Analisis Sub Sektor Unggul Pertanian di Kabupaten Lamongan dan Kabupaten Tuban

The most successful agricultural sub-sectors in Lamongan and Tuban districts are the subjects of this study. In Lamongan and Tuban districts, agriculture is the most significant industry. Agriculture, forestry, and fisheries, as well as energy and gas procurement, clean water, waste management, construction, and wholesale and retail trade, have the potential to become basic industries in Lamongan Regency. Automotive repair, information technology (ICT), military cooperation and manufacturing are some of the other sectors in the area. Lamongan Regency has a population of 200,000 people. Agriculture, forestry and fisheries account for an average of 2.9 percent of basic sector production. Agriculture is one of the fastest growing businesses in the United States, according to shift share data. are in the second or third best quadrant. There are several industries in Tuban Regency that can become the backbone of the economy. These industries include food production and forestry as well as fisheries and mining. Tuban Regency can also be at the forefront in the fields of technology, defense, government administration, and social security. Agriculture, forestry, and fisheries make up the majority of the output of the fundamental sector. By shifting share, agriculture is one of America's fastest growing businesses. They are in the upper quartile, which indicates that they are very good. Keywords: Location Quotient Analysis, Shift Share, Klassen Typology

Credit and Recognition for Contributions to Data-Sharing Platforms Among Cohort Holders and Platform Developers in Europe: Interview Study

Background The European Commission is funding projects that aim to establish data-sharing platforms. These platforms are envisioned to enhance and facilitate the international sharing of cohort data. Nevertheless, broad data sharing may be restricted by the lack of adequate recognition for those who share data. Objective The aim of this study is to describe in depth the concerns about acquiring credit for data sharing within epidemiological research. Methods A total of 17 participants linked to European Union–funded data-sharing platforms were recruited for a semistructured interview. Transcripts were analyzed using inductive content analysis. Results Interviewees argued that data sharing within international projects could challenge authorship guidelines in multiple ways. Some respondents considered that the acquisition of credit for articles with extensive author lists could be problematic in some instances, such as for junior researchers. In addition, universities may be critical of researchers who share data more often than leading research. Some considered that the evaluation system undervalues data generators and specialists. Respondents generally looked favorably upon alternatives to the current evaluation system to potentially ameliorate these issues. Conclusions The evaluation system might impede data sharing because it mainly focuses on first and last authorship and undervalues the contributor’s work. Further movement of crediting models toward contributorship could potentially address this issue. Appropriate crediting mechanisms that are better aligned with the way science ought to be conducted in the future need to be developed.

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

With the rapid development of communication technology, connected vehicles (CV) have the potential, through the sharing of data, to enhance vehicle safety and reduce vehicle energy consumption and emissions. Numerous research efforts have been conducted to quantify the impacts of CV applications, assuming instant and accurate communication among vehicles, devices, pedestrians, infrastructure, the network, the cloud, and the grid, collectively known as V2X (vehicle-to-everything). The use of cellular vehicle-to-everything (C-V2X), to share data is emerging as an efficient means to achieve this objective. C-V2X releases 14 and 15 utilize the 4G LTE technology and release 16 utilizes the new 5G new radio (NR) technology. C-V2X can function without network infrastructure coverage and has a better communication range, improved latency, and greater data rates compared to older technologies. Such highly efficient interchange of information among all participating parts in a CV environment will not only provide timely data to enhance the capacity of the transportation system but can also be used to develop applications that enhance vehicle safety and minimize negative environmental impacts. However, before the full benefits of CV can be achieved, there is a need to thoroughly investigate the effectiveness, strengths, and weaknesses of different CV applications, the communication protocols, the varied results with different CV market penetration rates (MPRs), the interaction of CVs and human driven vehicles, the integration of multiple applications, and the errors and latencies associated with data communication. This paper reviews existing literature on the environmental, mobility and safety impacts of CV applications, identifies the gaps in our current research of CVs and recommends future research directions. The results of this paper will help shape the future research direction for CV applications to realize their full potential benefits.

Federated Learning in Edge Computing: A Systematic Survey

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.

Multi-Keyword Searchable Encryption for E-Health System With Multiple Data Writers and Readers

E-health is a cloud-based system to store and share medical data with the stakeholders. From a security perspective, the stored data are in encrypted form that could further be searched by the stakeholders through searchable encryption (SE). Practically, an e-health system with support of multiple stakeholders (that may work as either data owner [writer] or user [reader]) along with the provision of multi-keyword search is desirable. However, the existing SE schemes either support multi-keyword search in multi-reader setting or offer multi-writer, multi-reader mechanism along with single-keyword search only. This chapter proposes a multi-keyword SE for an e-health system in multi-writer multi-reader setting. With this scheme, any registered writer could share data with any registered reader with optimal storage-computational overhead on writer. The proposed scheme offers conjunctive search with optimal search complexity at server. It also ensures security to medical records and privacy of keywords. The theoretical and empirical analysis demonstrates the effectiveness of the proposed work.

Ethics, Digital Rights Management, and Cyber Security

Cloud content hosting and redistribution is enabling convenient and easy access to online content thereby accelerating the adoption and penetration of internet in past two decades. The current Industry 4.0 revolution and adoption and acceleration efforts are leveraging cloud computing as a means to store, retrieve, and share data. This makes the internet a relatively vulnerable to content abuse and increase the demand of clear consent before data consumption and redistribution. The growth of cloud computing and management technologies is penetrating in the market, and digital rights management (DRM) practices are needed for better and ethically safe online space. This chapter talks about state-of-the-art DRM paradigms being proposed in the literature and critically discusses their technical performance, flexibility, and immutability challenges. This chapter will clarify internet governance implementation roadmap for Industry 4.0 revolution by critically analyzing the cloud technology stack and ethical features by advocating Cloud DRM.

Accelerated Learning Is Possible for Special Education Students

Accelerated learning has been historically absent in conversations driving the instruction of students being served in special education. A prevailing deficit mindset commonly exists within the special education community leaving expectations of increased student learning to chance. This chapter aims to share data from a large-scale, national sample of special education students receiving the powerful literacy intervention, Literacy Lessons. These data detail the possibility of accelerated student learning by measuring the text reading level of students at entry and exit of the intervention, and even more, compared to their rate of progress before entering the intervention.

Barriers and facilitators to qualitative data sharing in the United States: A survey of qualitative researchers

Qualitative health data are rarely shared in the United States (U.S.). This is unfortunate because gathering qualitative data is labor and time-intensive, and data sharing enables secondary research, training, and transparency. A new U.S. federal policy mandates data sharing by 2023, and is agnostic to data type. We surveyed U.S. qualitative researchers (N = 425) on the barriers and facilitators of sharing qualitative health or sensitive research data. Most researchers (96%) have never shared qualitative data in a repository. Primary concerns were lack of participant permission to share data, data sensitivity, and breaching trust. Researcher willingness to share would increase if participants agreed and if sharing increased the societal impact of their research. Key resources to increase willingness to share were funding, guidance, and de-identification assistance. Public health and biomedical researchers were most willing to share. Qualitative researchers need to prepare for this new reality as sharing qualitative data requires unique considerations.

Научное сотрудничество: мониторинг вечной мерзлоты циркумполярной зоны и обмен данными

Scientific cooperation is a well-supported narrative and theme, but in reality, presents many challenges and counter-productive difficulties. Moreover, data sharing specifically represents one of the more critical cooperation requirements, as part of the “scientific method [which] allows for verification of results and extending research from prior results.” One of the important pieces of the climate change puzzle is permafrost. Currently, most permafrost data remain fragmented and restricted to national authorities, including scientific institutes. Important datasets reside in various government or university labs, where they remain largely unknown or where access restrictions prevent effective use. A lack of shared research—especially data—significantly reduces effectiveness of understanding permafrost overall. Whereas it is not possible for a nation to effectively conduct the variety of modeling and research needed to comprehensively understand impacts to permafrost, a global community can. However, decision and policy makers, especially on the international stage, struggle to understand how best to anticipate and prepare for changes, and thus support for scientific recommendations during policy development. This article explores the global data systems on permafrost, which remain sporadic, rarely updated, and with almost nothing about the subsea permafrost publicly available. The authors suggest that the global permafrost monitoring system should be real time (within technical and reasonable possibility), often updated and with open access to the data. Following a brief background, this article will offer three supporting themes, 1) the current state of permafrost data, 2) rationale and methods to share data, and 3) implications for global and national interests.

Roles of GARCH and ARCH effects on the stability in stock market crash

We theoretically stochastic simulate and empirically analyze the escape process of stock market price nonequilibrium dynamics under the influence of GARCH and ARCH effects, and explore the impact of ARCH and GARCH effects on stock market stability. Based on the nonlinear GARCH model of econophysics, and combined with GARCH and ARCH effects of volatility, we propose a delay stochastic monostable potential model. We use the mean escape time, or mean hitting time, as an indicator for measuring price stability, as first introduced in Ref. [1]. Based on the comparative analysis of actual Chinese A-share data, the theoretical and empirical findings of this paper are as follows} (1) The theoretical simulation results and actual data are consistent. (2) There exist optimal GARCH and ARCH effects maximally enhancing stock market stability.