Miracle-dz/UniFAR-FTUs huggingface dataset:
Rare Species-Driven Diversity–Ecosystem Multifunctionality Relationships are Promoted by Stochastic Community Assembly

The relative functional importance of rare and abundant species in driving relationships between biodiversity and ecosystem functions remains unknown. Here, we highlighted the importance of rare species for ecosystem multifunctionality. ABSTRACT The relative functional importance of rare and abundant species in driving relationships between biodiversity and ecosystem functions (BEF) remains unknown. Here, we investigated the functional roles of rare and abundant species diversity (multitrophic soil organism groups) on multifunctionality derived from 16 ecosystem functions in 228 agricultural fields relating to soil and crop health. The results revealed that the diversity of rare species, rather than of abundant species, was positively related to multifunctionality. Abundant taxa tended to maintain a larger number of functions than rare taxa, while rare subcommunity contributed more phylotypes supporting to the single ecosystem functions. Community assembly processes were closely related to the ecosystem functional performance of soil biodiversity, only observed in rare subcommunity. Higher relative contributions of stochastic assembly processes promoted the positive effects of diversity of rare taxa on multifunctionality, while reducing their diversity and multifunctionality overall. Our results highlight the importance of rare species for ecosystem multifunctionality and elucidate the linkage between ecological assembly processes and BEF relationships. IMPORTANCE The relative functional importance of rare and abundant species in driving relationships between biodiversity and ecosystem functions remains unknown. Here, we highlighted the importance of rare species for ecosystem multifunctionality. In addition, community assembly processes were closely related to the ecosystem functional performance of soil biodiversity in rare subcommunity. Stochastic assembly processes promoted the positive effects of diversity of rare taxa on multifunctionality, while reducing their diversity and multifunctionality overall. This study expands current understanding of the mechanisms underpinning the relationships between biodiversity and ecosystem functions and suggests that stochastic community assembly enhances BEF relationships.
Chronic Social Defeat Stress Shifts Peripheral Circadian Clocks in Male Mice in a Tissue-Specific and Time-of-Day Dependent Fashion

Uncontrollable stress is linked to the development of many diseases, some of which are associated with disrupted daily rhythms in physiology and behavior. While available data indicate that the master circadian pacemaker in the suprachiasmatic nucleus (SCN) is unaffected by stress, accumulating evidence suggest that circadian oscillators in peripheral tissues and organs can be shifted by a variety of stressors and stress hormones. In the present study, we examined effects of acute and chronic social defeat stress in mice and addressed the question of whether effects of uncontrollable stress on peripheral clocks are tissue specific and depend on time of day of stress exposure. We used mice that carry a luciferase reporter gene fused to the circadian clock gene Period2 (PER2::LUC) to examine daily rhythms of PER2 expression in various peripheral tissues. Mice were exposed to social defeat stress in the early (ZT13-14) or late (ZT21-22) dark phase, either once (acute stress) or repeatedly on 10 consecutive days (chronic stress). One hour after the last stressor, tissue samples from liver, lung, kidney, and white adipose tissue (WAT) were collected. Social defeat stress caused a phase delay of several hours in the rhythm of PER2 expression in lung and kidney, but this delay was stronger after chronic than after acute stress. Moreover, shifts only occurred after stress in the late dark phase, not in the early dark phase. PER2 rhythms in liver and WAT were not significantly shifted by social defeat, suggesting a different response of various peripheral clocks to stress. This study indicates that uncontrollable social defeat stress is capable of shifting peripheral clocks in a time of day dependent and tissue specific manner. These shifts in peripheral clocks were smaller or absent after a single stress exposure and may therefore be the consequence of a cumulative chronic stress effect.
Intelligent Robust Nonlinear Controller for MEMS Angular Rate Sensor

This paper presents a control strategy for micro-electro-mechanical systems (MEMS) z-axis gyroscope, based on the coupling of the fuzzy logic control with the so-called sliding mode control (SMC) approach. An adaptive model reference state tracking controller which can estimate the angular velocity vector, and the damping and stiffness model coefficients in real-time is proposed. The motivation for using SMC in MEMS mainly relies on its appreciable features, such as design simplicity and robustness. The tracking performance of pure adaptive SMC is affected by high range chattering due to modeling uncertainties and exogenous inputs. In this paper, this problem is suitably circumvented by adopting an adaptive fuzzy sliding mode control (AFSMC) approach which results in chattering free tracking and improved estimation accuracy. For this proposed approach, we have used a fuzzy logic control to generate the reaching control signal. Therefore, the simple and intelligent Mamdani-type controller is resulted with high robustness against parametric uncertainties and exogenous disturbances. The stability of the system is guaranteed in the sense of the Lyapunov stability theorem. Numerical simulations using the nonlinear dynamic model of a MEMS z-axis gyroscope with uncertainties show the effectiveness of the approach in trajectory tracking problems and robustness in estimating the gyroscope parameters and also the angular velocity. The simulation results that are compared with the results of conventional adaptive SMC indicate that the control performance of the gyroscope system is satisfactory and the proposed AFSMC can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2012 IFSA.
Design of a new synthetic nanocatalyst resulting high fuel quality based on multiple supports: experimental investigation and modeling

Abstract In order to meet the environmental legislations related to sulfur content, it is important to find an alternative techniques for deep removal of sulfur components from fuels. So, in this study, a novel nano-catalyst based on iron oxide (Fe2O3) as active component prepared over composite support (γ-Alumina + HY-zeolite) is developed here for efficient removal of sulfur compounds from fuel via oxidation process. The precipitation method is employed first to prepare the composite support and then the impregnation method is utilized to generate a novel synthetic homemade (Fe2O3/composite support) nanocatalysts that has not been developed in the literature (iron oxide over composite support). The characterizations of the prepared catalysts display that the surface area of the catalyst increases with increasing the amount of Y-zeolite in composite support. The effectiveness of the catalysts is tested by utilizing oxidative desulfurization (ODS) operation under several operating conditions. The results of the experimental work show that the activity of oxidative desulfurization enhances with increasing Y-zeolite, temperature, and batch time under moderate operating conditions. The oxidative desulfurization efficiency followed the order: CAT-1 < CAT-2 < CAT-3. The CAT-3 performed the high removal of sulfur compounds (90.73%) at 100 min and 423 K. The best values of the kinetic parameters of the ODS process are then determined based on experimental data and model based techniques within gPROMS package. Finally, the reactor model is used to determine the optimal operating conditions while maximizing the removal of sulfur compounds leading to cleaner fuel. Where, 99.3% of the sulfur removal has achieved at batch time of 190.6 min, temperature of 543.56 K and initial sulfur content at 0.8668 wt% in the presence of CAT-3 based on the optimal kinetic parameters (order of reaction (n) of 1.9865719, activation energy (EA) at 29.942 kJ/mol and pre-exponential factor (k 0) with 622.926 wt−0.9865719 min−1).
Does Innovation Promote Regional Economic Development? Evidence for Countries in Europe and Central Asia

The aim of this study is to assess the relationship between innovation and economic development in a sample of 47 countries from Europe and Central Asia for the years 1990-2020. We use patent applications per 100,000 people as a proxy for innovation. Our results suggest increase in the number of patents per 100,000 people by 10 (slightly less than 1 standard deviation) is associated with 3.4% increase in GDP per capita. The results remain robust after accounting for determinants of economic growth.
Linear-time Mixed-Cell-Height Legalization for Minimizing Maximum Displacement

Due to the aggressive scaling of advanced technology nodes, multiple-row-height cells have become more and more common in VLSI design. Consequently, the placement of cells is no longer independent among different rows, which makes the traditional row-based legalization techniques obsolete. In this work, we present a highly efficient linear-time mixed-cell-height legalization approach that optimizes both the total cell displacement and the maximum cell displacement. First, a fast window-based cell insertion technique introduced in [4] is applied to obtain a feasible initial row assignment and cell ordering which is known to be good for total displacement consideration. In the second stage, we use an iterative cell swapping algorithm to change the row assignment and the cell order of the critical cells for maximum displacement reduction. Then we develop an optimal linear time DAG-based fixed row and fixed order legalization algorithm to minimize the maximum cell displacement. Finally, we propose a cell shifting heuristic to reduce the total cell displacement without increasing the maximum cell displacement. Using the proposed approach, the quality provided by the global placement can be preserved as much as possible. Compared with the state-of-the-art work [4], experimental results show that our proposed algorithm can reduce the maximum cell displacement by more than 11% on average with similar average cell displacement.
Global Placement Exploiting Soft 2D Regularity

Cell placement is such a critical step for chip physical design that it needs many kinds of efforts for improvement. Recently, designs with 2D processing element arrays have become popular primarily due to their deep neural network computing applications. The 2D array regularity is similar to but different from the regularity of conventional datapath designs. To exploit the 2D array regularity, this work develops a new global placement technique built upon RePlAce, the latest state-of-the-art placement framework. Experimental results from various designs show that the proposed technique can reduce half-perimeter wirelength and Steiner tree wirelength by about $6%$ and $12%$, respectively.
A Comparison of Deep Learning Methods for Vision-based Fire Detection in Surveillance System

Fire detection by analyzing video and images from surveillance cameras is increasingly being used as an early warning system. Fire detection using video surveillance as input is much more efficient than fire sensors because of the broader coverage area and is relatively cheaper. Several previous studies used handcrafted features as a differentiator between fire and other objects. Features like color, texture, and motion can be used as a reference for detecting fire. Object detection techniques using video as input are overgrowing. Some researchers use deep learning architecture to detect objects from video input. The deep learning architecture is capable of extracting features directly from video input by leveraging neural network layers. This study tries to compare several deep learning methods for fire detection from video input. The methods compared are Faster RCNN, Yolov4, and Yolov5. After being evaluated, the method with the best True Positive Rate is Yolov4 with a value of 84.62%, while the method with the best True Negative Rate value is Yolov5 with a value of 97.06%. Yolov5 is also the method with the best computational time that is 23.26 FPS. Faster RCNN got an excellent positive rate, almost close to the positive rate of Yolov4 and Yolov5. However, the negative rate of Faster RCNN is quite alarming. The computation time required is also longer than Yolov4 and Yolov5.
Reconciling the Quality vs Popularity Dichotomy in Online Cultural Markets

We propose a simple model of an idealized online cultural market in which N items, endowed with a hidden quality metric, are recommended to users by a ranking algorithm possibly biased by the current items’ popularity. Our goal is to better understand the underlying mechanisms of the well-known fact that popularity bias can prevent higher-quality items from becoming more popular than lower-quality items, producing an undesirable misalignment between quality and popularity rankings. We do so under the assumption that users, having limited time/attention, are able to discriminate the best-quality only within a random subset of the items. We discover the existence of a harmful regime in which improper use of popularity can seriously compromise the emergence of quality, and a benign regime in which wise use of popularity, coupled with a small discrimination effort on behalf of users, guarantees the perfect alignment of quality and popularity ranking. Our findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.
Metrics for Robot Proficiency Self-assessment and Communication of Proficiency in Human-robot Teams

As development of robots with the ability to self-assess their proficiency for accomplishing tasks continues to grow, metrics are needed to evaluate the characteristics and performance of these robot systems and their interactions with humans. This proficiency-based human-robot interaction (HRI) use case can occur before, during, or after the performance of a task. This article presents a set of metrics for this use case, driven by a four-stage cyclical interaction flow: (1) robot self-assessment of proficiency (RSA), (2) robot communication of proficiency to the human (RCP), (3) human understanding of proficiency (HUP), and (4) robot perception of the human’s intentions, values, and assessments (RPH). This effort leverages work from related fields including explainability, transparency, and introspection, by repurposing metrics under the context of proficiency self-assessment. Considerations for temporal level (a priori, in situ, and post hoc) on the metrics are reviewed, as are the connections between metrics within or across stages in the proficiency-based interaction flow. This article provides a common framework and language for metrics to enhance the development and measurement of HRI in the field of proficiency self-assessment.
ART-3D: Analytical 3D Placement with Reinforced Parameter Tuning for Monolithic 3D ICs

In this paper, we show that true 3D placement approaches, enhanced with reinforcement learning, can offer further PPA improvements over pseudo-3D approaches. To accomplish this goal, we integrate an academic true 3D placement engine into a commercial-grade 3D physical design flow, creating ART-3D flow (Analytical 3D Placement with Reinforced Parameter Tuning-based 3D flow). We use a reinforcement learning (RL) framework to find optimized placement parameter settings of the true 3D placement engine for a given netlist and perform high-quality 3D placement. We then use an efficient 3D optimization and routing engine based on a commercial place and route (P&R) tool to maintain or improve the benefits reaped from true 3D placement till design signoff. We evaluate our 3D flow by designing several gate-only and processor benchmarks on a commercial 28nm technology node. Our proposed 3D flow involving true 3D placement offers the best PPA results compared to existing 3D P&R flows and reduces power consumption by up to 31%, improves effective frequency by up to 25%, and therefore reduces power-delay product by up to 43% compared with commercial 2D IC design flow. These improvements predominantly come from RL-based parameter tuning, as it improves the performance of the 3D placer by up to 12%.
Predicting Depression Symptoms from Discord Chat Messaging Using AI Medical Chatbots

Depression is a chronic illness with even Olympic athletes [1] and top tennis players [2] withdrawing from competitions due to it. It's important to diagnose depression early. Traditional methods rely on questionnaires to evaluate depression. But they have their limitations due to inherent bias and inhibitions in self reporting. We propose an intelligent chatbot powered by AI approach to assist medical professionals diagnose depression. Specifically, the AI could predict depression symptoms in conversations with pre-care health care personnel and/or personal chats. Notably, due to sensitivity and privacy regulation (HIPPA) such data is not readily available [3]. To use AI driven medical assistants, and to overcome this limitation in training AI models, we propose to seed the models with recent chat engine (Discord) conversation dataset in the channel #depression. We achieve at least 73% accuracy in predicting seven key symptoms for depression using our best ML models. Secondly, with our ensemble random forest model we could recall 30-69% of depression symptoms with 60-99% depression diagnosis accuracy which could be further tuned by medical professional if they know which ones of these symptoms is a key predictor of depression.
Vocal fold mucus layer: Comparison of histological protocols for visualization in mice

The epithelial associated mucus layer of vocal fold (VF) mucosa, plays an essential role in protecting and lubricating the tissue, as well as promoting normal voice quality. Serving as a habitat for laryngeal microbiota involved in the regulation of host immunity, VF mucus contributes to laryngeal health and disease. However, its unstable structure renders its' investigation challenging. We aim to establish a reproducible histological protocol to recover the natural appearance of the VF mucus layer for investigation.
Effects of Primary Kidney Disease Etiology on Renal Osteodystrophy in Pediatric Dialysis Patients

Congenital diseases of the kidney and urinary tract (CAKUT) and glomerulonephritis are the main causes of chronic kidney disease (CKD) in children. Although renal osteodystrophy (ROD) and indices of mineral metabolism have been characterized in dialyzed children, the impact of primary kidney disease on ROD is unknown. We performed a cross‐sectional study of bone biopsies performed in 189 pediatric dialysis patients aged 12.6 ± 5.4 years. Patients were classified into three groups according to primary kidney disease: CAKUT (n = 82), hereditary (n = 22), or glomerular disease (n = 85). Serum concentrations of calcium, phosphate, alkaline phosphatase (ALP), parathyroid hormone (PTH), and 25(OH) vitamin D were measured at the time of biopsy. Fibroblast growth factor 23 (FGF23) levels were measured in a subset of 59 patients. Levels of calcium, phosphate, PTH, and 25(OH) vitamin D were similar across groups. CAKUT patients had higher serum ALP and lower C‐terminal FGF23 levels. Bone turnover and bone volume parameters did not differ across groups. However, osteoid volume (OV/BV), osteoid surface (OS/BS), and osteoid maturation time (OMT) were highest in the CAKUT group and lowest in the hereditary group. Multiple regression analysis revealed that calcium, phosphate, ALP, and PTH were independently associated with OV/BV and osteoid thickness (O.Th). PTH was an independent factor affecting bone formation rate. The relationship between CKD etiology and bone histomorphometric variables was abrogated after adjustment for biochemical parameters in the multivariable models. Overall, bone histology differed according to CKD etiology in the unadjusted analysis; however, this association could not be confirmed independently of biochemical parameters. Although CAKUT patients had a greater mineralization defect with elevated serum ALP levels, longitudinal studies will be needed to elucidate mediation pathways that might be involved in the complex interplay of CKD‐mineral bone disease (MBD). © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.
Measurement and Analysis of the Microphysical Properties of Arctic Precipitation Showing Frequent Occurrence of Riming

Detailed ground‐based observations of snow are scarce in remote regions, such as the Arctic. Here, Multi‐Angle Snowflake Camera measurements of over 55,000 solid hydrometeors—obtained during a two‐year period from August 2016 to August 2018 at Oliktok Point, Alaska—are analyzed and compared to similar measurements from an earlier experiment at Alta, Utah. In general, distributions of hydrometeor fall speed, fall orientation, aspect ratio, flatness, and complexity (i.e., riming degree) were observed to be very similar between the two locations, except that Arctic hydrometeors tended to be smaller. In total, the slope parameter defining a negative exponential of the size distribution was approximately 50% steeper in the Arctic as at Alta. Sixty‐six percent of particles were observed to be rimed or moderately rimed with some suggestion that riming is favored by weak boundary layer stability. On average, the fall speed of rimed particles was not notably different from aggregates. However, graupel density and fall speed increase as cloud temperatures approach the melting point.