Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-driven real-time control coordination for distribution grids with medium-scale photovoltaic generation

Increasing penetration of photovoltaic (PV) generation may significantly impact existing voltage regulation schemes. In particular, the lack of coordination between control devices and fast change in PV generation can cause voltage magnitude to go beyond limits. This study addresses the issue by providing a novel real-time centralised voltage control algorithm to tackle voltage fluctuations caused by variability of PV power generation. The new method considers autonomous response of upstream voltage regulation devices and provides optimised set-points for the chosen downstream controllers. Furthermore, the investigated method was tested in the real-time digital simulator on a model of 11kV grid located in a semi-rural area of Queensland, Australia. The control algorithm was designed in MATLAB with a multidimensional, non-linear, mixed-integer optimisation. The parallel computing implementation of the KNITRO optimiser helped to provide quicker and more accurate response than the existing uncoordinated control schemes. As a result, optimised control coordination leads to a substantial reduction in voltage fluctuations and decrease in regulation time. Moreover, the proposed method detects and eliminates unnecessary control operations. Thus, it can also prevent voltage hunting in distribution networks.
Study on the Risk of Transmission of COVID-19 Based on Population Migration

Since the emergence of COVID-19, migration of people has transferred the virus to new locations, causing the epidemic to expand, and local governments have put in place control measures to prevent the virus from spreading further. As of January 24, 2020, we calculated the population immigration from Wuhan to the rest of mainland China using migration statistics from the Gaode Map. In addition, we utilized machine learning methods to simulate the curve of the COVID-19 epidemic in different regions and over different time periods. Furthermore, we used machine learning methods to simulate the COVID-19 epidemic curve in various regions and over various time periods. Based on the Wuhan exodus, we built a migration transmission risk model. From January 24 to February 19, 2020, we predicted the location, severity, and timing of epidemics in various parts of mainland China. We showed how discrepancies in model predictions might be utilized to measure transmission load in different parts of the country. Higher transmission risk indices suggest more community transmission in the region. According to the study, states with lower transmission risk indices but fewer cases than expected may have taken highly effective public health measures.
An Algebra of Dependent Data Types

We extend the standard categorical approach to algebraic data types to dependent algebraic data types, so that dependency between two algebraic data types has natural semantics. Specifically, for two inductive data types S and A characterized by two F -algebra F and G, any natural transformation η : F → G gives rise to a dependency of S on A. This natural dependency is the initial object of what we call a Fηalgebra. The initiality further allows us to describe certain dependencies in functions that both involve S and A. We have used Objective Caml to write functional programs where dependencies among data types (and in the relevant functions) are made explicit. This is done by a systematic mapping of layers of categorical constructions to layers of Objective Caml modules.
Assessment of disease activity with simplified joint ultrasonography method in rheumatoid arthritis patients

Background/Aim: Ultrasound (US) is a highly useful tool for assessing the disease activity of rheumatoid arthritis (RA). On the other hand, examining all joints could be time-consuming and unfeasible. Defining the number of joints and which joints should be tested is essential to accurately measuring RA activity. Several simplified US methods are undergoing development for this purpose. The aim of this study was to assess the correlation between simplified 12-joint US findings and physical examination findings/disease activity in RA patients. Methods: This cohort study included 62 RA patients who had been undergoing treatment for at least three months. Multiplanar grayscale images and power Doppler (PD) of the 12 joints (bilateral elbow, wrist, second and third metacarpophalangeal [MCP] joints, knee, and ankle) were acquired and compared with clinical assessments. Disease activity was assessed using the clinical disease activity and simplified disease activity indices and disease activity score-28 (CDAI, SDAI, and DAS28, respectively). Synovial effusion, synovial proliferation, and PD US scores were calculated for 12 joints. Correlations between US scores and disease activity, clinical examination, and acute phase reactants were assessed. Results: The number of joints with PD activity and US total and US synovial proliferation scores showed weak correlations with clinical activity scores (r = 0.25, r = 0.26, and r = 0.28 for SDAI and r = 0.23, r = 0.26, and r = 0.28 for DAS28, respectively). The CDAI did not present any statistically significant correlations. The agreement between US findings and clinical joint examination was generally weak. PD activities of the second MCP joints (r = 0.84, P < 0.01) and knees (r = 0.42, P < 0.01) mostly correlated with clinical examination although it was weakly correlated at the third MCP (r = 0.152) and wrist (r = 0.148), and not correlated at the elbow (r = 0.125). Conclusion: The weak correlation between US findings and clinical examination/disease activity suggests that clinical examination alone may not be sufficient to determine joint inflammation and disease activity. US could provide a more accurate assessment of RA patients and aid in medication selection.
Palliative tumor surgery for incurable head and neck cancer: indications and outcomes: A retrospective case review

Purpose: Despite the promising palliative effects of radiation treatment, few reports have studied the role of palliative tumor surgery (PTS) in patients with unresectable head and neck cancer (HNC). Thus, we aimed to present the outcomes of PTS in HNC, and suggest a possible surgical indication for PTS.Methods: We retrospectively reviewed the medical records of 18 patients who underwent PTS for HNC between 2002 and 2017. PTS was defined as surgical debulking of tumor or surgery of loco-regionaltumors in patients with distant metastasis. As functional outcomes, we evaluated changes in pain, diet, respiration, and wound care before and after PTS.Results: Squamous cell carcinoma was the common cancer type (72.2%), followed by salivary gland cancers and others. The median overall survival time was 17 months (95% confidence interval, 7.3 to 26.7). PTS significantly reduced the pain score (P= 0.013), and improved cancer-related wounds (P=0.003 in wound infection). Oral swallowing and respiration status did not change after PTS. The recurrenttumor atthe operation bed was clinically detected at post-operative 1 to 2 months with intact skin (without wound problems). Of note, further chemotherapy or other additional cancer treatments was possible in 66.7% of patients with PTS (P=0.002). Conclusion: PTS could provide a meaningful benefitto selected patients with incurable HNC, in terms of pain control and cancer wound management. Thus, PTS is a considerable option for selected HNC patients, based on the accurate evaluation oftumor extent along with multi-disciplinary consultation as well as patient counseling.
Classification of hyperspectral image based on dual-branch feature interaction network

ABSTRACT Hyperspectral image (HSI) classification method based on convolutional neural network (CNN) has been widely used and has achieved remarkable results. However, the convolution operation is good at extracting local features, it is difficult to capture the global representation. Recently, Vision Transformer (ViT) shines with cascading self-attention modules that can aggregate global representation between patch embeddings. The self-attention module cascaded in ViT can capture long-distance feature dependencies, but it is prone to ignore the local feature details. In order to address those series of problems, this article proposes a dual-branch feature interaction (DBFI) network based on CNN and ViT, which allows global information and local feature to fully interact to enhance feature representation capabilities. To resolve the problem of the semantic gap between the two branches, a feature interaction unit (FIU) based on the attention mechanism is designed as an information interactive bridge to connect the two branches. The features are branched from ViT to CNN through the upsampling stream to enhance the global perception of the network. Similarly, the down-sampling stream is used as a coupling to connect the CNN branch to the Transformer branch to supplement the local detailed features of the network. In addition, the channel attention and spectral attention in FIU can further emphasize the distinguishability of HSI features and improve the performance of network. Experiments are conducted on three benchmark HSI datasets demonstrate that the DBFI network is better than other classification methods.
Enhanced Synchronization Stability of Grid-Forming Inverters With Passivity-Based Virtual Oscillator Control

In this article, a passivity-based virtual oscillator control strategy with enhanced synchronization stability for grid-forming inverters (GFMs) is proposed. By adopting the port-controlled Hamiltonian system theory for orbital stabilization problems, an energy pumping-and-damping block is proposed to render GFMs globally asymptotically stable with respect to the prespecified solutions of the power-flow equations from any initial condition. This allows for stable integrations of GFMs to any other globally asymptotically stable systems without their explicit knowledge, e.g., helping maintain synchronism with the bulk power system in a wide range of short-circuit-ratio conditions or under large disturbances and keeping synchronism among multiple GFMs in power systems. Both simulations and experiments are presented to demonstrate the proposed control approach.
Effect of Gentamicin Collagen Sponge on Wound Healing: In Vivo Study and Assessment of Its Effectiveness against Nosocomial Bacteria

Background: Gentamicin collagen sponge (Genta-Coll) reportedly has a hemostatic effect and promotes wound healing. However, it may also interfere with wound healing by causing foreign body reactions. We planned to analyze the effect of Genta-Coll on wound healing by inducing muscle defects in the rectus abdominis muscle of rats. We also assessed the effectiveness of Genta-Coll in preventing surgical site infections (SSIs) using the zone of inhibition tests to determine the sensitivity of pathogenic bacteria.Methods: We created an 8×8-mm muscle defect in both recti abdominis of 15 white Sprague-Dawley rats and distributed them into the control group (no care) and group A (Genta-Coll). Three biopsies were performed for histologic analysis (on days 3, 7, and 27). In the second study, we placed Genta-Coll on Mueller–Hinton agar plates seeded with pathogenic microorganisms commonly responsible for SSIs. The zone of inhibition was measured after 24 and 48 hours of incubation.Results: Group A showed a slightly higher score for foreign body giant cells and inflammation, with no significant difference. Granulation and neovascularization were constantly high in group A, and muscle regeneration was also high in group A. Extracellular matrix formation advanced at a similar pace in both groups. Genta-Coll created a zone of inhibition against all microorganisms except for Candida albicans. The effect lasted without change between 24 and 48 hours.Conclusion: While foreign body reactions and inflammation increased in group A, wound healing was unaffected. Genta-Coll can promote faster wound healing with an anti-bactericidal effect against SSI pathogens.
Mediating Effect of Self-Efficacy on the Relationship Between Perceived Social Support and Resilience in Patients with Recurrent Schizophrenia in China

Purpose To explore the mediating effect of self-efficacy on the relationship between perceived social support and resilience in patients with recurrent schizophrenia in China. Patients and Methods In this cross-sectional study, a total of 176 patients with recurrent schizophrenia who were hospitalized in a tertiary hospital in Hunan Province, China, completed a general data questionnaire, the Connor Davidson Resilience Scale (CD-RISC), the Perceived Social Support Scale (PSSS) and the General Self-Efficacy Energy Scale (GSES). Results Among the 176 patients, the mean GSES score was 2.02±0.61, the mean PSSS score was 56.77±14.61, and the mean CD-RISC score was 58.06±17.26. Self-efficacy played a partial mediating role between social support and resilience, and the mediating effect accounted for 42.56% of the total effect. Conclusion The resilience level of patients with recurrent schizophrenia in China is moderate and needs to be improved. This research revealed that self-efficacy played a part in mediating perceived social support and resilience in patients with recurrent schizophrenia in China. Perceived social support can indirectly affect resilience in patients with recurrent schizophrenia through self-efficacy. Comprehensive interventions in perceived social support and self-efficacy would help to improve the resilience of patients with recurrent schizophrenia.
Construction of 3D models of buildings damaged by earthquakes using UAV aerial images

Unmanned aerial vehicles (UAVs) are becoming very popular recently due to their accessibility to targets, promptness of dispatch, and high-spatial resolution of acquired images. In this study, the authors tried to obtain digital images from air and ground for a turned-down and washed-away building in Onagawa Town, Miyagi Prefecture, Japan due to the 2011 Tohoku earthquake and tsunami. A small multirotortype UAV was used in this aerial survey. Then 3D models were constructed in computer using the acquired images based on the Structure-from-Motion (SfM) technique. The most accurate model was made when both the aerial and ground images were used for 3D modelling. As the second example, an aerial video taken from UAVs was used to model affected districts in Kathmandu, Nepal, due the Mw7.8 Gorkha earthquake on April 25, 2015. The video was taken by the Canadian humanitarian organization and released on Internet. The 3D models were developed from the video footage, and they show the details of damage situation in the urban districts.
Functionalized Multi-Walled Carbon Nanotubes and Micro Cellulose Reinforced Poly (Vinyl Alcohol) Hybrid Composite Films: Characterization and Reprocessing Performance

The aim of this research paper was to investigate the synergetic effect of micro cellulose and multi-walled carbon nanotubes (MWCNTs) combination on physicochemical, mechanical and reprocessing performance of Poly (Vinyl Alcohol) hybrid composite films. The hybrid composite films production was carried out by the solution casting method, and the reprocessed films were produced using defective (torn, faulty) films from primary production by the same method. The combined use of MWCNTs and micro cellulose improved the hydrophobicity approximately 85% in ratio. After the reprocessing, decreased intensities of the peaks in the FTIR and Raman results confirmed the reduced molecular interaction between all components of the hybrid composite film, moreover, SEM and TEM revealed the negative defects such as agglomeration which caused mechanical strength decrease. The tensile strength of PVA hybrid composite films loaded cellulose/MWCNTs, reached 97 MPa strength value and elongation at break decreased to 33% according to the neat PVA film. The study results revealed that hybridization had a remarkable impact on improving characteristic properties of composite films and reprocessing process might have been compatible with solution casting method.
Technical Developments for Rehabilitation of Mobility

Abstract Technically assisted rehabilitation of mobility after stroke has been well established for several years. There is good evidence for the use of end-effector devices, exoskeletons and treadmill training with and without body weight support. New developments provide the possibility for functional training during mobilization, even in intensive care units. Mobile exoskeleton devices have been developed, but their clinical effects need still to be evaluated. All devices should not only focus on increasing the number of repetitions, but also include motivational aspects such as virtual reality environments. Hygienic aspects impose a special challenge. All devices should be integrated into a rational and clearly-defined therapy concept.
Air pollution exposure and mammographic breast density in Tehran, Iran: a cross-sectional study

Background Air pollution is one of the major public health challenges in many parts of the world possibly has an association with breast cancer. However, the mechanism is still unclear. This study aimed to find an association between exposure to six criteria ambient air pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and mammographic breast density (MBD), as one of the strongest predictors for developing breast cancer, in women living in Tehran, Iran. Methods Participants were selected from women attending two university hospitals for screening mammography from 2019 to 2021. Breast density was rated by two expert radiologists. Individual exposures to 3-year ambient air pollution levels at the residence were estimated. Results The final analysis in 791 eligible women showed that low and high breast density was detected in 34.8 and 62.2 of participants, respectively. Logistic regression analysis after considering all possible confounding factors represented that an increase in each unit of NO2 (ppb) exposure was associated with an increased risk of breast density with an OR equal to 1.04 (95CI: 1.01 to 1.07). Furthermore, CO level was associated with a decreasing breast density (OR = 0.40, 95CI = 0.19 to 0.86). None of the other pollutants were associated with breast density. Conclusion Higher MBD was associated with an increased level of NO2, as a marker of traffic-related air pollution. Furthermore, CO concentration was associated with a lower MBD, while other criteria air pollutants were not related to MBD. Further studies are needed to evaluate the association between ambient air pollutants with MBD. Supplementary information The online version contains supplementary material available at https://doi.org/10.1265/ehpm.22-00027.
Influence maximization algorithm based on community structure

In recent years, how to identify the most influential nodes has become the forefront of network science. Considering the influence of community structure and neighbor nodes within the second order on node influence diffusion, this paper proposes an influence maximization algorithm based on community structure (IMCS). Firstly, the CPM algorithm is used to detect the community of the network to obtain the community structure of network. Then, select the nodes belonging to multiple communities in the community and some potential nodes in each community to form a candidate node set. Finally, use the improved Prob-Degree algorithm to screen all seed nodes. Experimental data show that compared with Prob-degree, CoFIM and DD, the algorithm proposed in this paper has relatively good overall performance in Oregon network, and there are seed node intervals with relatively good performance in other networks too.
A novel CNN+LSTM classification model based on fashion-MNIST

Nowadays, Convolutional Neural Network (CNN) based image recognition is a popular research direction. This study uses the Fashion-Mnist dataset, which is more challenging than the Mnist dataset. aims to add Long short-term memory (LSTM) to the structure of CNN to create a hybrid model of CNN and LSTM, called CNN+LSTM model. This model is used to complete and optimize the image classification problem on Fashion-Mnist dataset. The final image classification accuracy of the obtained model is 91.36%, which still needs to be improved, but the accuracy results are better compared to the accuracy of other models.
Functional Porous Ionic Polymers as Efficient Heterogeneous Catalysts for the Chemical Fixation of CO2 under Mild Conditions

The development of efficient and metal-free heterogeneous catalysts for the chemical fixation of CO2 into value-added products is still a challenge. Herein, we reported two kinds of polar group (−COOH, −OH)-functionalized porous ionic polymers (PIPs) that were constructed from the corresponding phosphonium salt monomers (v-PBC and v-PBH) using a solvothermal radical polymerization method. The resulting PIPs (POP-PBC and POP-PBH) can be used as efficient bifunctional heterogeneous catalysts in the cycloaddition reaction of CO2 with epoxides under relatively low temperature, ambient pressure, and metal-free conditions without any additives. It was found that the catalytic activities of the POP-PBC and POP-PBH were comparable with the homogeneous catalysts of Me-PBC and PBH and were higher than that of the POP-PPh3-COOH that was synthesized through a post-modification method, indicating the importance of the high concentration catalytic active sites in the heterogeneous catalysts. Reaction under low CO2 concentration conditions showed that the activity of the POP-PBC (with a conversion of 53.8% and a selectivity of 99.0%) was higher than that of the POP-PBH (with a conversion of 32.3% and a selectivity of 99.0%), verifying the promoting effect of the polar group (−COOH group) in the porous framework. The POP-PBC can also be recycled at least five times without a significant loss of catalytic activity, indicating the high stability and robustness of the PIPs-based heterogeneous catalysts.
AN ARRAY PARTITIONING SCHEME OF AIRBORNE PHASED-MIMO RADAR BASED ON STAP SINR

An airborne phased-multiple-input-multiple-output (Phased-MIMO) radar with collocated antenna array is a tradeoff of phased array radar and MIMO radar. Its transmitting array is divided into multiple subarrays that are allowed to be overlapped. In this letter, we mainly study the array partitioning scheme of the airborne Phased-MIMO radar with equal uniform linear subarrays that are fully overlapped on the basis of space-time adaptive processing (STAP). A mathematical formula is derived to determine the number of subarrays and the elements in each subarray according to the principle of maximum STAP signal-to-interference-plus-noise ratio (SINR). The SINR performances corresponding to different partitioning schemes are simulated and discussed to demonstrate the effectiveness of the proposed mathematical formula for array partitioning in the sense of maximum STAP SINR.
Is Female a More Pro-Environmental Gender? Evidence from China

The purpose of this study is to determine whether there are gender differences in people’s pro-environmental psychology and behaviors in China. An online survey was conducted with the snowball sampling technique, and a sample of 532 Chinese respondents was obtained for the research. This study finds that gender does affect green psychology and behaviors, with females reporting a higher level of environmentalism in China. Specifically, females are more concerned with environmental problems, more supportive of plastic ban policies, more positive towards reducing plastics (reduce), and have stronger intention to bring a reusable bag for shopping (reuse and recycle). Moreover, females use fewer disposable toiletries when checking in a hotel and require less disposable tableware when ordering takeout. This study contributes to the current literature by identifying the relationship between gender and environmentalism in China. Implications for anti-plastic policy design and environmental management are also presented.