bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Low-Precision Structured Subnetworks Using Joint Layerwise Channel Pruning and Uniform Quantization
Authors: Zhang Xinyu , Colbert Ian , Das Srinjoy
Published: August 2022

Pruning and quantization are core techniques used to reduce the inference costs of deep neural networks. Among the state-of-the-art pruning techniques, magnitude-based pruning algorithms have demonstrated consistent success in the reduction of both weight and feature map complexity. However, we find that existing measures of neuron (or channel) importance estimation used for such pruning procedures have at least one of two limitations: (1) failure to consider the interdependence between successive layers; and/or (2) performing the estimation in a parametric setting or by using distributional assumptions on the feature maps. In this work, we demonstrate that the importance rankings of the output neurons of a given layer strongly depend on the sparsity level of the preceding layer, and therefore, naïvely estimating neuron importance to drive magnitude-based pruning will lead to suboptimal performance. Informed by this observation, we propose a purely data-driven nonparametric, magnitude-based channel pruning strategy that works in a greedy manner based on the activations of the previous sparsified layer. We demonstrate that our proposed method works effectively in combination with statistics-based quantization techniques to generate low precision structured subnetworks that can be efficiently accelerated by hardware platforms such as GPUs and FPGAs. Using our proposed algorithms, we demonstrate increased performance per memory footprint over existing solutions across a range of discriminative and generative networks.
Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
Authors: Huo Weiyue , Zhu Jihong , Zhou Jing , Dan Yangqing , Dai Pan , Lin Ling
Published: August 2022

Switched-capacitor converters (SCCs) have been developed for decades and applied successfully in many applications thanks to their merits of high power density, high efficiency, and low cost. Existing SCC evaluation methods do not provide an efficient and effective way to estimate their performance. This paper proposes a new evaluation performance method for SCCs that transforms the topology optimization issue into a more typical non-linear regression problem. The SCC topologies are described by the adjacent matrix. A CNN model is then selected to solve the regression problem. Experimental results confirm that this CNN model greatly shortens the calculation time while performing better than other machine learning algorithms.
Vision-Based Robotic Grasping of Reels for Automatic Packaging Machines
Authors: Comari Simone , Carricato Marco
Published: August 2022

In this work, we present a vision system particularly suited to the automatic recognition of reels in the field of automatic packaging machines. The output of the vision system is used to guide the autonomous grasping of the reels by a robot for a subsequent manipulation task. The proposed solution is built around three different methods to solve the ellipse-detection problem in an image. Such methods leverage standard image processing and mathematical algorithms, which are tailored to the targeted application. An experimental campaign demonstrates the efficacy of the proposed approach, even in the presence of low computational power and limited hardware resources, as in the use-case at hand.
Missing Well Logs Prediction Based on Hybrid Kernel Extreme Learning Machine Optimized by Bayesian Optimization
Authors: Qiao Lei , Cui You , Jia Zhining , Xiao Kun , Su Haonan
Published: August 2022

Geophysical logging is an essential measurement tool in the oil/gas exploration and development field. In practice, predicting missing well logs is an effective way to reduce the exploration expenses. Because of the complexity and heterogeneity of the reservoir, there must be strong nonlinear correlations between the well logs. To improve the accuracy and stability of the missing well logs prediction, a method based on a Bayesian optimized hybrid kernel extreme learning machine (BO-HKELM) algorithm is proposed. Firstly, the LightGBM algorithm is applied to screen out important features related to the missing well logs and reduce the input dimension of the prediction model. Secondly, the hybrid kernel extreme learning machine (HKELM) algorithm is applied to construct the missing well logs prediction model, and the hyperparameters (C0,C1,d,σ,C) of the model are optimized by the Bayesian algorithm. Finally, the BO-HKELM model is applied to the prediction of the missing well logs in a block of the Ordos Basin in China. The results show that the RMSE, MAE, and R-square predicted by the BO-HKELM model are 0.0767, 0.0613, and 0.9029, respectively. It can be found that the BO-HKELM model has better regression accuracy and generalization ability, and can estimate missing logs more accurately than the traditional machine learning methods, which provides a promised method for missing well logs estimation.
Ab Initio Phase Diagram of Chromium to 2.5 TPa
Authors: Baty Samuel R. , Burakovsky Leonid , Luscher Darby J. , Sjue Sky K. , Errandonea Daniel
Published: August 2022

Chromium possesses remarkable physical properties such as hardness and corrosion resistance. Chromium is also a very important geophysical material as it is assumed that lighter Cr isotopes were dissolved in the Earth’s molten core during the planet’s formation, which makes Cr one of the main constituents of the Earth’s core. Unfortunately, Cr has remained one of the least studied 3d transition metals. In a very recent combined experimental and theoretical study (Anzellini et al., Scientific Reports, 2022), the equation of state and melting curve of chromium were studied to 150 GPa, and it was determined that the ambient body-centered cubic (bcc) phase of crystalline Cr remains stable in the whole pressure range considered. However, the importance of the knowledge of the physical properties of Cr, specifically its phase diagram, necessitates further study of Cr to higher pressure. In this work, using a suite of ab initio quantum molecular dynamics (QMD) simulations based on the Z methodology which combines both direct Z method for the simulation of melting curves and inverse Z method for the calculation of solid–solid phase transition boundaries, we obtain the theoretical phase diagram of Cr to 2.5 TPa. We calculate the melting curves of the two solid phases that are present on its phase diagram, namely, the lower-pressure bcc and the higher-pressure hexagonal close-packed (hcp) ones, and obtain the equation for the bcc-hcp solid–solid phase transition boundary. We also obtain the thermal equations of state of both bcc-Cr and hcp-Cr, which are in excellent agreement with both experimental data and QMD simulations. We argue that 2180 K as the value of the ambient melting point of Cr which is offered by several public web resources (“Wikipedia,” “WebElements,” “It’s Elemental,” etc.) is most likely incorrect and should be replaced with 2135 K, found in most experimental studies as well as in the present theoretical work.
The Role of a DirectDensity® CT Reconstruction in a Radiotherapy Workflow: A Phantom Study
Authors: Feliciani Giacomo , Guidi Claretta , Belli Maria Luisa , D’Errico Vincenzo , Loi Emiliano , Mezzenga Emilio , Sarnelli Anna
Published: August 2022

The DirectDensity® CT reconstruction algorithm provides a reconstruction approach independent of the tube voltage, directly reconstructing the CT projection data into CT numbers related to the electron densities of the materials. This work examines the efficacy of DirectDensity® in the treatment planning process with both tissues and metallic materials. CT scans of a Cheese phantom were acquired at 80, 100, 120 and 140 kVp and reconstructed with different algorithms. Calibration curves were built for each kVp and reconstruction technique. To evaluate the flexibility of the DirectDensity® in dose calculations, a prostate cancer treatment plan was simulated on phantom images with and without metal inserts. Moreover, the robustness of the algorithm was tested by simulating a possible error in the selection of the calibration curve. As expected, the calibration curves related to DirectDensity® showed a tube voltage dependence only for densities above 1.82 g/cm3. The maximum percentage differences in dose distributions comparations never exceeded the 3% of tolerance and the 3D gamma analysis always returned indices greater than 90%. The results suggest that the DD reconstruction algorithm can be employed in most clinical cases and allows for a personalized radiotherapy cancer treatment workflow, maintaining its robustness and simplicity.
Application of Three-Dimensional Direct Least Square Method for Ellipsoid Anisotropy Fitting Model of Highly Irregular Drill Hole Patterns
Authors: Muchtadi-Alamsyah Intan , Heriawan Mohamad Nur , Rachmaputri Gantina , Rahmadiantri Elvira , Lawiyuniarti Made Putri
Published: August 2022

Ellipsoid or geometric anisotropy is a widely used method in geostatistical analysis to obtain variograms with different ranges in different directions (azimuth) and relatively similar sill variance. Ellipsoid anisotropy is indispensable in mining when a resource geologist intends to understand the spatial continuity of variables related to any geological controls of the mineralization. For example, when dealing with mineralization related to tabular deposits, a porphyritic deposit with an irregular drill hole pattern (fan drilling), three-dimensional ellipsoid anisotropy is quite challenging to model. We assume that the variables’ spatial continuity is isotropic, and we model it using a three-dimensional omnidirectional variogram. However, if the actual spatial continuity of the variables has a three-dimensional anisotropy, then assuming a three-dimensional omnidirectional variogram will generate imprecise resource estimations. This study presents a new practical three-dimensional ellipsoid model-fitting method using a three-dimensional direct least square method. We investigated a zinc (Zn) dataset from thousands of irregular drill hole patterns from a porphyritic system associated with skarn orebodies for the case study.
A Conceptual Model of Factors Influencing Customer Relationship Management in Global Software Development: A Client Perspective
Authors: Khattak Kausar-Nasreen , Ahmed Mansoor , Iqbal Naeem , Khan Murad-Ali , Imran  , Kim Jungsuk
Published: August 2022

The software development industry or organizations increasingly emerging day by day have adopted global software development (GSD) practices due to the large significance of outsourcing. These industries face many challenges due to a lack of understanding customer perspective in the GSD environment. For any organization, the customer is the major stakeholder, and customer relationship management (CRM) plays a vital role in customer satisfaction with software development projects. These challenges create serious risks for any software development project’s success. Thus, CRM is a crucial challenge in the success of software projects in the GSD environment. This research study aims to address the factors that negatively influence CRM implementation in the global context and proposes a conceptual model based on the identified factors for enhancing software product quality. The systematic literature review (SLR) phase investigates the potential barriers to CRM implementation in GSD. Based on identified barriers, an initial conceptual model is developed. The proposed conceptual model is validated using a questionnaire survey of the GSD industry and CRM practitioners of Pakistan. Statistical analysis and several suitable tests are also performed to develop the final conceptual model for CRM implementation in the GSD environment. This research is performed from the client’s perspective. The results are promising and accommodating to avoid any software project failure due to customer-related issues in a GSD environment.
Biomechanical Assessment of Throwing Gesture and Performance in Female Water-Polo Players
Authors: Panero Elisa , Agostini Valentina , Gastaldi Laura
Published: August 2022

Water-polo is an aquatic sport based on the high level of coordination of several body movements, frequent changes in positions, speeds, and directions. Considering technical and tactical aspects, the power, the force, and the shot accuracy have been identified as important skills and specific training programs can be scheduled with the attempt to improve them. Moreover, the biomechanical investigation of kinematic parameters during the shot can contribute to the evaluation of player’s throwing performance and to the description of gesture. The principal aim of the current study is the comparative analysis of throwing kinematics in three different types of shots. Eleven female players were evaluated before and after 45 days of a specific water-polo training. In the experimental tests, three types of shots were performed, shoulder and elbow angles and throwing velocity were analyzed. In addition, power and precision parameters were estimated to describe players’ throwing performance. During the passing-feint shot, a significant lower maximum velocity (before: 14.7 m/s; after: 13.9 m/s; p-value = 0.005) and a higher shoulder angle (before: 56.7°; after: 63.9°; p-value = 0.003) were observed. During the passing-spontaneous shot, players’ precision showed a tendency to increase after the training (score before: 9.0; score after: 11.0 score; p-value = 0.05). This study proved the feasibility of an objective biomechanical assessment of the throwing kinematics and throwing performance of water-polo players, in ecological conditions.
Wavefront Aberration Measurement Deflectometry for Imaging Lens Tests
Authors: Yu Linzhi , Li Dahai , Ruan Yilang , Zhang Xinwei , Wang Ruiyang , Xu Kaiyuan
Published: August 2022

Lenses play an important role in imaging systems. Having an effective way to test the aberrations of imaging lenses is important. However, the existing methods cannot satisfy the requirements in some conditions. To overcome these difficulties, wavefront aberration measurement deflectometry (WAMD) is proposed in this paper, which can reconstruct the wavefront aberrations of imaging lenses by measuring the angular aberrations. The principle of WAMD is analyzed in detail, and the correctness and feasibility of the proposed method are verified by both a simulation and an experiment. A telephoto lens and a single imaging lens were tested in an experiment, and the RMS errors were 166.8 nm (5.71%) and 58.9 nm (4.74%), respectively, as compared with the interferometer’s results. This method is widely applicable with relatively reasonable accuracy. It has potential to be applied in the lens manufacturing and alignment process.
Optimal Investment Strategy for DC Pension Plan with Deposit Loan Spread under the CEV Model
Authors: Wang Yang , Xu Xiao , Zhang Jizhou
Published: August 2022

This paper is devoted to determining an optimal investment strategy for a defined-contribution (DC) pension plan with deposit loan spread under the constant elasticity of variance (CEV) model. As far as we know, few studies in the literature have taken loans into account when using the CEV model in financial market contexts. The contribution of this paper is to study the impact of deposit loan spread on DC pension investment strategy. By considering a risk-free asset, a risky asset driven by CEV model, and a loan in the financial market, we first set up the dynamic equation and the asset market model, which are instrumental in achieving the expected utility of ultimate wealth at retirement. Second, the corresponding Hamilton–Jacobi–Bellman (HJB) equation is derived by means of the dynamic programming principle. The explicit expression for the optimal investment strategy is obtained using the Legendre transform method. Finally, different parameters are selected to simulate the explicit solution, and the financial interpretation of the optimal investment strategy is provided. We find that the deposit loan spread has a great impact on the investment strategy of DC pension plans.
Comparative Genomics Insights into a Novel Biocontrol Agent Paenibacillus peoriae Strain ZF390 against Bacterial Soft Rot
Authors: Zhao Yurong , Xie Xuewen , Li Junhui , Shi Yanxia , Chai Ali , Fan Tengfei , Li Baoju , Li Lei
Published: August 2022

Bacterial soft rot, caused by Pectobacterium brasiliense, can infect several economically important horticultural crops. However, the management strategies available to control this disease are limited. Plant-growth-promoting rhizobacteria (PGPR) have been considered to be promising biocontrol agents. With the aim of obtaining a strain suitable for agricultural applications, 161 strains were isolated from the rhizosphere soil of healthy cucumber plants and screened through plate bioassays and greenhouse tests. Paenibacillus peoriae ZF390 exhibited an eminent control effect against soft rot disease and a broad antagonistic activity spectrum in vitro. Moreover, ZF390 showed good activities of cellulase, protease, and phosphatase and a tolerance of heavy metal. Whole-genome sequencing was performed and annotated to explore the underlying biocontrol mechanisms. Strain ZF390 consists of one 6,193,667 bp circular chromosome and three plasmids. Comparative genome analysis revealed that ZF390 involves ten gene clusters responsible for secondary metabolite antibiotic synthesis, matching its excellent biocontrol activity. Plenty of genes related to plant growth promotion, biofilm formation, and induced systemic resistance were mined to reveal the biocontrol mechanisms that might consist in strain ZF390. Overall, these findings suggest that strain ZF390 could be a potential biocontrol agent in bacterial-soft-rot management, as well as a source of antimicrobial mechanisms for further exploitation.
Exploring the Potential Enhancing Effects of Trans-Zeatin and Silymarin on the Productivity and Antioxidant Defense Capacity of Cadmium-Stressed Wheat
Authors: Ali Esmat F. , Aljarani Alshafei M. , Mohammed Fozia A. , Desoky El-Sayed M. , Mohamed Ibrahim A. A. , El-Sharnouby Mohamed , Tammam Suzan A. , Hassan Fahmy A. S. , Rady Mostafa M. , Shaaban Ahmed
Published: August 2022

Pot trials were performed to explore the impacts of seed priming (SPr) plus leaf treatment (LTr) with trans-zeatin-type cytokinin (tZck; 0.05 mM) and silymarin (Sim; 0.5 mM) on growth, yield, physio-biochemical responses, and antioxidant defense systems in Cd-stressed wheat. tZck + Sim applied as SPr + LTr was more effective than individual treatments, and the impacts were more pronounced under stress conditions. Cd stress (0.6 mM) severely declined growth and yield traits, and photosynthesis efficiency (pigment contents, instantaneous carboxylation efficiency, and photochemical activity) compared to the control. These negative impacts coincided with increased levels of Cd2+, O2•− (superoxide), H2O2 (hydrogen peroxide), MDA (malondialdehyde), and EL (electrolyte leakage). Non-enzymatic and enzymatic antioxidant activities, and tZck and Sim contents were also increased. However, tZck + Sim increased photosynthesis efficiency, and further boosted antioxidant activities, and contents of tZck and Sim, while minimizing Cd2+ levels in roots, leaves, and grains. The levels of O2•−, H2O2, MDA, and EL were also minimized, reflecting positively on growth and productivity. tZck + Sim applied as SPr + LTr was highly effective in promoting antioxidants and photosynthesis machineries, minimizing oxidative stress biomarkers and Cd2+ levels, boosting tolerance to Cd stress, and improving wheat productivity under Cd stress.
Opportunities and Challenges of Data-Driven Virus Discovery
Authors: Lauber Chris , Seitz Stefan
Published: August 2022

Virus discovery has been fueled by new technologies ever since the first viruses were discovered at the end of the 19th century. Starting with mechanical devices that provided evidence for virus presence in sick hosts, virus discovery gradually transitioned into a sequence-based scientific discipline, which, nowadays, can characterize virus identity and explore viral diversity at an unprecedented resolution and depth. Sequencing technologies are now being used routinely and at ever-increasing scales, producing an avalanche of novel viral sequences found in a multitude of organisms and environments. In this perspective article, we argue that virus discovery has started to undergo another transformation prompted by the emergence of new approaches that are sequence data-centered and primarily computational, setting them apart from previous technology-driven innovations. The data-driven virus discovery approach is largely uncoupled from the collection and processing of biological samples, and exploits the availability of massive amounts of publicly and freely accessible data from sequencing archives. We discuss open challenges to be solved in order to unlock the full potential of data-driven virus discovery, and we highlight the benefits it can bring to classical (mostly molecular) virology and molecular biology in general.
Mann–Kendall-Based Concrete Failure Trend Analysis and Its Implementation for Dynamic Building Monitoring
Authors: Yang Xu , Han Xueying
Published: August 2022

Analyzing monitoring data efficiently is a classic problem in structural health monitoring. A nonparametric test method, the Mann–Kendall (MK) method, was implemented in this study, which is commonly used to detect monotonic trends in a series of environmental data. Using the MK method, three types of time series were studied: the stress time series measured in the concrete prism compression test, the resultant force time series obtained from the pseudostatic test of a reinforced masonry shear wall, and the translation velocity time series detected in a high-rise building. The statistics calculated, as well as the intersections of curves, indicate the trend change in the time series. The results demonstrated that the MK method could efficiently analyze the trend in the engineering time series.
Prioritizing Green Building Attributes on Conventional Office Building Rental Depreciation Using Analytic Hierarchy Process (AHP)
Authors: Rodi Wan Norhishamuddin Wan , Che-Ani Adi Irfan , Norazman Norsafiah , Kamaruzzaman Syahrul Nizam , Chohan Afaq Hyder
Published: August 2022

This paper explores the green building attributes that significantly influenced rental depreciation for conventional buildings from expert perspectives using Malaysia as a case study. The objectives of this study include: (1) identifying the green building attributes for rental depreciation and (2) prioritizing the green attributes via the Analytic Hierarchy Process (AHP) technique. To achieve these objectives, firstly the study identified the green building attributes from the literature and from green building guidelines. The attributes were then validated by expert valuers via a semi-structured interview. Secondly, the attributes were utilized to develop the AHP-designed questionnaire and used to gather feedback from real estate experts. Ten (10) responses were analyzed using the descriptive and AHP techniques. This study has identified the rank of prioritized green building attributes, where the findings suggest the central role of indoor environment quality (EQ), where it ranked the highest in contributing to conventional purpose-built office (PBO) rental depreciation, followed by energy efficiency (EE), green site planning and management (SM), materials and resources (MR), innovation (IN), and water efficiency (WE). The findings allow researchers and practitioners to create strategies for reducing the impact of conventional building rental depreciation and obsolescence due to green building attributes.
Interactive Education on Sleep Hygiene with a Social Robot at a Pediatric Oncology Outpatient Clinic: Feasibility, Experiences, and Preliminary Effectiveness
Authors: van Bindsbergen Kelly L. A. , van der Hoek Hinke , van Gorp Marloes , Ligthart Mike E. U. , Hindriks Koen V. , Neerincx Mark A. , Alderliesten Tanja , Bosman Peter A. N. , Merks Johannes H. M. , Grootenhuis Martha A. , van Litsenburg Raphaële R. L.
Published: August 2022

Objectives: Children with cancer often experience sleep problems, which are associated with many negative physical and psychological health outcomes, as well as with a lower quality of life. Therefore, interventions are strongly required to improve sleep in this population. We evaluated interactive education with respect to sleep hygiene with a social robot at a pediatric oncology outpatient clinic regarding the feasibility, experiences, and preliminary effectiveness. Methods: Researchers approached children (8 to 12 years old) who were receiving anticancer treatment and who were visiting the outpatient clinic with their parents during the two-week study period. The researchers completed observation forms regarding feasibility, and parents completed the Children’s Sleep Hygiene Scale before and two weeks after the educational regimen. The experiences of children and parents were evaluated in semi-structured interviews. We analyzed open answers by labeling each answer with a topic reflecting the content and collapsed these topics into categories. We used descriptive statistics to describe the feasibility and experiences, and a dependent-samples t-test to evaluate the preliminary effectiveness. Results: Twenty-eight families participated (58% response rate) and all interactions with the robot were completed. The children and parents reported that they learned something new (75% and 50%, respectively), that they wanted to learn from the robot more often (83% and 75%, respectively), and that they applied the sleeping tips from the robot afterwards at home (54%). Regarding the preliminary effectiveness, children showed a statistically significant improvement in their sleep hygiene (p = 0.047, d = 0.39). Conclusions: Providing an educational regimen on sleep hygiene in a novel, interactive way by using a social robot at the outpatient clinic seemed feasible, and the children and parents mostly exhibited positive reactions. We found preliminary evidence that the sleep hygiene of children with cancer improved.
A Journey through the Inter-Cellular Interactions in the Bone Marrow in Multiple Myeloma: Implications for the Next Generation of Treatments
Authors: Hervás-Salcedo Rosario , Martín-Antonio Beatriz
Published: August 2022

Tumors are composed of a plethora of extracellular matrix, tumor and non-tumor cells that form a tumor microenvironment (TME) that nurtures the tumor cells and creates a favorable environment where tumor cells grow and proliferate. In multiple myeloma (MM), the TME is the bone marrow (BM). Non-tumor cells can belong either to the non-hematological compartment that secretes soluble mediators to create a favorable environment for MM cells to grow, or to the immune cell compartment that perform an anti-MM activity in healthy conditions. Indeed, marrow-infiltrating lymphocytes (MILs) are associated with a good prognosis in MM patients and have served as the basis for developing different immunotherapy strategies. However, MM cells and other cells in the BM can polarize their phenotype and activity, creating an immunosuppressive environment where immune cells do not perform their cytotoxic activity properly, promoting tumor progression. Understanding cell–cell interactions in the BM and their impact on MM proliferation and the performance of tumor surveillance will help in designing efficient anti-MM therapies. Here, we take a journey through the BM, describing the interactions of MM cells with cells of the non-hematological and hematological compartment to highlight their impact on MM progression and the development of novel MM treatments.
Crosstalk between Biomolecular Condensates and Proteostasis
Authors: Amzallag Emmanuel , Hornstein Eran
Published: August 2022

Proper homeostasis of the proteome, referred to as proteostasis, is maintained by chaperone-dependent refolding of misfolded proteins and by protein degradation via the ubiquitin-proteasome system and the autophagic machinery. This review will discuss a crosstalk between biomolecular condensates and proteostasis, whereby the crowding of proteostasis factors into macromolecular assemblies is often established by phase separation of membraneless biomolecular condensates. Specifically, ubiquitin and other posttranslational modifications come into play as agents of phase separation, essential for the formation of condensates and for ubiquitin-proteasome system activity. Furthermore, an intriguing connection associates malfunction of the same pathways to the accumulation of misfolded and ubiquitinated proteins in aberrant condensates, the formation of protein aggregates, and finally, to the pathogenesis of neurodegenerative diseases. The crosstalk between biomolecular condensates and proteostasis is an emerging theme in cellular and disease biology and further studies will focus on delineating specific molecular pathways involved in the pathogenesis of amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases.
Multi-Elemental Coatings on Zirconium Alloy for Corrosion Resistance Improvement
Authors: Sartowska Bożena , Starosta Wojciech , Waliś Lech , Smolik Jerzy , Pańczyk Ewa
Published: August 2022

Zirconium alloys are commonly used as a cladding material for fuel elements in nuclear reactors. This application is connected with zirconium alloy’s good resistance to water corrosion and radiation resistance under normal working conditions. In the case of severe accident conditions, the possibly very fast oxidation of zirconium alloys in steam or/and air atmosphere may result in the intense generation of hydrogen and explosion of the hydrogen oxide mixture. The development of a solution to minimize the aforementioned risk is of interest. One of the actual concepts is to improve the oxidation resistance of Zr alloy cladding with protective coatings. This study aimed to develop, form, and investigate new coatings for zirconium alloy Zry-2. Multi-elemental Physical Vapour Deposition (PVD) coatings with Cr, Si, and Zr were considered for Institute of Nuclear Chemistry and Technology) INCT as corrosion protective coatings for nuclear fuel claddings. Heat treatment at 850–1100 °C/argon, air oxidation processes at 700 °C/1–5 h, and a long-term corrosion test in standard conditions for Pressure Water Reactor (PWR) reactors (360 °C/195 bar/water simulating the water used in PWR) were carried out. Initial, modified, and oxidized materials were characterized with Scanning Electron Microscopy (SEM) (morphology observations), Energy Dispersive Spectroscopy (EDS) (elemental composition determination), and X-ray Diffraction (XRD) (phase composition analysis). Slower oxidation processes and a smaller oxidation rate, in the case of modified material investigations, were observed, as compared with the unmodified material. The obtained results displayed a protective character against the oxidation of formed layers in the defined range of parameters in the process.
Microstructure Evolution Behavior of Blast-Furnace Coke under Different Gasification Reaction Conditions
Authors: Wu Hongliang , Yu Laihao , Chang Shengchao , Zhang Yingyi , Yang Jialong
Published: August 2022

With the development of large-scale blast-furnace and oxygen-rich coal-injection technology, as well as national green and low-carbon policy requirements, the ironmaking process has increasingly strict requirements for blast-furnace raw materials, such as new and higher requirements for coke quality and thermal performance. In this study, the melting loss reaction in a blast furnace was simulated under laboratory conditions and the microstructure evolution of coke after melting loss under CO2 and CO2 + H2O conditions was studied. The results showed that under a CO2 atmosphere, the specific surface area and pore volume of coke and the number of micropores in coke first increased and then decreased with the increase in reaction time, while the average pore size first decreased from 17.289 to 8.641 nm and then increased to 9.607 nm. In the gasification reaction between CO2 and coke, the relative content of the graphitized structure (IG/IAll) increased first, then decreased and then increased with the increase in reaction time, while the change trends of disordered structure (ID3/IG) and unstable structure (ID4/IG) were opposite to IG/IAll, indicating that coke still tended to be more orderly with the increase in time. In the mixed atmosphere of CO2 and H2O, the specific surface area and IG/IAll of coke increased with the increase in H2O content. However, when the proportion of H2O exceeded 50%, the specific surface area decreased slightly, and the pore-size value corresponding to the peak value in the pore-size distribution curve shifted to the right, and the average pore-size increased in the nanosize range.
Drug Treatment for Advanced Hepatocellular Carcinoma: First-Line and Beyond
Authors: Feng Maple Ye , Chan Landon L. , Chan Stephen Lam
Published: August 2022

Hepatocellular carcinoma (HCC) has high mortality. The option of systemic therapy has increased significantly over the past five years. Sorafenib was the first multikinase inhibitor, introduced in 2007, as a treatment option for HCC, and it was the only effective systemic treatment for more than ten years. It was not until 2017 that several breakthroughs were made in the development of systemic strategies. Lenvatinib, another multikinase inhibitor, stood out successfully after sorafenib, and has been applied to clinical use in the first-line setting. Other multikinase inhibitors such as regorafenib, ramucirumab and cabozantinib, were approved in quick succession as second-line therapies. Concurrently, immune checkpoint inhibitors (ICIs) have readily become established treatments for many solid tumors, including HCC. The most studied ICIs to date, target programmed cell death-1 (PD-1), its ligand PD-L1, and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4). These ICIs have demonstrated efficacy in treating advanced HCC. More recently, combination of bevacizumab and atezolizumab (ICI targeting PD-L1) was approved as the gold-standard first-line therapy. Combination of ICIs with nivolumab and ipilimumab was also approved in the second-line setting for those who failed sorafenib. At the moment, numerous clinical trials in advanced HCC are underway, which will bring continuous change to the management, and increase the survival, for patients with advanced HCC. Our review article: (1) summarizes United States Food and Drug Administration (US FDA) approved systemic therapies in advanced HCC, (2) reports the evidence of currently approved treatments, (3) discusses potential drugs/drug combinations being currently tested in phase III clinical trials, and (4) proposes possible future directions in drug development for advanced HCC.
Somatostatin Receptor Theranostics for Refractory Meningiomas
Authors: Salgues Betty , Graillon Thomas , Horowitz Tatiana , Chinot Olivier , Padovani Laetitia , Taïeb David , Guedj Eric
Published: August 2022

Somatostatin receptor (SSTR)-targeted peptide receptor radionuclide therapy (PRRT) represents a promising approach for treatment-refractory meningiomas progressing after surgery and radiotherapy. The aim of this study was to provide outcomes of patients harboring refractory meningiomas treated by 177Lu-DOTATATE and an overall analysis of progression-free survival at 6 months (PFS-6) of the same relevant studies in the literature. Eight patients with recurrent and progressive WHO grade II meningiomas were treated after multimodal pretreatment with 177Lu-DOTATATE between 2019 and 2022. Primary and secondarily endpoints were progression-free survival at 6-months (PFS-6) and toxicity, respectively. PFS-6 analysis of our case series was compared with other similar relevant studies that included 86 patients treated with either 177Lu-DOTATATE or 90Y-DOTATOC. Our retrospective study showed a PFS-6 of 85.7% for WHO grade II progressive refractory meningiomas. Treatment was clinically and biologically well tolerated. The overall analysis of the previous relevant studies showed a PFS-6 of 89.7% for WHO grade I meningiomas (n = 29); 57.1% for WHO grade II (n = 21); and 0 % for WHO grade III (n = 12). For all grades (n = 86), including unknown grades, PFS-6 was 58.1%. SSTR-targeted PRRT allowed us to achieve prolonged PFS-6 in patients with WHO grade I and II progressive refractory meningiomas, except the most aggressive WHO grade II tumors. Large scale randomized trials are warranted for the better integration of PRRT in the treatment of refractory meningioma into clinical practice guidelines.
A Unique Observation of a Patient with Vulto-van Silfhout-de Vries Syndrome
Authors: Bodunova Natalia , Vorontsova Maria , Khatkov Igor , Baranova Elena , Bykova Svetlana , Degterev Daniil , Litvinova Maria , Bilyalov Airat , Makarova Maria , Sagaydak Olesya , Danishevich Anastasia
Published: August 2022

Introduction: Vulto-van Silfhout-de Vries Syndrome (VSVS; OMIM#615828) is a rare hereditary disease associated with impaired intellectual development and speech, delayed psychomotor development, and behavioral anomalies, including autistic behavioral traits and poor eye contact. To date, 27 patients with VSVS have been reported in the literature. Materials and Methods: We describe a 23-year-old male patient with autism spectrum disorder (ASD) who was admitted to the gastroenterological hospital with signs of pseudomembranous colitis. ASD was first noted in the patient at the age of 2.5 years. Later, he developed epileptic seizures and important growth retardation. Prior to the hospitalization, chromosomal aberrations, Fragile X syndrome, and aminoacidopathies/aminoacidurias associated with ASD were excluded. Whole-genome sequencing (WGS) was prescribed to the patient at 23 years old. Results: The patient had a heterozygous carrier of “de novo” variant c.662C > T (p.S221L) in exon 4 of the DEAF1 gene. c.662C > T had not been previously described in genomic databases. According to the ACMG criteria, this missense variant was considered to be pathogenic. VSVS was diagnosed in the patient. Conclusions: The phenotype of the patient is very similar to the data presented in the world literature. However, growth retardation and cachexia, which have not been described previously in the articles, are of interest.
Urachal Carcinoma, An Unusual Possibility of Hematuria; Case Report and Literature Review
Authors: Tiutiucă Răzvan Călin , Năstase Pușcașu Alina Ioana , Țarcă Elena , Stoenescu Nicoleta , Cojocaru Elena , Trandafir Laura Mihaela , Țarcă Viorel , Scripcariu Dragoș-Viorel , Moscalu Mihaela
Published: August 2022

Urachal cancer is very rare, accounting for only 0.5–2% of bladder-associated malignancies and 0.01% of all cancers in adults. It has an insidious appearance, an aggressive behavior and a poor prognosis. The most common symptoms are hematuria and the presence of a palpable hypogastric mass. The scarcity of cases and the low number of studies carried out explains the lack of an evidence-based management strategy, but it seems that surgical treatment (open, laparoscopy or robot-assisted) represents the gold standard, while neoadjuvant and adjuvant chemotherapy or radiotherapy has a limited impact on overall survival. Since mucinous cystadenocarcinoma of urachal origin is a very uncommon pathological condition the differential diagnosis may be difficult and pathological investigations have to elucidate this disorder. It is worth mentioning the psychological impact on the patient in addition to the medical aspects. A rare condition is associated with heightened risk for mental health and psychosocial difficulties and this must be taken into account in the subsequent follow-up of the patient. In order to increase awareness of this rare entity we report a case of a 40-year-old male with a urachal adenocarcinoma who was treated surgically, with a favorable outcome. We also perform a brief literature review about this type of tumor.
Reliability and Construct Validity of the Yale Pharyngeal Residue Severity Rating Scale: Performance on Videos and Effect of Bolus Consistency
Authors: Rocca Sara , Pizzorni Nicole , Valenza Nadia , Negri Luca , Schindler Antonio
Published: August 2022

The Yale Pharyngeal Residue Severity Rating Scale (YPRSRS) provides an image-based assessment of pharyngeal residue in the fiberoptic endoscopic evaluation of swallowing (FEES). Its performance was investigated only in FEES frames. This study analyzed the reliability and construct validity of the YPRSRS in FEES videos and the influence of bolus consistency. Thirty pairs of FEES videos and frames, 8 thin liquids (<50 mPa·s), 11 pureed (2583.3 mPa·s at 50 s−1, 697.87 mPa·s at 300 s−1), and 11 solid food; were assessed by 29 clinicians using the YPRSRS; 14 raters re-assessed materials at least 15 days from the first evaluation. Construct validity and intra-rater reliability were assessed using weighted Cohen’s Kappa. Inter-rater reliability was assessed using weighted Fleiss Kappa. Construct validity and inter-rater reliability were almost perfect or excellent for frames (0.82 ≤ k ≤ 0.89) and substantial or intermediate to good for videos (0.67 ≤ k ≤ 0.79). Intra-rater reliability was almost perfect for both frames and videos (k ≥ 0.84). Concerning bolus consistency, thin liquids had significantly lower values of construct validity, intra-, and inter-rater reliability than pureed and solid food. Construct validity and inter-rater reliability were significantly lower for solid food than for pureed food. The YPRSRS showed satisfactory reliability and construct validity also in FEES videos. Reliability was significantly influenced by bolus consistency.
Probabilistic Hierarchical Quantum Information Splitting of Arbitrary Multi-Qubit States
Authors: Tang Jie , Ma Song-Ya , Li Qi
Published: August 2022

By utilizing the non-maximally entangled four-qubit cluster states as the quantum channel, we first propose a hierarchical quantum information splitting scheme of arbitrary three-qubit states among three agents with a certain probability. Then we generalize the scheme to arbitrary multi-qubit states. Hierarchy is reflected on the different abilities of agents to restore the target state. The high-grade agent only needs the help of one low-grade agent, while the low-grade agent requires all the other agents’ assistance. The designated receiver performs positive operator-valued measurement (POVM) which is elaborately constructed with the aid of Hadamard matrix. It is worth mentioning that a general expression of recovery operation is derived to disclose the relationship with measurement outcomes. Moreover, the scheme is extended to multiple agents by means of the symmetry of cluster states.
Electrochemical Profiling of Plants
Authors: Gandhi Mansi , Amreen Khairunnisa
Published: August 2022

The profiling, or fingerprinting, of distinct varieties of the Plantae kingdom is based on the bioactive ingredients, which are systematically segregated to perform their detailed analysis. The secondary products portray a pivotal role in defining the ecophysiology of distinct plant species. There is a crucial role of the profiling domain in understanding the various features, characteristics, and conditions related to plants. Advancements in variable technologies have contributed to the development of highly specific sensors for the non-invasive detection of molecules. Furthermore, many hyphenated techniques have led to the development of highly specific integrated systems that allow multiplexed detection, such as high-performance liquid chromatography, gas chromatography, etc., which are quite cumbersome and un-economical. In contrast, electrochemical sensors are a promising alternative which are capable of performing the precise recognition of compounds due to efficient signal transduction. However, due to a few bottlenecks in understanding the principles and non-redox features of minimal metabolites, the area has not been explored. This review article provides an insight to the electrochemical basis of plants in comparison with other traditional approaches and with necessary positive and negative outlooks. Studies consisting of the idea of merging the fields are limited; hence, relevant non-phytochemical reports are included for a better comparison of reports to broaden the scope of this work.
Simulating the Dispersion of the Energy Flux Density of the Electromagnetic Field Generated by Antennas for Mobile Communications
Authors: Buckus Raimondas , Chlebnikovas Aleksandras , Strukcinskiene Birute , Stukas Rimantas , Austys Donatas , Caban Jacek , Bogucki Marcin , Sidlauskiene Aurelija , Seleviciene Vaiva , Kilikevičius Artūras , Matijošius Jonas , Kilikevičienė Kristina , Vainorius Darius
Published: August 2022

The last two decades have faced a significantly increased number of telecommunication antennas emitting electromagnetic radiation in residential areas. The theoretical simulation of the dispersion of the energy flux density of the electromagnetic field has been performed applying the physical peculiarities of the waves generating electromagnetic radiation. Having evaluated studies on simulation, the visual representation of the spread of electromagnetic radiation has been carried out according to the results obtained applying the AutoCad package. A comparison of the simulated value of the energy flux density radiated from antennas for mobile telecommunications with the measured one has disclosed an overlap of 30%. The simulation of the energy flux density showed that, in the close proximity zone (under a distance of 30 m), antennas radiate values within the range 10–10,000 µW/cm2. At a distance larger than 30 m, the values of energy flux density fluctuate from 10 to 0.001 µW/cm2.
A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information
Authors: Hu Zhiqiu , Shao Fengjing , Sun Rencheng
Published: August 2022

Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topological features of 97 traffic network nodes. Consequently, six complex network properties are discussed, namely, degree centrality, clustering coefficient, closeness centrality, betweenness centrality, point intensity, and shortest average path length. This study improves the graph convolutional neural network based on the above six complex network properties and proposes a graph spatial–temporal network consisting of a combination of several complex network properties. By comparison with existing baselines containing graph convolutional neural networks, it is verified that GSTNCNI possesses high traffic flow prediction accuracy and robustness. In addition, ablation experiments are conducted for six different complex network features to verify the effect of different complex network features on the model’s prediction accuracy. Experimental analysis indicates that the model with combined multiple complex network features has a higher prediction accuracy, and its performance is improved by 31.46% on average, compared with the model containing only one complex network feature.