The dynamic range (DR) of digital-input closed-loop class-D amplifiers (CDAs) is typically limited by the noise introduced by their resistive DAC (RDAC) or current-steering DAC (IDAC). It could be improved by using tri-level cells in the IDAC, but this has not yet been realized in high-voltage (HV) CDAs due to the large difference in the common-mode levels between the DAC and the CDA. This article describes an HV CDA directly driven by an HV IDAC. By using the same output common mode for the digital-to-analog converter (DAC) and CDA, the noise penalty associated with shifting the common mode is avoided. To address the distortion due to mismatch and intersymbol interference (ISI) in the IDAC, a transition-rate-balanced bidirectional real-time dynamic element matching (RTDEM) technique is also introduced. Fabricated in a 180-nm BCD process, the CDA prototype achieves a DR of 121.7 dB and a peak THD+N of -104.0 and -109.0 dB for 1- and 6-kHz inputs, respectively. It can deliver a maximum of 14 W into an 8-Ω load with a power efficiency of 90%.

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Evaluating the performance of a land administration system (LAS) is a critical task as it can provide input for improving the operational system. Through such an evaluation, the strengths and weaknesses of the existing system can be identified, and actions can be taken to improve it. Efforts have been made to develop frameworks and best practices for the assessment and comparison of the performance of LASs. Amongst the most prominent are the ‘Land Governance Assessment Framework’ (LGAF) of the World Bank and the ‘Global Land Indicators’ proposed by the Global Land Tool Network (GLTN) and the United Nations Human Settlements Programme (UN-Habitat) in its Global Land Indicators Initiative (GLII). The GLII indicators are closely related to the UN-Sustainable Development Goals (SDGs) indicators on land tenure security, namely SDGs 1.4.2 (%adults with secure tenure rights), 5.a.1 (%agricultural population with secure rights over agricultural land), and 5.a.2 (women's equal rights to land ownership).

The Land Administration Domain Model (LADM), an International Standard (ISO, 2012), can be used to monitor global indicators proposed by various international organizations and to evaluate the performance of LADM-based LASs, as LADM Edition II now provides full support for all land administration (LA) functions including marine georegulation, valuation information and spatial plan information. Interface classes to the LADM are designed to support the generation and management of products and services, such as the monitoring of global performance indicators for LASs.

This paper is a follow-up on Chen et al. (2024), which was focusing on formalizing SDG land related indicator using LADM. The objective of this study is to explore the extent to which LADM can be used to also monitor the indicators of LGAF and GLII. To this end, the indicators are categorized according to their degree of association with LADM (i.e. full computational association, partial computational association, indirect association, association with other standards and non-association), and interface classes are created based on the results. The results show that LADM can be used to monitor a significant portion of the indicators of LGAF and GLII, although most of the indicators are related to a country's national legislation, its implementation and organizational decisions and capability.@en

This study illustrates the linkages between the ISO’s Land Administration Domain Model (LADM) and the UN’s sustainable development goals (SDGs), highlighting the role of the LADM in promoting effective land administration suitable for efficient computation of land/water (space)-related SDG indicators. The main contribution of this study is the formalization of SDG indicators by using the ISO standard LADM. This paper proposes several SDG-indicator-related extensions to the multi-part LADM standard that is currently under revision. These extensions encompass the introduction of new procedures for calculating indicators, the integration of blueprints for external classes to fulfil additional information needs and the design of interface classes for presenting indicator values across specific countries and reporting years. In an innovative approach, this paper introduces the Four-Step Method—a powerful framework designed to formalize SDG indicators within the LADM framework. Detailed attention is devoted to specific indicators, including 1.4.2 (secure land rights), 5.a.1 (women’s agricultural land rights), 14.5.1 (protected marine areas) and 11.5.2 (valuation as a basis for direct economic loss). In short, the Four-Step Method is pivotal in eliminating ambiguities, enhancing the efficiency of indicator computation and securing more accurate indicator values that more truly reflect the progress towards SDG realization. This approach is also expected to work with other (ISO) standards for other SDG indicators.@en

The Sustainable Development Goals (SDGs), comprising of 17 Global Goals, serve as a global framework for addressing various facets of sustainable development. Several of these goals emphasize the crucial role of land management and equitable land distribution in achieving sustainable development objectives. ISO 19152, known as the Land Administration Domain Model (LADM), plays a pivotal role in land administration systems globally. It provides a standardized framework for land management, including land tenure, marine georegulation, valuation, and spatial plan information. This paper explores the integration of land administration indicators within the ISO 19152 standard, aligning them with the United Nations Agenda 2030 SDGs. The process involves a systematic approach to selecting and developing these indicators. In the indicator selection phase, firstly, we establish the foundational lexicon linked to LADM then extract lexemes from SDGs indicators, analyze their semantic relationships, and evaluate their alignment with LADM; secondly, we meticulously evaluated chosen indicators by analyzing their SDG indicator metadata, focusing on the “Method of Computation" section to align these indicators with LADM's basic classes; thirdly, categorizing them based on their association with LADM. This categorization ranges from indicators with no direct correlation to those with full computational interdependence, specifically, they are: Non-Association (Category 0), Full Computational Association (Category 1), Partial Computational Association (Category 2), Indirect Association (Category 3), Association with Other International Standards (Category 4). Following indicator selection, our approach to indicator development is summarized. This entails expressing information from UN SDG "Method of Computation" documents in UML class diagrams, adding operation names and parameters to the most relevant class, and specifying implementation methods for each operation. An in-depth analysis of SDG Indicator 1.4.2 demonstrates the feasibility of deriving indicators entirely from LADM data. Finally, the paper discusses potential future work, including the integration of semantic networks and ontologies for keyword extraction, further exploration of Category 1 Indicators, and practical implementation through case studies, data collection, indicator testing, validation, and reflection.@en