Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Warning Method of Postgraduate Education Quality in Colleges and Universities Based on Multisource Information Fusion

With the rapid development of postgraduate education in my country, the contradiction between its quantitative growth and quality improvement has become increasingly prominent. At the same time, due to the downward shift in the focus of postgraduate education management, the role of provincial governments in the reform and development of postgraduate education has become increasingly important. Therefore, strengthening the practice of quality supervision and evaluation of postgraduate education at the provincial level is not only a realistic result of the development of postgraduate education in my country but also an inevitable requirement for the further development of postgraduate education in my country in the future, which has an important theoretical and practical significance. With the rapid development and wide application of computer network technology, communication technology, and multisensor technology, multisource information fusion has been selected by many colleges and universities as a method to evaluate the quality of postgraduate study because of its closeness to the frontier of disciplines. Therefore, how to effectively improve the teaching quality of multisource information fusion courses is a practical problem that needs to be solved urgently, and the improvement of teaching quality is also a key factor affecting the comprehensive quality of students. This paper deeply analyzes the setting and requirements of the information fusion course, combines the characteristics of mathematical theory and tools in information fusion, uses multisource data to model student behavior, and proposes a serialized prediction framework based on a deep network. The network structure reasonably integrates multisource data and automatically extracts behavioral characteristics. Combined with the static characteristics of students, the paper uses the long-term and short-term memory network to model the overall online behavior of students and finally predicts the probability that students have academic risks. The model is tested on a real university course dataset, and the experimental results show that the performance of the proposed algorithm is better than the baseline comparison algorithm.
On Correlation Models for Longitudinal Failure Time Data

When repeated failure times are collected from a large number of independent individuals, interest is focused primarily on the efficient estimation of the effects of the associated covariates on the failure times. The construction of the efficient estimating equations requires the modelling of the t correlation structure of the repeated failure times. There exists several bivariate correlation structures, such as the Clayton model, Kendall’s rank and the so-called grade models, for the repeated failure times. These existing correlation structures are, however, limited to the equi-correlation case only. In this paper, as an alternative to the existing bivariate correlation structures, we introduce a class of observation-driven multivariate correlation structures appropriate for non-stationary failure times, which also accommodate the equi-correlation model as a special case. The proposed correlation structure for failure times is exploited to obtain the correlation structure of the cumulative hazard variates in order to construct the true correlation based estimating equations for the hazard ratio parameters. The efficiency lossdue to any model mis-specification is examined through a simulation study. A numerical example is considered to illustrate the estimation methodology. AMS (2000) subject classification. Primary 62N02, 62N01.
Assessing Donor Liver Quality and Restoring Graft Function in the Era of Extended Criteria Donors

Liver transplantation (LT) is the final treatment option for patients with end-stage liver disease. The increasing donor shortage results in the wide usage of grafts from extended criteria donors across the world. Using such grafts is associated with the elevated incidences of post-transplant complications including initial nonfunction and ischemic biliary tract diseases, which significantly reduce recipient survival. Although several clinical factors have been demonstrated to impact donor liver quality, accurate, comprehensive, and effective assessment systems to guide decision-making for organ usage, restoration or discard are lacking. In addition, the development of biochemical technologies and bioinformatic analysis in recent years helps us better understand graft injury during the perioperative period and find potential ways to restore graft function. Moreover, such advances reveal the molecular profiles of grafts or perfusate that are susceptible to poor graft function and provide insight into finding novel biomarkers for graft quality assessment. Focusing on donors and grafts, we updated potential biomarkers in donor blood, liver tissue, or perfusates that predict graft quality following LT, and summarized strategies for restoring graft function in the era of extended criteria donors. In this review, we also discuss the advantages and drawbacks of these potential biomarkers and offer suggestions for future research.
Modulation of KV4.3-KChIP2 Channels by IQM-266: Role of DPP6 and KCNE2

The transient outward potassium current (Itof) is generated by the activation of KV4 channels assembled with KChIP2 and other accessory subunits (DPP6 and KCNE2). To test the hypothesis that these subunits modify the channel pharmacology, we analyzed the electrophysiological effects of (3-(2-(3-phenoxyphenyl)acetamido)-2-naphthoic acid) (IQM-266), a new KChIP2 ligand, on the currents generated by KV4.3/KChIP2, KV4.3/KChIP2/DPP6 and KV4.3/KChIP2/KCNE2 channels. CHO cells were transiently transfected with cDNAs codifying for different proteins (KV4.3/KChIP2, KV4.3/KChIP2/DPP6 or KV4.3/KChIP2/KCNE2), and the potassium currents were recorded using the whole-cell patch-clamp technique. IQM-266 decreased the maximum peak of KV4.3/KChIP2, KV4.3/KChIP2/DPP6 and KV4.3/KChIP2/KCNE2 currents, slowing their time course of inactivation in a concentration-, voltage-, time- and use-dependent manner. IQM-266 produced an increase in the charge in KV4.3/KChIP2 channels that was intensified when DPP6 was present and abolished in the presence of KCNE2. IQM-266 induced an activation unblocking effect during the application of trains of pulses to cells expressing KV4.3/KChIP2 and KV4.3/KChIP2/KCNE2, but not in KV4.3/KChIP2/DPP6 channels. Overall, all these results are consistent with a preferential IQM-266 binding to an active closed state of Kv4.3/KChIP2 and Kv4.3/KChIP2/KCNE2 channels, whereas in the presence of DPP6, IQM-266 binds preferentially to an inactivated state. In conclusion, DPP6 and KCNE2 modify the pharmacological response of KV4.3/KChIP2 channels to IQM-266.
Factors Influencing the Adoption of Open Government Data at the Firm Level

In recent years, there has been an increasing interest in open government data (OGD) around the world. The governments of various countries have encouraged firms to adopt OGD to develop innovative products and services to bring about social and economic benefits. This paper examines the factors influencing the adoption of OGD among firms in Taiwan to fill a gap that prior studies do not adequately address. Based on the sociotechnical perspective, we developed a research model that integrates the following factors that are central to adoption decisions of firms: top management support, government support, competitive pressure, perceived benefits, and perceived barriers. We examined this model through survey data from firms with experience in OGD adoption in Taiwan. The results show a significant positive relationship among top management support, competitive pressure, and the adoption of OGD by firms. This paper serves as a first attempt to contribute to advancing knowledge on OGD adoption at the firm level via empirically testing a research model that integrates factors within social and technical systems. This paper has significant implications for practitioners of enterprise information management and creates a valuable reference for other countries in the early stages of OGD initiatives.
Homologous and Heterologous Boosting of the Chadox1-S1-S COVID-19 Vaccine With the SCB-2019 Vaccine Candidate: A Randomized, Controlled, Phase 2 Study

Abstract Background Ongoing outbreaks of coronavirus disease 2019 (COVID-19) are driven by waning immunity following primary immunizations and emergence of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that escape vaccine-induced neutralizing antibodies. It has been suggested that heterologous boosters could enhance and potentially maintain population immunity. Methods We assessed the immunogenicity and reactogenicity of booster doses of different formulations of aluminium hydroxide–adjuvanted SCB-2019 vaccine (9 μg of SCB-2019, with or without CpG-1018 adjuvant, or 30 μg of SCB-2019 with CpG-1018) in Brazilian adults primed with ChAdOx1-S vector vaccine. S-protein antibodies and ACE2-binding inhibition were measured by enzyme-linked immunosorbent assay (ELISA) on days 1, 15, and 29. Participants self-reported solicited adverse events and reactions. Results All SCB-2019 formulations increased S-protein ELISA antibodies and ACE2 binding inhibition to a greater extent than ChAdOx1-S. After 30 μg of SCB-2019 + CpG + aluminium hydroxide, titers against wild-type S-protein were significantly higher than after ChAdOx1-S on days 15 and 29, as were titers of neutralizing antibodies against the wild-type strain and Beta, Gamma, Delta, and Omicron variants. Boosting with SCB-2019 or ChAdOx1-S was well tolerated, with no vaccine-related serious or severe adverse events. Conclusions Boosting ChAdOx1-S-primed adults with SCB-2019 induced higher levels of antibodies against a wild-type strain and SARS-CoV-2 variants than a homologous ChAdOx1-S booster, with the highest responses being with the 30-μg SCB-2019 + CpG + aluminium hydroxide formulation. Clinical Trials Registration NCT05087368
Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic

The marine environment is a hostile setting for robotics. It is strongly unstructured, uncertain, and includes many external disturbances that cannot be easily predicted or modeled. In this work, we attempt to control an autonomous underwater vehicle (AUV) to perform a waypoint tracking task, using a machine learning-based controller. There has been great progress in machine learning (in many different domains) in recent years; in the subfield of deep reinforcement learning, several algorithms suitable for the continuous control of dynamical systems have been designed. We implemented the soft actor–critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm that allows fulfilling a learning task and encourages the exploration of the environment simultaneously. We compared a SAC-based controller with a proportional integral derivative (PID) controller on a waypoint tracking task using specific performance metrics. All tests were simulated via the UUV simulator. We applied these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater Vehicle (ROV) converted in an AUV. We propose several interesting contributions as a result of these tests, such as making the SAC control and guiding the AUV simultaneously, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm inputs. Moreover, our implementation of this controller allows facilitating the transfer towards real-world robots. The code corresponding to this work is available on GitHub.
Holes in the t-J_z model: a thorough study

The t-J_z model is the strongly anisotropic limit of the t-J model which captures some general properties of the doped antiferromagnets (AF). The absence of spin fluctuations simplifies the analytical treatment of hole motion in an AF background and allows us to calculate the single- and two-hole spectra with high accuracy using regular diagram technique combined with real-space approach. At the same time, numerical studies of this model via exact diagonalization (ED) on small clusters show negligible finite size effects for a number of quantities, thus allowing a direct comparison between analytical and numerical results. Both approaches demonstrate that the holes have tendency to pair in the p- and d-wave channels at realistic values of t/J. The interactions leading to pairing and effects selecting p and d waves are thoroughly investigated. The role of transverse spin fluctuations is considered using perturbation theory. Based on the results of the present study, we discuss the pairing problem in the realistic t-J-like model. Possible implications for preformed pairs formation and phase separation are drawn.
Breaking the Negative Feedback Loop of Disaster, Conflict, and Fragility: Analyzing Development Aid by Japan and South Korea

Disaster risk reduction (DRR) has become an important element of donor policy, because numerous governments have expressed their commitment to helping countries vulnerable to natural hazards by mainstreaming DRR into their development programs. Meanwhile, countries that are considered fragile, as well as conflict-affected states, have faced a high risk of disasters brought on by natural hazards. However, there has been little research that addresses the complex relationship between disasters, conflict, and fragility in the context of development cooperation. Against this backdrop, this study analyzed the determinants of DRR aid allocation from Japan and South Korea—two East Asian countries that have shown a strong commitment to disaster resilience and peacebuilding—to investigate whether they are responsive to countries experiencing the combined risks of disasters and conflicts and/or fragility. Despite the vulnerable countries being in the most need, the study found that both Japan and Korea’s aid allocation has not been influenced much by the concurrence of disasters and conflict. Rather, it has been more driven by the level of a country’s climate vulnerability than the level of a country’s fragility. This suggests that developing countries facing multiple risks and challenges are at a major disadvantage in terms of the responsiveness of donors toward their needs and vulnerability.
Current Prospects of Rome Criteria for the Diagnosis of Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a worldwide health problem. It is often diagnosed by periodically- updated Rome symptom-based criteria, but the effects of recent updates on IBS diagnosis are not well clarified. This article overviews Rome III and IV IBS criteria, highlighting changes and updates that may impact IBS diagnosis. PubMed, Medline, Science Direct, Clinical trials.gov, and WHO databases were searched through to July 2021. Rome IV criteria are more restrictive, specific, and precise in patients′ diagnosis, selection, prognosis, and follow-up than Rome III criteria, in turn, IBS prevalence and number of patients with IBS-unspecified and IBS-mixed subtypes decrease, but overall symptom severity and patients′ homogeneity increase. In essence, the slight difference between Rome III and IV criteria reflects a considerable influence on IBS diagnosis, subtyping, and prevalence. Rome IV IBS patients form a homogeneous subset of Rome III IBS patients with more severe symptoms. Thus, in-depth research is warranted.
Managing risk with chance-constrained programming

Reducing catastrophic fire risk is an important objective of many fuel treatment programs (Kent et al. 2003; Machlis et al. 2002; USDA/USDI 2001a). In practice, risk reductions can be accomplished by lowering the probability of a given loss to forest fires, the amount of probable loss, or both. Forest fire risk objectives are seldom quantified, however, making it difficult to compare alternative management options. Instead, fuel treatment programs are often selected based on expert judgment.
Machine Learning: Crown Diameter Predictive Modeling for Open-Grown Trees in the Cerrado Biome, Brazil

The Brazilian Cerrado biome is a hotspot due to its ecological importance and high diversity of fauna and flora. We aimed to develop statistical models to predict the crown diameter of open-growing trees using several forest attributes. Potential crown diameter trends in the measured trees were determined by quantile regression. Crown diameter models were developed by regression analyses, artificial neural networks, support vector machine, and random forest techniques. We evaluated 200 trees characterized into 60 species belonging to 30 botanical families. Our equation for potential crown diameter predicts the derived basal area, number of trees, and the necessary growth space of crown diameter at breast height. Artificial neural networks (with the following validation statistics: R2 = 0.90, RMSE = 1.21, MAE = 0.93, and MAPE = 16.25) predicted crown diameter more accurately than the other evaluated techniques. Modeling crown diameter via machine learning represents an important step toward the assessment of crown dynamics by species and can support the decision making of silvicultural practices and other related activities in several rural properties within the Cerrado biome.
Simulating Wind Disturbances over Rubber Trees with Phenotypic Trait Analysis Using Terrestrial Laser Scanning

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a lifting steel truss carrying a large forced draft fan near a rubber plantation on Hainan Island, and we aligned three selected small rubber trees in a row in front of the fan (with separation distances from the forced draft fan outlet of approximately 1.3, 3.3, and 5.3 m) to explore the susceptibility of rubber trees to the mechanical loading of hurricane-level winds. By adjusting the power of the forced draft fan, four wind speeds were emitted: 0 m/s, 10.5 m/s, 13.5 m/s, and 17.5 m/s. Meanwhile, point clouds of the three rubber trees under different continuous wind speeds were acquired using two terrestrial laser scanners. Computer algorithms were applied to derive the key parameters of the three rubber trees, namely, the zenith and azimuth angles of each leaf, effective leaf area index (LAI), windward area of each tree, volume of the tree canopy, and trunk tilt angle, from these point clouds under all four wind speeds. The results show that by increasing the wind speed from 0 m/s to 17.5 m/s, the leaf zenith angles of the three rubber trees were unimodally distributed with the peak concentrated at 0°, while the leaf azimuth angles were bimodally distributed with the peaks concentrated at 0° and 360°. The effective LAI values of the three trees increased from 2.97, 4.77, and 3.63 (no wind) to 3.84, 5.9, and 4.29 (wind speed of 17.5 m/s), respectively, due to a decrease in the vertical crown projection area caused by the compression of the tree canopy. We also found that the effective LAI, windward area, and canopy volume of the third rubber tree (the tree farthest from the forced draft fan) varied less than those of the other two trees, reflecting the attenuation of the wind speed by the crowns of the two trees closer to the fan. The experimental results also indicate that the joint use of light detection and ranging (LiDAR) data with computer graphics algorithms to analyse the dynamic changes in tree phenotypic characteristics during the passage of a hurricane is promising, enabling the development of a novel strategy for mitigating wind hazards. The proposed method with the designed device capable of producing an adjustable wind speed also has the potential to study the impacts of wind damage under various forest conditions by further modifying the tree spacing and tree species.
Psychological impact and mental health effect of COVID-19 among Jordanian population

Background During public health emergencies, the major efforts of policy makers are directed towards physical and medical consequences, and little is directed towards the psychological impact of these outbreaks. Aim To assess the psychological impact and mental health effects of the current pandemic of Corona virus (COVID 19) on the general population in Jordan. Methodology A cross-sectional study was conducted using an online survey directed towards the general population in Jordan during the period March 25th to April 2nd, 2020. Mental health effect was measured using the Arabic version of the Depression, Anxiety and Stress Scale (DASS-21), while the psychological impact was measured using questions from the Arabic version of the Impact of Event Scale-Revised (IES-R). Total number of respondents was 1946. Results The levels of depression, anxiety and stress as measured by DASS-21 questionnaire showed that 13% had severe depression, 10% had severe anxiety, and 6.3% had severe stress. There was more psychological impact compared with mental health effects on the study population, as more than quarter of the sample (26.7%) reported severe psychological impact (score >38). Factors associated with higher psychological impact and mental health effects include younger age group, being female, childless, living in the remote areas, being a bachelor’s degree student or graduate, having low monthly income and having physical symptoms in the 14 days prior to the study. Conclusion Findings of this study can inspire health projects at the national level to better deal with these psychological issues in response to the current or any future health emergencies in Jordan.
Nanoscale Zeolitic Imidazolate Framework (ZIF)–8 in Cancer Theranostics: Current Challenges and Prospects

Simple Summary The biomedical application of metal–organic frameworks in cancer theranostics has become a research hotspot with rapid progress. As a typical representative, ZIF–8 attracts increasing interest from researchers due to its good performance and potential. In this review, we updated recent discoveries on the ZIF–8–based nanoplatforms for cancer, discussed the problems in current research and the obstacles for clinical translation of ZIF–8, and also proposed an outlook on its future development. Abstract Cancer severely threatens human health and has remained the leading cause of disease–related death for decades. With the rapid advancement of nanomedicine, nanoscale metal–organic frameworks are believed to be potentially applied in the treatment and biomedical imaging for various tumors. Zeolite imidazole framework (ZIF)–8 attracts increasing attention due to its high porosity, large specific surface area, and pH–responsiveness. The designs and modifications of ZIF–8 nanoparticles, as well as the strategy of drug loading, demand a multifaceted and comprehensive understanding of nanomaterial features and tumor characteristics. We searched for studies on ZIF–8–based nanoplatforms in tumor theranostics on Web of Science from 2015 to 2022, mainly focused on the research published in the past 3 years, summarized the progress of their applications in tumor imaging and treatment, and discussed the favorable aspects of ZIF–8 nanoparticles for tumor theranostics as well as the future opportunities and potential challenges. As a kind of metal–organic framework material full of potential, ZIF–8 can be expected to be combined with more therapeutic systems in the future and continue to contribute to all aspects of tumor therapy and diagnosis.