bluuebunny/crossref_metadata_2025 huggingface dataset:
Management and sequelae of dental implant removal
Authors: Monje Alberto , Nart José
Published: February 2022

Inappropriate and unnecessary implant therapy driven by an erroneous belief that dental implants provide enhanced function and esthetics over diseased or failing teeth has led to a growing burden of implant complications across the globe. Specifically, esthetic and biological complications frequently lead to the unfavorable prognosis of dental implants. Often, these cannot be managed predictably to improve the condition or satisfy patients' demands. In such circumstances, implant removal needs to be considered. Currently, minimally invasive methods based on reverse torque engineering are key to preserve peri‐implant soft and hard tissues. Implant replacement is now feasible, as evidenced by the high survival rates of implants placed at previously failed sites. Notwithstanding these data, clinicians should still consider carefully the expendability of an implant and whether its replacement will satisfy the prosthetic, biomechanical, and esthetic demands of the patient. In the scenario where future implant placement is desired, protocols undertaken for soft/hard tissue grafting and implant placement should be based upon defect morphology and soft and hard‐tissue characteristics. Currently, however, a lack of knowledge of the biological events and dimensional changes that arise following implant removal renders decision‐making complex and challenging, and recommendations remain largely based upon empirical speculation. This chapter will review the indications for implant replacement for prosthetic, biomechanical, and esthetic complications, alongside considerations in decision‐making, planning, implementation, and outcomes of implant replacement.
Polymorphisms and association of GRM1, GNAQ and HCRTR1 genes with seasonal reproduction and litter size in three sheep breeds
Authors: Zhu Mengting , Zhang Hongmei , Yang Hua , Zhao Zongsheng , Blair Hugh T. , Zhai Manjun , Yu Qian , Wu Pei , Fang Chenhui , Xie Mengting
Published: February 2022

Litter size is one of the important economic traits of livestock. Seasonal oestrus, ovulation and lambing of sheep have severely restricted the development of sheep farming in Xinjiang, China. The purpose of this study was to investigate the polymorphisms and genetic correlation between GRM1, GNAQ and HCRTR1 genes and the seasonal reproduction and litter size in three sheep breeds. The DNA mixed pool sequencing and PCR‐SSCP methods were used to detect single nucleotide polymorphisms (SNPs) of GRM1, GNAQ and HCRTR1 genes in seasonal oestrous (Kazakh and Chinese Merino [Xinjiang Junken type]) and perennial oestrous (Hu) sheep breeds. The association between genetic polymorphism and litter size was also analysed. The results showed that T945C in exon 2 of GRM1 gene, C589T in exon 2 of HCRTR1 gene and A191G in exon 2 of GNAQ gene were identified by Sanger sequencing, and three genotypes were existed in each SNP site, which all belonged to the synonymous mutation. GRM1 (CC), GNAQ (GA) and HCRTR1 (TC) were the dominant genotypes of seasonal reproduction and litter size in Kazakh sheep and Chinese Merino sheep, respectively, while, in perennial oestrous Hu sheep populations, the dominant genotypes were GRM1 (TC), GNAQ (GA) and HCRTR1 (TC), respectively, and association analysis also confirmed the results. The above results implied that GRM1, GNAQ and HCRTR1 genes are significantly associated with lambing traits in Kazakh, Chinese Merino and Hu sheep. Among them, the locus of GRM1 (T945C), GNAQ (A191G) and HCRTR1 (C589T) might be considered as a potential molecular marker, which controls seasonal reproduction and litter size in sheep.
Are some narratives better than others?: The impact of different narrative forms on adolescents’ intentions to text and drive
Authors: Scherr Courtney L. , Lillie Helen , Ratcliff Chelsea L. , Krakow Melinda , Liu Miao , Jensen Jakob D.
Published: February 2022

Psychological reactance theory posits individuals seek to restore freedom when threatened. Communication scholars have hypothesized persuasive messages can constitute threats to freedom. The current study engages questions about the potential for different forms of narratives in public service announcements (PSAs) to trigger freedom threats by examining responses to a PSA campaign that utilized three forms of narrative (celebrity testimonials, peer testimonials, and accident stories) to decrease adolescent texting and driving intentions. Participants (N = 214) watched anti‐texting and driving narratives, and completed measures of threat to freedom, anger, negative cognition, and attitudes/intentions toward texting/driving. Compared to celebrity/peer testimonial PSAs, accident stories triggered increased anger and, indirectly, decreased intentions to drive safely. The results also suggest the need for continued examination of the best way to model psychological reactance theory, and the value of further research explicating anger as a mechanism of message effects.
La contabilidad del desarrollo insostenible. Un análisis de contracuentas sobre la pérdida de biodiversidad en el caso JBS Brasil
Authors: Valenzuela Jiménez Luis Fernando , Suárez Rico Yuli Marcela
Published: February 2022

Este estudio muestra la responsabilidad que la multinacional JBS S.A., un gran productor de proteína animal en el mundo, ha tenido sobre la pérdida de biodiversidad por deforestación en Brasil, por más de diez años. Para ello se construye una contracuenta basada en información de diversas ONG y de organizaciones sociales que evidencian cómo los reportes de sostenibilidad de las grandes compañías maquillan comportamientos que llevan a la insostenibilidad teniendo como punto de referencia una perspectiva teórica crítica de la contabilidad y tomando como base puntual la literatura que aborda las posibilidades de la contabilidad para la emancipación, en diálogo con literatura latinoamericana sobre temas ambientales y del desarrollo. El trabajo se basa en teorías emergentes bajo el concepto de contracontabilidad, que plantean la débil efectividad del desarrollo sostenible. Los hallazgos de esta investigación evidencian las posibilidades de nuevos discursos contables para la transformación social y ambiental en los países latinoamericanos a través de maneras alternativas de rendición de cuentas, ampliando la literatura contable sobre el rol de la contabilidad en la problemática ambiental, en tanto los informes tradicionales y oficiales sólo muestran la cara conveniente a los intereses de la empresa que reporta.
Contabilidad, economía de la cultura y sectores creativos: necesidades y desafíos de una vinculación técnica y disciplinar
Authors: Arias Suárez Juan David , Asuaga Carolina Asuaga , Cano Mejía Vanessa
Published: February 2022

Este artículo reflexiona y busca llamar la atención de la comunidad académica sobre las urgentes necesidades conceptuales, técnicas y normativas de co-construir una contabilidad que represente realidades diversas y reconozca el amplio contexto social de las prácticas contables. Se hace un análisis socioeconómico de la transformación de los modelos de producción y de negocios, contemporáneamente vinculados a estructuras de economías emergentes como industrias y servicios culturales y creativos; lo cultural, lo simbólico y lo creativo representan el futuro de las industrias, los cuales se aglutinan en el estudio de la economía de la cultura, enfocada en el patrimonio cultural, las artes escénicas y la industria cultural, donde lo intangible y los nuevos modelos de negocios retan a los saberes contables y económico-financieros. Se reflexiona sobre los desafíos del control y la representación contables frente a productos, servicios y estructuras organizacionales y comerciales, que no siguen los cánones habituales de la existencia material y el intercambio económico. La necesidad de una contabilidad sociocultural que controle, represente y de cuenta de las nuevas realidades económico-culturales, establece desafíos de una vinculación técnica y disciplinar de la contabilidad con la economía de la cultura.
Mechanical Characterization and Validation of the Dynamic Collimation System Prototype for Proton Radiotherapy
Authors: Geoghegan Theodore , Patwardhan Kaustubh , Nelson Nicholas , Hill Patrick , Flynn Ryan , Smith Blake , Hyer Daniel
Published: February 2022

Radiation therapy is integral to cancer treatments for more than half of patients. Pencil beam scanning (PBS) proton therapy is the latest radiation therapy technology that uses a beam of protons that are magnetically steered and delivered to the tumor. One of the limiting factors of PBS accuracy is the beam cross-sectional size, similar to how a painter is only as accurate as the size of their brush allows. To address this, collimators can be used to shape the beam along the tumor edge to minimize the dose spread outside of the tumor. Under development is a dynamic collimation system (DCS) that uses two pairs of nickel trimmers that collimate the beam at the tumor periphery, limiting dose from spilling into healthy tissue. Herein, we establish the dosimetric and mechanical acceptance criteria for the DCS based on a functioning prototype and Monte Carlo methods, characterize the mechanical accuracy of the prototype, and validate that the acceptance criteria are met. From Monte Carlo simulations, we found that the trimmers must be positioned within ±0.5 mm and ±1.0 deg for the dose distributions to pass our gamma analysis. We characterized the trimmer positioners at jerk values up to 400 m/s3 and validated their accuracy to 50 μm. We measured and validated the rotational trimmer accuracy to ±0.5 deg with a FARO® ScanArm. Lastly, we calculated time penalties associated with the DCS and found that the additional time required to treat one field using the DCS varied from 25–52 s.
Pyrazinamide Susceptibility Is Driven by Activation of the SigE-Dependent Cell Envelope Stress Response in Mycobacterium tuberculosis
Authors: Thiede Joshua M. , Dillon Nicholas A. , Howe Michael D. , Aflakpui Ranee , Modlin Samuel J. , Hoffner Sven E. , Valafar Faramarz , Minato Yusuke , Baughn Anthony D.
Published: February 2022

For decades, pyrazinamide has served as a cornerstone of tuberculosis therapy. Unlike any other antitubercular drug, pyrazinamide requires an acidic environment to exert its action.
Commensal Pseudomonas fluorescens Strains Protect
Arabidopsis
from Closely Related Pseudomonas Pathogens in a Colonization-Dependent Manner
Authors: Wang Nicole R. , Wiesmann Christina L. , Melnyk Ryan A. , Hossain Sarzana S. , Chi Myoung-Hwan , Martens Kitoosepe , Craven Kelly , Haney Cara H.
Published: February 2022

Microbiota can protect diverse hosts from pathogens, and microbiome dysbiosis can result in increased vulnerability to opportunistic pathogens. Here, we developed a rhizosphere commensal-pathogen model to identify bacterial strains and mechanisms that can protect plants from an opportunistic
Pseudomonas
pathogen.
Incidence of an Intracellular Multiplication Niche among Acinetobacter baumannii Clinical Isolates
Authors: Rubio Tristan , Gagné Stéphanie , Debruyne Charline , Dias Chloé , Cluzel Caroline , Mongellaz Doriane , Rousselle Patricia , Göttig Stephan , Seifert Harald , Higgins Paul G. , Salcedo Suzana P.
Published: February 2022

Multidrug-resistant
Acinetobacter baumannii
isolates are associated with significant morbidity and mortality in hospitals worldwide. Understanding their pathogenicity is critical for improving therapeutic management.
Dissemination Routes of Carbapenem and Pan-Aminoglycoside Resistance Mechanisms in Hospital and Urban Wastewater Canalizations of Ghana
Authors: Delgado-Blas Jose F. , Valenzuela Agüi Cecilia , Marin Rodriguez Elena , Serna Carlos , Montero Natalia , Saba Courage Kosi Setsoafia , Gonzalez-Zorn Bruno
Published: February 2022

Antimicrobial resistance (AMR) is one the major threats to public health today, especially resistance to last-resort compounds for the treatment of critical infections, such as carbapenems and aminoglycosides. Innumerable works have focused on the clinical ambit of AMR, but studies addressing the impact of wastewater cycles on the emergence and dissemination of resistant bacteria are still limited.
Bile Acids, Gut Microbes, and the Neighborhood Food Environment—a Potential Driver of Colorectal Cancer Health Disparities
Authors: Wolf Patricia G. , Byrd Doratha A. , Cares Kate , Dai Hanchu , Odoms-Young Angela , Gaskins H. Rex , Ridlon Jason M. , Tussing-Humphreys Lisa
Published: February 2022

Bile acids (BAs) facilitate nutrient digestion and absorption and act as signaling molecules in a number of metabolic and inflammatory pathways. Expansion of the BA pool and increased exposure to microbial BA metabolites has been associated with increased colorectal cancer (CRC) risk.
Supplemental Material: Shallow distributed faulting in the Imperial Valley, California, USA
Authors: Sahakian Valerie J. , al. et
Published: February 2022

Description of methods (seismic data processing, 14C dating, interpretations, and Coulomb stress models), supplemental figures, and interpreted fault locations.
Lens capsule-related complications in femtosecond laser-assisted cataract surgery: a study based on video analysis
Authors: Wang Wei , Chen Xinyi , Liu Xin , Zhang Xiaobo , Lyu Danni , Yao Ke
Published: February 2022

Purpose

To analyse the occurrence and potential causes of lens capsule-related complications during femtosecond laser-assisted cataract surgery (FLACS).

Methods

This prospective consecutive cohort study included the first 1600 eyes (from 1140 consecutive patients) who received FLACS performed by the same surgeon from May 2015 to December 2018. The potential causes and characteristic signs of capsulotomy-related complications, including incomplete capsulotomies and radial anterior capsule (AC) tears, were summarised based on the agreement of two ophthalmologists after they analysed the surgical videos. Subgroup analysis was conducted to characterise the capsulotomy learning curve.

Results

Of the 1600 eyes, 52 (3.25%) had incomplete capsulotomies and 22 (1.38%) had radial AC tears. The most common causes of incomplete capsulotomies were eye tilt (16 eyes, 30.77%), air bubbles or ocular secretions at the interface (14 eyes, 26.92%) and white cataracts (7 eyes, 13.46%). Additionally, 54.55% (12/22) of AC tears were due to incomplete capsulotomy and secondary capsulorhexis. A significant difference was noted between the first 200 eyes and subsequent groups in terms of the incidence of incomplete capsulotomies. No difference was observed in the incidence of AC tears after the initial 100 procedures.

Conclusion

The most common causes of incomplete capsulotomies were eye tilt and air bubbles or ocular secretions at the interface. Secondary capsulorhexis after incomplete capsulotomy is the main risk factor for AC tears. There was a steep learning curve for laser capsulotomy in the first 100 operated eyes, as evidenced by the higher complication rate, but this stabilised after 200 procedures.
Cerebrospinal fluid biomarkers of disease activity and progression in amyotrophic lateral sclerosis
Authors: Dreger Marie , Steinbach Robert , Otto Markus , Turner Martin R , Grosskreutz Julian
Published: February 2022

Amyotrophic lateral sclerosis (ALS) is a relentlessly progressive neurodegenerative disease, and only modest disease-modifying strategies have been established to date. Numerous clinical trials have been conducted in the past years, but have been severely hampered by the wide-ranging heterogeneity of both the biological origins and clinical characteristics of the disease. Thus, reliable biomarkers of disease activity are urgently needed to stratify patients into homogenous groups with aligned disease trajectories to allow a more effective design of clinical trial. In this review, the most promising candidate biomarkers in the cerebrospinal fluid (CSF) of patients with ALS will be summarised. Correlations between biomarker levels and clinical outcome parameters are discussed, while highlighting potential pitfalls and intercorrelations of these clinical parameters. Several CSF molecules have shown potential as biomarkers of progression and prognosis, but large, international, multicentric and longitudinal studies are crucial for validation. A more standardised choice of clinical endpoints in these studies, as well as the application of individualised models of clinical progression, would allow the quantification of disease trajectories, thereby allowing a more accurate analysis of the clinical implications of candidate biomarkers. Additionally, a comparative analysis of several biomarkers and ideally the application of a multivariate analysis including comprehensive genotypic, phenotypic and clinical characteristics collectively contributing to biomarker levels in the CSF, could promote their verification. Thus, reliable prognostic markers and markers of disease activity may improve clinical trial design and patient management in the direction of precision medicine.
An alternative simulation framework to evaluate the sustainability of annual harvest on large forest estates
Authors: Fortin Mathieu , Sattler Derek , Schneider Robert
Published: February 2022

Sustainability is central to forest management. To determine the sustainable annual harvest, practitioners rely on a simulation framework that combines inventory data, growth models, and optimization software. Because this standard simulation framework is based on model predictions aggregated into yield tables, it may not properly capture natural dynamics. In this paper, we designed an alternative simulation framework that does not require aggregated model predictions. However, the growth model must implement a harvest submodel and produce stochastic predictions. To showcase this alternative simulation framework, we used a forest management unit in southwestern Quebec, Canada, and compared our simulation results with those of the standard simulation framework. Our alternative simulation framework showed that the standing volume of most coniferous species would decrease, whereas that of maple species would increase over the 21st century. The annual harvest of one species as determined through the standard simulation framework was found to be unsustainable in the alternative simulation framework. Being much lighter in terms of computation, this alternative simulation framework can be used as a complement to the standard simulation framework, notably for checking if the optimization-based annual harvest is sustainable.
Northern West Siberian–South Kara Composite Tectono-Sedimentary Element, Siberian Arctic
Authors: Deev E. V. , Shemin G. G. , Vernikovsky V. A. , Drachev S. S. , Matushkin N. Yu. , Glazyrin P. A.
Published: February 2022

The Northern West Siberian–South Kara Composite Tectono-Sedimentary Element (NWSSK CTSE) occupies over 1 million km
2
of the northern West Siberian Basin and the South Kara Basin. It formed as a result of the latest Permian–earliest Triassic crustal extension following thermal subsidence. The Early Triassic palaeorifts are buried under 10–12 km-thick post-rift Middle Triassic–Quaternary siliciclastic sediments. In the Tithonian–Early Berriasian period, a deep-water anoxic depression formed in the West Siberian Basin and accumulated the organic-rich carbonate-siliceous Bazhenov shales that are the main source of hydrocarbons.

The West Siberian Basin, including the NWSSK CTSE, is one of the major petroleum provinces worldwide. Oil and gas deposits have been identified in Paleozoic carbonate basement rocks and Triassic, Jurassic and Cretaceous reservoirs. The main gas reserves are concentrated in the Lower Aptian–Cenomanian play. Significant gas and oil reserves are located in the Berriasian–lowest Aptian and Jurassic plays.

The CTSE comprises 18–20% of the world's known gas reserves and provides approximately 90% of all Russian natural gas, which comprises 22% of global gas extraction. In this chapter we provide a summary of the CTSE geology and petroleum geology based on bulk results published mostly in the Russian literature.
NLUBroker
: A QoE-driven Broker System for Natural Language Understanding Services
Authors: Xu Lanyu , Iyengar Arun , Shi Weisong
Published: February 2022

Cloud-based Natural Language Understanding (NLU) services are becoming more popular with the development of artificial intelligence. More applications are integrated with cloud-based NLU services to enhance the way people communicate with machines. However, with NLU services provided by different companies powered by unrevealed AI technology, how to choose the best one is a problem for developers. Existing tools that can provide guidance to developers and make recommendations based on their needs are severely limited. This article comprehensively evaluates multiple state-of-the-art NLU services, and the results indicate that there is no absolute winner for different usage requirements. Motivated by this observation, we provide several insights and propose 
NLUBroker
, a Quality of Experience-driven (QoE-driven) broker system, to select the proper service according to the environment.
NLUBroker
senses the client and service status and leverages a solution to the multi-armed bandit problem to conduct online learning, aiming to achieve maximum expected QoE. The performance of 
NLUBroker
is evaluated in both simulation and real-world environments, and the evaluation results demonstrate that 
NLUBroker
is an efficient solution for selecting NLU services. It is adaptive to changes in the environment, outperforms three baseline methods we evaluated and improves overall QoE up to 1.5× for the evaluated state-of-the-art NLU services.
Simulating and Modeling the Risk of Conversational Search
Authors: Wang Zhenduo , Ai Qingyao
Published: February 2022

In conversational search, agents can interact with users by asking clarifying questions to increase their chance of finding better results. Many recent works and shared tasks in both natural language processing and information retrieval communities have focused on identifying the need to ask clarifying questions and methodologies of generating them. These works assume that asking a clarifying question is a safe alternative to retrieving results. As existing conversational search models are far from perfect, it is possible and common that they could retrieve/generate bad clarifying questions. Asking too many clarifying questions can also drain a user’s patience when the user prefers searching efficiency over correctness. Hence, these models can backfire and harm a user’s search experience due to these risks from asking clarifying questions.

  In this work, we propose a simulation framework to simulate the risk of asking questions in conversational search and further revise a risk-aware conversational search model to control the risk. We show the model’s robustness and effectiveness through extensive experiments on three conversational datasets — MSDialog, Ubuntu Dialog Corpus, and Opendialkg — in which we compare it with multiple baselines. We show that the risk-control module can work with two different re-ranker models and outperform all of the baselines in most of our experiments.
Network Temperature: A Novel Statistical Index for Networks Measurement and Management
Authors: Wang Changda , Li Xiaowei , Bertino Elisa
Published: February 2022

Being able to monitor each packet path is critical for effective measurement and management of networks. However, such detailed monitoring can be very expensive especially for large-scale networks. To address such problem, inspired by thermodynamics, which uses the statistical characteristics of a large number of molecules’ motion but not each molecule’s trajectory for analysis, we propose the new concept of
network temperature
together with the notions of
network-specific heat
and
network temperature gradient
. Our approach does not only provide a statistical view of the current network state consisting of all the active packet paths at each time instant, but can be used to represent transitions among network states. Our network temperature-based methods have a broad applicability, such as to DDoS detection, dynamic node importance ranking, network stability and robustness evaluation, reliable packets routing, provenance compression assessment, and so on. Numerical and/or the experimental results show that our methods are effective.
Low-Voltage Cathodoluminescent Phosphors
Authors: Shea Lauren E.
Published: February 2022

Phosphors have been used for the display of information since the invention of the cathode-ray tube (CRT) by Karl Ferdinand Braun in 1897. With the development of color television, an effort spanning approximately 30 years, came the most significant advances in phosphor technology. The most noteworthy was the shift to the all-sulfide system, and discovery of the red, rare-earth oxysulfide phosphors.
Full Solar Spectrum Photocatalytic H2 Production Base on Polynary Composite
Authors: Zhang Junying
Published: February 2022

Developing photocatalysts working from ultraviolet (UV) to near-infrared (NIR) region is a challenge. To improve the absorption to the visible and NIR lights, and to enhance the photocatlytic activity, we focus on researches in two aspects: (1) Fine-tuning the morphology of the component materials to expose the active facets, form successive surface heterojunction or phase junction. (2) Composing the wide band-gap semiconductors with narrow band-gap semiconductors, plasmonic Au nanoparticles or nanorods, and NIR absorbing black phosphorus. The thus obtained polynary composites could make full use of solar energy to photocatalytically release H2 from water.
Characterization of temporospatial distribution of renal tubular casts by nephron tracking after ischemia-reperfusion injury
Authors: Shin Naomi S. , Marlier Arnaud , Xu Leyuan , Lam TuKiet , Cantley Lloyd G. , Guo Jian-Kan
Published: February 2022

This detailed characterization of the dynamic distribution of dead cell debris in ischemically injured kidney tubules reveals which cells in the kidney are most severely injured, when and where tubular casts form, and when (and to a lesser extent, how) they are cleared.
Short-term daytime restricted feeding in rats with high salt impairs diurnal variation of Na+excretion
Authors: Rhoads Megan K. , Speed Joshua S. , Roth Kaehler J. , Zhang Dingguo , Jin Chunhua , Gamble Karen L. , Pollock David M.
Published: February 2022

Timing of food intake normally has little effect on the diurnal pattern of Na+and water excretion. However, rats on a high-salt diet were unable to maintain this pattern, yet K+excretion was more readily adjusted to match timing of intake. These data support the hypothesis that Na+and water homeostasis are impacted by timing of high-salt diets.
Potential renal stem/progenitor cells identified by in vivo lineage tracing
Authors: Zhang Wenzheng , Gao Chao , Tsilosani Akaki , Samarakoon Rohan , Plews Robert , Higgins Paul J.
Published: February 2022

Mammalian kidneys consist of more than 30 different types of cells. A challenging task is to identify and characterize the stem/progenitor subpopulations that establish the lineage relationships among these cellular elements during nephrogenesis in the embryonic and neonate kidneys and during tissue homeostasis and/or injury repair in the mature kidney. Moreover, the potential clinical utility of stem/progenitor cells holds promise for the development of new regenerative medicine approaches for the treatment of renal diseases. Stem cells are defined by unlimited self-renewal capacity and pluripotentiality. Progenitor cells have pluripotentiality but no or limited self-renewal potential. Cre-LoxP-based in vivo genetic lineage tracing is a powerful tool to identify stem/progenitor cells in their native environment. Hypothetically, this technique enables investigators to accurately track the progeny of a single cell or a group of cells. The Cre/LoxP system has been widely used to uncover the function of genes in various mammalian tissues and to identify stem/progenitor cells through in vivo lineage tracing analyses. In this review, we summarize the recent advances in the development and characterization of various Cre drivers and their use in identifying potential renal stem/progenitor cells in both developing and mature mouse kidneys.
Reinforcement Learning-Based Service-Oriented Dynamic Multipath Routing in SDN
Authors: Chiu Kai-Cheng , Liu Chien-Chang , Chou Li-Der
Published: February 2022

The increasing quality and various requirements of network services are guaranteed because of the advancement of the emerging network paradigm, software-defined networking (SDN), and benefits from the centralized and software-defined architecture. The SDN not only facilitates the configuration of the network policies for traffic engineering but also brings convenience for network state obtainment. The traffic of numerous services is transmitted within a network, whereas each service may demand different network metrics, such as low latency or low packet loss rate. Corresponding quality of service policies must be enforced to meet the requirements of different services, and the balance of link utilization is also indispensable. In this research, Reinforcement Discrete Learning-Based Service-Oriented Multipath Routing (RED-STAR) has been proposed to understand the policy of distributing an optimal path for each service. The RED-STAR takes the network state and service type as input values to dynamically select the path a service must be forwarded. Custom protocols are designed for network state obtainment, and a deep learning-based traffic classification model is also integrated to identify network services. With the differentiated reward scheme for every service type, the reinforcement learning model in RED-STAR gradually achieves high reward values in various scenarios. The experimental results show that RED-STAR can adopt the dynamic network environment, obtaining the highest average reward value of 1.8579 and the lowest average maximum bandwidth utilization of 0.3601 among all path distribution schemes in a real-case scenario.
Data Marketing Optimization Method Combining Deep Neural Network and Evolutionary Algorithm
Authors: Wang Wei
Published: February 2022

The design and optimization of personalized marketing strategy have become an important direction for e-commerce enterprises to meet the differentiated needs of consumers, innovate service content, and improve their core competitiveness. It is important to analyze the characteristics of personalized marketing in the e-commerce environment and to study and establish the optimization model of personalized marketing strategy and the model solving method, which are suitable for the application environment. In order to develop a new e-commerce model for consumers, innovate the online service content of enterprises, and improve consumer satisfaction, this paper improved two topological weight evolution methods of evolutionary neural networks and used them as tools for model solving, with the objectives of attracting potential consumers, improving consumer satisfaction, and maximizing revenue. The results of the experiments show that the proposed model is a good one. The experimental results show that the optimization model and model solving method proposed in this paper can efficiently build consumer demand and preference models from large-scale data and can help e-commerce enterprises to formulate accurate personalized promotion and pricing strategies to maximize their profits.
Evaluation Method of Human Resource Management Efficiency of Chain Retail Enterprises Based on Distributed Database
Authors: Jiang Xiaolong , Sohail Mishal
Published: February 2022

The calculation of human resource management efficiency evaluation method is large, which affects the technical efficiency and scale efficiency of evaluation results. Based on distributed database, an evaluation method of human resource management efficiency of chain retail enterprises is proposed. Genetic algorithm is applied to the design of distributed database to realize the best data allocation scheme. The distributed database is used to store the human resource information of chain retail enterprises to ensure the data consistency and information availability. Select the input and output elements that can best reflect the human resource management status of chain retail enterprises in the distributed database, design the management efficiency evaluation index system, and construct the DEA evaluation model. After testing, the technical efficiency and scale efficiency of the design method in this paper are higher than the evaluation methods of human resource management efficiency of chain retail enterprises based on principal component analysis and fuzzy comprehensive evaluation, which is conducive to management decision-making.
Research on Big Data-Driven Rural Revitalization Sharing Cogovernance Mechanism Based on Cloud Computing Technology
Authors: Liu Zhuan , Gao Peng , Li Weixiao
Published: February 2022

The political arrangement and strategic action to realize socialist sharing in the new era is the rural revitalization strategy, which is a development strategy to complete the historical task of farmers living a better life and solve the problem of insufficient rural development. Data sharing and driving, on the other hand, remain the weak links in China’s digital village construction. This paper proposes to develop a BD (big data)-driven rural revitalization governance, sharing, and monitoring platform based on CC (cloud computing) technology to promote the reshaping of rural governance patterns and to provide systematic support for the implementation of rural revitalization strategies. Knowledge governance rules and processes for rural community public affairs are created in a BD environment. Clustering in feature space is considered to address the low efficiency caused by clustering algorithms in high-dimensional data. The method proposed in this paper is perfectly matched in both the preprocessing and clustering steps, which not only ensures the algorithm’s accuracy but also significantly improves its efficiency.
On q‐Convex Functions Defined by the q‐Ruscheweyh Derivative Operator in Conic Regions
Authors: Jabeen Mehwish , Nawaz Malik Sarfraz , Mahmood Shahid , Riaz S. M. Jawwad , Ali Md. Shajib
Published: February 2022

The core objective of this article is to introduce and investigate a new class of convex functions associated with the conic domain defined by the Ruscheweyh q‐differential operator. Many interesting properties such as sufficiency criteria, coefficient bounds, partial sums, and radius of convexity of order α for the functions of the said class are investigated here.
Management of Edentulous Microstomia Patient: A Case Report and Classification System
Authors: Abbasi Maria S. , Ahmed Naseer , Irfan Adil Bin , Al-Saleh Samar , Abduljabbar Tariq , Vohra Fahim
Published: February 2022

Microstomia is an abnormally reduced oral aperture. In the literature, it is not classified by any particular size criteria, rather defined by its effects on function and esthetics. Prosthodontic management of edentulous patients with microstomia is a challenging task. Use of conventional methods for recording an impression and fabricating prosthesis is not effective in such patients. To fabricate well-fitting prosthesis, accuracy of impression recording important anatomic landmarks is essential. Formation of an exacting custom tray and diagnostic cast is critical for final impression accuracy. Provision of a well-fitting prosthesis in microstomia patient will restore esthetics, comfort, and function with oral and systemic patient wellbeing. This paper presents a case report of managing an edentulous microstomia patient with sectional removable prosthesis. Furthermore, it proposes a novel classification system for microstomia patients according to severity of the condition.
Neurobiological Promises of the Bitter Diterpene Lactone Andrographolide
Authors: Hossain Rajib , Quispe Cristina , Herrera-Bravo Jesús , Beltrán Jorge F. , Islam Muhammad Torequl , Shaheen Shabnum , Cruz-Martins Natália , Martorell Miquel , Kumar Manoj , Sharifi-Rad Javad , Ozdemir Fethi Ahmet , Setzer William N. , Alshehri Mohammed M. , Calina Daniela , Cho William C.
Published: February 2022

Andrographolide (ANDRO), a bitter diterpene lactone found in Andrographis paniculata (Burm.f.) Nees, possesses several biological effects such as antioxidant, anti-inflammatory, and organo-protective effects. Scientific reports suggest that it also has neuroprotective capacity in various test systems. The purpose of this review was to synthesize the neuropharmacological properties of ANDRO and highlight the molecular mechanisms of action that highlight these activities. A careful search was done in PubMed and Google Scholar databases using specific keywords. Findings suggest that ANDRO possess neuroprotective, analgesic, and antifatigue effects. Prominent effects were stated on neuro-inflammation, cerebral ischemia, Alzheimer’s and Parkinson’s diseases, multiple sclerosis, and brain cancer in mice and rats. Furthermore, ANDRO and its derivatives can enhance memory and learning capacity in experimental animals (rats) without causing any toxicity in the brain. Thus, ANDRO may be one of the most promising plant-based psychopharmacological lead compounds for new drug development.
Research on Robot Positioning and Navigation Algorithm Based on SLAM
Authors: Dai Yue
Published: February 2022

In the industrial field, industrial robots have taken over the heavy lifting that used to be done by traditional handicraft assembly lines, greatly freeing up human resources and improving production efficiency and safety. As a result, the focus of this paper is on the SLAM-based robot localization and navigation algorithm (simultaneous localization and mapping). An attitude estimation algorithm based on KF (Kalman filtering) information fusion of vision SLAM and IMU (Inertial Measurement Unit) is proposed, and the ORB-SLAM algorithm is studied and perfected. The fusion of the two postures improves the accuracy and frequency of the robot’s attitude estimation during motion. In addition, PSO (Particle Swarm Optimization) technology is used to optimize the resampling process, and PSO optimizes the particle set to alleviate the problem of particle degradation and exhaustion caused by resampling in the FastSLAM algorithm. Finally, the algorithm is verified to meet the requirements of positioning and composition accuracy, as well as the feasibility and effectiveness of robot autonomous navigation, using the open simulation platform.
Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers
Authors: Pang Gefeng , Bao Anze
Published: February 2022

Text makes up a large portion of network data because it is the vehicle for people’s direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.
A Comprehensive Convolutional Neural Network Survey to Detect Glaucoma Disease
Authors: Zafar Afia , Aamir Muhammad , Nawi Nazri Mohd , Ali Sikandar , Husnain Mujtaba , Samad Ali
Published: February 2022

Due to recent advances in computer technology and the accessibility of large datasets, deep learning has become at the forefront of artificial intelligence and on various tasks, especially those related to image classification and modelling, its performance is often equal to or even better than human appreciation. Ophthalmology has always been in an ideal position to employ one of CNN’s most popular deep learning algorithms to evaluate vast volumes of data from these tests since it is one of the health practices that focuses extensively on imaging. Glaucoma is one among the situations in which CNN can benefit from the enormous quantity of data collected by tests that assess the anatomy as well as function of the optic nerve and macula. We discussed the recommended use of CNN algorithm for specific glaucoma scenarios, such as fundus photography screening and diagnosis and detection of the course of glaucoma through OCT imaging modality. The purpose of this article is not only to critically examine and discuss the latest applications of CNN models in glaucoma but also to focus on the challenges associated with developing such models for screening, diagnosis, and progress detection. After a brief overview of the clinical practices and their comparison with conventional clinical methods, we discussed training and validation of CNN algorithm and how it was developed and why it is particularly suitable for glaucoma. The following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to detection of the glaucoma disease using conventional and nonconventional approaches; (ii) it covers a very different outlook of the glaucoma disease by providing in-depth discussions of the existing works at different granularity levels, that is, from primary to mediatory to the severe level; (iii) this state-of-the-art review covers each article in the following dimensions: the computer-based approach to tomographic model; analysis of different datasets; and summarizing the literature review in a disciplined way by mentioning the research gap concluded with discussion on future work.
Avian Communities of Alatish National Park, Ethiopia
Authors: Tesfahun Takele , Ejigu Dessalegn
Published: February 2022

Alatish National Park, which is located in the Amhara National Regional State at the border with the Republic of Sudan, is rich in its avian resources. To study the park’s avian communities, the study area was classified based on its vegetation structures into riverine woodland, wooded grassland, woodland, and bamboo woodland. Data were collected from July 2017 to April 2018 using line transect and point count methods. As a result, 132 species of birds belonging to 18 orders and 55 families were identified. The highest species diversities during the wet (H’ = 3.53) and dry (H’ = 3.55) seasons were recorded in the riverine woodland habitat. Species evenness was also the highest during the wet (E = 0.79) and dry (E = 0.77) seasons in the riverine woodland habitat. The bamboo woodland habitat harboured the lowest species diversity during the wet (H’ = 2.81) and dry (H’ = 2.45) seasons. More species similarity (SI = 0.90) was recorded between wooded grassland and bamboo woodland habitats during the dry season. Relative abundance of birds among the different species showed significant differences (F131, 1452 = 2.94,P<0.05), and most birds in the area were identified as uncommon species. Forest fire, logging, and overgrazing by cattle have significantly contributed to habitat destruction in the park which might act as the cause for reduction in avian diversity. Therefore, appropriate conservation strategies should be designed and implemented to minimize avian habitat destruction due to various anthropogenic activities within the park.
Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor
Authors: Sheng Jianqiang , Wang Fei , Zhao Baoquan , Jiang Junkun , Yang Yu , Cai Tie
Published: February 2022

With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.
Digital Planning and Design of Landscape Based on Intelligent Sensor Network
Authors: Wu Wei , Zou Jiaxi
Published: February 2022

According to the development of landscape digitization and the actual market demand, a digital landscape system based on intelligent sensor network is designed and implemented. The system consists of two parts: sensor node and display terminal, forming a star intelligent sensor network. Sensor node measurement is sent to display control terminal through intelligent sensor network. The display control terminal serves as the aggregation node. Based on geometry transformation, free form, and bionics, the method of constructing complex surface and the method strategy of optimizing complex surface are put forward from geometry and bionics theory. Then, the material types and construction methods of landscape composite surface are discussed and studied. According to the process and project of site cognition and landscape planning and design, six special models are established: ecological sensitivity evaluation model, construction suitability evaluation model, project site selection model, road line selection model, quasinatural waterscape construction model, and vertical design model. According to the characteristics of each landscape planning and design project, the logic generation and parameter composition of the model are discussed, and the application of the model is empirically studied and discussed based on actual cases.