Helicobacter pylori Eradication Therapy: Current Availabilities.

Background. Though Helicobacter pylori (HP) infections have progressively declined throughout most of the industrialized countries, a gradual increase in failure of HP eradication treatments is observed. Aim. To critically review evidence on the efficacy of the therapeutic availabilities for HP eradication, as yet. Methods. A selection of Clinical Trials, Systematic Reviews and Meta-analyses within the time period 2010-2012, was performed through a Medline search. Previous references were included when basically supporting the first selection. Results. An increasing rise in HP resistance rates for antimicrobial agents is currently observed. Further causes of HP treatment failure include polymorphisms of the CYP 2C19, an increased body mass index (BMI), smoking, poor compliance and re-infections. Alternative recent approaches to standard triple therapy have been attempted to increase the eradication rate, including bismuth-containing quadruple therapy, non-bismuth containing quadruple therapy, sequential therapy and levofloxacin-containing regimens. Conclusions. The main current aims should be the maintenance of a high eradication rate (>85%) of HP and the prevention of any increase in antimicrobial resistance. In the next future, the perspective of a tailored therapy could optimize eradication regimens within the different countries

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

A Survey on Text Classification Algorithms: From Text to Predictions

In recent years, the exponential growth of digital documents has been met by rapid progress in text classification techniques. Newly proposed machine learning algorithms leverage the latest advancements in deep learning methods, allowing for the automatic extraction of expressive features. The swift development of these methods has led to a plethora of strategies to encode natural language into machine-interpretable data. The latest language modelling algorithms are used in conjunction with ad hoc preprocessing procedures, of which the description is often omitted in favour of a more detailed explanation of the classification step. This paper offers a concise review of recent text classification models, with emphasis on the flow of data, from raw text to output labels. We highlight the differences between earlier methods and more recent, deep learning-based methods in both their functioning and in how they transform input data. To give a better perspective on the text classification landscape, we provide an overview of datasets for the English language, as well as supplying instructions for the synthesis of two new multilabel datasets, which we found to be particularly scarce in this setting. Finally, we provide an outline of new experimental results and discuss the open research challenges posed by deep learning-based language models

Microestrutura da fibra alimentar do albedo de laranja: um estudo por técnicas físicas e análise de imagens.

As fibras alimentares são consumidas naturalmente em cereais, frutas e hortaliças, mas também são adicionadas na forma concentrada em alimentos processados. Diferentes fontes de fibras têm diferentes estruturas e composições químicas, que irão definir a sua finalidade nutricional ou tecnológica. Este trabalho tem como objetivo a caracterização de fibra alimentar do albedo da laranja submetida a dois métodos de secagem (liofilização e convencional) e divididas por intervalos granulométricos. Para as determinações de densidade, área superficial, porosidade e distribuição de tamanho de poro, utilizaram-se técnicas clássicas da tecnologia de sistema particulado e foram comparadas com imagens de microscopia eletrônica de varredura. Os resultados apontaram que o método de secagem é o que mais influenciou na redução do volume de poros do material, também visualizado nas características microestruturais reveladas pela análise de imagem. O intervalo granulométrico da fibra particulada apresentou-se inversamente proporcional à densidade e à área superficial específica do material. A fibra alimentar do albedo da laranja apresentou características estruturais que permitem a diversificação de novos produtos alimentícios com alto valor nutritivo e comercial

A parametric approach for evaluating the stability of agricultural tractors using implements during side-slope activities

A methodological approach for evaluating a priori the stability of agricultural vehicles equipped with different mounted implements and operating on sloping hillsides is shown here. It uses a Matlab simulator in its first phase and, subsequently, the Response Surface Modelling (RSM) to evaluate the coefficients of a set of regression equations able to account for the Type-I and Type-II stability of the whole vehicle (tractor + implement with known dimensions and mass). The regression equations can give reliable punctual numeric estimations of the minimum value of the Roll Stability Index (RSI) and can verify the existence of a Type-I equilibrium without the need of using the simulator or knowing any detail about the model implemented in it. The same equations can also be used to generate many intuitive graphs (\u201cequilibrium maps\u201d) useful to verify quickly the possible overturning of the vehicle. A case-study concerning a 4-wheel drive articulated tractor is then presented to show the potential of the approach and how using its tools. The tractor has been studied in three scenarios, differing on where the implement has to be connected to the tractor (1: frontally; 2: frontally-laterally; 3: in the back). After performing a series of simulations, a set of polynomial models (with 6 independent variables) has been created and verified. Then, these models were used, together with the related equilibrium maps, to predict the stability of 8 implements for scenario 1, 7 implements for scenario 2, and 3 implements for scenario 3, evidencing in particular the danger of using a lateral shredder with a mass greater than 245 kg. The proposed approach and its main outcomes (i.e., the regression equations and the equilibrium maps) can give an effective contribution to the preventive safety of the tractor driver, so it could be useful to integrate it in the homologation procedures for every agricultural vehicle and to include the resulting documentation within the tractor logbook

Eficiência e viabilidade econômica da aplicação de fungicidas no controle da ferrugem asiática da soja em Campo Grande, MS.

Objetivou-se avaliar a eficiência e a viabilidade econômica da aplicação de fungicidas no controle da ferrugem asiática da soja, Phakopsora packyrhizi, em Campo Grande, MS. O ensaio foi conduzido em na safra 2007-2008. Foram avaliados os fungicidas (g i.a./ha): picoxistrobina + ciproconazole (40 + 16, 50 + 20 e 60 + 24) + nimbus 0,25% (v/v), piraclostrobina + epoxiconazole (66,5 + 25); azoxistrobina + ciproconazole (60 + 24) + nimbus 0,25% (v/v); trifloxistrobina + tebuconazole (50 + 100) + auero 0,13% (v/v) e trifloxistrobina + ciproconazole (56,25 + 24) + aureo 0,13% (v/v). Três aplicações foram realizadas, quinzenalmente, a partir do estádio fenológico R2. Foram realizadas 10 avaliações da severidade da doença (terços inferior e superior da planta) e três da desfolha (a partir do estádio R7), com intervalos de sete dias. Após plotagem das curvas de progresso (CP), foram calculadas as áreas abaixo das CP para a severidade da doença (AACPD) e desfolha (AACPDes). Ao final do ensaio, avaliou-se o rendimento de grãos (Rend - kg/ha), a massa de 1.000 grãos (MMG - g) e a viabilidade econômica do controle da doença. O clima durante a condução do ensaio foi favorável ao desenvolvimento da ferrugem asiática, constatando-se relação positiva entre a precipitação e a severidade da doença. Todos os fungicidas apresentaram valores de AACPD inferiores ao da testemunha, com destaque para picoxistrobina + ciproconazole (maior dose). Esse tratamento também apresentou o menor índice de AACPDes. Os fungicidas apresentaram índices semelhantes de Rend, embora superiores à testemunha. Quanto a MMG, maior índice foi atribuído a azoxistrobina + ciproconazole. A mistura trifloxistrobina + tebuconazole apresentou a menor eficiência de controle da ferrugem asiática. Com base na severidade da doença, rendimento de grãos e nos benefícios econômico gerados pela aplicação de fungicidas, identificou-se os fungicidas picoxistrobina + ciproconazole (60 + 24), azoxistrobina + ciproconazole e trifloxistrobina + ciproconazole como os mais promissores para o controle de P. packyrhizi

Wage dispersion and sports performance: does gender matter?

Purpose Previous studies focused predominantly on wage dispersion within men’ sports teams. This study aims to reveal how the relationship between wage dispersion and team performance applies for women’s sport. Design/methodology/approach The sample comprises 168 observations of four consecutive National Basketball Association (NBA) and Women’s National Basketball Association (WNBA) regular seasons (2018–2021). Eight econometric models are performed for comparing the leagues. Findings The findings indicate that the wage dispersion within the squads affects the women’s and men’s basketball teams differently. Cohesiveness theory is applicable for WNBA teams, while NBA teams follow the tournament theory. Originality/value To the best of the authors’ knowledge, this is the first paper which inspects the relationship between wage dispersion and team performance using data from women’s sports. Further research may examine whether the differences found in sports also apply in different labor markets

Quantization-Aware NN Layers with High-throughput FPGA Implementation for Edge AI

Over the past few years, several applications have been extensively exploiting the advantages of deep learning, in particular when using convolutional neural networks (CNNs). The intrinsic flexibility of such models makes them widely adopted in a variety of practical applications, from medical to industrial. In this latter scenario, however, using consumer Personal Computer (PC) hardware is not always suitable for the potential harsh conditions of the working environment and the strict timing that industrial applications typically have. Therefore, the design of custom FPGA (Field Programmable Gate Array) solutions for network inference is gaining massive attention from researchers and companies as well. In this paper, we propose a family of network architectures composed of three kinds of custom layers working with integer arithmetic with a customizable precision (down to just two bits). Such layers are designed to be effectively trained on classical GPUs (Graphics Processing Units) and then synthesized to FPGA hardware for real-time inference. The idea is to provide a trainable quantization layer, called Requantizer, acting both as a non-linear activation for neurons and a value rescaler to match the desired bit precision. This way, the training is not only quantization-aware, but also capable of estimating the optimal scaling coefficients to accommodate both the non-linear nature of the activations and the constraints imposed by the limited precision. In the experimental section, we test the performance of this kind of model while working both on classical PC hardware and a case-study implementation of a signal peak detection device running on a real FPGA. We employ TensorFlow Lite for training and comparison, and use Xilinx FPGAs and Vivado for synthesis and implementation. The results show an accuracy of the quantized networks close to the floating point version, without the need for representative data for calibration as in other approaches, and performance that is better than dedicated peak detection algorithms. The FPGA implementation is able to run in real time at a rate of four gigapixels per second with moderate hardware resources, while achieving a sustained efficiency of 0.5 TOPS/W (tera operations per second per watt), in line with custom integrated hardware accelerators