Kesesakan Dan Agresivitas Pada Remaja Di Kawasan Tambak Lorok Semarang

Penelitian ini bertujuan untuk mengetahui hubungan antara kesesakan dengan agresivitas pada remaja yang tinggal di Kawasan Tambak Lorok Semarang. Populasi dalam penelitian ini adalah remaja yang tinggal di Kawasan Tambak Lorok Semarang. Pengumpulan data menggunakan dua buah skala yaitu, Skala Agresivitas (22 aitem; α=0,864) dan Skala Kesesakan (16 aitem; α=0,828). Subjek penelitian berjumlah 230 remaja yang tinggal di Kawasan Tambak Lorok Semarang yang dipilih melalui teknik simple random sampling. Hasil analisis data menggunakan teknik analisis regresi sederhana menunjukkan terdapat hubungan positif antara kesesakan dengan agresivitas pada remaja yang tinggal Kawasan Tambak Lorok Semarang (r=0,578; p=0,000). Semakin tinggi kesesakan yang dirasakan subjek maka semakin tinggi agresivitas. Kesesakan memberikan sumbangan efektif sebesar 33,4% pada agresivitas dan sisanya sebesar 66,6% dipengaruhi oleh faktor lain yang tidak diteliti dalam penelitian ini

Structural Control and Chiroptical Response in Intrinsically Tetra- and Pentanuclear Chiral Gold Clusters

Controlling the synthesis of chiral metal clusters in the aspects of nuclearity number, metal–metal interaction, and spatial arrangement of metal atoms is crucial for establishing the correlation of detailed structural factors with chiroptical activity. Herein, a series of enantiopure gold complexes with nuclearity numbers ranging from 2 to 5 were constructed and structurally characterized. On the basis of the annulation reaction between two aurated μ2-imido nucleophilic units with various aldehydes, we finely adjusted the metal–metal interaction and torsion angles of a characteristic tetranuclear metal cluster by introducing different substituents into the resulting imidazolidine dianionic chiral skeleton. Further structural investigations, contrast experiments, and time-dependent density functional theory calculations confirmed that the chiroptical response of the acquired asymmetric metal clusters was mainly affected by the geometrically twisted arrangement of metal atoms. Finally, the tetranuclear gold cluster compound with the shortest intermetallic interaction and the largest torsion angle of a Au4 core showed the highest absorption anisotropy factor up to 2.2 × 10–3. In addition, the correlation of structural factors with the stability of chiral gold clusters was thoroughly evaluated by monitoring the CD, UV–vis, and NMR spectra at elevated temperatures. Insight into the relationship between the structural factors with the chiroptical property and stability of chiral gold clusters in this work will help us to design and achieve more stable chiral metal clusters and stimulate their practical applications in chiroptical functional materials

fdata-02-00006_Attending Over Triads for Learning Signed Network Embedding.xml

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches.</p

fdata-02-00006_Attending Over Triads for Learning Signed Network Embedding.pdf

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches.</p

fdata-02-00006-g0002_Attending Over Triads for Learning Signed Network Embedding.tif

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches.</p

fdata-02-00006-g0001_Attending Over Triads for Learning Signed Network Embedding.tif

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches.</p

Revisiting the Elasticity of Tetra-Poly(ethylene glycol) Hydrogels

The elasticity of polymer gels was conventionally analyzed based on the classical rubbery theories considering predominantly the entropic stress. Nevertheless, the recent studies of Yoshikawa et al. revealed that the extrapolation of the plots of elastic modulus against T to 0 K gave a negative characteristic modulus intercept, Gint < 0. They suggested that the elastic modulus stemmed not only from an increase of free energy owing to the reduced conformational entropy but also from a reduction of free energy owing to the facilitation of a favorable polymer/solvent interaction upon deformation. In this study, we choose a tetra-poly(ethylene glycol) gel (Tetra-PEG gel) dissolved in water/ethylene glycol mixtures as a model system and investigate how the elasticity of the gel samples changes with the quality of the solvent. We find that the negative Gint should stem from an improvement of the solvent quality with decreasing T. Owing to this improvement, the network strands exhibit a more expanded conformation at lower T, leading to an extra softening effect

A comfortable soundscape perspective in acoustic environmental planning and management: a case study based on local resident audio-visual perceptions

Soundscapes are an important factor related to audio-visual perception and human health; however, research on how local residents perceive the audio-visual environment remains insufficient. This study, therefore, was mainly conducted to examine the effects of sound sources, the sociodemographic factors of the local residents, visual aesthetic quality and quiet landscape experiences on rural soundscapes through on-site and in-home questionnaire surveys focusing on three components of a soundscape. The results indicated that although there were significant differences in the audio-visual perceptions among typical locations, road traffic sounds were the dominant sound category affecting acoustic comfort on site and in memory. The residents’ age affected the acoustic comfort of background sounds and sound marks in certain ways, while positive landscape experiences made sound marks predominantly perceived and the acoustic comfort of each sound category remarkably improved. This study also developed an agile practical soundscape resource optimization process through an audio-visual perceptual investigation.</p

fdata-02-00006-g0003_Attending Over Triads for Learning Signed Network Embedding.tif

Network embedding, which aims at learning distributed representations for nodes in networks, is a critical task with wide downstream applications. Most existing studies focus on networks with a single type of edges, whereas in many cases, the edges of networks can be derived from two opposite relationships, yielding signed networks. This paper studies network embedding for the signed network, and a novel approach called TEA is proposed. Similar to existing methods, TEA (Triad+Edge+Attention) learns node representations by predicting the sign of each edge in the network. However, many existing methods only consider the local structural information (i.e., the representations of nodes in an edge) for prediction, which can be biased especially for sparse networks. By contrast, TEA seeks to leverage the high-order structures by drawing inspirations from the Structural Balance Theory. More specifically, for an edge linking two nodes, TEA predicts the edge sign by considering the triangles connecting the two nodes as features. Meanwhile, an attention mechanism is proposed, which assigns different weights to the different triangles before aggregating their predictions for more precise results. We conduct experiments on several real-world signed networks, and the results prove the effectiveness of TEA over many strong baseline approaches.</p