Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing Mandatory Training Systems? Investigating the Introducing Mandatory Training Systems? Investigating the Implementation of Compulsory Pre-site Construction Training Implementation of Compulsory Pre-site Construction Training

There can be no more effective learning than that achieved by training systems to reduce death and injury. In such cases the imposition of a mandatory course would appear justifiable, especially where there is a history of unfortunate incidents and current rapid workforce growth. Installing learning as an imperative within a vibrant industry requires considerable negotiation between stakeholders to turn evidence into policy, industry intent, regulatory curriculum and subsequent workplace practice. This paper reflects back and reviews the introduction of such training within the construction industry in Western Australia as it is adapts to the additional pressures of the development boom. This paper reviews the impact of such training on the commercial sector of the industry, the training organisations, and the employees, using a mixed methods study reviewing both stakeholder perceptions and accident statistics. The study presents a landscape of construction workplaces where the culture is in a state of change. The final discussion projects forward , tracing the complex relationships between research, policy formation and implementation that underpins the development and embedding of such national programmes.
GENOME-WIDE ASSOCIATION STUDIES OF DROUGHT RESISTANCE AND YIELD POTENTIAL IN WHEAT (Triticum aestivum L.) USING AGRONOMIC AND REMOTELY SENSED TRAITS

by Shiferaw Abate Gizaw, Ph.D. Washington State University December 2015 Chair: Arron H. Carter Emerging phenotyping methods and increasingly abundant genotyping platforms have become valuable tools for the discovery and introgression of yield-positive and stress-adaptive genes into modern wheat (Triticum aestivum L.). The main goal of this research was to identify genomic regions that contribute to yield potential and drought tolerance in wheat using agronomic and remotely sensed phenotyping approaches. Phenotypic interrelations, genetic variability of yield, yield components, phenology, and spectral reflectance indices (SRIs) were evaluated in a Pacific Northwest (PNW) winter wheat diversity panel (n = 402). The SRIs showed moderate to high phenotypic correlations with grain yield (|r| = 0.27 – 0.84, p < 0.0001). Genetic variability and selection response were generally high for all traits in moist and cool rainfed environments whereas relative selection efficiency using SRIs was highest in drought environments. A yield selection model based on multiple SRIs had predictive power (R) ranging from 41 82%. Post-spike emergence stay green was also strongly correlated with SRIs (r = 0.26 – 0.76). Next, we conducted association mapping using 3,653 single nucleotide polymorphism (SNP) markers in the PNW winter wheat panel and identified 173 quantitative trait loci (QTL). Pleiotropic QTL for yield and component traits were identified on chromosomes 1A, 2A, 2B, 3B,
A very rare case of spontaneous regression of basaloid squamous cell carcinoma of the lung

Abstract Spontaneous regression of non‐small cell lung cancer is relatively rare. Here, we present a very rare case of spontaneous regression of lung cancer which occurred in a patient with basaloid squamous cell lung cancer. To the best of our knowledge, this is the first report of such a case. A 76‐year old man was referred to our hospital with nodules in the right upper lobe determined by chest computed tomography. The nodules spontaneously regressed during follow‐up. Two years later, the tumor had regrown and the patient subsequently underwent surgery. The pathological findings showed basaloid squamous cell carcinoma. Stimulation of the immune system was considered to be the cause of the spontaneous regression and CD‐8 positive and CD‐4 positive lymphocytes might play an important role.
Mental Health Care for People Who Identify as Two Spirit, Lesbian, Gay, Bisexual, Transgender, and (or) Queer (2SLGBTQ+)

This position paper has been revised by the Canadian Psychiatric Association ’ s (CPA) Professional Standards and Practice (PSP) Committee and approved by the Board of Directors on February 13, 2023. The original position paper was developed by the PSP and approved by the Board of Directors on April 22, 2014.
Plio-Pleistocene North-South and East-West Extension at the Southern Margin of the Tibetan Plateau

N-S–directed extension on the South Tibetan fault system (STFS) played a critical role in the Himalayan-Tibetan orogenic system in the Miocene Period, but it is generally assumed that orogen-perpendicular extension on this fault system ceased prior to Pliocene time. Previous structural mapping in the Annapurna Range of central Nepal by Hurtado et al. (2001) revealed evidence of Quaternary activity on one N-directed extensional detachment – likely a reactivated basal strand of the STFS – but the amount of displacement on that fault could not be tightly constrained. New (U-Th)/He thermochronology of apatite and zircon from samples collected across the fault suggest no more than a few hundred meters of throw. However, new mapping in this region and in the nearby Dhaulagiri Himalaya has revealed the existence of a new low-angle detachment – described here as the “Dhaulagiri detachment” – sub-parallel to, but structurally higher than, the basal detachment of the STFS. (U-Th)/He apatite and zircon cooling ages across this structure, combined with previous Ar/Ar muscovite and zircon fission track data from the region, demonstrate that the Dhaulagiri detachment likely initiated after significant Miocene STFS activity and has accommodated NS extension as recently as the Early Pleistocene, with a total Quaternary throw (inferred from the thermochronologic data) of 6.5 km or more.
Mechanism of lysine oxidase-like 1 promoting synovial inflammation mediating rheumatoid arthritis development

Rheumatoid arthritis (RA) is a chronic inflammatory joint disease that causes great distress to patients and society. Early diagnosis is the key to the successful treatment of RA. The basement membrane, one of the oldest tissue structures, is localized under the epithelium. Its complex composition and rich biological functions have made it a focus of research in recent years, while basement membrane-associated genetic variants are involved in most human disease processes. The aim of this study is to find new diagnostic biomarkers for RA and explore their role and possible mechanism in rheumatoid arthritis. The GSE12021, GSE55235 and GSE55457 datasets were downloaded from the GEO database. Their fraction associated with basement membrane genes was analyzed and differentially expressed genes between the disease and normal groups were explored. We identified two basement membrane-associated genes, lysine oxidase-like 1 (LOXL1) and discoid peptide receptor 2 (DDR2). Focusing on the more interesting LOXL1, we found that LOXL1 expression was significantly elevated in the synovium of patients with rheumatoid arthritis, and LOXL1 mRNA and protein levels were elevated in tumor necrosis factor α-stimulated human synovial sarcoma cells (SW982). And LOXL1 knockdown inhibited tumor necrosis factor α-induced inhibition in SW982 cells expression of inducible nitric oxide synthase (INOS), cyclooxygenase-2 (COX2), and interleukin-6 (IL-6). Interestingly, knockdown of LOXL1 inhibited the phosphorylation of PI3K and AKT. In summary, LOXL1 may become a novel diagnostic gene for RA, and knockdown of LoxL1 may inhibit synovial inflammation by affecting PI3K/AKT pathway.
Culex erraticus (Diptera: Culicidae) utilizes gopher tortoise burrows for overwintering in North Central Florida

Abstract Mosquito-borne diseases represent a significant threat to human and animal health in the United States. Several viruses, including West Nile, Saint Louis encephalitis, and Eastern equine encephalitis are endemic. In humans, the disease is typically detected during the summer months, but not during the winter months. The ability of these viruses to reemerge year after year is still not fully understood, but typically involves persistence in a reservoir host or vector during periods of low transmission. Mosquito species are known to overwinter at different life stages (adults, larvae, or eggs) in manufactured or natural sites. Gopher tortoise burrows are known to serve as refuge for many vertebrate and invertebrate species in pine savannas. In this study, we surveyed the interior of gopher tortoise burrows for overwintering mosquitoes. We identified 4 species (Anopheles crucians s.l., Culex erraticus, Mansonia dyari, and Uranotaenia sapphirina). Cx. erraticus was the most abundant, and its presence and abundance increased in winter months, implying that this species utilized gopher tortoise burrows for overwintering. Bloodfed Cx. erraticus and An. crucians s.l. females were detected. While An. crucians s.l. fed exclusively on the white-tailed deer, Cx. erraticus had a more diverse host range but fed primarily on the gopher tortoise. Tortoises and other long-lived reptiles like the American alligator have been shown to sustain high viremia following West Nile virus (WNV) and Eastern equine encephalitis virus (EEEV) infection and therefore could play a role in the maintenance of these viruses. In addition, Cx. erraticus is naturally infected with WNV and is a known bridge vector for EEEV. As such, these overwintering sites may play a role in perpetuating over-winter arboviral activity in Florida.
Natural trait variation across Saccharomycotina species

Abstract Among molecular biologists, the group of fungi called Saccharomycotina is famous for its yeasts. These yeasts in turn are famous for what they have in common—genetic, biochemical, and cell-biological characteristics that serve as models for plants and animals. But behind the apparent homogeneity of Saccharomycotina species lie a wealth of differences. In this review, we discuss traits that vary across the Saccharomycotina subphylum. We describe cases of bright pigmentation; a zoo of cell shapes; metabolic specialties; and species with unique rules of gene regulation. We discuss the genetics of this diversity and why it matters, including insights into basic evolutionary principles with relevance across Eukarya.
A Histogram Comparison Approach for Assessing Hydrologic Regime Alteration

The histogram comparison approach (HCA) is proposed to assess the alteration of hydrologic regimes, which are characterized by the hydrologic metrics, for example, the indicators of hydrologic alteration. The HCA method considers alterations within the whole range of the hydrologic regime and estimates the alteration degree by comparing the pre‐impact and post‐impact histograms. A key parameter of the HCA method is the similarity degree, which considers both the class‐by‐class and cross‐class information of histograms and reflects how many features of the pre‐impact histogram remain in the post‐impact histogram. The alteration degree is defined as the mathematical maximum similarity degree, that is, 100%, subtracted by the actual similarity degree in the HCA method. Comparing the HCA method to the existing range of variability approach and histogram matching approach through theoretical analysis and case studies, the HCA method can eliminate some limitations of the range of variability approach and histogram matching approach methods and can more accurately and effectively assess the hydrologic regime alteration. The Ob and Yangtze Rivers, which are located in different climatic zones, are used as case studies. Hydrologic regime alterations impacted by reservoirs with different regulation abilities (daily, seasonally and yearly) were assessed using the HCA method, the results of which verified its rationality and practicability. More sites with a wider range of flow variations will be examined in the future to assess the applicability of the HCA method. Copyright © 2017 John Wiley & Sons, Ltd.
Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet

Automated segmentation of distinct tumor regions is critical for accurate diagnosis and treatment planning in pediatric brain tumors. This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregions across three challenging datasets: Pediatrics Tumor Challenge (PED), Brain Metastasis Challenge (MET), and Sub-Sahara-Africa Adult Glioma (SSA). These datasets represent diverse scenarios and anatomical variations, making them suitable for assessing the robustness and generalization capabilities of the MPUnet model. By utilizing multi-planar information, the MPUnet architecture aims to enhance segmentation accuracy. Our results show varying performance levels across the evaluated challenges, with the tumor core (TC) class demonstrating relatively higher segmentation accuracy. However, variability is observed in the segmentation of other classes, such as the edema and enhancing tumor (ET) regions. These findings emphasize the complexity of brain tumor segmentation and highlight the potential for further refinement of the MPUnet approach and inclusion of MRI more data and preprocessing.
Even-body interactions favour asymmetry as a resource in metrological precision

Genuine multiparty entangled probes lead to minimum error in estimating the phase corresponding to the generator of a unitary encoder, if the generator comprises of only local terms. We ask if genuine multiparty entanglement remains indispensable in attaining the best metrological precision if we employ higher-order interaction terms in the generator. We identify a dichotomy in the answer. Specifically, we find that generators having odd-body interactions necessarily require genuine multipartite entanglement in probes to attain the best metrological precision, but the situation is opposite in the case of generators with even-body interactions. The optimal probes corresponding to generators that contain even-body interaction terms, may be entangled, but certainly not so in all bipartitions, and particularly, for certain ranges of the number of parties including the large number limit, the optimal state is asymmetric. Asymmetry, which therefore is a resource in this scenario rather than genuine multiparty entanglement, refers to the disparity between states of local parts of the global system. Additionally, we provide an upper bound on the number of parties up to which one can always obtain an asymmetric product state that gives the best metrological precision for even-body interactions. En route, we find the quantum Fisher information in closed form for two- and three-body interactions for an arbitrary number of parties, and prove, in both the cases, that when the number of parties is large, the metrological precision is non-optimal if we consider only symmetric product probes. Further, we identify conditions on the local component of the generator, for which these results hold for arbitrary local dimensions.
Adapting Large Language Models for Document-Level Machine Translation

Large language models (LLMs) have made significant strides in various natural language processing (NLP) tasks. Recent research shows that the moderately-sized LLMs often outperform their larger counterparts after task-specific fine-tuning. In this work, we delve into the process of adapting LLMs to specialize in document-level machine translation (DocMT) for a specific language pair. Firstly, we explore how prompt strategies affect downstream translation performance. Then, we conduct extensive experiments with two fine-tuning methods, three LLM backbones, and 18 translation tasks across nine language pairs. Our findings indicate that in some cases, these specialized models even surpass GPT-4 in translation performance, while they still significantly suffer from the off-target translation issue in others, even if they are exclusively fine-tuned on bilingual parallel documents. Furthermore, we provide an in-depth analysis of these LLMs tailored for DocMT, exploring aspects such as translation errors, discourse phenomena, training strategy, the scaling law of parallel documents, additional evaluation on recent test sets, and zero-shot crosslingual transfer. Our findings not only shed light on the strengths and limitations of LLM-based DocMT models but also provide a foundation for future research.
Teaching Code LLMs to Use Autocompletion Tools in Repository-Level Code Generation

Recent code large language models (LLMs) have shown promising performance in generating standalone functions but face limitations in repository-level code generation due to their lack of awareness of repository-level dependencies (e.g., user-defined attributes), resulting in dependency errors such as undefined-variable and no-member errors. In this work, we introduce ToolGen, an approach that integrates autocompletion tools into the code LLM generation process to address these dependencies. ToolGen comprises two main phases: Trigger Insertion and Model Fine-tuning (Offline), and Tool-integrated Code Generation (Online). During the offline phase, ToolGen augments functions within a given code corpus with a special mark token, indicating positions to trigger autocompletion tools. These augmented functions, along with their corresponding docstrings, are then used to fine-tune a selected code LLM. In the online phase, ToolGen iteratively generates functions by predicting tokens step-by-step using the fine-tuned LLM. Whenever a mark token is encountered, ToolGen invokes the autocompletion tool to suggest code completions and selects the most appropriate one. We conduct comprehensive experiments to evaluate ToolGen's effectiveness in repository-level code generation. To facilitate this evaluation, we create a benchmark comprising 680 real-world code repositories and introduce two new repository-level metrics: Dependency Coverage and Static Validity Rate. The results demonstrate that ToolGen significantly improves Dependency Coverage by 15.2% to 45.8% and Static Validity Rate by 10.9% to 42.2% across three distinct code LLMs, while maintaining competitive performance in widely-recognized similarity metrics. Furthermore, our generalizability evaluation confirms ToolGen's consistent performance when applied to diverse code LLMs, including various model architectures and scales.
Nanoalignment by Critical Casimir Torques

The manipulation of microscopic objects requires precise and controllable forces and torques. Recent advances have led to the use of critical Casimir forces as a powerful tool, which can be finely tuned through the temperature of the environment and the chemical properties of the involved objects. For example, these forces have been used to self-organize ensembles of particles and to counteract stiction caused by Casimir-Liftshitz forces. However, until now, the potential of critical Casimir torques has been largely unexplored. Here, we demonstrate that critical Casimir torques can efficiently control the alignment of microscopic objects on nanopatterned substrates. We show experimentally and corroborate with theoretical calculations and Monte Carlo simulations that circular patterns on a substrate can stabilize the position and orientation of microscopic disks. By making the patterns elliptical, such microdisks can be subject to a torque which flips them upright while simultaneously allowing for more accurate control of the microdisk position. More complex patterns can selectively trap 2D-chiral particles and generate particle motion similar to non-equilibrium Brownian ratchets. These findings provide new opportunities for nanotechnological applications requiring precise positioning and orientation of microscopic objects.
The cohomology of the connective spectra for {K}-theory revisited

The stable mod 2 cohomologies of the spectra for connective real and complex K-theories are well known and easy to work with. However, the known bases are in terms of the anti-automorphism of Milnor basis elements. We offer simple bases in terms of admissible sequences of Steenrod operations that come from the Adem relations. In particular, the basis for the complex case is that you don't use any Steenrod operations in degree one or $2^n+1$, $n>0$.
Functions that are uniquely maximized by sparse quasi-star graphs, and uniquely minimized by quasi-complete graphs

We show that for a certain class of convex functions $f$, including the exponential functions $x\mapsto e^{\lambda x}$ with $\lambda>0$ a real number, and all the powers $x\mapsto x^\beta$, $x\geq 0$ and $\beta\geq 2$ a real number, with a unique small exception, if $(d_1,\ldots,d_n)$ ranges over the degree sequences of graphs with $n$ vertices and $m$ edges and $m\leq n-1$, then the maximum of $\sum_i f(d_i)$ is uniquely attained by the degree sequence of a quasi-star graph, namely, a graph consisting of a star plus possibly additional isolated vertices. This result significantly extends a similar result in [D. ~Ismailescu, D. ~Stefanica, Minimizer graphs for a class of extremal problems, J.~Graph Theory,~39~(4)~(2002)]. Dually, we show that for a certain class of concave functions $g$, including the negative exponential functions $x\mapsto 1-e^{-\lambda x}$ with $\lambda>\ln(2)$ a real number, all the powers $x\mapsto x^\alpha$, $x\geq 0$ and $0<\alpha\leq \frac{1}{2}$ a real number, and the function $x\mapsto \frac{x}{x+1}$ for $x\geq 0$, if $(d_1,\ldots,d_n)$ ranges over the degree sequences of graphs with $n$ vertices and $m$ edges, then the minimum of $\sum_i g(d_i)$ is uniquely attained by the degree sequence of a quasi-complete graph, i.e., a graph consisting of a complete graph plus possibly an additional vertex connected to some but not all vertices of the complete graph, plus possibly isolated vertices. This result extends a similar result in the same paper.
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs

Neuro-symbolic programs -- programs containing both machine learning components and traditional symbolic code -- are becoming increasingly widespread. However, we believe that there is still a lack of a general methodology for verifying these programs whose correctness depends on the behaviour of the machine learning components. In this paper, we identify the ``embedding gap'' -- the lack of techniques for linking semantically-meaningful ``problem-space'' properties to equivalent ``embedding-space'' properties -- as one of the key issues, and describe Vehicle, a tool designed to facilitate the end-to-end verification of neural-symbolic programs in a modular fashion. Vehicle provides a convenient language for specifying ``problem-space'' properties of neural networks and declaring their relationship to the ``embedding-space", and a powerful compiler that automates interpretation of these properties in the language of a chosen machine-learning training environment, neural network verifier, and interactive theorem prover. We demonstrate Vehicle's utility by using it to formally verify the safety of a simple autonomous car equipped with a neural network controller.