Complementary and alterative treatments in functional dyspepsia

INTRODUCTION AND AIM: The popularity of complementary and alternative medicine (CAM) in treating functional gastrointestinal disorders (FGIDs) has steadily increased in Western countries. We aimed at analyzing available data on CAM effectiveness in functional dyspepsia (FD) patients. METHODS: A bibliographical search was performed in PubMed using the following keywords: "complementary/alternative medicine”, “hypnosis”, “acupuncture” and/or “functional dyspepsia”. RESULTS: In community settings, almost 50% of patients with FGIDs used CAM therapies. Herbal remedies consist in multi-component preparations, whose mechanisms of action have not been systematically clarified. Few studies analyzed the effectiveness of Acupuncture in Western countries, yielding conflicting results and possibly reflecting a population bias of this treatment. Hypnosis has been extensively used in irritable bowel syndrome, but few data support its role in treating FD. CONCLUSIONS: Although some supporting well-designed studies have been recently performed, additional randomized control trials are needed before stating any recommendation on CAM effectiveness in treating FD

Complementary and alternative treatment in functional dyspepsia

The popularity of complementary and alternative medicine (CAM) in treating functional gastrointestinal disorders (FGIDs) has steadily increased in Western countries. We aimed at analyzing available data on CAM effectiveness in functional dyspepsia (FD) patients

Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband

Indoor autonomous navigation requires a precise and accurate localization system able to guide robots through cluttered, unstructured and dynamic environments. Ultra-wideband (UWB) technology, as an indoor positioning system, offers precise localization and tracking, but moving obstacles and non-line-of-sight occurrences can generate noisy and unreliable signals. That, combined with sensors noise, unmodeled dynamics and environment changes can result in a failure of the guidance algorithm of the robot. We demonstrate how a power-efficient and low computational cost point-to-point local planner, learnt with deep reinforcement learning (RL), combined with UWB localization technology can constitute a robust and resilient to noise short-range guidance system complete solution. We trained the RL agent on a simulated environment that encapsulates the robot dynamics and task constraints and then, we tested the learnt point-to-point navigation policies in a real setting with more than two-hundred experimental evaluations using UWB localization. Our results show that the computational efficient end-to-end policy learnt in plain simulation, that directly maps low-range sensors signals to robot controls, deployed in combination with ultra-wideband noisy localization in a real environment, can provide a robust, scalable and at-the-edge low-cost navigation system solution.Comment: Accepted by ICAART 2021 - http://www.icaart.org

Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology

Robust ultra-wideband range error mitigation with deep learning at the edge

Ultra-wideband (UWB) is the state-of-the-art and most popular technology for wireless localization. Nevertheless, precise ranging and localization in non-line-of-sight (NLoS) conditions is still an open research topic. Indeed, multipath effects, reflections, refractions, and complexity of the indoor radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques to achieve effective ranging error mitigation at the edge. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions. Extensive experimentation with different settings and configurations has proved the effectiveness of our methodology and demonstrated the feasibility of a robust and low computational power UWB range error mitigation

Ultra-Low-Power Range Error Mitigation for Ultra-Wideband Precise Localization

Precise and accurate localization in outdoor and indoor environments is a challenging problem that currently constitutes a significant limitation for several practical applications. Ultra-wideband (UWB) localization technology represents a valuable low-cost solution to the problem. However, non-line-of-sight (NLOS) conditions and complexity of the specific radio environment can easily introduce a positive bias in the ranging measurement, resulting in highly inaccurate and unsatisfactory position estimation. In the light of this, we leverage the latest advancement in deep neural network optimization techniques and their implementation on ultra-low-power microcontrollers to introduce an effective range error mitigation solution that provides corrections in either NLOS or LOS conditions with a few mW of power. Our extensive experimentation endorses the advantages and improvements of our low-cost and power-efficient methodology

Lo stoccaggio di carbonio organico nei suoli come indicatore addizionale negli studi di Product Environmental Footprint: il modello RothC applicato a un uliveto biologico in Italia

Questo articolo presenta l’applicazione del modello RothC, suggerito dalle Product Environmental Footprint Category Rules (PEFCR) dell’olio di oliva, attualmente in fase di bozza, per il calcolo dello stoccaggio del carbonio organico nel suolo in un’area coltivata ad ulivi della regione Lazio. Il quadro di riferimento è il metodo Product Environmental Footprint (PEF) sviluppato dalla Commissione Europea, che è in fase di sperimentazione all’interno del progetto LIFE EFFIGE. I risultati mostrano che la scelta delle pratiche colturali, ed in particolare delle colture di copertura, può agire come fattore di mitigazione su un orizzonte temporale di un centinaio di anni. Si osserva inoltre che il modello RothC, nonostante permetta di evidenziare informazioni addizionali importanti relative allo stoccaggio di carbonio organico negli studi PEF, richiede un utilizzo di dati e conoscenze tali da metterne in dubbio l’applicabilità in autonomia da parte delle imprese

The RothC Model to Complement Life Cycle Analyses: A Case Study of an Italian Olive Grove

Soil organic carbon (SOC) plays a fundamental role in soil health, and its storage in soil is an important element to mitigate climate change. How to include this factor in Life Cycle Assessment studies has been the object of several papers and is still under discussion. SOC storage has been proposed as an additional environmental information in some applications of the Product Environmental Footprint (PEF). In the framework of wider activity aimed at producing the PEF of olive oil, the RothC model was applied to an olive cultivation located in Lazio region (Italy) to calculate the SOC storage and assess four scenarios representing different agricultural practices. RothC applicability, possible use of its results for improving product environmental performance, and relevance of SOC storage in terms of CO2eq compared to greenhouse gas emissions of the life-cycle of olive oil are discussed in this paper. According to the results, in all scenarios, the contribution in terms of CO2eq associated with SOC storage is remarkable compared to the total greenhouse gas emissions of the olive oil life-cycle. It is the opinion of the authors that the calculation of the SOC balance allows a more proper evaluation of the agricultural products contribution to climate change, and that the indications of the scenarios analysis are useful to enhance the environmental performance of these products. The downside is that the application of RothC requires additional data collection and expertise if compared to the execution of PEF studies

The RothC Model to Complement Life Cycle Analyses: A Case Study of an Italian Olive Grove

Soil organic carbon (SOC) plays a fundamental role in soil health, and its storage in soil is an important element to mitigate climate change. How to include this factor in Life Cycle Assessment studies has been the object of several papers and is still under discussion. SOC storage has been proposed as an additional environmental information in some applications of the Product Environmental Footprint (PEF). In the framework of wider activity aimed at producing the PEF of olive oil, the RothC model was applied to an olive cultivation located in Lazio region (Italy) to calculate the SOC storage and assess four scenarios representing different agricultural practices. RothC applicability, possible use of its results for improving product environmental performance, and relevance of SOC storage in terms of CO2eq compared to greenhouse gas emissions of the life-cycle of olive oil are discussed in this paper. According to the results, in all scenarios, the contribution in terms of CO2eq associated with SOC storage is remarkable compared to the total greenhouse gas emissions of the olive oil life-cycle. It is the opinion of the authors that the calculation of the SOC balance allows a more proper evaluation of the agricultural products contribution to climate change, and that the indications of the scenarios analysis are useful to enhance the environmental performance of these products. The downside is that the application of RothC requires additional data collection and expertise if compared to the execution of PEF studies