Miracle-dz/UniFAR-FTUs huggingface dataset:
Aflatoxin M1 survey in dairy households in Kenya

Introduction Kenya has the largest dairy herd and highest per capita milk consumption in East Africa. Kenya has also experienced multiple aflatoxicosis outbreaks in recent years, and several surveys have reported high levels of contamination in maize. When lactating cows consume feed contaminated with aflatoxins, they secrete aflatoxin M1in milk. This metabolite is harmful to humans. There is no current information on the risk to human health posed by aflatoxins in milk in Kenya. To fill this gap, a risk assessment is being conducted in four agro-ecological zones (AEZ; semi-arid, temperate, sub-humid and humid) in Kenya.
Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification

Decision trees are widely used in machine learning due to their simplicity in construction and interpretability. However, as data sizes grow, traditional methods for constructing and retraining decision trees become increasingly slow, scaling polynomially with the number of training examples. In this work, we introduce a novel quantum algorithm, named Des-q, for constructing and retraining decision trees in regression and binary classification tasks. Assuming the data stream produces small increments of new training examples, we demonstrate that our Des-q algorithm significantly reduces the time required for tree retraining, achieving a poly-logarithmic time complexity in the number of training examples, even accounting for the time needed to load the new examples into quantum-accessible memory. Our approach involves building a decision tree algorithm to perform k-piecewise linear tree splits at each internal node. These splits simultaneously generate multiple hyperplanes, dividing the feature space into k distinct regions. To determine the k suitable anchor points for these splits, we develop an efficient quantum-supervised clustering method, building upon the q-means algorithm of Kerenidis et al. Des-q first efficiently estimates each feature weight using a novel quantum technique to estimate the Pearson correlation. Subsequently, we employ weighted distance estimation to cluster the training examples in k disjoint regions and then proceed to expand the tree using the same procedure. We benchmark the performance of the simulated version of our algorithm against the state-of-the-art classical decision tree for regression and binary classification on multiple data sets with numerical features. Further, we showcase that the proposed algorithm exhibits similar performance to the state-of-the-art decision tree while significantly speeding up the periodic tree retraining.
Understanding the drivers of sustainable entrepreneurial practices in Pakistan’s leather industry: A multi-level approach

Purpose The purpose of this paper is to analyse the drivers of sustainable entrepreneurial practices in SMEs operating in a developing economy. The secondary objectives are to explore the relationship between these drivers and to draw out the implications for policy and practice. Design/methodology/approach The paper is informed by the literature on sustainable entrepreneurship, and on the drivers of pro-environmental practices in SMEs. It reports on the results of an intensive multi-level empirical study, which investigates the environmental practices of SMEs in Pakistan’s leatherworking industry using a multiple case study design and grounded analysis, which draws on relevant institutional theory. Findings The study identifies that coercive, normative and mimetic isomorphic pressures simultaneously drive sustainable entrepreneurial activity in the majority of sample SMEs. These pressures are exerted by specific micro-, meso- and macro-level factors, ranging from international customers’ requirements to individual-level values of owners and managers. It also reveals the catalytic effect of the educational and awareness-raising activities of intermediary organisations, in tandem with the attraction of competitiveness gains, (international) environmental regulations, industrial dynamism and reputational factors. Practical implications The evidence suggests that, in countries where formal institutional mechanisms have less of an impact, intermediary organisations can perform a proto-institutional role that helps to overcome pre-existing barriers to environmental improvement by sparking sustainable entrepreneurial activity in SME populations. Originality/value The findings imply that the drivers of sustainable entrepreneurial activity do not operate in a “piecemeal” fashion, but that particular factors mediate the emergence and development of other sustainability drivers. This paper provides new insights into sustainable entrepreneurship and motivations for environmental practices in an under-researched developing economy context.
The Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design

The grand challenge of protein engineering is the development of computational models that can characterize and generate protein sequences for any arbitrary function. However, progress today is limited by lack of 1) benchmarks with which to compare computational techniques, 2) large datasets of protein function, and 3) democratized access to experimental protein characterization. Here, we introduce the Protein Engineering Tournament, a fully-remote, biennial competition for the development and benchmarking of computational methods in protein engineering. The tournament consists of two rounds: a first in silico round, where participants use computational models to predict biophysical properties for a set of protein sequences, and a second in vitro round, where participants are challenged to design new protein sequences, which are experimentally measured with open-source, automated methods to determine a winner. At the Tournament's conclusion, the experimental protocols and all collected data will be open-sourced for continued benchmarking and advancement of computational models. We hope the Protein Engineering Tournament will provide a transparent platform with which to evaluate progress in this field and mobilize the scientific community to conquer the grand challenge of computational protein engineering.
Efeitos da cessação do tabagismo na alteração ponderal : estudo de coorte prospectiva de vida real

Introducao: O ganho de peso e uma grande preocupacao para os pacientes que tentam parar de fumar, tornando-se uma barreira tanto para iniciar uma tentativa de cessacao quanto para manter a abstinencia em longo prazo. No entanto, alguns dos que conseguem manter-se em abstinencia nao engordam e outros ate perdem algum peso. Objetivo: Avaliar as alteracoes de peso apos 12 meses de abstinencia tabagica confirmada. Tambem pretende-se identificar fatores de risco para ganho de peso. Metodos: Foi realizado estudo de coorte no Ambulatorio de Cessacao do Tabagismo, Hospital Sao Lucas, em Porto Alegre, Brasil, de junho de 2007 a junho de 2016 nos pacientes que buscaram ajuda para cessacao do tabagismo. Foram incluidos fumantes submetidos a terapia cognitivocomportamental combinada com farmacoterapia para cessacao tabagica. O peso no inicio e no final foi registrado e a cessacao do tabagismo foi confirmada pelo COex <10ppm e pela confirmacao dos pacientes de serem abstinentes. Resultados: Foram incluidos 348 voluntarios no estudo, dos quais, 187(53,7%) falharam em cessar o tabagismo, mas 161 (46,3%) pacientes conseguiram manter abstinencia de no minimo 12 meses. Em relacao a alteracao ponderal, 104(64,6%) ex-fumantes mantiveram o peso ou apresentaram alteracao para mais ou para menos de ate 5% do peso inicial. Outro grupo teve aumento de peso maior de 5% (n=57; 35.4%). Apenas 18 (11,2%) pacientes aumentaram mais de 10% de seu peso inicial. Nao foi detectado nenhum fator de risco com significância estatistica associado com aumento de peso maior que 5%. O numero necessario para causar dano (NNH) associado a cessacao tabagica foi 3.4 (IC 95% 2,6-5,2). Conclusao: Estes resultados mostram que o ganho de peso nao esta necessariamente associado a cessacao do tabagismo e que um percentual nao negligenciavel de ex-fumantes mantem ou mesmo perde peso ao parar de fumar. Esta informacao pode ser util para o fumante ainda indeciso por estar preocupado com o possivel ganho de peso. Esta estrategia sera provavelmente util em todos os niveis de atendimento clinico, mas especialmente nas Unidades de Atencao Basica.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree

1. Trait-environment relationships have been described at the community level across tree species. However, whether interspeciﬁc trait-environment relationships are consistent at the intraspeciﬁc level is yet unknown. Moreover, we do not know how consistent is the response between organ vs. whole-tree level. 2. We examined phenotypic variability for 16 functional leaf (dimen-sions, nutrient, chlorophyll) and wood traits (density)
Kaupang in Skiringssal

Introduction Exploring Skiringssal 1771-1999 Preparing the New Campaign Skiringssal, Kaupang, Tjolling -- the Toponumic Evidence The Kaupang Cemeteries Revisited Evidence from the Settlement Area 1956-1984 The Fieldwork 1998-2003: Overview and Methods The Settlement: Extent and Dating The Settlement: Artefacts and Site Periods The Settlement: Character, Structures and Features Geology, Soils, Vegetation and Sea-Levels in the Kaupang Area Dendrochronological Dates From Kaupang Interpreting the Plant and Animal remains from Viking-age Kaupang Soils and Sediments in the Settlement and Harbour at Kaupang The Skiringssal Cemetary The Skiringssal Thing Site pjodalyng The Emergence of a Central Place: Skiringssal in the 8th Century Towns and Markets, Kings and Central Places in South-Western Scandinavia c.AD 800-950.
Attribute Signatures : Dynamic Visual Summaries for Analyzing Multivariate Geographical Data

The visual analysis of geographically referenced datasets with a large number of attributes is challenging due to the fact that the characteristics of the attributes are highly dependent upon the locations at which they are focussed, and the scale and time at which they are measured. Specialized interactive visual methods are required to help analysts in understanding the characteristics of the attributes when these multiple aspects are considered concurrently. Here, we develop attribute signatures – interactively crafted graphics that show the geographic variability of statistics of attributes through which the extent of dependency between the attributes and geography can be visually explored. We compute a number of statistical measures, which can also account for variations in time and scale, and use them as a basis for our visualizations. We then employ different graphical configurations to show and compare both continuous and discrete variation of location and scale. Our methods allow variation in multiple statistical summaries of multiple attributes to be considered concurrently and geographically, as evidenced by examples in which the census geography of London and the wider UK are explored.
Fitting 3D morphable models using implicit representations

JVRB, 4(2007), no. 18. - We consider the problem of approximating the 3D scan of a real object through an affine combination of examples. Common approaches depend either on the explicit estimation of point-to-point correspondences or on 2-dimensional projections of the target mesh; both present drawbacks. We follow an approach similar to [IF03] by representing the target via an implicit function, whose values at the vertices of the approximation are used to define a robust cost function. The problem is approached in two steps, by approximating first a coarse implicit representation of the whole target, and then finer, local ones; the local approximations are then merged together with a Poisson-based method. We report the results of applying our method on a subset of 3D scans from the Face Recognition Grand Challenge v.1. 0.
Realising a Layered Digital Library: Exploration and Analysis of the Live Music Archive through Linked Data

Building upon a collection with functionality for discovery and analysis has been described by Lynch as a ‘layered’ approach to digital libraries. Meanwhile, as digital corpora have grown in size, their analysis is necessarily supplemented by automated application of computational methods, which can create layers of information as intricate and complex as those within the content itself. is combination of layers – aggregating homogeneous collections, specialised analyses, and new observations – requires a exible approach to systems implementation which enables pathways through the layers via common points of understanding, while simultaneously accommodating the emergence of previously unforeseen layers. In this paper we follow a Linked Data approach to build a layered digital library based on content from the Internet Archive Live Music Archive. Starting from the recorded audio and basic information in the Archive, we rst deploy a layer of catalogue metadata which allows an initial – if imperfect – consolidation of performer, song, and venue information. A processing layer extracts audio features from the original recordings, workow provenance, and summary feature metadata. A further analysis layer provides tools for the user to combine audio and feature data, discovered and reconciled using interlinked catalogue and feature metadata from layers below. Finally, we demonstrate the feasibility of the system through an investigation of ‘key typicality’ across performances. is highlights the need to incorporate robustness to inevitable ‘imperfections’ when undertaking scholarship within the digital library, be that from mislabelling, poor quality audio, or intrinsic limitations of computational methods. We do so not with the assumption that a ‘perfect’ version can be reached; but that a key benet of a layered approach is to allow accurate representations of information to be discovered, combined, and investigated for informed interpretation.
Evaluation of Functional Family Therapy in an Irish Context

In an Irish context we conducted a retrospective archival study of Functional Family Therapy for adolescents with behavioural problems. Strengths and Difficulties Questionnaire data were collected from 118 families at the beginning and end of therapy (at baseline and follow-up for dropouts) in a community-based clinic in a socially disadvantaged Dublin suburb. Analyses of improvement in mean scores and clinical recovery rates showed that outcome was associated with treatment completion and therapist adherence. Therapy completers treated by high-adherent therapists had the most favourable outcome.
Characterization of a Putative Pathogenicity Island from Bovine Staphylococcus Aureus Encoding Multiple Superantigens

'Characterization of a putative pathogenicity island from bovine Staphylococcus aureus encoding multiple superantigens' Journal of Bacteriology, vol 183, no. 1, pp. 63-70. General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac. uk providing details, and we will remove access to the work immediately and investigate your claim.
Ripped from the Headlines: Juror Perceptions in the Law & Order Era

Film and Media Studies scholars have long claimed that television is a primary source of information about the criminal justice system for most Americans. These scholars have also found that television can significantly impact viewer’s perceptions of the world. In the last decade, Jerry Bruckheimer’s CSI: Crime Scene Investigation has been the subject of significant scrutiny. Many have claimed that CSI’s highly dramatized, romanticized, and generally unrealistic portrayal of forensic science and the investigative process has significantly altered juror expectations and poses a challenge for lawyers trying criminal cases. The concept has been discussed in nearly 400 news articles and more than 100 academic journal articles. With all of this attention paid to CSI, the impact of Law & Order on jurors’ perceptions of the criminal justice system has been largely overlooked.In this Article, I analyze the dominant narrative in the Law & Order franchise, demonstrating that the show is rooted in Packer’s Crime Control Model. This Article discusses the various techniques the Law & Order franchise employs to construct that narrative and shape our views, through the use of basic psychological principles, detailed character backstories, casting choices of various characters, and dialogue and specific plot events. This Article considers the potential impact of this narrative on peoples’/jurors’ perceptions of various aspects of criminal investigations and the justice system, including interrogation techniques, eyewitness identification and lineup procedures, and the presumption of innocence. Finally, this Article makes suggestions for future research.
Multi-CNN Voting Method for Improved Arabic Handwritten Digits Classification

Handwritten Arabic recognition poses a unique set of challenges due to the intricate nature of the script, its diverse styles, and the inherent complexity of the Arabic language. With the increasing need for automated recognition systems in various domains, such as document analysis, postal services, and historical document preservation, finding the best system for accurate and efficient recognition becomes crucial. The primary objective of this paper is to propose a method for enhancing the performance of handwritten Arabic written classification tasks by incorporating ensemble learning that employs multiple CNN classical networks, including Inception, Lenet-5, AlexNet, Dense, MobileV2, and ResNet. The final r esults a re t hen c hosen using a voting approach. To validate the effectiveness of our proposed method, we evaluated it on the MADBase dataset and compared it with the latest works in the field. The results demonstrate that our proposed method achieved better accuracy, Fl-score, precision, and recall, with 99.31, 99.31, 99.31, and 99.30, respectively, outperforming existing research. This proposed method can be adapted and utilized for various classification tasks.1.
Uint-Packing: Multiply Your DNN Accelerator Performance via Unsigned Integer DSP Packing

DSP blocks are undoubtedly efficient solutions for implementing multiply-accumulate (MAC) operations on FPGA. Since DSP resources are scarce in FPGA, the advanced solution is to pack parallel multiplication operations into a single DSP. However, available methods are based on signed-type multiplication, leading to both loss of accuracy and increased area. To solve these issues simultaneously, we propose an unsigned integer DSP packing generalization model called uint-packing. Guided by this generalization model, we design the novel computational structure of the DNN accelerator. Our system design is state-of-the-art, with 2.8× throughput and 4× energy efficiency compared to the third-place DAC-SDC’22 design.
Asymptotic performance limits of vehicular location and velocity detection towards 6G mmWave integrated communication and sensing

In this paper, joint location and velocity estimation (JLVE) of vehicular terminals for 6G integrated communication and sensing (ICAS) is studied. We aim to provide a unified performance analysis framework for ICAS-based JLVE, which is challenging due to random fading, multipath interference, and complexly coupled system models, and thus the impact of channel fading and multipath interference on JLVE performance is not fully understood. To address this challenge, we exploit structured information models of the JLVE problem to render tractable performance quantification. Firstly, an individual closed-form Cramer-Rao lower bound for vehicular localization, velocity detection and channel estimation, respectively, is established for gaining insights into performance limits of ICAS-based JLVE. Secondly, the impact of system resource factors and fading environments, e.g., system bandwidth, the number of subcarriers, carrier frequency, antenna array size, transmission distance, spatial channel correlation, channel covariance, the number of interference paths and noise power, on the JLVE performance is theoretically analyzed. The associated closed-form JLVE performance analysis can not only provide theoretical foundations for ICAS receiver design but also provide a performance benchmark for various JLVE methods.
Research on the Safety Production Risk Assessment in the field of Maritime Communication from Navigation Supporting

As a very important part, maritime communication filed having an irreplaceable position in terms of navigation supporting. However, due to the increasingly intense technological warfare, the way that how to reduce communication risk, eliminate hidden dangers for protecting maritime communication supporting field safety, had become one of the important subjects from navigation supporting. This paper used the data that came from Tianjin Communication Center of Northern Navigation Service Center, maritime safety Administration, People’s Republic of CHINA, based on risk management process, using fault tree analysis method, brainstorming method and flow charts method etc. defined related marine communication operation risk events; using cconditional hazard assessment method to analyse and evaluate related risk events’ risk level. At the end of this paper, it has been introduced related prevention and improving measures suggestion toward the serious risks during risk treatments process.
Semantic feature extraction and classification inference of ship trajectories using coding sequences: A case study of the Taiwan strait

In the process of monitoring a myriad of vessels, maritime administrators recurrently confront situations where crucial static fields, such as vessel type, are frequently missing from automatic identification system (AIS) data. This omission presents a significant obstacle to effective safety management, prompting the need for a method to rectify incomplete data. In terrestrial traffic, constraints dictated by road widths do not apply to marine traffic, thus allowing vessels of an identical type to opt for routes that diverge by numerous nautical miles. This condition renders trajectory clustering insufficient. To tackle this issue, this paper proposes a division of the sea area into spatiotemporal grids and a transformation of vessel trajectories into a coded sequence of navigational grids. Word2vec embedding is merged with deep neural networks predicated on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms through the utilization of natural language processing techniques. The goal is to facilitate efficient vessel classification via deep learning. Applying the Taiwan Strait as a case study, it is shown that under the conditions of a five-dimensional word embedding, the F1 score for vessel classification using RNN models can attain 0.94. This investigation provides a unique method for processing vessel trajectory data, and it also assists in the effective completion of missing vessel types.