Building Innovation System for the Diffusion of Renewable EnergyTechnology: Practices in Ethiopia and Bangladesh

AbstractThe diffusion of renewable energy technologies (RETs) has been progressing very slowly in global scope, particularly in developing countries where the diffusion challenges for renewable are greater. Among potential actors in the promotion and diffusion of rural-based renewable energy innovations, NGOs and NPOs have been mentioned as promising actors. However, empirical studies that show the role of the actors and the way they can besystem builders by diffusing existing technologies have been very rare. This paper discusses the practices of an NGO in Ethiopia (Solar Energy Foundation) and an NPO in Bangladesh (Grameen Shakti) and showshowlocal technological innovation systems can be built bykey actors in the context of developing countries. The studysheds light on the process of system building for accelerated diffusion of RETs in the context of developing countries. Using a theoretical framework, we compared the approach, technology adoption trend (solar home systems diffusion), and common challenges facing both actors in their respective countries. The two empirical cases which are in different geographical contexts provided lessons on thesimilarities and differences of system building practices and emerging innovation systems for diffusion of RETs in developing countries

Evaluating surgical skills from kinematic data using convolutional neural networks

The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.Comment: Accepted at MICCAI 201

Surgical robotics beyond enhanced dexterity instrumentation: a survey of machine learning techniques and their role in intelligent and autonomous surgical actions

PURPOSE: Advances in technology and computing play an increasingly important role in the evolution of modern surgical techniques and paradigms. This article reviews the current role of machine learning (ML) techniques in the context of surgery with a focus on surgical robotics (SR). Also, we provide a perspective on the future possibilities for enhancing the effectiveness of procedures by integrating ML in the operating room. METHODS: The review is focused on ML techniques directly applied to surgery, surgical robotics, surgical training and assessment. The widespread use of ML methods in diagnosis and medical image computing is beyond the scope of the review. Searches were performed on PubMed and IEEE Explore using combinations of keywords: ML, surgery, robotics, surgical and medical robotics, skill learning, skill analysis and learning to perceive. RESULTS: Studies making use of ML methods in the context of surgery are increasingly being reported. In particular, there is an increasing interest in using ML for developing tools to understand and model surgical skill and competence or to extract surgical workflow. Many researchers begin to integrate this understanding into the control of recent surgical robots and devices. CONCLUSION: ML is an expanding field. It is popular as it allows efficient processing of vast amounts of data for interpreting and real-time decision making. Already widely used in imaging and diagnosis, it is believed that ML will also play an important role in surgery and interventional treatments. In particular, ML could become a game changer into the conception of cognitive surgical robots. Such robots endowed with cognitive skills would assist the surgical team also on a cognitive level, such as possibly lowering the mental load of the team. For example, ML could help extracting surgical skill, learned through demonstration by human experts, and could transfer this to robotic skills. Such intelligent surgical assistance would significantly surpass the state of the art in surgical robotics. Current devices possess no intelligence whatsoever and are merely advanced and expensive instruments

Towards interoperability of entity-based and event-based IoT platforms: The case of NGSI and EPCIS standards

With the advancement of IoT devices and thanks to the unprecedented visibility and transparency they provide, diverse IoT-based applications are being developed. With the proliferation of IoT, both the amount and type of data items captured have increased dramatically. The data generated by IoT devices reside in different organizations and systems, and a major barrier to utilizing the data is the lack of interoperability among the standards used to capture the data. To reduce this barrier, two major standards have emerged: the Global Standards One (GS1) Electronic Product Code Information Service (EPCIS) and the FIWARE Next Generation Services Interface (NGSI). However, the two standards differ not only in the data encoding but also in the underlying philosophy of representing IoT data; namely, EPCIS is event-based, and NGSI is entity-based. Interoperability between FIWARE and EPCIS is essential for system integration. This paper presents OLIOT Mediation Gateway, now one of the incubated generic enablers offered by the FIWARE Foundation, that realizes the required interoperability between NGSI and EPCIS systems. It also demonstrates the applicability and feasibility of the Gateway by applying it to a real-life case study of integrating transparency systems used in a meat supply chain

Residential food environment, household wealth and maternal education association to preschoolers’ consumption of plant-based vitamin A-rich foods: the EAT Addis survey in Addis Ababa

Vitamin A deficiency is common among preschoolers in low-income settings and a serious public health concern due to its association to increased morbidity and mortality. The limited consumption of vitamin A-rich food is contributing to the problem. Many factors may influence children’s diet, including residential food environment, household wealth, and maternal education. However, very few studies in low-income settings have examined the relationship of these factors to children’s diet together. This study aimed to assess the importance of residential food availability of three plant-based groups of vitamin A-rich foods, household wealth, and maternal education for preschoolers’ consumption of plant-based vitamin A-rich foods in Addis Ababa. A multistage sampling procedure was used to enroll 5467 households with under-five children and 233 residential food environments with 2568 vendors. Data were analyzed using a multilevel binary logistic regression model. Overall, 36% (95% CI: 34.26, 36.95) of the study children reportedly consumed at least one plant-based vitamin A-rich food group in the 24-h dietary recall period. The odds of consuming any plant-based vitamin A-rich food were significantly higher among children whose mothers had a higher education level (AOR: 2.55; 95% CI: 2.01, 3.25), those living in the highest wealth quintile households (AOR: 2.37; 95% CI: 1.92, 2.93), and in residentials where vitamin A-rich fruits were available (AOR: 1.20; 95% CI: 1.02, 1.41). Further research in residential food environment is necessary to understand the purchasing habits, affordability, and desirability of plant-based vitamin A-rich foods to widen strategic options to improve its consumption among preschoolers in low-income and low-education communities

Capacity of health facilities for diagnosis and treatment of HIV/AIDS in Ethiopia

Background: There are dearth of literature on the capacity of the health system to diagnose and treat HIV/AIDS in Ethiopia. In this study we evaluated the capacity of health facilities for HIV/AIDS care, its spatial distribution and variations by regions and zones in Ethiopia. Methods: We analyzed the Service Provision Assessment plus (SPA+) survey data that were collected in 2014 in all regions of Ethiopia. We assessed structural, process and overall capacity of the health system based on the Donabedian quality of care model. We included 5 structural and 8 process indicators and overall capacity score was constructed by taking the average of all indicators. Multiple linear regression was done using STATA 14 to assess the association of the location and types of health facilities with overall capacity score. Maps displaying the average capacity score at Zonal level were produced using ArcGIS Desktop v10.3 (Environmental Systems Research Institute Inc., Redlands CA, USA). Results: A total of 873 health facilities were included in the analysis. Less than 5% of the private facilities provided antiretroviral therapy (ART); had national ART guideline, baseline CD4 count or viral load and tuberculosis screening mechanisms. Nearly one-third of the health centers (34.9%) provided ART. Public hospitals have better capacity score (77.1%) than health centers (45.9%) and private health facilities (24.8%). The overall capacity score for urban facilities (57.1%) was higher than that of the rural (38.2%) health facilities (β = 15.4, 95% CI: 11.7, 19.2). Health centers (β = − 21.4, 95% CI: -25.4, − 17.4) and private health facilities (β = − 50.9, 95% CI: -54.8, − 47.1) had lower overall capacity score than hospitals. Facilities in Somali (β = − 13.8, 95% CI: -20.6, − 7.0) and SNNPR (β = − 5.0, 95% CI: -9.8, − 0.1) regions had lower overall capacity score than facilities in the Oromia region. Zones located in emerging regions such as Gambella and Benishangul Gumz and in remote areas of Oromia and SNNPR had lower capacity score in terms of process indicators. Conclusions: There is a significant geographical heterogeneity on the capacity of health facilities for HIV/AIDS care and treatment in Ethiopia. Targeted capacity improvement initiatives are recommended with focus on health centers and private health facilities, and emerging Regions and the rural and remote areas

Multispacer Sequence Typing Relapsing Fever Borreliae in Africa

In Africa, relapsing fevers are caused by four cultured species: Borrelia crocidurae, Borrelia duttonii, Borrelia hispanica and Borrelia recurrentis. These borreliae are transmitted by the bite of Ornithodoros soft ticks except for B. recurrentis which is transmitted by louse Pediculus humanus. They cause potentially undifferentiated fever infection and co-infection with malaria could also occur. The exact prevalence of each Borrelia is unknown and overlaps between B. duttonii and B. crocidurae have been reported. The lack of tools for genotyping these borreliae limits knowledge concerning their epidemiology. We developed multispacer sequence typing (MST) and applied it to blood specimens infected by B. recurrentis (30 specimens), B. duttonii (18 specimens) and B. crocidurae (13 specimens), delineating these 60 strains and the 3 type strains into 13 species-specific spacer types. B. crocidurae strains were classified into 8 spacer types, B. duttonii into 3 spacer types and B. recurrentis into 2 spacer types. These findings provide the proof-of-concept that that MST is a reliable tool for identification and genotyping relapsing fever borreliae in Africa

Mechanism-based pharmacokinetic-pharmacodynamic modeling of the dopamine D-2 receptor occupancy of olanzapine in rats

A mechanism-based PK-PD model was developed to predict the time course of dopamine D-2 receptor occupancy (D2RO) in rat striatum following administration of olanzapine, an atypical antipsychotic drug. A population approach was utilized to quantify both the pharmacokinetics and pharmacodynamics of olanzapine in rats using the exposure (plasma and brain concentration) and D2RO profile obtained experimentally at various doses (0.01-40 mg/kg) administered by different routes. A two-compartment pharmacokinetic model was used to describe the plasma pharmacokinetic profile. A hybrid physiology- and mechanism-based model was developed to characterize the D-2 receptor binding in the striatum and was fitted sequentially to the data. The parameters were estimated using nonlinear mixed-effects modeling . Plasma, brain concentration profiles and time course of D2RO were well described by the model; validity of the proposed model is supported by good agreement between estimated association and dissociation rate constants and in vitro values from literature. This model includes both receptor binding kinetics and pharmacokinetics as the basis for the prediction of the D2RO in rats. Moreover, this modeling framework can be applied to scale the in vitro and preclinical information to clinical receptor occupancy