Miracle-dz/UniFAR-FTUs huggingface dataset:
Basal VEGF-A and ACE Plasma Levels of Metastatic Colorectal Cancer Patients Have Prognostic Value for First-Line Treatment with Chemotherapy Plus Bevacizumab

Simple Summary Molecular biology knowledge has enabled the incorporation of targeted therapies, such as the anti-angiogenic drug bevacizumab, into combined chemotherapy regimens for the treatment of metastatic colorectal cancer. However, to date, there are no reliable useful biomarkers to predict the efficacy of this anti-angiogenic therapy. The objective of this prospective study was to evaluate potential circulating plasma biomarkers in mCRC patients prior to the start of first-line treatment with chemotherapy plus bevacizumab. We found that high VEGF-A and low ACE plasma levels were associated with poor OS after treatment. Moreover, a simple scoring system combining both biomarkers efficiently stratified patients into high- or low-risk groups, which allows the selection of patients for anti-angiogenic therapy. Abstract The identification of factors that respond to anti-angiogenic therapy would represent a significant advance in the therapeutic management of metastatic-colorectal-cancer (mCRC) patients. We previously reported the relevance of VEGF-A and some components of the renin–angiotensin-aldosterone system (RAAS) in the response to anti-angiogenic therapy in cancer patients. Therefore, this prospective study aims to evaluate the prognostic value of basal plasma levels of VEGF-A and angiotensin-converting enzyme (ACE) in 73 mCRC patients who were to receive bevacizumab-based therapies as a first-line treatment. We found that high basal VEGF-A plasma levels were significantly associated with worse overall survival (OS) and progression-free survival (FPS). On the other hand, low ACE levels were significantly associated with poor OS. Importantly, a simple scoring system combining the basal plasma levels of VEGF-A and ACE efficiently stratified mCRC patients, according to OS, into high-risk or low-risk groups, prior to their treatment with bevacizumab. In conclusion, our study supports that VEGF-A and ACE may be potential biomarkers for selecting those mCRC patients who will most benefit from receiving chemotherapy plus bevacizumab treatment in first-line therapy. Additionally, our data reinforce the notion of a close association between the RAAS and the anti-angiogenic response in cancer.
Improved Deep Learning Model for Static PE Files Malware Detection and Classification

Static analysis and detection of malware is a crucial phase for handling security threats. Most researchers stated that the problem with the static analysis is an imbalance in the dataset, causing invalid result metrics. It requires more time for extracting features from the raw binaries, and methods like neural networks require more time for the training. Considering these problems, we proposed a model capable of building a feature set from the dataset and classifying static PE files efficiently. The research work was conducted to emphasize the importance of feature extraction rather than focusing on model building. The well-extracted features help to provide better results when fed to neural networks with minimal numbers of layers. Using minimum layers will enhance the performance of the model and take fewer resources and time for the processing and evaluation. In this research work, EMBER datasets published by Endgame Inc. containing PE file information are used. Feature extraction, data standardization, and data cleaning techniques are performed to handle the imbalance and impurities from the dataset. Later the extracted features were scaled into a standard form to avoid the problems related to range variations. A total of 2381 features are extracted and pre-processed from both the 2017 and 2018 datasets, respectively. The pre-processed data is then given to a deep learning model for training. The deep learning model created using dense and dropout layers to minimize the resource strain on the model and deliver more accurate results in less amount of time. The results obtained during experimentation for EMBER v2017 and v2018 datasets are 97.53% and 94.09%, respectively. The model is trained for ten epochs with a learning rate of 0.01, and it took 4 minutes/epoch, which is one minute lesser than the Decision Tree model. In terms of precision metrics, our model achieved 98.85%, which is 1.85% more as compared to the existing models.
Two-dimensional partial orders

This thesis presents new algorithms dealing with a class of graphs called two dimensional partial orders. Algorithms are given which recognize two dimensional partial orders, and determine whether a pair of two dimensional partial orders are isomorphic. These algorithms have a time complexity of O(n('2)) time, while previous algorithms required O(n('3)) time. A new characterization of bipartite two dimensional partial orders is presented. A polynomial time algorithm is developed which solves the minimum dummy task problem for PERT networks if the input graph is a two dimensional partial order.
Update of a biogeochemical model with process-based algorithms to predict ammonia volatilization from fertilized cultivated uplands and rice paddy fields

Abstract. Accurate simulation of ammonia (NH3) volatilization from fertilized croplands is crucial to enhancing fertilizer-use efficiency and alleviating environmental pollution. In this study, a process-oriented model, CNMM–DNDC (Catchment Nutrient Management Model–DeNitrification–DeComposition), was evaluated and modified using NH3 volatilization observations from 44 and 19 fertilizer application events in cultivated uplands and paddy rice fields in China, respectively. The major modifications for simulating NH3 volatilization from cultivated uplands were primarily derived from a peer-reviewed and published study. NH3 volatilization from cultivated uplands was jointly regulated by wind speed, soil depth, clay fraction, soil temperature, soil moisture, vegetation canopy, and rainfall-induced canopy wetting. Moreover, three principle modifications were made to simulate NH3 volatilization from paddy rice fields. First, the simulation of the floodwater layer and its pH were added. Second, the effect of algal growth on the diurnal fluctuation in floodwater pH was introduced. Finally, the Jayaweera–Mikkelsen model was introduced to simulate NH3 volatilization. The results indicated that the original CNMM–DNDC not only performed poorly in simulating NH3 volatilization from cultivated uplands but also failed to simulate NH3 volatilization from paddy rice fields. The modified model showed remarkable performances in simulating the cumulative NH3 volatilization of the calibrated and validated cases, with drastically significant zero-intercept linear regression of slopes of 0.94 (R2 = 0.76, n = 40) and 0.98 (R2 = 0.71, n = 23), respectively. The simulated NH3 volatilization from cultivated uplands was primarily regulated by the dose and type of the nitrogen fertilizer and the irrigation implementation, while the simulated NH3 volatilization from rice paddy fields was sensitive to soil pH; the dose and depth of nitrogen fertilizer application; and flooding management strategies, such as floodwater pH and depth. The modified model is acceptable to compile regional or national NH3 emission inventories and develop strategies to alleviate environmental pollution.
DLMP using three‐phase current injection OPF with renewables and demand response

With increasing penetration of demand response and distributed renewable generation in the distribution system, distribution locational marginal price (DLMP) helps to provide a right price signal for participants in an active distribution system. Unlike the transmission system, the distribution system is unbalanced, which may result in different phase price at each bus. In this study, a three-phase current injection based optimal power flow (OPF) with robust convergence is proposed to compute DLMP at each bus and phase. Several scenarios have been modelled to validate the application of the proposed DLMP to manage distributed generation, demand response and line congestion for a modified IEEE test system.
A Survey on 3D Object Detection Methods for Autonomous Driving Applications

An autonomous vehicle (AV) requires an accurate perception of its surrounding environment to operate reliably. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. Object detection is a fundamental function of this perception system, which has been tackled by several works, most of them using 2D detection methods. However, the 2D methods do not provide depth information, which is required for driving tasks, such as path planning, collision avoidance, and so on. Alternatively, the 3D object detection methods introduce a third dimension that reveals more detailed object’s size and location information. Nonetheless, the detection accuracy of such methods needs to be improved. To the best of our knowledge, this is the first survey on 3D object detection methods used for autonomous driving applications. This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs. It then discusses and categorizes the recent works based on sensors modalities into monocular, point cloud-based, and fusion methods. We then summarize the results of the surveyed works and identify the research gaps and future research directions.
PENGARUH CORPORATE IMAGE, STORE ATMOSPHERE DAN KEPUASAN PELANGGAN TERHADAP LOYALITAS PELANGGAN (Studi pada pelanggan Aluna Home Spa Semarang)

This study aims to determine and analyze the effect of corporate image, store atmosphere on customer satisfaction and its impact on customer loyalty. The population is customers from Aluna Home Spa, with a sample of 100 respondents. The data testing technique used in this study uses a validity test with a loading factor, a reliability test with Cronbach's alpha and multiple regression analysis. The results of the study show that corporate image has a positive effect on customer satisfaction, store atmosphere has a positive effect on customer satisfaction and customer satisfaction has a positive effect on customer loyalty
A Fully-Human Antibody Specifically Targeting a Membrane-Bound Fragment of CADM1 Potentiates the T Cell-Mediated Death of Human Small-Cell Lung Cancer Cells

Small-cell lung cancer (SCLC) is the most aggressive form of lung cancer and the leading cause of global cancer-related mortality. Despite the earlier identification of membrane-proximal cleavage of cell adhesion molecule 1 (CADM1) in cancers, the role of the membrane-bound fragment of CAMD1 (MF-CADM1) is yet to be clearly identified. In this study, we first isolated MF-CADM1-specific fully human single-chain variable fragments (scFvs) from the human synthetic scFv antibody library using the phage display technology. Following the selected scFv conversion to human immunoglobulin G1 (IgG1) scFv-Fc antibodies (K103.1–4), multiple characterization studies, including antibody cross-species reactivity, purity, production yield, and binding affinity, were verified. Finally, via intensive in vitro efficacy and toxicity evaluation studies, we identified K103.3 as a lead antibody that potently promotes the death of human SCLC cell lines, including NCI-H69, NCI-H146, and NCI-H187, by activated Jurkat T cells without severe endothelial toxicity. Taken together, these findings suggest that antibody-based targeting of MF-CADM1 may be an effective strategy to potentiate T cell-mediated SCLC death, and MF-CADM1 may be a novel potential therapeutic target in SCLC for antibody therapy.
A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.
Modified Increased-Level Model Predictive Control Methods With Reduced Computation Load for Modular Multilevel Converter

It is considerable to reduce the computation load while keeping a good total harmonic distortion (THD) performance for the modular multilevel converter (MMC) with model predictive control (MPC). In this paper, the discrete-time mathematical model of the MMC is derived and the output voltage ripples of the cascaded submodules (SMs) are analyzed in detail. Accordingly, a modified increased-level MPC method A (IL-MPC-A) is proposed with 2N + 1 ac-side output voltage levels. The computation load is significantly reduced without losing the harmonic circulating currents suppression and capacitor voltage ripples compensation capabilities. In this method, the cost function minimization step and capacitor voltage sorting algorithm are combined to determine the optimal control option. In addition, to avoid the selection of weighting factors and further reduce the computation load as the increasing of the SM number, another modified IL-MPC-B is proposed by directly calculating the optimal ac-side output voltage level and arm summation voltage according to the discrete-time mathematical model. As a result, the ac-side output current and circulating current controls are decoupled. Furthermore, the capacitor voltages are balanced with a proposed control scheme by adjusting the circulating current reference. The effectiveness of the proposed MPC methods is verified by simulation and experimental results.
Survey on cooperatively V2X downloading for intelligent transport systems

Energy conservation has always been crucial issues faced by the development of academia and the automotive industry. Traditional cellular networks cannot meet people's needs for video, large files and entertainment. To tackle these problems, cooperatively vehicles-to-everything (V2X) downloading is a typical solution of driving experience, which is in line with the trend of automobile development. Cooperative downloading can better achieve energy efficiency, low-emission and resource sharing. Using cooperative vehicles to download files for intelligent transport systems (ITS) is attracting increasing attention. The future commercial potential is unlimited and a win-win situation is achieved. One of the key challenges in building cooperative downloading today is the provisioning of multimedia services requiring actuator algorithm, intermittent connectivity and real-time computation. This survey summarises recent efforts of the key technologies, routing protocols and incentives mechanism in cooperative downloading.
Likelihood Estimation with Normal Mixture Models

We consider some of the problems associated with likelihood estimation in the context of a mixture of multivariate normal distributions. Unfortunately with mixture models, the likelihood equation usually has multiple roots and so there is the question of which root to choose. In the case of equal covariance matrices the choice of root is straightforward in the sense that the maximum likelihood estimator exists and is consistent. However, an example is presented to demonstrate that the adoption of a homoscedastic normal model in the presence of some heteroscedasticity can considerably influence the likelihood estimates, in particular of the mixing proportions, and hence the consequent clustering of the sample at hand.
More is simpler: Decomposition of ligand‐binding affinity for proteins being disordered

In statistical mechanics, it is well known that the huge number of degrees of freedom does not complicate the problem as it seems, but actually greatly simplifies the analysis (e.g., to give a Boltzmann distribution). Here, we reveal that the ensemble averaging from the vast conformations of intrinsically disordered proteins (IDPs) greatly simplifies the nature of binding affinity, which can be reliably decomposed into a sum of the ligandability of IDP and the capacity of ligand. Such an unexpected regularity is applied to facilitate the virtual screening upon IDPs. It also provides essential insight in understanding the specificity difference between IDPs and conventional ordered proteins since the specificity is caused by deviation from the baseline behavior of protein–ligand binding.
Particle Flow Analysis of the Influences of Joints on Rock Mechanical Properties

The failure of natural rock mainly forms along the joint planes; therefore, the existence of joints heavily affects the rock strength. The influences of joints with a different dip angle and cross double joints on rock mass strength were analyzed using theoretical calculation and particle flow PFC2D software to explore the mechanical properties of joints on rock failure. Results show that the strength of a jointed rock has obvious angle-range effects in the uniaxial compression text, and the peak strength of rock specimens is typical of the “U” type with the increase of the joint dip angle. For the rock mass of cross double joints, the rock mass strength is determined by the strength of joints when the joint dip angle of either joint is between 45° and 75°. The rock mass strength is determined by the superposition of the effects of these two joints when the joint dip angles of the two joints are both between 45° and 75°; the influences of joints on the rock mass strength are not obvious when neither of the joint dip angles of the two joints is between 45° and 75°. The conclusions obtained in the study have significant implications for understanding the laws of joint rock strength.
High pressure CO2 amplifiers for picosecond pulse amplification

A high-pressure CO2 laser with large discharge x-section of 22×22 mm and gain length of 800 mm has been developed for use as regenerative or multi-pass amplifier in high energy ps laser systems. A test system with reduced electrode length was constructed to establish initial design parameters for the full-size system. The design of the final amplifier is described, and measured performance characteristics are presented. The system can be operated at gas pressures of up to 12 bar and provides stable discharges with a small signal gain of >3 %/cm. Further scaling of the laser to x-sections of up to 50×100 mm will be discussed and preliminary results of a discharge test system for development of novel discharge excitation techniques will be presented.
Mediating Role of Psychological Capital in the Relationship Between Social Support and Self-Neglect Among Chinese Community-Dwelling Older Adults

Objectives Self-neglect in older adults has become an important public health issue and is associated with negative health outcomes and increased morbidity and mortality. Social support has been recognized as a prominent predictor of self-neglect, but the underlying mechanism is unclear. This study aims to investigate and illustrate the associations among social support, psychological capital, and self-neglect. Methods This study used a cross-sectional convenience sampling design. A total of 511 older adults were recruited in Chinese communities. Spearman’s correlation coefficient and hierarchical multiple regression analysis were performed to assess the influencing factors of self-neglect. A structural equation model was applied to test the hypothesized mediation model. Results Social support and psychological capital were found to be negatively related to self-neglect. Social support and psychological capital explained 5.1 and 11.9% of the incremental variances of older adults’ self-neglect, respectively. Psychological capital acts as a mediator between social support and self-neglect. Conclusion Self-neglect among older adults is a rising problem in China. Social support and higher psychological capital could decrease the risk of self-neglect in older adults. It is crucial to improve social networks and facilitate psychological interventions to reduce such self-neglect.
Tooth recognition of 32 tooth types by branched single shot multibox detector and integration processing in panoramic radiographs

Abstract. Purpose: The purpose of our study was to analyze dental panoramic radiographs and contribute to dentists’ diagnosis by automatically extracting the information necessary for reading them. As the initial step, we detected teeth and classified their tooth types in this study. Approach: We propose single-shot multibox detector (SSD) networks with a side branch for 1-class detection without distinguishing the tooth type and for 16-class detection (i.e., the central incisor, lateral incisor, canine, first premolar, second premolar, first molar, second molar, and third molar, distinguished by the upper and lower jaws). In addition, post-processing was conducted to integrate the results of the two networks and categorize them into 32 classes, differentiating between the left and right teeth. The proposed method was applied to 950 dental panoramic radiographs obtained at multiple facilities, including a university hospital and dental clinics. Results: The recognition performance of the SSD with a side branch was better than that of the original SSD. In addition, the detection rate was improved by the integration process. As a result, the detection rate was 99.03%, the number of false detections was 0.29 per image, and the classification rate was 96.79% for 32 tooth types. Conclusions: We propose a method for tooth recognition using object detection and post-processing. The results show the effectiveness of network branching on the recognition performance and the usefulness of post-processing for neural network output.
Design of L1/L2 GPS BPF Using Closed-Loop Rectangular Ring Resonator

In this paper, a new compact dual-band filter based on closed-loop rectangular ring resonator is presented. Two passbands are designed to operate at L1 and L2 global positioning system (GPS) bands, respectively. The first passband is established by utilizing the dimension of square ring resonator that resonates at L1 band. Then, a perturbation mode element is applied to the upper and lower sides of the ring to separate the other degenerate mode and allocate the second passband of the filter at L2 GPS band. The dual passband can be adjusted to meet the designer requirements. The frequency selectivity of the filter is improved by using two symmetric parallel-coupled 0-degree feed structure that introduce a pair of transmission zeros around the passband. A practical dual-band bandpass filter is fabricated and measured. The measured results show good agreement with the simulated ones.