Atheism is only skin deep: Geertz and Markusson rely mistakenly on sociodemographic data as meaningful indicators of underlying cognition

Jesse Beringa*

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Atheism is only skin deep: Geertz and Markusson rely mistakenly on sociodemographic data as meaningful indicators of underlying cognition

机译：无神论只是肤浅的：Geertz和Markusson错误地依赖于社会人口统计学数据作为潜在认知的有意义指标

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《Religion 》 |2010年第3期| p.166-168| 共3页
作者
Jesse Beringa*;
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a Institute of Cognition and Culture, Queen's University, Belfast, BT7 1NN, United Kingdom Phone: +44 2890 971170 Fax: +44 2890 971170 E-mail:;

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机译：姿势认知双重任务中与年龄相关的表现差异的潜在皮质活动调节
2. SOS teeth with advanced caries and sociodemographic indicators, health-related habits and dental attendance patterns: data from the Dental, Oral, Medical Epidemiological (DOME) nationwide records-based study [J] . Abramovitz Itzhak, Zini Avraham, Kessler Baruch Ortal, BMC Oral Health . 2021 ,第1期

机译：具有先进龋齿和社会渗透指标的SOS牙齿，健康相关的习惯和牙科考勤模式：来自牙科，口服，医学流行病学（DOME）的数据全国范围内的基于记录的研究
3. Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal [J] . Schmid Timo, Bruckschen Fabian, Salvati Nicola, Journal of the Royal Statistical Society . 2017 ,第pta4期

机译：使用手机数据为国家统计机构建立社会人口统计学指标：估计塞内加尔的识字率
4. More than Skin Deep: Measuring Effects of the Underlying Model on Access-Control System Usability [C] . Robert W. Reeder, Lujo Bauer, Lorrie Faith Cranor, Annual CHI conference on human factors in computing systems . 2011

机译：不仅仅是深入了解：衡量基础模型对访问控制系统可用性的影响
5. SOS teeth with advanced caries and sociodemographic indicators health-related habits and dental attendance patterns: data from the Dental Oral Medical Epidemiological (DOME) nationwide records-based study [O] . Itzhak Abramovitz, Avraham Zini, Ortal Kessler Baruch, 2021

机译：SOS牙齿具有先进的龋齿和社会渗透指标健康相关的习惯和牙科考勤模式：来自牙科口服医学流行病学（圆顶）的数据全国范围内的记录基础研究
6. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

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Atheism is only skin deep: Geertz and Markusson rely mistakenly on sociodemographic data as meaningful indicators of underlying cognition

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