Miracle-dz/UniFAR-FTUs huggingface dataset:
RP-UPLC method development and validation for simultaneous estimation of Etoricoxib and Thiocolchicoside in tablets

INTRODUCTION There are several combination drugs in the market formulated to meet multiple disorders that are associated with a disease. Etoricoxib (60 mg) and Thiocolchicoside (4 mg) are one such combination medicine. Etoricoxib is a nonsteroidal inflammatory drug with and aim to relieve pain and swelling of the skeleton muscles, whereas Thiocolchicoside is a muscle relaxant and is used as an add-on treatment for pain due to permanent tightening of muscles. Etoricoxib (Fig. 1) chemically is 5-chloro2-(6-methylpyridin-3-yl)-3-[4-(trideuteriomethylsulfonyl) phenyl]pyridine and Thiocolchicoside (Fig. 2) chemically is N-[(7S)-1,2-dimethoxy-10-methylsulfanyl-9-oxo-3[(2S,3R,4S,5S,6R)-3,4,5-trihydroxy-6-(hydroxymethyl)oxan2-yl]oxy-6,7-dihydro-5H-benzo[a]heptalen-7-yl]acetamide. The literature reveals the availability of several techniques and methods for the simultaneous estimation of Etoricoxib and Thiocolchicoside like UV spectroscopy, High Performance Thin Layer Chromatography (HPTLC), and High Performance Liquid Chromatography (HPLC) methods (Gulhane et al., 2021; Palte and Kondalkar, 2015; Sujit and Nitin, 2016). With the advent of lean laboratories, there is a strong need to develop methods which are quick as well as cost-competitive. Published methods in the literature provide several HPLC methods which require about 30 minutes of HPLC run time. However, there is no published data on the use of the ultra performance liquid chromatography (UPLC) method for simultaneous estimation of both Etoricoxib and Thiocolchicoside. From an operations point of view, both HPLC and UPLC work on the same principle. HPLCs utilize RP-UPLC method development and validation for simultaneous estimation of Etoricoxib and Thiocolchicoside in tablets
A distributed road traffic signal control system based on 5G communication

In response to the disadvantages of the traditional road traffic signal control field, such as the large number of traffic information devices, high maintenance cost, many failure hazards, and lack of scalability of the system, we propose a distributed road traffic signal control system method based on 5G network. The system includes a radar-video integrated unit, which collects traffic flow data at intersections through radar and video sensors, and sends the collected flow data to the intersection control center through a 5G network; a distributed control signal, which collects signal unit operation status through voltage and current sensors, sends the signal unit status to the intersection control center through a 5G network, and accepts and executes the intersection control center's issued operation scheme; intersection control center, according to the received intersection traffic flow data and signal status information analysis to generate intersection direction scheme, send the intersection release scheme through 5G network to the radar video integrated machine and distributed control signal, and complete the control node autonomous monitoring function. We tested the proposed method by building a verification platform. The experimental results show that the adoption of this method can greatly reduce the intersection wiring workload and improve the system usability.
Consensus tracking for singular multi‐agent systems with Lipschitz nonlinear dynamics and external disturbances

The consensus tracking control problem for singular multi‐agent systems (SMASs) with Lipschitz nonlinearities and unknown disturbances is investigated. An extended state observer using only the relative output information is introduced and then a distributed consensus protocol is proposed, whose performance is analyzed using matrix analysis, singular system theory and Lyapunov stability theory. Sufficient conditions for ensuring consensus tracking with disturbance rejection of linear and Lipschitz nonlinear SMASs are, respectively, obtained under directed networks. Specifically, an equivalent formulation of achieving consensus tracking for SMASs is established, and the design procedures of gain matrices in the proposed consensus protocols are presented by the linear matrix inequality solver. Finally, simulation examples are provided to illustrate the theoretical results.
Open Problems in Numerical Fluxes: Proposed Resolutions

With undeniably enormous successes and capabilities achieved over the last few decades, there however remain some open issues in numerical methods: some are fundamental in nature and some resolvable by operational changes. Clearly the first is the most difficult one because in some instances a method can lead to an erroneous solution, which can not be corrected by operational choices a seasoned CFD practitioner often opts to invoke. In this paper, we will specifically focus on two open problems: one is the so-called “carbuncle” phenomenon and the second is about an overheating problem, here coined as “volcano” phenomenon for its physical manifestation and figurative similarity. The former is considered open because it seems perennial, appearing in nearly all CFD conferences and journals in the last two decades. The latter is, to the author’s knowledge, has not been raised as a problem previously, but it fails every method known to him. It is argued in this paper that the carbuncle problem is purely a numerical artifact because it is correctable by operational variations and it is only peculiar to certain methods. A solution to the volcano problem, however, remains elusive, an open problem still to all the upwind fluxes considered herein.
Issues on Solar-Generator Interfacing with Voltage-Fed MPP-Tracking Converters

Abstract The large-scale harvesting of solar energy is an important action to decelerate the observed climate changes and ensuring the availability of energy also in the future. Reliably operating solar-energy systems composing of solar arrays and their interfacing converters are of prime importance to maximize solar-energy harvesting. The main obstacle for the improvements in the reliability is turned out to be the lack of full understanding of the solar-generator interfacing. The paper investigates the interfacing in terms of voltage-fed maximum-power-point (MPP) tracking converters. The investigations confirm that there are two reasons for the observed problems of which the one is the violation of Kirchhoff’s current law governing the operation of a solar generator and the other is the phenomenon known as negative resistance oscillation (NRO) causing instability. In practice, this means that the voltage-fed converter can reliably operate only at the voltages higher than the maximum-power-point (MPP) voltage. Practical evidence is provided based on a coupled-inductor superbuck converter supplied by a real solar panel and loaded with a storage battery.
Designing a Groupware Implementation of a Manual Participatory Design Process

TelePICTIVE is an experimental groupware prototype based on the paper-and-pencil PICTIVE participatory design technique. This paper describes a number of design issues that arose during the implementation of a computerized version of the manual process. The major classes of issues included: (1) Transformation: How well do the manual process and low-tech object model serve as metaphors for the groupware implementation? Which new computer-supported capabilities are desirable? (2) Embodiment and Skill: What changes may result from the more formal and technological groupware implementation of a deliberately low-tech and informal manual process? We discuss these issues, strategies for resolving them, and the lessons learned in using the manuaiPICTIVE process to design the groupware TelePICTIVE prototype.
Cooperation paradigms for overcoming communication limitations in multirobot wide area coverage

Multi-robot systems are an important research topic in wide area coverage applications such as hazardous waste clean-up, bomb detection, surveillance, and search and rescue missions. They can work in parallel and complete tasks faster than a single robot. Communications can support cooperation to speed up execution, reduce duplication, and prevent interference. Communication among team members is achieved explicitly or implicitly. In explicit communication, messages are intentionally transmitted and received from robot to robot. In implicit communication, robots observe the environment and other robot actions. Although many systems use explicit communications, in exploration of large, open areas (e.g. stadiums and parks), persistent intra-team digital communications is not guaranteed. Therefore, alternative approaches that do not rely upon message passing throughout exploration are needed. Novel contributions of overcoming communication limitations in wide area coverage include: (1) insight on how information shared between robots that are close has more influence on immediate action selection than information shared between robots that are farther apart. Spatial and temporal locality can be instrumental in determining relevance in subsequent action selection; (2) an approach in which observation leverages spatial and temporal locality to infer state rather than rely on digital messaging; and (3) an approach in which robots use spatial rendezvous to exchange information instead of continuously passing messages. Robots explore an environment in sectors, or designated areas, and periodically meet to communicate map information of what they explored. Simulations and physical experiments were conducted and results suggest both approaches can serve as alternatives to cooperation based on continuous point-to-point communications.
Pseudo-Label-Free Weakly Supervised Semantic Segmentation Using Image Masking

Weakly-supervised semantic segmentation (WSSS) aims to train a semantic segmentation network using weak labels. Recent approaches generate the pseudo-label from the image-level label and then exploit it as a pixel-level supervision in the segmentation network training. A potential drawback of the conventional WSSS approaches is that the pseudo-label cannot accurately express the object regions and their classes, causing a degradation of the segmentation performance. In this paper, we propose a new WSSS technique that trains the segmentation network without relying on the pseudo-label. Key idea of the proposed approach is to train the segmentation network such that the object erased by the segmentation map is not detected by the classification network. From extensive experiments on the PASCAL VOC 2012 benchmark dataset, we demonstrate that our approach is effective in WSSS.
Kelvin Probe Microscopy Investigation of Poly-Octylthiophene Aggregates

Conductive polymers have fundamental relevance as well as novel technological applications in the organic optoelectronics field. Their photophysical and transport properties strongly depend on the molecular arrangement, and nanoscale characterization is needed to fully understand the optoelectronic processes taking place in organic devices. In this work, we study the electrostatic properties of poly-3-octylthiophene isolated structures: disordered low-packed polymer chains and crystalline layered lamellar assemblies. We characterize the electronic ground state using Kelvin probe microscopy. This allows us to resolve a rich variety of surface potential regions that emerge over the different polymer structures. These SP regions are correlated with different molecular aggregates.
Comparison of BLSTM-Attention and BLSTM-Transformer Models for Wind Speed Prediction

Accurate estimation of wind speed is essential for many meteorological applications. A novel short-term wind speed prediction method of Bi-directional LSTM and Transformer Network (BLSTM-TRA) model is proposed by combining the Transformer model and LSTM model, and a hybrid model of Bi-directional Long Short-term Memory and Attention Network (BLSTM-ATT) is proposed based on Attention mechanism and LSTM model. The proposed BLSTM-ATT and BLSTM-TRA model are used for predicting the wind speed of seven meteorological stations in Qingdao. In combination with historical ob- servation data, the proposed models outperform the Numerical Weather Prediction (NWP) system of European Centre for Medium-Range Weather Forecasts (ECMWF). By comparing the results of BLSTM-ATT, BLSTM-TRA and ECMWF forecast model, RMSE and MAE of BLSTM-ATT are reduced by 44.7% and 50.3% on average, respectively, as well as an average decrease of 43.0% in the RMSE, an average decrease of 47.4% in the MAE of the BLSTM-TRA model. This demonstrates that the BLSTM-ATT model and the BLSTM-TRA model are more accurate than the ECMWF model in wind speed prediction.
Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms.
Pharmacogene Variants Associated with Liver Transplant in a Twelve-Year Clinical Follow-Up

Some gene polymorphisms have been previously associated individually with tacrolimus efficacy and toxicity, but no long-term study to determine the role of pharmacogene variants in the clinical evolution of liver-transplanted patients has been addressed so far. In the present work, we analyzed the relation between highly-evidenced genetic polymorphisms located in relevant pharmacogenes and the risk of suffering premature death and other comorbidities such as cancer, diabetes mellitus, arterial hypertension, graft rejection, infections and nephrotoxicities in a cohort of 87 patients (8 were excluded due to early loss of follow-up) transplanted at Hospital La Fe in Valencia (Spain) during a 12-year follow-up. Employing a logistic regression model with false discovery rate penalization and Kaplan–Meier analyses, we observed significant association between survival rates and metabolizer genes. In this sense, our results show an association between MTHFR gene variants in donor rs1801133 (HR: 7.90; p-value: 0.032) and recipient rs1801131 (HR: 7.34; p-value: 0.036) and the group of patients who died during the follow-up period, supporting the interest of confirming these results with larger patient cohorts. In addition, donor polymorphisms in UGT1A9 metabolizer gene rs6714486 (OR: 0.13; p-value: 0.032) were associated with a lower risk of suffering from de novo cancer. Genetic variants in CYP2B6 metabolizer gene rs2279343 demonstrated an association with a risk of infection. Other variants in different locations of SLCO1A2, ABCC2 and ABCB1 transporter genes were associated with a lower risk of suffering from type 2 diabetes mellitus, chronic and acute nephrotoxicities and arterial hypertension. Results suggest that pharmacogenetics-derived information may be an important support for personalized drug prescription, clinical follow-up and the evolution of liver-transplanted patients.
A Design Method for Estimating the Reliability of Lead Free Solder Joints in Ball-Grid-Array-Type Packages under Impact Load

衝撃負荷に対するPbフリーはんだBGA接続部の信頼性設計技術を確立するため, 衝撃曲げ試験による種々の構造仕様での寿命評価を行い, 破壊寿命と有限要素法解析で得た接続部の界面応力を対応させることで, 界面応力を用いた寿命評価が可能であることを明らかにした。 Pbフリーはんだ接続部の破壊は, Pb共晶はんだの場合と異なり, 接続界面近傍の金属間化合物内で発生し, 寿命には, 実装温度, 基板ひずみの立上り時間およびバンプ高さが影響することを解明した。 実験条件と同等の有限要素解析を行い, はんだ接続部の界面応力を算出して, はんだ接続部の界面応力と寿命の関係を求めた。 本評価法の妥当性を検討するために, バンプ配置と接続部構造が信頼性へ及ぼす影響を本方法を用いて検討した。 その結果, 界面応力を寿命評価に用いることで, PbフリーはんだBGA接続部の定量的な信頼性設計が可能であることが明らかになった。
NovelADS: A Novel Anomaly Detection System for Intra-Vehicular Networks

Modern vehicular electronics is a complex system of multiple Electronic Control Units (ECUs) communicating to provide efficient vehicle functioning. These ECUs communicate using the well-known Controller Area Network (CAN) protocol. The increasing amount of research in the Intelligent Transportation System (ITS) domain has demonstrated that this protocol is vulnerable to various types of security attacks, compromising the safety of passengers and pedestrians on the roads. Hence, there is a need to develop novel anomaly detection systems to address this problem. This work presents a novel deep learning-based Intrusion Detection System incorporating thresholding and error reconstruction approaches. We train and explore multiple neural network architectures and compare their performance. The proposed anomaly detection system is tested on four kinds of attacks - Denial of Service (DoS), Fuzzy, RPM Spoofing and Gear Spoofing using evaluation metrics such as Precision, Recall and F1-Score. We also present reconstruction-error distribution plots to give a qualitative intuition about the proposed system’s ability to distinguish between genuine and anomalous sequences.
The Combined Effect of Nanobubble-IR783-HPPH-Affibody Complex and Laser on HER2-Positive Breast Cancer

Introduction Nanobubble is an innovative ultrasound contrast agent that triggers the development of targeted imaging of HER2-positive breast cancer by combining with HER2 affibody and IR783. HPPH is a second-generation photosensitiser that is effective in treating tumours. Hence, the nanobubble-IR783-HPPH-affibody (NIHA) complex demonstrates considerable potential in the treatment of HER2-positive breast cancer. Methods We fabricated the NIHA complex via an advanced thin-film hydration method and detected its characteristics such as particle size, morphology, stability, and cytotoxicity. Moreover, the effect of NIHA complex with laser on HER2-positive breast cancer was confirmed via in vitro and in vivo experiments. Results The NIHA complex was spheroid, stable and exhibited no cytotoxicity; moreover, its particle size was 524.8 ± 53.3 nm (n = 5). In combination with laser treatment, NIHA complex reduced the cell viability and tumour volume, induced apoptosis of HER2-positive breast cancer cells, and prolonged survival of nude mice. Conclusion The newly prepared NIHA complex with laser treatment has the potential on treating HER2-positive breast cancer.
What You Like, What I Am: Online Dating Recommendation via Matching Individual Preferences With Features

Dating recommendation becomes a critical task since the rapidly development of the online dating sites and it is beneficial for users to find their ideal relationships from a large number of registered members. Different users usually have different tastes when choosing their dating partners. Therefore, it is necessary to distinguish the user’s personal features and preferences in dating recommendation methods. However, present approaches don’t capture enough user preferences from social graph and attribute data. They also ignore user attributes, which is the complementary and consistent side information of user social graphs. In this paper, we propose a Matching Individual Preferences with Features (MIPF) model to recommend dating partners jointly using user attributes and social graphs. Our specific design in the model is that user preferences and user features are completely different. User features are users’ personal characteristics, while user preferences indicate which kind of other users they are seeking. We aim to model user features and preferences to identify what the user is and what the user likes. We also distinguish user preferences into explicit preference and implicit preferences. The implicit preferences are mined from social graphs, while the explicit preferences are captured from the social links. Additionally, convolutional neural networks are used to extract the latent non-linear information in user attributes. Experiments on real-world online dating datasets demonstrate our MIPF model is superior to existing methods.
Analysis of Orbital Parameters for live Tracking of Nanosatellite

Over several years satellite technologies are used to serve to ever- increasing demands of wireless communication in terms of volume of traffic and quality of communication services. Satellite has many subsystems; few of them are Attitude and orbit control subsystem, Thermal control. Tracking, telemetry, command and monitoring subsystem, Power subsystem, Communication subsystems, Antennas and Bus subsystem, Ground Station subsystems, Space Qualification subsystem and so on. Long Range communication has also started to contribute to applications in the space sector. We propose a concept that allows continuous monitoring of remote application in nano-satellite constellations by implementing an analogue of an existing open platform network called LoRa Wide Area Network in the constellations making their own LPWAN (Low Power Wide Area Network). Such a network among satellites allows the tracking and monitoring of IoT data over the satellite network. The role of Machine-to-Machine and Internet of market applications in satellite technology, which is now having annual revenue of $1.5 billion, is expected to reach $5.8 million in-service satellite by 2023. The Main performance parameters of IoT-Satellite consortium for value propositions are going to be Lowest Cost, Lowest Energy, Global and Secure availability, Dedicated Networks, Speed, Reliability and System Continuing Integration. The satellite-based applications leading the market in space sector and provides the opportunities for increase in revenue in space sector.
A Cognitive Human Model for Virtual Commissioning of Dynamic Human-Robot Teams

Virtual commissioning can be used for planning human-robot workflows. To this end, digital human models enable consideration of human ergonomics and motion times as optimization criteria. This leads to optimal but fixed human-robot schedules with clearly quantified benefits over the manual process. By contrast, recent research on robot co-workers has been putting emphasis on dynamic task sharing. Respective methods enable robots to make decisions recurrently and adapt to unforeseen situations. Measuring the gain in productivity when deploying such systems to the shop floor while leveraging the advantages of virtual commissioning requires replicating the indeterminism of human behaviour in simulation. We, hence, contribute a cognitive human model that captures aspects of dynamic human decision making relevant to everyday work. Our model covers phases of working, pausing, fatigue, and error using Markov Decision Processes with dynamic transition probabilities. Experiments demonstrate the scalability of generated behaviour with regard to different human characteristics.