Search results

This research informs the intersection of climate and heritage policy development by examining the history of US energy policy as it relates to historic buildings, emerging policy tools to reduce greenhouse gas emissions, and the implications of a changing legislative landscape on historic buildings through the case of New York City.

This study employs a multi-method approach, including a review of US energy codes; discourse analysis of government records, energy studies, and reports related to historic buildings and energy; select research into energy-related heritage policy at the municipal level; and geospatial and statistical methods to analyze policy implications in the case study of New York City.

Historic buildings have long been afforded exemptions from energy code compliance in the US, and these waivers are widespread. Contemporary operating energy and greenhouse gas data, as well as energy justice findings about whom these waivers privilege, challenge these exemptions and signal a need for significant policy reform in light of climate change.

This study questions longstanding rhetoric about historic buildings being inherently green and supports the need for more evidence-based research to undergird heritage policy reform that is equitable and climate-responsive.

The COVID-19 outbreak has significant psychological effects because of reduced support system and social quarantine, making women the worst-hit population of shadow pandemic, i.e. domestic violence. While food shortages, unemployment and increased domestic-work burdens are the immediate effects of the lockdown, women at home have to bear its far-reaching impacts in the long term in the form of domestic abuse, making the study of the psychological impact of domestic violence against women imperative. This paper aims to identify the factors and causes responsible for domestic violence and its psychological impacts on women in different aspects. This paper further focuses on the reasons behind an escalation in psychological violence against women.

This paper is based on extrapolating data from various journal articles, Indian Government reports, newspaper articles and other printed materials that are recent, relevant and discuss domestic violence and mental stress during the COVID-19 pandemic. Researchers use Indian National Commission for Women’s (NCW) data on complaints received regarding violence against women and domestic abuse in the year 2020 and 15 journal articles that discuss domestic violence against women during the COVID-19 period in different countries to discuss social inequalities and power relations impact on women’ mental health.

The findings suggest that economic instability during the pandemic and social and cultural norms of India ignited psychological abuse against women during the pandemic. The number of monthly complaints of dowry death, dowry harassment and protection of women against domestic violence reflect on increased registered complaints in the postlockdown period in the year 2020. The number of monthly complaints received by the NCW from January 2020 to December 2020 in India represents that WhatsApp chat is a powerful tool for reporting domestic violence.

The pandemic lockdown has an adverse psychological impact on women, making them suffer from posttraumatic symptoms, substance abuse, panic attacks, depressions, hallucinations, eating disorders, self-harm, etc. This paper strives to reflect upon mitigation strategies to curb domestic violence in India.

The purpose of this study/paper is to integrate distributed generation optimally in power system using plant propagation algorithm. Distributed generation is a growing concept in the field of electricity generation. It mainly comprises small generation units installed at calculated points of a power system network. The challenge of optimal allocation and sizing of DG is of utmost importance.

Plant propagation algorithm and particle swarm optimisation techniques have been implemented where a weighting factor-based multi-objective function is minimised. The objective is to cut down real losses and to improve the voltage profile of the system.

The results obtained using plant propagation algorithm technique for IEEE 33-bus systems are compared to those attained using particle swarm optimisation technique. The paper deals with the optimisation of weighting factor-based objective function, which counterpoises the losses and improves the voltage profile of the system and, therefore, helps to deliver the best outcomes.

This paper fulfils an identified need to study the multi-objective optimisation techniques for integration of distributed generation in the concerned power system network. The paper proposes a novel plant-propagation-algorithm-based technique in appropriate allocation and sizing of distributed generation unit.

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.

The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.

The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.

The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.

The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.

The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.

There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.