Miracle-dz/UniFAR-FTUs huggingface dataset:
Interindividual Brain and Behavior Differences in Adaptation to Unexpected Uncertainty

Simple Summary To adapt to a new environment, people must choose between using strategies that previously produced the expected effect in a similar context and trying new strategies with no certainty of achieving the expected effect. This is known as the exploration/exploitation trade-off. Its underlying brain mechanisms and factors accounting for interindividual differences have been overlooked. Resting-state functional connectivity highlights the synchronicity of different brain regions at rest, i.e., basal or intrinsic connectivity. Here, we investigated the evolution of this synchronicity before/after a task involving environmental uncertainty. To study the trade-off, we introduced uncertainty and cheated participants by introducing false feedbacks during an associative learning task. We examined the associations among the participants’ behaviors, changes in the brain intrinsic functional connectivity, and psychological factors. The sensitivity to uncertainty was used to create two groups. The groups displayed different patterns of evolution of regional connectivity from before to after the task. We also found a trend whereby higher scores of anxiety and greater propensity to doubt about actions were positively linked to these patterns. These results provide additional arguments in favor of short-term plasticity in basal brain connectivity and clues about the interindividual factors influencing neural and behavioral adaptation. Abstract To adapt to a new environment, individuals must alternate between exploiting previously learned “action–consequence” combinations and exploring new actions for which the consequences are unknown: they face an exploration/exploitation trade-off. The neural substrates of these behaviors and the factors that may relate to the interindividual variability in their expression remain overlooked, in particular when considering neural connectivity patterns. Here, to trigger environmental uncertainty, false feedbacks were introduced in the second phase of an associative learning task. Indices reflecting exploitation and cost of uncertainty were computed. Changes in the intrinsic connectivity were determined using resting-state functional connectivity (rFC) analyses before and after performing the “cheated” phase of the task in the MRI. We explored their links with behavioral and psychological factors. Dispersion in the participants’ cost of uncertainty was used to categorize two groups. These groups showed different patterns of rFC changes. Moreover, in the overall sample, exploitation was correlated with rFC changes between (1) the anterior cingulate cortex and the cerebellum region 3, and (2) the left frontal inferior gyrus (orbital part) and the right frontal inferior gyrus (triangular part). Anxiety and doubt about action propensity were weakly correlated with some rFC changes. These results demonstrate that the exploration/exploitation trade-off involves the modulation of cortico-cerebellar intrinsic connectivity.
Outlook on genome editing application to cattle

In livestock industry, there is growing interest in methods to increase the production efficiency of livestock to address food shortages, given the increasing global population. With the advancements in gene engineering technology, it is a valuable tool and has been intensively utilized in research specifically focused on human disease. In historically, this technology has been used with livestock to create human disease models or to produce recombinant proteins from their byproducts. However, in recent years, utilizing gene editing technology, cattle with identified genes related to productivity can be edited, thereby enhancing productivity in response to climate change or specific disease instead of producing recombinant proteins. Furthermore, with the advancement in the efficiency of gene editing, it has become possible to edit multiple genes simultaneously. This cattle breed improvement has been achieved by discovering the genes through the comprehensive analysis of the entire genome of cattle. The cattle industry has been able to address gene bottlenecks that were previously impossible through conventional breeding systems. This review concludes that gene editing is necessary to expand the cattle industry, improving productivity in the future. Additionally, the enhancement of cattle through gene editing is expected to contribute to addressing environmental challenges associated with the cattle industry. Further research and development in gene editing, coupled with genomic analysis technologies, will significantly contribute to solving issues that conventional breeding systems have not been able to address.
Deep Ontology Alignment Using a Natural Language Processing Approach for Automatic M2M Translation in IIoT

The technical capabilities of modern Industry 4.0 and Industry 5.0 are vast and growing exponentially daily. The present-day Industrial Internet of Things (IIoT) combines manifold underlying technologies that require real-time interconnection and communication among heterogeneous devices. Smart cities are established with sophisticated designs and control of seamless machine-to-machine (M2M) communication, to optimize resources, costs, performance, and energy distributions. All the sensory devices within a building interact to maintain a sustainable climate for residents and intuitively optimize the energy distribution to optimize energy production. However, this encompasses quite a few challenges for devices that lack a compatible and interoperable design. The conventional solutions are restricted to limited domains or rely on engineers designing and deploying translators for each pair of ontologies. This is a costly process in terms of engineering effort and computational resources. An issue persists that a new device with a different ontology must be integrated into an existing IoT network. We propose a self-learning model that can determine the taxonomy of devices given their ontological meta-data and structural information. The model finds matches between two distinct ontologies using a natural language processing (NLP) approach to learn linguistic contexts. Then, by visualizing the ontological network as a knowledge graph, it is possible to learn the structure of the meta-data and understand the device’s message formulation. Finally, the model can align entities of ontological graphs that are similar in context and structure.Furthermore, the model performs dynamic M2M translation without requiring extra engineering or hardware resources.
Overview on Interest Point Detection Over 40 Year’s Development: A Review

Interest point detection (IPD) is one of the most fundamental studies in computer vision and other communities. The methods for IPD have evolved from early simple mathematical model-based to current artificial intelligence (AI)-based methods, from early 2-D gray image-based to 3-D point cloud data-based methods, and from early personal computer (PC)-based to field programmable gate array (FPGA) chip-based onboard methods. Many scholars are very concerned about these methods’ advantages and disadvantages and what the future development on the IPD method is; therefore, this article makes a deep and comprehensive overview for two major types of the IPD methods, the handcrafted and machine learning, including their advantages, disadvantages, localization accuracy, noise immunity and others, for over 40 years. A comprehensive analysis using average repeatability (AR) and localization error (LE) is also evaluated. The future development for the IPD is finally given.
Variation of transpiration within a canopy of silver birch: effect of canopy position and daily versus nightly water loss

Sap flux density (FLA) in branches of Betula pendula trees was examined with respect to environmental variables and canopy position. FLA of branches sampled within the basal and top thirds of crowns was analysed with respect to stomatal (gs), canopy (gc) and soil‐to‐leaf hydraulic conductance (KT). The canopy positions differed significantly (P < 0·001) by FLA: the day‐time values in the upper canopy were on average 1·8 times greater than in the lower canopy (59·2 vs 33·2 g m−2 h−1), associated with greater gs, gc and KT observed in the upper canopy. The differences in gc among the canopy positions resulted primarily from a ∼10‐fold greater aerodynamic boundary‐layer conductance (gbl) in the upper canopy (mean gbl was 1695 vs 161 mmol m−2 s−1, respectively) in the day time. The asymptotic nature of gs versus gc relationship observed in the lower canopy suggests that stomata limit transpiration from the shade foliage at higher gbl. The linear relationship (R2 = 0·49–0·58, P < 0·001) between KT and FLA alludes to the role of hydraulic capacity in the control of sap fluxes. The dominant environmental factor affecting FLA in the day time was irradiance, and air relative humidity at night. The strong dependence of FLA on atmospheric evaporative demand in the night time proved that nocturnal branch sap flow indeed represents transpiration, but not tissue water recharge. Nocturnal FLA was about 24·7 and 19·5% of the respective mean day‐time rates for the upper and lower canopy. Owing to short northern nights in summer, nocturnal transpiration constituted only 6–8% of the daily total water loss. The variation in branch‐level sap flow is determined not merely by environmental gradients existing within the forest canopy, but depends largely on trees' internal properties, including stomatal conductance and plant hydraulic properties. Copyright © 2010 John Wiley & Sons, Ltd.
A review of advances in flash flood forecasting

Flash flooding is one of the most hazardous natural events, and it is frequently responsible for loss of life and severe damage to infrastructure and the environment. Research into the use of new modelling techniques and data types in flash flood forecasting has increased over the past decade, and this paper presents a review of recent advances that have emerged from this research. In particular, we focus on the use of quantitative precipitation estimates and forecasts, the use of remotely sensed data in hydrological modelling, developments in forecasting models and techniques, and uncertainty estimates. Over the past decade flash flood forecast lead‐time has expanded up to six hours due to improved rainfall forecasts. However the largest source of uncertainty of flash flood forecasts remains unknown future precipitation. An increased number of physically based hydrological models have been developed and used for flash flood forecasting and they have been found to give more plausible results when compared with the results of conceptual, statistical, and neural network models. Among the three methods for deciding flash flood occurrence discussed in this review, the rainfall comparison method (flash flood guidance) is most commonly used for flash flood forecasting as it is easily understood by the general public. Unfortunately, no existing model is capable of making reliable flash flood forecasts in urban watersheds even though the incidence of urban flash flooding is increasing due to increasing urbanisation. Copyright © 2011 John Wiley & Sons, Ltd.
Not all bad decisions are alike: approach and avoidant bad decisions are associated with distinct network organization

Introduction Decisions under ambiguity occurs daily for everyone. Subsequently, we all deliberate upon options to initiate an action most appropriate for current goal demands. Researchers has attempted to identify factors which contribute to risk taking, alongside the neurocircuitry underpinning it. Empirically, uncertain decision making is frequently assessed using the Iowa Gambling Task (IGT). Research have reliably identified varying regions implicating two broader circuits known as the reward and salience networks. However, considerable work has focused on contrasting “good” versus “bad” decisions. Methods The present investigation attempted a unique approach to analyzing the modified IGT acquired during fMRI (n = 24) and focused on active and passive bad decisions to identify potential internetwork connectivity, dissociable connectivity patterns between approach and avoidant bad decisions, and their relationship with personality traits, which can be linked with behavioral approach styles. Results Network cluster analyses revealed general internetwork connectivity when passing (avoiding) good decks; however, the OFC was functionally disconnected from the rest of the selected brain regions when playing (approaching) bad decks. Decreased reward responsiveness was linked to increased functional connectivity between the lateral OFC and aSMG, while drive was associated with increased functional connectivity between dACC and aINS. Discussion We report evidence that approach and avoidant bad decisions are associated with distinct neural communication patterns. Avoidant decisions were marked by substantial network integration and coherence, contrasted with the general scarcity of internetwork communication observed for approach decisions. Furthermore, the present investigation observed preliminary evidence of personality traits linked with neural communication between salience and reward evaluative networks.
Response of Sunflowers to Quantities of Irrigation Water, Irrigation Regimes and Salinities in the Water and Soil

Summary The production of sunflower grains for roasting was investigated in two soil types under different quantities of applied saline and non-saline irrigation water, different irrigation managements, soil salinity due to previous use of saline water or due to a raised water table. It was shown in one experiment, conducted in a loess type soil, that sunflowers extracted water at least to a soil depth of 120 cm, when the available water from the top layers was used up. The crop in this soil consumed all the available soil water from nearly the entire root zone, while in the clay soil limited water was consumed from deep layers, due to the high salinity and lack of aeration. No decrease in yield was found in the loess soil when 75% of the full amount of water (which was 0.8 of Class A pan evaporation rate) was applied. When only 50% was applied a significant decrease in yield was obtained. In contrast, in the clay soil even 75% of the full amount of water decreased the yield remarkably. Under dry-land conditions approximately 65% of maximum yield was found in the loess soil but only 45% in the clay soil. These differences are all attributed to a shallow active root system in the clay soil. Residual soil salinity from previously use of saline water had no effect on grain production in the loess soil, while saline irrigation water applied during the irrigation season decreased production, but only when water supply was not rate limiting. The combination of saline water and residual soil salinity had a marked effect on the decrease of grain yield under limited irrigation. In both soils a reduction in the amount of water applied per single irrigation and maintaining the entire irrigation period caused a significantly smaller decrease in yield than shortening the irrigation period and applying the full demand.
Thermal-Aware Fixed-Outline 3-D IC Floorplanning: An End-to-End Learning-Based Approach

High temperature and temperature nonuniformity pose significant challenges in 3-D integrated circuits (3-D ICs). Numerous studies have explored thermal issues in 3-D IC floorplanning. However, most existing handcrafted heuristic algorithms suffer from long iteration cycles, resulting in inefficient thermal management and no guarantee of optimal performance. In addition, with the increasing complexity of modern integrated circuit design, current floorplanning techniques encounter the “curse of dimensionality” and struggle to optimize large-scale cases. To address these challenges, this article proposes a novel end-to-end learning-based approach for thermal-aware fixed-outline 3-D IC floorplanning. In the tier assignment stage, we utilize a deep ${k}$ -means clustering algorithm to allocate modules to different tiers, aiming to achieve an improved cross-tier power distribution. In the global distribution (GD) stage, we formulate the floorplanning problem as a Markov decision process (MDP). By combining graph convolutional networks (GCNs) with a multiagent deep reinforcement learning (MADRL) algorithm, we optimize the positions of modules and through-silicon vias (TSVs), while incorporating an attention mechanism in the centralized critic to enhance cooperation among agents. Finally, in the TSV assignment (TA) stage, we refine the TSV positions using the MADRL algorithm, further reducing wirelength and temperature in 3-D ICs. Experimental results demonstrate that our proposed approach outperforms state-of-the-art heuristic-based 3-D IC floorplanner in terms of wirelength and temperature optimization.
Identification of Determinants Entrepreneurial Intentions during the Covid Pandemic on Open Unemployment in Tasikmalaya

During the Covid-19 pandemic, the business sector experienced a slowdown due to declining purchasing power. Dozens of companies had their revenue decline, forcing them to focus on cost efficiencies, including laying off workers. As a result, the unemployment rate has surged. Becoming an entrepreneur is a viable option to generate income for those who are unemployed. This research aims to identify the factors driving individual motivation to become entrepreneurs in adverse conditions, especially during a pandemic. The conceptual model employed in this research was modified from the Theory of Planned Behavior by adding resilience as an additional independent variable. Using simple random sampling for sample selection, a total of 225 respondents who live in Tasikmalaya City and Tasikmalaya Regency participated in this study. The multiple linear regression methods was deployed for further analysis and hypothesis testing. The results reveal that the formation of entrepreneurial intentions in the unemployed in Tasikmalaya City and Tasikmalaya Regency is influenced by attitudes, subjective norms, perceptions of behavioral control, and resilience.
Diurnal variation of ionization rate close to ground

Ionization rates for different times on two fair-weather days are computed using Hoppel's (1985) theoretically deduced aerosol-size-dependent attachment coefficients and the aerosol-size spectra derived from our measurements of mobility spectra of atmospheric ions made at a height of 1 m above ground at Pune (18°32′N, 73°51′E, 559 m above mean sea level), India. The ionization rates are minimum in the afternoon and increase to their maximum values of 116.52 and 103.93 ion pairs cm−3 s−1 in the early morning. Increase in ionization rate is found to accompany with the increase in the total aerosol concentration. Our results suggest that this increase in ionization rate may be due to addition of some radioactive aerosol particles, primarily in the size range of 0.002 to 0.004 μm.
Yogākṣara: Kalepasan road map

Yogākṣara is yoga of a specific nature, as it uses the medium of bhijākṣara [seed script] as a kalêpasan roadmap. In the Śiwa Tattwa it is stated that the Ākṣara relates to something magical, seen as the universal code for the true self [Ātman] as a link to the soul of the universe [Paramātman]. The yogākṣara system, as one of the religious systems of the Balinese Hindu community, has never been studied seriously based on academic rules, therefore this research is expected to explain yogākṣara scientifically. This type of research is qualitative by referring to Śiwa Tattwa text as primary data. Data collection in this research uses observation, interview, documentation, and literature study techniques. To dissect the problem formulation, semiotic theory and religious theory are used. This study found that Ākṣara is the rope that binds the Ātman to remain in the body. Kalêpasan is the process of how we release ākṣara so that the Ātman is released from its bonds. This is where yogis take the ultimate path of death [kalêpasan] by knowing and understanding the secret of ākṣara within and the secret of ākṣara in the universe through yogākṣara.
Intercomparison of satellite observations and atmospheric model simulations of a meso-scale cyclone in the Mediterranean Sea

The paper investigates the possibility of integrating satellite observations and limited-area atmospheric model simulations in the study of the meso-scale processes in the Mediterranean Sea. A tropical-like cyclone, which occurred on 9 and 10 December 1996 over the central Mediterranean Sea, has been used as a case study. The main features of the cyclone, derived from satellite wind and vertically integrated liquid water content (LWC) fields, have been compared with the results of a limited-area model, obtained with and without scatterometer data assimilation. The results indicate an acceptable comparability of the wind fields, despite the model‐scatterometer wind bias and the misplacement of the cyclone position in the simulations. The vertically integrated quantities, such as the LWC and the model vertically integrated cloud mixing ratio (CWINT), do not show the same degree of comparability. This is because they reflect only large-scale features, whereas the meso-scale features are only detected by the satellite observations. As expected, the results of assimilating the scatterometer data into the model show an improvement over the cyclone positioning but do not provide a better description of its meso-scale features. The lack of improvement in the determination of the CWINT horizontal structure after scatterometer data assimilation indicates the need to assimilate the LWC fields in future studies.