Miracle-dz/UniFAR-FTUs huggingface dataset:
A meta-analytic review of the effects of intergenerational programs for youth and older adults

ABSTRACT This is the first meta-analysis to examine the outcomes of nonfamilial, intergenerational programs (IGP) on youth and older adults. Relevant studies were identified by screening the abstracts from five systematic reviews on the impact of intergenerational programs on psychosocial outcomes. After all studies were screened 23 independent studies were included in the meta-analysis. An effect size, d, was calculated for the change in youth attitudes toward older adults, as well as older adults’ change in generativity, quality of life, depressive symptoms, and physical health. Results suggested that IGP have a small, but significant association with improved youth attitudes toward older adults (d = 0.42). For older adults IGP were associated with a small, significant, reduction in depressive symptoms (d = > −0.30) and increased generativity (d = 0.25), quality of life (d = 0.48) and physical health (d = 0.10). Nonsignificant heterogeneity precluded the use of moderator variables to explain variation among the studies. With a better understanding of the effectiveness of IGP, researchers can more effectively develop IGP and assess their outcomes.
Stemness Correlates Inversely with MHC Class I Expression in Pediatric Small Round Blue Cell Tumors

Simple Summary Tumors occurring at a young age are distinct from tumors in older individuals, clinically and pathologically. As small round blue cell tumors (SRBCTs), they often show a resemblance to stem cells and immature precursor cells during embryonal development. Recently, immunotherapy has become an option for a subset of patients with limited success. We observed that in almost all the pediatric SRBCT types investigated (n = 1134) there was an inverse relationship, when comparing genes highly expressed in stem cells with genes encoding MHC class I molecules. MHC class I molecules are important in tumor cell recognition by cytotoxic T cells. We suspect that these tumors are derived from multipotent precursor cells that naturally show a low MHC class I expression and a lack of immune recognition necessary for prenatal proliferation and development. Abstract Recently, immunotherapeutic approaches have become a feasible option for a subset of pediatric cancer patients. Low MHC class I expression hampers the use of immunotherapies relying on antigen presentation. A well-established stemness score (mRNAsi) was determined using the bulk transcriptomes of 1134 pediatric small round blue cell tumors. Interestingly, MHC class I gene expression (HLA-A/-B/-C) was correlated negatively with mRNAsi throughout all diagnostic entities: neuroblastomas (NB) (n = 88, r = −0.41, p < 0.001), the Ewing’s sarcoma family of tumors (ESFT) (n = 117, r = −0.46, p < 0.001), rhabdomyosarcomas (RMS) (n = 158, r = −0.5, p < 0.001), Wilms tumors (WT) (n = 224, r = −0.39, p < 0.001), and central nervous system-primitive neuroectodermal tumors CNS-PNET (r = −0.49, p < 0.001), with the exception of medulloblastoma (MB) (n = 76, r = −0.24, p = 0.06). The negative correlation of MHC class I and mRNAsi was independent of clinical features in NB, RMS, and WT. In NB and WT, increased MHC class I was correlated negatively with tumor stage. RMS patients with a high expression of MHC class I and abundant CD8 T cells showed a prolonged overall survival (n = 148, p = 0.004). Possibly, low MHC class I expression and stemness in pediatric tumors are remnants of prenatal tumorigenesis from multipotent precursor cells. Further studies are needed to assess the usefulness of stemness and MHC class I as predictive markers.
Design of Broadband Implantable Antenna for Biomedical Application

A broadband implantable antenna is presented for the biomedical implantable system applications. The proposed antenna adopts a spiral monopole structure to realize the purpose of miniaturization size. The total size of the antenna including the biocompatible superstrates is 15 × 15 × 1.42 mm3 operating frequency at 402 MHz. The effects of some design parameters on performance of proposed antenna are discussed. The simulated and measured in skin-mimicking gel show that the broad bandwidths of 150 MHz (310–460 MHz) and 260 MHz (280–540 MHz) at return loss of 10 dB can be achieved covering the entire Medical Device Radiocommunications Service (MedRadio) and Industrial Scientific Medical (ISM, 433–438 MHz) bands, respectively. The broadband implantable antenna performs an omnidirectional pattern, showing a good candidate for biomedical implantable systems application.
Design Of Loosely Coupled Magnetic Systems Based On Finite Element Method For Inductive Power Transfer Applications

This paper proposes an iterative approach for the design of loosely coupled inductors for inductive power transfer applications. The procedure is based on the finite element method and is suitable for realistic coil geometry development where non-linear magnetic materials are required. Dedicated algorithms represent self-inductances and mutual inductances in terms of equivalent geometric parameters and vice-versa thus allowing an approach that is not possible in analytical formulations. Also, finite element method is used to analyze the coil system, with two or more coils, under axial, lateral and angular misalignment prior to the development of prototypes. The iterative method can be used to support development of wireless power converters for biomedical implants, electric vehicles recharging systems and chargers for portable devices, with simple and minor modifications. Also, it has specific tools for geometry optimization that can lead to maximized power transfer. Experimental results are presented for coupling coefficient under spatial misalignment. 1
Resolution of the Thelymitra canaliculata R.Br. (Orchidaceae) complex in southern Australia

The five cuiTently known taxa in the Thelymitra canaliculata R.Br. complex from southern Australia are discussed and descriptions are presented tor each. Thelymitra jonesii Jeanes from Tasmania, Thelymitra latiloha Jeanes from southwestern Australia and Thelymitra occidentalis Jeanes from southern Australia are described as new and illustrated. The key diagnostic features relating to the size, shape and relative position of the auxiliary lobes of the column and the size and shape ot the post-anther lobe of the column are elucidated. Information on distribution, habitat, pollination biology and conservation status is given tor all five taxa. The relationships between Thelymitra canaliculata, Thelymitra azurea R.S. Rogers and the three new species are discussed. A key is provided to distinguish all five members of the Thelymitra canaliculata complex.
Immune Checkpoint Inhibitors in Cancer Therapy—How to Overcome Drug Resistance?

Simple Summary Immune checkpoint inhibitors (ICIs) are an important strategy in cancer therapy. However, with the widespread clinical use of ICIs, people gradually found that ICIs may not be effective enough to eliminate tumor tissue for certain patients. The resistance to ICI treatment makes some patients unable to benefit from their antitumor effects. Therefore, it is vital to understand their antitumor and drug resistance mechanisms to better narrow the ICI-resistant patient population. This review outlines the antitumor action sites and mechanisms of different types of ICIs and lists the main reason of ICI resistance based on recent studies. Finally, we propose current and future solutions for resistance to ICIs. Abstract Immune checkpoint inhibitors (ICIs), antagonists used to remove tumor suppression of immune cells, have been widely used in clinical settings. Their high antitumor effect makes them crucial for treating cancer after surgery, radiotherapy, chemotherapy, and targeted therapy. However, with the advent of ICIs and their use by a large number of patients, more clinical data have gradually shown that some cancer patients still have resistance to ICI treatment, which makes some patients unable to benefit from their antitumor effect. Therefore, it is vital to understand their antitumor and drug resistance mechanisms. In this review, we focused on the antitumor action sites and mechanisms of different types of ICIs. We then listed the main possible mechanisms of ICI resistance based on recent studies. Finally, we proposed current and future solutions for the resistance of ICIs, providing theoretical support for improving their clinical antitumor effect.
S05 Molecular mechanisms which predict response to chemotherapy

LumB, anti-HER2 therapy for HER2-enriched, but no targeted agent exists yet for Basal-like patients for whom the majority are triple-negatives). However, all these high risk patients also typically receive some form of multiagent chemotherapy. Analysis of adjuvant and neoadjuvant trials suggests that not all chemotherapeutics are equally effective on each of these high risk subtypes, therefore, further individualization of chemotherapy choices may be possible. Data on differences in chemotherapy sensitivity to taxanes and anthracyclines will be presented and suggest that there are significant differences across these disease subtypes, which if further validated, could be used to guide patient treatment decision making. Disclosure of Interest: None Declared
Reducing COVID-19 Racial Disparities: Why Some Counties Make Data-driven Decisions and Others Do Not?

Abstract Despite the progress in understanding the theoretical underpinnings behind governments’ use of performance information, there is limited understanding of whether governments use performance data to reduce racial and ethnic inequalities. This study uses the COVID-19 pandemic as a case study to examine what actions local governments have taken to remedy the disproportionally negative impact of the COVID-19 pandemic on racial and ethnic minorities. Specifically, the study examines the antecedents of the use of disaggregated performance data and studies the influence of the organizational culture and organizational learning on the use of disaggregated data in decision-making. An online survey was administered to 295 counties in the U.S., and the results indicate that attention dedicated to discussing and analyzing COVID-19 data and developmental organizational culture are positively associated with making data-driven decisions. Contrary to the widespread expectations, the percentage of minority population and prevalence of COVID-19 cases do not result in greater efforts to assist minorities in dealing with the pandemic.
UAV-Based Urban Monitoring Using on-Board 802.11ad Radar

The unmanned aerial vehicles (UAVs) offer low-cost, flexible, and line-of-sight deployment for radar remote sensing as well as building temporary communications base stations in inaccessible or crowded areas. Their limited battery power and small footprints is driving innovations to combine multiple functions in single hardware operating in the same spectrum. In particular, UAV-borne joint radar-communications (JRC) systems are of significant interest. In this paper, we focus on a wide-bandwidth millimeter-wave JRC system for UAVs that employ IEEE 802.11ad protocol for sensing. We model such a system for detecting dynamic users on the ground such as pedestrians and vehicles. Our method incorporates a realistic electromagnetic scattering center model of road targets and surface clutter in the pyBullet software to examine 802.11ad for UAV-borne sensing. Numerical experiments demonstrate that 802.11ad UAV-borne sensing is able to capture several micro-Doppler characteristics.
Semiconductor Innovations, from Device to System

This year marks the 75th anniversary since the invention of the transistor and the beginning of the semiconductor industry that has had profound impact on the world and society at large. Technology scaling has been the key driving force behind the numerous innovations that usher in the Information Age. As the fast-expanding applications in 5G, artificial intelligence (AI), advanced driver assistance systems (ADAS), augmented reality and virtual reality (AR/VR), and robotics continue to propel the demand for data-centric products and services, future generations of semiconductor technology will require innovations across the entire stack – from material and device to design infrastructure, architecture and system. In this paper, we will share our perspective of the applications trends and technology advancements that have brought us thus far. We will also outline key innovations that could lay the foundation for future logic technologies. We will conclude the paper with a vision to extend scaling beyond existing frontier into the era of system integration using 3D stacking technologies, thus enabling a roadmap of energy-efficient computing to continue into the foreseeable future.
Encalypta driva (sp. nov.) and its relationship to E. vulgaris in Scandinavia

Encalypta vulgaris is well known for its wide morphological variation, here we study morphological and genetic variation of E. vulgaris in Scandinavia and compare this with the closely related E. rhaptocarpa and the morphologically similar E. mutica. Our morphological and genetic results indicate that Scandinavian plants of E. vulgaris s.l. includes two taxa, E. vulgaris s.s. and a new species here described as E. driva. The two species differ genetically by plastid markers while the included nuclear markers do not differentiate between the two. Morphologically they can be separated both by gametophytic and sporophytic characters. We also clarify the distinction between E. mutica and the newly described E. driva which have been confused in Scandinavia.
Design and Realization of Solar-powered IoT-based Flood Early Warning System with Telegram Messaging, Auto-restart Watchdog, and Power Management

A Flood is inevitable in some regions in Indonesia during rainy season. Flood can cause problems ranging from simple problem such as lightly flooded street to a complex one such as water that enters the house. For community living at river side, the flood often comes from excessive water due to rain in the upstream that outflows the river as drainage which can happen unnoticeably. An early warning system is needed to detect flood so that people who lived in a flood-prone area can be warned and be prepared to mitigate the flood. With the help of internet of things (IoT) technology, such a system can be made. In this work, an early warning system was developed using off-grid solar system as power source, ultrasonic sensor to detect water level, NodeMCU as microcontroller, and 4G modem to connect to the internet. The system uses Blynk as its IoT platform and sends alarm messages to locals using Telegram so they are vigilant. The system can detect how much the water level and categorizes it into preset alarm levels. Lab. experiments on the proposed system was performed using power from PV-module installed on the lab. ’s rooftop. The system functionality works well for 3 weeks of experiments with battery voltage monitored to vary from 12 V at dawn to 13.7 V at daytime. The experiments show that the system exhibits water level measurement average relative error of 2.22% and accuracy of 97.78%. The system is also furnished with power management and auto restart self-healing functionality which were also observed to work well during the test. Measurements show that the system only consumes energy of 11.52 Wh/day which is equivalent to 8% of battery depth of discharge, hence the system can operate for 3.75 days without charging during successive rainy and cloudy days and has sufficient solar energy harvesting margin to cope with site imperfections like shading and soiling of the PV-module.
Dual-Function Radar-Communication Systems with Constant-Modulus and Similarity Constraints

In this paper, the problem of joint transmit waveform and receive filter design for dual-function radar-communication (DFRC) systems is studied. A multiple antenna base station (BS) serving multiple single antenna users on the downlink is assumed. Furthermore, the BS simultaneously accommodates sensing capabilities in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. The core objective of the present paper is to design the optimal waveform and radar receive filter such that the derived waveform satisfies constant-modulus and similarity to known waveform constraints that are of particular importance to the radar's part operation. The proposed designs are derived through the solution to difficult non-convex optimization problems. To that end, novel algorithmic solutions with guaranteed convergence are developed for the solution of the aforementioned problems. The effectiveness of the proposed solutions is verified via simulations.
SELF-DUAL INTEGRAL EQUATION FOR SCATTERING ANALYSIS FROM BODIES OF REVOLUTION WITH MULTIPLE IMPEDANCE BOUNDARY CONDITIONS

|In this paper, electromagnetic scattering from multi-impedance body of revolutions (BORs) is formulated using self-dual integral equations (SDIEs) and is solved numerically by the method of moments using BOR basis functions. Using the axial symmetry advantage of BORs, a 3D problem is converted to a 2D one, and a signi(cid:12)cant reduction in unknowns is obtained. This in turn leads to an increase in the speed of scattering problem solving. Numerical results show that monostatic and bistatic RCS calculation with the proposed method is about 85 and 18 times faster than the commercial software, respectively.
Spatial-Temporal Variability of Soil Organic Carbon Density and Its Related Factors in Fengqiu County of Yellow River Basin, China: A Model and GIS Technique Approach

The accurate estimation of the soil organic carbon (SOC) sequestration rate is very important for studying farmland soil fertility and environmental effects. In this research, a typical fluvor-aquic soil area, Fengqiu county, located in the Yellow River basin of the Huang-Huai-Hai Plain of China, was chosen as a study area. The physicochemical properties of 70 soil samples collected from the surface layer (at a depth of 0–20 cm) in 2011 were analyzed, and related data about the sampling sites were also collected from the Second State Soil Survey of China (SSSSC), conducted in 1981. The results revealed that the SOC density (SOCD) in Fengqiu county increased greatly on a spatio-temporal scale. The average SOCD increased from 15.66 to 26.09 Mg ha−1, and the SOCD sequestration rate was more than 0.20 Mg C ha−1 year−1 in most regions. Few areas showed lost carbon in the past 30 years (1981–2011). In addition, the study suggested that all the areas present strong carbon sequestration potential in the coming decades from 2011, and the carbon sequestration potential was mainly between 32–40 Mg ha−1. Finally, the SOCD sequestration rate was not only affected by natural factors, such as soil type and pH, but also positively correlated with artificial soil management measures, such as fertilization and straw returning. Therefore, we concluded that the farmland in Fengqiu county showed significant carbon sequestration characteristics in the past 30 years (1981–2011). Considering that soil has a great potential for carbon sequestration in the future, the trend of carbon sequestration in farmland soil might continue for a period of time. Furthermore, the results of this study emphasized that strengthening soil scientific management may play a positive role in improving soil carbon sequestration.
Physical Fitness and Cardiometabolic Disease

The increased energy demands of the muscular sys-tem at work require that several physiological systems ad-just their functions accordingly to meet this demand. During this process, specific adaptations occur that make all systems involved more efficient in their respective role and more resilient to the wear and tear imposed by the increased demand. This results in the organism being more resilient to environmental stresses, injuries, diseases and demise. In short, the capacity to do physical work has played a crucial role in our survival.
Multi‐view face detection using frontal face detector

The development of frontal face detection has matured for application to everyday life. However, various profile face views are still difficult to deal with practically. A common solution is to build different detectors organized by a decision tree and ensure that each detector handles a single view or a few views. However, collecting the training images for each face view is time consuming and laborious. Moreover, many profile face detectors cannot perform as well as the frontal face detector because of insufficient training images. The management of many detectors also leads to too complex a logic structure to classify a face into its corresponding view. In this paper,we propose a novel method to reuse a frontal face detector to detect multi‐view faces, which do not need any data collection or training processes for various profile views. We focus on exploiting the potential of a general frontal face detector and aim to extend its application to multi‐view faces. We conduct a theoretical analysis to explain why our methodology works from different perspectives, and implement the proposed method based on the sliding window strategy. Furthermore, the searching process is optimized by a genetic algorithm with an original fitness function. Experimental results verify that our method can successfully detect human faces in almost all head poses in the dataset containing a complete collection of head poses in yaw and pitch axes. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Predicting the language of depression from multivariate twitter data using a feature‐rich hybrid deep learning model

Depression is a clinical entity that might be difficult for a psychiatrist to diagnose it effectively on time. A depressed person usually suffers from distress and anxiety, leading to serious consequences if not diagnosed early. Social media platforms facilitate users to exchange ideas and dialogs, resulting in the collection of a huge volume of data. Analyzing user's online behavior to categorize depression is a challenging task for researchers. This motivated researchers to investigate machine learning, deep learning, and natural language processing techniques supporting research related to depression prediction. The dataset used in the study is a large‐scale Twitter dataset. This article aims to investigate a hybrid CNN‐LSTM deep learning model with the Word2Vec feature extraction technique for classifying depressive sentiments from Twitter data. By using TF‐IDF, PCA, and Word2Vec approaches, this model utilizes significant linguistic features present within the text. The proposed model is evaluated on four benchmark datasets and its efficiency is compared with four traditional machine learning models. Moreover, the proposed model's performance is compared to three deep learning‐based hybrid models. The proposed model showed comparable performance with the hybrid deep learning‐based models and outperformed state‐of‐the‐art machine learning techniques with an accuracy of 96.78% and an MSE score of 3.21.