Towards the development of a wireless network node lifetime calculation tool

The designers, optimizers and maintenance personnel of a wireless sensor network are frequently challenged by system level energy budget considerations. Minimizing the need for battery replacement is often the design goal while ensuring that a balance is maintained between capability and current consumption in order to address application needs. In this paper, a tool is introduced which can be used to calculate the lifetime of a battery operated wireless node. It allows the user to configure different wireless sensor platforms, select a battery of choice, and specify the application which needs to be executed over the configured hardware. As a result, the tool computes an estimate for the expected lifetime of the wireless sensor node. Furthermore, the tool also provides a detailed overview of the energy consumed by each component during a duty cycle. © 2013 IEEE

Entanglement of Gaussian states using beam splitter

We study an experimental scheme to generate Gaussian two-mode entangled states via beam splitter. Specifically, we consider a nonclassical Gaussian state (squeezed state) and a thermal state as two input modes, and evaluate the degree of entanglement at the output. Experimental conditions to generate entangled outputs are completely identified and the critical thermal noise to destroy entanglement is analytically obtained. By doing so, we discuss the possibility to link the resistance to noise in entanglement generation with the degree of single-mode nonclassicality.Comment: 6 pages, 3 figures, references added (particularly [19]), more detailed discussion

Wireless Communication in Process Control Loop: Requirements Analysis, Industry Practices and Experimental Evaluation

Wireless communication is already used in process automation for process monitoring. The next stage of implementation of wireless technology in industrial applications is for process control. The need for wireless networked control systems has evolved because of the necessity for extensibility, mobility, modularity, fast deployment, and reduced installation and maintenance cost. These benefits are only applicable given that the wireless network of choice can meet the strict requirements of process control applications, such as latency. In this regard, this paper is an effort towards identifying current industry practices related to implementing process control over a wireless link and evaluates the suitability of ISA100.11a network for use in process control through experiments

Seasonality of cognitive function in the general population:the Rotterdam Study

Seasonal variation in cognitive function and underlying cerebral hemodynamics in humans has been suggested, but not consistently shown in previous studies. We assessed cognitive function in 10,276 participants from the population-based Rotterdam Study, aged 45 years and older without dementia, at baseline and at subsequent visits between 1999 and 2016. Seasonality of five cognitive test scores and of a summary measure of global cognition were determined, as well as of brain perfusion. Using linkage with medical records, we also examined whether a seasonal variation was present in clinical diagnoses of dementia. We found a seasonal variation of global cognition (0.05 standard deviations [95% confidence interval: 0.02–0.08]), the Stroop reading task, the Purdue Pegboard test, and of the delayed world learning test, with the best performance in summer months. In line with these findings, there were fewer dementia diagnoses of dementia in spring and summer than in winter and fall. We found no seasonal variation in brain perfusion. These findings support seasonality of cognition, albeit not explained by brain perfusion. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11357-021-00485-0

Clinical Relevance of Cortical Cerebral Microinfarcts on 1.5T Magnetic Resonance Imaging in the Late-Adult Population

Background and Purpose: Cortical cerebral microinfarcts (CMIs) have been linked with dementia and impaired cognition in cross-sectional studies. However, the clinical relevance of CMIs in a large population-based setting is lacking. We examine the association of cortical CMIs detected on 1.5T magnetic resonance imaging with cardiovascular risk factors, cerebrovascular disease, and brain tissue volumes. We further explore the association between cortical CMIs with cognitive decline and risk of stroke, dementia, and mortality in the general population. Methods: Two thousand one hundred fifty-six participants (age: 75.7±5.9 years, women: 55.6%) with clinical history and baseline magnetic resonance imaging (January 2009-December 2013) were included from the Rotterdam Study. Cortical CMIs were graded based on a previously validated method. Markers of cerebrovascular disease and brain tissue volumes were assessed on magnetic resonance imaging. Cognition was assessed using a detailed neuropsychological test at baseline and at 5 years of follow-up. Data on incident stroke, dementia, and mortality were included until January 2016. Results: Two hundred twenty-seven individuals (10.5%) had ≥1 cortical CMIs. The major risk factors of cortical CMIs were male sex, current smoking, history of heart disease, and stroke. Furthermore, presence of cortical CMIs was associated with infarcts and smaller brain volume. Persons with cortical CMIs showed cognitive decline in Stroop tests (color-naming and interference subtasks; β for color-naming, 0.18 [95% CI, 0.04-0.33], P interaction ≤0.001 and β for interference subtask, 1.74, [95% CI, 0.66-2.82], P interaction ≤0.001). During a mean follow-up of 5.2 years, 73 (4.3%) individuals developed incident stroke, 95 (5.1%) incident dementia, and 399 (19.2%) died. People with cortical CMIs were at an increased risk of stroke (hazard ratio, 1.18 [95% CI, 1.09-1.28]) and mortality (hazard ratio, 1.09 [95% CI, 1.00-1.19]). Conclusions: Cortical CMIs are highly prevalent in a population-based setting and are associated with cardiovascular disease, cognitive decline, and increased risk of stroke and mortality. Future investigations will have to show whether cortical CMIs are a useful biomarker to intervene upon to reduce the burden of stroke.</p

Dietary nitrate intake in relation to the risk of dementia and imaging markers of vascular brain health:a population-based study

Background: Nitric oxide is a free radical that can be produced from dietary nitrate and positively affects cardiovascular health. With cardiovascular health playing an important role in the etiology of dementia, we hypothesized a link between dietary nitrate intake and the risk of dementia. Objectives: This study aimed to find the association of total, vegetable, and nonvegetable dietary nitrate intake with the risk of dementia and imaging markers of vascular brain health, such as total brain volume, global cerebral perfusion, white matter hyperintensity volume, microbleeds, and lacunar infarcts. Methods: Between 1990 and 2009, dietary intake was assessed using food-frequency questionnaires in 9543 dementia-free participants (mean age, 64 y; 58% female) from the prospective population-based Rotterdam Study. Participants were followed up for incidence dementia until January 2020. We used Cox models to determine the association between dietary nitrate intake and incident dementia. Using linear mixed models and logistic regression models, we assessed the association of dietary nitrate intake with changes in imaging markers across 3 consecutive examination rounds (mean interval between images 4.6 y). Results: Participants median dietary nitrate consumption was 85 mg/d (interquartile range, 55 mg/d), derived on average for 81% from vegetable sources. During a mean follow-up of 14.5 y, 1472 participants developed dementia. A higher intake of total and vegetable dietary nitrate was associated with a lower risk of dementia per 50-mg/d increase [hazard ratio (HR): 0.92; 95% confidence interval (CI): 0.87, 0.98; and HR: 0.92; 95% CI: 0.86, 0.97, respectively] but not with changes in neuroimaging markers. No association between nonvegetable dietary nitrate intake and the risk of dementia (HR: 1.15; 95% CI: 0.64, 2.07) or changes in neuroimaging markers were observed. Conclusions: A higher dietary nitrate intake from vegetable sources was associated with a lower risk of dementia. We found no evidence that this association was driven by vascular brain health.</p

PERTUMBUHAN DAN HASIL BEBERAPA GALUR PADI MUTAN ANORGANIK GREEN SUPER RICE (ORYZA SATIVA L.) DAN HUBUNGAN DENGAN KONSENTRASI KNO3 TERHADAP PEMATAHAN DORMANSI AFTER RIPENING

Penelitian ini bertujuan untuk mengetahui pertumbuhan dan hasil beberapa padi galur mutan anorganik Green Super Rice. Penelitian ini dilaksanakan di Rumah Kasa Fakultas Pertanian Universitas Syiah Kuala. Penelitian ini dimulai dari Februari sampai Juni 2018. Analisis data yang digunakan dalam penelitian ini yaitu Rancangan Acak Kelompok (RAK) pola Non-Faktorial yang menggunakan uji lanjut BNT taraf 5% untuk pengujian pertumbuhan dan hasil galur padi mutan. Parameter pengamatan yang dilakukan pada penelitian ini yaitu tinggi tanaman 15, 30, 45 HST, umur mulai berbunga, jumlah anakan produktif, berat gabah bernas, berat gabah hampa, berat 1000 butir, dan potensi hasil. Galur terbaik pada penelitian ini adalah galur UF-1 berdasarkan potensi hasil

Application of an Imaging-Based Sum Score for Cerebral Amyloid Angiopathy to the General Population: Risk of Major Neurological Diseases and Mortality

Objective: To assess the relation between a sum score of imaging markers indicative of cerebral amyloid angiopathy (CAA) and cognitive impairment, stroke, dementia, and mortality in a general population. Methods: One thousand six hundred twenty-two stroke-free and dementia-free participants of the population-based Rotterdam Study (mean age 73.1 years, 54.3% women) underwent brain MRI (1.5 tesla) in 2005–2011 and were followed for stroke, dementia and death until 2016–2017. Four MRI markers (strictly lobar cerebral microbleeds, cortical superficial siderosis, centrum semiovale perivascular spaces, and white matter hyperintensities) were combined to construct the CAA sum score, ranging from 0 to 4. Neuropsychological testing measured during the research visit closest to scan date were used to assess general cognitive function and cognitive domains. The associations of the CAA sum score with cognition cross-sectionally and with stroke, dementia, and mortality longitudinally were determined using linear regression and Cox proportional hazard modeling adjusted for age, sex, hypertension, cholesterol, lipid lowering medication, atrial fibrillation, antithrombotic medication and APOE-ε2/ε4 carriership. Additionally, we accounted for competing risks of death due to other causes for stroke and dementia, and calculated absolute risk estimates. Results: During a mean follow-up of 7.2 years, 62 participants suffered a stroke, 77 developed dementia and 298 died. Participants with a CAA score of 1 showed a lower Mini-Mental-State-Exam (fully-adjusted mean difference −0.21, 9

Transfer learning improves supervised image segmentation across imaging protocols

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%