Miracle-dz/UniFAR-FTUs huggingface dataset:
Cultivated Manatee Meat Aiding Amazon Biodiversity Conservation: Discussing a Proposed Model

: Cultivated meat (CM) is a disruptive technology that provides an alternative to animal protein. In this context, the Amazon manatee ( Trichechus inunguis ) emerges as an important case. Although it is illegal to hunt this large mammal, its meat continues to be consumed, causing several threats to its natural habitat. The aim of this study is to explore the impacts of introducing the Amazon manatee CM into the traditional meat value chain as a tool to aid the biodiversity of the Amazon Basin. Thus, we developed a strengths, weaknesses, opportunities, and threats matrix from the content analysis of 11 interviews conducted between October 2021 and May 2022. The intervie-wees were experts in different ﬁelds, ranging from ﬁnancial analysts of novel food technologies to biologists, researchers, and others. We presented the theme of illegal hunting and its consequences during the interviews, followed by the CM process, and explained how the royalties from the sale of this innovative product could help to preserve Amazon biodiversity through the proposal of a new business model. The main ﬁndings suggest that the proposed model would produce good results, but the threat of a rebound effect from the consumption of wild animals was mentioned in most responses, especially by actors involved in conservation. The strengths and opportunities of this disruptive narrative mainly focused on preserving biodiversity and promoting environmental awareness, combining the conservation of wildlife and the consumption of novel food. The weaknesses included the lack of knowledge and the non-existent market. This framework is relevant for policymakers, nongovernmental organizations, and researchers seeking to improve the sustainability not only of the species found in the Amazon, but also around the world.
Deconvolution for Ill-Conditioned Systems

We propose an efficient, hybrid Fourier-wavelet reg- ularized deconvolution (ForWaRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and wavelet domains. The Fourier shrinkage exploits the Fourier transform's economical representation of the colored noise inherent in de- convolution, whereas the wavelet shrinkage exploits the wavelet domain's economical representation of piecewise smooth signals and images. We derive the optimal balance between the amount of Fourier and wavelet regularization by optimizing an approximate mean-squared error (MSE) metric and find that signals with more economical wavelet representations require less Fourier shrinkage. ForWaRD is applicable to all ill-conditioned deconvolu- tion problems, unlike the purely wavelet-based wavelet-vaguelette deconvolution (WVD); moreover, its estimate features minimal ringing, unlike the purely Fourier-based Wiener deconvolution. Even in problems for which the WVD was designed, we prove that ForWaRD's MSE decays with the optimal WVD rate as the number of samples increases. Further, we demonstrate that over a wide range of practical sample-lengths, ForWaRD improves on WVD's performance.
An IoT Controlled Smart Grid System for Theft Detection and Remote Power Redirection

It is important to have a smart energy monitoring system in this era of Industry 4.0. Internet of Things (IoT) technologies have been widely used in smart energy monitoring and controlling of grid statistics for reliable and efficient operation of the grid. This study is aimed at developing a control system for monitoring power use, detecting power theft and for electricity redirection. Hence, features such as: current theft detection, load parameter analysis, automatic power re-routing will be incorporated in the developed system. This will be achieved by the use of a microprocessor, which will act as the central processing unit, and other components such as, current sensors, relay module, an AC-DC isolated power Module (220VAC-12VDC), Bluetooth module, energy meter, internet of things (IoT). The implementation of this system helps solve problems that arise due to failure of the electricity grid leading to black out of an entire area solely dependent on that particular grid and also provides power theft information which is quite valuable to the energy producing companies
Therapeutic Potential of Nanomedicine in Management of Alzheimer’s Disease and Glioma

Abstract Neoplasm (Glioblastoma) and Alzheimer’s disease (AD) comprise two of the most chronic psychological ailments. Glioblastoma is one of the aggressive and prevalent malignant diseases characterized by rapid growth and invasion resulting from cell migration and degradation of extracellular matrix. While the latter is characterized by extracellular plaques of amyloid and intracellular tangles of tau proteins. Both possess a high degree of resistance to treatment owing to the restricted transport of corresponding drugs to the brain protected by the blood–brain barrier (BBB). Development of optimized therapies using advanced technologies is a great need of today. One such approach is the designing of nanoparticles (NPs) to facilitate the drug delivery at the target site. The present article elaborates the advances in nanomedicines in treatment of both AD as well as Gliomas. The intention of this review is to provide an overview of different types of NPs with their physical properties emphasizing their importance in traversing the BBB and hitting the target site. Further, we discuss the therapeutic applications of these NPs along with their specific targets. Multiple overlapping factors with a common pathway in development of AD and Glioblastoma are discussed in details that will assist the readers in developing the conceptual approach to target the NP for an aging population in the given circumstances with limitations of currently designed NPs, and the challenges to meet and the future perspectives.
CuO-Based Solar-Blind Photodetector for Flexible Electronics

Flexible UV-C i.e. solar-blind detectors are in high demand, not only due to their potential for non-line of sight communication, and environmental monitoring but also for medical technology. Now-a-days, the development of UV-C (200 nm to 270 nm) light sources and detectors is highly essential. Herein, we report the fabrication of UV-C photodetector using ultra-wide bandgap CuO nanostructures (NSs) with <inline-formula> <tex-math notation="LaTeX">$\text{E}_{\mathbf {g}}$ </tex-math></inline-formula>=3.98 eV, on a flexible substrate. All solution-based technique was followed for the growth of CuO NSs on poly-ethylene terephthalate (PET) substrate. The as-fabricated device has shown photo-sensitivity of <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>15, photo-responsivity of <inline-formula> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula>485 mA/W and photo-detectivity of <inline-formula> <tex-math notation="LaTeX">$\sim \,\,1.4\times 10^{11}$ </tex-math></inline-formula> Jones with 220 nm excitation (at −20 V applied bias). Moreover, the external quantum efficiency (EQE) and linear-dynamic range (LDR) of the device were calculated to be 74 % and 56 dB, respectively at 220 nm excitation.
DAO Governance

Decentralized autonomous organizations (DAOs) operate based on a set of decision-making rules encoded into smart contracts utilizing blockchain technology. They lack a central point of authority in contrast to traditional corporations. In our paper, we develop a theoretical model of DAO governance featuring strategic token trading and a token-based voting system to investigate potential conflicts of interest between a large participant (“whale”) and many small participants. Our results show that ownership concentration is negatively related to platform growth, but platform size, token illiquidity, and long-term incentives can mitigate the negative effects. We confirm these predictions using novel voting data on more than 200 DAO projects from July 2020 to July 2022.
A Model El Niñ–Southern Oscillation

Abstract A coupled atmosphere-ocean model is developed and used to study the ENSO (El Niñ/Southern Oscillation) phenomenon. With no anomalous external forcing, the coupled model reproduces certain key features of the observed phenomenon. including the recurrence of warm events at irregular intervals with a preference for three to four years. It is shown that the mean sea surface temperature, wind and ocean current fields determine the characteristic spatial structure of ENSO anomalies. The tendency for phase-locking of anomalies is explained in terms of a variation in coupling strength associated with the annual cycle in the mean fields. Sensitivity studies reveal that both the amplitude and the time of scale of the oscillation are sensitive to several parameters that affect the strength of the atmosphere–ocean coupling. Stronger coupling implies larger oscillations with a longer time scale. A critical element of the model oscilliation is the variability in the equatorial heat content of the upper o.
Facing the infinity: tackling large samples of challenging Chironomidae (Diptera) with an integrative approach

Background Integrative taxonomy is becoming ever more significant in biodiversity research as scientists are tackling increasingly taxonomically challenging groups. Implementing a combined approach not only guarantees more accurate species identification, but also helps overcome limitations that each method presents when applied on its own. In this study, we present one application of integrative taxonomy for the highly abundant and particularly diverse fly taxon Chironomidae (Diptera). Although non-biting midges are key organisms in merolimnic systems, they are often cast aside in ecological surveys because they are very challenging to identify and extremely abundant. Methods Here, we demonstrate one way of applying integrative methods to tackle this highly diverse taxon. We present a three-level subsampling method to drastically reduce the workload of bulk sample processing, then apply morphological and molecular identification methods in parallel to evaluate species diversity and to examine inconsistencies across methods. Results Our results suggest that using our subsampling approach, identifying less than 10% of a sample’s contents can reliably detect >90% of its diversity. However, despite reducing the processing workload drastically, the performance of our taxonomist was affected by mistakes, caused by large amounts of material. We conducted misidentifications for 9% of vouchers, which may not have been recovered had we not applied a second identification method. On the other hand, we were able to provide species information in cases where molecular methods could not, which was the case for 14% of vouchers. Therefore, we conclude that when wanting to implement non-biting midges into ecological frameworks, it is imperative to use an integrative approach.
Concept Parser With Multimodal Graph Learning for Video Captioning

Conventional video captioning methods are either stage-wise or simple end-to-end. While the former might introduce additional noise when exploiting off-the-shelf models to provide extra information, the latter suffers from lacking high-level cues. Therefore, a more desired framework should be able to capture multi-aspects of videos consistently. To this end, we present a concept-aware and task-specific model named CAT that accounts for both low-level visual and high-level concept cues, and incorporates them effectively in an end-to-end manner. Specifically, low-level visual and high-level concept features are obtained from the video transformer and concept parser of CAT. And a concept loss is further introduced to regularize the learning process of concept parser w.r. t. generated pseudo ground truth. To combine multi-level features, a caption transformer is later introduced in CAT, where visual and concept features are the inputs and caption is its output. In particular, we make critical design choices in the caption transformer to learn to exploit these cues with a multi-modal graph. This is achieved by a graph loss that enforces effective learning of intra and inter correlations between multi-level cues. Extensive experiments on three benchmark datasets demonstrate that CAT achieves 2.3 and 0.7 improvements in the CIDEr metric on MSVD and MSR-VTT compared to the state-of-the-art method SwinBERT and also achieves a competitive result on VATEX.
Estimation of redshifts from early galaxies

Dating early galaxies is a current technical, empirical and theoretical problem. Progress has been made recently calculating these dates using the observed redshift, z, alone. While this relaxes the need for simultaneous determination of luminosity distances with redshifts; accurate redshift determination beyond z ≥ 3 is difficult. We have shown that light emissions tend toward blue with increasing emission age and this is supported by recent data. Here we combine calculations of cosmological times from redshifts alone with the emission frequency model for lookback times. This allows a better gauge between recombination and the first galaxies and may provide additional 50-200 M years between these events.
Deciphering the Molecular Mechanisms of Autonomic Nervous System Neuron Induction through Integrative Bioinformatics Analysis

In vitro derivation of human neurons in the autonomic nervous system (ANS) is an important technology, given its regulatory roles in maintaining homeostasis in the human body. Although several induction protocols for autonomic lineages have been reported, the regulatory machinery remains largely undefined, primarily due to the absence of a comprehensive understanding of the molecular mechanism regulating human autonomic induction in vitro. In this study, our objective was to pinpoint key regulatory components using integrated bioinformatics analysis. A protein–protein interaction network construction for the proteins encoded by the differentially expressed genes from our RNA sequencing data, and conducting subsequent module analysis, we identified distinct gene clusters and hub genes involved in the induction of autonomic lineages. Moreover, we analyzed the impact of transcription factor (TF) activity on target gene expression, revealing enhanced autonomic TF activity that could lead to the induction of autonomic lineages. The accuracy of this bioinformatics analysis was corroborated by employing calcium imaging to observe specific responses to certain ANS agonists. This investigation offers novel insights into the regulatory machinery in the generation of neurons in the ANS, which would be valuable for further understanding and precise regulation of autonomic induction and differentiation.
On Chaplygin models in f(G) gravity

The current work treats cosmological perturbation in a mixture of standard matter, Chaplygin gas as well as Gauss-bonnet fluids using a 1+3 covariant approach in the context of modified $f(G)$ gravity. We define the gradient variables to obtain linear perturbation equations. After scalar and redshift transformations, we consider both an original Chaplygin and generalized Chaplygin gas models under Gauss-bonnet gravity. For pedagogical purposes, the consideration of polynomial $f(G)$ gravity model was used to solve the perturbation equations for short- and long- wavelength modes and investigate the late time evolution. The numerical solutions were obtained. The results show that the energy overdensity perturbations decay with an increase in redshift. The treatment recovers GR results under limiting cases.
Subject choice motivation and students’ conceptions of employability: thin and thick

Abstract This study aims to fill a gap in our understanding of the relationship between two major topics in educational research: higher education choices and students’ conceptions of employability. It explores, first, the balance between vocational calling, instrumental considerations and chance in subject choice and, second, the relation between vocational calling and two alternative conceptions of employability (thin and thick) articulated in the article. Using survey data from ‘Education’ students in Spain, the results show an association between a strong vocational calling in subject choice and a ‘thick’ conception of employability that goes beyond credential performance and underlines the importance of an economy of experience. By contrast, students with lower vocational calling adopt a narrower (‘thin’) conception of employability, based on credential performance. The consequences of these results for theory and for practice are discussed, highlighting the need to better acknowledge the role of interests, and not only skills, in the employability agenda.
Assessment of the Environmental Impacts of a Localized Food System and Food Waste Reduction in a Water-Scarce Region Using Diet Optimization Models

Despite growing interest in fresh local produce across the United States, scaling up local agricultural development might impose new environmental pressures on increasingly scarce water and land resources in specific localities. Drawing upon the case of the Palouse of the US Inland Northwest, this study evaluates land and water footprints of local foods along with food waste reduction in a water-scarce region. We used both non-robust and robust diet-optimization techniques to estimate the minimum amounts of irrigation water necessary to grow foods locally and to satisfy the local population’s caloric or nutrition needs. Our modeling results indicate that, on an annual basis, an increase of less than 5% of the current freshwater withdrawal on the Palouse could satisfy 10% of the local population’s aspirational demand for locally grown food products, while more than 35% of local foods (by mass) may be wasted. Furthermore, reducing food waste by 50% could simultaneously reduce water use by up to 24%, cropland use by 13%, and pastureland use by 20%. Our findings not only provide intriguing information for access to local food but could also be used to stimulate new efforts to increase consumers’ and retailers’ awareness of environmental benefits associated with food waste reduction.
Validity and Reliability of the Empowered Veteran Index Among Military Veterans

We sought to develop a tool designed to measure psychosocial reintegration outcomes among military veterans. We used a sequential mixed-methods study design for this research project. We conducted semi-structured interviews about veteran reintegration with 30 veteran members of The Mission Continues (TMC). Transcripts were coded/thematized based on three TMC program areas: personal growth (PG), connectedness (C), and community impact (CI). We developed/pilot-tested 65 Likert-scale items across three constructs. We collected survey data (n = 851) and performed exploratory factor analysis. Cronbach’s alpha was used to measure internal consistency reliability. Results showed that the three-factor model was satisfactory, with eigenvalues of 8.11, 5.50, and 4.92, respectively, on the PG, C, and CI constructs. Model fit indices were also acceptable (RMSEA = 0.06) and Cronbach’s α’s were >. 90. This study provided initial validity and reliability evidence for a tool that can be used to assess psychosocial reintegration outcomes among veterans.
Classification of Nilpotent Lie Algebras of Dimension 7 (over Algebraically Closed Field and R)

This thesis is concerned with the dassitication of 7-dimensional nilpotent Lie algebras. Skjdbred and Sund have published in 1977 their method of constructing all nilpotent Lie algebras of dimension rr given those algebras of dimension < n, and their automorphism groups. By using this method, we constmct ail nonisomorphic 7-dimensional nilpotent Lie algebras in the following two cases: (1) over an algebraically closed field of arbitrary characteristic except 2; (2) over the real field R. We have compared our lists with three of the most recent lists (those of Seeley, Ancochea-Goze, and Romdhani). While our list in case (1) over C cliffers greatly fkom that of Ancochea-Goze, which contains too many errors to be usable, it agrees with that of Seeley apart fkom a few corrections that should be made in his Est, Our list in case (2) over R contains all the algebras on Romdhani's list, which omits many algebras.
The Diagnostic Value of Inflammatory Markers (CRP, IL6, CRP/IL6, CRP/L, LCR) for Assessing the Severity of COVID-19 Symptoms Based on the MEWS and Predicting the Risk of Mortality

Introduction Various diagnostic tools are used to assess the severity of COVID-19 symptoms and the risk of mortality, including laboratory tests and scoring indices such as the Modified Early Warning Score (MEWS). The diagnostic value of inflammatory markers for assessing patients with different severity of COVID-19 symptoms according to the MEWS was evaluated in this study. Materials and Methods The concentrations of CRP (C-reactive protein) (immunoassay) and IL6 (interleukin 6) (electrochemiluminescence assay) were determined, and CRP/IL6, CRP/L, and LCR ratios were calculated in blood serum samples collected from 374 COVID-19 patients. Results We demonstrated that CRP, IL6, CRP/IL6, CRP/L, LCR inflammatory markers increase significantly with disease progression assessed based on the MEWS in COVID-19 patients and may be used to differentiating patients with severe and non-severe COVID-19 and to assess the mortality. Conclusion The diagnostic value of inflammatory markers for assessing the risk of mortality and differentiating between patients with mild and severe COVID-19 was confirmed.
Control of Linear Dynamic Systems with Set Constrained Disturbances

A linear dynamic system with input and observation uncertainties is studied. The uncertainties are constrained to be contained in specified sets. No probabilistic structure is assumed. The problem of keeping the state of the system in a specified region is investigated. Necessary and sufficient conditions for a solution to the problem and an algorithm that constructs the control are derived for open-loop and closed-loop control laws. The algorithm is also approximated by a bounding ellipsoid algorithm. Two special control laws, linear and L'linear-plus-dead-band,'y are studied, and the regions that contain the state and control are characterized. El- lipsoids that bound the state and control are also derived, and a simple example of linear and linear-plus-dead-band control laws is presented. "keep t.he state in the t.arget tube;" i.e., a cont,rol that keeps the state wit.hin some prespecified region of state space is desired. Explicit equations suit,able for comput.er study and actual implementa.tion are given. The motivation for this research came from considera- tion of the "load frequency control" problem of electric power systems. However the results are presented in a general mathematical framework without discussing t.he part,icular application. In a general context,, the use of set constrained disturbances can be motivat.ed by the fa.ct that bounds are often availa.ble, while necessary sta,tistics a.nd probability dist.ributions needed for st,o- chast,ic models are often not. available. Emphasis on t.he state-in-t,he-tube problem can be motiva.t,ed by the fact that the real purpose of a cont.roller is often just to keep t,he stat.e (or some function thereof) sufficiently close to some desired operating point or trajectory. Minimization of some scalar cost criteria is oft,en of secondary importance The present. control t.heory arose out, of Schweppe's (l) state estimation concepts. The ideas a.re related to the work of Witsenhausen (2), (3) and Delfour and Mitt.er (4). The results reported in Section 111-A of this paper have also been obtained by Bel-tsekas (7). A precise problem statement is given in Sect-ion 11. General c0ncept.s needed to keep the state in the tube are developed in Sect,ion 111, n-hile in Sect.ions IV and TT linear cont.rols and linear-plus-dead-band controls are studied. Section VI conta.ins a simple example a.nd Section VII, conclusions and extensions. 11. PROBLEM STATEMENT