Formation of Prussian-Blue-Analog Nanocages via a Direct Etching Method and their Conversion into Ni-Co-Mixed Oxide for Enhanced Oxygen Evolution by Han, Lei, Yu, Xin-Yao, Lou, Xiong Wen David

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Investigating human trafficking recruitment online: a study of fraudulent job offers on social media platforms by Moyo, TM, Gunes, O, Jirotka, M

An affine invariant salient region detector by Kadir, T, Zisserman, A, Brady, M

The challenge of hyper-sexual femininity and binge drinking: A feminist psychoanalytic response by Stepney, M

Le mot carien pour ‘chef’, une nouvelle racine anatolienne et un changement phonétique carien-grec by Bianconi, M

“It would be pretty immoral to choose a random algorithm”: Opening up algorithmic interpretability and transparency by Webb, HM, Patel, M, Rovatsos, M, Davoust, A, Ceppi, S, Koene, A, Dowthwaite, L, Portillo, V, Jirotka, M, Cano, M

On the higher-order edge toughness of a graph by Chen, C.C., Koh, K.M., Peng, Y.H.

“…For an integer c, 1≤c≤{curly logical or}V(G){curly logical or}-1, we define the cth-order edge toughness of a graph G as tc(G)=min |X| ω(G-X)-cX⊆E(G) & ω(G-X)>c The objective of this paper is to study this generalized concept of edge toughness. …”
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Fungal colonization of rice straw and palm press fibre in the rumen of cattle and buffalo by Ho, Y.W., Abdullah, N., Jalaludin, S.

“…In both animal species, attachment to rice straw by rumen fungal zoospores was rapid, within 15 min of rumen incubation. At 6 h of rumen incubation, thin- and thick-walled tissues were colonized by fungal hyphae and by 24 h fungal colonization was extensive. …”
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Mobility patterns are associated with experienced income segregation in large US cities by Moro, Esteban, Calacci, Dan, Dong, Xiaowen, Pentland, Alex

“…To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). …”
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Exploiting irregular parallelism to accelerate FPGA routing by Zhu, Alan Y.

“…This parallelism can be exploited on parallel architectures that provide hardware support for ordered tasks and fine-grained speculation, such as the Swarm architecture. …”
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Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling by Blum, Yannick, Mikelson, Jan, Dobrzyński, Maciej, Ryu, Hyunryul, Jacques, Marc‐Antoine, Jeon, Noo L., Khammash, Mustafa, Pertz, Olivier

“…Our results provide novel insights into how different receptor tyrosine kinase (RTK) systems differentially wire the MAPK network to fine‐tune fate decisions at the cell population level.…”
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A guide to being the elephant in the room : a study on the residual behaviours stemming from bodyweight stigma by Jayakumar Sandhya Pillai

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Generating domain-specific paraphrases of questions from FAQ by Ng, Jing Rui

“…Firstly, T5 is used to fine-tune on the paraphrase dataset for the task of paraphrase generation. …”
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Mu (em-power-you) : a jewelry brand inspired by the stories of different communities by Cho, Cecilia Hyunjae

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Aspect-based sentiment analysis using BERT by Kheriwala, Hussain Khozema

“…Aspect-based sentiment analysis extracts and identifies fine-grained sentiment polarities for a specific aspect. …”
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The city you'll never meet by Lim, Ying Quan

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Soulvurn by Tan, Sherneese

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Visual mapping art : data visualization & crowdsourcing for an ostensive definition of art by Ong, Zi Feng

“…The core data will be gathered from existing art databases, such as the Library of Congress (LCC) (Class-N - Fine Arts) and the Universal Decimal Classification (UDC) (73/76 Various arts & crafts); and controlled vocabulary systems such as Art & Architecture Thesaurus (AAT). …”
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Deep learning for fabric defect detection by Nangia, Saniya

“…The base models, YOLOv8 and RT-DETR, are fine-tuned on a dataset of patterned fabric defects to find the optimal model configurations. …”
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Value investment with machine learning by Wang, Jiwei

“…Further optimization led to the creation of the Improved Fundamental and Technical Factors Model, which achieved an impressive annualized return of 50\% and a Sharpe ratio of 1.85. After fine-tuning key parameters, the final optimized model demonstrated exceptional performance, with an annualized return of 59.82\%, a Sharpe ratio of 2.13, and a win rate of 75\%. …”
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