Planning and governance under the LGA: Lessons from the RMA experience.

The purpose of this report is to identify ways in which experiences gained from the RMA as a devolved and co-operative planning mandate can enable local and central government and other stakeholders to more effectively implement the LGA. The report is based on findings from the FRST-funded research programme on Planning under Co-operative Mandates (PUCM). We argue in this report that the experiences gained from the RMA can inform effective implementation of the LGA in three important respects: Preparation and implementation of LTCCPs; The community consultation process for formulating community outcomes; and Māori participation in planning and governance

AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video

Developing useful interfaces between brains and machines is a grand challenge of neuroengineering. An effective interface has the capacity to not only interpret neural signals, but predict the intentions of the human to perform an action in the near future; prediction is made even more challenging outside well-controlled laboratory experiments. This paper describes our approach to detect and to predict natural human arm movements in the future, a key challenge in brain computer interfacing that has never before been attempted. We introduce the novel Annotated Joints in Long-term ECoG (AJILE) dataset; AJILE includes automatically annotated poses of 7 upper body joints for four human subjects over 670 total hours (more than 72 million frames), along with the corresponding simultaneously acquired intracranial neural recordings. The size and scope of AJILE greatly exceeds all previous datasets with movements and electrocorticography (ECoG), making it possible to take a deep learning approach to movement prediction. We propose a multimodal model that combines deep convolutional neural networks (CNN) with long short-term memory (LSTM) blocks, leveraging both ECoG and video modalities. We demonstrate that our models are able to detect movements and predict future movements up to 800 msec before movement initiation. Further, our multimodal movement prediction models exhibit resilience to simulated ablation of input neural signals. We believe a multimodal approach to natural neural decoding that takes context into account is critical in advancing bioelectronic technologies and human neuroscience

Serum urocortin in preterm labor is it an effective biomarker?

Background: Preterm labor classically defined as delivery before completed 37 gestational weeks. Urocortin a biomarker that have raised recent research interest is a 40-amino acid neuropeptide related to the corticotrophin-releasing factor molecular family. Interestingly urocortin is produced by gestational tissue such as amnion and chorion predictability of preterm labor by biomarker assay could enhance management levels particularly in cases of preterm labor that are considered a frequent clinical scenario in obstetric practice. Aim of the study was to assess and evaluate the serum levels of urocortin predictability capacity in cases that develop preterm labor.Methods: The current research clinical trial was conducted in a prospective way there was two research groups 60 study subjects had threatened preterm labor and 60 normal research study subjects that delivered at term. Comparative analysis was performed for urocortin assay conducted in both research groups in correlation to gathered clinical data obtained from both research groups.Results: Receiver operating characteristic curve (ROC) between preterm and term delivery research groups as regards plasma urocortin level (pg/ml) as a predictor of pre term delivery showing that a cut-off point level >101.3 pg/ml in which statistical sensitivity=88.33%, statistical specificity=75%, positive predictive value=77.9, negative predictive value=86.5.Conclusions: This research finding reveal that maternal serum urocortin is an effective biomarker in predictability of preterm labor; however future research studies should be multicentric in fashion putting in consideration the racial and ethnic differences besides the impact of BMI on maternal serum urocortin indices

Pengaruh Terapi Musik Keroncong Terhadap Insomnia pada Menopause di Wilayah Kerja Puskesmas Kota Tengah Kota Gorontalo

Insomnia is more common in menopausal women.  In menopausal women,  50 % of the most common is hard to be in a state of sleep and wake up too early.  Non-pharmacological therapy can be done using keroncong music. In essence, keroncong music can break through a person's state of consciousness and can cause psycho-physiological responses.  As this process continues, it will follow the remaining state of consciousness, increasing the sensory phase, so that it can dream and sleep normally. This study aimed to analyze the effect of keroncong music on the level of insomnia in menopause in the working area of Kota Tengah public health center, Gorontalo city. This research used a pre-experimentaal design with pretest posttest group to analyze the effeect of keroncong music therapy on insomnia. The sample in this study were 60 menopausal women. The results of statistical tests using the Nonparametric Wilcoxon sample, obtained α = 0.05 with a value of ρ = 0.002 (ρ <α). It means that there was an effect of keroncong music therapy on insomnia in menopausal women. The conclusion is was an effect of keroncong music therapy on insomnia in menopausal women. it is hoped that with this research, keroncong music can be used as an alternative method that is easy, safe, and without risk to treat insomnia in menopausal women so that it can be applied in everyday life

ROBUST AND PARALLEL SEGMENTATION MODEL (RPSM) FOR EARLY DETECTION OF SKIN CANCER DISEASE USING HETEROGENEOUS DISTRIBUTIONS

Melanoma is the most common dangerous type of skin cancer; however, it is preventable if it is diagnosed early. Diagnosis of Melanoma would be improved if an accurate skin image segmentation model is available. Many computer vision methods have been investigated, yet the problem of finding a consistent and robust model that extracts the best threshold value, persists. This paper suggests a novel image segmentation approach using a multilevel cross entropy thresholding algorithm based on heterogeneous distributions. The proposed strategy searches the problem space by segmenting the image into several levels, and applying for each level one of the three benchmark distributions, including Gaussian, Lognormal or Gamma, which are combined to estimate the best thresholds that optimally extract the segmented regions. The classical technique of Minimum Cross Entropy Thresholding (MCET) is considered as the objective function for the applied method. Furthermore, a parallel processing algorithm is suggested to minimize the computational time of the proposed segmentation model in order to boost its performance. The efficiency of the proposed RPSM model is evaluated based on two datasets for skin cancer images: The International Skin Imaging Collaboration (ISIC) and Planet Hunters 2 (PH2). In conclusion, the proposed RPSM model shows a significant reduced processing time and reveals better accuracy and stable results, compared to other segmentation models. Design/methodology – The proposed model estimates two optimum threshold values that lead to extract optimally three segmented regions by combining the three benchmark statistical distributions: Gamma, Gaussian and lognormal. Outcomes – Based on the experimental results, the suggested segmentation methodology using MCET, could be nominated as a robust, precise and extremely reliable model with high efficiency. Novelty/utility –A novel multilevel segmentation model is developed using MCET technique and based on a combination of three statistical distributions: Gamma, Gaussian, and Lognormal. Moreover, this model is boosted by a parallelized method to reduce the processing time of the segmentation. Therefore, the suggested model should be considered as a precious mechanism in skin cancer disease detection

Assia Djebar et la réécriture de l’histoire au féminin

Dans L'Amour, la fantasia, Assia Djebar tend la main aux sans-voix, les femmes analphabètes de la guerre en Algérie, transmettant leurs récits oraux dans son écrit en français. Nous montrerons que, d'une autobiographie anonyme à une autobiographie plurielle, Djebar négocie un lieu spécifique pour l’autobiographie féminine écrite, dans une société traditionnelle inhibitrice du « je » de la femme. Ainsi, nous nous sommes intéressée à sa posture de « conteuse » : revisitant les archives françaises de la conquête de l'Algérie, elle donne une voix aux témoignages des femmes autrement silencieuses. Nous étudierons la façon dont Djebar écrit pour naître au monde, mais aussi pour aider les autres femmes à naître au monde en inscrivant leurs récits dans le discours de l’Histoire. Ce faisant, elle réécrit l’Histoire en un palimpseste au féminin.Assia Djebar and the Rewriting of the History in the Feminine In L’Amour, la fantasia, Assia Djebar lends a hand to the voiceless, the illiterate women of the Algerian war, transmitting their oral testimonies in her French text. We argue that Djebar negotiates a specific place between an anonymous autobiography and a collective one for the female autobiography written in a society that frowns upon the first-person pronoun of a woman. We address the author’s role as a storyteller: revisiting the French archives of the conquest of Algeria, Djebar gives a voice to the stories of the otherwise silenced women. We explore how writing for Djebar is a means of coming to life, not just for herself, but also for the women of Algeria whose stories she inscribes in the narrative of History. So doing, she rewrites History into a feminized palimpsest

Addressing Health Disparities Among Homeless in Alachua County through Community-Based Participatory Research.

Introduction. In states such as Florida that did not expand Medicaid, a large number of economically disadvantaged individuals do not qualify for subsidies to buy health insurance through the Affordable Care Act (ACA) 2. This leaves the health needs of Florida’s homeless population largely unaddressed. Nearly 48.1% of Alachua County’s homeless population has disabling conditions 16. This confirms a pressing need to understand the homeless population\u27s healthcare needs, knowledge, and barriers in accessing healthcare. Methods. We used a Community-Based Participatory Research model in conducting health fairs and needs assessment surveys, incentivizing participation, and providing education about existing resources. The surveys were conducted at two homeless meal service sites and consisted of 22 questions regarding access to healthcare, utilization, and satisfaction. Health fairs consisted of blood pressure, blood glucose, and mental health screening. Patient participation was encouraged through games, prizes and food. Results. Of the population we surveyed, 100% have income levels below $11,490, therefore all uninsured fall into the ACA coverage gap. Those less than 65 years of age do not qualify for Medicare unless disabled. Some qualify for Medicaid as shown in tables. Fifty-eight percent were uninsured and did not get any treatment for their illnesses. Additionally, 67% had no knowledge of free local healthcare clinics. Discussion/Conclusion. The majority of this population falls into the ACA Coverage Gap, lacks knowledge about free community clinics, and inappropriately uses the ED. Future implications of this research involve advocacy to expand Medicaid in Florida and enroll those who are eligible for health insurance. Vital goals include outreach by free healthcare clinics to make healthcare more accessible, as well as building trust with the community through continued outreach initiatives. A community-Based Participatory Research Model is an effective tool to increasing collaboration among diverse members of the community in order to bring meaningful and positive change to the health of populations

The Influence of Selected Factors of Motivation on Women’s Participation in Contract Sugar Cane Farming in Mumias Division, Kakamega County, Kenya

Challenges facing women are a major source of inequality often discriminating against them specifically in the agricultural labour market. Unless women farmers in households are motivated to be actors in contract farming activities and programs, the impact of such interventions remain insignificant in transforming their lives and the lives of others.As the contribution of women in contract farming diminishes in the rural households where they form the majority in crop production within the regional and national economy, family livelihoods are severely affected. This paper examines the influence of selected factors on women’s participation in contract sugarcane farming in Mumias Division, Kakamega County, Kenya. The study used cross-sectional design. Systematic sampling technique was used to select 118 women engaged in contract sugarcane farming in the households.  In addition, two-focus discussion groups (FGDs) each comprising eight women were selected purposively. Data was collected using interviews and FGD guides. The validity and reliability of the instruments were ascertained using content validity and Cronbach’s coefficient alpha respectively. Data was analyzed using both descriptive and inferential statistics. Results of the study indicated that most of the women were highly influenced by property ownership, membership in advocacy bodies, and representation in investment institutions with a significance of 0.000, 0.000 and 0.000 respectively. The study recommends that sound policies and mechanisms by sugar Industry management and government be adopted. This would serve to ensure that the energy of women that is directed into contract sugarcane farming to upscale Industry production indeed benefits the women’s livelihoods at household level. Keywords: Women, contract sugarcane farming, households, livelihoods, participation

Assessment of the calendar aging of lithium-ion batteries for a long-term—Space missions

Energy availability is a critical challenge for space missions, especially for those missions designed to last many decades. Space satellites have depended on various combinations of radioisotope thermoelectric generators (RGTs), solar arrays, and batteries for power. For deep space missions lasting as long as 50 + years, batteries will also be needed for applications when there is no sunlight and RTGs cannot support peak power demand due to their insufficient specific power. This paper addresses the potential use of lithium-ion batteries for long-term space missions. Using data collected from the literature and internal experiments, a calendar aging model is developed to assess the capacity fade as a function of temperature, state-of-charge and time. The results for various LIB chemistries are used to identify the best candidate chemistries and determine the conditions, with a focus on low temperatures, that can best enable deep space missions