bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between religiosity or spirituality and internet addiction: A systematic review
Authors: Dossi Francesca , Buja Alessandra , Montecchio Laura
Published: December 2022

Introduction

The literature provides evidence of religiosity being associated with physical and mental health, and also with behavioral addictions. This systematic review examines the data on the link between religiosity or spirituality and the emerging internet addictions.

Methods

A systematic literature review was conducted in the PubMed and Scopus databases to identify observational (cross-sectional, cohort, and case-control) studies conducted on adolescents and young adults to investigate the association between religiosity or spirituality and internet addiction. Of the 854 articles identified in the databases, 13 met our inclusion criteria and were included in our systematic review.

Results

Eleven of the 13 studies reviewed specifically investigated religiosity and internet addiction: six found an inverse association between religiosity and internet addiction; three found no evidence of any association; and one found a direct association. One study examining both religiosity and spirituality generated mixed results. Only one study investigated spirituality unrelated to religion, and found a direct association with internet addiction. Two of three studies specifically considering internet gaming addiction found it inversely associated with high levels of religiosity, while the third found no association.

Conclusion

This review supports a possible role for religiosity as a protective factor, as emerged from the majority of the studies examined. Religiosity also seemed to be associated with lower internet gaming rates among adolescents.
Human microbiome and microbiota identification for preventing and controlling healthcare-associated infections: A systematic review
Authors: Tozzo Pamela , Delicati Arianna , Caenazzo Luciana
Published: December 2022

Objective

This systematic review describes the role of the human microbiome and microbiota in healthcare-associated infections (HAIs). Studies on the microbiota of patients, healthcare environment (HE), medical equipment, or healthcare workers (HCW) and how it could be transmitted among the different subjects will be described in order to define alarming risk factors for HAIs spreading and to identify strategies for HAIs control or prevention.

Methods

This review was performed in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After retrieval in databases, identification, and screening of available records, 36 published studies were considered eligible and included in the review.

Results

A multifaceted approach is required and the analyses of the many factors related to human microbiota, which can influence HAIs onset, could be of paramount importance in their prevention and control. In this review, we will focus mainly on the localization, transmission, and prevention of ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) bacteria and Clostridium difficile which are the most common pathogens causing HAIs.

Conclusions

Healthcare workers' microbiota, patient's microbiota, environmental and medical equipment microbiota, ecosystem characteristics, ways of transmission, cleaning strategies, and the microbial resistome should be taken into account for future studies on more effective preventive and therapeutic strategies against HAIs.
Acrylamide, a toxic maillard by-product and its inhibition by sulfur-containing compounds: A mini review
Authors: Augustine Dahryn Andilla , Bent Grace-Anne
Published: December 2022

The Maillard reaction not only results in the formation of flavor compounds, but also harmful by-products, including the infamous toxicant acrylamide. Dietary acrylamide cannot be eliminated, but its levels in foods can be minimized. This review discusses the mechanisms of inhibition, effectiveness under varied conditions, and limitations of the sulfur-containing compounds: thiols, sulfites, thioethers, and thiosulfinates. These compounds have proven to be effective inhibitors of acrylamide formation. Among these compounds, the thiols are deemed the most effective. However, such success is masked by their negative effects on food taste and odor.
Unmet challenges in septoplasty–nordic studies from a uniform healthcare and geographical area
Authors: Hellgren J , Lundberg M , Rubek N , von Buchwald C , Steinsvåg S , Mäkitie A
Published: December 2022

Purpose

Nasal septoplasty is one of the most common surgical procedures in otorhinolaryngology and optimising both patient selection and the surgery is a challenge. The Nordic countries have similar public healthcare systems and comparable populations in terms of size.

Methods

This is a review of studies of outcome and predictors related to septoplasty from Denmark, Finland, Norway and Sweden, published during the last decade. The aim of this review was to identify areas in need of further research to meet the challenges of septoplasty in the Nordic countries with reference to international data.

Results

Postoperative patient satisfaction at 6–12 months was reported in around 2/3 of the patients and well in line with international data. Patients with more severe symptoms had a higher chance of improvement. Lack of standardisation in patient selection, surgical methods and skills, and follow up procedures, still makes it difficult to explain the 25% failure rate in septoplasty surgery.

Conclusion

This review of the Nordic studies from the last decade shows that septoplasty in general is effective in relieving nasal obstruction. There is a need for studies addressing the standardisation of diagnostic tools and algorithms and the systematic and continuous implementation of follow-up of the surgical results at both departmental and personal level. This includes an awareness of how surgical skills in septoplasty are obtained and maintained.
Beyond antioxidants: Selenium and skeletal muscle mitochondria
Authors: Wesolowski Lauren T. , Semanchik Pier L. , White-Springer Sarah H.
Published: December 2022

The element, Selenium (Se), has an essential nutritive and biological role as a trace mineral known primarily for its vital antioxidant functions as a constituent of the selenoenzyme, glutathione peroxidase. However, Se also has a much more global biological impact beyond antioxidant function. The objective of this review is to present an overview of prior research on the extra-antioxidant effects of Se with a key focus on skeletal muscle mitochondrial energetics. Cognizance of these additional functions of Se is requisite when formulating and recommending dietary supplementation of Se in humans or animals. Chief amongst its myriad of biological contributions, Se influences mitochondrial capacity and function and, subsequently, muscular health. Dietary Se supplementation has been shown to increase skeletal muscle mitochondrial volume density and within some cell lines, Se treatment increases mitochondrial biogenesis and respiratory capacity. In addition, the selenoproteins H, N, W, and O and deiodinases exhibit varying effects on mitochondrial and/or skeletal muscle function. Selenoprotein H enhances mitochondrial biogenesis whereas selenoproteins N and W appear to influence muscle calcium homeostasis which impacts mitochondrial function. Moreover, selenoprotein O's intramitochondrial residence facilitates Se's redox function. Deiodinases regulate thyroid hormone activation which impacts muscle cell regeneration, metabolism, and reactive oxygen species production. Although the precise relationships between dietary Se and skeletal muscle mitochondria remain unclear, previous research constitutes a firm foundation that portends promising new discoveries by future investigations.
Owned dog population size and ownership patterns in Costa Rica
Authors: Flockhart D. T. Tyler , Rowan Andrew N. , Boone John D.
Published: December 2022

Dogs (Canis lupus familiaris) are one of the most common pets around the world but ownership patterns and human-dog interactions have been changing, particularly in developing nations. We conducted household surveys in Costa Rica to characterize dog ownership, the owned dog population, where dogs were confined at night and in the morning, and behaviors regarding selected dog care issues. We also compared these results to similar questionnaires used in Costa Rica over the past 20 years. We found 76% of households in Costa Rica owned at least one dog and on average there were about 1.4 dogs owned per household. These dog ownership rates are higher than previous estimates. The probability of owning a dog was highest on farms and lowest in single family dwellings without a yard, higher among respondents that owned their homes and decreasing with increasing human population density The total number of owned dogs in Costa Rica was estimated to be 2,222,032 (95% confidence intervals: 1,981,497–2,503,751). The sterilization rate for homed dogs in 2020 was approximately 62% (females: 67%, males: 61%) which is higher than the 18% of owned dogs that were sterilized in a 2003 survey. Overall, only 1.2% (95% CI: 0.3–2.5%) of owned dogs slept on the street with a slightly higher proportion on the street at 8 am. The number of owned dogs roaming the streets at night nation-wide was estimated to be 27,208 (95% CI: 7,557–56,619) compared to 43,142 (95% CI: 20,118–73,618) on the street at 8 am. The number of unowned free-roaming dogs in Costa Rica has never been estimated but we can generate some idea of the size of the unowned dog population by determining the proportion of free-roaming dogs on the street wearing collars. There was a negative relationship between human population density and owned dogs being on the street meaning fewer dogs roam the streets in highly populated areas compared to less populated areas. Overall, we identify trends against which future progress can be measured and provide information that are critical in designing effective humane dog management programs in Costa Rica in the future.
Gestión de costos como ventaja competitiva en empresas productoras de banano: abordaje teórico
Authors: Lalangui-Balcázar Margot Isabel , Meleán-Romero Rosana
Published: December 2022

El sector de banano es importante para la economía ecuatoriana, genera empleo e ingresos a las familias que se insertan en las actividades operativas desarrolladas en el agro, así como contribuye a la generación de ingresos a la economía ecuatoriana, aportando al Producto Interno Bruto del país. La gestión de costos es fundamental en las empresas, más aún, para el área gerencial: permite tomar decisiones que, apoyadas en la contabilidad de costos aportan a la competitividad de estas empresas. Por ello, es importante analizar la gestión de costos en empresas bananeras para determinar como esta se constituye en una ventaja competitiva, que resalte el liderazgo en costos en los mercados. Mediante un abordaje teórico, se parte de la revisión de documentos procedentes de bases de datos especializadas, básicamente, artículos académicos, que abordan las problemáticas empresariales de los últimos 5 años en torno a esta temática. La revisión realizada conlleva a precisar problemas relevantes como el control requerido a nivel del proceso productivo pues en él se desarrollan actividades que consumen recursos y que son generadoras de costos. Por otro lado, la gestión de costos se perfila como una herramienta que conduce a su reducción, conllevando los buenos desempeños en esta área al liderazgo de costos. Después de la pandemia, las prioridades se direccionan hacia la nueva estrategia de diferenciación, la innovación en los procesos productivos, la calidad en el producto. La tendencia es una producción orgánica, por la inocuidad en el producto, amigable al medio ambiente y con responsabilidad con la sociedad.
Liquidez, endeudamiento y rentabilidad de las mipymes en Ecuador: un análisis comparativo
Authors: Sánchez-Pacheco María Estefanía , Bermúdez-Fajardo Paula Ninoska , Zea-Franco Robert Daniel , Zambrano-Farías Fernando José
Published: December 2022

La presente investigación tiene como objetivo analizar las principales ratios financieros, de liquidez, de endeudamiento, gestión y rentabilidad de las micros, pequeñas y medianas empresas (mipymes) de las regiones Costa y Sierra de Ecuador utilizando un enfoque cuantitativo de carácter descriptivo y exploratorio con datos suministrados por 701 empresas que registraron su información contable y financiera a la Superintendencia de Compañías, Valores y Seguros en el año 2020. Los resultados de esta investigación mostraron que la liquidez general y la liquidez estricta de las mipymes de la región Costa son significativamente mayores que la mostrada por las empresas de la región Sierra. Este estudio también mostró que las empresas de la región Sierra tienen mejores rendimientos, en términos de inversión de accionistas, que las empresas de la Costa; además, se evidenció que no existen diferencias significativas en cuanto al financiamiento y administración de sus activos. Las conclusiones de esta investigación ofrecen orientaciones a futuras investigaciones sobre los factores que inciden en la rentabilidad corporativa y fracaso empresarial.
Research on Landing Stability of Four-Legged Adaptive Landing Gear for Multirotor UAVs
Authors: Ni Xinlei , Yin Qiaozhi , Wei Xiaohui , Zhong Peilin , Nie Hong
Published: December 2022

Rotorcraft Unmanned Aerial Vehicles (UAVs) often need to take off and land under complex working conditions. The rugged terrains may cause the UAV to tilt during takeoff and landing and even cause rollover and other accidents in severe cases. In this paper, a new four-legged landing gear of multirotor UAVs with a passive cushioning structure is designed, aiming at the landing stability requirement of rotorcraft UAVs in complex terrains. The mathematical model of the landing gear dynamics is established in MATLAB/Simulink, and the drop test simulation is carried out under different landing terrain conditions. By comparing the simulation results of the drop test multibody dynamic model in Simcenter3D dynamics software, the adaptive landing and cushioning capacity of the landing gear and the accuracy of the mathematical model are verified. Combined with the landing stability criterion and control strategy of adaptive landing gear adjustment, the landing stability of adaptive landing gear under different slope angles of landing surface and horizontal velocities is studied. The landing stability boundary under different combinations of these two parameters is found.
A Leakage Rate Model for Metal-to-Metal Seals Based on the Fractal Theory of Porous Medium
Authors: Liu Yong , Du Hao , Ren Xinjiang , Li Baichun , Qian Junze , Yan Fangchao
Published: December 2022

Due to the complexity of sealing surface topography, it is difficult to take the surface topography into consideration when building a leakage rate model theoretically. Therefore, a theoretical model for estimating the leakage rate of metal-to-metal seals based on the fractal theory of porous medium, which can objectively reflect the influence of sealing surface topography from a microscopic perspective, is proposed in the present work. In the approach, fractal parameters are adopted to characterize the sealing surface. The sealing interface is supposed to be a porous medium space and the intrinsic parameters are obtained through rigorous theoretical derivation. The results show that the topography parameters of the sealing surface have a significant effect on the intrinsic parameters of the pore space and lead to a significant influence on the leakage rate of metal-to-metal seals. Specifically, the smoother the sealing surface, the lower the leakage rate of the metal-to-metal seal. Moreover, the leakage rate decreases with an increase in the contact pressure, and, if the fluid pressure difference is too large, the sealing performance will be seriously reduced. The proposed model provides a novel way to calculate the leakage rate of metal-to-metal seals.
Smart Automation for Production of Panchagavya Natural Fertilizer
Authors: V. Sumathi , J. Mohamed Abdullah
Published: December 2022

Modern agricultural farming techniques employ the usage of chemical supplements to improve crop yield in terms of quantity and quality. This practice has brought down the fertility of the soil and has led to secondary impacts and necessitates a significant financial investment. Awareness of the side effects of artificially enriched food has made people move towards organically grown food, and the consumption has also increased significantly. One of the ancient organic fertilizers used in India is panchagavya. As the name implies, pancha means five and gavya means cow. The five products of the cow are combined as per the compositions and procedure described in the literature, to provide economical and meaningful value to organic farming. The objective of this work is to design, develop, and implement an automated system to manufacture panchagavya with reduced operator assistance. The system implements an ATmega 328 microcontroller to automate the entire process by interfacing sensors such as pH, moisture, temperature, and pressure. The system is also provided with a SIM900A GSM modem to provide information to the user regarding the status of the process. The developed pilot scale design discussed in this work has several advantages in the world of farming technologies in terms of enriching the soil, thereby improving the crop yield. This technology will benefit the farmers as this natural fertilizer can be mass-produced and turn them into entrepreneurs, which benefits society at large.
Too Much of a Good Thing: Rethinking Feed Formulation and Feeding Practices for Zinc in Swine Diets to Achieve One Health and Environmental Sustainability
Authors: Shurson Gerald C. , Urriola Pedro E. , Hung Yuan-Tai
Published: December 2022

The objectives of this review were to summarize current knowledge of Zn in swine nutrition, environmental concerns, potential contribution to antimicrobial resistance, and explore the use of alternative feeding strategies to reduce Zn excretion in manure while capturing improvements in productivity. Zinc is a required nutrient for pigs but is commonly supplemented at concentrations that greatly exceed estimated requirements. Feeding pharmacological concentrations of Zn from ZnO to pigs for 1 to 2 weeks post-weaning reduces post-weaning diarrhea and improves growth performance. Feeding elevated dietary levels of Zn to sows during the last 30 days of gestation can reduce the incidence of low-birth-weight pigs and pre-weaning mortality. Most of the dietary Zn consumed by pigs is not retained in the body and is subsequently excreted in manure, which led several countries to impose regulations restricting dietary Zn concentrations to reduce environmental impacts. Although restricting Zn supplementation in swine diets is a reasonable approach for reducing environmental pollution, it does not allow capturing health and productivity benefits from strategic use of elevated dietary Zn concentrations. Therefore, we propose feeding strategies that allow strategic use of high dietary concentrations of Zn while also reducing Zn excretion in manure compared with current feeding practices.
Gold(III) Complexes Activity against Multidrug-Resistant Bacteria of Veterinary Significance
Authors: Ratia Carlos , Sueiro Sara , Soengas Raquel G. , Iglesias María José , López-Ortiz Fernando , Soto Sara María
Published: December 2022

The emergence and spread of multidrug-resistant bacteria are a global concern. The lack of new antibiotics in the pipeline points to the need for developing new strategies. In this sense, gold(III) complexes (G3Cs) could be a promising alternative due to their recently described antibacterial activity. The aim of this study was to evaluate the antimicrobial activity of G3Cs alone and in combination with colistin against pathogenic bacteria from veterinary sources. Minimal inhibitory concentration (MIC) values were determined by broth microdilution and compared with clinically relevant antibiotics. Antibiofilm activity was determined by crystal violet staining. Combinations of selected G3Cs with colistin and cytotoxicity in commercial human cell lines were evaluated. Four and seven G3Cs showed antibacterial effect against Gram-negative and Gram-positive strains, respectively, with this activity being higher among Gram-positive strains. The G3Cs showed antibiofilm activity against Gram-negative species at concentrations similar or one to four folds higher than the corresponding MICs. Combination of G3Cs with colistin showed a potential synergistic antibacterial effect reducing concentrations and toxicity of both agents. The antimicrobial and antibiofilm activity, the synergistic effect when combined with colistin and the in vitro toxicity suggest that G3Cs would provide a new therapeutic alternative against multidrug-resistant bacteria from veterinary origin.
Parametric Numerical Modeling and Fabrication of PCL Scaffolds for Bone Tissue Engineering Applications
Authors: Früh Anton , Rolauffs Bernd , Seidenstuecker Michael
Published: December 2022

Bone tissue engineering (BTE) is an interdisciplinary discipline that focuses on bone structure–function relationships for improving the replacement and/or regeneration of bone tissues. Thereby, the architecture and load-bearing capacity of embedded scaffolds play an important role in the generation of artificial tissues. The aim of this study was to develop a parametric numerical model and the accompanying fabrication of polycaprolactone (PCL) scaffolds for BTE applications. Therefore, we manufactured layered PCL-based constructs using three-dimensional (3D) printing. The material properties of PCL and constructs were determined by mechanical testing, and numerical models based on Beam188 Timoshenko elements were developed in the software environment ANSYS. PCL constructs were coated with collagen and seeded with osteoblasts, mesenchymal stem cells (MSCs), MLO-Y4 and MG63 cell types. We demonstrated the successful production of PCL constructs with 3D interconnected pores suitable for BTE applications. Furthermore, we provided for the first time geometrical parametric numerical models that determined the mechanical behavior of layered PCL scaffolds consisting of interconnected compartments for strains up to 3%. The parametric structures of the model allowed us to flexibly study new geometries in silico, which demonstrated its role as an important tool for supporting the fabrication of customized PCL constructs in planning and performing suitable mechanical characterizations for BTE applications.
Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
Authors: Singh Divya , Shukla Aasheesh , Hui Kueh Lee , Sain Mangal
Published: December 2022

Due to the increasing demand for fast data rates and large spectra, millimeter-wave technology plays a vital role in the advancement of 5G communication. The idea behind Mm-Wave communications is to take advantage of the huge and unexploited bandwidth to cope with future multigigabit-per-second mobile data rates, imaging, and multimedia applications. In Mm-Wave systems, digital precoding provides optimal performance at the cost of complexity and power consumption. Therefore, hybrid precoding, i.e., analog–digital precoding, has received significant consideration as a favorable alternative to digital precoding. The conventional methods related to hybrid precoding suffer from low spectral efficiency and large processing time due to nested loops and the number of iterations. A manifold optimization-based algorithm using the gradient method is proposed to increase the spectral efficiency to be near optimal and to speed up the processing speed. A comparison of performances is shown using the simulation outcomes of the proposed work and those of the existing techniques.
Assessing the Crashworthiness Analysis on Frontal and Corner Impacts of Vehicle on Street Poles Using FEA
Authors: Alardhi Mohsen , Sequeira Rahul , Fahed Melad , Alrajhi Jasem , Alkhulaifi Khalid
Published: December 2022

The impact analysis of vehicle collision on street poles was investigated, as well as an assessment of which type of impact—frontal or corner—contributes to the most damages on both the car and the streetlamp. This work was accomplished using Abaqus/Explicit software to numerically simulate the crashes at three different velocities, 12, 17, and 22 m·s−1, and extract relations such as the energy models, the specific energy absorption (SEA) of the materials tested, and the impact forces. Two materials were used for the street pole: aluminum Al-6061 and ASTM A36 grade steel. Findings such as the influence of the SEA on the vehicle’s velocity, the relationship between the deformation of the street pole and the vehicle’s velocity, as well as the improvement of previously studied models by including damage parameters are presented.
Strategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
Authors: Lang Jonas , Zielinski Sebastian , Feld Sebastian
Published: December 2022

In this work, we introduce a new workflow to solve portfolio optimization problems on annealing platforms. We combine a classical preprocessing step with a modified unconstrained binary optimization (QUBO) model and evaluate it using simulated annealing (classical computer), digital annealing (Fujitsu’s Digital Annealing Unit), and quantum annealing (D-Wave Advantage). Starting from Markowitz’s theory on portfolio optimization, our classical preprocessing step finds the most promising assets within a set of possible assets to choose from. We then modify existing QUBO models for portfolio optimization, such that there are no limitations on the number of assets that can be invested in. Furthermore, our QUBO model enables an investor to also place an arbitrary amount of money into each asset. We apply this modified QUBO to the set of promising asset candidates we generated previously via classical preprocessing. A solution to our QUBO model contains information about what percentage of the whole available capital should be invested into which asset. For the evaluation, we have used publicly available real-world data sets of stocks of the New York Stock Exchange as well as common ETFs. Finally, we have compared the respective annealing results with randomly generated portfolios by using the return, variance, and diversification of the created portfolios as measures. The results show that our QUBO formulation is capable of creating well-diversified portfolios that respect certain criteria given by an investor, such as maximizing return, minimizing risk, or sticking to a certain budget.
Multi-Body Dynamics Modeling and Analysis of Planetary Gearbox Combination Failure Based on Digital Twin
Authors: Zhu Daoyong , Li Zhinong , Hu Niaoqing
Published: December 2022

Existing studies on the dynamic characteristics of planetary gearboxes are generally based on traditional modeling methods. The models were idealized because of various uncertain factors that affect the dynamic characteristics of the planetary gearbox. The influence of non-mechanical components, such as driving motors, on dynamic analysis, is often ignored. Digital twin technology can not only simulate the data characteristics of physical entities to realize the real-time state evaluation and fault diagnosis of complex equipment but also reflect the entity characteristics of the modeled object completely. Therefore, a digital twin multi-body dynamic model of a planetary gearbox is established for detecting the combined fault of the sun gear missing teeth and the planetary gear missing teeth or pitting under different working conditions. The dynamic characteristics of a planetary gearbox subjected to electromechanical coupling were studied. The sun gear fault frequency, planetary gear fault frequency, and both related modulation frequencies appear in the experiment, and the corresponding fault characteristic frequencies also appear in the digital twin simulation. The validity of the proposed model was experimentally verified. Finally, the root-mean-square index was used to analyze the fault sensitivity of the traditional and digital twin models. The results show that the fault diagnosis effect of the digital twin model has a significant advantage, thus verifying the necessity of considering the effect of electromechanical coupling on the dynamic characteristics of the planetary gearbox.
Random-Delay-Corrected Deep Reinforcement Learning Framework for Real-World Online Closed-Loop Network Automation
Authors: Du Keliang , Wang Luhan , Liu Yu , Niu Haiwen , Huang Shaoxin , Wen Xiangming
Published: December 2022

The future mobile communication networks (beyond 5th generation (5G)) are evolving toward the service-based architecture where network functions are fine-grained, thereby meeting the dynamic requirements of diverse and differentiated vertical applications. Consequently, the complexity of network management becomes higher, and artificial intelligence (AI) technologies can improve AI-native network automation with their ability to solve complex problems. Specifically, deep reinforcement learning (DRL) technologies are considered the key to intelligent network automation with a feedback mechanism similar to that of online closed-loop architecture. However, the 0-delay assumptions of the standard Markov decision process (MDP) of traditional DRL algorithms cannot directly be adopted into real-world networks because there exist random delays between the agent and the environment that will affect the performance significantly. To address this problem, this paper proposes a random-delay-corrected framework. We first abstract the scenario and model it as a partial history-dependent MDP (PH-MDP), and prove that it can be transformed to be the standard MDP solved by the traditional DRL algorithms. Then, we propose a random-delay-corrected DRL framework with a forward model and a delay-corrected trajectory sampling to obtain samples by continuous interactions to train the agent. Finally, we propose a delayed-deep-Q-network (delayed-DQN) algorithm based on the framework. For the evaluation, we develop a real-world cloud-native 5G core network prototype whose management architecture follows an online closed-loop mechanism. A use case on the top of the prototype namely delayed-DQN-enabled access and mobility management function (AMF) scaling is implemented for specific evaluations. Several experiments are designed and the results show that our proposed methodologies perform better in the random-delayed networks than other methods (e.g., the standard DQN algorithm).
Fractional Importance of Various Moisture Sources Influencing Precipitation in Iran Using a Comparative Analysis of Analytical Hierarchy Processes and Machine Learning Techniques
Authors: Heydarizad Mojtaba , Pumijumnong Nathsuda , Sorí Rogert , Salari Pouya , Gimeno Luis
Published: December 2022

Studying the moisture sources responsible for precipitation in Iran is highly important. In recent years, moisture sources that influence precipitation across Iran have been studied using various methods. In this study, moisture uptake rate from individual sources that influences precipitation across Iran has been determined using the (E − P) values obtained by the FLEXPART model for the 1981–2015 period. Then, moisture uptake rate from individual sources has been used as independent parameters to investigate the fractional importance of moisture sources that influence precipitation in Iran using analytical hierarchy process (AHP) as well as machine learning (ML) methods including artificial neural networks, Decision Tree, Random Forest, Gboost, and XGboost. Furthermore, the average annual precipitation in Iran was simulated using ML methods. The results showed that the Arabian Sea has a dominant fractional influence on precipitation in both wet (November to April) and dry (May to October) periods. Simulation of precipitation amounts using the ML methods presented accurate models during the wet period, whereas the developed models for the dry period were not adequate. Finally, validation of the accuracy of the ML models using RMSE and R2 values showed that the models developed using XGboost had the highest accuracy.
Prediction of Battery Cycle Life Using Early-Cycle Data, Machine Learning and Data Management
Authors: Celik Belen , Sandt Roland , dos Santos Lara Caroline Pereira , Spatschek Robert
Published: December 2022

The prediction of the degradation of lithium-ion batteries is essential for various applications and optimized recycling schemes. In order to address this issue, this study aims to predict the cycle lives of lithium-ion batteries using only data from early cycles. To reach such an objective, experimental raw data for 121 commercial lithium iron phosphate/graphite cells are gathered from the literature. The data are analyzed, and suitable input features are generated for the use of different machine learning algorithms. A final accuracy of 99.81% for the cycle life is obtained with an extremely randomized trees model. This work shows that data-driven models are able to successfully predict the lifetimes of batteries using only early-cycle data. That aside, a considerable reduction in errors is seen by incorporating data management and physical and chemical understanding into the analysis.
NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters
Authors: Alsaif Suleiman Ali , Sassi Hidri Minyar , Ferjani Imen , Eleraky Hassan Ahmed , Hidri Adel
Published: December 2022

For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.
Chemical Defence by Sterols in the Freshwater Ciliate Stentor polymorphus
Authors: Buonanno Federico , Trenti Francesco , Achille Gabriele , Vallesi Adriana , Guella Graziano , Ortenzi Claudio
Published: December 2022

Heterotrich ciliates typically retain toxic substances in specialized ejectable organelles, called extrusomes, which are used in predator-prey interactions. In this study, we analysed the chemical defence strategy of the freshwater heterotrich ciliate Stentor polymorphus against the predatory ciliate Coleps hirtus, and the microturbellarian flatworm Stenostomum sphagnetorum. The results showed that S. polymorphus is able to defend itself against these two predators by deploying a mix of bioactive sterols contained in its extrusomes. Sterols were isolated in vivo and characterized by liquid chromatography-mass spectrometry (LC-MS), and nuclear magnetic resonance (NMR), as ergosterol, 7-dehydroporiferasterol, and their two peroxidized analogues. The assessment of the toxicity of ergosterol and ergosterol peroxide against various organisms, indicated that these sterols are essential for the effectiveness of the chemical defence in S. polymorphus.
Succinate at the Crossroad of Metabolism and Angiogenesis: Roles of SDH, HIF1α and SUCNR1
Authors: Atallah Reham , Olschewski Andrea , Heinemann Akos
Published: December 2022

Angiogenesis is an essential process by which new blood vessels develop from existing ones. While adequate angiogenesis is a physiological process during, for example, tissue repair, insufficient and excessive angiogenesis stands on the pathological side. Fine balance between pro- and anti-angiogenic factors in the tissue environment regulates angiogenesis. Identification of these factors and how they function is a pressing topic to develop angiogenesis-targeted therapeutics. During the last decade, exciting data highlighted non-metabolic functions of intermediates of the mitochondrial Krebs cycle including succinate. Among these functions is the contribution of succinate to angiogenesis in various contexts and through different mechanisms. As the concept of targeting metabolism to treat a wide range of diseases is rising, in this review we summarize the mechanisms by which succinate regulates angiogenesis in normal and pathological settings. Gaining a comprehensive insight into how this metabolite functions as an angiogenic signal will provide a useful approach to understand diseases with aberrant or excessive angiogenic background, and may provide strategies to tackle them.
Evolutionary Implications of Environmental Toxicant Exposure
Authors: Bolognesi Giorgia , Bacalini Maria Giulia , Pirazzini Chiara , Garagnani Paolo , Giuliani Cristina
Published: December 2022

Homo sapiens have been exposed to various toxins and harmful compounds that change according to various phases of human evolution. Population genetics studies showed that such exposures lead to adaptive genetic changes; while observing present exposures to different toxicants, the first molecular mechanism that confers plasticity is epigenetic remodeling and, in particular, DNA methylation variation, a molecular mechanism proposed for medium-term adaptation. A large amount of scientific literature from clinical and medical studies revealed the high impact of such exposure on human biology; thus, in this review, we examine and infer the impact that different environmental toxicants may have in shaping human evolution. We first describe how environmental toxicants shape natural human variation in terms of genetic and epigenetic diversity, and then we describe how DNA methylation may influence mutation rate and, thus, genetic variability. We describe the impact of these substances on biological fitness in terms of reproduction and survival, and in conclusion, we focus on their effect on brain evolution and physiology.
Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population
Authors: Ding Kexin , Zhou Zechen , Ma Yujia , Li Xiaoyi , Xiao Han , Wu Yiqun , Wu Tao , Chen Dafang
Published: December 2022

The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177–13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250–5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.
Bio-Inspired Nanomembranes as Building Blocks for Nanophotonics, Plasmonics and Metamaterials
Authors: Jakšić Zoran , Obradov Marko , Jakšić Olga
Published: December 2022

Nanomembranes are the most widespread building block of life, as they encompass cell and organelle walls. Their synthetic counterparts can be described as freestanding or free-floating structures thinner than 100 nm, down to monatomic/monomolecular thickness and with giant lateral aspect ratios. The structural confinement to quasi-2D sheets causes a multitude of unexpected and often counterintuitive properties. This has resulted in synthetic nanomembranes transiting from a mere scientific curiosity to a position where novel applications are emerging at an ever-accelerating pace. Among wide fields where their use has proven itself most fruitful are nano-optics and nanophotonics. However, the authors are unaware of a review covering the nanomembrane use in these important fields. Here, we present an attempt to survey the state of the art of nanomembranes in nanophotonics, including photonic crystals, plasmonics, metasurfaces, and nanoantennas, with an accent on some advancements that appeared within the last few years. Unlimited by the Nature toolbox, we can utilize a practically infinite number of available materials and methods and reach numerous properties not met in biological membranes. Thus, nanomembranes in nano-optics can be described as real metastructures, exceeding the known materials and opening pathways to a wide variety of novel functionalities.
Correlative Raman–Electron–Light (CREL) Microscopy Analysis of Lipid Droplets in Melanoma Cancer Stem Cells
Authors: Pagliari Francesca , Sogne Elisa , Panella Davide , Perozziello Gerardo , Liberale Carlo , Das Gobind , Turdo Alice , Di Franco Simone , Seco Joao , Falqui Andrea , Gratteri Santo , Pujia Arturo , Di Fabrizio Enzo , Candeloro Patrizio , Tirinato Luca
Published: December 2022

Among all neoplasms, melanoma is characterized by a very high percentage of cancer stem cells (CSCs). Several markers have been proposed for their identification, and lipid droplets (LDs) are among them. Different techniques are used for their characterization such as mass spectrometry, imaging techniques, and vibrational spectroscopies. Some emerging experimental approaches for the study of LDs are represented by correlative light–electron microscopy and by correlative Raman imaging–scanning electron microscopy (SEM). Based on these scientific approaches, we developed a novel methodology (CREL) by combining Raman micro-spectroscopy, confocal fluorescence microscopy, and SEM coupled with an energy-dispersive X-ray spectroscopy module. This procedure correlated cellular morphology, chemical properties, and spatial distribution from the same region of interest, and in this work, we presented the application of CREL for the analysis of LDs within patient-derived melanoma CSCs (MCSCs).
Ir(III) Complexes with AIE Characteristics for Biological Applications
Authors: Pei Yu , Sun Yan , Huang Meijia , Zhang Zhijun , Yan Dingyuan , Cui Jie , Zhu Dongxia , Zeng Zebing , Wang Dong , Tang Benzhong
Published: December 2022

Both biological process detection and disease diagnosis on the basis of luminescence technology can provide comprehensive insights into the mechanisms of life and disease pathogenesis and also accurately guide therapeutics. As a family of prominent luminescent materials, Ir(III) complexes with aggregation-induced emission (AIE) tendency have been recently explored at a tremendous pace for biological applications, by virtue of their various distinct advantages, such as great stability in biological media, excellent fluorescence properties and distinctive photosensitizing features. Significant breakthroughs of AIE-active Ir(III) complexes have been achieved in the past few years and great progress has been witnessed in the construction of novel AIE-active Ir(III) complexes and their applications in organelle-specific targeting imaging, multiphoton imaging, biomarker-responsive bioimaging, as well as theranostics. This review systematically summarizes the basic concepts, seminal studies, recent trends and perspectives in this area.
An Investigation of the Mechanical Properties of Basalt Fibre-Reinforced Graphite Tailings Cement Mortar
Authors: Zhang Chen , Li Ben , Yu Ying , Zhang Yu , Xu Hu , Wang Wen-xue
Published: December 2022

In order to solve the limitation of graphite tailings in improving the toughness of cement-based materials, this paper aims to study the effect of basalt fibres (BF) on the mechanical properties of graphite tailings cement mortar (GTCM). Basic physical and mechanical tests such as fluidity, water absorption, surface water content, flexural strength, compressive strength and modulus of elasticity were conducted on basalt fibre-reinforced graphite tailings cement mortar (BFR-GTCM), and combined with microscopic tests such as scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) and mercury intrusion porosimetry (MIP) for the enhancement mechanism were deeply analysed. The results show that 0.3% BF and 20% GT are the optimal doping amounts for the mechanical response of BFR-GTCM, which especially significantly improves the flexural and crack resistance. In addition, the synergistic effect of basalt fibres and graphite tailings optimizes the spatial structure and pore distribution of the cement matrix and promoted the hydration of the cementitious material, thus improving the mechanical properties of BFR-GTCM.
Building Energy Models at Different Time Scales Based on Multi-Output Machine Learning
Authors: Li Guangchen , Tian Wei , Zhang Hu , Chen Bo
Published: December 2022

Machine learning techniques are widely applied in the field of building energy analysis to provide accurate energy models. The majority of previous studies, however, apply single-output machine learning algorithms to predict building energy use. Single-output models are unable to concurrently predict different time scales or various types of energy use. Therefore, this paper investigates the performance of multi-output energy models at three time scales (daily, monthly, and annual) using the Bayesian adaptive spline surface (BASS) and deep neural network (DNN) algorithms. The results indicate that the multi-output models based on the BASS approach combined with the principal component analysis can simultaneously predict accurate energy use at three time scales. The energy predictions also have the same or similar correlation structure as the energy data from the engineering-based EnergyPlus models. Moreover, the results from the multi-time scale BASS models have consistent accumulative features, which means energy use at a larger time scale equals the summation of energy use at a smaller time scale. The multi-output models at various time scales for building energy prediction developed in this research can be used in uncertainty analysis, sensitivity analysis, and calibration of building energy models.