Miracle-dz/UniFAR-FTUs huggingface dataset:
Major point and nonpoint sources of nutrient pollution to surface water have declined throughout the Chesapeake Bay watershed

Understanding drivers of water quality in local watersheds is the first step for implementing targeted restoration practices. Nutrient inventories can inform water quality management decisions by identifying shifts in nitrogen (N) and phosphorus (P) balances over space and time while also keeping track of the likely urban and agricultural point and nonpoint sources of pollution. The Chesapeake Bay Program’s Chesapeake Assessment Scenario Tool (CAST) provides N and P balance data for counties throughout the Chesapeake Bay watershed, and these data were leveraged to create a detailed nutrient inventory for all the counties in the watershed from 1985–2019. This study focuses on three primary watershed nutrient balance components—agricultural surplus, atmospheric deposition, and point source loads—which are thought to be the leading anthropogenic drivers of nutrient loading trends across the watershed. All inputs, outputs, and derived metrics (n=53) like agricultural surplus and nutrient use efficiency, were subjected to short- and long-term trend analyses to discern how sources of pollution to surface water have changed over time. Across the watershed from 1985–2019, downward trends in atmospheric deposition were ubiquitous. Though there are varying effects, long-term declines in agricultural surplus were observed, likely because nutrients are being managed more efficiently. Multiple counties’ point source loads declined, primarily associated with upgrades at major cities that discharge treated wastewater directly to tidal waters. Despite all of these positive developments, recent increases in agricultural surpluses from 2009–2019 highlight that water quality gains may soon be reversed in many agricultural areas of the basin. Besides tracking progress and jurisdictional influence on pollution sources, the nutrient inventory can be used for retrospective water quality analysis to highlight drivers of past improvement/degradation of water quality trends and for decision makers to develop and track their near- and long-term watershed restoration strategies.
A Novel LOC101927967 Intergenic Region ALK Fusion Identified by NGS and Validated by IHC and FISH in a Patient with Early Stage Adenocarcinoma of Lung

Abstract Anaplastic lymphoma kinase (ALK) gene rearrangement is an essential driver mutation identified in approximately 5% of non-small cell lung cancers (NSCLCs). The results of clinical trials have demonstrated the impressive efficacy of ALK tyrosine kinase inhibitors (ALK-TKIs). Besides the classic EML4-ALK fusions, a growing list of gene fusion partners for ALK in NSCLC have been identified with heterogeneous clinical responses to ALK-TKIs. However, a LOC101927967-ALK fusion has not been reported in NSCLC. Herein, a novel LOC101927967 downstream intergenic region ALK fusion in an early-stage patient with lung adenocarcinoma was first identified by next-generation sequencing (NGS) and verified by immunohistochemical staining (IHC) and fluorescence in situ hybridization (FISH), which might provide a treatment option for postoperative recurrence.
Analysis on the Construction of Sustainable Operation Mechanism of Agricultural Product Supply Chain under the Background of Wireless Communication and Internet of Things

Historical experience shows that the people’s happiness index is highly correlated with the development of agriculture. The prosperity of agriculture and industrial production has also brought most people into a well-off life. Since the innovation of agricultural production equipment and concepts, the efficiency of agricultural production in my country has also been greatly improved compared to before the reform and opening up. In the era when robots will gradually become mainstream work machinery tools, the production efficiency and supply of agricultural products will become the focus of attention of the people of all countries. In such a general environment, the competition between agricultural products markets is no longer solely based on the competitiveness of agricultural products but also depends on the advantages of the agricultural supply chain to win. This article analyzes the construction of the sustainable operation mechanism of the agricultural supply chain in the context of wireless communication and the Internet of Things. First, the method of literature research is used to explain the characteristics of the agricultural supply chain and the problems existing in the development of the current agricultural supply chain model in the context of wireless communication and the Internet of Things. Next, study the current situation of the sustainability mechanism of the agricultural supply chain, and finally optimize the construction of the sustainability mechanism of the agricultural supply chain products based on the research results. According to the survey results, the main problem with the current mechanism of the agricultural product supply chain at this stage is that the logistics information technology is difficult to update in time, accounting for about 44%, followed by the lack of effective monitoring methods, accounting for about 34%.
Knowledge, Skills, and Attitudes of Faculty Members and Residents-in-Training of the Department of Rehabilitation Medicine of the Philippine General Hospital towards Virtual Learning: A Cross-sectional Study

Background and Objectives. Virtual learning has been utilized in residency programs to continue training amid the COVID-19 pandemic. This study aimed to determine the knowledge, skills, and attitudes of faculty members and residents of the Department of Rehabilitation Medicine of the Philippine General Hospital towards virtual learning. Method. This is a descriptive cross-sectional study. Respectively, residents and faculty members answered the Online Learning Readiness Scale (OLRS) and Faculty Readiness to Teach Online (FRTO) through Google Forms. Results. Twenty (20) residents and 19 faculty members participated in the study. The majority of the residents preferred asynchronous learning (50%), while faculty members preferred the hybrid mode (74%). Residents’ readiness for online learning was generally high, though problems with easy distractibility (60%) and time management (40%) were revealed. Female residents had higher online communication self-efficacy compared to males (p = 0.0367). Faculty members’ perceived attitude was significantly higher than ability in course design (p = 0.00102), time management (p = 0.00159), and technical competence (p < 0.0001). Males had higher perceived ability in course design (p = 0.0320). Older age groups had lower perceived abilities in course design (p = 0.0301) and technical competence (p = 0.0371). Conclusion. This study revealed the levels of readiness of residents and faculty for virtual learning. Finding indicate the need to address both issues by developing programs to enhance faculty’s online teaching abilities and observing best practices to minimize problems such as distractibility. Large-scale studies with longer time frames are also recommended.
Passivity-based One-cycle Control of Swiss Rectifier in Electric Vehicle Battery Chargers

With the widespread use of electric vehicles (EVs), the development of high-quality and efficient battery chargers has become the last mile problem. This paper describes a high-performance control scheme for the Swiss rectifier (SR) in EV battery chargers, named passivity-based one-cycle control (POCC). By combining passivity-based control (PBC) and one-cycle control (OCC), the range of sampling error for the SR’s output current is strictly limited, and improve the reliability of battery charging. Thus, the qualities of input-output currents can be improved, simultaneously. Besides, compared with the classic dual closed-loop PI control (DCLPIC), the proposed passivity-based one-cycle control method can further reduce the total harmonic distortion (THD) of the input current, thus decreases the rectifier’s negative feedback to the grid. Meanwhile, the amplitude of charging current ripple can be reduced to improve the reliability of battery charging. Lastly, simulation examples are applied to validate the proposed POCC method.
A Robust and Privacy-Preserving Anonymous User Authentication Scheme for Public Cloud Server

Everyone desires to avail online services provided by different service providers securely, efficiently, and effectively. In this regard, security is still a significant concern for them. However, no one guarantees secure communication by browsing different applications remotely. To ensure confidentiality, authorization, availability, nonrepudiation, and removing eavesdropping, without a robust authentication scheme, nothing will go right. Therefore, we attempted to design a robust and privacy-preserving authentication scheme for end-users to securely access public cloud servers’ services remotely without losing performance. Our proposed scheme security has been evaluated formally using the random oracle model (ROM) and ProVerif2.03 and informally using proposition and discussion. At the same time, the performance metric has been analyzed by considering the scheme’s computation and communication costs. Upon comparing the proposed scenario with state-of-the-artwork, it has been demonstrated that the scheme is much better in terms of security and performance, as these are contradicting metrics, and the change in one conversely affects the other.
Zero-current-switching bidirectional interleaved switched-capacitor DC–DC converter: analysis, design and implementation

The study presents experimental verification of a zero-current-switching (ZCS) bidirectional interleaved switched-capacitor (BISC) DC-DC converter that features bidirectional power flow capability and a high-voltage conversion ratio. In addition, the interleaved configuration of the converter and its modular design enable both its current rating and voltage gain to be increased. At the same time, the converter does not exhibit current spike issues, which is achieved by employing stray inductance distributed throughout the circuit, rather than relying on an additional magnetic-core inductor. Moreover, the ZCS operation of the converter improves the converter efficiency. The operating principle and theoretical analysis of the BISC converter under both forward and reverse modes are presented in detail. Simulation and experimental results from a 1.5 kW prototype validate the operation and characteristics of the BISC DC-DC converter.
Effect of Li-ESWT on Testicular Tissue and Function in Androgen-Deficient Rat Model

Low-intensity extracorporeal shockwave therapy (Li-ESWT), as a microenergy therapy, has the effects of inhibiting oxidative stress, antiapoptosis, and tissue repair, which is increasingly applied to a variety of diseases. Our research aims to explore the protective effects of Li-ESWT in the aging rat model and its possible molecular mechanism through in vivo and in vitro experiments. In vitro, TM3 Leydig cells incubated with H2O2 were treated with Li-ESWT at 4 energy levels (0.01, 0.05, 0.1, and 0.2 mJ/mm2). In vivo, we employed an androgen-deficient rat model to simulate male aging and treated it with Li-ESWT at three different energy levels (0.01, 0.05, and 0.2 mJ/mm2). Li-ESWT increased the expression of vascular endothelial growth factor (VEGF) in TM3 cells, improved antioxidant capacity, and reduced apoptosis, with the effect being most significant at 0.05 mJ/mm2 energy level. In androgen-deficient rat model, LI-ESWT can improve sperm count, motility, and serum testosterone level, enhancing tissue antioxidant capacity and antiapoptotic ability, and the effect is most significant at 0.05 mJ/mm2 energy level. Therefore, Li-ESWT at an appropriate energy level can improve sperm count, motility, and serum testosterone levels in androgen-deficient rat models, reduce oxidative stress in the testis, and increase antioxidant capacity and antiapoptotic abilities. The mechanism of this condition might be related to the increased VEGF expression in Leydig cells by Li-ESWT.
Cascaded Deep Learning Frameworks in Contribution to the Detection of Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairment, as well as tremors, stiffness, and rigidity. Besides the typical motor symptomatology, some Parkinsonians experience non-motor symptoms such as hyposmia, constipation, urinary dysfunction, orthostatic hypotension, memory loss, depression, pain, and sleep disturbances. The correct diagnosis of PD cannot be easy since there is no standard objective approach to it. After the incorporation of machine learning (ML) algorithms in medical diagnoses, the accuracy of disease predictions has improved. In this work, we have used three deep-learning-type cascaded neural network models based on the audial voice features of PD patients, called Recurrent Neural Networks (RNN), Multilayer Perception (MLP), and Long Short-Term Memory (LSTM), to estimate the accuracy of PD diagnosis. A performance comparison between the three models was performed on a sample of the subjects’ voice biomarkers. Experimental outcomes suggested that the LSTM model outperforms others with 99% accuracy. This study has also presented loss function curves on the relevance of good-fitting models to the detection of neurodegenerative diseases such as PD.
Reduced CCR2 Can Improve the Prognosis of Sarcoma by Remodeling the Tumor Microenvironment

Background The tumor microenvironment (TME) plays a very important role in the development of sarcoma (SARC), but it is still unknown how to effectively regulate the TME. Aim Our study aims to identify core molecules that can concurrently regulate immune and stromal cells in TME as potential therapeutic targets. Methods and Results We used the ESTIMATE algorithm to score the immune and stromal components of 265 SARC samples and determined that increased immune and stromal components in TME were both associated with poor prognosis in SARC. Next, we identified differential genes that regulate both immune and stromal cells, and identified the core prognostic gene CCR2 through the protein–protein interaction (PPI) network, COX analysis, survival analysis, and GSEA enrichment analysis. Next, we calculated the content of infiltrating immune cells and stromal cells in tumors using the CIBERSORT and xcell algorithms, respectively. Using differential analysis and Spearman correlation analysis, we identified 12 immune cells and 7 stromal cells, including CD4+T cells, CD8+T cells, monocytes, macrophages, dendritic cells, NK cells, mesenchymal stem cells (MSC), Fibroblasts and Endothelial cells, all of which were regulated by CCR2. Conclusion Increased immune and stromal cell components were associated with poor prognosis in SARC, and CCR2 had a prognostic role in TME, regulating multiple immune and stromal cells, and was an important target for TME remodeling as well as immunotherapy in SARC.
Joint Social-Aware and Mobility-Aware Computation Offloading in Heterogeneous Mobile Edge Computing

With increasing computation-intensive tasks of various applications running on mobile devices, the limitation of computing resources and battery capacity on mobile devices makes it impossible to meet the users’ Quality of Service (QoS). Fortunately, with the emergence of mobile edge computing (MEC), mobile devices can offload tasks to edge servers to efficiently solve the above problems. However, meeting the users’ QoS requirements with the help of deployed MEC facilities is still challenging since mobile users’ service demands vary depending on their dynamic location. In addition, increasing the number of edge servers to meet the requirements of applications would burden the initial investment and maintenance fee accordingly. In this case, using idle resources from nearby mobiles may become an effective solution. Most of the existing works do not consider the mobility of devices and users’ willingness to share. Therefore, in this paper, we propose the mobile device selection algorithms (MDSA), in which the social relationship, location correlation, and mobile activity of mobile devices were considered in the selection of target mobile devices, providing device-to-device offloading. In addition, we propose the joint social-aware and mobility-aware computation offloading algorithm (JSMCO) based on the improved Kuhn–Munkres (KM) algorithm to obtain a resource allocation strategy that minimizes the energy consumption while satisfying the minimum latency condition. The proposed algorithms have been verified to reduce the offloading success rate and decrease the users’ time and energy consumption in extended real datasets.
Study on Tourist Guide Vocational Competency Index System Construction ——Based on the Double Perspective of Industry and Academic Discussion

Through investigating the teachers of tourist management and the guides in tourist agency,this study constructs the index system of guide vocational competency by the methods of documents analyse,deep interviewing and Delphi.From this study,we can see that tourist guide vocational competency index system is divided into three hierarchies.The first hierarchy is the final objective of tourist guide vocational competency;The second is the label hierarchy,containing professional knowledge,professional skill and professional quality.The third is the attribute hierarchy,containing 18 indexes.Getting the experts' weight on each ability index by AHP,and comparing the academic experts with industry experts' opinions,the result indicates that there are many outstanding differences by experts in professional quality,attractions knowledge,marketing and so on.The paper gives some explanation to those differences.
Liver Abscess Secondary to Crohn’s Disease: A Case Report

Crohn’s disease (CD) is a type of inflammatory bowel disease (IBD) and extra-intestinal manifestations are common. Although common features of CD include fistulation and abscess formation, they typically manifest exclusively in the lower gastrointestinal (GI) tract and in patients who do not have adequate control over their disease. Pyogenic liver abscess is rare in the general population and is an unusual and extra-intestinal manifestation of CD. Herein, we present a patient with Crohn’s ileo-colitis who presented with generalized abdominal pain and fevers and was found to have multiple pyogenic liver abscesses biopsy-proven to be secondary to CD. The patient’s liver abscesses were refractory to repeated CT-guided drainage and antibiotic therapy. This paper illustrates a rare condition in the general population and those with CD. We intend to discuss the differences of pyogenic liver abscesses in CD compared to the general population, the rarity of this presentation and propose a unique mechanism by which the patient may have developed this liver abscess. It is common for clinicians to mistake the diagnosis of febrile illness with or without abdominal pain as a simple reactivation of CD, and thus it is important to keep pyogenic liver abscess on the differential even if their disease state is otherwise well controlled.
Image denoising with heterogeneous low rank matrix factorization

Low rank matrix factorization (LRMF) is an important research direction in computer vision. It can learn low dimensional subspace from high dimensional data. LRMF is mostly constructed by L1 loss function and L2 loss function in optimization problems. And whether LRMF is constructed by L1 loss function or L2 loss function, it is important to describe the noise in the dataset. In order to describe the noise in the dataset well, this paper describes the noise from a pixel-level perspective to construct LRMF. Considering that there maybe uneven light intensity and differences in light reflection intensity between different parts of each picture in the dataset, this paper assumes that all noise in the image dataset is heterogeneous. Based on this assumption, this paper describes the noise in the dataset by the Student-t distributions with different parameters, and constructs two hierarchical Bayesian models, and deduces all the parameters of the models design through variational Bayesian inference. It not only improves the calculation accuracy of the LRMF, but also improves the calculation speed of the model to a certain extent. A large number of experiments on face reconstruction and medical image denoising prove the superiority of the methods.
Feasibility Study of Anaerobic Codigestion of Municipal Organic Waste in Moderately Pressurized Digesters: A Case for the Russian Federation

Anaerobic digestion (AD) is a promising option to obtain renewable energy in the form of biogas and reduce the anthropogenic impact on the environment. In recent years there has been increasing interest in using pressurized digesters to improve the quality of biogas. However, maintaining high overpressure increases the requirements for the explosion safety of digesters. Consequently, there are natural limitations in the available technologies and facilities suitable for full-scale operation. In this work, we aimed to evaluate the possibility of using overpressure in the digester to improve the efficiency of codigestion of common municipal organic waste–sewage sludge and the organic fraction of municipal solid waste. Three levels of moderate excess pressure (100, 150 and 200 kPa) were used to meet requirements of existing block-modular anaerobic bioreactors based on railway tanks, which are widely utilized for AD in the Russian Federation. There was no significant change in methane content in biogas (65% ± 3%) at different values of overpressure, hydraulic retention time (HRT) and organic loading rate (OLR). The maximum methane and energy production rates (2.365 L/(L·day) and 94.27 kJ/(L·day), respectively) were obtained at an overpressure of 200 kPa, HRT of 5 days and OLR of 14 kg VS/(m3·day). However, the maximum methane yield (202.44 mL/g VS), energy yield (8.07 kJ/g VS) and volatile solids (VS) removal (63.21%) were recorded at an overpressure of 150 kPa, HRT of 7 days and OLR of 10.4 kg VS/(m3·day). The pressured conditions showed better performance in terms of AD stability at high OLRs.
Non-fragile state estimation for Markov jump systems by event-triggered scheme

This article focus on the non-fragile dissipative state estimator design issue for delayed Markov jump systems(MJSs). The event-triggered scheme is employed to alleviate the network bandwidth resource. Based on Lyapunov stability theory, a state estimator is established, which guarantees the estimation error system is stochastically stable with dissipative. Then, the simulate results are presented to demonstrate the validness of the proposed state estimator design method.