bluuebunny/crossref_metadata_2025 huggingface dataset:
Architecting for Artificial Intelligence with Emerging Nanotechnology
Authors: Kulkarni Sourabh , Bhat Sachin , Moritz Csaba Andras
Published: August 2021

Artificial Intelligence is becoming ubiquitous in products and services that we use daily. Although the domain of AI has seen substantial improvements over recent years, its effectiveness is limited by the capabilities of current computing technology. Recently, there have been several architectural innovations for AI using emerging nanotechnology. These architectures implement mathematical computations of AI with circuits that utilize physical behavior of nanodevices purpose-built for such computations. This approach leads to a much greater efficiency vs. software algorithms running on von Neumann processors or CMOS architectures, which emulate the operations with transistor circuits. In this article, we provide a comprehensive survey of these architectural directions and categorize them based on their contributions. Furthermore, we discuss the potential offered by these directions with real-world examples. We also discuss major challenges and opportunities in this field.
Chasing Convex Bodies with Linear Competitive Ratio
Authors: Argue C. J. , Gupta Anupam , Tang Ziye , Guruganesh Guru
Published: August 2021

We study the problem of chasing convex bodies online: given a sequence of convex bodies

the algorithm must respond with points

in an online fashion (i.e.,

is chosen before

is revealed). The objective is to minimize the sum of distances between successive points in this sequence. Bubeck et al. (STOC 2019) gave a

-competitive algorithm for this problem. We give an algorithm that is

-competitive for any sequence of length

.
Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow Platforms
Authors: Yasudo Ryota , Coutinho José G. F. , Varbanescu Ana-Lucia , Luk Wayne , Amano Hideharu , Becker Tobias , Guo Ce
Published: August 2021

Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today’s technology. A promising path in achieving the next leap in high-performance computing is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators such as FPGAs, which have been shown to speed up compute-intensive tasks with reduced power consumption. However, assessing the feasibility of large-scale heterogeneous systems requires fast and accurate performance prediction. This article proposes Performance Estimation for Reconfigurable Kernels and Systems (PERKS), a novel performance estimation framework for reconfigurable dataflow platforms. PERKS makes use of an analytical model with machine and application parameters for predicting the performance of multi-accelerator systems and detecting their bottlenecks. Model calibration is automatic, making the model flexible and usable for different machine configurations and applications, including hypothetical ones. Our experimental results show that PERKS can predict the performance of current workloads on reconfigurable dataflow platforms with an accuracy above 91%. The results also illustrate how the modelling scales to large workloads, and how performance impact of architectural features can be estimated in seconds.
Exploring Topic-language Preferences in Multilingual Swahili Information Retrieval in Tanzania
Authors: Telemala Joseph P. , Suleman Hussein
Published: August 2021

Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences—topic-language preferences. This article is based on a carefully controlled study using Swahili-speaking Web users in Tanzania who interacted with a guided multilingual search engine. From the statistical analysis of queries and click-through logs, it was revealed that language preferences may be associated with the topics of search. The results also suggest that language preferences are not static; they vary along the course of Web search from query to results selection. In most of the topics, users either had significantly no language preference or preferred to query in Kiswahili and changed their preference to either English or no preference for language when selecting/clicking on the results. The findings of this study might provide researchers with more insights in developing better MLIR systems that support certain types of users and in certain scenarios.
Process Variability Analysis in Interconnect, Logic, and Arithmetic Blocks of 16-nm FinFET FPGAs
Authors: Taka Endri , Maragos Konstantinos , Lentaris George , Soudris Dimitrios
Published: August 2021

In the current work, we study the process variability of logic, interconnect, and arithmetic/DSP resources in commercial 16-nm FPGAs. We create multiple, soft-macro sensors for each distinct resource under evaluation, and we deploy them across the FPGA fabric to measure intra-die variation, as well as across multiple FPGAs to measure inter-die variation. The derived results are used to create device-signature variability maps characterizing the distribution of variability across the die. Our study includes decoupling of variability to systematic and stochastic parts, exploration of variability under various voltage and temperature conditions and correlation analysis between the variability maps of the different resources. Furthermore, we scrutinize the impact of variability on the performance of actual test circuits and correlate the retrieved results with the sensor-based maps. Our experimental results on four Zynq XCZU7EV FPGAs showed significant intra- and inter-die variability, up to 7.8% and 8.9%, respectively, with a small increase under certain operating conditions. The correlation analysis demonstrated a strong correlation between the logic and arithmetic resources, whereas the interconnects showed a slightly weaker correlation in specific devices. Finally, a relatively moderate correlation was calculated between the variability maps and performance of test circuits due their dissimilar operating behavior versus our sensors.
Hardware Context Switch-based Cryptographic Accelerator for Handling Multiple Streams
Authors: Sasongko Arif , Kumara I. M. Narendra , Wicaksana Arief , Rousseau Frédéric , Muller Olivier
Published: August 2021

The confidentiality and integrity of a stream has become one of the biggest issues in telecommunication. The best available algorithm handling the confidentiality of a data stream is the symmetric key block cipher combined with a chaining mode of operation such as cipher block chaining (CBC) or counter mode (CTR). This scheme is difficult to accelerate using hardware when multiple streams coexist. This is caused by the computation time requirement and mainly by management of the streams. In most accelerators, computation is treated at the block-level rather than as a stream, making the management of multiple streams complex.

This article presents a solution combining CBC and CTR modes of operation with a hardware context switching. The hardware context switching allows the accelerator to treat the data as a stream. Each stream can have different parameters: key, initialization value, state of counter. Stream switching was managed by the hardware context switching mechanism. A high-level synthesis tool was used to generate the context switching circuit. The scheme was tested on three cryptographic algorithms: AES, DES, and BC3. The hardware context switching allowed the software to manage multiple streams easily, efficiently, and rapidly. The software was freed of the task of managing the stream state. Compared to the original algorithm, about 18%–38% additional logic elements were required to implement the CBC or CTR mode and the additional circuits to support context switching. Using this method, the performance overhead when treating multiple streams was low, and the performance was comparable to that of existing hardware accelerators not supporting multiple streams.
Enhancing the Scalability of Multi-FPGA Stencil Computations via Highly Optimized HDL Components
Authors: Reggiani Enrico , Del Sozzo Emanuele , Conficconi Davide , Natale Giuseppe , Moroni Carlo , Santambrogio Marco D.
Published: August 2021

Stencil-based algorithms are a relevant class of computational kernels in high-performance systems, as they appear in a plethora of fields, from image processing to seismic simulations, from numerical methods to physical modeling. Among the various incarnations of stencil-based computations,Iterative Stencil Loops (ISLs)andConvolutional Neural Networks (CNNs)represent two well-known examples of kernels belonging to the stencil class. Indeed, ISLs apply the same stencil several times until convergence, while CNN layers leverage stencils to extract features from an image. The computationally intensive essence of ISLs, CNNs, and in general stencil-based workloads, requires solutions able to produce efficient implementations in terms of throughput and power efficiency. In this context, FPGAs are ideal candidates for such workloads, as they allow design architectures tailored to the stencil regular computational pattern. Moreover, the ever-growing need for performance enhancement leads FPGA-based architectures to scale to multiple devices to benefit from a distributed acceleration. For this reason, we propose a library of HDL components to effectively compute ISLs and CNNs inference on FPGA, along with a scalable multi-FPGA architecture, based on custom PCB interconnects. Our solution eases the design flow and guarantees both scalability and performance competitive with state-of-the-art works.
Coherent Dialog Generation with Query Graph
Authors: Xu Jun , Lei Zeyang , Wang Haifeng , Niu Zheng-Yu , Wu Hua , Che Wanxiang , Huang Jizhou , Liu Ting
Published: August 2021

Learning to generate coherent and informative dialogs is an enduring challenge for open-domain conversation generation. Previous work leverage knowledge graph or documents to facilitate informative dialog generation, with little attention on dialog coherence. In this article, to enhance multi-turn open-domain dialog coherence, we propose to leverage a new knowledge source, web search session data, to facilitate hierarchical knowledge sequence planning, which determines a sketch of a multi-turn dialog. Specifically, we formulate knowledge sequence planning or dialog policy learning as a graph grounded Reinforcement Learning (RL) problem. To this end, we first build a two-level query graph with queries as utterance-level vertices and their topics (entities in queries) as topic-level vertices. We then present a two-level dialog policy model that plans a high-level topic sequence and a low-level query sequence over the query graph to guide a knowledge aware response generator. In particular, to foster forward-looking knowledge planning decisions for better dialog coherence, we devise a heterogeneous graph neural network to incorporate neighbouring vertex information, or possible future RL action information, into each vertex (as an RL action) representation. Experiment results on two benchmark dialog datasets demonstrate that our framework can outperform strong baselines in terms of dialog coherence, informativeness, and engagingness.
BYANJON: A Ground Truth Preparation System for Online Handwritten Bangla Documents
Authors: Sen Shibaprasad , Bhattacharyya Ankan , Sarkar Ram , Roy Kaushik
Published: August 2021

The work reported in this article deals with the ground truth generation scheme for online handwritten Bangla documents at text-line, word, and stroke levels. The aim of the proposed scheme is twofold: firstly, to build a document level database so that future researchers can use the database to do research in this field. Secondly, the ground truth information will help other researchers to evaluate the performance of their algorithms developed for text-line extraction, word extraction, word segmentation, stroke recognition, and word recognition. The reported ground truth generation scheme starts with text-line extraction from the online handwritten Bangla documents, then words extraction from the text-lines, and finally segmentation of those words into basic strokes. After word segmentation, the basic strokes are assigned appropriate class labels by using modified distance-based feature extraction procedure and the
MLP
(
Multi-layer Perceptron
) classifier. The Unicode for the words are then generated from the sequence of stroke labels. XML files are used to store the stroke, word, and text-line levels ground truth information for the corresponding documents. The proposed system is semi-automatic and each step such as text-line extraction, word extraction, word segmentation, and stroke recognition has been implemented by using different algorithms. Thus, the proposed ground truth generation procedure minimizes huge manual intervention by reducing the number of mouse clicks required to extract text-lines, words from the document, and segment the words into basic strokes. The integrated stroke recognition module also helps to minimize the manual labor needed to assign appropriate stroke labels. The freely available and can be accessed at
https://byanjon.herokuapp.com/
.
Improving Data Augmentation for Low-Resource NMT Guided by POS-Tagging and Paraphrase Embedding
Authors: Maimaiti Mieradilijiang , Liu Yang , Luan Huanbo , Pan Zegao , Sun Maosong
Published: August 2021

Data augmentation is an approach for several text generation tasks. Generally, in the machine translation paradigm, mainly in low-resource language scenarios, many data augmentation methods have been proposed. The most used approaches for generating pseudo data mainly lay in word omission, random sampling, or replacing some words in the text. However, previous methods barely guarantee the quality of augmented data. In this work, we try to build the data by using paraphrase embedding and POS-Tagging. Namely, we generate the fake monolingual corpus by replacing the main four POS-Tagging labels, such as noun, adjective, adverb, and verb, based on both the paraphrase table and their similarity. We select the bigger corpus size of the paraphrase table with word level and obtain the word embedding of each word in the table, then calculate the cosine similarity between these words and tagged words in the original sequence. In addition, we exploit the ranking algorithm to choose highly similar words to reduce semantic errors and leverage the POS-Tagging replacement to mitigate syntactic error to some extent. Experimental results show that our augmentation method consistently outperforms all previous SOTA methods on the low-resource language pairs in seven language pairs from four corpora by 1.16 to 2.39 BLEU points.
A Deep Learning Framework to Predict Routability for FPGA Circuit Placement
Authors: Al-Hyari Abeer , Szentimrey Hannah , Shamli Ahmed , Martin Timothy , Gréwal Gary , Areibi Shawki
Published: August 2021

The ability to accurately and efficiently estimate the routability of a circuit based on its placement is one of the most challenging and difficult tasks in the Field Programmable Gate Array (FPGA) flow. In this article, we present a novel, deep learning framework based on a Convolutional Neural Network (CNN) model for predicting the routability of a placement. Since the performance of the CNN model is strongly dependent on the hyper-parameters selected for the model, we perform an exhaustive parameter tuning that significantly improves the model’s performance and we also avoid overfitting the model. We also incorporate the deep learning model into a state-of-the-art placement tool and show how the model can be used to (1) avoid costly, but futile, place-and-route iterations, and (2) improve the placer’s ability to produce routable placements for hard-to-route circuits using feedback based on routability estimates generated by the proposed model. The model is trained and evaluated using over 26K placement images derived from 372 benchmarks supplied by Xilinx Inc. We also explore several opportunities to further improve the reliability of the predictions made by the proposed DLRoute technique by splitting the model into two separate deep learning models for (a) global and (b) detailed placement during the optimization process. Experimental results show that the proposed framework achieves a routability prediction accuracy of 97% while exhibiting runtimes of only a few milliseconds.
DILSA+: Predicting Urban Dispersal Events through Deep Survival Analysis with Enhanced Urban Features
Authors: Khezerlou Amin Vahedian , Zhou Xun , Li Xinyi , Street W. Nick , Li Yanhua
Published: August 2021

Urban dispersal events occur when an unexpectedly large number of people leave an area in a relatively short period of time. It is beneficial for the city authorities, such as law enforcement and city management, to have an advance knowledge of such events, as it can help them mitigate the safety risks and handle important challenges such as managing traffic, and so forth. Predicting dispersal events is also beneficial to Taxi drivers and/or ride-sharing services, as it will help them respond to an unexpected demand and gain competitive advantage. Large urban datasets such as detailed trip records and
point of interest
(
POI
) data make such predictions achievable. The related literature mainly focused on taxi demand prediction. The pattern of the demand was assumed to be repetitive and proposed methods aimed at capturing those patterns. However, dispersal events are, by definition, violations of those patterns and are, understandably, missed by the methods in the literature. We proposed a different approach in our prior work [32]. We showed that dispersal events can be predicted by learning the complex patterns of arrival and other features that precede them in time. We proposed a survival analysis formulation of this problem and proposed a two-stage framework (DILSA), where a deep learning model predicted the survival function at each point in time in the future. We used that prediction to determine the time of the dispersal event in the future, or its non-occurrence. However, DILSA is subject to a few limitations. First, based on evidence from the data, mobility patterns can vary through time at a given location. DILSA does not distinguish between different mobility patterns through time. Second, mobility patterns are also different for different locations. DILSA does not have the capability to directly distinguish between different locations based on their mobility patterns. In this article, we address these limitations by proposing a method to capture the interaction between POIs and mobility patterns and we create vector representations of locations based on their mobility patterns. We call our new method DILSA+. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014 to 2016. Results show that DILSA+ can predict events in the next 5 hours with an F1-score of 0.66. It is significantly better than DILSA and the state-of-the-art deep learning approaches for taxi demand prediction.
Pandemic Pedagogy and Possibilities
Authors: Stafford Tom
Published: August 2021

Greetings, scholars and friends of The DATA BASE. It's been a long and interesting year we just lived through, and everybody has an interesting story to tell about it, to be sure. I'm no different in that respect, but I have not yet written on the matter, as so many already have. Special issues on the topic of technological remediation of pandemic restrictions on pedagogy abound; it's quite interesting.
Rubric for Evaluating the Alignment of the IT Project Portfolio with IT Governance in Universities
Authors: Valverde-Alulema Francisco , Llorens-Largo Faraón
Published: August 2021

Organizations are currently using the potential of IT to achieve their strategic objectives. IT governance ensures that decisions related to information technologies are aligned with business needs and strategies. One of the best practices to introduce a culture of IT governance is the implementation of the IT project portfolio. However, there is a lack of studies that support this claim. We developed a rubric to evaluate the portfolio of IT projects of a university to determine its strategic nature and its alignment with the principles of IT governance. After a phase of qualitative research, we designed a survey to collect quantitative data in a real scenario. We contrasted these data with the estimated scenario through the results obtained in the previous phase. The objective of this research is to validate and improve the starting proposal as a result of qualitative research. This survey has been answered by Spanish public universities that have implemented a portfolio of IT projects and whose IT managers are aware of the ISO/IEC 38500 standard and its principles. The results have allowed us to design a rubric that can be used by universities to evaluate the relationship between the elements and the advantages of the IT project portfolio and its alignment with IT governance.
Effects of Implant Temperature, Backside Contamination and Scribe Lines on Room Temperature Photoluminescence Measurements on Silicon
Authors: Yoo Woo Sik , Ishigaki Toshikazu , Kim Jung Gon , Kang Kitaek
Published: August 2021

To investigate the effect of implantation temperature on the damage to a Si lattice, room temperature photoluminescence (PL) spectra were measured from highly-channeled MeV 11B+ implanted Si wafers with different implant temperatures (25 °C and 450 °C) after annealing at 950 °C for 3 min in N2. Small pieces from the wafers were used for PL characterization. The implanted Si wafer piece at the elevated temperature resulted in higher overall PL intensity under both excitation wavelengths suggesting lesser lattice damage than the Si wafer implanted at room temperature. Unexpectedly large PL intensity variations were observed from PL area mapping of both wafer pieces. In addition, strange behaviors of localized PL intensity variations, with opposite trends between 670 nm and 827 nm excitation PL measurements were observed near the scribe lines and sample number markings on the backside. The PL intensity was increased under 670 nm excitation while it was decreased under 827 nm excitation. Possible reasons for this strange behavior were discussed based on experimental results and analysis. PL measurement is verified to be very sensitive to the surface condition, interface, bulk and backside conditions of the Si. For reliable PL measurement, backside contamination and scratches, such as scribe lines, should be avoided.
Rub‐Impact Force Induces Periodic, Quasiperiodic, and Chaotic Motions of a Controlled Asymmetric Rotor System
Authors: Saeed N. A. , Awwad Emad Mahrous , Maarouf Ali , Farh Hassan M. H. , Alturki Fahd A. , Awrejcewicz Jan
Published: August 2021

This article aims to explore the oscillatory characteristics of a controlled asymmetric rotor system when subjected to rub and impact forces between the rotor and stator. Four electromagnetic poles are used to control the whirling motion of the rotor system through a linear proportional‐derivative control law. The equations of motion that govern the whole system dynamics are derived including the rub and impact forces. The derived mathematical model is analyzed in two basic steps. Firstly, the obtained model is treated as a weakly nonlinear system using perturbation analysis to obtain the slow‐flow modulating equations when neglecting the rub and impact forces. Depending on the obtained slow‐flow equations, different response curves are plotted to explore the system’s periodic vibrations and determine the conditions at which the system can exhibit rub and impact force. Secondly, the whole system model including the rub and impact forces is investigated by using the bifurcation diagrams, Poincare map, frequency spectrums, and temporal oscillations. The obtained results revealed that the applied control law could mitigate the system whirling oscillations and prevent the rub and impact forces if the control gains are tuned properly. However, the system can perform period‐n, quasiperiodic, or chaotic motion depending on the shaft spinning speed if the controller fails to eliminate the contact between the rotor and stator.
Contribution Degree of Different Surface Factors in Urban Interior to Urban Thermal Environment
Authors: Wan Jikang , Zhu Min
Published: August 2021

The contribution degree of different surface factors (complexity and heterogeneity) in the urban interior to the urban thermal environment has become an issue of increasing concern under changing climate. In this paper, the multiple linear regression analysis methods to analyze the contribution degree of different surface factors to the urban thermal environment were based on seven urban built-up areas. At the same time, the LST of the same type of factors in the same city will have a difference of ±2.5°C due to the different surrounding features. At the same time, the LST of the same ground object in the same city will be ±2.5°C different because of the difference of the surrounding ground object. The environmental LST and the mean LST of other surface factors were significantly correlated, and the root mean square error was 3.52. This study first classifies the ground features with different attributes, conducts LST statistics for each category, and conducts multivariate linear analysis, instead of setting some fuzzy exponent and forcing a threshold to calculate. The purpose is to explore the contribution of different reflectivity ground objects to the urban thermal environment.
The Predictive Effect of Health Examination in the Incidence of Diabetes Mellitus in Chinese Adults: A Population-Based Cohort Study
Authors: Fu Xiaomin , Jia Yingmin , Liu Jing , Lei Qinghua , Li Lele , Li Nan , Hu Yanyan , Wang Shanshan , Liu Hongzhou , Yan Shuangtong
Published: August 2021

Background. The incidence of diabetes mellitus (DM) was increasing in recent years, and it is important to screen those nondiabetic populations through health examination to detect the potential risk factors for DM. We aimed to find the predictive effect of health examination on DM. Methods. We used the public database from Rich Healthcare Group of China to evaluate the potential predictive effect of health examination in the onset of DM. The colinear regression was used for estimating the relationship between the dynamics of the health examination index and the incident year of DM. The time-dependent ROC was used to calculate the best cutoff in predicting DM in the follow-up year. The Kaplan-Meier method and Cox regression were used to evaluate the HR of related health examination. Results. A total of 211,833 participant medical records were included in our study, with 4,172 participants diagnosing as DM in the following years (among 2-7 years). All the initial health examination was significantly different in participants’ final diagnosing as DM to those without DM. We found a negative correlation between the incidence of years of DM and the average initial FPG (

r
=
−
0.1862

,

P
<
0.001

). Moreover, the initial FPG had a strong predictive effect in predicting the future incidence of DM (

AUC
=
0.961

), and the cutoff was 5.21 mmol/L. Participants with a higher initial FPG (>5.21 mmol/L) had a 2.73-fold chance to develop as DM in follow-up (

95
%
CI
=
2.65
–
2.81

,

P
<
0.001

). Conclusion. Initial FPG had a good predictive effect for detecting DM. The FPG should be controlled less than 5.21 mmol/L.
An Efficient and Robust Method to Predict Multifractured Horizontal Well Production in Shale Oil and Gas Reservoirs
Authors: Chen Ling , Bai Yuhu , Xu Bingxiang , Li Yanzun , Dong Zhiqiang , Wang Suran
Published: August 2021

Shale oil and gas reservoirs are developed by MFHWs. After large-scale hydraulic fracturing, it is hard to forecast the production rate using the theoretical method. In the engineering application field, the empirical method of DCA is often used to forecast the production rate of shale oil and gas produced by MFHWs. However, there are some problems in using DCA, like how to find out the proper decline model and switch point of two contiguous flowing periods and how to deal with the unsteady operation condition which causes a lot of uncertainty in production forecast. In order to solve these problems, firstly, a straight line model, representing the linear flow period in the life cycle of shale oil and gas produced by MFHWs, in the

Q
,
lg
 
q

coordinate system is proven to be theoretically proper. Secondly, the duration of the linear flow period is verified to be over 10~15 years by using an analytical model to do the calculation with the method of Monte Carlo random sampling taking a large amount of parameter combinations of Eagle Ford shale oil and gas reservoirs into calculation. And a field data analysis of Barnett and Eagle Ford also shows that the duration of linear flow period can be more than 10~15 years. Thus, a method of production forecast taking advantage of the straight line feature in the

Q
,
lg
 
q

coordinate system is raised. After practical use, it is found that the method is robust and can increase the forecast efficiency and decrease the manual error. Moreover, it can increase the accuracy of production forecast and deal with some unsteady operation conditions. Therefore, this new method has good promotional value in the engineering field.
Robust Estimation Methods Used to Study the Reasons behind Increasing Divorce Cases in Saudi Society
Authors: Abdulrahman Alanazi Talal , Alamri Osama
Published: August 2021

Background. Increasing divorce rates is a major problem in Saudi society. Divorce has become the primary solution for many couples who are experiencing problems in their relationships, and the language of divorce has become prevalent in both the daily register of Saudi courts on a daily basis and in fictional works. This trend has become a threat to marital life and is particularly damaging for children and young people. There has been previous research on the increase in divorce rates; however, no one addresses this important issue statistically. The specific research question is what are the actual reasons for the increase in divorce rates mentioned in previous studies? Objective. This study contains a statistical analysis of the underlying reasons for rising divorce rates in Saudi society. Methods. An online survey was conducted that gathered quantitative data from 800 participants. The responses of factual relevance were examined via edge plan analysis, regression analysis, and analyses with SPSS software to determine the causes for the rise in divorces in Saudi society. A predesign of edge consisting of six factors and twelve trials is selected. The design is examined in all the data for each row of it and according to the factor or reason chosen, provided that all design items are fulfilled; after that, it is analyzed in two ways. Results. Some examples are presented, so the findings indicated that differences on the couple’s characters and the length of time the wife spent doing housework and amount of attention to the husband are factors that contributed to divorce. Conclusion. The Saudi Arabian government needs to develop an operational plan to study these causes of divorce and take action. Future studies could analyze the data using supersaturated prototypes, where a large number of variables are studied in just a few simulation trials.
Downregulated KIF3B Induced by miR-605-3p Inhibits the Progression of Colon Cancer via Inactivating Wnt/β-Catenin
Authors: Wang Qilong , Hao Xiaomin , Xu Gang , Lv Tiesheng
Published: August 2021

Colon cancer is a common malignant disease with high morbidity and mortality, and miRNA dysfunction has been confirmed as an important reason for cancer development. Several studies have verified miR-605-3p as a tumor inhibitor while its roles in colon cancer remain uncertain. In this study, the specimen of the patients and the cell lines of colon cancer were used to observe the expression of miR-605-3p, and the CCK-8, Transwell assay, and flow cytometry assay were used to observe the functions of miR-605-3p in colon cancer cells. The downstream factors of miR-605-3p were predicted by TargetScan and then were verified by dual-luciferase reporter assay. Moreover, western blot was used to investigate the effect of miR-605-3p on Wnt/β-catenin signal pathway. The result showed that miR-605-3p was extremely downregulated in the pathological tissues and tumor cells, and miR-605-3p could effectively induce the apoptosis and impede the proliferation and invasion of the tumor cells. It was found that KIF3B was a target of KIF3B; decreased KIF3B could reverse the effects of miR-605-3p on colon cancer. Besides, the inactivated Wnt/β-catenin pathway was also observed in colon cells when miR-605-3p was upregulated, and the phenomenon could be rescued by KIF3B upregulation. In conclusion, miR-605-3p could inactivate the Wnt/β-catenin pathway induced via promoting KIF3B expression.
Hospital-Acquired COVID-19: Case Discussions of Two Patients Treated at a Level I Trauma Center
Authors: Desrochers Ryan M. , Gates Jonathan D. , Ricaurte Daniel , Keating Jane J.
Published: August 2021

The community spread of COVID-19 is well known and has been rigorously studied since the onset of the pandemic; however, little is known about the risk of transmission to hospitalized patients. Many practices have been adopted by healthcare facilities to protect patients and staff by attempting to mitigate internal spread of the disease; however, these practices are highly variable among institutions, and it is difficult to identify which interventions are both practical and impactful. Our institution, for example, adopted the most rigorous infection control methods in an effort to keep patients and staff as safe as possible throughout the pandemic. This case report details the hospital courses of two trauma patients, both of whom tested negative for the COVID-19 virus multiple times prior to producing positive tests late in their hospital courses. The two patients share many common features including history of psychiatric illness, significant injuries, ICU stays, one-to-one observers, multiple consulting services, and a prolonged hospital course prior to discharge to a rehabilitation facility. Analysis of these hospital courses can help provide a better understanding of potential risk factors for acquisition of a nosocomial COVID-19 infection and insight into which measures may be most effective in preventing future occurrences. This is important to consider not only for COVID-19 but also for future novel infectious diseases.
Abundance and Symbiotic Rhizobia Colonizing Soybean (Glycine max) in Soils of Kakamega County, Western Kenya
Authors: Mulama Samuel , Onamu Rose , Odongo Fredrick , Muoma John
Published: August 2021

Identification of effective indigenous rhizobia strain nodulating soybean (Glycine max) was conducted in Kakamega County, Western Kenya. This study was carried out to establish the abundance and symbiotic efficiency of native rhizobia strains in soil samples collected from 12 sites in four agroecological zones within the region. The MPN enumeration technique was used to estimate the indigenous rhizobia numbers and showed that 54% of the sampled sites had high abundance. Rhizobia number in the soil collected from the six regions varied from 2.5 × 102 to 3.12 × 103 cells·g− soil. Thirty isolates from the soil collected in the four regions were tested for efficiency using soybean as the trap plant in Leonard jars containing sterilized vermiculite irrigated with nitrogen-free media. Determination of the relationship between rhizobial abundance and symbiotic efficiency with respect to the soil chemical conditions was performed using ANOVA, and the means were compared using Tukey’s honest significant difference test. Inoculation with bacterial isolates significantly (

P
<
0.05

) increased the nodule number and shoot dry weight, while weight of the seeds showed variations across the groups. The soil pH across the 4 regions ranged between 5.41 and 6.52.
Schizophrenia and Hyperostosis Frontalis Interna with History of Head Injury
Authors: Elghazouani Fatima
Published: August 2021

Hyperostosis frontalis interna is an irregular thickening of the frontal bone. Its etiology is unknown. It has been rarely linked with schizophrenia and head injury. Case Presentation. We describe an unusual case of a 44-year-old female with schizophrenia and hyperostosis frontalis interna having a history of head trauma. At the age of 3 years, she had a head injury that could be classified as mild traumatic brain injury. She presents a family history of schizophrenia. She was admitted for resistant schizophrenic disorder. The cranial computed tomography showed bilateral and asymmetrical hyperostosis of the frontal bone that was more pronounced on the right side. This corresponds to the impact of the trauma with frontal atrophy without any metabolic or endocrinal abnormalities. Conclusion. We surmise that the long-term pathological effects of traumatic brain injury, including hyperostosis frontalis interna, are likely to interact with genetic vulnerability and may lead to schizophrenic disorder.
Shear Strength of Reinforced Recycled Aggregate Concrete Corbels
Authors: Hamoodi Ahid Zuhair , Chkheiwer Aqeel Hatem , Kadim Jaffar Ahmed
Published: August 2021

This paper is related to a laboratory program for the shear strength of reinforced concrete corbels (RCC) cast with or without recycled aggregate (RA) by investigating the main parameters affecting the corbels behavior including the replacement aggregate recycling ratio, fcu, and shear span to effective depth ratio a/d. Eight specimens were cast and tested. The obtained results were compared with ACI and EC2 codes. It is found that the ACI code and E2 code give sensibly conservative results when compared with the findings of the present work for all tested specimens regarding RA, concrete strength, and a/d. Also, the experimental results show that the presence of recycled aggregate decreases slightly both cracking and failure loads. Furthermore, the failure load development due to the effect of compressive strength is more effective with the presence of recycled aggregate, and the 50% ratio of RA was the suitable ratio in elaborate crack and failure loads. Finally, the reduction of the span-depth ratio (from 0.50 to 0.35) increases the crack and failure load by 8.1% and 20.2%, respectively, leading to confirm that the corbel strength is much sensitive to decreasing span-depth ratio compared to the associated deflections.
Effects of Fatty Acid Oxidation and Its Regulation on Dendritic Cell-Mediated Immune Responses in Allergies: An Immunometabolism Perspective
Authors: Sun Shanfeng , Gu Yanjun , Wang Junjuan , Chen Cheng , Han Shiwen , Che Huilian
Published: August 2021

Type 1 allergies, involve a complex interaction between dendritic cells and other immune cells, are pathological type 2 inflammatory immune responses against harmless allergens. Activated dendritic cells undergo extensive phenotypic and functional changes to exert their functions. The activation, differentiation, proliferation, migration, and mounting of effector reactions require metabolic reprogramming. Dendritic cells are important upstream mediators of allergic responses and are therefore an important effector of allergies. Hence, a better understanding of the underlying metabolic mechanisms of functional changes that promote allergic responses of dendritic cells could improve the prevention and treatment of allergies. Metabolic changes related to dendritic cell activation have been extensively studied. This review briefly outlines the basis of fatty acid oxidation and its association with dendritic cell immune responses. The relationship between immune metabolism and effector function of dendritic cells related to allergic diseases can better explain the induction and maintenance of allergic responses. Further investigations are warranted to improve our understanding of disease pathology and enable new treatment strategies.
Value of ddPCR in the Preoperative Diagnosis of Solitary Pulmonary Nodules Based on the Observation of Virtual Reality Images of Smart Medical Treatment
Authors: Mou Xuri , Sun Guiying , Shi Yubo , Zhang Liangdong , Li Runjie , Yu Shuling , Liu Shuliang
Published: August 2021

At present, lung cancer ranks the first cause of tumor death in the world, and malignant tumors in the SPN detected by imaging account for 5–40%, most of which are peripheral lung cancer. The recovery of the solitary nodules in the lung after treatment has also been paid attention to. In order to explore the correlation of solitary pulmonary nodule (SPN) with microvessel density (MVD), vascular parameters, and vascular ratio under virtual reality images of smart medical treatment and evaluate the differentiation of SPN by ddPCR under virtual reality image observation diagnosis value, this article collects relevant information by investigating patients, investigating relevant literature, interviewing professionals, and constructing a case template, using a comprehensive quantitative and qualitative analysis method to create a damage assessment matrix. Experimental results prove that there are significant differences in the microvascular architecture within the SPN in the benign, inflammatory, and malignant groups. The correlation between ddPCR and vascular parameters (especially the ratio of luminal vessels) under the virtual reality image observation of smart medical treatment is better than other detection methods, and its accuracy is about 10% higher. Based on the observation of smart medical virtual reality images, ddPCR can be used as an index for noninvasive evaluation of tumor angiogenesis, which is helpful for the differential diagnosis of SPN.
The Marichev-Saigo-Maeda Fractional Calculus Operators Pertaining to the
V
-Function
Authors: Chandak S. , Shimelis Biniyam , Abeye Nigussie , Padma A.
Published: August 2021

In the present paper, we establish some composition formulas for Marichev-Saigo-Maeda (MSM) fractional calculus operators with

V

-function as the kernel. In addition, on account of

V

-function, a variety of known results associated with special functions such as the Mittag-Leffler function, exponential function, Struve’s function, Lommel’s function, the Bessel function, Wright’s generalized Bessel function, and the generalized hypergeometric function have been discovered by defining suitable values for the parameters.
Carveol Attenuates Seizure Severity and Neuroinflammation in Pentylenetetrazole‐Kindled Epileptic Rats by Regulating the Nrf2 Signaling Pathway
Authors: Alvi Arooj Mohsin , Al Kury Lina Tariq , Alattar Abdullah , Ullah Ikram , Muhammad Asmaa Jan , Alshaman Reem , Shah Fawad Ali , Khan Arif Ullah , Feng Jinxing , Li Shupeng
Published: August 2021

Epilepsy is a neurodegenerative brain disorder characterized by recurrent seizure attacks. Numerous studies have suggested a strong correlation between oxidative stress and neuroinflammation in several neurodegenerative disorders including epilepsy. This study is aimed at investigating the neuroprotective effects of the natural compound carveol against pentylenetetrazole‐ (PTZ‐) induced kindling and seizure model. Two different doses of carveol (10 mg/kg and 20 mg/kg) were administered to male rats to determine the effects and the effective dose of carveol and to further demonstrate the mechanism of action of nuclear factor E2‐related factor (Nrf2) in PTZ‐induced kindling model. Our results demonstrated reduced levels of innate antioxidants such as superoxide dismutase (SOD), catalase, glutathione‐S‐transferase (GST), and glutathione (GSH), associated with elevated lipid peroxidation (LPO) and inflammatory cytokines level such as tumor necrosis factor‐alpha (TNF‐α), and mediators like cyclooxygenase (COX‐2) and nuclear factor kappa B (NFκB). These detrimental effects exacerbated oxidative stress and provoked a marked neuronal alteration in the cortex and hippocampus of PTZ‐intoxicated animals that were associated with upregulated Nrf2 gene expression. Furthermore, carveol treatment positively modulated the antioxidant gene Nrf2 and its downstream target HO-1. To further investigate the role of Nrf2, an inhibitor of Nrf2 called all‐trans retinoic acid (ATRA) was used, which further exacerbated PTZ toxicity. Moreover, carveol treatment induced cholinergic system activation by mitigating acetylcholinesterase level which is further linked to attenuated neuroinflammatory cascade. The extent of blood‐brain barrier disruption was evaluated based on vascular endothelial growth factor (VEGF) expression. Taken together, our findings suggest that carveol acts as an Nrf2 activator and therefore induces downstream antioxidants and mitigates inflammatory insults through multiple pathways. This eventually alleviates PTZ‐induced neuroinflammation and neurodegeneration.
iPSC-Derived Neoantigen-Specific CTL Therapy for Ewing Sarcoma
Authors: Ishii Midori , Ando Jun , Yamazaki Satoshi , Toyota Tokuko , Ohara Kazuo , Furukawa Yoshiki , Suehara Yoshiyuki , Nakanishi Mahito , Nakashima Kazutaka , Ohshima Koichi , Nakauchi Hiromitsu , Ando Miki
Published: August 2021

The prognosis of Ewing sarcoma caused by EWS/FLI1 fusion is poor, especially after metastasis. Although therapy with CTLs targeted against altered EWS/FLI1 sequences at the gene break/fusion site may be effective, CTLs generated from peripheral blood are often exhausted because of continuous exposure to tumor antigens. We addressed this by generating induced pluripotent stem cell (iPSC)–derived functionally rejuvenated CTLs (rejT) directed against the neoantigen encoded by the EWS/FLI1 fusion gene. In this study, we examined the antitumor effects of EWS/FLI1-rejTs against Ewing sarcoma. The altered amino acid sequence at the break/fusion point of EWS/FLI1, when presented as a neoantigen, evokes an immune response that targets EWS/FLI1+ sarcoma. Although the frequency of generated EWS/FLI1-specific CTLs was only 0.003%, we successfully established CTL clones from a healthy donor. We established iPSCs from a EWS/FLI1-specific CTL clone and redifferentiated them into EWS/FLI1-specific rejTs. To evaluate cytotoxicity, we cocultured EWS/FLI1-rejTs with Ewing sarcoma cell lines. EWS/FLI1-rejTs rapidly and continuously suppressed the proliferation of Ewing sarcoma for >40 hours. Using a Ewing sarcoma xenograft mouse model, we verified the antitumor effect of EWS/FLI1-rejTs via imaging, and EWS/FLI1-rejTs conferred a statistically significant survival advantage. “Off-the-shelf” therapy is less destructive and disruptive than chemotherapy, and radiation is always desirable, particularly in adolescents, whom Ewing sarcoma most often affects. Thus, EWS/FLI1-rejTs targeting a Ewing sarcoma neoantigen could be a promising new therapeutic tool.