Miracle-dz/UniFAR-FTUs huggingface dataset:
The Antibacterial activity of some Essential Oils Nano Particles on Escherichia coli infection in dairy farms

Bacterial resistance has a major role in the failure of infectious illness treatments. E.coli is one of the most common causes of mastitis, 100 samples (50 each from milk and feces) were collected from dairy cows from various farms in Gharbia Governorate Egypt, to determine the prevalence of E.coli , isolates were analyzed for antibiotic susceptibility. The purpose of our research was to assess the antibacterial activity and mechanism of action of two nanoemulsion EOs (Citronella and Mentha spicata) against Escherichia coli . Antibacterial action was assessed by utilizing agar well diffusion technique, with an electron microscope used to confirm the effect of minimum inhibitory concentration (MIC) on tested bacterial cell and minimum bactericidal concentration (MBC). E. coli was more susceptible to nanoemulsion EOs with a larger inhibition zone diameter; the present research demonstrates the antimicrobial activity that nanoemulsions EOs have an effect on E. coli by acting on the cell surface and disrupting the cell membrane; bacterial cells treated with both essential oils nanoemulsions demonstrated varying degrees of deformation and disruption. On the surface of strains treated with 2 MIC, which was substantially more effective than MIC, considerable morphological alterations were observed.
Voltage band based improved particle swarm optimization technique for maximum power point tracking in solar photovoltaic system

The extraction of maximum power from solar photovoltaic (PV) using Maximum Power Point Tracking (MPPT) methods is a promising research area in the recent past. Many methods including conventional methods, such as Hill Climbing and Incremental Conductance, and methods based on neural network, Fuzzy logic and bio-inspired algorithms, were proposed for MPPT application. However, all these methods suffer from drawbacks such as slower convergence, reduced power output, predominant steady state oscillations, larger memory requirement, and complex structure. Hence, in this paper an attempt is made to enhance existing Particle Swarm Optimization technique by emphasizing proper initial value selection. The key features of this method include the ability to track the global peak power accurately under partial shading conditions with almost zero steady state oscillations, faster dynamic response, and easy implementation. Simulations are carried out for different shading patterns and the results obtained are compared with existing methods. Further, simulation results are validated via experimental values.
On the capability of preprocessing techniques for suspended sediment load prediction using artificial intelligence methods

ABSTRACT With growing interest in global environmental change, it is crucial to have a reliable model to accurately predict the suspended sediment load (SSL). This study investigates the efficiency and capability of two time-series data decomposition algorithms, namely empirical mode decomposition (EMD) and ensemble EMD (EEMD), to address the complexity and non-stationary behaviour of the monthly SSL time series data. These algorithms were coupled with three artificial intelligence models – gene expression programming (GEP), extreme learning machine (ELM), and extreme gradient boosting (XGBoost) – to predict SSL at two stations in Tajan Basin, northern Iran. The results indicate that EEMD, with coefficient of determination values higher than 0.9, performed better than EMD when coupled with XGBoost as the ensemble model. To verify the the predicted SSL data, Monte Carlo simulation was used. The results confirmed the lower uncertainty of the EEMD-XGBoost model in predicting SSL at a monthly scale.
Development and evaluation of a dedicated breast CT scanner

Kai Yang, Ph.D. Department of Biomedical Engineering and Department of Radiology, University of California, Davis, CA 95616 Ph.D. degree in Biomedical Engineering awarded by University of California, Davis, December 2007 Dissertation Committee Members: John M. Boone, Ph.D. (Supervisor); Anthony J. Seibert, Ph.D.; Simon R. Cherry, Ph.D.; Jinyi Qi, Ph.D. Email: jmboone@ucdavis.edu, yangk11@hotmail.com
Study on Rheological Properies of Full Tailing Paste Filling Slurry of Jinchuan Mine

A study is made of the rheological properties of full tailing paste filling slurry of Jinchuan non ferrous metal mining company. The authors analyse the relation between all parameters and the quantity of all these materials, and calculate the engineering average of optimal ratioes of filling slurry. A rheology model of filling slurry is set up and a formula for calculating resistance loss in a pump pileline. This provides a technical bases for designing Jinchuan Mine's paste filling system and ensuring smooth slurry transport.
Nuclear Molecular Imaging of Cardiac Remodeling after Myocardial Infarction

The role of molecular imaging technologies in detecting, evaluating, and monitoring cardiovascular disease and their treatment is expanding rapidly. Gradually replacing the conventional anatomical or physiological approaches, molecular imaging strategies using biologically targeted markers provide unique insight into pathobiological processes at molecular and cellular levels and allow for cardiovascular disease evaluation and individualized therapy. This review paper will discuss currently available and developing molecular-based single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging strategies to evaluate post-infarction cardiac remodeling. These approaches include potential targeted methods of evaluating critical biological processes, such as inflammation, angiogenesis, and scar formation.
Spatiotemporal convolutional network for time-series prediction and causal inference

Making predictions in a robust way is not easy for nonlinear systems. In this work, a neural network computing framework, i.e., a spatiotemporal convolutional network (STCN), was developed to efficiently and accurately render a multistep-ahead prediction of a time series by employing a spatial-temporal information (STI) transformation. The STCN combines the advantages of both the temporal convolutional network (TCN) and the STI equation, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the prediction of the target variable. From the observed variables, the STCN also infers the causal factors of the target variable in the sense of Granger causality, which are in turn selected as effective spatial information to improve the prediction robustness. The STCN was successfully applied to both benchmark systems and real-world datasets, all of which show
Size of the prize: the value of closing pasture yield gaps on heterogeneous soil types in a dairy farm in Canterbury, New Zealand

Identifying opportunities to further improve pasture production on high producing, irrigated Canterbury dairy farms is complex. An innovation systems approach was used to identify the factors contributing to variation in pasture production on a case study farm such as: soil characteristics, irrigation management, and grass grub populations. This paper focuses on the value proposition of managing soil zones to optimise pasture production. Using EM38 mapping, area under a single centre pivot irrigator were characterised into soil zones of ‘low’, ‘medium’ and ‘high’. Actual measurements of irrigation application and soil characteristics were modelled in APSIM to estimate the pasture production of ‘low’ and ‘medium’ soil zones under a constant irrigation regime, giving an annual difference of 2900 kg DM/ha between the zones. In a ‘typical irrigated Canterbury System 4 dairy farm’ modelled in FARMAX, with 20% ‘low’ and 80% ‘medium’ soil zones, increasing the pasture growth in the ‘low’ zone to that of the ‘medium’ zone gave an increase of 580 kg DM/ha. This produced an increase of 51 kg/ha of milk solids and increased the stocking rate by 0.2 cow/ha, which resulted in a profit increase of $298/ha/year. The value of this on a 255 ha farm would be $75,000 per annum, while at a regional scale, increasing the productivity of the 52,900 ha of ‘low’ zone soils on irrigated dairy farms in Canterbury would add around $14 M of profit. Taking a data-driven spatial management approach to understanding the drivers of variability in pasture production has potential to identify opportunities and their potential value even in high-performing systems.
100 MHz Symmetric Current-Mode Class D Wireless Power Transfer

Power conversion for wireless power transfer (WPT) at 100 MHz is important for miniaturization and integration, which is critical for charging wearable consumer electronics and devices for the Internet of Things (IoT). Design and implementation at this frequency are challenging in the selection of components and circuit configurations for operation over a wide load range. The optimization of the tuned compensation networks together with a step-by-step guide for a systematic and rigorous optimal design is derived in this article from fundamental circuit models for symmetric current-mode class D (CMCD) WPT systems. Symmetric CMCD WPT systems can have inherently constant output current for a wide load range with small current ripple for the optimal design, which is useful in direct battery charging applications. Both uncontrolled and synchronous rectifiers are analyzed, demonstrated, and compared in hardware with the optimal designs. A 100MHz prototype with an output peak power of 6.9 W has a power circuit dc–dc efficiency of 81.9% despite only natural convection cooling of miniaturized GaN devices. The prototype maintains nearly constant power circuit efficiency above 80.5% with a peak of 83.2% over a wide power range that spans from 1.7 to 6.9 W.
Artificial Intelligence-Assisted Design and Virtual Diagnostic for the Initial Condition of a Storage-Ring-Based Quantum Information System

Developments in Artificial Intelligence (AI) are helping to solve complex physical problems that otherwise may be too computationally demanding to solve using traditional approaches. Universal Approximation Theorems tell us that we can model any physical system if we can approximate the system with some continuous function (i.e., compact convergence topology and algorithmically generated sets of functions, such as the convolutional neural network), whether for an arbitrary depth or arbitrary width neural network. We consider the problem of solving a set of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> coupled algebraic equations as <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> becomes very large and apply machine learning (ML) to solve this problem for any value of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>. The physical problem we are focusing on is to model the equilibrium positions of ions in an ion trap. A storage ring quantum computer could contain well over tens of thousands of ions. Quickly determining the equilibrium positions will be important to minimize the time to target and observe each ion. As each ion serves as a single qubit, this is important for setting and measuring the individual qubit states. The phonon modes from a collection of ions acts as another qubit, useful for gate operations. Measuring the phonon modes, where ions are oscillating around their respective equilibrium positions also means understanding the equilibrium positions very well. Turning all of this into a virtual diagnostic allows real time prediction and comparison to ensure unique definition of each ion.
Potential impact assessment between sst and extreme precipitation: an observational study in Southeastern Brazil

This work aims to identify, by means of statistical analysis, the relationship between extreme precipitation events and SST anomalies (SSTA) in the municipalities surrounding the Ilha Grande (IGB) and Sepetiba (SB) bays in the state of Rio de Janeiro evaluating its annual distributions using measured data from rainfall stations and relating these events to potential SST anomalies found in this region. Data from 10 rainfall stations distributed around the bays and SST dataset obtained from the Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR) from 2003 to 2014 were used. SSTA and their average were generated for each of the three subareas delimited in the study area. The obtained time series of each subarea define the thresholds for significant SSTA, based on their respective standard deviation. The number of days with SSTA were obtained from these significant anomaly values, and days with positive or negative SSTA, for each subarea, were computed. For SSTA analysis, only events with significant anomalies were considered. Only SSTA occurring on the days of intense precipitation highlighted in P95 and up to 3 days before the extreme event date were analyzed. The results had shown that about 35% of extreme precipitation events were potentially related to the presence of significant SST anomalies in the studied area. Finally, it is pointed that regional anomalies in SST, such as those described in this study, can also contribute to the formation of extreme rain events.
Application of Repetitive Transcranial Magnetic Stimulation over the Dorsolateral Prefrontal Cortex in Alzheimer’s Disease: A Pilot Study

Repetitive transcranial magnetic stimulation (rTMS) is reportedly a potential tool to understand the neural network; however, the pathophysiological mechanisms underlying cognitive function change remain unclear. This study aimed to explore the cognitive function changes by rTMS over the bilateral dorsolateral prefrontal cortex (DLPFC) in Alzheimer’s disease (AD). We evaluated the feasibility of rTMS application for mild cognitive dysfunction in patients with AD in an open-label trial (UMIN000027013). An rTMS session involved 15 trains at 120% resting motor threshold on each side (40 pulses/train at 10 Hz). Efficacy outcome measures were changes from baseline in cognitive function, assessed based on the AD Assessment Scale-cognitive subscale, Mini-Mental State Examination, Japanese version of Montreal Cognitive Assessment (MoCA-J), Behavioral and Psychological Symptom of Dementia, and Instrumental Activity of Daily Living scores. Sixteen patients with AD underwent five daily sessions of high-frequency rTMS over the bilateral DLPFC for 2 weeks. All participants completed the study; no major adverse effects were recorded. The MoCA-J score increased by 1.4 points (±0.15%) following 2 weeks of stimulation. At 1 month following rTMS cessation, all cognitive functional scores returned to the original state. Our findings suggest that the DLPFC plays an important role in the neural network in AD.
A Novel Mathematical Approach to Model Multi-Agent-Based Main Grid and Microgrid Networks for Complete System Analysis

Penetration of renewable energy resources in modern power systems has increased rapidly. The integration of different renewable and nonrenewable resources for the purpose of electricity generation is referred to as Distributed Generation (DG) units. The penetration of DG units gave birth to the concept of power microgrid. Power inverters play a major role in the integration of DGs in the power system. Control and stability analysis of microgrids in power systems is a challenging task for the control community. Dynamic microgrid modeling demands knowledge of fundamental engineering laws to detailed theoretical analysis. To model the dynamic behavior of the power microgrid, a basic understanding of the power converter operation modes and their control schemes is necessary. The main microgrid modeling components are power converters, power lines, transformers, protection systems, load, and faults. In this paper, preliminary concepts of power systems along with graph theoretic approach are used to develop the model of the microgrid and main grid networks. A mathematical model of a power microgrid in islanded mode, as well as the grid-connected mode, is developed and comprises of generation sources, power inverter interface, protection mechanism, load, faults, and transmission lines. The developed mathematical model can be used to address the stability issues as well as resilience in the power networks for complete system analysis. To validate the mathematical model, a renewable energy-based main grid and microgrid model is simulated. The graphical result of simulated model presents the generation and load curves.
Empirical Evidence on the Sustainability of the IT Innovation Capability

Approaching IT innovation (ITI) from a strategic management standpoint, this paper sheds light on the issue of the capability of a firm to innovate with IT over time. In this study we argue that ITI is a cumulative and path-dependent capability that is not easily replicated. Companies that have developed ITI capability and attained ITI status among their peers are likely to maintain this status over time, thus demonstrating that ITI is a sustained capability. We tested our proposition with cross-sectional data of large US firms that have attained ITI status from 1997-2004. Exploratory results based on transitional probabilities and econometric analysis based on a dynamic random effect Probit model seem to strongly support our position.
Evaluating The Effectiveness Of Production Process Using Pareto Analysis

Abstract The aim of this paper is to present the possibilities of using the Pareto method in evaluating the effectiveness of machine production processes. The paper deals with the production process of material cutting using progressive technology and subsequent evaluation of its effectiveness and quality. In the production process, we have used the method of material cutting by abrasive water jet. The Pareto analysis was used for eliminating the shortcomings in the quality of the final part.
A Method to Locate Disturbance Source due to Capacitor Switching in Distribution Network Based on Integration of Wavelet Transform With Support Vector Machine

An algorithm to locate disturbance source due to capacitor switching in distribution network,in which the wavelet transform is integrated with support vector machine (SVM),is proposed.Using wavelet transform,the proposed algorithm analyzes the transient disturbance signal due to capacitor switching and extracts two characteristic quantities,i.e.,the disturbance energy and disturbance duration,as the inputs of SVM classifier to recognize disturbances and automatically locate the switched capacitor.Utilizing simulation software PSCAD/EMTDC,a model of typical 10 kV distribution network is built and three capacitor banks at different positions are chosen to be switched,then the transient disturbances due to the switching of capacitor banks are simulated.Taking different switching events obtained by changing the switching-on times of capacitor banks as the samples of the proposed algorithm,the simulation is performed.Simulation results show that the accuracy of locating disturbance source due to capacitor switching can reach to 99.17%.
A Fractional Analysis of Hyperthermia Therapy on Breast Cancer in a Porous Medium along with Radiative Microwave Heating

Cancer is a prominent source of mortality and morbidity globally, but little is known about how it develops and spreads. Tumor cells are unable to thrive in high-temperature environments, according to recent research. Hyperthermia is the name for this therapy method. This study provides insights into hyperthermia therapy on breast cancer in the presence of a porous material with fractional derivative access when using radiative microwave heating. The mathematical model is formulated by PDE, while the time-fractional Caputo derivative is applied to make our equation more general as compared to the classical model. To produce a more efficient analysis of blood temperature distributions inside the tissues of the breast, the unsteady state is calculated by using the Laplace transform technique. The Laplace inversion is found by Durbin’s and Zakian’s algorithms. The treatment involves mild temperature hyperthermia, which causes cell death by enhancing cell sensitivity to radiation therapy and blood flow in the tumor. The variations of different parameters to control the temperate profile during therapy are discussed; we can also see how a fractional parameter makes our study more realistic for further experimental study.
Research on Control DC Side Neutral-point Potential Balance of SAPF

The structure of three-level PWM is applied to the shunt active power filter(APF). The reason which the three-level inverter midpoint potential volatility inherent problems is analyzed.In order to solve overshoot static error larger issues existing in the traditional DC voltage control,a solution is proposed.The waveforms show that the algorithm has good control effect and the experiment verify the feasibility of the algorithm.
Eutrophication and Deoxygenation Forcing of Marginal Marine Organic Carbon Burial During the PETM

The Paleocene‐Eocene Thermal Maximum (PETM) is recognized globally by a negative excursion in stable carbon isotope ratios (δ13C) in sedimentary records, termed the carbon isotope excursion (CIE). Based on the CIE, the cause, duration, and mechanisms of recovery of the event have been assessed. Here, we focus on the role of increased organic carbon burial on continental margins as a key driver of CO2 drawdown and global exogenic δ13C during the recovery phase. Using new and previously published sediment proxy data, we show evidence for widespread enhanced primary production, low oxygen waters, and high organic carbon (Corg) burial in marginal and restricted environments throughout the δ13C excursion. With a new biogeochemical box model for deep and marginal environments, we show that increased phosphorus availability and water column stratification on continental margins can explain the increased Corg burial during the PETM. Deoxygenation and recycling of phosphorus relative to Corg were relatively mild, compared to modern day anoxic marine systems. Our model reproduces the conditions reconstructed by field data, resulting in a burial of 6,000 Pg across the PETM, in excess of late Paleocene burial, and ∼3,300 Pg C for the critical first 40 kyr of the recovery, primarily located on continental margins. This value is consistent with prior data and model estimates (∼2,000–3,000 Pg C). To reproduce global exogenic δ13C patterns, this Corg burial implies an injection of 5,000–10,000 Pg C during the first ∼100–150 kyr of the PETM, depending on the source's δ13C (−11‰ to −55‰).