bluuebunny/crossref_metadata_2025 huggingface dataset:
Nonlinear interaction detection through partial dimension reduction with missing response data
Authors: Xu Hong-Xia , Fan Guo-Liang , Li Jin-Chang
Published: July 2022

In this paper, we are concerned with nonlinear interaction detection based on partial dimension reduction with missing response data. The covariates are grouped through linear combinations in a general class of semi-parametric models to detect their joint interaction effects. The joint interaction effects are estimated by a profile least squares approach with the help of the inverse probability weighted technique. The asymptotic properties of the resulting estimate for the central partial mean subspace are established. In addition, a Wald type test is proposed to detect the interactions between the covariates. A BIC-type criterion is applied to determine the structural dimension of the central partial mean subspace and its consistency is also obtained. Simulations are conducted to examine the finite sample performances of our proposed method and a real data set is analyzed for illustration.
Modulation of the electronic and magnetic properties of pyridinic N-doped graphene with Ni/Cr
Authors: Aggarwal Seema , Anand Monica
Published: July 2022

We have explored the interaction of Ni/Cr with pyridinic N-doped graphene and its influence on the electronic and magnetic characteristics of graphene using a density functional theory (DFT) approach. A [Formula: see text] graphene supercell with a single vacancy is modeled. Initially, the concentration of nitrogen atoms is varied in the pyridinic configuration of graphene. The structures, thus obtained, are decorated with Ni/Cr atoms. For configurations without transition metals (TMs), the calculated electronic band structures show a downward shift in Fermi level that decreases as nitrogen concentration increases. Furthermore, it is found that these structures manifest metallic behavior along with some flat bands near the Fermi level. With the addition of Ni/Cr atoms, the movement of the Fermi level toward the conduction band is noticed. The opening of the bandgap at the K-point also varies slightly with the concentration of N atoms and/or the presence of TM atoms. The structures P1N-Ni and P2N-Ni transmute into semiconductors. A significant increase in the magnetic moment is found with the adsorption of the Ni/Cr atom. The analysis of spin-polarized partial density of states proves that higher magnetic moments following TM absorption are mainly on account of the Ni/Cr-3d orbitals and their hybridization with C-2p orbitals. These findings suggest that functionalizing TMs on pyridinic N (Nitrogen)-doped graphene can drastically alter their electronic and magnetic properties.
A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot
Authors: Ma Liling , Guo Jian , Li Jiehao , Wang Junzheng
Published: July 2022

This research provides a novel approach for detecting multi-legged robot actuator faults. The most significant concept is to design the Fault Diagnosis Generative Adversarial Network (FD-GAN) to fully adapt to the fault diagnosis problem with insufficient data. We found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data. A straightforward solution is to use massive amounts of normal data to drive the diagnostic model. We introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data. A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal category. It uses a generator network as a feature extractor, and uses a discriminator network as a fault probability evaluator, which creates a new use of GAN in the field of fault diagnosis. Among the many learning strategies of GAN, we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the excitation. We also design a fault location method based on binary search, which greatly improves the search efficiency and engineering value of the entire method. We have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working modes. We compared FD-GAN with popular diagnostic methods. The results show that our method has the highest accuracy and recall rate.
Biomechanical Study of Modified Massachusetts General Hospital Flexor Tendon Repair Using Looped Sutures
Authors: WANG Qiao , WONG Yoke Rung , McGROUTHER Duncan Angus
Published: July 2022

Background: Massachusetts General Hospital (MGH) repair is one of the widely used 4-strand flexor tendon repair techniques. However, it uses two single strand sutures that are each passed twice across the repair site. This is time consuming and may cause imbalance of the load across the repair. We modified the MGH repair by using a looped suture and call it the looped MGH repair. The aim of this study is to compare the strength of the looped MGH repair performed with three different looped sutures against the strength of original MGH repair.

Methods: Forty porcine flexors were used for the study. The original MGH repair was performed with Prolene® 4-0. Looped MGH repair was performed with three different loop sutures, Supramid® 4-0, Tendo-Loop® 4-0 and FiberLoop® 4-0. Mechanism of failure, ultimate tensile strength, stiffness, load to 2-mm gap formation and repair time were recorded for comparison.

Results: There was no significant difference between the original MGH repair and the looped repair using Supramid® regarding their biomechanical performance. Looped MGH repair using Tendo-Loop® and FiberLoop® showed significantly higher ultimate tensile strength and FiberLoop® had highest 2-mm gap force. All looped MGH repairs required significant less time compared to original MGH repair.

Conclusions: Our modification of the MGH repair using a looped Supramid® 4-0 suture took significantly lesser time to perform while providing the same strength as the original MGH repair using Prolene® 4-0. The use of the FiberLoop® 4-0 provided significantly greater strength while taking lesser time.
The Effect of Hand Dominance, Age, Gender, Fracture Comminution and ASA Status on Time to Fracture Healing Following Surgical Fixation of Distal Radius Fractures
Authors: TANG Camelia Qian Ying , CHIOW Si Min , LAI Sean Han Sheng , CHIA Dawn Sinn Yii
Published: July 2022

Background: There is recent renewed interest in the effect of hand dominance on distal radius fractures. Current studies focus on functional or patient-reported outcomes, with lack of studies pertaining to radiological outcomes. The primary aim of this study is to examine the effect of hand dominance on time to fracture healing following surgical fixation of distal radius fracture. We also looked at the effect of age, gender, fracture comminution and American Society of Anaesthesiologists (ASA) status on time to fracture healing.

Methods: Patients who underwent distal radius fracture fixation surgery in our department from 1 January 2015 to 31 December 2015 were included. Time to fracture healing was taken from the day of surgery to when radiographic union was present as evidence by bridging callus or osseous bone. We looked at the effect of hand dominance, age, gender, fracture comminution and ASA status on time to fracture healing.

Results: One hundred and forty-five consecutive patients (80 females and 65 males) had a mean period of 56.2 ± 41.8 days to fracture healing post-operatively. Patients with dominant hand injury had a shorter duration to fracture healing than patients with non-dominant hand injury (mean 47.3 ± 31.1 days versus 62.1 ± 46.8 days, p = 0.023). Patients ≥ 65 years and with pre-existing medical conditions (ASA Class II and above) had a longer duration to fracture healing (mean 63.7 ± 53.0 days versus 51.9 ± 33.4 days, p = 0.036 and mean 47.9 ± 30.0 days versus 62.0 ± 47.7 days, p = 0.016, respectively). In addition, patients with comminuted fractures took longer to heal than patients with non-comminuted fractures (mean 57.6 ± 33.4 days versus 48.3 ± 20.8 days, p = 0.038).

Conclusion: Time to fracture healing post distal radius fracture fixation surgery was significantly related to hand dominance, as well as age, ASA classification and fracture configuration. Dominant wrist injuries had shorter time to fracture healing.

Level of Evidence: Level IV (Therapeutic)
Limited Active Metacarpophalangeal Extension of the Ring Finger Due to Scarring of the Juncturae Tendinum: A Case Report
Authors: HASHIMOTO Akira , TSUBOKAWA Naoto , SONOHATA Motoki , MAWATARI Masaaki
Published: July 2022

A limitation of finger extension resulting from scarring of the juncturae tendinum (JT) is a rare condition. We report a patient with limitation in active extension of the metacarpophalangeal joint (MCPJ) of the ring finger due to a scarred JT in a 37-year-old man. Surgical exploration showed a hard, fixed and scarred JT in the third intermetacarpal space. After resection of the scarred JT, the patient achieved full range of active extension of the ring finger MCPJ. Scarring of the JT may decrease excursion of the extensor digitorum communis and must be considered as one of the differentials in patients with limitation in MCPJ extension.

Level of Evidence: Level V (Therapeutic)
Acquired Constriction Band Syndrome Affecting Multiple Digits – A Case Report
Authors: KENDIR Munur Selcuk , DADACI Mehmet , UYAR Ilker , YILDIRIM Mehmet Emin Cem
Published: July 2022

Rubber band syndrome is a condition that usually affects children because of wearing a rubber band on the wrist or the ankle. Depending on the degree of pressure caused by the band, patients may present acutely with ischemia and necrosis of the tissues distal to the bands or chronically with change in shape, oedema, loss of function, sensation and rarely amputation. This condition is very rare in adults and most reports in literature are in patients with cognitive impairment or psychiatric illness. We report 62-year-old lady with a background of a psychiatric illness who presented with an acquired constriction band syndrome affecting multiple digits of both hands.

Level of Evidence: Level V (Therapeutic)
Tenosynovitis with Psammomatous Calcifications Masquerading as Pyogenic Flexor Tenosynovitis
Authors: MULLEN Connor R. , KUBIAK Carrie A. , GRANT David W. , CHUNG Kevin C.
Published: July 2022

We present a case report examining the clinical management of tenosynovitis with psammomatous calcifications presenting like pyogenic flexor tenosynovitis in a pregnant patient. Discussion details overlapping symptoms, important distinctions on imaging studies and the appropriateness of surgery.

Level of Evidence: Level V (Therapeutic)
Molecular Interaction Analysis of SPARC–Collagen with Human Serum Albumin
Authors: Are Ramakrishna Prasad , Babu Anju R.
Published: July 2022

In this study, in-silico interaction analysis between Human serum albumin (HSA) and serum protein acidic and rich in cysteine (SPARC)–Collagen complex was performed to determine the binding affinity between the two proteins. Structure-based molecular interaction studies of the HSA–SPARC–Collagen complex were performed using ClusPro 2.0, ZDOCK and PatchDock servers. Molecular dynamics analysis was performed for the first-ranked complex structure (HSA–SPARC–Collagen complex) using GROMACS for a period of 125 nanoseconds to determine the stability of the complex. The results show higher structural interactive cumulative scores between the HSA and SPARC–Collagen complex. HSA–SPARC–Collagen complex predominantly has the Pi-alkyl and Pi-sigma interactions. The root mean square fluctuation (RMSF), root mean square deviation, solvent accessible surface area, radius of gyration and the number of hydrogen bonds show that formed complex is stable. In conclusion, our study provides an add-on for tuning drugs concerning albumin as a drug carrier by targeting the SPARC–Collagen complex for treating different ailments.
Post-hoc Interpretability for Neural NLP: A Survey
Authors: Madsen Andreas , Reddy Siva , Chandar Sarath
Published: July 2022

Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability. Interpretability serves to provide these explanations in terms that are understandable to humans. Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a categorization of how recent post-hoc interpretability methods communicate explanations to humans, it discusses each method in-depth, and how they are validated, as the latter is often a common concern.
A Neural Model to Jointly Predict and Explain Truthfulness of Statements
Authors: Brand Erik , Roitero Kevin , Soprano Michael , Rahimi Afshin , Demartini Gianluca
Published: July 2022

Automated fact-checking (AFC) systems exist to combat disinformation, however, their complexity usually makes them opaque to the end-user, making it difficult to foster trust in the system. In this article, we introduce the E-BART model with the hope of making progress on this front. E-BART is able to provide a veracity prediction for a claim and jointly generate a human-readable explanation for this decision. We show that E-BART is competitive with the state-of-the-art on the e-FEVER and e-SNLI tasks. In addition, we validate the joint-prediction architecture by showing (1) that generating explanations does not significantly impede the model from performing well in its main task of veracity prediction, and (2) that predicted veracity and explanations are more internally coherent when generated jointly than separately. We also calibrate the E-BART model, allowing the output of the final model to be correctly interpreted as the confidence of correctness. Finally, we also conduct an extensive human evaluation on the impact of generated explanations and observe that: Explanations increase human ability to spot misinformation and make people more skeptical about claims, and explanations generated by E-BART are competitive with ground truth explanations.
A Survey of Joint Intent Detection and Slot Filling Models in Natural Language Understanding
Authors: Weld Henry , Huang Xiaoqi , Long Siqu , Poon Josiah , Han Soyeon Caren
Published: July 2022

Intent classification, to identify the speaker’s intention, and slot filling, to label each token with a semantic type, are critical tasks in natural language understanding. Traditionally the two tasks have been addressed independently. More recently joint models that address the two tasks together have achieved state-of-the-art performance for each task and have shown there exists a strong relationship between the two. In this survey, we bring the coverage of methods up to 2021 including the many applications of deep learning in the field. As well as a technological survey, we look at issues addressed in the joint task and the approaches designed to address these issues. We cover datasets, evaluation metrics, and experiment design and supply a summary of reported performance on the standard datasets.
A Survey on the Fairness of Recommender Systems
Authors: Wang Yifan , Ma Weizhi , Zhang Min , Liu Yiqun , Ma Shaoping
Published: July 2022

Recommender systems are an essential tool to relieve the information overload challenge and play an important role in people’s daily lives. Since recommendations involve allocations of social resources (e.g., job recommendation), an important issue is whether recommendations are fair. Unfair recommendations are not only unethical but also harm the long-term interests of the recommender system itself. As a result, fairness issues in recommender systems have recently attracted increasing attention. However, due to multiple complex resource allocation processes and various fairness definitions, the research on fairness in recommendation is scattered. To fill this gap, we review over 60 papers published in top conferences/journals, including TOIS, SIGIR, and WWW. First, we summarize fairness definitions in the recommendation and provide several views to classify fairness issues. Then, we review recommendation datasets and measurements in fairness studies and provide an elaborate taxonomy of fairness methods in the recommendation. Finally, we conclude this survey by outlining some promising future directions.
Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale
Authors: Nguyen Thien , Lyakh Dmitry , Dumitrescu Eugene , Clark David , Larkin Jeff , McCaskey Alexander
Published: July 2022

The numerical simulation of quantum circuits is an indispensable tool for development, verification, and validation of hybrid quantum-classical algorithms intended for near-term quantum co-processors. The emergence of exascale high-performance computing (HPC) platforms presents new opportunities for pushing the boundaries of quantum circuit simulation. We present a modernized version of the Tensor Network Quantum Virtual Machine (TNQVM) that serves as the quantum circuit simulation backend in the eXtreme-scale ACCelerator (XACC) framework. The new version is based on the scalable tensor network processing library ExaTN (Exascale Tensor Networks). It provides multiple configurable quantum circuit simulators that perform either an exact quantum circuit simulation via the full tensor network contraction or an approximate simulation via a suitably chosen tensor factorization scheme. Upon necessity, stochastic noise modeling from real quantum processors is incorporated into the simulations by modeling quantum channels with Kraus tensors. By combining the portable XACC quantum programming frontend and the scalable ExaTN numerical processing backend, we introduce an end-to-end virtual quantum development environment that can scale from laptops to future exascale platforms. We report initial benchmarks of our framework, which include a demonstration of the distributed execution, incorporation of quantum decoherence models, and simulation of the random quantum circuits used for the certification of quantum supremacy on Google’s Sycamore superconducting architecture.
Prehistory of Kinship
Authors: Bentley R. Alexander
Published: July 2022

As observed in recent centuries, the contemporary variety of kinship systems reflects millennia of human migration, cultural inheritance, adaptation, and diversification. This review describes key developments in prehistoric kinship, from matricentric hominin evolution to the Neolithic transition to agriculture and the heterogeneous resilience of matriliny. Starting with our hominin ancestors, kinship evolved among a cooperative breeding species to multilevel group structure among human hunter-gatherers, to substantial kinship changes brought on by the origins of intensified farming, to permanent settlements and unequal resource access. This review takes the approach that new forms of subsistence facilitated new equations of reproductive success, which changed cultural norms of kinship systems and heritable wealth. Subsequently, the formation of complex societies diminished kinship as the primary organizing principle of society. The article describes new methodologies and theoretical developments, along with critiques of bioarchaeological interpretations of prehistoric kinship.
Wound Culture
Authors: Solomon Harris
Published: July 2022

This review offers new perspectives on the anthropology of injuries and wounds. It maps how theories, methods, and ethnographic sensibilities converge on wounds, on the act of wounding, and on the wounded as instructive objects. The review assesses how anthropologists understand social forces to cause wounds and how they accord wounds the power to generate meaning about sociality. Organized across two themes, “breach” and “repair,” the review tests concepts of embodiment across clinical boundaries, manifestations of harm, and formations of justice. It examines how anthropological thought connects to wound culture and assesses links between embodiment and politics that develop in the domains of critical theory and medical anthropology. Ultimately, it aims to shed light on the connections between body politics and ethnography and to ask what wounds might generate as an anthropological concern.
Gender Development in Gender Diverse Children
Authors: deMayo Benjamin E. , Jordan Ashley E. , Olson Kristina R.
Published: July 2022

Within “mainstream” developmental science, gender researchers largely study the developmental trajectory of children considered to be “gender typical,” while research housed primarily in psychiatry and clinical psychology often documents the trajectories of gender diverse children. This article aims to bridge the studies of gender diversity and “mainstream” gender development. First, we review literature on the development of four commonly studied subgroups of gender diverse children—children referred to medical clinics because of their gender identity and expression, transgender children, female children with congenital adrenal hyperplasia, and tomboys—highlighting how these gender trajectories do or do not align with modal developmental patterns. We then describe social, cognitive, and biological determinants of gender in light of their implications for understanding diverse gender development. Finally, we note methodological suggestions for future research, with an eye toward better integrating research on gender diversity into “mainstream” gender development research.
Probing the Neutrino-Mass Scale with the KATRIN Experiment
Authors: Lokhov Alexey , Mertens Susanne , Parno Diana S. , Schlösser Magnus , Valerius Kathrin
Published: July 2022

The absolute mass scale of neutrinos is an intriguing open question in contemporary physics. The as-yet-unknown mass of the lightest and, at the same time, most abundant massive elementary particle species bears fundamental relevance to theoretical particle physics, astrophysics, and cosmology. The most model-independent experimental approach consists of precision measurements of the kinematics of weak decays, notably tritium β decay. With the KATRIN experiment, this direct neutrino-mass measurement has entered the sub-eV domain, recently pushing the upper limit on the electron-based neutrino mass down to 0.8 eV (90% CL) on the basis of first-year data out of ongoing, multiyear operations. Here, we review the experimental apparatus of KATRIN, the progress of data taking, and initial results. While KATRIN is heading toward the target sensitivity of 0.2 eV, other scientific goals are pursued. We discuss the search for light sterile neutrinos and an outlook on future keV-scale sterile-neutrino searches as well as further physics opportunities beyond the Standard Model.
Electroweak Penguin Decays of b-Flavored Hadrons
Authors: Egede Ulrik , Nishida Shohei , Patel Mitesh , Schune Marie-Hélène
Published: July 2022

In the past decade, electroweak penguin decays have provided a number of precision measurements and have become one of the most competitive ways to search for New Physics describing phenomena beyond the Standard Model. An overview of the measurements made at the B factories and hadron colliders is given, and the experimental methods are presented. Experimental measurements required to provide further insight into present indications of New Physics are discussed.
Exploring Stars in Underground Laboratories: Challenges and Solutions
Authors: Aliotta Marialuisa , Boeltzig Axel , Depalo Rosanna , Gyürky György
Published: July 2022

For millennia, mankind has been fascinated by the marvel of the starry night sky. Yet, a proper scientific understanding of how stars form, shine, and die is a relatively recent achievement, made possible by the interplay of different disciplines as well as by significant technological, theoretical, and observational progress. We now know that stars are sustained by nuclear fusion reactions and are the furnaces where all chemical elements continue to be forged out of primordial hydrogen and helium. Studying these reactions in terrestrial laboratories presents serious challenges and often requires developing ingenious instrumentation and detection techniques. Here, we reveal how some of the major breakthroughs in our quest to unveil the inner workings of stars have come from the most unexpected of places: deep underground. As we celebrate 30 years of activity at the first underground laboratory for nuclear astrophysics, LUNA, we review some of the key milestones and anticipate future opportunities for further advances both at LUNA and at other underground laboratories worldwide.
Serum Glial Cell Line-Derived Neurotrophic Factor (sGDNF) Is a Novel Biomarker in Predicting Cirrhosis in Patients with Chronic Hepatitis B
Authors: Yang Guangyue , Zhuang Liping , Sun Tiantian , Yeo Yee Hui , Tao Le , Zhang Wei , Ma Wenting , Wu Liu , Yang Zongguo , Yang Yanqin , Xue Dongying , Zhang Jie , Feng Rilu , Matthias P. Ebert , Dooley Steven , Seki Ekihiro , Liu Ping , Liu Cheng
Published: July 2022

Objectives. We assessed the potential of glial cell line-derived neurotrophic factor (GDNF) as a useful biomarker to predict cirrhosis in chronic hepatitis B (CHB) patients. Methods. A total of 735 patients from two medical centers (385 CHB patients and 350 healthy controls) were included to determine the association of serum and tissue GDNF levels with biopsy-proven cirrhosis. The diagnostic accuracy of serum GDNF (sGDNF) was estimated and compared with other indices of cirrhosis. Results. We showed significantly higher levels of sGDNF in CHB patients with fibrosis (28.4 pg/ml vs. 11.6 pg/ml in patients without) and patients with cirrhosis (33.8 pg/ml vs. 23.5 pg/ml in patients without). The areas under receiver operating curve (AUROCs) of sGDNF were 0.83 (95% confidence interval (CI): 0.80–0.87) for predicting liver fibrosis and 0.84 (95% CI: 0.79–0.89) for cirrhosis. Findings from the serum protein level and hepatic mRNA expression were consistent. Using the best cutoff to predict cirrhosis, we categorized the patients into sGDNF-high and sGDNF-low groups. The sGDNF-high group had significantly larger Masson’s trichrome and reticulin staining-positive area, higher Scheuer score, and METAVIR fibrosis stage (all

p
<
0.001

) but not steatosis. On multivariable regression, sGDNF was independently associated with cirrhosis with an odds ratio of 6.98 (95% CI: 1.10–17.94). Finally, we demonstrated that sGDNF outperformed AST to platelet ratio index, FIB-4, fibroscore, forn index, and fibrometer in differentiating F4 vs. F3. Conclusion. Using serum, tissue mRNA, and biopsy data, our study revealed a significant potential of sGDNF as a novel noninvasive biomarker for cirrhosis in CHB patients.
A Business Management Resource-Scheduling Method based on Deep Learning Algorithm
Authors: Wang Jing
Published: July 2022

As China’s economic level and industrial volume continue to develop and expand, the traditional methods of business management and resource scheduling demonstrate significant limitations and misalignments with China’s daily economic activities. Traditional industrial and commercial management resource scheduling is highly reliant on manual labor. When confronted with the requirements of large-scale project management, the pure manual mode cannot track the real-time progress of the project flexibly, effectively, and in a timely manner, resulting in the inability to complete the corresponding resource-scheduling work efficiently, which will affect the overall operation progress of the project. Deep neural networks have a high-application potential for solving extremely complex and highly nonlinear optimization problems. Artificial intelligence and deep learning research have advanced at a rapid pace over the past few decades, thanks to the efforts of numerous researchers. Combined with GPU technology, the deep learning framework can provide an extremely complex optimization problem with a practical and feasible optimization scheme and corresponding solution path in a very short amount of time. Therefore, this paper explores the potential application of deep learning technology to industrial and commercial resource-scheduling management. By analyzing the benefits of deep learning technology and the bottleneck issues of existing industrial and commercial resource scheduling, a real-time optimized industrial and commercial resource-scheduling model based on deep learning technology is developed. The model is evaluated using the respective data set. The test results demonstrate that the resource-scheduling model proposed in this paper has strong real-time and high-operation efficiency and can assist engineers in completing the corresponding resource-scheduling tasks.
Synthesis, Characterization, X-Ray Single-Crystal Structure, Potentiometric Measurements, Molecular Modeling, and Bioactivity Screening of Some Thiosemicarbazones
Authors: El-Sisi Abeer A. , Ali Mohamed , Ramdan Sohair F. , AlTaweel Osama , Abd El-Mageed Ahmed I. A. , El-Sherif Ahmed A.
Published: July 2022

A series of thiosemicarbazone (TSCN) compounds including ((E)-2-((E)-1-(2-(p-tolyl)hydrazono)propan-2-ylidene)hydrazine-1-carbothioamide (TSC1), (E)-N-ethyl-2-((E)-1-(2-(p-tolyl)hydrazono)propan-2-ylidene)hydrazine-1-carbothioamide (TSC2), and (E)-N-phenyl-2-((E)-1-(2-(p-tolyl)hydrazono)propan-2-ylidene)hydrazine-1-carbothioamide) (TSC3) were synthesized and fully characterized by diverse spectroscopies, such as X-ray single-crystal, infrared, mass, proton nuclear magnetic resonance, and ultraviolet-visible. Potentiometric measurements, molecular modeling, and biological and antitumor activity screening were studied. The thermodynamics and protonation constants of TSC1 as a representative of the synthesized TSCs were calculated and discussed. The solution speciation of different species was studied with pH. The molecular parameters of the optimized structures were calculated and discussed. The X-ray single crystals of TSC2 and TSC3 were established. Considering the antimicrobial activities and correlating structure-activity relationship of the synthesized compounds, the TSC1 molecule was considered a promising candidate as an antifungal agent against Candida albicans. Thus, it would be extremely helpful in the field of medicinal chemistry, particularly as an antimicrobial agent. The results are of significance to the chemistry of antimicrobial agents.
lncRNA PINK1-AS Aggravates Cerebral Ischemia/Reperfusion Oxidative Stress Injury through Regulating ATF2 by Sponging miR-203
Authors: Yang Zhong-Bao , Xiang Yu , Zuo Mei-Lin , Mao Li , Hu Guo-Huang , Song Gui-Lin , Sheikh Md Sayed Ali , Tan Li-Ming
Published: July 2022

Ischemic stroke is a common disease that led to high mortality and high disability. NADPH oxidase 2- (NOX2-) mediated oxidative stress and long noncoding RNA have important roles in cerebral ischemia/reperfusion (CI/R) injury, whereas whether there is interplay between them remains to be clarified. This study was performed to observe the role of lncRNA PINK1-antisense RNA (PINK1-AS) in NOX2 expression regulation. An in vivo rat model (MCAO) and an in vitro cell model (H/R: hypoxia/reoxygenation) were utilized for CI/R oxidative stress injury investigation. The expression levels of lncRNA PINK1-AS, activating transcription factor 2 (ATF2), NOX2, and caspase-3 and the production level of ROS and cell apoptosis were significantly increased in CI/R injury model rats or in H/R-induced SH-SY5Y cells, but miR-203 was significantly downregulated. There was positive correlation between PINK1-AS expression level and ROS production level. PINK1-AS and ATF2 were found to be putative targets of miR-203. Knockdown of lncRNA PINK1-AS or ATF2 or the overexpression of miR-203 significantly reduced oxidative stress injury via inhibition of NOX2. Overexpression of lncRNA PINK1 significantly led to oxidative stress injury in SH-SY5Y cells through downregulating miR-203 and upregulating ATF2 and NOX2. lncRNA PINK1-AS and ATF2 were the targets of miR-203, and the lncRNA PINK1-AS/miR-203/ATF2/NOX2 axis plays pivotal roles in CI/R injury. Therefore, lncRNA PINK1-AS is a possible target for CR/I injury therapy by sponging miR-203.
An Unusual Case of a Large Extraovarian Endometrioma in an Adolescent
Authors: Loh Wei-Guo Nicholas , Borok Nira , Anderson Lyndal , Rao Tanushree
Published: July 2022

Endometriosis in adolescents is often underrecognized and is a contributing factor to significant delays in its diagnosis and management. Extraovarian endometrioma is uncommon, especially in the adolescent age range. We report on a rare case of a large extraovarian endometrioma involving the terminal ileum in an adolescent who presented with abdominal pain and a pelvic mass and its subsequent surgical management. It emphasizes the importance of having a broad differential diagnosis and considers endometriosis in any adolescent with pelvic pain and mass, especially in an atypical context. Increased awareness, education, and research on endometriosis in this young population are essential in order to overcome existing challenges in the early diagnosis and optimal management of this chronic gynaecological condition and avoid morbidity associated with advanced disease.
Effect of Biosynthesized Silver Nanoparticles on the Properties of Chemically Modified Agave tequilana Weber var. Azul Fibers
Authors: Alvarado-Arámburo Arahí , Velázquez Gilberto , Cid Hernández Margarita , Reyes Oscar-Kevin , González-Ortiz Luis J. , Cruz-Estrada Ricardo H. , Vázquez-Lepe Milton O. , Jimenez-Aguilar Jaime A. , Navarro-Partida José , López-Naranjo Edgar J.
Published: July 2022

Agave tequilana Weber var. azul fibers (ATF) are widely used as a reinforcement material despite their polarity makes them incompatible with hydrophobic matrices. Consequently, ATF are commonly modified employing different chemical processes (e.g., mercerization and coupling agents) to change their surface characteristics to improve the interface between the fibers and the polymeric matrix. Nevertheless, these treatments could damage the fibers during the process, negatively affecting their natural properties. The use of nanotechnology to repair this natural material could help to restore its intrinsic properties and give it new ones as antibacterial activity. In this work, chemically treated ATF were used as templates for the biosynthesis of silver nanoparticles (AgNPs) using a natural extract obtained from Agave tequilana Weber var. azul leaves (ATL) as reducing agent. Scanning and transmission electron microscopy images as well as dynamic light scattering results indicate that stable nanometric particles were successfully synthesized on all fibers. X‐ray photoelectron spectroscopy and X‐ray diffraction results confirm the composition of the nanoparticles. Tensile tests indicate that AgNPs improved the mechanical properties of fibers previously mercerized and treated with maleic anhydride grafted polyethylene as coupling agent. Additionally, an antibacterial effect against S. enterica was conferred to ATF due to the presence of AgNPs.
[Retracted] Correlation Analysis between the Emotion and Aesthetics for Chinese Classical Garden Design Based on Deep Transfer Learning
Authors: Guanglong Bao , Qian Gao
Published: July 2022

Garden design with healthy psychological characteristics is a design method that mines positive psychological expressions and converts them into garden design elements. Chinese classical gardens are cultural heritage of China. Studying the beauty of space in classical gardens is of great significance to inheriting traditional culture, traditional art, and traditional aesthetics. At present, the research hotspots of garden design with healthy psychological characteristics mainly focus on the construction of relevant research theories and methods with the help of various intelligent tools. In this study, we propose a deep learning‐based end‐to‐end model to recognize the positive psychological design of a Chinese classical garden. The model is designed based on Inception V3 that is proposed by Google. The innovation lies in that transfer learning which is integrated into Inception V3 to improve the generalization ability. Also, it is not necessary to encode the characteristics of the garden design style due to the end‐to‐end structure used in our proposed model. We design a positive psychological characteristics classification task to recognize high aesthetic feeling and low aesthetic feeling of rockery design. Experimental results indicate that our proposed model wins the best performance compared with other comparison models.
Direction-of-Arrival Estimation Method for Principal Singular Vectors Based on Multiple Toeplitz Matrices
Authors: Tang Yaofeng , Fan Kuangang , Lei Shuang , Cui Junfeng
Published: July 2022

A principal singular vector based on multiple Toeplitz matrices is proposed to solve the accuracy problem of direction-of-arrival (DOA) estimation for coherent signals. First, the data matrix received by uniform linear array (ULA) is transformed into a Toeplitz matrix. An equivalent covariance matrix is obtained by square weighted summation method using the Toeplitz matrix. Then, a polynomial containing DOA information is constructed because the signal space and the steering matrix have the same column space; the Toeplitz matrix is built using polynomial coefficients. The problem is transformed into solving linear equations by establishing the relationship between the Toeplitz matrix and the signal subspace. Furthermore, the weighted least square method is used to obtain multiple candidates for linear equations. Finally, the maximum likelihood (ML) rule is used to select source signal candidates from multiple candidates. In comparison with currently known algorithm, the proposed algorithm has the characteristics of high estimation accuracy, low-complexity, and strong anti-interference ability and resolution. Even when the signal-to-noise ratio (SNR) is low, the snapshot number is small, and multiple signals exist; this method can still provide good estimation performance and resolution, which is more than 90% in most cases. Simulation experiments verify the superiority of the algorithm.
A Grey Prediction Algorithm for Enterprise Human Resource Management Validity Model Construction and Improvement
Authors: Yang Yajing
Published: July 2022

With the deepening development of economic globalization and world economic integration, all enterprises are faced with an increasingly complex and competitive living environment. Therefore, it is particularly important to formulate a human resource strategy suitable for the enterprise’s own development to cope with the external challenges and external competition of the enterprise. However, in the current management practice of state-owned enterprises including power supply enterprises in our country, it is rare to make strategic decisions on human resources by applying scientific, effective, and complete mathematical models. Taking construction enterprises as an example, this study combines human resource management, enterprise management, project management, comprehensive evaluation, and grey system and other theories to systematically study the effect of human resource management in enterprises, and finally constructs a suitable evaluation method for construction enterprises, and an assessment model for diagnosing its human resource management status. We provide practical guidance for the management of human resources in construction enterprises, which is conducive to human resource workers to discover existing problems in time and make improvements, give full play to the potential of human resources, and improve the level of human resource management and the value of enterprises.
Statistical Analysis of COVID-19 Data for Three Different Regions in the Kingdom of Saudi Arabia: Using a New Two-Parameter Statistical Model
Authors: Al-Dayel Ibrahim , Alshahrani Mohammed N. , Elbatal Ibrahim , Alotaibi Naif , Shawki A. W. , Elgarhy Mohammed
Published: July 2022

Since December 2019, the COVID-19 outbreak has touched every area of everyday life and caused immense destruction to the planet. More than 150 nations have been affected by the coronavirus outbreak. Many academics have attempted to create a statistical model that may be used to interpret the COVID-19 data. This article extends to probability theory by developing a unique two-parameter statistical distribution called the half-logistic inverse moment exponential (HLIMExp). Advanced mathematical characterizations of the suggested distribution have explicit formulations. The maximum likelihood estimation approach is used to provide estimates for unknown model parameters. A complete simulation study is carried out to evaluate the performance of these estimations. Three separate sets of COVID-19 data from Al Bahah, Al Madinah Al Munawarah, and Riyadh are utilized to test the HLIMExp model’s applicability. The HLIMExp model is compared to several other well-known distributions. Using several analytical criteria, the results show that the HLIMExp distribution produces promising outcomes in terms of flexibility.
Pattern Generation and Design of Floral Patterns Based on Newton’s Iterative Algorithm
Authors: Wang Luotao
Published: July 2022

Fashionable FMCG clothing brands have emerged in large numbers, and fast fashionable and personalized clothing is increasingly favored by young people. In this era of rapid renewal, clothing has obviously become a fast moving consumer good. China’s textile industry continues to develop, and the speed of development accelerates, coupled with the introduction and promotion of the use of Western textile technology, making the pattern design of Chinese textiles develop more quickly. The long-term disconnection between product pattern design and product production has led to the fact that the research on product pattern has been only on the plane, so there is an urgent need to improve the design level of product pattern and the market competitiveness of products through systematic research on product pattern. Newton’s iteration is an important research content of nonlinear theory, which is a hot spot in the field of scientific research at present. Newton’s iteration is a common method for finding the roots of nonlinear equations, which has at least second-order convergence speed, but requires the calculation of first-order derivative values. In this paper, a preliminary discussion on the design method of pattern design based on Newton’s iterative algorithm is conducted to explore a new way of personalized pattern design while using nonlinear graphs as pattern design materials. The experimental results show that the average value of EME of Newton’s iteration algorithm is 3.479 and 2.072 higher than that of AVG Filter and CB Filter, respectively, and the difference of pattern generation accuracy between CB Filter and AVG Filter is not much, so the accuracy of Newton’s iteration algorithm for pattern generation is better than that of CB Filter and AVG Filter. Therefore, it is proved that it is feasible to transform the special texture effect based on Newton’s iteration algorithm into fabric.
Effectiveness of Computer-Aided Technology for Teaching English Courses in the Internet Era
Authors: Xie Caihong
Published: July 2022

Computer and Internet technologies have advanced significantly in recent years. They have been incorporated into people’s daily lives and workplaces. The traditional English teaching approach has several problems. It includes poor student learning interest and lack of active learning. English teaching courses are set up in colleges and universities for non-English majors. The contradiction between students’ needs for foreign language learning and personal interests is increasing day by day. Traditional single classroom teaching has been unable to meet the basic requirements. With the rapid advancement of Internet technology, computer-aided technology is being proposed and applied in English education to investigate the effectiveness of English instruction. This article goes into detail on the idea, classification structure, and courseware design process of computer-aided technology. It employs the AI teaching approach in English instruction, creates a similarity matrix of English learners, and assesses English learners who exhibit similar behavior. Finally, the students are tested by answering the proportion of 1000 words correctly in English teaching to measure the effect of course learning. The results show that the number of words memorized by students increases after using computer-aided technology. This article analyzes students’ capacity to compose in English and evaluates the efficacy of students’ English teaching. It proclaims that after teachers adopt computer-aided technology, the error rate in English composition is reduced. The total score in English increased from 58.6% to 69.6%.
International Trade of Masks and COVID‐19 Pandemic Containment
Authors: Fan Xiayang , Li Bing , Xiu Yuanyuan
Published: July 2022

This study analyzes the impact of the exports of China’s masks and other antivirus supplies on the people from the importing countries who are subject to the severe pandemic during the coronavirus disease 2019 (COVID‐19) pandemic. Our main data include the COVID‐19 cases data of countries around the world published by Johns Hopkins University and the data of China’s exports of masks or other antivirus supplies to these countries from the Chinese Customs Database. Using cross‐sectional data of about 180 countries and multiple regression analysis, we find that the antivirus supplies from China have played an important role in combating the COVID‐19 pandemic. Specifically, China’s masks are shipped to countries around the world, and these masks can improve the recovery rate and protect people against the COVID‐19 pandemic. Our findings are relevant to global efforts in the COVID‐19 pandemic containment.
The Effect of Heparin and Its Preparations on Disseminated Intravascular Coagulation Mortality and Hospitalization: A Systematic Review
Authors: Omidkhoda Navid , Abedi Farshad , Ghavami Vahid , Rahimi Hossein , Samadi Sara , Arasteh Omid , Mohammadpour Amir Hooshang
Published: July 2022

Background and Aim. Disseminated intravascular coagulation (DIC) is a critical clinical condition that is expressed by systemic activation of the homeostatic system, leading to elevation of thrombin deposition and result in microvascular thrombi. Heparin makes a good effect on hypercoagulability states through inhibition of thrombin. The present study aimed to summarize and discuss the results of randomized clinical trials and cohort studies regarding the effect of heparin and its preparations on DIC mortality and duration of hospitalization. Methods. The databases of PubMed, Scopus, Embase, and Web of Science were searched systematically up to November 2021. The quality of RCTs was assessed by Cochrane Collaboration’s tool and the risk of bias was assessed for cohort studies through NOS score. Results. Out of 3288 articles, eight studies were eligible to be included in this study. Our review retrieved six RCTs and two retrospective cohort studies consisting of 950 participants diagnosed by DIC. A significant effect of heparin on DIC mortality was identified in four studies. Furthermore, heparin was used as a control group in three studies. Conclusions. We concluded that administration of heparin and its preparations in DIC patients could reduce the mortality rate and duration of hospitalization, especially in the earlier stages of DIC.