Triboelectric nanogenerators based on elastic electrodes

Nanoscale ◽  
2020 ◽  
Vol 12 (39) ◽  
pp. 20118-20130 ◽  
Author(s):  
Yike Liu ◽  
Chenguo Hu

New technologies such as the Internet of Things and big data have become the strategic focus of national development in the world.

2019 ◽  
pp. 553-560
Author(s):  
John Child ◽  
David Faulkner ◽  
Stephen Tallman ◽  
Linda Hsieh

In concluding the book, Chapter 25 argues that cooperation is becoming the preferred strategy for business and public organizations to adopt in the new economy. It is taking on new forms that are adapted to changing market expectations and new technological possibilities in the rapidly evolving business environment. New technologies such as ICT and blockchain are reducing the potential for and value of partner opportunism, making cooperation more efficient and less costly while enabling managers to pinpoint potential partners for ever more focused purposes. Cooperative strategy offers a viable solution for pooling together the required resources to seize the opportunities offered by Industry 4.0, which is driven by AI, the Internet of Things, and Big Data. Looking ahead, the personalization of technologies envisaged in Industry 5.0 will require an increasing number of collaborations between organizations from different sectors, both firms and social/public bodies.


2021 ◽  
Vol 8 (4) ◽  
pp. 685-733
Author(s):  
Jennifer Zwagerman

Technology advancements make life, work, and play easier and more enjoyable in many ways. Technology issues are also the cause of many headaches and dreams of living out the copier destruction scene from the movie “Office Space.” Whether it be user error or technological error, one key technology issue on many minds right now is how all the data produced every second of every day, in hundreds of different ways, is used by those that collect it. How much data are we talking about here? In 2018, the tech company Domo estimated that by 2020 “1.7 MB of data will be created every second” for every single person on Earth. In 2019, Domo’s annual report noted that “Americans use 4,416,720 GB of internet data including 188,000,000 emails, 18,100,000 texts and 4,497,420 Google searches every single minute.” And this was before the pandemic of 2020, which saw reliance on remote technology and the internet skyrocket. It is not just social media and working from home that generates data—the “Internet of Things” (“IoT”) is expanding exponentially. From our homes (smart appliances and thermostats), to entertainment (smart speakers and tablets), to what we wear (smartwatches and fitness devices), we are producing data constantly. Over 30 billion devices currently make up the IoT, and that number will double by 2025. The IoT is roughly defined as “devices—from simple sensors to smartphones and wearables—connected together.” That connection allows the devices to “talk” to each other across networks that stretch across the world, sharing information that in turn can be analyzed (alone or combined with data from other users) in ways that may be beneficial to the user or the broader economy. The key word in that last sentence is “may.” When it comes to the data that individuals and businesses across the world produce every second of every day, some of it—perhaps most of it—could be used in ways that are not beneficial to the user or the entire economy. Some data types can be used to cause harm in obvious ways, such as personal identifying information in cases of identity theft. While some data types may seem innocuous or harmful when viewed on their own, when combined with other data from the same user or even other users, it can be used in a wide variety of ways. While I find it beneficial to know how many steps I take in a day or how much time I sleep at night, I am not the only individual or entity with access to that information. The company that owns the device I wear also takes that information and uses it in ways that are beyond my control. Why would a company do that? In many instances, “[t]he data generated by the Internet of Things provides businesses with a wealth of information that—when properly collected, stored, and processed—gives businesses a depth of insight into user behavior never before seen.” Data security and privacy in general are issues that all companies manage as they work to protect the data we provide. Some types of data receive heightened protections, as discussed below, because they are viewed as personal, as private, or as potentially dangerous since unauthorized access to them could cause harm to the user/owner. Some states and countries have taken a step further, focusing not on industry-related data that needs particular types of protection, but in-stead looking at an individual’s overall right to privacy, particularly on the internet. Those protections are summarized below. It makes sense, you might say, to worry about financial or healthcare data remaining private and to not want every website you have ever visited to keep a file of information on you. But why might we care about the use of data in agricultural operations? Depending on who you ask, the answer may be that agricultural data needs no more care or concern than any other type of business data. Some argue that the use of “Big Data” in agriculture provides opportunities for smaller operations and shareholders. These opportunities include increased power in a market driven for many years by the mantra “bigger is better” and increased production of food staples across the world—both in a more environmentally-friendly fashion. While the benefits of technology and Big Data in the agricultural sector unarguably exist, questions remain as to how to best manage data privacy concerns in an industry where there is little specific law or regulation tied to collection, use, and ownership of this valuable agricultural production data. In the following pages, this Article discusses what types of data are currently being gathered in the agricultural sector and how some of that data can and is being used. In addition, it focuses on unique considerations tied to the use of agricultural data and why privacy concerns continue to increase for many producers. As the Article looks at potential solutions to privacy concerns, it summarizes privacy-related legislation that currently exists and ends by looking at whether any of the current privacy-related laws might be used or adapted within the agricultural sector to address potential misuse of agricultural data.


Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1711 ◽  
Author(s):  
Guobao Xu ◽  
Yanjun Shi ◽  
Xueyan Sun ◽  
Weiming Shen

Marine environment monitoring has attracted more and more attention due to the growing concern about climate change. During the past couple of decades, advanced information and communication technologies have been applied to the development of various marine environment monitoring systems. Among others, the Internet of Things (IoT) has been playing an important role in this area. This paper presents a review of the application of the Internet of Things in the field of marine environment monitoring. New technologies including advanced Big Data analytics and their applications in this area are briefly reviewed. It also discusses key research challenges and opportunities in this area, including the potential application of IoT and Big Data in marine environment protection.


2020 ◽  
Vol 10 (2) ◽  
pp. 106-112
Author(s):  
Ahmed Burhan Mohammed

    One of the most important topics in the last decade is the Big Data (BD) and how to link it and benefit from its consumption in different fields, included as the introduction in this research analysis of the BD belonging to devices of the Internet of Things. The concept of managing objects and exploring devices is connected to the Internet and sensors deployed in the world, all these devices are pumping a lot of data through the Internet of Things (IoT) into the world. In order to make the right decisions for people and things, BD using data mining techniques and machine language algorithms help make decisions. The Internet of Things that insert large amounts of data need to be studied, analysed and disseminated in order to access valuable, useful and bug-free information for the purpose of making the right decision and avoiding problems. In this paper, two clustering algorithms simple K-means and self-organising map (SOM) in IoT are presented. Next, comparing the clustering models’ output in the IoT data set that improved the SOM is better than K-means, but it is slower in creating the model.   Keywords: Internet of things (IoT), big data, machine learning, filtered cluster, K-means, SOM.    


2020 ◽  
Vol 9 (2) ◽  
pp. 136-138 ◽  
Author(s):  
Md. Siddikur Rahman ◽  
Noah C. Peeri ◽  
Nistha Shrestha ◽  
Rafdzah Zaki ◽  
Ubydul Haque ◽  
...  

2016 ◽  
Vol 44 (4) ◽  
pp. 18-25 ◽  
Author(s):  
Saul J. Berman ◽  
Peter J. Korsten ◽  
Anthony Marshall

Purpose Digital reinvention helps organizations create unique, compelling experiences for their customers, partners, employees and other stakeholders. Design/methodology/approach Digital reinvention combines the capabilities of multiple technologies, including cloud, cognitive, mobile and the Internet of Things (IoT) to rethink customer and partner relationships from a perspective of fundamental customer need, use or aspiration. Findings The most successful digitally reinvented businesses establish a platform of engagement for their customers, with the business acting as enabler, conduit and partner Practical implications For successful digital reinvention, organizations need to pursue a new strategic focus, build new expertise and establish new ways of working. Originality/value The article offers a blueprint for digital reinvention that involves rethinking customer and partner relationships from a perspective of fundamental customer need, use or aspiration.


Sign in / Sign up

Export Citation Format

Share Document