Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Computing-Complexity Touch Signal Detection Method and Analog Front-End Circuits Based on Cross-Correlation Technology for Large-Size Touch Panel

This paper proposes a low-computing-complexity touch signal detection method and analog front-end (AFE) circuits based on cross-correlation technology for large mutual capacitance touch screen panels (TSPs). To solve the traditional touch signal detection method problem of lots of invalid data being sampled and processed in a large-size TSP, the proposed method only samples and processes the signals around the touch points rather than full-screen data to decrease the computing complexity and analog–digital convertor (ADC) acquisition number. Compared with the traditional method, the proposed touch points search algorithm complexity decreases from MN to M + nN where M, N, and n are the number of RX channels, TX channels, and touch points, respectively. The maximum ADC acquisition number of the proposed method decreases from MN to 18n. Based on the proposed touch signal detection method, the AFE circuits are designed by a 0.11 μm process. The proposed dual cross-correlation AFE achieves detection of the weak touch signal submerged in the large display panel noise. The average channel area and power consumption are decreased to 0.015 mm2 and 0.227 mW, respectively. The maximum frame rate is 384.6 Hz with 10 touch points. The proposed cross-correlation AFE achieves a high frame rate while reducing the die area and power consumption.
Efficient Application of AI for Target Tracking and Monitoring in Airborne Images:

With evolution of computing technology, its capability of solving complex problems faced in real life scenario have certainly escalated. One such problem of extraction and counting of building footprints using aerial images obtained from drone is looked upon in this paper.A typical image obtained from drone has many characteristic features like the spatial resolution i.e. the area that a single pixel of the image covers, pixel resolution and many more, and provides users with a 3 channel(RGB) image of a certain region which helps in assessing the landscape distribution, vegetation cover urbanization and monitoring the area. With so many efficient and intriguing applications, drone images have seen a definite upsurge in recent years. However, all such applications are painstakingly tedious and certainly require automation. Researchers are constantly working towards solving this problem and providing an optimal solution economically and computationally feasible.This paper proposes to solve this problem by carrying out basic image processing steps enhanced using Deep Learning by efficiently harnessing the statistical features of image and applying adaptive thresholding to semantically segment and count building footprints, thus deriving a holistic algorithm that is capable of handling image with different spatial or pixel resolution.
TRISHNA: An Indo-French Space Mission to Study the Thermography of the Earth at Fine Spatio-Temporal Resolution

TRISHNA (Thermal infraRed Imaging Satellite for High-Resolution Natural resource Assessment) is a cross-purpose high spatial and temporal resolution thermal infrared Earth Observation (EO) mission that will provide observations in the domains of terrestrial and marine ecosystems, inland and coast, urban, cryosphere, solid Earth and atmosphere. It is an Indo-French innovative polar-orbiting mission that will overcome the limitations of TIR-optical observations from Landsat series and ASTER. The high-quality optical-thermal imagery will be used to provide precise surface temperature, emissivity and albedo of vegetation, manmade structures, snow, ice and sea. Atmospheric fields such as cloud mask and type, aerosol load, water vapour content will be well described. TRISHNA products will improve our knowledge of radiative and heat transfer to quantify evapotranspiration, fresh water discharge, snow-melt runoff, bio-geochemical cycle, urban heat island.
SIMULATION OF THE EFFECTIVENESS OF THE SCOURING PREVENTION STRUCTURE AT THE EXTERNAL RAIL BALLAST USING PHYSICAL MODEL

Rail ballast is a rockfill construction that serves to bind sleepers and rail which distributes trainload to ground. The function of rail ballast is vital to the quality and strength of the rail track, but are prone to overtopping. The relationship between overtopping high and scouring has been investigated, as well as the effect of the high external prevention structure against the length of scouring. This study focuses on the effectiveness of external prevention structure at downstream in its ability to dampen and reduce the length of scouring due to overtopping by the mean the physical model. This research simulates the scouring prevention structure at downstream of ballast with several variations of type (T) and structures distance (L) so that the most effective structure is obtained, i.e. the structure which can maximum reduce the length of scouring. From the research, the most effective structure is the Type T1 at L1 distance. The modeling was carried out at the Hydraulic Laboratory of Civil Engineering Diponegoro University, with the prototype of the Mangkang-Semarang Railway KM.12 Indonesia, with the scale of 1: 5. The structure types which are simulated consist of T, T1 and T2 with the variation of structure distance L1 = 40 cm and L2 = 60 cm from downstream of toe ballast. The results of this study can be utilized by railway operator as a safety measure and especially against the danger of scouring due to flooding.
TVENet: Transformer-based Visual Exploration Network for Mobile Robot in Unseen Environment

This paper presents a Transformer-based Visual Exploration Network (TVENet) that capably serves as a solution for active perception problems, especially the visual exploration problem: How could a robot that is equipped with a camera explore an unknown 3D environment? The TVENet consists of a Mapper, a Global Policy and a Local Policy. The mapper is trained by supervised learning to take the visual observation as input and generate an occupancy grid map for the explored environment. The Global Policy and the Local Policy are trained by reinforcement learning in order to make navigation decision. Most state-of-the-art methods in visual exploration domain use ResNet as feature extractor, and few of them pay attention to the extraction capability of the extractor. Therefore, this paper focuses on enhancing the extraction capability, and proposes a Transformer-based Feature Pyramid Module (TFPM). Moreover, two tricks for training process are introduced to improve the performance. Our experiments in photo-realistic simulated environment (Habitat) demonstrate the higher-accuracy mapping of TVENet. Experimental results prove that the TFPM and tricks have positive impacts on the mapping accuracy of the visual exploration and increase it by 5.31% compared with the state-of-the-art. Most importantly, the TVENet is deployed on a real robot (NVIDIA Jetbot) to prove the feasibility of Embodied AI approaches. To the authors’ knowledge, this paper is the first one that proves the viability of the Embodied AI style approach for visual exploration tasks and deploys the pre-trained model on the NVIDIA Jetson robot.
Survey of Pharmacological Activity and Pharmacokinetics of Selected β-Adrenergic Blockers in Regard to Their Stereochemistry

The present survey concentrates on pharmacodynamics and pharmacokinetics of selected β-adrenergic blockers from the point of view of their stereochemistry. It could be shown that the activity in the arylaminoethanol and aryloxyaminopropanol group of β-blockers is higher in their (–)-enantiomers as compared with the (+)-enantiomers. The stereoisomers differ also in other types of bioactivity as well as in toxicity. The particular pharmacokinetic stages such as resorption, distribution, and metabolism are discussed in regard to their stereochemistry.
Synchronized switch technique based on dynamical state of bi-stable energy harvesters

Vibration energy harvesters have been proposed as an autonomous power source for meeting the limited power requirements of present-day sensors and electronics that find extensive usage in structural health monitoring systems. Recent research reveals that nonlinear energy harvesters outperform their linear counterparts, designed to operate on the principle of resonance, owing to their wide frequency bandwidth which allows for better performance in realistic operational environments. Particularly, bi-stable energy harvesters designed to exploit piezoelectricity to achieve the mechanical to electrical energy conversion have been widely investigated in literature. Additionally, several investigations have been also proposed to enhance power conversion in linear harvesters by introducing nonlinear circuits, e.g. based on synchronized switching (SS). In this respect, unveiling the effects on the bandwidth and coexisting solutions in the response of strongly nonlinear electrical SS shunts interacting with multi-stable structures requires further investigation. In particular, synchronized switch harvesting on inductor (SSHI) circuits, when connected in parallel with bi-stable energy harvesters, facilitate an increase in the harvested voltage, thus allowing for higher power generation as compared to a standard shunt load. This paper investigates the effects of utilizing a SSHI circuit for enhancing the power output in the different dynamical regimes of a bi-stable energy harvester. In particular, we present a comprehensive study of the efficiency of the SSHI circuit when the response configuration of the system is shifted between coexisting dynamical states of the bi-stable harvester. The herein presented semi-analytical and numerical results show that the qualitative performance of the SSHI circuit is quite robust in terms of superiority over the standard load circuit. However, the quantitative nature of the increase in power harvested by the SSHI circuit is sensitive to the optimal load in the circuit, which in turn varies with the dynamical state of the system.
ReSNN-DCT: Methodology for Reduction of the Spiking Neural Network using Discrete Cosine Transform and Elegant Pairing

In recent years, the use of artificial neural network applications to perform object classification and event prediction has increased, mainly from research about deep learning techniques running on hardware such as GPU and FPGA. The interest in the use of neural networks extends to embedded systems, due to the development of applications in smart mobile devices, such as cell phones, drones, autonomous cars and industrial robots. But when it comes to embedded systems, it is necessary to observe the hardware limitations, such as memory, scalability and power consumption, which significantly impact the processing of a neural network. In this article, a methodology is proposed that allows to reduce a spiking neural network, applying the discrete cosine transform (DCT) and elegant pairing, contributing to the scalability of the neural network layers in hardware. The results demonstrate the effectiveness of the methodology, showing the feasibility of reducing synapses and neurons, while maintaining the correctness of the spiking neural network response.
Effects of Lactobacillus plantarum P9 Probiotics on Defecation and Quality of Life of Individuals with Chronic Constipation: Protocol for a Randomized, Double-Blind, Placebo-Controlled Clinical Trial

Background Although probiotics have been shown to improve constipation-related symptoms, a clear consensus on the use of probiotics as a constipation-relieving agent has not been reached, which is attributed to the limited available evidence and inconsistent protocols used in existing studies. Method A randomized, double-blind, placebo-controlled clinical trial is designed to study the efficiency and possible mechanism of action of probiotics for chronic constipation, in which 200 eligible volunteers with chronic constipation will be randomly assigned to a probiotic group (oral Lactobacillus plantarum P9 probiotic powder, 100 billion colony-forming units (CFUs)/day) or a placebo group. Volunteers, treatment distributors, data collectors, and data analysts will be blinded. The primary outcome is the weekly mean frequency of complete spontaneous bowel movements (CSBMs), and secondary outcomes include weekly mean frequency of CSBMs ≥3, weekly mean frequency of spontaneous bowel movements (SBMs), weekly mean stool appearance score, weekly mean difficulty of passing stool score, weekly percentage of volunteers who use auxiliary measures to assist with defecation (WPUAMA), quality-of-life (QOL) score, emotional status score, gut microbiome, and faecal metabolome. Each outcome measure will be assessed at the time points of preadministration (day 0), administration (day 14 and/or 28), and postadministration (day 42) to identify inter- and intragroup differences. Adverse events will be recorded to evaluate the safety of L. plantarum P9. Discussion. The protocol will provide methodological guidance for other similar studies, avoiding methodological bias and ultimately facilitating the formulation of consensus on the use of probiotics as a constipation-relieving agent. In addition, the results are more comprehensive than those of existing studies and may objectively and scientifically reflect the effectiveness of L. plantarum P9 on constipation. If the expected study findings are obtained, L. plantarum P9, taken as a probiotic, may become a complementary choice for chronically constipated patients. This trial is registered with Chinese Clinical Trial Registry (ChiCTR) (no. ChiCTR2000038396) registered on November 22, 2020, https://www.chictr. org.cn/showproj.aspx? proj=54024.
A Multi-Phase Research Program Investigating Project Management Offices (PMOS): The Results of Phase 1

Over the last decade, the project management office (PMO) has become a prominent feature in many organizations. Despite the proliferation of PMOs in practice, our understanding of this phenomenon remains sketchy at best. No consensus exists as to the way PMOs are or should be structured nor as to the functions they should or do fill in organizations. In addition, there is no agreement as to the value of PMOs. Despite the importance of this phenomena and the lack of understanding, there has been very little research on this topic. A three-phase research program has been undertaken in order to develop a better understand of PMOs. This paper presents the research strategy, the overall program, and the results of the first phase of the research.
Green Synthesis of Silver Nanoparticles Using Aqueous Citrus limon Zest Extract: Characterization and Evaluation of Their Antioxidant and Antimicrobial Properties

The current work concentrated on the green synthesis of silver nanoparticles (AgNPs) through the use of aqueous Citrus limon zest extract, optimizing the different experimental factors required for the formation and stability of AgNPs. The preparation of nanoparticles was confirmed by the observation of the color change of the mixture of silver nitrate, after the addition of the plant extract, from yellow to a reddish-brown colloidal suspension and was established by detecting the surface plasmon resonance band at 535.5 nm, utilizing UV-Visible analysis. The optimum conditions were found to be 1 mM of silver nitrate concentration, a 1:9 ratio extract of the mixture, and a 4 h incubation period. Fourier transform infrared spectroscopy spectrum indicated that the phytochemicals compounds present in Citrus limon zest extract had a fundamental effect on the production of AgNPs as a bio-reducing agent. The morphology, size, and elemental composition of AgNPs were investigated by zeta potential (ZP), dynamic light scattering (DLS), SEM, EDX, X-ray diffraction (XRD), and transmission electron microscopy (TEM) analysis, which showed crystalline spherical silver nanoparticles. In addition, the antimicrobial and antioxidant properties of this bioactive silver nanoparticle were also investigated. The AgNPs showed excellent antibacterial activity against one Gram-negative pathogens bacteria, Escherichia coli, and one Gram-positive bacteria, Staphylococcus aureus, as well as antifungal activity against Candida albicans. The obtained results indicate that the antioxidant activity of this nanoparticle is significant. This bioactive silver nanoparticle can be used in biomedical and pharmacological fields.
Mechanistic Approach of Nano Carriers for Targeted in Cancer Chemotherapy: A Newer Strategy for Novel Drug Delivery System

The application of nanomedicine represents an innovative approach for the treatment in the modern field of cancer chemotherapy. In the present research work, tamoxifen citrate loaded nanolipid vesicles were prepared conjugated with phosphoethanolamine as the linker molecule, and the specific antibody was tagged with the linker molecule on the bilayer surface of the vesicles. The main objective of this study is to determine the efficacy and biological behavior of antibody conjugated nanoliposome in breast cancer cell lines. Percentage of drug loading and loading efficiency was done and their results were compared to theoretical drug loading. The average diameter of those vesicles was within the 100 nm range, which is revealed in FESEM and TEM images and their lamellarity was observed in cryo-TEM images. The hydrodynamic diameter was done by particle size analysis and the surface charge was determined by the zeta potential parameter. Predominant cellular uptake was observed for antibody conjugated nanolipid vesicles in MCF-7 and MDA-MB-453 human breast cancer cell lines. A cellular apoptosis assay was conducted by flow cytometer (FACS). All experimental data would be more beneficial for the treatment of breast cancer chemotherapy. Further studies are warranted to investigate the efficacy and safety of antibody conjugated nanolipid vesicles in vivo for breast cancer animal model.
Compact unequal filtering power divider operating in two bands with controllable characteristic impedances

A design method for unequal filtering power dividers (PDs) working at 2 bands is presented in this article. Compared with conventional unequal PDs, in this method, each section of the proposed circuit uses transmission lines whose characteristic impedances are controllable to avoid high‐impedance line. Meanwhile, the conventional λ/4 transmission line in PDs is substituted with a pair of proper dual‐mode resonators. To prove the effectiveness of the proposed design principle, a miniaturized unequal filtering PD working at 3.5 and 4.9 GHz is designed, fabricated and measured. Eventually, it is clear that the simulated results and the measurements have favorable agreement.
Changes of the Freshwater Microbial Community Structure and Assembly Processes during Different Sample Storage Conditions

A long-standing dilemma for microbial analyses is how to handle and store samples, as it is widely assumed that the microbial diversity and community patterns would be affected by sample storage conditions. However, it is quite challenging to maintain consistency in field sampling, especially for water sample collection and storage. To obtain a comprehensive understanding of how sample storage conditions impact microbial community analyses and the magnitude of the potential storage effects, freshwater samples were collected and stored in bottles with lid closed and without lid at room temperature for up to 6 days. We revealed the dynamics of prokaryotic and eukaryotic microbial communities under different storage conditions over time. The eukaryotic microbial communities changed at a faster rate than the prokaryotic microbial communities during storage. The alpha diversity of the eukaryotic microbial communities was not substantially influenced by container status or storage time for up to 12 h, but the beta diversity differed significantly between the control and all treatment samples. By contrast, no significant changes of either the alpha or beta diversity of the prokaryotic microbial communities were observed within 12 h of room-temperature storage, regardless of the container status. The potential interactions between microbial taxa were more complex when samples were stored in sealed bottles, and the deterministic processes played an increasingly important role in shaping the freshwater microbial communities with storage time. Our results suggest that water samples collected and stored without refrigeration for no more than 12 h may still be useful for downstream analyses of prokaryotic microbial communities. If the eukaryotic microbial communities are desired, storage of water samples should be limited to 3 h at room temperature.
Model to cope with Requirements Engineering Issues for Software Development Outsourcing

The anticipated benefits of Software Development Outsourcing (SDO) are not achieved in case of several projects because of the issues that emanate from Requirements Engineering (RE) process. This research work presents a Requirements Engineering Practices (REP) model to cope with the customarily occurring issues of the RE process for SDO. To formulate the model, five workshops have been conducted and Root Cause Analysis has been performed by considering 43 commonly occurring SDO RE process issues, and 147 RE practices to tackle the issues. To discover the root causes for commonly transpiring issues, 5- Whys technique has been employed. The relevant RE practices that can be used to deal with the root causes, have been endorsed by applying Brainstorming technique. For the 43 frequently occurring issues, 89 root causes have been discovered. Afterwards, 124 relevant RE practices have been recommended to eradicate the root causes and hence to address the corresponding issues. Thus, REP model postulates the root causes for commonly occurring issues of the SDO RE process, maps the root causes to the relevant best RE practices to address the corresponding issues. The model has been evaluated by an expert panel and evaluation results have been analysed through Inter-Rater Reliability analysis and Analysis of Means. The REP model supports the RE process for SDO by i). evading the adoption of random and inappropriate RE practices for dealing with the common issues of the process, ii) helping to attain the expected benefits of SDO.
Grey Relation Analysis Methodology and its Application

Article History Published Online: 10 February 2019 Grey Relation analysis was developed by Deng and most widely used model of Grey system. Model is applied between known and unknown information, which is grey in nature. Condition with clearly defined information is named as white and no information as black, in between as Grey. The grey theory is applied in various filed including manufacturing, process and service operations. The theory is also applied in selection process which involves multiple selection attributes. The paper focuses on fundamentals of method, steps required to follow and various applications of method. This paper will be of assistance to the researchers and industries to understand basics of theory and to know its application in real life problems.
A Simplified Theoretical Understanding of Price Discrimination as a Business Management Strategy

The paper delves into the five different types of price discrimination while giving graphical and illustrative examples of Pigou (1920) price classifications, marketing strategies, and more. The paper shows the differences and similarities between all price discriminations during transactions from first degree, second degree, third degree, fourth degree, and fifth-degree pricing [discrimination] strategies while graphically showing the ratio to marginal cost.