Miracle-dz/UniFAR-FTUs huggingface dataset:
A Filtering Patch Antenna With Reconfigurable Quad-Polarization Diversity

This brief presents a filtering patch antenna with reconfigurable quad-polarization diversity. By loading parasitic patches and a Jerusalem-cross-shaped metal strip on a patch antenna, two radiation nulls are introduced to realize frequency selectivity. Then, to achieve reconfigurability, a feeding network with four switchable output modes is proposed. By switching the PIN diodes, the antenna realizes vertical linear-polarized (V-LP), horizontal linear-polarized (H-LP), left-hand circular-polarized (LHCP) and right-hand circular-polarized (RHCP) radiations while maintaining its intrinsic filtering characteristic. A prototype operating at 2.28 GHz is fabricated, and the measured 10-dB overlapped bandwidth of the four polarization states is 5.7%. The peak gains for the four states are higher than 6.65 dBi, and the overlapped 3-dB AR bandwidth is 7.89%.
Abiotic and biotic factors in the nutrient solution and filter skin (Schmutzdecke ) of slow filters integrated to closed hydroponic greenhouse systems: Potential predictors for assessment of efficacy

Furtner, B. 2006. Abiotic and biotic factors in the nutrient solution and filter skin (Schmutzdecke) of slow filters integrated to closed hydroponic greenhouse systems: Potential predictors for assessment of efficacy. Licentiate thesis. ISSN 1652-6880 , ISBN 91-576-7174-5. Rapid pathogen dispersal by circulating nutrient solutions in closed hydroponic systems poses a major threat to this production method. Slow filtration is very effective against important fungal pathogens such as Phytophthora cryptogea and Pythium aphanidermatum. Filter efficacy against pathogens can partly be predicted by observation of autochthonous microbial communities in slow filters and filter efficacy can be improved by manipulation of these communities. However, data about factors influencing filter efficacy are scarce. Generally, slow filter efficacy is the result of complex interactions between abiotic and biotic attributes of nutrient solution and filter body. Selected abiotic (electric conductivity, pH, oxygen content, chemical oxygen demand, dissolved organic carbon) and biotic factors (general bacterial and fungal microflora, fluorescent pseudomonads, filamentous actinomycetes, Fusarium oxysporum, P. aphanidermatum, biochemical oxygen demand, enzyme activity) were monitored in a two-year study of two distinct commercial hydroponic systems (tomatoes and ornamentals) with integrated slow filters in supernatant, filter skin and effluent. The same filter skin parameters were monitored in six small-scale experimental systems with slow filters to establish possible correlations with filter efficacy against F. oxysporum f.sp. cyclaminis. In half these systems, filter skins were enriched with fungal cell wall preparation to enhance biological activity of microfauna. Total polysaccharide and extracellular biofilm polysaccharide content of the filter skin were determined and a method for extracellular biofilm polymer
Location-Aided mm Wave Train-to-Ground Beam Alignment: Optimal Beamformers and Performance Bounds

The millimeter-wave (mmWave) train-to-ground (T2G) communications is an essential enabling technology for future intelligent railways, where the acquisition of beam alignment information is one of the most challenging and significant issues. Hence, in this paper, we investigate the optimal beamformers and performance bounds for the mmWave T2G beam alignment with the aid of train location information. We first propose a mmWave T2G system model, which can be used by arbitrary array geometry and identifies a clear relationship between the T2G scenario and the wireless channel. Then, based on the Cramer Rao bound (CRB), we provide two bounds characterizing the average and worst minimum mean square error (MMSE) of beam alignment with the bounded error model of train location. Next, two non-convex optimization problems are formulated aiming to find the transmitting beamformers that can minimize the bounds, which is solved optimally by relaxation and recovery techniques. Finally, numerical simulations are conducted to validate the proposed beamformers and bounds for the mmWave T2G beam alignment. In particular, the results show that the MSE performance of optimal beamformers converges to the bounds by applying the maximum likelihood estimator (MLE) in the high SNR regime.
Analyzing the Sensitivity of Crops Classification Accuracy Based on MODIS EVI Time Series and History Ground Reference Data

Abstract An improved spectral profile–based classification method was developed to discriminate corn, alfalfa, and winter wheat in the U.S. state of Kansas. Unlike other classification procedures, this method uses historical field reference data as training samples. An artificial immune network (AIN) algorithm, namely the artificial antibody network (ABNet), was tested as a classifier, combining historical field reference data and moderate-resolution imaging spectroradiometer (MODIS)-enhanced vegetation index (EVI) images. Historical field reference data from the years 2009 to 2012 were used to classify the three crops for 2013 data. A new method was developed to select the purest pixels from cropland data layer (CDL). Historical reference data were used in two different methods to classify crops in 2013: (i) single-year historical data and (ii) multiyear data used in four different combinations. Using method (i), classification was most accurate when the most recent year of training data was utilized. The accuracy of method (ii) increased with the number of years of data used for training the classifier. Results ranged from 81% to 92% overall accuracies, with the exception of the year 2012, where a severe drought created anomalous spectral profiles for all crops in the study area.
The Role of a Local Health Department in Advancing Health Equity: Universal Postpartum Home Visiting in a Large Urban Setting

Introduction: Racial and ethnic inequities persist among birthing families in urban U.S. communities, despite public health efforts to improve outcomes. To address these inequities, in 2020, the Chicago Department of Public Health (CDPH) launched Family Connects Chicago (FCC), an evidence-based, universal, postpartum home visiting program. We examine CDPH's transition from “high risk” to universal home visiting to determine whether and how this change represent an explicit commitment to advancing maternal and child health equity. Methods: We conducted a secondary analysis of key informant interview data (n=45 interviews) collected from stakeholders involved in FCC's early implementation. Our analysis involved identifying processes used by CDPH in their planning and early implementation of FCC and examining the alignment of these processes with approaches for promoting health equity proposed by Calancie et al. Results: The processes used by CDPH to plan and implement the FCC pilot are reflected in two major themes: (1) CDPH emphasized improving outcomes for all birthing families, and (2) CDPH prioritized engaging multiple stakeholders throughout planning and implementation. Alignment of these themes and their subthemes with the approaches proposed by Calancie et al. demonstrated that CDPH's implementation of FCC represents a commitment to advancing health equity. Discussion: In their planning and implementation of FCC, CDPH appears to have exhibited a concerted effort to address Chicago's persistent health inequities. Institutional commitment, continued stakeholder engagement, ongoing data sharing, and sustainable funding will be crucial to implementing and expanding FCC. Health Equity Implications: The implementation of FCC, a new service delivery approach for maternal and infant health, marks a new beginning in tackling inequities for Chicago's birthing families.
Locating microseismic events using borehole data

Constraining microseismic hypocentres in and around hydrocarbon reservoirs and their overburdens is essential for the monitoring of deformation related to hydraulic fracturing, production and injection and the assessment of reservoir security for CO2 and wastewater storage. Microseismic monitoring in hydrocarbon reservoirs can be achieved via a variety of surface and subsurface acquisition geometries. In this study we use data from a single, subsurface, vertical array of sensors. We test an existing technique that uses a 1D velocity model to constrain locations by minimizing differential S‐to‐P arrival times for individual sensors. We show that small errors in either arrival time picks or the velocity model can lead to large errors in depth, especially near velocity model discontinuities where events tend to cluster. To address this issue we develop two methods that use all available arrival times simultaneously in the inversion, thus maximizing the number of potential constraints from N/2 to N, where N is the number of phase picks. The first approach minimizes all available arrival time pairs whilst the second approach, the equal distance time (EDT) method defines the hypocentre as the point where the maximum number of arrival time surfaces intersect. We test and compare the new location procedures with locations using differential S‐to‐P times at each individual sensor on a microseismic data set recorded by a vertical array of sensors at the Ekofisk reservoir in the North Sea. Specifically, we test each procedure's sensitivity to perturbations in measured arrival times and the velocity model using Monte Carlo analysis. In general, location uncertainties increase with increasing raypath length. We show that errors in velocity model estimates are the most significant source of uncertainty in source location with these experiments. Our tests show that hypocentres determined by the new procedures are less sensitive to erroneous measurements and velocity model uncertainties thus reducing the potential for misinterpretation of the results.
Internalising and Externalising Symptoms and Their Association with the Family Environment in Young Children with Williams Syndrome: A Longitudinal Study

Williams Syndrome (WS) involves high rates of psychopathology across the lifespan. However, little is known about the early, longitudinal trajectory of internalising/externalising symptoms or the association between these and the family environment in WS. WS (n = 16; aged 2 years, 2 months to 9 years, 5 months) and typically developing or TD (n = 46; aged 2 years, 2 months to 11 years, 1 month) children were assessed on two occasions over 2.5 years utilising parent report questionnaires—the Child Behaviour Checklist and the Family Environment Scale. No statistically significant changes were found in CBCL/psychopathology profiles across timepoints, on average, for either WS or TD children. However, reliable change scores showed WS children had considerable variability in CBCL scores over time. Cross-sectionally, the WS group showed higher scores (reflecting more psychopathology) compared to TD controls at both time points across most CBCL subscales, with elevated overall psychopathology problems identified in 56–68% of WS children (versus 8% in TD controls). Psychopathology was not associated with sex, chronological age, or cognitive ability in WS. Conflict in the family environment was positively associated with higher Attention Problems at Time 1 in the WS group, whilst the TD group showed associations between family conflict and total psychopathology problems at both time points and between family cohesion and total psychopathology problems at Time 2. Family environment did not differ between groups, except for lower engagement in intellectual and cultural activities in WS. Findings highlight variable Internalising and Externalising Problems in young WS children over time, with greater biological than environmental contributions to psychopathology in WS.
Machine Learning-Based Approach for Cardiovascular Disease Detection and Classification

Cardiovascular disease (CVD) remains the primary cause of mortality worldwide and continues to exhibit an alarming upward trend. Detecting CVD efficiently and accurately in large populations has become an urgent necessity. The proposed framework aims to detect and classify five major cardiovascular disorders in the heart, including Heart Attack, Heart Failure, Heart Valve disease, pericardial disease, and vascular disease (Blood Vessel Disease). Developed within the Matlab environment, the system will undergo comprehensive simulation to ensure its effectiveness. To evaluate the system’s performance, a two-pronged approach will be employed: psycho-visual and parametric analysis. Through psycho-visual analysis, human experts will visually assess the system’s outputs, offering qualitative insights into its accuracy and reliability. Meanwhile, parametric analysis will utilize objective metrics to quantitatively measure the system’s efficiency in detecting and categorizing the cardiovascular conditions. The successful implementation of this proposed system holds promise for early and precise identification of cardiovascular disorders, facilitating timely medical interventions and improving patient outcomes. By incorporating both subjective and objective evaluations, this research seeks to develop a robust and efficient tool for cardiovascular disease screening, ultimately contributing to enhanced healthcare and reduced disease burden.
Implications of Climate Change on Biodiversity in Nepal: Some Observations and Opportunities

To support the arguments, cotemporary literatures and some empirical evidences of climate change impacts in parts of Nepal Himalaya are discussed. Highlighting the preliminary findings of an action research called ‘Kyoto: Think global, act local’, that shows community forests as a source for carbon sequestration, the paper concludes with an emphasis on promoting international advocacy in favour of making the community forestry activities of Nepal and other developing countries eligible for global climate funds. Effects of global warming Effects of global warming encompass all vital systems supporting world populations. Human health, agriculture, forest, water resources and biodiversity will suffer at different scales depending on local conditions. Assessing the effects of global warming with reliable accuracy is very challenging at macro level and almost impossible at micro or local level. It is further difficult task in a country like Nepal where climatic database is comparatively weak in terms of network coverage, duration and quality. Visible changes such as glacier retreat in the Himalaya are well monitored; therefore, it is possible to project future scenarios of water discharge in snow-fed rivers. Case studies on various aspects of climate change impacts are also useful to analysis the situation. Nepal’s Initial National Communication Report on Climate Change to UNFCCC mentions increased seasonal and annual air temperature over the last few decades. Observed annual trend of temperature rise per decade is 0.41o C while seasonal rising trend for temperature during pre-monsoon, monsoon and winter periods are 0.43 0 C, 0.43 0 C and 0.37 0 C per decade respectively (HMG/MOPE 2004). This is much higher
Recent Development on the Extraction Process of Plants Essential Oil and its Effect on Chemical Composition: A Review

This study presents a thorough review on different innovative extraction techniques of different plant essential oil derived from its peel, leaves, seeds and shrubs. The review also entails the different pre-treatment processes that affects the quality and recovery of the plant essential oil. Also, discussed the recently developed green extraction method such as Microwave Assisted Hydrodistillation (MAHD), Solvent Free Microwave Extraction (SFME), Ultrasound Assisted Extraction (UAE), Supercritical Fluid Extraction (SFE) and Ohmic Heated Assisted Hydrodistillation (OHAD). The impact of these extraction process on the important chemical composition of essential oil is further analyzed in this review. The important parameters in extraction is also studied such as the water to material ratio, extraction temperature, power input and extraction time. Based on the different articles reviewed, it signifies that the innovative extraction method improves the quality and quantity of the essential oil yield. It also improved the number of component that can be extracted in the plant essential oil and its biological activities such as antioxidant and antimicrobial activities. Thus this review aims to emphasize the potential of the newly developed essential oil extraction techniques that would help to boon the pharmaceuticals, food and agriculture industry.
Ppb-Level Ammonia Sensors Based on SnS2/rGO Nanohybrid Operating at Room Temperature

The importance of testing for ammonia is underlined by the fact that its adverse effects on the environment, ecosystems and human health are widely acknowledged. However, detecting low concentrations of ammonia at lower temperatures poses a significant challenge. In this study, a two-dimensional tin sulfide (SnS2) combined with graphene was synthesized using a straightforward one-step hydrothermal method, followed by comprehensive characterization and analysis of the material. The SnS2/rGO (reduced graphene oxide) sensors exhibited the best performance at room temperature (25 °C). The composite material effectively reduced the operating temperature and resistance value of the sensor. Notably, the sensor achieved a minimum detectable concentration of 8 ppb of ammonia. The response of the sensor displayed a favorable linear relationship within the concentration range of 25 ppm to 125 ppm for ammonia. This behavior was accompanied by excellent repeatability. In addition, the gas-sensing mechanism of the ammonia sensor is thoroughly analysed. This study contributes to the progress of ammonia detection technology, a vital aspect of environmental monitoring and safeguarding human well-being.
Distributed MMSE Detection with One-Shot Combining for Cooperative Aerial Networks in Presence of Imperfect CSI

In this paper, we investigate the multiple access technique for the aerial networks, where the multi-antenna base stations are carried by unmanned aerial vehicles (UAVs) to serve ground users. The uplink signals are transmitted by the users simultaneously, then detected and recovered by the aerial networks. On one hand, compared to the case that each UAV individually detects the signals of serving users, we aim to improve the quality of the recovered signals by using cooperative techniques that leverage the signals from multiple UAVs. On the other hand, the fully-centralized cooperation requires signaling exchange between UAVs, which incurs huge signaling overhead and latency, and is infeasible for aerial networks. Therefore, we propose a two-stage distributed minimum mean squared error (MMSE) detection with one-shot signal combining. Specifically, the users' signals are first locally detected by each UAV via the MMSE detector, and then weighted combined at the central UAV in a one-shot manner, where the combining weights are designed to minimize the MSE between the combined signals and the original signals. When only imperfect channel state information is locally available at each UAV, by using the random matrix theory, these combining weights are shown to depend on the long-term channel statistics and thus, greatly reduce the interaction overhead and latency. Numerical results show that the proposed scheme outperforms the non-cooperative detection in terms of the achievable spectral efficiency. Meanwhile, with a much smaller interaction overhead, the proposed scheme achieves comparable spectral efficiency as the fully centralized signal detection.
An MLE of Interferometric Coherence Matrix and its Applications in Multipolarimetric Interferometric Phase Optimization and Phase Series Estimation

Multipolarimetric synthetic aperture radar (SAR) interferometric phase optimization and phase series estimation have received a lot of attentions recently from the polarimetry SAR interferometry (PolInSAR) community. In this article, a maximum likelihood estimation (MLE) method for the interferometric coherence matrix (ICM) is proposed, which is further applied to both interferometric phase optimization and phase series estimation. By modeling the PolInSAR coherence matrix as the Kronecker product of the polarimetric coherence matrix and ICM, the MLE of ICM under complex circular Gaussian distribution hypothesis is acquired through an alternate iterative optimization method. In addition, it is theoretically proved in this article that the two state-of-the-art methods, i.e., the TP (total power) method and the MLE-MPPL method, are suboptimal compared to the proposed method regarding the MLE of ICM. Numerical experiments are conducted on simulated fully polarimetric data, airborne fully polarimetric E-SAR data, and spaceborne dual polarimetric Sentinel-1A data, to confirm the effectiveness and superiority of the proposed method.
Nonlinear Optical Response of a Plasmonic Nanoantenna to Circularly Polarized Light: Rotation of Multipolar Charge Density and Near-Field Spin Angular Momentum Inversion

The spin and orbital angular momentum carried by electromagnetic pulses open new perspectives to control nonlinear processes in light–matter interactions, with a wealth of potential applications. In this work, we use time-dependent density functional theory (TDDFT) to study the nonlinear optical response of a free-electron plasmonic nanowire to an intense, circularly polarized electromagnetic pulse. In contrast to the well-studied case of the linear polarization, we find that the nth harmonic optical response to circularly polarized light is determined by the multipole moment of order n of the induced nonlinear charge density that rotates around the nanowire axis at the fundamental frequency. As a consequence, the frequency conversion in the far field is suppressed, whereas electric near fields at all harmonic frequencies are induced in the proximity of the nanowire surface. These near fields are circularly polarized with handedness opposite to that of the incident pulse, thus producing an inversion of the spin angular momentum. An analytical approach based on general symmetry constraints nicely explains our numerical findings and allows for generalization of the TDDFT results. This work thus offers new insights into nonlinear optical processes in nanoscale plasmonic nanostructures that allow for the manipulation of the angular momentum of light at harmonic frequencies.
Recent Advances in Biomimetic Nanocarrier-Based Photothermal Therapy for Cancer Treatment

Nanomedicine presents innovative solutions for cancer treatment, including photothermal therapy (PTT). PTT centers on the design of photoactivatable nanoparticles capable of absorbing non-toxic near-infrared light, generating heat within target cells to induce cell death. The successful transition from benchside to bedside application of PTT critically depends on the core properties of nanoparticles responsible for converting light into heat and the surface properties for precise cell-specific targeting. Precisely targeting the intended cells remains a primary challenge in PTT. In recent years, a groundbreaking approach has emerged to address this challenge by functionalizing nanocarriers and enhancing cell targeting. This strategy involves the creation of biomimetic nanoparticles that combine desired biocompatibility properties with the immune evasion mechanisms of natural materials. This review comprehensively outlines various strategies for designing biomimetic photoactivatable nanocarriers for PTT, with a primary focus on its application in cancer therapy. Additionally, we shed light on the hurdles involved in translating PTT from research to clinical practice, along with an overview of current clinical applications.