Miracle-dz/UniFAR-FTUs huggingface dataset:
MICROSTRIP LOW PASS FILTER DESIGNS USING DEFECTED GROUND STRUCTURE

The microwave filters play an important role in most RF/microwave applications. They are designed to remove undesired harmonics to reduce the system noise or to remove spurious mixing products. DGS slot with an interdigital shape are introduced here in two elliptic low pass filter designs. Here a fifth order low pass filter was designed, simulated and fabricated for a cut off frequency of 3GHz. Finally a prototype model were designed based on the simulation results obtained. These prototype filter designs have more than 20 dB of stop band rejection and a good return loss in the pass band. The fabricated results proved to be better than the simulation results. In the first low pas filter design the central aperture was replaced with the interdigital slot structure which improved the stop band response at the resonant frequency around 7.8 GHz. To improve the stop band rejection two interdigital structures were introduced in the bottom layer (Ground).
A 24–29.5-GHz Highly Linear Phased-Array Transceiver Front-End in 65-nm CMOS Supporting 800-MHz 64-QAM and 400-MHz 256-QAM for 5G New Radio

This article presents a four-element phased-array transceiver (TRX) front-end for millimeter-wave (mm-Wave) 5G new radio (NR). The effects of amplitude-to-phase (AM–PM) and amplitude-to-amplitude (AM–AM) distortions on error vector magnitude (EVM) are detailed. A three-stage highly linear wideband class-AB power amplifier (PA) is described, mitigating AM–PM and AM–AM distortions without degrading other performances. A matching network embedded transmitting/receiving (T/R) switch is proposed to support time division duplex (TDD). With the proposed technique, RF switch insertion losses are reduced in both transmitter (TX) and receiving (RX) modes. In each TRX element, phase and gain controls are achieved by a 6-bit vector-modulated phase shifter (VMPS) with a phase tuning range of 360° and a 6-bit attenuator with a gain tuning range of 31.5 dB, respectively. Fabricated in 65-nm bulk CMOS, the proposed packaged TRX demonstrates the measured peak gains of 25.5/14.2 dB with the gain ripples of less than 2.5/1.9 dB across 24–29.5 GHz in the TX/RX mode. The measured peak $P_{\text {1 dB}}$ is 17.6 dBm with a power added efficiency (PAE) of 20.4%. The measured minimum noise figure (NF) is 4.3 dB. The TRX achieves an output power of 7.9–8.9 dBm and an EVM of −25 dB with 8 $\times $ 100-MHz 5G NR frequency range 2 (FR2) orthogonal frequency division multiplexing (OFDM) 64-quadrature amplitude modulation (QAM) signals across 24–29.5 GHz, covering 3rd generation partnership project (3GPP) 5G NR FR2 operating bands (i.e., n257, n258, and n261) around 26 GHz.
High efficiency Tm3+-doped fiber seed source of optimum fiber length

A high efficiency Tm3+-doped fiber seed source system has demonstrated. The pumping laser and output laser in the optical fiber distribution were analyzed. High performance output laser would be obtained by reasonable design of laser parameters, selecting the optimum length of gain fiber, and increasing injection power. Tm3+-doping concentration of 7.5×1024 m-3 and the optimum length of gain fiber of 2 m were used in the experiments. The continuous wave laser with output power of 5.01 W and the beam quality of M2x = 1.35, M2y = 1.51 was obtained by fiber coil diameters of 10 cm and injection power of 17.61 W. A slope efficiency and conversion efficiency were up to 36.2% and 28.4%, respectively. The fiber length of 1 m, 1.5 m, 2 m, 2.5 m, 3 m were chosen in the experiments, and the effects of different fiber lengths on the output characteristic were researched. The laser loss was reduced, and the conversion efficiency and beam quality were improved effectively by selecting the length of gain fiber and Tm3+-doping concentration reasonably. Theoretical analysis and experimental results show that a higher output power can be achieved by increasing the injection power. It provides a strong experimental foundation for further researching on high power Tm3+-doped pulse fiber amplifier by using this Tm3+-doped fiber laser as a seed source system.
Human Biomonitoring Guidance Values (HBM-GVs) for Bisphenol S and Assessment of the Risk Due to the Exposure to Bisphenols A and S, in Europe

Within the European Joint Programme HBM4EU, Human Biomonitoring Guidance Values (HBM-GVs) were derived for several prioritised substances. In this paper, the derivation of HBM-GVs for the general population (HBM-GVGenPop) and workers (HBM-GVworker) referring to bisphenol S (BPS) is presented. For the general population, this resulted in an estimation of the total urinary concentration of BPS of 1.0 µg/L assuming a 24 h continuous exposure to BPS. For workers, the modelling was refined in order to reflect continuous exposure during the working day, leading to a total urinary concentration of BPS of 3.0 µg/L. The usefulness for risk assessment of the HBM-GVs derived for BPS and bisphenol A (BPA) is illustrated. Risk Characterisation Ratios (RCRs) were calculated leading to a clear difference between risk assessments performed for both bisphenols, with a very low RCR regarding exposure to BPA, contrary to that obtained for BPS. This may be due to the endocrine mediated endpoints selected to derive the HBM-GVs for BPS, whereas the values calculated for BPA are based on the temporary Tolerable Daily Intake (t-TDI) from EFSA set in 2015. A comparison with the revised TDI recently opened for comments by EFSA is also discussed. Regarding the occupational field, results indicate that the risk from occupational exposure to both bisphenols cannot be disregarded.
Preliminary Assessment of the Population Status and Diurnal Activity Pattern of Common Bushbuck (Tragelaphus scriptus decula Rüppell, 1835) in Maze National Park, Ethiopia

The common bushbuck (Tragelaphus scriptus decula) is a widely distributed antelope species in Africa that utilises a variety of habitat types. A study was conducted on the population status and diurnal activity pattern of T. s. decula in Maze National Park, Ethiopia, from January to July 2020 during the wet and dry seasons. Data were collected using randomly sampled blocks covering a total area of 34 km2. The data were analysed using descriptive statistics and compared with Chi-square and one-way ANOVA tests. The average population size throughout both the wet and the dry season was 21 ± 8, of which 10.5 ± 3.5, 4.0 ± 2.0 and 6.5 ± 2.5 were adults, subadults and juveniles. There was a significant difference ( χ2 = 3.4, df = 4, p < 0.05) in the T. s. decula population between the two seasons. The sex ratio of adult males to adult females was 0.56:1 and 0.75:1 in the dry and the wet seasons, respectively, indicating a population predominated by females. The mean group size was x̄ = 2.16 and 2.00 for the dry and wet seasons, respectively. The riverine forest had the greatest number of individuals and hence highest density and population distribution per square kilometre; however, distribution between the habitats did not vary significantly (F = 2.71, df = 4, p = 0.16). The overall density of the population was very low in the sampled blocks, with the population density not exceeding one per 1.7 km2. The dominant activity pattern of T. s. decula was feeding followed by resting and running/fighting. Even though its population was predominantly female, a good indicator for future breeding success, the low numbers and density of common T. s. decula due to the high impact of illegal hunting and predation, especially of subadults, indicates a vulnerable population requiring enhanced conservation efforts.
The Complete Mitochondrial Genome of Stichopus naso (Aspidochirotida: Stichopodidae: Stichopus) and Its Phylogenetic Position

The mitochondrial genome is widely used to study the molecular evolution of and perform phylogenetic analyses on animals. In this study, the complete mitochondrial genome (mitogenome) of Stichopus naso was sequenced. The mitogenome was 16,239 bp in length and contained 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), and 2 ribosomal RNA genes (rRNAs). The genome composition showed positive AT-skew (0.023) and negative GC-skew (−0.158). The order of the mitochondrial genes was consistent with those from the Stichopus and Isostichopus species, whereas it was different from those of other species of Aspidochirotida. The phylogenetic analysis, based on the nucleotide sequences of 13 PCGs through the methods of Bayesian inference (BI) and maximum likelihood (ML), indicated that S. naso has close relationships with S. horrens and S. monotuberculatus, and belongs to a member of Stichopodidae. Our study provides a reference mitogenome for further molecular evolution studies and phylogenetic research on sea cucumbers.
Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

In multidisciplinary engineering (MDE) projects, for example, automation systems or manufacturing systems, stakeholders from various disciplines, for example, electrics, mechanics and software, have to collaborate. In industry practice, engineers apply individual and highly specialized tools with strong limitation regarding defect detection in early engineering phases. Experts typically execute reviews with limited tool support which make engineering projects defective and risky. Semantic Web Technologies (SWTs) can help to bridge the gap between heterogeneous sources as foundation for efficient and effective defect detection. Main questions focus on (a) how to bridge gaps between loosely coupled tools and incompatible data models and (b) how SWTs can help to support efficient and effective defect detection in context of engineering process improvement. This chapter describes success-critical requirements for defect detection in MDE and shows how SWTs can provide the foundation for early and efficient defect detection with an adapted review approach. The proposed defect detection framework (DDF) suggests different levels of SWT contributions as a roadmap for engineering process improvement. Two selected industry-related real-life cases show different levels of SWT involvement. Although SWTs have been successfully applied in real-life use cases, SWT applications can be risky if applied without good understanding of success factors and limitations. of selected industrial cases and prototype implementations with respect to SWT contributions and defect detection in MDE environments? In the context of the CDL-Flex 8 research laboratory, researchers and industry collaborators developed application scenarios and real-life cases that focus on engineering process improvement and defect detection in industry contexts. Selected industry prototypes are presented for every level of the DDF including discussions on the benefits and limitations in context of requirements and
A Method for Modeling and Prediction of Ground Vehicle Dynamics and Stability in Autonomous Systems

A future limitation of autonomous ground vehicle technology is the inability of current algorithmic techniques to successfully predict the allowable dynamic operating ranges of unmanned ground vehicles. A further difficulty presented by real vehicles is that the payloads may and probably will change with unpredictably time as will the terrain on which it is expected to operate. To address this limitation, a methodology has been developed to generate real-time estimations of a vehicle’s Instantaneous Maneuvering Manifold. This approach uses force-moment method techniques to create an adaptive, parameterized vehicle model. A technique is developed for estimation of vehicle load state using internal sensors combined with low-magnitude maneuvers. An unscented Kalman filter based estimator is then used to estimate tire forces for use in determining the ground/tire coefficient of friction. Probabilistic techniques are then combined with a combined-slip pneumatic trail based estimator to estimate the coefficient of friction in real-time. This data is then combined to map out the instantaneous maneuvering manifold while applying techniques to account for dynamic rollover and stability limitations. The algorithms are implemented in MATLAB, simulated against TruckSim models, and results are shown to demonstrate the validity of the techniques. The developed methodology is shown to be a novel approach that is capable of addressing the problem of successfully estimating the available maneuvering manifold for autonomous ground vehicles.
Magnetite Nanoparticles Functionalized with Therapeutic Agents for Enhanced ENT Antimicrobial Properties

In the context of inefficient antibiotics, antibacterial alternatives are urgently needed to stop the increasing resistance rates in pathogens. This study reports the fabrication and characterization of four promising magnetite-based antibiotic delivery systems for ENT (ear, nose and throat) applications. Magnetite nanoparticles were functionalized with streptomycin and neomycin and some were entrapped in polymeric spheres. The obtained nanomaterials are stable, with spherical morphology, their size ranging from ~2.8 to ~4.7 nm for antibiotic-coated magnetite nanoparticles, and from submicron sizes up to several microns for polymer-coated magnetite–antibiotic composites. Cell viability and antimicrobial tests demonstrated their biocompatibility on human diploid cells and their antibacterial effect against Gram-negative (Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) opportunistic bacteria. The presence of the polymeric coat proved an enhancement in biocompatibility and a slight reduction in the antimicrobial efficiency of the spheres. Our results support the idea that functional NPs and polymeric microsystems containing functional NPs could be tailored to achieve more biocompatibility or more antimicrobial effect, depending on the bioactive compounds they incorporate and their intended application.
Analyzing IoT Attack Feature Association with Threat Actors

Internet of Things (IoT) refers to the interconnection via the Internet of computing devices embedded in everyday objects, enabling them to send and receive data. These devices can be controlled remotely, which makes them susceptible to exploitation or even takeover by an attacker. The lack of security features on many IoT devices makes them easy to access confidential information, issue commands from a distance, or even use the compromised device as part of a DDoS attack against another network. Feature selection is an important part of problem formulation in machine learning. To overcome the above problems, this paper proposes a novel feature selection framework RFS for IoT attack detection using machine learning (ML) techniques. The RFS is based on the concept of effective feature selection and consists of three main stages: feature selection, modeling, and attacks detection. For feature selection, three different models are proposed. Based on these approaches, three different algorithms are proposed. A set of 40 features was included in the model, derived from combinatorial optimization and statistical analysis methods. Our experimental study shows that the proposed frame work significantly improves over state-of-the-art cyberattacks techniques for time series data with outliers.
Modelling of Dependent Competing Risks and Semi-Competing Risks by Means of First Passage Times of Gamma Processes

In this thesis we model both dependent competing risks and semi-competing risks by means of first passage times in a gamma process. In both cases, we consider a terminal event, such as death of a person or component failure, and a non-terminal event like for instance disease recurrence or preventive maintenance of a component. We let the time to the terminal event equal the first passage time to a fixed level c in a gamma process. The time to the non-terminal event is represented by the first passage time to a stochastic level S. We have assumed that S is independent of the gamma process so that we have random signs censoring. In the competing risks case, a similar model based on Wiener processes has been considered before. For semi-competing risks this is a new modelling approach, as semi-competing risks data have mostly been analysed through copula models in the past. We conduct simulation studies that show that the parameters in the gamma process model can be estimated satisfactorily for both competing and semicompeting risks data. The model is also applied to real datasets and seems to be able to fit the data well, at least for certain chosen distributions of the random level S. It is particularly interesting to note that our results for semi-competing risks are consistent with earlier published results.
Modeling and Fault Detection of Brushless Direct Current Motor by Deep Learning Sensor Data Fusion

Only with new sensor concepts in a network, which go far beyond what the current state-of-the-art can offer, can current and future requirements for flexibility, safety, and security be met. The combination of data from many sensors allows a richer representation of the observed phenomenon, e.g., system degradation, which can facilitate analysis and decision-making processes. This work addresses the topic of predictive maintenance by exploiting sensor data fusion and artificial intelligence-based analysis. With a dataset such as vibration and sound from sensors, we focus on studying paradigms that orchestrate the most optimal combination of sensors with deep learning sensor fusion algorithms to enable predictive maintenance. In our experimental setup, we used raw data obtained from two sensors, a microphone, and an accelerometer installed on a brushless direct current (BLDC) motor. The data from each sensor were processed individually and, in a second step, merged to create a solid base for analysis. To diagnose BLDC motor faults, this work proposes to use data-level sensor fusion with deep learning methods such as deep convolutional neural networks (DCNNs) for their ability to automatically extract relevant information from the input data, the long short-term memory method (LSTM), and convolutional long short-term memory (CNN-LSTM), a combination of the two previous methods. The results show that in our setup, sound signals outperform vibrations when used individually for training. However, without any feature selection/extraction step, the accuracy of the models improves with data fusion and reaches 98.8%, 93.5%, and 73.6% for the DCNN, CNN-LSTM, and LSTM methods, respectively, 98.8% being a performance that, according to our reading, has never been reached in the analysis of the faults of a BLDC motor without first going through the extraction of the characteristics and their fusion by traditional methods. These results show that it is possible to work with raw data from multiple sensors and achieve good results using deep learning methods without spending time and resources on selecting appropriate features to extract and methods to use for feature extraction and data fusion.
Helium recovery and purification at CHMFL

Currently, rising demand and declining reserves of helium have led to dramatic increases in the helium price. The High Magnetic Field Laboratory of Chinese Academy of Sciences (CHMFL) has made efforts since its foundation to increase the percentage of helium recovered. The piping network connects all the helium experimental facilities to the recovery system, and even exhaust ports of pressure relief valves and vacuum pumps are also connected. In each year, about 30,000 cubic meters helium gas is recovered. The recovery gas is purified, liquefied and supplied to the users again. This paper will provide details about the helium recovery and purification system at CHMFL, including system flowchart, components, problems and solutions.
Influence of Communication on Strategy Implementation among Pharmaceutical Companies in Nairobi Kenya

vii TABLE OF CONTENTS . ix LIST OF FIGURES . xii LIST OF TABLES . xiii CHAPTER ONE . 1 1.0 INTRODUCTION. 1 1.1 Background of the Problem . 1 1.2 Statement of the Problem . 4 1.3 Purpose of the Study . 5 1.4 Research Questions . 5 1.5 Significance of the Study . 5 1.6 Scope of the Study . 7 1.7 Definition of Terms. 7 1.8 Chapter Summary . 7 CHAPTER TWO . 9 2.0 LITERATURE REVIEW . 9
The Presence of Blood–Brain Barrier Modulates the Response to Magnesium Salts in Human Brain Organoids

Magnesium (Mg) is fundamental in the brain, where it regulates metabolism and neurotransmission and protects against neuroinflammation. To obtain insights into the molecular basis of Mg action in the brain, we investigated the effects of Mg in human brain organoids, a revolutionary 3D model to study neurobiology and neuropathology. In particular, brain organoids derived from human induced pluripotent stem cells were cultured in the presence or in the absence of an in vitro-generated blood–brain barrier (BBB), and then exposed to 1 or 5 mM concentrations of inorganic and organic Mg salts (Mg sulphate (MgSO4); Mg pidolate (MgPid)). We evaluated the modulation of NMDA and GABAergic receptors, and BDNF. Our data suggest that the presence of the BBB is essential for Mg to exert its effects on brain organoids, and that 5 mM of MgPid is more effective than MgSO4 in increasing the levels of GABA receptors and BDNF, and decreasing those of NMDA receptor. These results might illuminate novel pathways explaining the neuroprotective role of Mg.
Recent Advances on Capacitive Proximity Sensors: From Design and Materials to Creative Applications

Capacitive proximity sensors (CPSs) have recently been a focus of increased attention because of their widespread applications, simplicity of design, low cost, and low power consumption. This mini review article provides a comprehensive overview of various applications of CPSs, as well as current advancements in CPS construction approaches. We begin by outlining the major technologies utilized in proximity sensing, highlighting their characteristics and applications, and discussing their advantages and disadvantages, with a heavy emphasis on capacitive sensors. Evaluating various nanocomposites for proximity sensing and corresponding detecting approaches ranging from physical to chemical detection are emphasized. The matrix and active ingredients used in such sensors, as well as the measured ranges, will also be discussed. A good understanding of CPSs is not only essential for resolving issues, but is also one of the primary forces propelling CPS technology ahead. We aim to examine the impediments and possible solutions to the development of CPSs. Furthermore, we illustrate how nanocomposite fusion may be used to improve the detection range and accuracy of a CPS while also broadening the application scenarios. Finally, the impact of conductance on sensor performance and other variables that impact the sensitivity distribution of CPSs are presented.
Who Gets to Farm? Agricultural Renewal, Quotas, and the Governance of Alternative Food Networks in Quebec

: In Canada, chicken, turkey, egg, and milk production is regulated through a supply management system. As a result, any Canadian farmer wishing to raise poultry or produce eggs or milk is legally required to hold a production quota. However, quota prices have risen sharply over time, creating considerable entry barriers for small farmers interested in selling supply-managed specialty products directly to consumers. The impact of the current system on small-scale farming and alternative food networks has led to growing calls for reform. In our study, we examine the debate around quota policies in the province of Quebec from a governance perspective. Our findings indicate that stakeholders disagree on the potential impact that supply management reforms would have on market stability, equity, farmer professionalism