Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete‐time decentralized linear quadratic control for linear time‐varying systems

This article addresses the problem of designing a decentralized control solution for a network of agents modeled by linear time‐varying (LTV) dynamics, in a discrete‐time framework. A general scheme is proposed, in which the problem is formulated as a classical linear quadratic regulator problem, for the global system, subject to a given sparsity constraint on the gain, which reflects the decentralized nature of the network. A method able to compute a sequence of well‐performing stabilizing regulator gains is presented and validated resorting to simulations of two randomly generated LTV systems, one stable and the other unstable. Moreover, a tracking solution is developed, building on the solution to the regulator problem. Both methods rely on a closed‐form solution, thus they can be computed very rapidly. Similarly to the centralized solution, both the presented methods require that a window of the future system dynamics is known. Both methods are validated resorting to simulations of: (i) a nonlinear network of four interconnected tanks; and (ii) a large‐scale nonlinear network of interconnected tanks. When implemented to a nonlinear network, approximated by an LTV system, the proposed methods are able to compute well‐performing gains that track the desired output. Finally, both algorithms are scalable, being adequate for implementation in large‐scale networks.
track2KBA: An R package for identifying important sites for biodiversity from tracking data

Identifying important sites for biodiversity is vital for conservation and management. However, there is a lack of accessible, easily applied tools that enable practitioners to delineate important sites for highly mobile species using established criteria. We introduce the R package ‘track2KBA’, a tool to identify important sites at the population level using tracking data from individual animals based on three key steps: (a) identifying individual core areas, (b) assessing population‐level representativeness of the sample and (c) quantifying spatial overlap among individuals and scaling up to the population. We describe package functionality and exemplify its application using tracking data from three taxa in contrasting environments: a seal, a marine turtle and a migratory land bird. This tool facilitates the delineation of sites of ecological relevance for diverse taxa and provides output useful for assessing their importance to a population or species, as in the Key Biodiversity Area (KBA) Standard. As such, ‘track2KBA’ can contribute directly to conservation planning at global and regional levels.
A systematic review of constructive and solutions journalism research

Academic activity surrounding constructive and solutions journalism has surged in recent years; thus, it is important to pause and reflect on this growing body of work in order to understand where the field can and should go in the future. We conducted a systematic review of existing literature on solutions and constructive journalism (N = 94), in an effort to (1) describe the state of this field by identifying the patterns and trends in the methodological and conceptual approaches, topics, institutions, countries and practices involved in this research, and (2) illuminate potentially important gaps in the field and suggest recommendations for future research.
Extraction of underlying factors causing construction projects delay in Nigeria

Purpose This paper aims to establish the most underlying factors causing construction projects delay from the most applicable. Design/methodology/approach The paper conducted survey of experts using systematic review of vast body of literature which revealed 23 common factors affecting construction delay. Consequently, this study carried out reliability analysis, ranking using the significance index measurement of delay parameters (SIDP), correlation analysis and factor analysis. From the result of factor analysis, this study grouped a specific underlying factor into three of the six applicable factors that correlated strongly with construction project delay. Findings The paper finds all factors from the reliability test to be consistent. It suggests project quality control, project schedule/program of work, contractors’ financial difficulties, political influence, site conditions and price fluctuation to be the six most applicable factors for construction project delay, which are in the top 25% according to the SIDP score and at the same time are strongly associated with construction project delay. Research limitations/implications This paper is recommending that prospective research should use a qualitative and inductive approach to investigate whether any new, not previously identified, underlying factors that impact construction projects delay can be discovered as it followed an inductive research approach. Practical implications The paper includes implications for the policymakers in the construction industry in Nigeria to focus on measuring the key suppliers’ delivery performance as late delivery of materials by supplier can result in rescheduling of work activities and extra time or waiting time for construction workers as well as for the management team at site. Also, construction stakeholders in Nigeria are encouraged to leverage the amount of data produced from backlog of project schedules, as-built drawings and models, computer-aided designs (CAD), costs, invoices and employee details, among many others through the aid of state-of-the-art data driven technologies such as artificial intelligence or machine learning to make key business decisions that will help drive further profitability. Furthermore, this study suggests that these stakeholders use climatological data that can be obtained from weather observations to minimize impact of bad weather during construction. Originality/value This paper establishes the three underlying factors (late delivery of materials by supplier, poor decision-making and Inclement or bad weather) causing construction projects delay from the most applicable.
Transition to Adulthood: Perspectives of Korean Immigrant Parents of Individuals With Intellectual and Developmental Disabilities

As the United States grows more racially and ethnically diverse, Koreans have become one of the largest ethnic minority populations. We conducted this qualitative study to explore the perspectives of Korean immigrant parents about their child’s future and the factors that shape those perspectives. We used modified grounded theory methods. Twenty Korean immigrant parents of children and adults with intellectual and developmental disabilities participated in the study. Four themes emerged: navigating complicated and limited service systems, maintaining safety and relationships through work and higher education, ongoing parental care at home, and the need for culturally relevant adult services. We discuss implications for culturally responsive practice and inclusive research.
Pre-Stack Seismic Data-Driven Pre-Salt Carbonate Reef Reservoirs Characterization Methods and Application

Carbonate reservoirs have significant reserves globally, but the substantial heterogeneity brings intractable difficulties to exploration. In the work area, the thick salt rock reduces the resolution of pre-salt seismic signals and increases the difficulty of reservoir characterization. Therefore, this paper proposes to utilize wavelet frequency decomposition technology to depict the seismic blank reflection area’s signal and improve the pre-salt signal’s resolution. The high-precision pre-stack inversion based on Bayesian theory makes full use of information from various angles and simultaneously inverts multiple elastic parameters, effectively depicting reservoirs with substantial heterogeneity. Integrating the high-precision inversion results and the Kuster-Toksöz model, a porosity prediction method is proposed. The inversion results are consistent with the drilling rock samples and well-logging porosity results. Moreover, the reef’s accumulation and growth, which conform to the geological information, proves the accuracy of the above methods. This paper also discusses the seismic reflection characteristics of reefs and the influence of different lithological reservoirs on the seismic waveform response characteristics through forward modeling, which better proves the rationality of porosity inversion results. It provides a new set of ideas for future pre-salt carbonate reef reservoirs’ prediction and characterization methods.
Resource allocation in Chlamydomonas reinhardtii exposed to sulphur starvation and anaerobiosis

ABSTRACT When environmental conditions change, microalgae can modulate the size of their macromolecular pools and their elemental composition in order to accommodate their nutritional and energetic demands. In this context, we evaluated how O2 levels and S starvation alter the resource partitioning in terms of organic and elemental composition in Chlamydomonas reinhardtii. The analysis of gene transcription, photosynthetic energy production and organic pool allocation allowed us to identify a two-phase cellular response related to external O2 concentration: (1) a short-term response where S starvation induced the expression of S starvation genes immediately after the start of the perturbation; (2) a long-term response that consisted of a drastic reduction of photosynthetic energy production and changes in C allocation following S deprivation, which was related to oxygen conditions. In anaerobic conditions, S starvation did not affect the pools of carbohydrates and lipids, whereas the protein pool decreased slightly over time. On the other hand, in aerobic conditions, S starvation modulated all organic pools: carbohydrates, lipids and proteins. We conclude that the energy levels produced by fermentation metabolism are not sufficient to sustain the acclimation process in C. reinhardtii; moreover, S availability affects C. reinhardtii more than do oxygen levels.
Bayesian Analysis of Partially Linear Additive Spatial Autoregressive Models with Free-Knot Splines

This article deals with symmetrical data that can be modelled based on Gaussian distribution. We consider a class of partially linear additive spatial autoregressive (PLASAR) models for spatial data. We develop a Bayesian free-knot splines approach to approximate the nonparametric functions. It can be performed to facilitate efficient Markov chain Monte Carlo (MCMC) tools to design a Gibbs sampler to explore the full conditional posterior distributions and analyze the PLASAR models. In order to acquire a rapidly-convergent algorithm, a modified Bayesian free-knot splines approach incorporated with powerful MCMC techniques is employed. The Bayesian estimator (BE) method is more computationally efficient than the generalized method of moments estimator (GMME) and thus capable of handling large scales of spatial data. The performance of the PLASAR model and methodology is illustrated by a simulation, and the model is used to analyze a Sydney real estate dataset.
AN ANALYSIS OF MORAL VALUES IN “ZOOTOPIA” MOVIE DIRECTED BY BYRON HOWARD AND RICH MOORE

This thesis attempts to analyze in the movie Zootopia directed by Byron Howard and Rich Moore. The study is based on two research questions, first about kind of moral values and second about the effects of moral values through the main character. The methodology of the study is descriptive qualitative. It means that it does not need statistic approach to explore the material. The primary data is taken from the original movie itself and script from Zootopia movie. The secondary data is taken from many journals, literary books and some relevant materials to support and complete the primary data sources. The result shows that there are ten types of kind of moral values which the writers found by in the script of the movie Zootopia. They are brave, respect, kind and friendly, confidence, loyalty, self-discipline, fair and human, love and affection, honesty and sensitive and not selfish. Then, the effects of moral values that reflected through the main character are optimistic, brave, ambitious, caring, and enthusiastic. The writers hope that movie lovers can see the positive aspect and take it to imply their life
Visualizing the ‘global knowledge economy’

ABSTRACT In a structuralist reading, the hegemonies of the global economy are perceived as threateningly fixed. We suggest understanding the global knowledge economy as an always unfinished project of ordering socio-spatial relations. To better communicate the struggles of peripherally located places/companies associated with this process of spatial ordering, we provide a simplified visualization of the global knowledge economy.
Dynamics of a power hacksaw mechanism, contact interaction with the workpiece, and material removal

ABSTRACT During the metal cutting process, dynamics of a mechanism influence the cutting phenomenon at the contact interface while the contact interaction also has its reciprocal effect on the mechanism. Modeling the dynamics and the contact interaction between the cutting tool and workpiece, causing material removal, is a complex and challenging task. The mechanism of a machine involves a number of interconnected rigid bodies that interact with each other dynamically. Modeling dynamics of such mechanisms is necessary from the point of view of design and analysis. In this paper, the multibond graph approach has been used fxor modeling the dynamics of a power hacksaw machine. The power hacksaw machine is quite commonly used for cutting materials in industries. This graphical approach offers scope for analysis of causality, which is an important aspect of physical systems, and its application, a feature not offered by the energy-based formulations, such as the Euler-Lagrange’s method and Hamilton’s method. These energy-based formulations, commonly used for modeling dynamics of physical systems, also tend to be too mathematically inclined. MATLAB coding for simulation has been done directly from the multibond graph model without explicitly deriving system equations. The simulation results have been presented, analyzed, and discussed.
What Have We Learned from the First 500 Avian Genomes?

The increased capacity of DNA sequencing has significantly advanced our understanding of the phylogeny of birds and the proximate and ultimate mechanisms molding their genomic diversity. In less than a decade, the number of available avian reference genomes has increased to over 500—approximately 5% of bird diversity—placing birds in a privileged position to advance the fields of phylogenomics and comparative, functional, and population genomics. Whole-genome sequence data, as well as indels and rare genomic changes, are further resolving the avian tree of life. The accumulation of bird genomes, increasingly with long-read sequence data, greatly improves the resolution of genomic features such as germline-restricted chromosomes and the W chromosome, and is facilitating the comparative integration of genotypes and phenotypes. Community-based initiatives such as the Bird 10,000 Genomes Project and Vertebrate Genome Project are playing a fundamental role in amplifying and coalescing a vibrant international program in avian comparative genomics. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Urban tree species classification using UAV-based multi-sensor data fusion and machine learning

ABSTRACT Urban tree species classification is a challenging task due to spectral and spatial diversity within an urban environment. Unmanned aerial vehicle (UAV) platforms and small-sensor technology are rapidly evolving, presenting the opportunity for a comprehensive multi-sensor remote sensing approach for urban tree classification. The objectives of this paper were to develop a multi-sensor data fusion technique for urban tree species classification with limited training samples. To that end, UAV-based multispectral, hyperspectral, LiDAR, and thermal infrared imagery was collected over an urban study area to test the classification of 96 individual trees from seven species using a data fusion approach. Two supervised machine learning classifiers, Random Forest (RF) and Support Vector Machine (SVM), were investigated for their capacity to incorporate highly dimensional and diverse datasets from multiple sensors. When using hyperspectral-derived spectral features with RF, the fusion of all features extracted from all sensor types (spectral, LiDAR, thermal) achieved the highest overall classification accuracy (OA) of 83.3% and kappa of 0.80. Despite multispectral reflectance bands alone producing significantly lower OA of 55.2% compared to 70.2% with minimum noise fraction (MNF) transformed hyperspectral reflectance bands, the full dataset combination (spectral, LiDAR, thermal) with multispectral-derived spectral features achieved an OA of 81.3% and kappa of 0.77 using RF. Comparison of the features extracted from individual sensors for each species highlight the ability for each sensor to identify distinguishable characteristics between species to aid classification. The results demonstrate the potential for a high-resolution multi-sensor data fusion approach for classifying individual trees by species in a complex urban environment under limited sampling requirements.
Abscisic Acid in Coordination with Nitrogen Alleviates Salinity-Inhibited Photosynthetic Potential in Mustard by Improving Proline Accumulation and Antioxidant Activity

This investigation was done to assess the role of abscisic acid (ABA; 25 µM) and/or nitrogen (N; 10 mM) in the alleviation of salinity (NaCl; 100 mM)-induced reduction in photosynthetic activity and growth, N and sulfur (S) assimilation of mustard (Brassica juncea L.) cv. RH0-749. Salinity treatment caused oxidative stress and significantly elevated the content of both H2O2 and thiobarbituric acid reactive substances (TBARS), and impaired photosynthetic activity and growth, but increased the content of nitrogenous osmolyte proline and the activity of antioxidant enzymes involved in the metabolism of reactive oxygen species. The application of 25 µM ABA under a controlled condition negatively affected photosynthesis and growth. However, ABA, when combined with N, minimized oxidative stress and mitigated the salinity-inhibited effects by increasing the activity of antioxidant enzymes (superoxide dismutase, SOD; glutathione reductase, GR; ascorbate peroxidase, APX) and proline content. Overall, the supplementation of 10 mM N combined with 25 µM ABA provides an important strategy for enhancing the photosynthetic potential of B. juncea under saline conditions.
Trophic gauntlet effects on fisheries recovery: a case study in Sansha Bay, China

ABSTRACT Low biological productivity causes ecosystem energy deficiency. Sansha Bay is an important spawning and nursery ground for migratory fish in the East China Sea (ECS). There is significant mariculture in this area, but stock enhancement programs and seasonal fishery closures have failed to recover populations of the commercially important species, Larimichthys crocea. We want to figure out the potential reason of unsuccessful L. crocea resource in recent 30 years. In this study, the trophic status of L. crocea, the food source proportions of L. crocea and zooplankton, and the food web structure and functioning of Sansha Bay was analyzed. A high nutrient low chlorophyll phenomenon was observed: this prevented harmful algal blooms, and phytoplankton growth was restrained by intensive macroalgal culture, resulting in a low abundance of zooplankton in Sansha Bay. Phytoplankton was the most important food source of zooplankton, and zooplankton was the greatest food source of juvenile L. crocea. Analyzed L. crocea suffered from starvation. Crucially, most of the phytoplankton was not used efficiently in the Sansha Bay ecosystem. This study suggests that trophic bottleneck, caused by food limitation, is a potential reason for unsuccessful fishery recovery in enclosed bay with macroalgal culture.
Lumped-Parameters Thermal Network of PM Synchronous Machines for Automotive Brake-by-Wire Systems

Thermal analysis represents a key factor in electrical machine design due to the impact of temperature increase on insulation lifetime. In this context, there has been a wide investigation on thermal modeling, particularly for machines used in harsh working conditions. In this perspective, brake-by-wire (BBW) systems represent one of the most challenging applications for electrical machines used for automotive smart actuators. Indeed, electro-actuated braking systems are required to repeatedly operate the electric machine in high overload conditions in order to limit the actuator response time, as well as to enhance gravimetric and volumetric specific performance indexes. Moreover, BBW systems often impose unconventional supply conditions to the electric machine, consisting of dc currents in three-phase windings to keep the rotor fixed during the braking intervals. However, a dc supply leads to uneven temperature distributions in the machine, and simplified thermal models may not accurately represent the temperature variations for the different machine parts. Considering such unconventional supply conditions, this paper initially investigates the applicability of a conventional lumped-parameters thermal network (LPTN) based on symmetry assumptions for the heat paths and suitable for surface-mounted PM synchronous machines used in BBW systems. An extensive test campaign consisting of pulses and load cycle tests representative of the real machine operations was conducted on a prototype equipped with several temperature sensors. The comparison between measurements and predicted average temperatures, together with insights on the unbalanced heat distribution under the dc supply obtained by means of finite element analyses (FEA), paved the way for the proposal of a phase-split LPTN with optimized parameters. The paper also includes a critical analysis of the optimized parameters, proposing a simplified, phase-split lumped-parameters thermal model suitable to predict the temperature variations in the different machine parts for PM synchronous electric machines used in BBW systems.
Assessment of IsPETase-Assisted Depolymerization of Terephthalate Aromatic Polyesters and the Effect of the Thioredoxin Fusion Domain

Terephthalate polyesters such as poly(ethylene terephthalate) (PET) have been massively produced over the last few decades due to their attractive properties in multiple applications. However, due to their limited biodegradability, they have accumulated in landfills and oceans, posing an environmental threat. Enzymatic recycling technologies are predicted to generate long-term socioeconomic benefits. In the present work, we compared the IsPETase (from Ideonella sakaiensis 201-F6) activity on a series of polyesters, including poly(butylene) terephthalate (PBT), poly(hexamethylene) terephthalate (PHT) and Akestra™, with PET. The IsPETase showed remarkable activity toward PET (39% degradation of the original polyester) that was higher than that toward Akestra™ (0.13%), PBT (0.25%) and PHT (0.13%) after 72 h. Thus, based on experimental data and computational analysis, we report insights into IsPETase activity on a series of terephthalate-based polyesters. Aside from that, the fusion domain (Trx) effect in the production and activity of a recombinant Trx-IsPETase is reported.