Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, Synthesis, and In Vitro and In Vivo Antifungal Activity of Novel Triazoles Containing Phenylethynyl Pyrazole Side Chains

A series of triazole derivatives containing phenylethynyl pyrazole moiety as side chain were designed, synthesized, and most of them exhibited good in vitro antifungal activities. Especially, compounds 5k and 6c showed excellent in vitro activities against C. albicans (MIC = 0.125, 0.0625 μg/mL), C. neoformans (MIC = 0.125, 0.0625 μg/mL), and A. fumigatus (MIC = 8.0, 4.0 μg/mL). Compound 6c also exerted superior activity to compound 5k and fluconazole in inhibiting hyphae growth of C. albicans and inhibiting drug-resistant strains of C. albicans, and it could reduce fungal burdens in mice kidney at a dosage of 1.0 mg/kg. An in vivo efficacy evaluation indicated that 6c could effectively protect mice models from C. albicans infection at doses of 0.5, 1.0, and 2.0 mg/kg. These results suggested that compound 6c deserves further investigation.
Continuous Conduction Mode Soft-Switching Boost Converter and its Application in Power Factor Correction

Continuous conduction mode (CCM) boost converters are commonly used in home appliances and various industries because of their simple topology and low input current ripples. However, these converters suffer from several disadvantages, such as hard switching of the active switch and reverse recovery problems of the output diode. These disadvantages increase voltage stresses across the switch and output diode and thus contribute to switching losses and electromagnetic interference. A new topology is presented in this work to improve the switching characteristics of CCM boost converters. Zero-current turn-on and zero-voltage turn-off are achieved for the active switches. The reverse-recovery current is reduced by soft turning-off the output diode. In addition, an input current sensorless control is applied to the proposed topology by pre-calculating the duty cycles of the active switches. Power factor correction is thus achieved with less effort than that required in the traditional method. Simulation and experimental results verify the soft-switching characteristics of the proposed topology and the effectiveness of the proposed input current sensorless control.
RELIABILITY ASSESSMENT TOOLS FOR MULTI- COMPONENT COMPLEX SYSTEMS: AN OVERVIEW

The global business competitiveness has increased the challenges of industries having multi component complex systems to deliver their products as well as services both, on demand maintaining their quality standards. This paper discusses the scope of reliability, availability and maintainability (RAM) assessment tools which includes qualitative and quantitative methods and commercially available software’s which might be quite helpful in reducing the fear of uncertain failures and their effects on overall performance. The integration of RAM tools in conceptual and complex process design will assist managers to select an optimum maintenance policy, requirement of spare parts, software tools, keeping in mind the various factors such as operational environment, skill of operator’s etc. and comply with other environmental requirements. We draw some conclusions about the degree to which different classes of problem have been solved, and discuss challenges for the future.
FBG-Galfenol Integrated Magnetic Field Sensors for Harsh Environments

The paper aims to discuss the basic issues related to the analysis and design of magnetic sensors based on the employment of magneto-active materials. In particular, the basic idea is based on the integration of a Galfenol magnetostrictive alloy to a Fiber Bragg Grating (FBG) embedded into an optic fiber, able to sense the deformation of the material induced by magnetic field. The structure of the alloy and the characteristics of the fiber, make the device suitable to work also in harsh envi- ronments. One of the basic goals is to provide a sensor as simple as possible, with high field range detection and, at the same time, low reconstruction error. It has been observed that the increase of the field range could be achieved by exploiting the effects of the demagnetizing field, without exploit- ing the well-known magnetic hardening induced by the applied stress. In fact, the latter requires a clamping system, resulting in the increase of the sensor size. The demagnetizing field, conversely, provides a shielding of the external field, turning away the undesired approach to saturation. Finally, the employment of a material characterized by weak hysteresis phenomena avoids the use of complex compensation algorithm without losing accuracy. Some result of its characteristics and performances are provided.
Evaluation of physical health, mental wellbeing, and injury in a UK Police Firearms unit

ABSTRACT The aim was to examine the health and wellbeing of UK police firearms officers and to identify the incidence and severity of work-related injuries. Data from 96 officers were derived from an online self-report survey. General health indicators, physical activity levels, WHO-5 wellbeing score, and injury data from the previous 12-months were collected. Thirty work-related injuries (31%) occurred with an injury rate of 31 injuries per 100 FTE worked within the firearms unit in the previous year. Fifty per cent of officers took no time off for recovery. Twenty-nine per cent of injuries were classified as severe and the mechanisms of more severe injuries were linked to occupational demands. Officers who exercised ≥four times per week reported significantly less injuries, while low physical activity levels were associated with significantly lower wellbeing. UK police firearms officers are at a high risk of occupational injury, and physical activity can play an important role in reducing injury and improving wellbeing.
Frozen Vegetable Processing Plants Can Harbour Diverse Listeria monocytogenes Populations: Identification of Critical Operations by WGS

Frozen vegetables have emerged as a concern due to their association with foodborne outbreaks such as the multi-country outbreak of Listeria monocytogenes serogroup IVb linked to frozen corn. The capacity of L. monocytogenes to colonize food-processing environments is well-known, making the bacteria a real problem for consumers. However, the significance of the processing environment in the contamination of frozen foods is not well established. This study aimed to identify potential contamination niches of L. monocytogenes in a frozen processing plant and characterize the recovered isolates. A frozen vegetable processing plant was monitored before cleaning activities. A total of 78 points were sampled, including frozen vegetables. Environmental samples belonged to food-contact surfaces (FCS); and non-food-contact surfaces (n-FCS). Positive L. monocytogenes samples were found in FCS (n = 4), n-FCS (n = 9), and the final product (n = 1). A whole-genome sequencing (WGS) analysis revealed two clusters belonging to serotypes 1/2a-3a and 1/2b-3b). The genetic characterization revealed the presence of four different sequence types previously detected in the food industry. The isolate obtained from the final product was the same as one isolate found in n-FCS. A multi-virulence-locus sequence typing (MVLST) analysis showed four different virulence types (VT). The results obtained highlight the relevant role that n-FCS such as floors and drains can play in spreading L. monocytogenes contamination to the final product.
Investigation of a multi-layer perceptron network to model and control a non-linear system

This thesis describes the development and implementation of an on-line optimal predictive controller incorporating a neural network model of a non-linear process. The scheme is based on a Multi-Layer Perceptron neural net-work as a modelling tool for a real non-linear, dual tank, liquid level process. A neural network process model is developed and evaluated firstly in simulation studies and then subsequently on the real process. During the development of the network model, the ability of the network to predict the process output multiple time steps ahead was investigated. This led to investigations into a number of important aspects such as the network topology, training algorithms, period of network training, model validation and conditioning of the process data. Once the development of the neural network model had been achieved, it was included into a predictive control scheme where an on-line comparison with a conventional three term controller was undertaken. Improvements in process control performance that can be achieved in practice using a neural control scheme are illustrated. Additionally, an insight into the dynamics and stability of the neural control scheme was obtained in a novel application of linear system identification techniques. The research shows that a technique of conditioning the process data, called spread encoding, enabled a neural network to accurately emulate the real process using only process input information and this facilitated accurate multi-step-ahead predictive control to be performed.
Vision-Based Impedance Control of an Aerial Manipulator Using a Nonlinear Observer

Contact-based aerial interaction control in unknown environments is a challenging issue. To solve this problem, this paper presents an image-based impedance control scheme for an aerial manipulator. Unlike points and image moments, the line features considered in this paper cannot uniquely determine the pose of the onboard camera. Therefore, a nonlinear observer is proposed to online provide supplementary 3D information of the system. After that, a hierarchical tracking controller, which is equipped with an image-space impedance filter, is designed to implement a trajectory tracking task with tunable compliance, while at the same time achieving a subtask that aims to determine the final pose of the aerial manipulator. By planning task-space trajectories, desired interaction behavior can be specified without relying on the global position of the system. Furthermore, the effectiveness criterion of the observer is derived, and the stability of the system is proven. Several experiments, including a push-and-slide task, robustness analysis, and a comparison with the state of the art have been conducted to demonstrate the feasibility of the method. Note to Practitioners—This article presents a method for controlling an aerial manipulator to interact with unknown environments without measuring the global position of both the environment and the aerial manipulator. Furthermore, unlike the existing methods, the proposed visual servoing method is based on line features, which are generally more robust than point features. Moreover, it would be intuitive for users to specify the approaching path and the interaction behavior of the aerial manipulator by just planning the image feature trajectories and the desired distance between the aerial manipulator and the environment. This distance is online estimated using the visual feedback of a monocular camera, instead of relying on any costly 3D-vision sensors or lidars, making it an economical solution for aerial manipulation in field environments.
Minimal mean-square error for 3D MIMO beamforming weighting

The 3D MIMO beamforming system needs a weighting method to determine the direction of beam whist reducing the interference for other beam areas operating at the same carrier frequency. The challenge is to determine the weights of the 3D MIMO beams to direct each beam towards its cluster of user terminals while placing its nulls at undesired user directions to minimise undesired interference. Therefore, the signal-to-interference-plus-noise ratio should be increased while the interference from the side lobes of the other beams reduced. A weight determining method is presented that constructs horizontal and vertical array weights, respectively, by minimising the mean-square error between the array pattern vector and the unit vector, where the unit vector expresses the desired direction for the array pattern and zero vector expresses the undesired direction. Since the rectangular planar array can be viewed as M linear arrays of N elements, the weight of the M–Nth element can be obtained based on the horizontal and vertical array weights.
Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.
A Novel Rodent Model of Hypertensive Cerebral Small Vessel Disease with White Matter Hyperintensities and Peripheral Oxidative Stress

Cerebral small vessel disease (CSVD) is the second most common cause of stroke and a major contributor to dementia. Manifestations of CSVD include cerebral microbleeds, intracerebral hemorrhages (ICH), lacunar infarcts, white matter hyperintensities (WMH) and enlarged perivascular spaces. Chronic hypertensive models have been found to reproduce most key features of the disease. Nevertheless, no animal models have been identified to reflect all different aspects of the human disease. Here, we described a novel model for CSVD using salt-sensitive ‘Sabra’ hypertension-prone rats (SBH/y), which display chronic hypertension and enhanced peripheral oxidative stress. SBH/y rats were either administered deoxycorticosteroid acetate (DOCA) (referred to as SBH/y-DOCA rats) or sham-operated and provided with 1% NaCl in drinking water. Rats underwent neurological assessment and behavioral testing, followed by ex vivo MRI and biochemical and histological analyses. SBH/y-DOCA rats show a neurological decline and cognitive impairment and present multiple cerebrovascular pathologies associated with CSVD, such as ICH, lacunes, enlarged perivascular spaces, blood vessel stenosis, BBB permeability and inflammation. Remarkably, SBH/y-DOCA rats show severe white matter pathology as well as WMH, which are rarely reported in commonly used models. Our model may serve as a novel platform for further understanding the mechanisms underlying CSVD and for testing novel therapeutics.
Visual communication of performance meaures supporting continuous improvement : Challenges and opportunities for manufacturing SMEs

This paper provides a holistic view of visual communication of performance measures supporting continuous improvement in manufacturing SMEs. This is done by a theoretical part with a literature review, and an empirical part with case studies, including both the managers’ and the operators’ perspectives, and by focusing on manufacturing SMEs. The paper identifies five challenges and one opportunity in visual communication that SMEs perceive when using visual communication of performance measures, supporting continuous improvement.
Drug-Drug Interactions of Direct Oral Anticoagulants (DOACs): From Pharmacological to Clinical Practice

The direct oral anticoagulants (DOACs), dabigatran, rivaroxaban, apixaban, and edoxaban, are becoming the most commonly prescribed drugs for preventing ischemic stroke in patients with non-valvular atrial fibrillation (NVAF) and for the treatment and prevention of venous thromboembolism (VTE). Rivaroxaban was also recently approved for the treatment of patients with a recent acute coronary syndrome (ACS). Their use demonstrated to have a favorable risk-benefit profile, with significant reductions in stroke, intracranial hemorrhage, and mortality compared to warfarin, but with increased gastrointestinal bleeding. Nevertheless, their safety profile is compromised in multimorbidity patients requiring contemporary administration of several drugs. Comorbidity and polypharmacy have a high prevalence in elderly patients, who are also more susceptible to bleeding events. The combination of multiple treatments can cause relevant drug–drug interactions (DDIs) by affecting the exposure or the pharmacological activities of DOACs. Although important differences of the pharmacokinetic (PK) properties can be observed between DOACs, all of them are substrate of P-glycoprotein (P-gp) and thus may interact with strong inducers or inhibitors of this drug transporter. On the contrary, rivaroxaban and, to a lower extent, apixaban, are also susceptible to drugs altering the cytochrome P450 isoenzyme (CYP) activities. In the present review, we summarize the potential DDI of DOACs with several classes of drugs that have been reported or have characteristics that may predict clinically significant DDIs when administered together with DOACs. Possible strategies, including dosage reduction, avoiding concomitant administration, or different time of treatment, will be also discussed to reduce the incidence of DDI with DOACs. Considering the available data from specific clinical trials or registries analysis, the use of DOACs is associated with fewer clinically relevant DDIs than warfarin, and their use represents an acceptable clinical choice. Nevertheless, DDIs can be significant in certain patient conditions so a careful evaluation should be made before prescribing a specific DOAC.
Assessing Deep Generative Models on Time Series Network Data

To achieve zero touch automation in next generation wireless networks through artificial intelligence (AI), large amounts of training data is required. This training data is publicly unavailable and is a major hindrance in research on AI applications to wireless communication. One solution is using limited real data to generate synthetic data that can be used in lieu of real data. Generative Adversarial Networks (GAN) have been used successfully for this purpose. In this paper, we choose two publicly available GAN - based models and one deep learning - based autoregressive model. We then compare their performance at generating synthetic time-series wireless network traffic data. We also assess the impact of data scarcity on the generated data quality by varying the level of data available to the models for training. Moreover, in order to assess the usefulness of this generated data, we compare the performance of a gradient boosting regressor trained solely on generated data, real data, and a mix of both at forecasting network traffic. Our experiments show that the GANs perform better than the autoregressive approach in each aspect considered in this work and forecasting models trained to predict network load based on data generated by these GANs yield error rates comparable to models trained on real data. Finally, augmenting small amounts of real data with generated data leads to minor performance gains in some cases.
Novel Carboxylic Acid-Capped Silver Nanoparticles as Antimicrobial and Colorimetric Sensing Agents

The present work reports the synthesis, characterization, and antimicrobial activities of adipic acid-capped silver nanoparticles (AgNPs@AA) and their utilization for selective detection of Hg2+ ions in an aqueous solution. The AgNPs were synthesized by the reduction of Ag+ ions with NaBH4 followed by capping with adipic acid. Characterization of as-synthesized AgNPs@AA was carried out by different techniques, including UV–Visible spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), Dynamic Light Scattering (DLS), and zeta potential (ZP). In the UV–Vis absorption spectrum, the characteristic absorption band for AgNPs was observed at 404 nm. The hydrodynamic size of as-synthesized AgNPs was found to be 30 ± 5.0 nm. ZP values (−35.5 ± 2.4 mV) showed that NPs possessed a negative charge due to carboxylate ions and were electrostatically stabilized. The AgNPs show potential antimicrobial activity against clinically isolated pathogens. These AgNPs were found to be selectively interacting with Hg2+ in an aqueous solution at various concentrations. A calibration curve was constructed by plotting concentration as abscissa and absorbance ratio (AControl − AHg/AControl) as ordinate. The linear range and limit of detection (LOD) of Hg2+ were 0.6–1.6 μM and 0.12 μM, respectively. A rapid response time of 4 min was found for the detection of Hg2+ by the nano-probe. The effect of pH and temperature on the detection of Hg2+ was also investigated. The nano-probe was successfully applied for the detection of Hg2+ from tap and river water
An Optimization Strategy of Price and Conversion Factor Considering the Coupling of Electricity and Gas Based on Three-Stage Game

In order to improve the profits of electricity utility company (EUC) and gas utility company (GUC) and reduce the electricity and heat cost of users, an energy trading and pricing scheme based on three-stage game is proposed. Firstly, a three-stage optimization problem is established, and the conversion between electricity and gas is considered. Meanwhile, the conversion factor is introduced and coordinated with the energy price to adjust the balance of supply and demand. Then, the equilibrium solution of the game is obtained by using Lagrange function and backward induction method. In addition, an iterative algorithm is developed to obtain the optimal conversion factor between electricity and natural gas. Numerical results show that the profits of EUC and GUC are increased by 31.6% and 14.4%, the electricity and heat profits of energy hubs (EHs) are increased by 3% and 6.4%, and the electricity and heat cost of users are reduced by 9.25% and 14.05%. Note to Practitioners—In the multi-energy market, trading and pricing strategies have attracted more and more attention. Based on this, many scholars only studied pricing strategy to balance the supply and demand. However, in the context of multi-energy coupling, the previous pricing strategy is not effective. In this paper, we propose a new pricing strategy, with which the conversion factor can cooperate with the energy price to achieve the balance between supply and demand. The new pricing strategy can increase the profits of electricity utility companies and gas utility companies and reduce the costs of users.