イタリア・サルデーニャ紀行 : グラムシの家博物館を訪ねて

departmental bulletin pape

Hybrid IoT Programming with Data Flow Diagrams and mruby

IoT is increasingly being used in different areas of society. IoT is used in society by collecting values acquired by sensor devices through networks and analysing the data. IoT software is needed to use the IoT, but efficient software development methods are desired because software for an increasingly complex society tends to become more complex. This research aims to improve the efficiency of IoT software and develop an intuitive method for developing IoT software.This paper proposes a development methodology that combines a data-flow-based programming approach with a procedural programming language. When the focus is on data, data flow programming is intuitive and efficient. On the other hand, when the focus is on data processing, it is better to use a procedural programming language because data is processed sequentially[1].In the hybrid IoT programming environment proposed in this paper, the code of a procedural programming language is generated from the description by data flow programming, and the program is executed by a micro controller. The products of this research are available as open source software[2], [3].journal articl

Analysis of Obese Patients Using Machine Learning to Categorize Hidden Risk Factors in Explorative Assessment

Obesity has a multitude of detrimental impacts on both mental and physical health, along with an increased risk of acquiring chronic illnesses including coronary heart disease, diabetes, and stroke, as well as anxiety and depression conditions. Using forecasting analytics, researchers may develop therapies and prevention methods that are tailored to a particular behavior and medical knowledge. Data analytics examines the greater risk of contracting diseases associated with obesity in order to target a specific group. In order to undertake efficient treatments, it is primarily important to gather and evaluate data on issues associated with obesity. Gathering the abnormal diet, lifestyle, and previous medical history tends to indicate whether the individual is affected by an obese hidden risk factor. Machine learning can assist in early diagnosis and anticipate potential health problems associated with obesity. The proposed approach involves various machine learning techniques to analyze comprehensive data on diet, lifestyle.journal articl

Assessing multi-decadal climatic variability and its impact on cardamom cultivation in the Indian Cardamom Hills

This study examines the multi-decadal variability and trends of surface air temperature and precipitation in the Indian Cardamom Hills (ICH), a degraded tropical rainforest area unique for cardamom cultivation. Utilizing observed long-term climatic data (1958–2017), statistical methods such as the Mann–Kendall test (MKT), Sen’s Slope Estimator (SSE), and Incremental Trend Analysis (ITA) were applied to assess the impact of surface air temperature, rainfall, and the number of rainy days on cardamom yield. The analysis revealed a significant decline in annual rainfall by approximately 13.62 mm per year, with pronounced seasonal declines 0.87 mm for winter, 12.33 mm for pre-monsoon, 24.93 mm for southwest monsoon, and 18.10 mm for post-monsoon. Simultaneously, the number of rainy days dropped by nearly 19.75 days over the 40-year period. A noticeable increase in decadal minimum and average temperatures was observed, highlighting potential adverse effects on cardamom yield and irrigation water resources. The findings suggest that excessive rainfall during the southwest monsoon negatively correlates with cardamom yield, while slightly warmer temperatures show a weak positive correlation. The study also emphasizes the need for adaptive agricultural practices and climate-resilient policies to mitigate the effects of changing climatic conditions on cardamom production. This research contributes valuable insights for farmers and other stakeholders as well as policymakers aiming to ensure sustainable cardamom cultivation amidst climate change.journal articl

Observation of B+ → χc0K+

journal articl

A LSTM Neural Network applied to Mobile Robots Path Planning

Mobile robots path planning is a central problem in every situation where human intervention is not desired or not possible to accept: full automated industrial warehouses or general stocking areas and every domestic application that involves a mobile robot and special cases where environment is prohibited for human accessing like toxic wastes and bombs defusing [1]. Currently, neural networks are applied to problems related to mobile robot navigation. However, they are not as popular as in applications like image processing, speech recognition or machine translation, where they are commercially relevant. In this paper we propose a Long Short-Term Memory (LSTM) neural network as an online search agent to tackle the problem of mobile robots path planning in unknown environments, meaning that the agent relies only on local map awareness realized with a LRF sensor and relative information between robot and goal position. Specifically, a final structure of LSTM network is analyzed and its performance is compared with the A* algorithm, a widely known method that follows the best-first search approach. Subsequently, an analysis of the method developed on a real robot is described.journal articl

<翻訳>ローレンツ・シュタイン 社会の概念と本質(抄訳)

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Studies on Computer Measurement System of the Plowing Performances : II. Measurement System

departmental bulletin pape

Predictors of Serum Dioxin, Furan, and PCB Concentrations among Women from Chapaevsk, Russia

Dioxins, furans, and polychlorinated biphenyls (PCBs) are persistent and bioaccumulative toxic chemicals that are ubiquitous in the environment. We assessed predictors of their serum concentrations among women living in a Russian town contaminated by past industrial activity. Blood samples from 446 mothers aged 23−52 years were collected between 2003−2005 as part of the Russian Children’s Study. Serum dioxin, furan, and PCB concentrations were quantified using high-resolution gas chromatography−mass spectrometry. Potential determinants of exposure were collected through interviews. Multivariate linear regression models were used to identify predictors of serum concentrations and toxic equivalencies (TEQs). The median total PCB concentrations and total TEQs were 260 ng/g lipid and 25 pg TEQ/g lipid, respectively. In multivariate analyses, both total PCB concentrations and total TEQs increased significantly with age, residential proximity to a local chemical plant, duration of local farming, and consumption of local beef. Both decreased with longer breastfeeding, recent increases in body mass index, and later blood draw date. These demographic and lifestyle predictors showed generally similar associations with the various measures of serum dioxins, furans, and PCBs