Urban Administration Research Articles

Optimizing Vehicle Replacement in Sustainable Urban Freight Transportation Subject to Presence of Regulatory Measures

Since the 1990s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persist. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement with UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, an elasticity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

Dark stores in the City of Light: Geographical and transportation impacts of ‘quick commerce’ in Paris

Food e-commerce has long remained a limited phenomenon, which only changed noticeably during the COVID-19 pandemic. Not only did more consumers take advantage of the options offered online, it also prompted the launch of many food delivery start-ups around the world. Quick commerce in particular, offering grocery deliveries within twenty minutes or less, attracted significant sums of venture capital, alongside criticism from urban administrations and communities for the nuisances caused. Indeed, to offer instant grocery deliveries, quick commerce companies rely on a tightknit network of small, store-like warehouses, called ‘dark stores’, and a readily available vehicle fleet and staff. While plenty of ink has flowed about the urban implications of quick commerce in the press, a thorough scientific analysis lacks. To objectify and quantify the nuisances generated by quick commerce, we study geographical dimensions and transportation activities in Paris. By means of a press review, expert interviews, field observations and a cartography, we detail a supply chain consisting of various facility types; demonstrate the transportation-intensity of dark stores consisting mainly of electric two-wheelers; problematize the public space consumed by the vehicles in particular; and contrast the ultimately limited network of dark stores relative to traditional food retail.

Smart all-time vision: The battery-free video communication for urban administration and law enforcement

The Chinese government is dedicated to enhancing the level of informatization in administrative law enforcement to ensure fairness and increase credibility. Currently, law enforcement has exposed such problems as insufficient force, difficulties in collecting evidence, and low evidential power. These problems contradict the principles of rule of law and standardization. Many local governments have implemented advanced information technologies for urban administration and law enforcement, including big data, artificial intelligence, and IoT. Among these technologies, IoT is the core communication infrastructure for innovative law enforcement platforms. However, traditional video communications rely on batteries or dedicated power sources, leading to maintenance challenges and high power consumption. In this paper, we propose AT-Eye, a new video communication paradigm for all-time law enforcement video monitoring, which is battery-free and high-throughput. The key enabler of AT-Eye is ambient backscatter technology, which enables energy harvesting and video communication simultaneously. Unlike previous methods, our proposal introduces a novel CCK-based modulation for tag cameras and achieves 4-way concurrency. The modulation is simple because it uses phase difference and it is energy-efficient because generating square waves is adequate. Moreover, to ensure full compatibility with COTS WiFi, we modulate the physical service data unit. Therefore, data recovery is possible using only commercial NICs. We conduct comprehensive experiments to examine our proposal and experiment results show that AT-Eye achieves a throughput of 10.8 Mbps with COTS radios. We also simulate a 16-way battery-free tag camera system, demonstrating AT-Eye's feasibility of high-definition video communication.

The False King Olaf and His Necklace of Letters

The False King Olaf and His Necklace of Letters

A city-based PM2.5 forecasting framework using Spatially Attentive Cluster-based Graph Neural Network model

Urban environments globally are under threat due to recent climate changes caused by a variety of factors such as growing industrialization, rapid migration, increasing traffic flow, etc. An effective data-driven air pollution modeling system helps in increasing regular awareness regarding the severity of the air quality at the local level, play a preventive role in addressing the root causes and hence can be extremely useful for the urban administration. Graph Neural Networks have recently emerged for various classification and estimation tasks on graph-structured data. A Spatially Attentive Cluster-based Graph Neural Network-enabled PM2.5 concentration forecasting model (SA-GNN) is proposed to predict short-term PM2.5 concentrations by considering monitoring stations as nodes of a graph structure and exploring their spatial relationships. This modeling procedure takes into account relevant meteorological variables like wind speed, wind direction, relative humidity etc. An efficient clustering-based spatiotemporal feature extraction method is proposed within a graph neural network setting. This technique makes use of cluster-wise separated graph-structured spatiotemporal features by utilizing disjoint intermediate spatiotemporal GRU networks in order to handle spatial heterogeneity. Additionally, the use of graph attentional network (GATs) makes the modeling framework efficient. The proposed short-term PM2.5 concentrations forecasting framework is applied to the highly polluted Indian capital city, Delhi. The proposed SA-GNN model achieves R2 value 0.75, RMSE and MAE, 25.13 and 21.28 μg/m3 respectively on test data, achieving significant improvement with respect to the baseline models. In fact, even the high pollution episodes can be predicted by the SA-GNN model with better accuracy. Evidently, the proposed GNN-based air pollution modeling framework can be a potential option for forecasting of pollutants in other similar cities globally with high pollution records.

Introduction pages, Nova Geodesia 3(1), 2023

Nova Geodesia (https://novageodesia.ro), Issue 1, Volume 3, 2023: The papers published in this issue represent interesting novelties in different topics of geodetic science or related fields. Nova Geodesia publishes significant papers in geodesy and close topics, related to cartography, urban administration and environment, engineering projects and construction, landscape and ecology, environmental administration, geography, planetology, hydrography, etc. Among the exciting articles, we invite readers to find news about: Landslide susceptibility modelling in a part of Himachal Pradesh, India: An integrated method based on machine learning and geospatial techniques; Fertility and mortality differentials among the Paundra Kshatriya community living in a peri-urban setting, West Bengal, India; Comparative analysis of Road Scanning Techniques.

Spatiotemporal Monitoring of Urban Sprawl in a Coastal City Using GIS-Based Markov Chain and Artificial Neural Network (ANN)

Over the last two decades, globally coastal areas have urbanized rapidly due to various socioeconomic and demographic driving forces. However, urban expansion in towns and cities of the developing world has been characterized by entangled structures and trends exacerbating numerous negative consequences such as pollution, ecological degradation, loss of agricultural land and green areas, and deprived settlements. Substantially, spatial simulation of urban growth and their consequences on coastal areas particularly in Egypt is still very rare. Geospatial modelling coastal urban growth is crucial and has enormous potential for coastal land use transformation and urban sustainability. The key aim of this study was to analyze spatiotemporal changes (2010–2020) and simulate future dynamics (2030 to 2050) of land use/land cover (LULC) in Alexandria Governorate, Egypt. Artificial Neural Network–Multiple Layer Perceptron (ANN-MLP) and Markov Chain techniques were employed within the GIS platform to assess processes of land transitions and predict urban growth trends, patterns and dimensions. The forecasting process was based on three maps of LULC derived from classified Landsat images of 2000, 2010 and 2020. In addition, topographical, demographic, accessibility, proximity factors were generated and developed in the form of raster spatial parameters of urbanization driving forces. The findings revealed that the observed expansion of the built-up area during one decade (2010–2020) was 12,477.51 ha, with a decline in agricultural area (7440.39 ha) and bare land (4904.91 ha). The projected change was forecasted to be 71,544 ha by 2030 and 81,983 ha in 2040 with a total of 35,998 ha increase in the built-up area and residential expansion by 2050. Despite this expected pattern of rapid changes, urban growth will be shaped by the key drivers of proximity to coastline and agricultural land transformation. The analysis indicates that the vertical urban growth will be most likely dominant along the coastal zone due to the lack of vacant lands, whereas the horizontal urban expansion will primarily take place towards the east-northeastern and south-southeastern directions of the city. The present work provides a holistic framework for establishing initial coastal land use plans not only for planners and urban administrators in Alexandria but also for policymakers and coastal municipalities in developing nations.

From one flooding crisis to the next: Negotiating ‘the maybe’ in unequal Karachi

AbstractEvery few years, Karachi floods during the summer monsoon. The flooding brings latent manoeuvrings by political actors looking to establish their hold over the city to the surface. Politicians, urban administrators, and relevant state and non‐state institutions blame historical planning failures, informal and illegal constructions, institutional conflict, incapable municipal governance, and widespread corruption for the flooding. They move quickly to establish authority and consolidate power while offering ‘fixes’. Eviction drives against ‘illegal settlements’ built along storm‐water drains, heavy taxes, fines, and demolitions of non‐conforming constructions, institutional reforms, budget allocations, and project approvals for new infrastructure all happen at once. Once the emergency ceases, key players in urban politics – resident groups, community associations, political parties, municipal authorities, land developers, planners, international non‐governmental organisations, and military institutions – start working on projects of accumulation and entrenchment, in preparation for the next crisis. In this paper, we look at the space–time of Karachi's certain and yet uncertain flooding crisis as a moment to study the politics of the maybe in the Pakistani megacity. Outlining marginal and affluent residents' lived experiences in a flooding city and relating their politics with governmental responses to immediate and possible future floods, we study the conditions of inhabitation, citizenship claims, and governmental relations in Karachi. We argue that the monsoon's expectant arrival becomes a locus for articulating and modulating different kinds of popular vernaculars, governmental practices, and political manoeuvrings for institutional and individual actors seeking profit and power in and through Karachi. The politics of the maybe hinges on actors entrenching their political positions without care, taking away any possibility for a shared, coherent worldview for all Karachiites. In conclusion, we argue that distant interests and logics of this politics of governance and inhabitation are inherently exploitative, threatening to pull apart the very city they thrive on.

Breaking policy on land for Ho Chi Minh City: Some implications from the draft of adjustment of law on land

For the important role of a major economic and urban center, Ho Chi Minh City needs a distinguished legal, administrative, and economic institution. The advantage of a breakthrough mechanism for specific urban administrations not only creates resilience for HCMC but also brings positive externalities, pulling socio-economic activities of the region and the whole country, quickly approaching the national development goals, and then contributing to the budget. The efficiency after more than five years of implementing Resolution 54/2017/QH14 is a testament to the choice of policy model. However, the rapid growth of economic activities in Ho Chi Minh City once again poses new requirements as well as the need for a new and more breakthrough in this unique optional policy. More importantly, the drafting of a new draft resolution on a new particular sandbox mechanism for HCMC should aim to effectively solve the administrative bottlenecks that are currently arising. Urban governments especially HCMC need to be empowered to be more proactive, more flexible, and more creative. In most of the content that exists a great need for a breakthrough mechanism, the state management of land plays an important role and therefore should be enlisted to find possible solutions while drafting the revised Law on land, which is also being finalized for submission to the National Assembly. The discussion of the article thus proposes resolutions from the content of the draft Resolution on the specific mechanism for Ho Chi Minh City and the relevant provisions in the mentioned draft of the Law on land adjustment.

ГОРОДСКОЙ ГОЛОВА: РЕТРОСПЕКТИВА В КОНТЕКСТЕ СЕМЕЙНОЙ ПЕРЕПИСКИРЕЦЕНЗИЯ НА КНИГУ: МАРГАРИТА АГЕЕВА. УФИМСКИЙ ГОРОДСКОЙ ГОЛОВА АЛЕКСАНДР МАЛЛЕЕВ:ИСТОРИЯ В ПИСЬМАХ. УФА: ИНФОРМРЕКЛАМА, 2020. 448С.: ИЛ.

The new book by Margarita V. Ageeva presents the family correspondence of the Ufa mayor A.A. Malleev and studies his biography. The epistolary finding and the specific historical context of the story introduce different aspects of the history of urban administration and the everyday life of the provincial nobility in the late 19th – early 20th centuries. The book has become an original contribution to regional historiography, and its author has revised some of the conclusions of his predecessors, based on a metahistorical narrative. Information about the origin and genealogical relations of the family surname sheds light on the formation of the official elite in the eastern provinces as a result of the industrial development of the region in the 18th century and the reforms of local government and the court system in the post-reform period. The epistolary material recreates a portrait of personal qualities of the modernizers and testifies to the formation of new personnel in the provinces – the carriers of a genuine democratic culture. The author analyzes the influence of ethical views of officials on managerial decision-making as a central problem poorly developed in institutional history. The author traced it on the example of the events of the 1905 revolutionary crisis in Ufa. The discourse of private correspondence contains a lot of valuable evidence on the family life of the Malleevs, their religious identity and methods of raising children, on cultural and everyday life of the urban elite of a provincial town. The style and spelling of the letters, as well as the turns of phrase and the accepted spelling of certain words at that time, have remained unchanged, which increases the value of the source. The literal capture of the smallest details of everyday history make the book a practical tool for museum workers, fashion historians and other professionals.

Introduction pages, Nova Geodesia 2(4), 2022

Nova Geodesia (https://novageodesia.ro), Issue 4, Volume 2, 2022: The papers published in this issue represent interesting novelties in different topics of geodetic science or related fields. Nova Geodesia publishes significant papers in geodesy and close topics, related to cartography, urban administration and environment, engineering projects and construction, landscape and ecology, environmental administration, geography, planetology, hydrography, etc. Among the exciting articles, we invite readers to find news about: Testing the measurability of steel sections with terrestrial laser scanners; Use of UAS and TLS technology in 3D modeling of constructions with complex architecture; Influence of geographic provenance on phenotypic variation in seed and kernel traits of the African oil tree from southern Benin and implications for species breeding.

The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges

The challenges of urban administration are growing, as the population, automobiles, and cities rise. Making cities smarter is thus one of the most effective solutions to urban issues. A key feature of the “smart cities” of today is that they use cutting-edge technology in their infrastructure and services. With strategic planning, the smart city utilizes its resources in the most efficient manner. With reduced expenses and enhanced infrastructure, smart cities provide their residents with more and better services. One of these important urban services that can be very helpful in managing cities is structural health monitoring (SHM). By combining leading new technologies like the Internet of Things (IoT) with structural health monitoring, important urban infrastructure can last longer and work better. A thorough examination of recent advances in SHM for infrastructure is thus warranted. Bridges are one of the most important parts of a city’s infrastructure, and their building, development, and proper maintenance are some of the most important aspects of managing a city. The main goal of this study is to look at how artificial intelligence (AI) and some technologies, like drone technology and 3D printers, could be used to improve the current state of the art in SHM systems for bridges, including conceptual frameworks, benefits and problems, and existing methods. An outline of the role AI and other technologies will play in SHM systems of bridges in the future was provided in this study. Some novel technology-aided research opportunities are also highlighted, explained, and discussed.

Unsatisfying Transfer of Climate Research to Urban Planning: The Regulatory Trap in the Triple Helix

Abstract Making urban areas more sustainable by transferring scientific results into the building, shaping, and governance of cities is a complicated process which involves – amongst other dimensions – science, local governance, and regulatory processes. There are non-linear interactions within each of these three dimensions which are influenced and enhanced by interactions between the three dimensions. After a short analysis of different sustainability concepts, this conceptual paper considers each of the three dimensions and finally tries to find some suggestions as to how these dimensions could interact more smoothly also considering Triple Helix theory. One basic suggestion is that without updating laws, norms, and standards, urban administrations will often not be able to integrate new scientific findings into procedures for more sustainable cities. That is, all three dimensions need to be aligned in the process of building sustainable cities.

Performance Evaluation of Bias Correction Methods and Projection of Future Precipitation Changes Using Regional Climate Model over Thanjavur, Tamil Nadu, India

The use of regional climate models in climate change risk assessments is uncertain because of the threat of biases. Prior to using simulations, some corrections must be made. The aim of this study is to evaluate the effectiveness of simple and complex correction approaches on CORDEX (Coordinated Regional Climate Downscaling Experiment) precipitation datasets over Thanjavur in Tamil Nadu, India. The various bias correction approaches were assessed using the two-sample cross-validation technique, and the best method was chosen using statistical metrics such as the correlation coefficient, root mean square error, and percent bias for correcting the future scenario datasets. According to the findings, all methods significantly improved raw simulated estimations after correction. The Delta Change method was used to adjust the RCP2.6 and RCP8.5 scenarios datasets for this study region because it produced good agreement (0.98) and low wet bias (1%) against observed precipitation. In addition, this study found that the annual mean precipitation projections showed an increase in precipitation intensity of between 4.2 and 6.8% under the RCP2.6 scenario and between 14 and 20.1% under the RCP8.5 scenario, compared to observed data (1976–2005). Furthermore, the analysis showed that most years in the twenty-first century will experience heavy precipitation, resulting in floods. This research has offered reliable knowledge about future precipitation to environmentalists, urban administrators, and policy-makers, enable them to make plausible strategies to mitigate the detrimental effects of climate change.

PELATIHAN ADMINISTRASI PEMERITAHAN KELURAHAN BERBASIS ONLINE DI KECAMATAN NAMBO KOTA KENDARI TAHUN 2022

Community service activities integrated with thematic Real Work Lectures (KKN) of Halu Oleo University aim to contribute to the government and citizens, especially in public services in the midst of the New Normal period in Southeast Sulawesi. This activity is an online-based Urban Village Administration Training in Nambo District, Kendari City, Southeast Sulawesi Province. The method used in the Thematic KKN is carried out directly at the activity location. Students are active from the beginning to the end of KKN activities carried out in the domicile area. The results of community service activities through Thematic KKN for students provide information that the average village government apparatus does not yet know and understand how to use internet technology facilities in public services, especially in managing village administration online based. There is only 1 website in the sub-district and all kelurahan do not yet have a website. There has never been any technical training on website management for village officials. Local government support is very limited in providing resources in realizing online-based urban administration.

Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system’s renewable power projections.

Impact of Parents’ Income on Students’ Retention in Primary and Secondary Schools in Kakamega Municipality, Kenya

The goal of this study was to investigate the impact of parents’ income on students’ retention in primary and secondary schools in Kakamega Municipality - Kenya. This study was justified by the fact that Kakamega Municipality’s growth has largely been based on the expansion of educational institutions of higher studies and the inception of the devolved structures in the counties. The target population for the study was 99,987. This population was comprised of municipality residents, primary school pupils, secondary school students, Education officers, principals, urban administrators, and parents from low, middle, and high residential parts of the municipality. The sample size for the study was 172 and it was calculated using the formula proposed by Fisher et al. (1998). Data collection instruments included; questionnaires, interview guides, and focus group discussions. A pilot study was carried out in three residential areas in Bungoma municipality in order to test the validity of the instruments. Data was analysed using descriptive statistics and statistics package for social sciences (SPSS) version (20). The study established that poor income by parents results in low school retention as children engage in cheap labour with their parents for school fees. This is confirmed by low-class respondents, of whom the majority earn between 10,000 -30,000 (42%) and below 10,000 (26%) and as a result, there is low retention in education by their children at (6%). Child’s retention rate in school is low in the indigent social class than in other classes and this is attributed to their parent’s financial constraints and environmental factors. To address this state, the study recommends that more employment opportunities should be created for middle and majority low-income classes by reserving specific jobs in the county for these groups to enable better access to education by children from these groups.

Deep Learning–Based Monitoring Sustainable Decision Support System for Energy Building to Smart Cities with Remote Sensing Techniques

In modern society, energy conservation is an important consideration for sustainability. The availability of energy-efficient infrastructures and utilities depend on the sustainability of smart cities. The big streaming data generated and collected by smart building devices and systems contain useful information that needs to be used to make timely action and better decisions. The ultimate objective of these procedures is to enhance the city's sustainability and livability. The replacement of decades-old infrastructures, such as underground wiring, steam pipes, transportation tunnels, and high-speed Internet installation, is already a major problem for major urban regions. There are still certain regions in big cities where broadband wireless service is not available. The decision support system is recently acquiring increasing attention in the smart city context. In this article, a deep learning–based sustainable decision support system (DLSDSS) has been proposed for energy building in smart cities. This study proposes the integration of the Internet of Things into smart buildings for energy management, utilizing deep learning methods for sensor information decision making. Building a socially advanced environment aims to enhance city services and urban administration for residents in smart cities using remote sensing techniques. The proposed deep learning methods classify buildings based on energy efficiency. Data gathered from the sensor network to plan smart cities' development include a deep learning algorithm's structural assembly of data. The deep learning algorithm provides decision makers with a model for the big data stream. The numerical results show that the proposed method reduces energy consumption and enhances sensor data accuracy by 97.67% with better decision making in planning smart infrastructures and services. The experimental outcome of the DLSDSS enhances accuracy (97.67%), time complexity (98.7%), data distribution rate (97.1%), energy consumption rate (98.2%), load shedding ratio (95.8%), and energy efficiency (95.4%).

Post-displacement status of climate migrants in Rajshahi City, Bangladesh

Some big cities in Bangladesh have been experiencing a massive and rapid influx of rural people due to the impacts of climate change, and therefore the urban administration encounters enormous challenges. This study aims to investigate the drivers of climate-induced migration and the post-displacement status of the migrants living in the urban slum of Rajshahi City. Using a semi-structured questionnaire survey, this study conducted interviews with 50 migrants residing in two slums in Rajshahi City. An interpretive phenomenological analysis (IPA) approach was implemented to evaluate the survey data. This study finds that food insecurity and flood are the two significant climate drivers of migration. Among the non-climate drivers, lack of alternative livelihood is the major reason. It should be noticed that the climate migrants in many cases do not get the opportunity to improve their living standards; they are usually occupied with low-pay professions like maid, van and rickshaw puller, and scrap collector. The study also reveals that migrants, especially females and children, need several basic physiological, economic, social, and health services. Most children have no chance to attend school. Compared to males, females have more opportunities for some support and allowances. Overall, an inadequate level of change has taken place in the lives of migrants, which raises the concern if migration is ever a way to resolve a problem or the beginning of many other problems. Further researches may concentrate on the impact of migration on the dynamics of social capital among slum dwellers.

Urbanization’s environmental imprint: A review

The urban population interact with their environment and change it through the consumption of resources. The changed condition may impact the wellbeing and life nature of the urban population. The uncontrollable growth of urbanization has been reported to be dangerous for mental health and sanity of many citizens. Urban areas whether small or megacities each generate an ecological foot print.The present day urbanization, consequently requires a sustainable development pattern and changes in present day styles of boom to cause them to be more equitable and more resource and energy efficientAs of now, urban administration is to a great extent concentrated on single issues, for example, water, transportation, or waste. A multi scale administration framework that expressly inscribes interconnected asset chains and interconnected spots is essential as a way to transition closer to sustainable urbanizationwhich require, prevention of urban pollution, decreasing production capacity and supporting recycling, while discouraging non-profit development and fading poor and rich differences. . Therefore, the present study was conducted to clarify the effect of urbanization on the environmental quality, resource use and sustainability. The study conducted throughout the globe indicated sustainable urbanization can be achieved by following concept of development wherein natural resources are restored and not replaced by technology.