Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy consumption prediction of public buildings based on PCA-RF-AdaBoost

The influencing factors of public building energy consumption are complex, and accurate prediction of public building energy consumption is of great significance for energy conservation. Studying new energy consumption prediction methods can simplify the prediction process and improve the prediction accuracy. Taking the energy consumption data of a large public building as the sample, the principal component analysis is carried out to remove the information redundancy and eliminate the correlation between input quantities; Based on the analysis of random forest and AdaBoost algorithm, the RF AdaBoost prediction model is constructed. The results show that the prediction model has better prediction effect, which can be used to predict and supervise building energy consumption, improve energy consumption facilities, and achieve effective energy utilization.
Molluscicidal and Larvicidal Potency of N-Heterocylic Analogs against Biomophalaria alexandrina Snails and Schistosoma mansoni Larval Stages

This work describes the synthesis of quinoline-based N--heterocyclic arenes and their biological evaluation as molluscicides against adult Biomophalaria alexandrina snails as well as larvicides against Schistosoma mansoni larvae (miracidia and cercariae). Molecular docking studies were demonstrated to investigate their affinity for cysteine protease protein as an interesting target for antiparasitics. Compound AEAN showed the best docking results followed by APAN in comparison to the co-crystallized ligand D1R reflected by their binding affinities and RMSD values. The egg production, hatchability of B. alexandrina snails and ultrastructural topography of S. mansoni cercariae using SEM were assessed. Biological evaluations (hatchability and egg-laying capacity) revealed that the quinoline hydrochloride salt CAAQ was the most effective compound against adult B. alexandrina snails, whereas the indolo-quinoline derivative APAN had the most efficiency against miracidia, and the acridinyl derivative AEAA was the most effective against cercariae and caused 100% mortality. CAAQ and AEAA were found to modulate the biological responses of B. alexandrina snails with/without S. mansoni infection and larval stages that will affect S. mansoni infection. AEAA caused deleterious morphological effects on cercariae. CAAQ caused inhibition in the number of eggs/snail/week and reduced reproductive rate to 43.8% in all the experimental groups. CAAQ and AEAA can be recommended as an effective molluscicide of plant origin for the control program of schistosomiasis.
Outsourced and Privacy-Preserving Collaborative k-Prototype Clustering for Mixed Data via Additive Secret Sharing

Outsourced cloud computing can be considered as an effective way to overcome the data island among users and relieve the pressure of limited resources. However, due to the concerns about trust in cloud servers, outsourcing the users’ data and model training task has considerable privacy disclosure risks. This article presents a PriKPM scheme by using additive secret sharing (ASS), so as to implement the privacy-preserving ${k}$ -prototype clustering for mixed data (i.e., including numerical and categorical attributes). In PriKPM, data samples are randomly split into two shares and delivered offline to two collaborative servers. We design a secure initialization method for determining the location and number of cluster centers. Then, both servers securely calculate the mixed distance between samples and cluster centers, and execute the samples partion and cluster updating operations. An efficient and secure comparison protocol is developed to offer flexibly the “less than or equal” and “equal” functions during the entire clustering process. Furthermore, theoretical analysis proves the effectiveness and security of PriKPM. Sufficient experiments demonstrate that PriKPM is computationally more efficient than existing secure clustering works. PriKPM can achieve the approximate accuracy of the plaintext ${k}$ -prototype clustering scheme.
The Use of Esophageal Stents in the Management of Postoperative Fistulas—Current Status, Clinical Outcomes and Perspectives—Review

Esophageal fistula remains one of the main postoperative complications, with the treatment often requiring the use of stents. This article reviews the updates on the use of endoscopic stents for the treatment of postoperative esophageal leakage in terms of indications, types of stents used, efficiency, specific complications and perspectives. Materials and Methods: We searched the PubMed and MEDLINE databases for the keywords postoperative esophageal anastomotic leak and postoperative esophageal anastomotic leak stent, and retrieved relevant papers published until December 2022. Results: The endoscopic discovery of the fistula is usually followed by the insertion of a fully covered esophageal stent. It has an efficiency of more than 60% in closing the fistula, and the failure is related to the delayed application of the method, a situation more suitable for endo vac therapy. The most common complication is migration, but life-threatening complications have also been described. The combination of the advantages of endoscopic stents and vacuum therapy is probably found in the emerging VACstent procedure. Conclusions: Although the competing approaches give promising results, this method has a well-defined place in the treatment of esophageal fistulas, and it is probably necessary to refine the indications for each individual procedure.
Dynamic Alginate Hydrogel as an Antioxidative Bioink for Bioprinting

3D bioprinting holds great potential for use in tissue engineering to treat degenerative joint disorders, such as osteoarthritis. However, there is a lack of multifunctional bioinks that can not only support cell growth and differentiation, but also offer protection to cells against injuries caused by the elevated oxidative stress; this conditions is a common characteristic of the microenvironment of the osteoarthritis disease. To mitigate oxidative stress-induced cellular phenotype change and malfunction, an anti-oxidative bioink derived from an alginate dynamic hydrogel was developed in this study. The alginate dynamic hydrogel gelated quickly via the dynamic covalent bond between the phenylboronic acid modified alginate (Alg-PBA) and poly (vinyl alcohol) (PVA). It presented good self-healing and shear-thinning abilities because of the dynamic feature. The dynamic hydrogel supported long-term growth of mouse fibroblasts after stabilization with a secondary ionic crosslinking between introduced calcium ions and the carboxylate group in the alginate backbone. In addition, the dynamic hydrogel showed good printability, resulting in the fabrication of scaffolds with cylindrical and grid structures with good structural fidelity. Encapsulated mouse chondrocytes maintained high viability for at least 7 days in the bioprinted hydrogel after ionic crosslinking. Most importantly, in vitro studies implied that the bioprinted scaffold could reduce the intracellular oxidative stress for embedded chondrocytes under H2O2 exposure; it could also protect the chondrocytes from H2O2-induced downregulation of extracellular matrix (ECM) relevant anabolic genes (ACAN and COL2) and upregulation of a catabolic gene (MMP13). In summary, the results suggest that the dynamic alginate hydrogel can be applied as a versatile bioink for the fabrication of 3D bioprinted scaffolds with an innate antioxidative ability; this technique is expected to improve the regenerative efficacy of cartilage tissues for the treatment of joint disorders.
Bitter decoration of vortex patterns in superconducting Nb films with random, triangular, and Penrose arrays of antidots

We imaged Abrikosov vortex patterns in thin Nb films with random, periodic (triangular), and quasiperiodic (Penrose) arrays of antidots. Vortex positions were visualized by Bitter decoration for a range of applied fields $B$, antidot radii $r$, and densities ${n}_{p}$ after field-cooling through the transition temperature ${T}_{c}$ to a base temperature $T\ensuremath{\approx}2\phantom{\rule{0.16em}{0ex}}\phantom{\rule{0.16em}{0ex}}\mathrm{K}$. The observed vortex patterns correspond to snapshots of vortex positions at the time of decoration. The effectiveness of antidots as artificial pinning sites for vortices is found to be sensitive to several factors: array geometry, antidot size and density, and applied field. Overall, the triangular lattice provides the most effective pinning landscape, with antidots trapping the highest proportion of vortices, but for a wide range of parameters the Penrose lattice is equally effective. For a quantitative analysis, we determined the occupation number $n$ (average number of vortices trapped per antidot) from each image. This revealed a significantly more complicated dependence of antidot occupation on applied field and/or antidot density than that predicted by simple models considering pinning by an isolated antidot. In particular, upon increasing the antidot density ${n}_{p}$, we find a marked increase in $n$ for triangular arrays, which we attribute to the additional repulsion from interstitial vortices, pushing more vortices into antidots with decreasing antidot separation. This effect is also present but less pronounced for Penrose arrays, which can be explained by the variation of antidot spacing inherent to the Penrose geometry and accordingly more options for accommodating interstitial vortices.
An Extended Method to Analyze Boron Diffusion Defects in 16 nm Node High-Voltage FinFETs

This work proposed extended methods, which can analyze kinds of defects more easily with power spectrum density (PSD) and weighted time lag plot (W-TLP), to decouple single or multi-traps. To get additional high voltage tolerance, it is common to design different kinds of structures dispersing the electric field. In this work, boron and fluorine were doped in the source and drain extension regions to achieve higher voltage operation. However, boron diffusion could worsen the interface quality. Interestingly, after different stress conditions of hot carrier degradation (HCD) and positive bias temperature instability (PBTI), the degradation trends of the two devices show opposite behaviors. It is because the boron can bear the high voltage operation, but also weak the devices’ interface quality. Therefore, to analyze the influence of these defects plays an important role. With Agilent B1530A WGFMU and RTSDataAnalysis software, varied defects response to frequency can be simply detected. It can also use W-TLP to decouple single trap and multi-traps behaviors at the same time.
Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks

Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.
The Short-Term Labor Supply Response to the Expanded Child Tax Credit

We estimate the extensive and intensive margin labor supply response to the monthly Child Tax Credit disbursed in 2021 as a part of the American Rescue Plan Act. Using Current Population Survey microdata, we compare labor supply outcomes among households who qualify for varying relative increases in household income, as a result of their income level and household size. We do not find strong evidence of a change in labor supply for families receiving the credit. The results are robust to alternative labor supply models, where households respond mainly to cash on hand or changes in the annual budget set.
Research and Improvement of Greedy Projection Triangulation Algorithm

With the development of point cloud acquisition equipment, generating realistic 3D digital models based on various acquisition equipment has become one of the main means of acquiring object surface information, and has been widely used in computer vision, aerospace, 3D printing, terrain mapping, virtual Reality, archeology, reverse engineering and other fields. So the point cloud registration and 3D reconstruction technology are particularly important. This paper studies and improves point cloud registration algorithm and point cloud 3D reconstruction algorithm.
On the Influence of ENSO on Sudden Stratospheric Warmings

Using the extended ERA5 reanalysis and three state‐of‐the‐art models, this study explores how El Niño‐Southern Oscillation (ENSO) can influence the total frequency, seasonal cycle and preconditioning of sudden stratospheric warmings (SSWs). Reanalysis data shows that in the last seven decades, winters with SSWs were more common than winters without, regardless El Niño (EN) or La Niña (LN) occurrence or the ENSO/SSW definitions. In agreement with previous studies, our models tend to simulate a linear ENSO‐SSW relationship, with more SSWs for EN, around mid‐winter (January–February) as in reanalysis, and less for LN when compared to neutral conditions. Independently of ENSO, the main tropospheric precursor of SSWs appears to be an anomalous wave‐like pattern over Eurasia, but it is dominated by wavenumber 1 (WN1) for EN and shows an enhanced wavenumber 2 (WN2) for LN. The differences in this Eurasian wave pattern, which is largely internally generated, emerge from the distinct configuration of the background, stationary wave pattern induced by ENSO in the North Pacific, favoring a stronger WN1 (WN2) component during EN (LN). Our results suggest that the ENSO‐forced signal relies on modulating the seasonal‐mean polar vortex strength, becoming weaker and more displaced (stronger and more stable) for EN (LN), while ENSO‐unforced wave activity represents the ultimate trigger of SSWs. This supports the view that ENSO and SSWs are distinct sources of variability of the winter atmospheric circulation operating at different time‐scales and may reconcile previous findings in this context.
Novel Convolutions for Semantic Segmentation of Remote Sensing Images

The networks are required to be capable of learning low-level features well when applied to remote sensing image (RSI) semantic segmentation tasks. To capture accurate and abundant low-level semantic information, the early feature extractor layer is crucial to the whole network because all the subsequent features are inferred from that base. To address the low-level feature extraction issue and overcome the shortcomings of traditional convolution such as too many parameters or limited receptive field, some novel convolution units have been proposed in the literature. In this article, we propose two elaborately designed and portable yet effective convolution units, i.e., directional convolution (DC) and large field convolution (LFC), combined as the extractor of low-level semantic features. DC is designed to extract directional features from specific directions, and LFC can achieve a large receptive field with few parameters. Experimental results on two public datasets provide evidence that our convolution units can help deep learning networks improve performance stably and comprehensively compared to the baseline networks.
ERRATUM: “CLASSICAL T TAURI-LIKE OUTFLOW ACTIVITY IN THE BROWN DWARF MASS REGIME” (2009, ApJ, 706, 1054)

Figures 3–11 have an error in that the direction of the offsets should be reversed. Thus, in the spectro-astrometric plots, positive offsets are toward the top of the figure and negative offsets toward the bottom. In the position–velocity diagram shown in Figure 8, positive offsets are toward the left and negative toward the right. The result of this correction is that the orientations of the blueshifted and redshifted lobes of the outflows are now reversed. Thus, for ISO-Cha1 217 the blueshifted flow is the in northeast quadrant with a position angle (P.A.) of 20° and the redshifted flow is in the southwest quadrant (see Figure 2). Similarly, for ISO-Oph 32, the blueshifted flow now lies in the northeast quadrant with a P.A. of 60°. Table 3 has been updated accordingly.
Classification of potato diseases using deep learning apprach

Non-destructive and rapid inspection utilising deep learning and image processing may be of tremendous use for potatoes and other agricultural products. In this study, we recommend employing a deep neural network to separate diseased potatoes into groups according to their outer appearance. Train, test, and evaluate the resulting DNN using a collection of potato pictures. Using Machine Learning, a Modified Support Vector Machine (MSVM) and a Conv. Neural Network (CNN) were used to make predictions and comparisons about the segmentation and feature extraction of a potato picture. Canada and the United States Department of Agriculture standard datasets were modified for the purpose of testing the suggested model. The results show that deep learning is superior to both traditional methods and the Machine learning model. In some sort of picture, more than 99 % accuracy recorded, while in others, approach 97 percent.
Simulation and design of pre-ionization systems for Alborz tokamak

Alborz tokamak is an educational system for studying plasma phenomena in many physics and engineering experiments. A hot filament and a reverse discharge loop are used in the tokamak as the pre-ionization system. The hot cathode prepares a local initial electron density, then the reverse discharge loop trigger the ionization avalanche by means of inducing a toroidal electric field. The parameters of the hot filament are determined in order to produce the desired electron source. Filament temperature is simulated by using three-dimensional finite element method. The average values of filament temperature and electron density at the plasma core (at the end of pre-ionization process) were calculated and are about 2750 K and 1019 m−3, respectively. The resultant electron density and equivalent plasma resistivity due to reverse discharge loop are also calculated. In this paper, the simulation results, optimum structural style, the obtained parameters, the temperature of different parts of the filament and produced electron density are presented and discussed.
Solute carrier-related signature for assessing prognosis and immunity in patients with clear-cell renal cell carcinoma

Background: Clear-cell renal cell carcinoma (ccRCC) is the most common malignant kidney cancer. However, the tumor microenvironment and crosstalk involved in metabolic reprogramming in ccRCC are not well-understood. Methods: We used The Cancer Genome Atlas to obtain ccRCC transcriptome data and clinical information. The E-MTAB-1980 cohort was used for external validation. The GENECARDS database contains the first 100 solute carrier (SLC)-related genes. The predictive value of SLC-related genes for ccRCC prognosis and treatment was assessed using univariate Cox regression analysis. An SLC-related predictive signature was developed through Lasso regression analysis and used to determine the risk profiles of patients with ccRCC. Patients in each cohort were separated into high- and low-risk groups based on their risk scores. The clinical importance of the signature was assessed through survival, immune microenvironment, drug sensitivity, and nomogram analyses using R software. Results: SLC25A23, SLC25A42, SLC5A1, SLC3A1, SLC25A37, SLC5A6, SLCO5A1, and SCP2 comprised the signatures of the eight SLC-related genes. Patients with ccRCC were separated into high- and low-risk groups based on the risk value in the training and validation cohorts; the high-risk group had a significantly worse prognosis (p < 0.001). The risk score was an independent predictive indicator of ccRCC in the two cohorts according to univariate and multivariate Cox regression (p < 0.05). Analysis of the immune microenvironment showed that immune cell infiltration and immune checkpoint gene expression differed between the two groups (p < 0.05). Drug sensitivity analysis showed that compared to the low-risk group, the high-risk group was more sensitive to sunitinib, nilotinib, JNK-inhibitor-VIII, dasatinib, bosutinib, and bortezomib (p < 0.001). Survival analysis and receiver operating characteristic curves were validated using the E-MTAB-1980 cohort. Conclusions: SLC-related genes have predictive relevance in ccRCC and play roles in the immunological milieu. Our results provide insight into metabolic reprogramming in ccRCC and identify promising treatment targets for ccRCC.
Photonic-assisted Modulation Format Identification Using Convolutional Neural Networks

A photonic-assisted architecture is used to implement self-interference on radio-frequency (RF) signals to create modulation-biased amplitude patterns. The interfered signals are identified from their modulation formats using a convolutional neural network (CNN). In the proof-of-concept experiments, the average accuracy reaches 97.06% for signals with 10 modulation formats at 1 G Baud.
Variable Form LADRC-Based Robustness Improvement for Electrical Load Interface in Microgrid: A Disturbance Response Perspective

As the cornerstone of adjusting microgrid output, the electrical load interface unit is difficult to continuously output desired dynamics under various uncertainties. To handle the problem, a variable form linear active disturbance rejection control (VFLADRC), including extended state differential compensation, is proposed for load interface unit according to the separating response. In this technology, the extended state differential is embedded in the linear extended state observer (LESO) to construct a modified LESO, alongside the control law is generated via a feedforward way. By doing this, the attenuation capability of generalized disturbance response in the system output is improved, thereby optimizing output dynamics. In addition, the stability and output characteristics of the studied scheme are theoretically investigated. It is revealed that the electrical load interface unit with VFLADRC exhibits a higher level of robustness. Finally, comparative experiments are conducted on a 40-kW test platform, which validates the correctness of the analysis and the superiority of the proposed approach.