Miracle-dz/UniFAR-FTUs huggingface dataset:
YIELD AND PRODUCTIVITY OF COMPONENT CROPS IN A MAIZE- SOYBEAN INTERCROPPING SYSTEM AS AFFECTED BY TIME OF PLANTING AND SPATIAL ARRANGEMENT

Two field experiments were carried out in 2007 and 2008 to determine the appropriate sequence of planting and spatial arrangement of component crops in the maize-soybean intercropping system for optimum grain yield and production efficiency. The experimental design was a randomized complete block design with three replications. Treatments consisted of combinations of five relative times of planting either maize or soybean and two spatial arrangements of soybean (alternate or double rows). One sole crop each of maize and soybean was added for comparison. The results indicated that the relative time of planting and spatial arrangement are important factors determining the productivity of the maize - soybean intercropping system. The crops must be planted simultaneously to obtain adequate yields of both crops. However, the spatial arrangement to adopt in order to obtain high yields for the component crops differed. For maize spatial arrangement of single rows of maize alternating with single rows of soybean gave the best yields. In contrast, a spatial arrangement of single rows of maize alternating with double rows of soybean recorded the best yields with respect to soybean. LER values recorded were in general greater than unity, implying that it will be more productive to intercrop maize and soybean than grow them in monoculture.
G ENERALIZATION G AMES IN R EINFORCEMENT L EARNING

In reinforcement learning (RL), the term generalization has either denoted introducing function approximation to reduce the intractability of large state and action spaces problems or designated RL agents’ ability to transfer learned experiences to one or more evaluation tasks. Recently, many subﬁelds have emerged to understand how distributions of training tasks affect an RL agent’s performance in unseen environments. While the ﬁeld is extensive and ever-growing, recent research has underlined that variability among the different approaches is not as signiﬁcant. We leverage this intuition to demonstrate how current methods for generalization in RL are specializations of a general framework. We obtain the fundamental aspects of this formulation by rebuilding a Markov Decision Process (MDP) from the ground up by resurfacing the game-theoretic framework of games against nature. The two-player game that arises from considering nature as a complete player in this formulation explains how existing methods rely on learned and randomized dynamics and initial state distributions. We develop this result further by drawing inspiration from mechanism design theory to introduce the role of a principal as a third player that can modify the payoff functions of the decision-making agent and nature. The games induced by playing against the principal extend our framework to explain how learned and randomized reward functions induce generalization in RL agents. The main contribution of our work is the complete description of the Generalization Games for Reinforcement Learning, a multiagent, multiplayer, game-theoretic formal approach to study generalization methods in RL. We offer a preliminary ablation experiment of the different components of the framework. We demonstrate that a more simpliﬁed composition of the objectives that we introduce for each player leads to comparable, and in some cases superior, zero-shot
Research on the Current Status of Installation and Awareness of Fire Extinguishers and Awareness of Fire Extinguishers

The main purpose of this study is to propose a directionfor fire safety education and to discuss the current issues of fire extinguisher training A survey was conducted, and the results show that only 52.1% of homes have a fire extinguisher, whereas over 93% of public and private work places (e.g., schools and businesses) have a fire extinguisher. Approximately 90% of the survey participants had fire extinguisher training experience. However, only 65.8% of the public and private work places conducted fire extinguisher training regularly. 42.5% of survey participants had not been trained in the past year. In the survey result on fire extinguisher knowledge, participants showed a score of 3.95/5.00 for how to use a fire extinguisher and a score of 3.00/5.00 for the meaning of the ABC ratings of fire extinguishers. An independent T-test showed that the "the installation of fire extinguishers in the house" and "regular fire safety education in your group" have influences on the "the knowledge of fire extinguishers" (p
From the origin of life to pandemics: emergent phenomena in complex systems

When a large number of similar entities interact among each other and with their environment at a low scale, unexpected outcomes at higher spatio-temporal scales might spontaneously arise. This non-trivial phenomenon, known as emergence, characterizes a broad range of distinct complex systems—from physical to biological and social—and is often related to collective behaviour. It is ubiquitous, from non-living entities such as oscillators that under specific conditions synchronize, to living ones, such as birds flocking or fish schooling. Despite the ample phenomenological evidence of the existence of systems’ emergent properties, central theoretical questions to the study of emergence remain unanswered, such as the lack of a widely accepted, rigorous definition of the phenomenon or the identification of the essential physical conditions that favour emergence. We offer here a general overview of the phenomenon of emergence and sketch current and future challenges on the topic. Our short review also serves as an introduction to the theme issue Emergent phenomena in complex physical and socio-technical systems: from cells to societies, where we provide a synthesis of the contents tackled in the issue and outline how they relate to these challenges, spanning from current advances in our understanding on the origin of life to the large-scale propagation of infectious diseases. This article is part of the theme issue ‘Emergent phenomena in complex physical and socio-technical systems: from cells to societies’.
Fatigue Driving Vigilance Detection Using Convolutional Neural Networks and Scalp EEG Signals

Fatigue driving is one of the important factors that cause traffic accidents. To solve this problem, this paper proposes a classification model based on the traditional convolutional neural network (CNN) to distinguish the vigilance state. First, the raw electroencephalogram (EEG) signals were converted into two-dimensional spectrograms by the short-time Fourier transform (STFT). Then, the CNN model was used for automatic features extraction and classification from these spectrograms. Finally, the performance of the trained CNN model was evaluated. The average of area under ROC Curve (AUC) was 1, the sensitivity was 91.4%, the average false prediction rate (FPR) was 0.02/h, and the accuracy rate was as high as 97%. The effectiveness of the CNN model was verified by the evaluation results.
Frequency Domain Filtering Based Compressed Sensing Applied on Sparse-angle CT Image Reconstruction

In the process of CT scanning, multi-angle projection data needs to be obtained from a large number of projection actions, which makes the scanned individual bear the risk of high radiation exposure. In order to solve such problems, the use of sparse projection data for CT image reconstruction is proposed as a new type of solution. The previous research can obtain good quality reconstructed images when the projection data is sparse by using the CT reconstruction technology based on the nonlinear sparsity transformation of compressed sensing. However, the heavy time loading of the image reconstruction is a practical problem that needs to be solved urgently. This study optimizes the non-linear filtering process of the regularization term of the original scheme, and proposes a novel method which replaces the original non-linear filter with a low-pass frequency domain filter. This strategy effectively utilizes the properties of low-pass frequency domain filtering in image processing. The excellent properties include high efficiency and low time complexity for image smoothing. The simulation experiment results show that in the process of CT image reconstruction using compressed sensing algorithm, the low-pass frequency domain filtering of the new scheme can greatly reduce the required time in the reconstruction of sparse projection data, and the image quality is feasibly guaranteed.
Overlapping Community Discovery based on Seed Expansion and Shortest Path of Graph

This article proposes an overlapping community discovery algorithm based on the combination of seed extension and the shortest path of the graph (Overlapping Communities Detecting Based on Seed Extension and Shortest Path of Graph, the following referred to as SESPG algorithm). First, the seed node is selected according to the influence of the node. Secondly, the nodes of the community are preliminarily divided by calculating the similarity between the nodes and the seed community. Then calculate the maximum value of the shortest path between the seeds in the community and the nodes in the community. Use this as the radius. For nodes within the radius but not in the community, calculate the similarity with the community, and join the community if it exceeds the threshold. Finally, the communities with high similarity are merged to complete the discovery of overlapping communities. After comparing with other algorithms, the SESPG algorithm has a good performance when the complexity is high, and the number of nodes is large.
A Novel Architecture Design for Output Significance Aligned Flow with Adaptive Control in ReRAM-based Neural Network Accelerator

Resistive-RAM-based (ReRAM-based) computing shows great potential on accelerating DNN inference by its highly parallel structure. Regrettably, computing accuracy in practical is much lower than expected due to the non-ideal ReRAM device. Conventional computing flow with fixed wordline activation scheme can effectively protect computing accuracy but at the cost of significant performance and energy savings reduction. For such embarrassment of accuracy, performance and energy, this article proposes a new Adaptive-Wordline-Activation control scheme (AWA-control) and combines it with a theoretical Output-Significance-Aligned computing flow (OSA-flow) to enable fine-grained control on output significance with distinct impact on final result. We demonstrate AWA-control-supported OSA-flow architecture with maximal compatibility to conventional crossbar by input retiming and weight remapping using shifting registers to enable the new flow. However, in contrast to the conventional computing architecture, the OSA-flow architecture shows the better capability to exploit data sparsity commonly seen in DNN models. So we also design a sparsity-aware OSA-flow architecture for further DNN speedup. Evaluation results show that OSA-flow architecture can provide significant performance improvement of 21.6×, and energy savings of 96.2% over conventional computing architecture with similar DNN accuracy.
Memory-aware Partitioning, Scheduling, and Floorplanning for Partially Dynamically Reconfigurable Systems

Partially dynamic reconfiguration (PDR) technology can accelerate the reconfiguration process and overcome hardware resource constraints when facing the challenge of high performance with respect to applications and resources constraints on field-programmable gate arrays (FPGAs). On FPGAs with PDR technology, the available on-chip Block RAM (BRAM) resources may not satisfy the memory requirements for all data. If we reserve more BRAM resources, then the total area of the dynamically reconfigurable region (DRR) that is used for calculation will decrease, with a reduction in system performance. We propose a memory-aware optimization framework to search for the optimal solution considering partitioning, scheduling, and floorplanning, where we make a tradeoff between performance and on-chip memory resources utilization. We then propose methods for memory allocation: An ILP model and a heuristic algorithm are provided to determine the minimum memory requirements and the number of corresponding memory blocks for data, as well as to determine whether the memory block with its stored data is assigned on-chip or off-chip by formulating the problem into a 0-1 knapsack problem and solving it using dynamic programming. Experimental results show that the memory-aware optimization framework and methods of memory allocation can increase the amount of on-chip data access to 29.65% of the total data volume with guaranteed performance.
Dissecting the Prefrontal Network With Pathway-Selective Manipulation in the Macaque Brain—A Review

Macaque monkeys are prime animal models for studying the neural mechanisms of decision-making because of their close kinship with humans. Manipulation of neural activity during decision-making tasks is essential for approaching the causal relationship between the brain and its functions. Conventional manipulation methods used in macaque studies are coarse-grained, and have worked indiscriminately on mutually intertwined neural pathways. To systematically dissect neural circuits responsible for a variety of functions, it is essential to analyze changes in behavior and neural activity through interventions in specific neural pathways. In recent years, an increasing number of studies have applied optogenetics and chemogenetics to achieve fine-grained pathway-selective manipulation in the macaque brain. Here, we review the developments in macaque studies involving pathway-selective operations, with a particular focus on applications to the prefrontal network. Pathway selectivity can be achieved using single viral vector transduction combined with local light stimulation or ligand administration directly into the brain or double-viral vector transduction combined with systemic drug administration. We discuss the advantages and disadvantages of these methods. We also highlight recent technological developments in viral vectors that can effectively infect the macaque brain, as well as the development of methods to deliver photostimulation or ligand drugs to a wide area to effectively manipulate behavior. The development and dissemination of such pathway-selective manipulations of macaque prefrontal networks will enable us to efficiently dissect the neural mechanisms of decision-making and innovate novel treatments for decision-related psychiatric disorders.
Chicken Incubation Conditions: Role in Embryo Development, Physiology and Adaptation to the Post-Hatch Environment

The chicken hatching egg is a self-contained life-supporting system for the developing embryo. However, the post-hatch performance of birds depends on several factors, including the breeder management and age, egg storage conditions and duration before incubation, and the incubation conditions. Studies have determined the effect of incubation factors on chick post-hatch growth potential. Therefore, chick physical quality at hatch is receiving increasing attention. Indeed, although incubation temperature, humidity, turning and ventilation are widely investigated, the effects of several variables such as exposure of the embryo to high or low levels, time of exposure, the amplitude of variations and stage exposures on embryo development and post-hatch performance remain poorly understood. This review paper focuses on chick quality and post-hatch performance as affected by incubation conditions. Also, chick physical quality parameters are discussed in the context of the parameters for determining chick quality and the factors that may affect it. These include incubation factors such as relative humidity, temperature, turning requirements, ventilation, in ovo feeding and delay in feed access. All these factors affect chick embryo physiology and development trajectory and consequently the quality of the hatched chicks and post-hatch performance. The potential application of adapted incubation conditions for improvement of post-hatch performance up to slaughter age is also discussed. It is concluded that incubation conditions affect embryo parameters and consequently post-hatch growth differentially according to exposure time and stage of exposure. Therefore, classical physical conditions are required to improve hatchability, chick quality and post-hatch growth.
Opioid-Use, COVID-19 Infection, and Their Neurological Implications

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an imminent threat to human health and public safety. ACE2 and transmembrane serine protease 2 proteins on host cells provide the viral entry point to SARS-CoV-2. Although SARS-CoV-2 mainly infects the respiratory system, there have been reports of viral neurotropism and central nervous system injury as indicated by plasma biomarkers, including neurofilament light chain protein and glial fibrillary acidic protein. Even with a small proportion of infections leading to neurological manifestation, the overall number remains high. Common neurological manifestations of SARS-CoV-2 infection include anosmia, ageusia, encephalopathy, and stroke, which are not restricted to only the most severe infection cases. Opioids and opioid antagonists bind to the ACE2 receptor and thereby have been hypothesized to have therapeutic potential in treating COVID-19. However, in the case of other neurotropic viral infections such as human immunodeficiency virus (HIV), opioid use has been established to exacerbate HIV-mediated central nervous system pathogenesis. An analysis of electronic health record data from more than 73 million patients shows that people with Substance Use Disorders are at higher risk of contracting COVID-19 and suffer worse consequences then non-users. Our in-vivo and in-vitro unpublished studies show that morphine treatment causes increased expression of ACE2 in murine lung and brain tissue as early as 24 h post treatment. At the same time, we also observed morphine and lipopolysaccharides treatment lead to a synergistic increase in ACE2 expression in the microglial cell line, SIM-A9. This data suggests that opioid treatment may potentially increase neurotropism of SARS-CoV-2 infection. We have previously shown that opioids induce gut microbial dysbiosis. Similarly, gut microbiome alterations have been reported with SARS-CoV-2 infection and may play a role in predicting COVID-19 disease severity. However, there are no studies thus far linking opioid-mediated dysbiosis with the severity of neuron-specific COVID-19 infection.
Does Oral Endotoxin Contribute to Systemic Inflammation?

The oral microbiome, with a unique emphasis on Porphyromonas gingivalis has been associated with a constellation of inflammatory diseases such as cardiovascular disease, rheumatoid arthritis, Alzheimer's disease, type II diabetes, and non-alcoholic associated fatty liver disease. Periodontal disease has also been shown to induce “leaky gut” leading to metabolic endotoxemia. Several recent studies investigating the habitants of the blood microbiome have found the majority of species appear to be derived from oral and skin bacterial communities in otherwise healthy individuals. Many of the same pathologies associated with perturbations of oral health, such as cardiovascular disease, show alterations to the composition of the blood microbiome as well as circulating neutrophil phenotypes. Gingival inflammation is associated with activated blood neutrophil phenotypes that can exacerbate a distal inflammatory insult which may explain the connection between oral and systemic inflammatory conditions. While in the oral cavity, neutrophils encounter oral microbes that are adept in manipulating neutrophil activity which can re-enter the vasculature thereafter. Endotoxin from oral microbes can differ significantly depending on bacterial community and state of oral health to alter cellular LPS tolerance mechanisms which may contribute to the primed neutrophil phenotype seen in periodontitis and provide a mechanism by which the oral-microbes can affect systemic health outcomes. This review synthesizes the studies between inflammatory diseases and oral health with emphasis on microbiome and corresponding lipopolysaccharides in immune tolerance and activation.
Low Female Gametophyte Fertility Contributes to the Low Seed Formation of the Diploid Loquat [Eriobotrya Japonica (Thunb.) Lindl.] Line H30-6

Loquat is a widely grown subtropic fruit because of its unique ripening season, nutrient content, and smooth texture of its fruits. However, loquat is not well-received because the fruits contain many large seeds. Therefore, the development of seedless or few-seed loquat varieties is the main objective of loquat breeding. Polyploidization is an effective approach for few-seed loquat breeding, but the resource is rare. The few-seed loquat line H30-6 was derived from a seedy variety. Additionally, H30-6 was systematically studied for its fruit characteristics, gamete fertility, pollen mother cell (PMC) meiosis, stigma receptivity, in situ pollen germination, fruit set, and karyotype. The results were as follows. (1) H30-6 produced only 1.54 seeds per fruit and the fruit edible rate was 70.77%. The fruit setting rate was 14.44% under open pollination, and the other qualities were equivalent to those of two other seedy varieties. (2) The in vitro pollen germination rate was only 4.04 and 77.46% of the H30-6 embryo sacs were abnormal. Stigma receptivity and self-compatibility in H30-6 were verified by in situ pollen germination and artificial pollination. Furthermore, the seed numbers in the fruits of H30-6 did not significantly differ among any of the pollination treatments (from 1.59 ±0.14 to 2 ± 0.17). (3) The chromosome configuration at meiotic diakinesis of H30-6 was 6.87I + 9.99II + 1.07III +0.69IV +0.24V (H30-6), and a total of 89.55% of H30-6 PMCs presented univalent chromosomes. Furthermore, chromosome lagging was the main abnormal phenomenon. Karyotype analysis showed that chromosomes of H30-6 had no recognizable karyotype abnormalities leading to unusual synapsis on the large scale above. (4) The abnormal embryo sacs of H30-6 could be divided into three main types: those remaining in the tetrad stage (13.38%), those remaining in the binucleate embryo sac stage (1.41%), and those without embryo sacs (52.82%). Therefore, we conclude that the loquat line H30-6 is a potential few-seed loquat resource. The diploid loquat line H30-6 was with low gametophyte fertility, which may be driven by abnormal meiotic synapses. The low female gamete fertility was the main reason for the few seeds. This diploid loquat line provides a new possibility for breeding a few-seed loquat at the diploid level.