytic cells using lipotransfection based procedures and a dual guide RNA delivery strategy. We observed differences in monocyte and macrophage functions involving phagocytosis, lipid accumulation, cell migration, inflammatory gene expression, cytokine release patterns, secreted proteomics (cytokines) and whole-genome transcriptomics between wild-type, HZ and NZ FURIN clones. These studies provide a mechanistic basis on the possible roles of myeloid cell FURIN in cardiovascular disorders.

Aging and obesity make humans more prone to cardiovascular and metabolic syndrome diseases, leading to several serious health conditions, including dyslipidemia, high blood pressure, and sleep disturbance. Hyperlipidemia means elevated blood lipid levels, including cholesterol and triglycerides. This study aimed to explore the hypolipidemic effect of fermented citrus lemon juice using a hyperlipidemic hamster model. Sugar-free lemon juice’s fermentation was optimized, and the characteristics of fresh and fermented lemon juice (FLJ) were evaluated and compared. In addition, tests were conducted to determine the appropriate hamster feed for the functional animal tests. Then, experiments were conducted to evaluate the hypolipidemic effect of the FLJ, which contained polyphenols and superoxide dismutase-like activity, on a hyperlipidemic hamster model. This study’s pre-fermentation efficiency evaluation found that 21–30 days of bacterial DMS32004 and DMS32005 fermentation of fresh lemon juice provided the best fermentation benefits. In addition, the fermentation evaluation showed that a sugar-free fermentation method provided FLJ with the best benefits. Results showed that the absorption and utilization efficiency of FLJ was higher compared with the unfermented lemon juice. After six weeks of feeding, the total cholesterol and triglyceride values in the blood and liver of the FLJ treatment groups were de-creased compared with the high-fat diet (HFD) group. In addition, the blood low-density lipoprotein cholesterol levels were significantly reduced in the FLJ treatment groups compared with the HFD group. In contrast, the blood high-density lipoprotein to low-density lipoprotein cholesterol ratio increased considerably in the FLJ treatment groups, and the total to high-density lipoprotein ratio was significantly lower than in the HFD group. Compared with the HFD group, the total cholesterol content in the FLJ treatment groups’ feces increased significantly. This study demonstrated that the sugar-free fermentation method and fermentation cycle management provided FLJ with the potential to regulate blood lipids. Further research and verification will be carried out to isolate specific substances from the FLJ and identify their mechanisms of action.

Additive manufacturing (AM) processes, including stereolithography (SL), can fabricate complex ceramic parts layer by layer using computer-aided design (CAD) models. A ceramic slurry with high solid loading is usually used in SL to fabricate the desired shape, which is further sintered to produce the final part. The traditional SL system utilizes a tank filled with printable material, known as a vat, which for ceramic slurry contributes several limitations and operational difficulties, and further renders it non-recyclable mainly due to its high viscosity and the fragility of the green state. In this study, we utilized a continuous film supply (CFS) printer integrated with a tape casting system using in-house-designed ceramic slurry to print standard prototype specimens. Various printing parameters, including viscosity, layer thickness control, and slurry recycling efficiency, were studied. In addition, post-processing optimizations of the prototype, characterizations, and the microhardness of sintered samples were studied to determine their properties and compare them with traditional methods. The effectiveness of slurry reusability was demonstrated by printing with original and recycled slurry to produce consistent densification of final parts. Post-processing was optimized to achieve a relative sinter density of 99.02% and microhardness of 12.59 GPa. This method provides new opportunities to realize dense complex ceramic features with final properties comparable to those produced by subtractive machining and traditional SL. Furthermore, slurry recycling helps to reduce the overall cost and material consumption.

In the intricate landscape of software development, the distinction between average and exceptional outcomes often hinges on the approach taken by the engineers involved. This paper explores the core aspects that characterize responsible and professional software engineering, emphasizing the importance of stakeholder engagement, innovative problem-solving, and adherence to standards and best practices. Through detailed real-life examples, we illustrate how professional engineers ensure the creation of robust, efficient, and adaptable software solutions. Additionally, the paper delves into advanced strategies for enhancing database security, including the implementation of Sealed Secrets in Kubernetes environments, continuous improvement, and future enhancements. The goal is to provide a comprehensive understanding of professional software engineering practices and their impact on delivering superior value to projects and organizations.

Amphetamine is a psychostimulant drug with a high risk of toxicity and death when misused. Abuse of amphetamines is associated with an altered organic profile, which includes omega fatty acids. Low omega fatty acid levels are linked to mental disorders. Using the Comparative Toxicogenomic Database (CTD), we investigated the chemical profile of the brain in amphetamine-related fatalities and the possibility of neurotoxicity. We classified amphetamine cases as low (0-0.5 g/ml), medium (&gt;0.5 to 1.5 g/ml), and high (&gt;1.5 g/ml) based on amphetamine levels in brain samples. All three groups shared 1-octadecene, 1-tridecene, 2,4-di-tert-butylphenol, arachidonic acid, docosahexaenoic acid (DHA), eicosane, and oleylamide. We identified chemical-disease associations using the CTD tools and predict an association between docosahexaenoic and arachidonic acids and curated conditions like autistic disorder, disorders related to cocaine, Alzheimer's disease, and cognitive dysfunction. An amphetamine challenge may cause neurotoxicity in the human brain due to a decrease in omega-3 fatty acids and an increase in oxidative products. Therefore, in cases of amphetamine toxicity, supplement therapy may necessitate preventing omega-3 fatty acid deficiency.

Background: Substance use continues to be on the rise in the United States and has been linked to new onset cardiovascular (CVDs) and cerebrovascular disorders (CeVDs) leading to hospitalizations. We aimed to study the association of different subtypes of substance use disorders (SUDs) among hospitalized patients, with the different subtypes of CVDs and CeVDs, using the National Inpatient Sample (NIS) Database. Additionally, we aimed to assess the odds of hospitalizations with new onset CVDs and CeVDs among patients with different types of SUDs. Methods: A retrospective study of the NIS database (2016-2017) using the ICD-10-CM codes was performed. The hospitalizations with a secondary diagnosis of SUDs were identified. Weighted univariate analysis using the chi-square test and multivariate survey logistic regression analysis was performed to evaluate for the incidence, prevalence, and odds of association between vascular events and SUDs. Results: There were a total of 58,259,589 hospitalizations, out of which 21.42% had SUDs. Out of all the hospitalized patients between the age 18-50, more patients had SUDs than not (31.83%, p< 0.0001). This difference existed for all the different subtypes of SUDs including alcohol related disorder (42.61%), amphetamine dependence (76.17% vs 31.83%), cannabis related disorder (75.17%), cocaine related disorders (57.87%), hallucinogen related disorder (82.91%), inhalant related disorders (67.25%), opioid related disorders (52.86%), and nicotine dependence (35.72%). We found a significant association of acute ischemic stroke with amphetamine dependence (OR 1.23, 95%CI 1.14-1.33), cocaine related disorders (1.17, 1.12-1.23) and nicotine dependence (1.42, 1.40-1.43). Similarly, the association of intracerebral hemorrhage was higher with amphetamine dependence (2.58, 2.26-2.93), and cocaine related disorders (1.62, 1.46-1.79). The association of subarachnoid hemorrhage was noted to be higher with amphetamine dependence (1.82, 1.48-2.24) and nicotine dependence (1.47, 1.39-1.55). In terms of association of cardiovascular disorders with SUDs,the patients with myocardial infarction had higher odds of nicotine dependence (1.85, 1.83-1.87) than not, Similarly, the patients with angina pectoris were noted to have a higher association with cocaine related disorders (2.21, 1.86-2.62), and those with atrial fibrillation had a higher association alcohol related disorders (1.14, 1.11-1.17). Conclusion: Our study demonstrates the variability of CVD and CeVD in patients hospitalized for SUD. Findings from our study may help promote increased awareness and early management of these events. Further studies are needed to evaluate specific effects of frequency and dose on the incidence and prevalence of CVD and CeVD in patients with SUD.

The performance consistency of an RF MEMS switch matrix is a crucial metric, which directly impacts its operational lifespan. An improved crossbar-based RF MEMS switch matrix topology, the SR-Crossbar, was investigated in this article. An optimized port configuration scheme was proposed for the RF MEMS switch matrix. Both the utilization probability of individual switch nodes and path lengths in the switch matrix achieve best consistency simultaneously under the proposed port configuration scheme. One significant advantage of this scheme lies in that it only adjusts positions of input and output ports, with topology and individual switch nodes kept unchanged. This grants it a high level of generality and feasibility, and also introduces an additional degree of freedom for optimizations. The article also constructed a universal utilization probability function of single nodes, and formulated an optimization objective function for the SR-Crossbar RF MEMS switch matrix, which provides a convenient approach to directly solving the optimized port configuration scheme for practical applications.

In order to balance the conflicts among mass flow rate, total pressure ratio, adiabatic efficiency and stall margin in a low-bypass transonic fan, the optimization of the rotor blade is implemented in this study. An advanced 3D optimization design system is adopted, and flow diagnostic methods based on vorticity dynamics are employed to discuss the flow field. Results indicate that by controlling the blade camber line curvature, significant improvements in the aerodynamic performance for the fan stage are achieved, the total pressure ratio is increased by 1.90%, while the adiabatic efficiency and mass flow rate are increased by 4.45% and 5.82%, respectively. Vorticity diagnosis suggest that there exists a close link between performance parameters and vorticity parameters in the axial fan/compressor, both azimuthal vorticity and boundary vortex flux have significant influences on the stage performance. Moreover, the boundary layer separation is accompanied by the spike of entropy and static pressure, while the derivation/gradient of these flow parameters would also suffer drastic changes under the effect of shock wave. The vorticity parameters could provide detailed flow information about the on-wall flow with high accuracy, which provides the researchers with a novel method for the turbomachinery aerodynamic design and analysis.

The advancement of technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of Finite Impulse Response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of the comprehensive analysis of the impact of different genetic operators show that the genetic algorithm-based filter outperforms conventional filter designs.

This paper proposes a compact planar Wi-Fi antenna with optimized radiation patterns for small uncrewed aerial vehicle (UAV) applications in both urban and open areas. It is suitable for mounting on the outermost side of the non-metallic wing of small UAVs. It has small dimensions of 16.5 mm (L) by 30.3 mm (W) by 1.6 mm (h), and the measured results of its prototype are in agreement with simulated data. Its impedance bandwidths over the two frequency ranges are 2.11 to 2.58 GHz and 5.06 to 7.5 GHz ( ). At 5.8 GHz, it has stronger radiation below the small UAV to reduce interference from rare-use directions. Its maximum radiations, the directions of the maximum radiation in each elevation plane, are below the UAV and between 14° and 29° from the horizontal plane. At 2.4 GHz, it has quasi-omnidirectional radiation to ensure a stable link in all directions, and its maximum radiations are near the horizontal plane. The optimized radiation patterns at 5.8 and 2.4 GHz can provide more antenna gain when the small UAV flies farther in urban and open areas, respectively. In addition, it has good vertically polarized radiation for long-distance applications.

Optimizing the magnetic coupling of two coils of a dynamic wireless charging power supply for electric vehicles (EVs) is a challenging and crucial task. This article examines three situations of the magnetic coupling between two identical rectangular spiral coils with the same number of turns as a function of both the horizontal (X axis) and vertical (Z axis) alignment. In the first one, the analysis of the magnetic coupling between two copper air coils is presented. However, the obtained coupling coefficient was very low with a high leakage flux which affected the performance of the charging system. In the second case, a straightforward shielding method that involves inserting a magnetic material of the ferrite type is proposed to circumvent the aforementioned problems. Unfortunately, the obtained results show that simple shielding is still only a partial and insufficient solution. In the third situation, an aluminum sheet was consequently placed on the top of the ferrite to provide an adequate shielding structure. Additionally, a 3D analysis of the proper and mutual induction parameters separating the two coils as well as a magnetic field or induction distribution is also performed using the ANSYS Maxwell software. The results highlight the significance of the enhanced structure.

Desalinators are traditionally driven by fossil-fuels but in order to avoid greenhouse emissions, renewable energy must be used. In this paper, a coupling between multi-stage flash distillation apparatus and a parabolic trough solar collector is analyzed. The purpose of this study is to determine the economic feasibility of the system, considering four cities of Saudi Arabia and three different potable water productions. To avoid solar energy intermittency and unavailability at night, thermal storage is implemented. Whereas other researchers made parametric analyses, in this paper, the authors developed a mathematical program which was optimized with the help of GAMS software, where the capital cost of the plant was the objective function. After that, a life cycle cost analysis was carried out for each scenario. Depending on the region and water production, the costs of drinking water vary from 2.26 to 3.93 US$/m³, and from 7440 to 23825 tons of carbon dioxide emissions are avoided. As a consequence of the approach used, low costs are obtained; even though no auxiliary heater is implemented and the lowest irradiance conditions are considered. The results of this study reflect that the proposed process is competitive with respect to the traditional one.

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

The important sampling parameters of a headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) procedure, extraction temperature, extraction time and sample volume were optimized to quantify 23 important impact odorants in reduced alcohol red and white wines. A three-factor design of Box-Behnken experiments was used to determine optimized sampling conditions for each analyte, and a global optimized condition at every ethanol concentration of interest determined using a desirability function that accounts for a low signal response for compounds. Shiraz and Chardonnay wines were dealcoholized from 13.7 and 12.2% v/v ethanol respectively, to 8 and 5% v/v, using a commercially available membrane-based technology. A sample set of the reduced alcohol wines were also reconstituted to their natural ethanol level to evaluate the effect of ethanol content reduction on volatile composition. The three-factor Box-Behnken experiment ensured an accurate determination of the headspace concentration of each compound at each ethanol concentration, allowing comparisons between wines at varying ethanol levels to be made. Overall, the results showed that the main effect of extraction temperature was considered the most critical factor when studying the equilibrium of reduced alcohol wine impact odorants. The impact of ethanol reduction upon the concentration of volatile compounds clearly resulted in losses of impact odorants from the wines. The concentration of most analytes decreased with dealcoholization compared to that of the natural samples. Significant differences were also found between the reconstituted volatile composition and 5% v/v reduced alcohol wines, revealing that the dealcoholization effect is the result of a combination between the type of dealcoholization treatment and reduction in wine ethanol content.

The diagnosis of papillary thyroid carcinoma has always been a concerned and challenging issue and it is very important and meaningful to have a definite diagnosis before the operation. In this study, we tried to use an artificial intelligence algorithm instead of medical statistics to analyze the genetic fingerprint from gene chip results to identify papillary thyroid carcinoma. We trained 20 artificial neural network models with differential genes and other important genes related to the cell metabolic cycle as the list of input features, and apply them to the diagnosis of papillary thyroid cancer in the independent validation data set. The results showed that when we used the DEGs and all genes lists as input features the models got the best diagnostic performance with AUC=98.97% and 99.37% and the accuracy were both 96%. This study revealed that the proposed artificial neural network models constructed with genetic fingerprints could achieve a prediction of papillary thyroid carcinoma. Such models can support clinicians to make more accurate clinical diagnoses. At the same time, it provides a novel idea for the application of artificial intelligence in clinical medicine.

Adding steel fibers to concrete improves the capacity in tension-driven failure modes. An example is the shear capacity in steel fiber reinforced concrete (SFRC) beams with longitudinal reinforcement and without shear reinforcement. Since no mechanical models exist that can fully describe the behavior of SFRC beams without shear reinforcement failing in shear, a number of empirical equations have been suggested in the past. This paper compiles the existing empirical equations and code provisions for the prediction of the shear capacity of SFRC beams failing in shear as well as a database of 487 experiments reported in the literature. The experimental shear capacities from the database are then compared to the prediction equations. This comparison shows a large scatter on the ratio of experimental to predicted values. The practice of defining the tensile strength of SFRC based on different experiments internationally makes the comparison difficult. For design purposes, the code prediction methods based on the Eurocode shear expression provide reasonable results (with coefficients of variation on the ratio of tested to predicted results of 27% - 29%). None of the currently available methods properly describe the behavior of SFRC beams failing in shear. As such, this work shows the need for studies that address the different shear-carrying mechanisms in SFRC and its crack kinematics.

Pregnancy is a dynamic state with multiple metabolic changes occurring including insulin resistance. Gestational diabetes mellitus (GDM), a form of diabetes that appears during pregnancy, develops if metabolic aberrations occur, in particular, in normal pregnancy-induced insulin resistance. Multi-omics is a powerful approach for uncovering the mechanisms driving metabolic change in different physiologic and pathologic states. A recent study demonstrated that the gestational gut microbiome mediates pregnancy metabolic adaptations through effects on gut indoleamine-2,3 dioxygenase 1 activity and the production of kynurenine. Using the dataset generated from this highly controlled study, we performed a comprehensive analysis of the pregnancy-specific physiological and metabolic profiles, 16S rRNA microbiome, and plasma untargeted LC-MS metabolome data. To facilitate the utilization of these analysis results by other researchers, we developed MOMMI-MP, a database that provides an easy-to-use platform to browse and search differential abundant microbial taxa and metabolites, and to examine metabolic pathways. The datasets consist of data collected from 3 genetically diverse strains of mice (C57BL/6J, CD1, and NIH-Swiss) over 6 time points during the gestational (days 0, 10, 15, and 19 during gestation) and postpartum (days 3 and 20 after delivery) states, totaling 180 samples for each strain. The computational results are presented in various tables and plots, and organized in MOMMI-MP to empower exploratory analyses by other researchers. In conclusion, MOMMI-MP is a resource to facilitate the investigation of novel mechanisms governing metabolic changes during pregnancy.

Background/ObjectivesMental health and substance use disorders (MHD, SUD) affect cardiac allograft and VAD recipients and impact their quality of life and compliance. Limited research on MHDs and SUDs in transplant and MCS candidates and recipients. This study compares the incidence of MHDs and SUDs in transplant list, VAD, and post-transplant patients with that in heart failure patients. MethodsStudy cohorts were derived from the TriNetX using ICD-10 codes. Differences in incidence were examined using the log-rank test. Adults with mental health disorders before the window of time were excluded. Propensity score matching was performed using TriNetX to balance demographic and medical comorbidities. Statistical significance was set at p<0.05. All comparisons were made between propensity-matched cohorts. ResultsTransplant waitlist patients showed a statistically significant increase in the incidence of anxiety, panic disorder, adjustment disorder, depression, mood disorder, alcohol use disorder, and eating disorder. Post-transplant patients showed a statistically significant increase in the incidence of depression and opioid use compared to the heart failure population. VAD patients showed a statistically significant increase in the incidence of depression and a statistically significant decrease in panic disorder and anxiety. ConclusionsThe results pave the pathway for further investigations on prevention and coping strategies because deterioration of mental health will significantly impact compliance with medications as well as patient survival and quality of life. The use of opioids for pain management in the early postoperative period should be further investigated to assess its impact on long-term substance use and addiction. .

To address the large hysteresis and susceptibility to instability in high-speed deposition fusion moulding (FDM) 3D printing’s temperature control, this paper proposes an improved ant-lion optimization algorithm (OCALO) to optimize the PID parameters.The improvement aims to solve the problem that the traditional ant-lion optimization algorithm (ALO) reduces the population diversity and tends to fall into the local optimal solution when it is close to the global optimal solution.The generation of the initial solution is enhanced by the introduction of a new Tent-Logistic-Cotangent composite chaotic mapping. This integration into Elite Opposition-Based Learning aids in optimizing population diversity. Concurrently, the incorporation of cosine factors of a specified parameter into the elitist formula of the traditional ALO algorithm is aimed at reducing the tendency of ants to randomly gravitate towards ant-lions with lower fitness values. This adjustment helps in balancing the exploration-exploitation trade-off in the algorithmic process. Compared with two existing classical algorithms and three improved ALO algorithms, the algorithm improves the convergence speed, global search ability and the ability to jump out of the local optimal solution. The improved algorithm was combined with PID control to design an OCALO-PID temperature controller and simulated on a high-speed 3D printing temperature model identified by modelling. The results show that the method improves the transient and steady-state performance of reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a physical experimental platform to verify the feasibility of the algorithm.

To address the problems of wind abandonment in the regional integrated energy system (RIES), which occurs when cogeneration unit operates in a heat-determined electricity operation mode, and the low efficiency of the energy storage system in a low-temperature environment, we propose an optimal scheduling method for RIES based on fine energy storage and wind power dissipation. First, a fine energy storage model is established on the source side and the conditional value at risk(CvaR) theory is used to quantify the uncertainty of wind power; then a combined heat and power demand response mechanism is introduced on the load side to reduce the peak-to-valley difference between heat and power loads, and to promote the consumption of wind power. Finally, with the objective of minimizing the total cost of RIES optimal dispatch, the example is solved on the MATLAB platform. The simulation results show that compared with the traditional model, the proposed model is not only more adaptable to the low-temperature environment, but also can effectively improve the system economy and wind power consumption.

Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18.

Renewable energy sources will play a central role in the sustainable energy systems of the future. Scenario analyses of such hypothesized energy systems require sound knowledge of the techno-economic potential of renewable energy technologies. Although there have been various studies concerning the potential of offshore wind energy, higher spatial resolution, as well as the future design concepts of offshore wind turbines, has not yet been addressed in sufficient detail. Here, we aim to overcome this gap by applying a high spatial resolution to the three main aspects of offshore wind potential analysis, namely ocean suitability, the simulation of wind turbines and cost estimation. A set of constraints is determined that reveal the available areas for turbine placement across Europe’s maritime boundaries. Then, turbine designs specific to each location are selected by identifying turbines with the cheapest levelized cost of electricity (LCOE), restricted to capacities, hub heights and rotor diameters of between 3-20 MW, 80-200 m and 80-280 m, respectively. Ocean eligibility and turbine design are then combined to distribute turbines across the available areas. Finally, LCOE trends are calculated from the individual turbine costs, as well as the corresponding capacity factor obtained by hourly simulation with wind speeds from 1980 to 2017. The results of cost-optimal turbine design reveal that the overall potential for offshore wind energy across Europe will constitute nearly 8.6 TW and 40.0 PWh at roughly 7 €_ct kWh^-1 average LCOE by 2050. Averaged design parameters at national level are provided in an appendix.

Digital healthcare systems play a pivotal role in providing efficient and accessible healthcare services. However, ensuring secure authentication and key agreement mechanisms is essential to protect sensitive patient data and maintain the integrity of the system. The existing methods face limitations in terms of vulnerability to cyber attacks, scalability, and resource utilization. Furthermore, the integration of blockchain technology introduces new complexities that need to be addressed. This research proposes an optimized fuzzy logic approach combined with blockchain technology to address the authentication and key agreement challenges in digital healthcare systems. The proposed solution leverages the flexibility and adaptability of fuzzy logic algorithms to handle uncertainty and imprecision in authentication decisions. By employing fuzzy logic, the system can effectively minimize false positives and false negatives, enhancing the robustness against adversarial attacks. Moreover, the integration of blockchain technology provides a decentralized and tamper-proof infrastructure for securely storing and managing authentication and key agreement data. This ensures transparency and trust in the system, mitigating the risks of unauthorized access and data manipulation. The blockchain-based architecture also enables efficient resource utilization and scalability, allowing the system to handle authentication requests in a timely manner, even in large-scale digital healthcare environments.The proposed method is evaluated by using the NIST Special Database 302 and it shows superior performance compared to existing methods, with minimum False Rejection Rate (FRR), False Acceptance Rate (FAR), and response time. Moreover, the proposed method minimizes communication overhead during the authentication process and resists different cyber attacks including a Replay attack, Man-in-the-middle attack, Denial of Service (DoS) attack, and Impersonation attack. The proposed method achieves excellent performance in terms of security, efficiency, and resistance to various cyber-attacks, making it a promising approach for secure data sharing in P2P cloud environments.

The natural distribution of stress in the femur is altered when total hip arthroplasty (THA) is performed. In fact, when a stem is inserted inside the femur, there is a variation in stress due to the difference in rigidity between the material with which the stem is made and the femur. This generates the phenomenon of stress-shielding. The aim of this study is to design an optimized prosthesis that guarantees excellent rotational stability and re-duced stress shielding. Methods: through the finite element method (FEM) the mechanical behavior of the stem subjected to the loads described by ISO 7206-4: 2010 is studied. Re-sults: Through topological optimization, there is a reduction in stress shielding in the proximal zone of 31.46%. The addition of ridges on the dorsal side of the stem also im-proves rotational stability by 27.82%. Conclusions: The decrease in stiffness that is rec-orded with the optimized stem, guarantees a greater distribution of stress on the bone. The presence of dorsal ridges also favors the corticalization of the bone as it loads the bone near the dorsal ensuring further stability. The perforated prosthesis presented in this study, shows an increase in primary stability and an improvement in rotational stability. As there is also a bone regrowth inside the prosthesis.