Surrogacy: A Question of Motherhood and the “Child’s Best Interests”

The Second Wave of Feminism of the mid-twentieth century created an ideal landscape to discuss and fight for women’s rights, with surrogacy and other reproductive rights issues as focal points of the movement. In 1985, Mary Beth Whitehead signed a surrogacy contract with Elizabeth and Bill Stern; Whitehead agreed to carry and give birth to a child for the Sterns in return for 10,000 dollars. However, the birth of the child rekindled Whitehead’s intense motherly instincts, and she battled a series of contradicting thoughts, for she understood that she had chosen to sign the contract and give the baby to the Sterns. In the days after the birth, Whitehead faced a choice that could alter the life of the child: should she keep or give up the baby to the Sterns? By examining the financial incentives for pursuing a surrogacy agreement, along with the competing arguments of what it means to be a mother, this case explores the validity of surrogacy contracts and the value of reproductive rights in American society

Autocracy to democracy: how African autocrats have fallen and what happens next

The past decade was not a good one for African democracies. Many autocrats are still in power and consolidating existing infrastructure to extend their rule indefinitely. However, autocrats eventually fall, both from reasons related to mortality and others. Looking at five cases of autocrats leaving office in the last decade, this paper looks to show that the different methods wherein autocrats are removed from office make a difference in how democracy may be consolidated in the power vacuum. The paper finds that not only does the method of removal not matter, but the power vacuum left by the autocrat does not give room for democracy to grow

Implementing Engineering Based STEM Programs in High School Classrooms in the Republic of Korea

In 2022, South Korea announced new national curriculum that implement it from 2023. High school curriculum is about to fully implement the high school credit system, which allows students to choose subjects that suit their needs and career paths. In South Korea, technology education in middle school is a common compulsory subject, but high school technology education is a selective subject and has the name of technology and home-economics. High school technology education experiences difficulties that are not selected in many schools due to the confusion of identity of subject names and social negative perception of technology. The purpose of this is to develop an engineering education program that can be used in high school technology education and to verify its effect on students. To achieve the purpose of this study, an engineering education program was developed and students’ changes through the program were measured. This study was based on a single-group pre-post test design and was conducted with 96 10th grade students. As a result of this study, students’ engineering interest, engineering self-efficacy, and engineering career awareness were statistically significantly improved through the developed engineering education program. This study provides great implications for actively including and utilizing engineering in technology education. In addition, it will give great implication for the direction and program development of high school technology education

Crack Detection in an Aluminium Oxide Grinding Wheel by Impact Hammer Tests

Grinding is widely used as the last step of the manufacturing process when a good surface finish and precise dimensional tolerances are required. However, if the grinding wheels have cracks, they may lead to a hazardous working environment and produce poor tolerance in machined products. Therefore, grinding wheels should be inspected for cracks before being mounted onto the machine. In this study, a novel method of finding possible internal cracks in the aluminium oxide grinding wheel will be explored by examining the natural frequency and displacement of wheels using an impact hammer testing method. Grinding wheels were cracked into two segments using a three-point bend fixture and then bonded intentionally to simulate cracks. The impact hammer test indicated that cracks in the grinding wheels caused a drop in natural vibration frequency and an increase in the maximum displacement of the accelerometer sensors

A Comparison of Network Clustering Algorithms in Keyword Network Analysis: A Case Study with Geography Conference Presentations

The keyword network analysis has been used for summarizing research trends, and network clustering algorithms play important roles in identifying major research themes. In this paper, we performed a comparative analysis of network clustering algorithms to find out their performances, effectiveness, and impact on cluster themes. The AAG (American Association for Geographers) conference datasets were used in this research. We evaluated seven algorithms with modularity, processing time, and cluster members. The Louvain algorithm showed the best performance in terms of modularity and processing time, followed by the Fast Greedy algorithm. Examining cluster members also showed very coherent connections among cluster members. This study may help researchers to choose a suitable network clustering algorithm and understand geography research trends and topical fields

What Geographers Research: An Analysis of Geography Topics, Clusters, and Trends Using a Keyword Network Analysis Approach and the 2000-2019 AAG Conference Presentations

The spectrum of geographic research topics is very broad, and several thousands of research projects are presented at AAG annual conferences. This research aims at analyzing geography research topics, clusters, and trends using conference presentation data. We analyzed the 2000-2019 AAG conference presentations with keyword network analysis methods. The most frequently used keywords during the 20-year span were GIS, followed by Remote Sensing, Climate Change, Urban, China, Education, Political Ecology, Migration, Gender, and Agriculture. Results showed that geographic research has focused on six major clusters during 2000-2019: GIS, Urban, Climate Change, Political Ecology, People, and Education. About 68.6 percent of keywords were about the GIS, People, and Urban issues. The GIS keyword showed very strong connections with Remote Sensing, Urban, Spatial, Education, Climate Change, and Health. Over the 2015-2019 period, big data analysis and artificial intelligence became popular as emerging fields. This research also shows that the keyword network analysis is an effective method to summarize research trends in geography using conference presentation data. To some fellow geographers, the findings in this research may also cast meaningful insights into what geography is and where it is heading

Multipar-T: Multiparty-Transformer for Capturing Contingent Behaviors in Group Conversations

As we move closer to real-world AI systems, AI agents must be able to deal with multiparty (group) conversations. Recognizing and interpreting multiparty behaviors is challenging, as the system must recognize individual behavioral cues, deal with the complexity of multiple streams of data from multiple people, and recognize the subtle contingent social exchanges that take place amongst group members. To tackle this challenge, we propose the Multiparty-Transformer (Multipar-T), a transformer model for multiparty behavior modeling. The core component of our proposed approach is the Crossperson Attention, which is specifically designed to detect contingent behavior between pairs of people. We verify the effectiveness of Multipar-T on a publicly available video-based group engagement detection benchmark, where it outperforms state-of-the-art approaches in average F-1 scores by 5.2% and individual class F-1 scores by up to 10.0%. Through qualitative analysis, we show that our Crossperson Attention module is able to discover contingent behavior.Comment: 7 pages, 4 figures, IJCA

Synchronizing BIM cost models and bills of quantities for lifecycle audit trail cost management

PurposeAudit trail cost management is crucial for ensuring accountability and enhancing quality assurance in construction management. Despite limited practical studies on audit trail management from a cost perspective; this study developed a lifecycle-based audit trail cost management framework. It used synchronized Building Information Modeling (BIM) cost models and Bills of Quantities (BoQs) to address the existing gap.Design/methodology/approachThis study employed a descriptive case study approach of a real-life hospital project in China. Data triangulation was achieved through interviews, observations, documents, and relevant artifacts.FindingsThe study identified three key factors contributing to cost variances between BIM cost models and BoQs: differences in measurement rules, model precision, and professional errors, particularly evident during the preliminary estimate stage. Notably, significant cost savings of approximately RMB 5.811 million were achieved during the detailed estimate stage. During the construction phase, a synchronized approach was deployed to improve precise payment verification and modifications to the BIM model. In the post-construction phase, the synchronized as-built BIM models and BoQs served as primary references to facilitate the resolution of operational discrepancies.Practical implicationsThe research contributes to the literature by proposing a synchronized approach of BIM cost models and BoQs. This approach enhances traceability and accountability of project information, catering to the digitalization needs of the construction industry.Originality/valueThis study unveils a pragmatic approach to enhancing transparency and accountability in audit-trail cost management by synchronizing BIM cost models and BoQs at various project stages. The synchronized approach offers a promising direction for future research and implementation of audit trail frameworks to enhance cost management in construction

Fine-Grained Socioeconomic Prediction from Satellite Images with Distributional Adjustment

While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like municipality. This study employs deep learning-based predictions from satellite images to close the gap. We propose a method that assigns a socioeconomic score to each satellite image by capturing the distributional behavior observed in larger areas based on the ground truth. We train an ordinal regression scoring model and adjust the scores to follow the common power law within and across regions. Evaluation based on official statistics in South Korea shows that our method outperforms previous models in predicting population and employment size at both the municipality and grid levels. Our method also demonstrates robust performance in districts with uneven development, suggesting its potential use in developing countries where reliable, fine-grained data is scarce