Miracle-dz/UniFAR-FTUs huggingface dataset:
Intergenerational transmission of parental neuroticism to emotional problems in 8‐year‐old children: Genetic and environmental influences

Abstract Background Children of parents with high levels of neuroticism tend to have high neuroticism themselves as well as increased risk of experiencing symptoms of anxiety and depression. It is not yet clear how much of this link is attributable to a potential effect of parent on child (e.g., via a socializing effect) versus to shared genetic risk. We aimed to determine whether there is an intergenerational association after accounting for genetic transmission and assortative mating. Methods We used data from the Norwegian Mother, Father and Child Cohort Study including 11,088 sibling pairs in the parental generation, their partners (N = 22,176) and their offspring (N = 26,091). Exposures were maternal and paternal neuroticism (self‐reported), and the outcomes were neuroticism, symptoms of depression, and symptoms of anxiety in 8‐year‐old children (mother‐reported). Results After accounting for assortative mating in parents (phenotypic r = 0.26) and genetic transmission (explaining 0%–18% of the mother‐offspring correlations), potential maternal effects explained 80% (95% CI = 47–95) of the association with offspring neuroticism (mother‐child r = 0.31), 78% (95% CI = 66–89) of the association with offspring depressive symptoms (r = 0.31), and 98% (95% CI = 45–112) of the association with offspring anxiety symptoms (r = 0.16). Intergenerational transmission of genetic variants associated with paternal neuroticism accounted for ∼40% (CI = 22%–58%) of the father‐offspring correlations with neuroticism and symptoms of depression (r = 0.13 and 0.13, respectively) but none with offspring symptoms of anxiety (r = 0.05). The remaining father‐offspring correlations were explained by maternal influences through assortative mating. Conclusions These results are consistent with direct effects between maternal and offspring neuroticism and between maternal neuroticism and offspring symptoms of anxiety and depression. Further understanding of these intergenerational processes will require an adequate model of how these constructs (neuroticism, anxiety and depression) relate to each other within generations.
A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study

The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company’s production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs.
A Survey on Deep Reinforcement Learning for Traffic Signal Control

Traffic congestion is one of the most important and complex problems in urban governance for a long time. Although traffic lights are used at intersections, traffic bottlenecks still appear with the increasing number of private cars. In recent years, with the continuous development of related technologies in the field of intelligent transportation, more attention has been paid to the automatic driving scheme with intelligent vehicle infrastructure cooperative systems as the core. As a kind of advanced artificial intelligence method, deep reinforcement learning (DRL) is applied to traffic signal control (TSC) to achieve the purpose of optimizing roadside traffic timing. In this paper, we introduce the background of TSC (i.e., main parameters, methods and simulation tools), and then summarize the representation of DRL model (i.e., state, action and reward) and the application of DRL in TSC. The research scenarios of TSC are divided into single-agent and multi-agent. Finally, according to existing works in this field, the problems to be solved are put forward and the paper is summarized.
MiSPiA: microelectronic single-photon 3D imaging arrays for low-light high-speed safety and security applications

This paper reports the achievements so far attained in the development of high-performance CMOS SPADs for single-photon sensitive 2D imagers, based on photon-counting and 3D ranging cameras. The latters are based on both the direct in-pixel measurement of the Time-of-Flight of each photon bouncing bounce from the scene back to the camera and the “indirect” phase-resolved method to count reflected photons in well-defined time slots, synchronous to the active illumination of the scene. MiSPiA SPADs are the new state-of-the-art among SPADs in CMOS technologies.
Numerical Modelling of Airflow and Temperature Distribution in a Living Room with Different Heat Exchange Conditions

Numerical Modelling of Airflow and Temperature Distribution in a Living Room with Different Heat Exchange Conditions Numerical mathematical modelling of the indoor thermal conditions and of the energy losses for separate rooms is an important part of the analysis of the heat-exchange balance and energy efficiency in buildings. The measurements of heat transfer coefficients for bounding structures, the air-tightness tests and thermographic diagnostics done for a building allow the influence of those factors to be predicted more correctly in developed numerical models. The temperature distribution and airflows in a typical room (along with the heat losses) were calculated for different heater locations and solar radiation (modelled as a heat source) through the window, as well as various pressure differences between the openings in opposite walls. The airflow velocities and indoor temperature, including its gradient, were also analysed as parameters of thermal comfort conditions. The results obtained show that all of the listed factors have an important influence on the formation of thermal comfort conditions and on the heat balance in a room. Gaisa Plūsmu un Temperatūras Sadalījumu Skaitliskā Modelēšana Dzīvojamā Telpā ar Dažādiem Siltuma Apmaiņas Nosacījumiem Atsevišķu telpu iekštelpu termisko apstākļu un siltuma zudumu no tām skaitliskā modelēšana ir būtiska visas ēkas siltuma apmaiņas bilances un energo-efektivitātes analīzes kontekstā. Iepriekš veiktie ēkas norobežojošo konstrukciju siltuma caurlaidības koeficientu mērījumi, tās telpu gaisa blīvējuma testi un termogrāfiskie apsekojumi ļauj precīzāk uzdot dažādu faktoru ietekmi skaitliskajos aprēķinos. Rakstā tiek salīdzināti skaitliskie temperatūras un gaisa plūsmu sada-lījumu aprēķinu rezultāti tipiskā dzīvojamā telpā, tiek analizēti telpas siltuma zudumi atšķirīgu apkures veidu, dažādu ventilācijas nosacījumu un mainīgā solārā starojuma gadījumos. Papildus tiek novērtēti arī gaisa kustības ātrumi, temperatūra un tās gradients no termiskā komforta apstākļu viedokļa. Ir parādīts, ka visiem minētiem faktoriem ir izteikti atšķirīga, bet būtiska nozīme gan termiskā komforta apstākļu veidošanā, gan telpas siltuma bilancē.
Fatty Acids and Oxylipins as Antifungal and Anti-Mycotoxin Agents in Food: A Review

Fungal contamination of food, especially by mycotoxigenic fungi, not only reduces the quality of the food, but can also cause serious diseases, thus posing a major food safety challenge to humans. Apart from sound food control systems, there is also a continual need to explore antifungal agents that can inhibit fungal growth and mycotoxin production in food. Many types of fatty acids (FAs) and their oxidized derivatives, oxylipins, have been found to exhibit such effects. In this review, we provide an update on the most recent literature on the occurrence and formation of FAs and oxylipins in food, their effects on fungal growth and mycotoxin synthesis, as well as the genetic and molecular mechanisms of actions. Research gaps in the field and needs for further studies in order to realizing the potential of FAs and oxylipins as natural antifungal preservatives in food are also discussed.
Broadband circularly-polarised antenna consisting of four notch slot radiators

A broadband antenna consisting of four notch slots is proposed to generate circularly-polarised radiation. Wide bandwidth is achieved by using notch slots operating in the double-resonance range. A separate feeding network is designed to meet the requirement of sequential rotation excitation for the four slots maintaining a corresponding phase difference of 90° between adjacent slots. Simulated and measured results are presented to demonstrate the high directivity and wide bandwidth of the circularly-polarised antenna. The designed antenna exhibits a bandwidth from 4.9 to 6.1GHz for both |S11|≤ −10 dB and AR≤ 3 dB, which is 21% of the centre frequency of 5.5GHz. Peak gain at 5.5 GHz is 7.6 dBic along the zenith direction, with less than 1dB gain variation within the operating bandwidth.
Pedati Model Online Learning in University

Since the COVID-19 pandemic hit the world, various changes have occurred in the world of education. Teachers are required to be able to develop online learning so that learning activities can run smoothly. The purpose of this study was to analyze the effectiveness of online learning in universities. The research subjects were lecturers totalling 22 people. The research object includes the effectiveness of online learning programs in terms of context, input, process, and product. The approach used in this study is evaluative. The research model design used is the CIPP evaluation model (Context, Input, Process and Product Evaluation Model). The data were analyzed descriptively by calculating the percentage. Next, the data were analyzed by the T-Score formula. The level of effectiveness of online learning was analyzed through the quadrant of the "Glickman" model. The results of data analysis showed that online learning in PGSD, in terms of context, descriptively classified as very good, with an effectiveness percentage of 18.18%. Online learning at PGSD Undiksha in terms of input, descriptively is very good, with an effectiveness percentage of 0%. In terms of process, online learning at PGSD Undiksha is descriptively classified as very good, with an effectiveness percentage of -9.09%. In terms of products, online learning at PGSD Undiksha descriptively is classified as very good, with an effectiveness percentage of 18.18%. It can be concluded that the online learning that has been held is excellent and effective.
Dielectric materials, devices, and circuits

Dielectric materials are continuing to play a very important role in the microwave communication systems. These materials are key in realization of low-loss temperature-stable resonators and filters for satellite and broadcasting equipment, and in many other microwave devices. High dielectric-constant materials are critical to the miniaturization of wireless systems, both for the terminals and base-stations, as well as for handsets. In this paper, a sequential evolution of the dielectric materials applications in microwave devices will be reviewed. This includes dielectric waveguides, low-loss temperature-stable ceramic materials, dielectric resonators, and filters. The recent advances in the multilayer circuit modules, dielectric antennas, and ferroelectrics are also described.
Well concentration: a novel scaling limitation factor derived from DRAM retention time and its modeling

A novel scaling limitation factor derived from DRAM retention time and its modeling has been proposed. So far, the well concentration has been optimized from the viewpoint of the scaling of the transistor dimensions. However, it has been found that the DRAM retention time strongly depends on the well concentration. Increase of the well concentration enhances thermionic field emission (TFE) current from the storage node. This leakage current makes "tail distribution" of the retention time. Therefore, the well concentration must be optimized taking into account the retention time distribution.
Influence of Beauty Service Workers' Psychological Capital on Their Creative Behavior According to Empowerment and Shared Leadership

This study looks into the influence of psychological capital of beauty service industry workers on their creative behavior and aims to empirically investigate causal relationships between variables by identifying the influence of empowerment and shared leadership, which are the mediator variables. For this purpose, convenient sampling was conducted on beauty service industry workers, and 711 was chosen as the final sample. The sampled data were analyzed using SPSS 24.0 and AMOS 23.0 programs. As a result, psychological capital was confirmed to have influence over empowerment with no direct influence on shared leadership and creative behavior but indirect influence mediated by empowerment on them. It was further confirmed that psychological capital has indirect influence mediated by empowerment and shared leadership. On the other hand, empowerment was found to have an influence on shared leadership and creative behavior, with also shared leadership having a significant influence on creative behavior.
Bridging the technological divide: Stigmas and challenges with technology in clinical studies of older adults.

The COVID-19 pandemic has increased adoption of remote assessments in clinical research. However, longstanding stereotypes persist regarding older adults’ technology familiarity and their willingness to participate in technology-enabled remote studies. We examined the validity of these stereotypes using a novel technology familiarity assessment (n = 342) and with a critical evaluation of participation factors from an intensive smartphone study of cognition in older adults (n = 445). The technology assessment revealed that older age was strongly associated with less technology familiarity, less frequent engagement with technology, and higher difficulty ratings. Despite this, the majority (86.5%) of older adults elected to participate in the smartphone study and showed exceptional adherence (85.7%). Furthermore, among those enrolled, neither technology familiarity, knowledge, perceived difficulty, nor gender, race, or education were associated with adherence. These results suggest that while older adults remain significantly less familiar with technology than younger generations, with thoughtful study planning that emphasizes participant support and user-centered design, they are willing and capable participants in technology-enabled studies. And once enrolled, they are remarkably adherent.
The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks

The imaging Cherenkov telescopes TAIGA-IACT, located in the Tunka valley of the republic Buryatia, accumulate a lot of data in a short period of time which must be efficiently and quickly analyzed. One of the methods of such analysis is the machine learning, which has proven its effectiveness in many technological and scientific fields in recent years. The aim of the work is to study the possibility of the machine learning application to solve the tasks set for TAIGA-IACT: the identification of the primary particle of cosmic rays and reconstruction their physical parameters. In the work the method of Convolutional Neural Networks (CNN) was applied to process and analyze Monte-Carlo events simulated with CORSIKA. Also various CNN architectures for the processing were considered. It has been demonstrated that this method gives good results in the determining the type of primary particles of Extensive Air Shower (EAS) and the reconstruction of gamma-rays energy. The results are significantly improved in the case of stereoscopic observations.
Comparing the MCMC Efficiency of JAGS and Stan for the Multi-Level Intercept-Only Model in the Covariance- and Mean-Based and Classic Parametrization

Bayesian MCMC is a widely used model estimation technique, and software from the BUGS family, such as JAGS, have been popular for over two decades. Recently, Stan entered the market with promises of higher efficiency fueled by advanced and more sophisticated algorithms. With this study, we want to contribute empirical results to the discussion about the sampling efficiency of JAGS and Stan. We conducted three simulation studies in which we varied the number of warmup iterations, the prior informativeness, and sample sizes and employed the multi-level intercept-only model in the covariance- and mean-based and in the classic parametrization. The target outcome was MCMC efficiency measured as effective sample size per second (ESS/s). Based on our specific (and limited) study setup, we found that (1) MCMC efficiency is much higher for the covariance- and mean-based parametrization than for the classic parametrization, (2) Stan clearly outperforms JAGS when the covariance- and mean-based parametrization is used, and that (3) JAGS clearly outperforms Stan when the classic parametrization is used.
New automatic equaliser employing modulo arithmetic

The limitations of present automatic and adaptive equalisers stem from the use of feedforward transversal filters. These drawbacks may be obviated by using a feedback transversal filter, the inverse filter, but this is only suitable for limited use since it can be an unstable circuit. It is shown that modulo arithmetic may be used in the inverse filter to eliminate completely the possibility of instability, and a very simple automatic or adaptive equalisation system is presented. Some interesting properties of the modulo inverse filter are included.
A Generation-based Text Steganography by Maintaining Consistency of Probability Distribution

Text steganography combined with natural language generation has become increasingly popular. The existing methods usually embed secret information in the generated word by controlling the sampling in the process of text generation. A candidate pool will be constructed by greedy strategy, and only the words with high probability will be encoded, which damages the statistical law of the texts and seriously affects the security of steganography. In order to reduce the influence of the candidate pool on the statistical imperceptibility of steganography, we propose a steganography method based on a new sampling strategy. Instead of just consisting of words with high probability, we select words with relatively small difference from the actual sample of the language model to build a candidate pool, thus keeping consistency with the probability distribution of the language model. What's more, we encode the candidate words according to their probability similarity with the target word, which can further maintain the probability distribution. Experimental results show that the proposed method can outperform the state-of-the-art steganographic methods in terms of security performance.
Dual-band microstrip-to-coplanar stripline Wilkinson balun using composite right- and left-handed transmission lines and its application in feeding dual-band bow-tie antenna

Using a composite right- and left-handed transmission lines, a dual-band microstrip-to-coplanar stripline balun is proposed that presents the benefits of bandwidth enlargement, excellent isolation and equal power split between output ports in the two passbands. Also, the dual-band nature of the balun, close space between output ports and ease of integration with a printed antenna in a compact size make it well suited for feeding dual-band planar two-wire antennas. As an example of an application, the proposed balun is designed and fabricated to work at two arbitrary frequency bands, 0.9 and 1.8 GHz. There is a good agreement between the measured and predicted results, which verifies the proposed design methodology. The fabricated balun shows a measured differential output phase bandwidth (180° ± 10°) of 22.95% and 18.76% for the first and second frequency bands, respectively. Moreover, this device is shown to have enhanced bandwidth performance in both isolation and 3-dB magnitude balance. To validate the performance of the proposed balun, the balun is employed to feed a dual-band metamaterial-loaded bow-tie antenna. The experimental results show that the return losses for both the operating bands remain below 10 dB. Likewise, the dual-band bow-tie antenna provides quasi-omnidirectional radiation over the frequency bands.
Emerging Application of Magnetic Nanoparticles for Diagnosis and Treatment of Cancer

Cancer is a disease that has resulted in millions of deaths worldwide. The current conventional therapies utilized for the treatment of cancer have detrimental side effects. This led scientific researchers to explore new therapeutic avenues with an improved benefit to risk profile. Researchers have found nanoparticles, particles between the 1 and 100 nm range, to be encouraging tools in the area of cancer. Magnetic nanoparticles are one of many available nanoparticles at present. Magnetic nanoparticles have increasingly been receiving a considerable amount of attention in recent years owing to their unique magnetic properties, among many others. Magnetic nanoparticles can be controlled by an external magnetic field, signifying their ability to be site specific. The most popular approaches for the synthesis of magnetic nanoparticles are co-precipitation, thermal decomposition, hydrothermal, and polyol synthesis. The functionalization of magnetic nanoparticles is essential as it significantly increases their biocompatibility. The most utilized functionalization agents are comprised of polymers. The synthesis and functionalization of magnetic nanoparticles will be further explored in this review. The biomedical applications of magnetic nanoparticles investigated in this review are drug delivery, magnetic hyperthermia, and diagnosis. The diagnosis aspect focuses on the utilization of magnetic nanoparticles as contrast agents in magnetic resonance imaging. Clinical trials and toxicology studies relating to the application of magnetic nanoparticles for the diagnosis and treatment of cancer will also be discussed in this review.
Experimental Assessment of Cooperative Sensors Network-based Dynamical Thermal Rating: the first evidences from the H2020 OSMOSE Project

The main purpose of the Working Package 5 of the H2020 Optimal System-Mix of flexibility Solutions for European electricity (Osmose) project is to develop an advanced Energy Management System (EMS), which allows the Transmission System Operators (TSOs) to reliably manage distributed Renewable Energy Sources (RES) and grid congestions, by properly coordinating innovative flexibility resources which include Dynamic Thermal Rating (DTR) and Demand Side Response (DSR). In this context, Ensiel, a consortium of Italian universities active in power systems research, developed a new DTR solution, which is based on a self-organizing sensor network composed by cooperative smart nodes deployed along the line route, having as final output the loadability curve of the line. This paper presents the first experimental results obtained by deploying this new DTR method on a real operation scenario, demonstrating the improved performance and effectiveness of the proposed method.