scholarly journals A Case Study on China’s Policies of Promoting Work Resumption During the COVID-19 Pandemic — Perspective of Policy Resilience

2021 ◽  
pp. 54-76
Author(s):  
Qicheng LU ◽  
◽  
Bin RONG ◽  
Yijia LI ◽  
◽  
...  
Keyword(s):  
Evaluation Model ◽  
Recovery Efficiency ◽  
High Recovery ◽  
Short Term ◽  
Index Model ◽  
Effective Policy ◽  
High Level ◽  
Work Resumption

During the COVID-19 pandemic, China has achieved high recovery efficiency. One of the most important reasons behind this is the effective poli­cies of promoting work resumption. Why can such policies maintain steady performance despite the high level of environmental uncertainties? This ques­tion can be answered from the perspective of policy resilience. This study employed a policy evaluation model for analyzing quantitative data of 342 poli­cies of promoting work resumption. We evaluate the policies through the Policy Modeling Consistency (PMC-index) model and text mining methods. The results show that: first, the contents and elements of all policies have consistent characteristics, including the combination of multiple policy tools, the combi­nation of support for work resumption and pandem­ic control, the incentives to support effective policy implementation, and the reasonable match between macro and micro policies as well as short-term and long-term policies. Second, among the nine policies that are randomly selected from the sample, one is rated excellent and the other eight are good, indicat­ing that China’s policies of promoting work resump­tion have good resilience.

From Users’ Intentions to IF-THEN Rules in the Internet of Things

2021 ◽  
Vol 39 (4) ◽  
pp. 1-33
Author(s):  
Fulvio Corno ◽  
Luigi De Russis ◽  
Alberto Monge Roffarello
Keyword(s):  
Internet Of Things ◽  
Smart Devices ◽  
The Internet ◽  
Short Term ◽  
Real World Data ◽  
Original Intention ◽  
Recommendation Accuracy ◽  
High Level ◽  
The Internet Of Things

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

scholarly journals A Framework for Property Developers to Survive in a Recession

10.29007/cfr2 ◽  
2018 ◽  
Author(s):  
Zunoon Parambath ◽  
Nilupa Udawatta
Keyword(s):  
Survival Strategies ◽  
Economic Activities ◽  
Short Term ◽  
Property Development ◽  
Market Shares ◽  
Negative Impacts ◽  
Set Up ◽  
High Level ◽  
Property Developers

Recession is considered as a major threat to the economy as it slows down economic activities. The property development sector is extremely responsive to these economic conditions. Thus, it is crucial to understand causes, effects and strategies for property developers to survive in a recession without any ill effects. Thus, this research aimed to develop a framework for property developers to identify appropriate survival strategies in recession. A comprehensive literature review was conducted in this research to achieve the above mentioned aim. The results of this study indicated that recession prompts negative impacts on property development sector resulting in unemployment, lower demand, production and revenue, decline in resources and high level of competition. According to the results, the survival strategies were classified into short-term and long-term strategies. The short term strategies include: implementing management tactics, cut down of operating costs, keeping financing lines set up, timely repayment of debts, setting vital new objectives for the future, undertaking shorter time span developments, specialisation in favoured market, renegotiating deals and contracts. The long-term strategies include retrenchment, restructuring, investment and ambidextrous strategies. Similarly, attention should be paid to predict any changes in the economic environment that can influence property development activities and it is necessary to carefully evaluate investment activities to increase sales, profits and market shares of property developers. Preparing for a crisis is doubtlessly the ideal approach as it can facilitate both survival and growth. Thus, the property developers can implement these suggested strategies in their businesses to enhance their practices.

scholarly journals Learning Long- and Short-Term User Literal-Preference with Multimodal Hierarchical Transformer Network for Personalized Image Caption

2020 ◽  
Vol 34 (05) ◽  
pp. 9571-9578 ◽  
Author(s):  
Wei Zhang ◽  
Yue Ying ◽  
Pan Lu ◽  
Hongyuan Zha
Keyword(s):  
State Of The Art ◽  
Natural Extension ◽  
Target Image ◽  
Short Term ◽  
Image Representations ◽  
High Level ◽  
Image Descriptions ◽  
Shed Light ◽  
Image Caption

Personalized image caption, a natural extension of the standard image caption task, requires to generate brief image descriptions tailored for users' writing style and traits, and is more practical to meet users' real demands. Only a few recent studies shed light on this crucial task and learn static user representations to capture their long-term literal-preference. However, it is insufficient to achieve satisfactory performance due to the intrinsic existence of not only long-term user literal-preference, but also short-term literal-preference which is associated with users' recent states. To bridge this gap, we develop a novel multimodal hierarchical transformer network (MHTN) for personalized image caption in this paper. It learns short-term user literal-preference based on users' recent captions through a short-term user encoder at the low level. And at the high level, the multimodal encoder integrates target image representations with short-term literal-preference, as well as long-term literal-preference learned from user IDs. These two encoders enjoy the advantages of the powerful transformer networks. Extensive experiments on two real datasets show the effectiveness of considering two types of user literal-preference simultaneously and better performance over the state-of-the-art models.

scholarly journals Development of Evaluation Index Model for Activation and Promotion of Public Space in the Historic District Based on AHP/DEA

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Fengwen Wu ◽  
Shiyu Qin ◽  
Chunyu Su ◽  
Mingyuan Chen ◽  
Qian Wang ◽  
...  
Keyword(s):  
Public Space ◽  
Evaluation Model ◽  
Evaluation Index ◽  
Superposition Method ◽  
Historic District ◽  
Historic Districts ◽  
Evaluation Indexes ◽  
The Public ◽  
Index Model

Historic districts represent an important characteristic of Beijing and are also a crucial carrier of Chinese historic culture. However, they are significantly affected by the rapid urban constructions. Thus, it is of great significance to maintain and promote the public space in historic districts. This paper uses a multisource data superposition method to select the evaluation index of public space. The AHP was also used to complete the single-level and total-level ranking and calculation of evaluation indexes. Finally, based on the DEA model, a vitality evaluation model of Beijing historic district public spaces was developed and its validity was verified through a case study of the Wanping historic district.

Cordaid’s Post-Disaster Shelter Strategy in Haiti: Linking Relief and Development

2014 ◽  
Vol 39 (3) ◽  
pp. 77-85
Author(s):  
Harmen Janse ◽  
Kees van der Flier
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Life Support ◽  
Comparative Case Study ◽  
Urban Context ◽  
Short Term ◽  
Stage Of Development ◽  
Post Disaster

Haiti was struck by a heavy earthquake in 2010 and international aid poured into the country. News reports in 2011 were not very positive about the results of post-disaster reconstruction: “The relief efforts are only putting Haiti on life-support instead of evolving into the next stage of development”. One of the non-governmental organisations (NGOs) involved in Haiti was Cordaid, implementing a ‘transitional shelter strategy’ to support the transformation of neigh-bourhoods from a state of life-support into a state of self-sustaining development. The strategy was implemented in both a rural and an urban area. The main feature of the strategy was the provision of structures that could be adapted from simple shelters to permanent houses. Since the results of the strategy were mixed and ambiguous, a comparative case study was conducted to evaluate the shelter strategy in both areas. The objective was to draw lessons about what has to be taken into account when formulating future urban shelter strategies. The case study is discussed in this article. The main finding from the case study is that producing the intended number of shelters within the financial and time budgets that were set (efficiency), was more difficult in the urban area than in the rural area. But the conditions for linking relief and development (effectiveness) are more favourable in the urban context. NGOs may achieve long-term (effective) results in the urban context when a lower efficiency can be justified. That is why NGOs need to engage in a debate about the extent to which they are able to focus on long-term shelter or housing strategies. The important element in the debate is communication with the donors who are often focused on short-term relief measures. However urban areas cannot be rebuilt with only short-term interventions. The link between relief and development has to be made by a process-orientated approach focusing on capacities of local participants.

Disaster Management and Continuity Planning in Libraries

2016 ◽  
Vol 5 (1) ◽  
pp. 26-41 ◽  
Author(s):  
Rebecca Hamilton ◽  
Diane Brown
Keyword(s):  
Success Factor ◽  
Short Term ◽  
Continuity Planning ◽  
Critical Success ◽  
Business Operations ◽  
Asset Recovery ◽  
Disaster Preparation

Libraries are taking on new roles in a disaster and with that comes strategic responsibilities beyond traditional asset recovery activities. In the past, library disaster plans have emphasized recovery of materials. Here, the emphasis is on continuing business operations. Libraries have become the centers of communication for their communities in a crisis. This article will demonstrate the essential role of libraries before, during and after a disaster, both short term and long term and how to get a seat at the table with community planners by demonstrating the functions that are critical to recovery. A literature review and case study are used to develop these recommendations. A critical success factor is to use a disaster preparation methodology that includes a business continuity plan.

scholarly journals Challenges in modelling isoprene and monoterpene emission dynamics of Arctic plants: a case study from a subarctic tundra heath

2016 ◽  
Vol 13 (24) ◽  
pp. 6651-6667 ◽  
Author(s):  
Jing Tang ◽  
Guy Schurgers ◽  
Hanna Valolahti ◽  
Patrick Faubert ◽  
Päivi Tiiva ◽  
...  
Keyword(s):  
Response Curve ◽  
Large Scale ◽  
Ecosystem Model ◽  
The Arctic ◽  
Short Term ◽  
Response Curves ◽  
Arctic Plants ◽  
Scale Modelling

Abstract. The Arctic is warming at twice the global average speed, and the warming-induced increases in biogenic volatile organic compounds (BVOCs) emissions from Arctic plants are expected to be drastic. The current global models' estimations of minimal BVOC emissions from the Arctic are based on very few observations and have been challenged increasingly by field data. This study applied a dynamic ecosystem model, LPJ-GUESS, as a platform to investigate short-term and long-term BVOC emission responses to Arctic climate warming. Field observations in a subarctic tundra heath with long-term (13-year) warming treatments were extensively used for parameterizing and evaluating BVOC-related processes (photosynthesis, emission responses to temperature and vegetation composition). We propose an adjusted temperature (T) response curve for Arctic plants with much stronger T sensitivity than the commonly used algorithms for large-scale modelling. The simulated emission responses to 2 °C warming between the adjusted and original T response curves were evaluated against the observed warming responses (WRs) at short-term scales. Moreover, the model responses to warming by 4 and 8 °C were also investigated as a sensitivity test. The model showed reasonable agreement to the observed vegetation CO2 fluxes in the main growing season as well as day-to-day variability of isoprene and monoterpene emissions. The observed relatively high WRs were better captured by the adjusted T response curve than by the common one. During 1999–2012, the modelled annual mean isoprene and monoterpene emissions were 20 and 8 mg C m−2 yr−1, with an increase by 55 and 57 % for 2 °C summertime warming, respectively. Warming by 4 and 8 °C for the same period further elevated isoprene emission for all years, but the impacts on monoterpene emissions levelled off during the last few years. At hour-day scale, the WRs seem to be strongly impacted by canopy air T, while at the day–year scale, the WRs are a combined effect of plant functional type (PFT) dynamics and instantaneous BVOC responses to warming. The identified challenges in estimating Arctic BVOC emissions are (1) correct leaf T estimation, (2) PFT parameterization accounting for plant emission features as well as physiological responses to warming, and (3) representation of long-term vegetation changes in the past and the future.

scholarly journals A Geolocation Analytics-Driven Ontology for Short-Term Leases: Inferring Current Sharing Economy Trends

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 59
Author(s):  
Georgios Alexandridis ◽  
Yorghos Voutos ◽  
Phivos Mylonas ◽  
George Caridakis
Keyword(s):  
Building Blocks ◽  
Sharing Economy ◽  
Urban Landscapes ◽  
Methodological Framework ◽  
Short Term ◽  
Multimodal Data ◽  
Labeled Graph ◽  
Current Sharing ◽  
High Level

Short-term property rentals are perhaps one of the most common traits of present day shared economy. Moreover, they are acknowledged as a major driving force behind changes in urban landscapes, ranging from established metropolises to developing townships, as well as a facilitator of geographical mobility. A geolocation ontology is a high level inference tool, typically represented as a labeled graph, for discovering latent patterns from a plethora of unstructured and multimodal data. In this work, a two-step methodological framework is proposed, where the results of various geolocation analyses, important in their own respect, such as ghost hotel discovery, form intermediate building blocks towards an enriched knowledge graph. The outlined methodology is validated upon data crawled from the Airbnb website and more specifically, on keywords extracted from comments made by users of the said platform. A rather solid case-study, based on the aforementioned type of data regarding Athens, Greece, is addressed in detail, studying the different degrees of expansion & prevalence of the phenomenon among the city’s various neighborhoods.

scholarly journals Alternatives to groundwater abstraction as a measure to stop land subsidence: a case study of Semarang, Indonesia

2019 ◽  
Vol 125 ◽  
pp. 01003 ◽  
Author(s):  
Wesley Beek ◽  
Bart Letitre ◽  
H. Hadiyanto ◽  
S. Sudarno
Keyword(s):  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Drinking Water Treatment ◽  
Decision Makers ◽  
Groundwater Abstraction ◽  
Short Term ◽  
Treatment Services

The Water as Leverage project aims to lay a blueprint for urban coastal areas around the world that are facing a variety of water-related issues. The blueprint is based upon three real case studies in Bangladesh, India and Indonesia. The case of Indonesia focuses on Semarang, a city that faces issues like flooding, increased water demand, and a lack of wastewater treatment. In this report I summarise the different techniques available to tackling these issues. Along with this I provide a cost-benefit analysis to support decision makers. For a short term it is recommended to produce industrial water from (polluted) surface water as a means to offer an alternative to groundwater abstraction. On a long term it is recommended to install additional wastewater and drinking water treatment services to facilitate better hygiene and a higher quality of life.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

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