bluuebunny/crossref_metadata_2025_split huggingface dataset:
Weighing temporal fluctuations of drought features in Rwanda through the implementation of the standardized precipitation evapotranspiration index and standardized precipitation index
Authors: Jonah Kazora , Weijun Zhu , Oo Than Kyaw , Asfaw Temesgen Gebremariam , Gyamfi Kedjanyi Emmanuel Adu , Okrah Abraham , Diane Akimana , Mugunga Mathieu Mbati 
Published: January 2025

Abstract
Climate change poses significant challenges globally, affecting weather patterns and leading to more frequent and severe extreme events such as droughts. In Rwanda, a country highly reliant on rainfed agriculture, understanding the changing nature of droughts is crucial for effective climate adaptation and resilience-building efforts. This study investigates temporal variations in drought characteristics across Rwanda using the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). Through a comprehensive analysis spanning multiple time scales (3-month and 12-month periods) and spatial extents, the research assesses drought occurrence, frequency, intensity, and severity from 1983 to 2020. Utilizing the homogeneity test, Mann-Kendall trend test, and Sen’s slope method, the study identifies significant trends in precipitation, SPEI, SPI, potential evapotranspiration (PET), and trends in PET changes over time. Results reveal substantial spatial and temporal variability in drought patterns, with distinct interdecadal shifts observed. While certain regions experienced increased drought frequency and severity, others exhibited wetter conditions. Notably, the Eastern and Southern provinces faced intensified drought impacts, with prolonged dry spells lasting over 3 to 6 months. The research emphasizes the necessity of comprehending the temporal and spatial fluctuations in drought attributes to enhance agricultural practices and adaptation measures. Through the utilization of complementary drought indices and robust statistical approaches, this study offers valuable insights into Rwanda’s susceptibility to climate variability and its impacts on socio-economic advancement. The findings offer essential guidance for policymakers, stakeholders, and communities in developing targeted interventions to mitigate the adverse impacts of droughts and enhance resilience to climate change.
A comprehensive review of computational diagnostic techniques for lymphedema
Authors: K R Jayasree , Vijayakumar D K , Sugumaran Vijayan , Pathinarupothi Rahul Krishnan 
Published: January 2025

Abstract
Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2–5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema. This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning (ML) approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies bioimpedance spectroscopy (BIS), Kinect sensor and ML integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas ML and artificial neural network achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. ML and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.
3D laser structuring of supermetalphobic microstructures inside elastomer for multilayer high-density interconnect soft electronics
Authors: Zhang Chengjun , Yang Qing , Li Haoyu , Luo Zexiang , Lu Yu , Zhang Jialiang , Li Cheng , Chen Feng 
Published: January 2025

Abstract
High-density interconnect (HDI) soft electronics that can integrate multiple individual functions into one miniaturized monolithic system is promising for applications related to smart healthcare, soft robotics, and human-machine interactions. However, despite the recent advances, the development of three-dimensional (3D) soft electronics with both high resolution and high integration is still challenging because of the lack of efficient manufacturing methods to guarantee interlayer alignment of the high-density vias and reliable interlayer electrical conductivity. Here, an advanced 3D laser printing pathway, based on femtosecond laser direct writing (FLDW), is demonstrated for preparing liquid metal (LM)-based any layer HDI soft electronics. FLDW technology, with the characteristics of high spatial resolution and high precision, allows the maskless fabrication of high-resolution embedded LM microchannels and high-density vertical interconnect accesses for 3D integrated circuits. High-aspect-ratio blind/through LM microstructures are formed inside the elastomer due to the supermetalphobicity induced during laser ablation. The LM-based HDI circuit featuring high resolution (∼1.5 μm) and high integration (10-layer electrical interconnection) is achieved for customized soft electronics, including various customized multilayer passive electric components, soft multilayer circuit, and cross-scale multimode sensors. The 3D laser printing method provides a versatile approach for developing chip-level soft electronics.
Construction of complex three-dimensional vascularized liver tissue model in vitro based on a biphasic cell-laden embedding medium
Authors: Lv Weikang , Yu Haoran , Aazmi Abdellah , Naren Tuya , Cheng Wanli , Yu Mengfei , Wang Zhen , Xu Xiaobin , Yang Huayong , Ma Liang 
Published: January 2025

Abstract
Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation, mimicking physiological heterogeneous structures, and recreating the cellular microenvironment. However, the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink, making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials. To overcome this limitation, we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel. This medium possesses yield stress and self-healing properties, facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it. By adjusting the printing speed, we controlled the filament diameter, achieving a range from 250 μm to 1000 μm, and ensuring precise control over ink deposition locations and filament shapes. Using the in situ endothelialization method, we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells. In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue. We also investigated the impact of varying vascular densities on liver tissue function. Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways. In summary, this method is adaptable to various materials, allowing the rheological properties of the supporting bath and the tissue’s porosity to be modified using microgels, thus enabling precise regulation of the liver tissue microenvironment. Additionally, it facilitates the rapid construction of three-dimensional vascular structures within liver tissue. The resulting vascularized liver tissue model exhibits enhanced biological functionality, opening new opportunities for biomedical applications.
Self-propelled ferroptosis nanoinducer for enhanced cancer therapy
Authors: Xu Wenxin , Tian Hao , Song Yanzhen , Qin Hanfeng , Gao Junbin , Chen Yichi , Huang Weichang , Lin Lin , Tan Haixin , Ye Yicheng , Zhang Xiaoting , Wilson Daniela A , Yang Guang , Peng Fei , Tu Yingfeng 
Published: January 2025

Abstract
Ferroptosis is a newly proposed type of programmed cell death, which has been associated with a variety of diseases including tumors. Researchers have thereby presented nanoplatforms to mediate ferroptosis for anti-cancer therapy. However, the development of ferroptosis-based nanotherapeutics is generally hindered by the limited penetration depth in tumors, poor active pharmaceutical ingredient (API) loading content and the systemic toxicity. Herein, self-propelled ferroptosis nanoinducers composed of two endogenous proteins, glucose oxidase and ferritin, are presented to show enhanced tumor inhibition via ferroptosis while maintaining high API and biocompatibility. The accumulation of our proteomotors at tumor regions is facilitated by the active tumor-targeting effect of ferritin. The enhanced diffusion of proteomotors is then actuated by efficiently decomposing glucose into gluconic acid and H2O2, leading to deeper penetration and enhanced uptake into tumors. Under the synergistic effect of glucose oxidase and ferritin, the equilibrium between reactive oxygen species and GSH is damaged, leading to lipid peroxidation. As a result, by inducing ferroptosis, our self-propelled ferroptosis nanoinducers exhibit enhanced tumor inhibitory effects. This work paves a way for the construction of a biocompatible anticancer platform with enhanced diffusion utilizing only two endogenous proteins, centered around the concept of ferroptosis.
Micropattern of core-shell Ag@MCS/PEGDA nanoparticles fabricated by femtosecond laser maskless optical projection lithography
Authors: Bin Fan-Chun , Wu Xin-Yi , Liu Jie , Dong Xian-Zi , Li Teng , Duan Qi , Zhang Jian-Miao , Fujita Katsumasa , Zheng Mei-Ling 
Published: January 2025

Abstract
Chitosan (CS)-based nanocomposites have been studied in various fields, requiring a more facile and efficient technique to fabricate nanoparticles with customized structures. In this study, Ag@methacrylamide CS/poly(ethylene glycol) diacrylate (Ag@MP) micropatterns are successfully fabricated by femtosecond laser maskless optical projection lithography (Fs-MOPL) for the first time. The formation mechanism of core-shell nanomaterial is demonstrated by the local surface plasmon resonances and the nucleation and growth theory. Amino and hydroxyl groups greatly affect the number of Ag@MP nanocomposites, which is further verified by replacing MCS with methacrylated bovine serum albumin and hyaluronic acid methacryloyl, respectively. Besides, the performance of the surface-enhanced Raman scattering, cytotoxicity, cell proliferation, and antibacterial was investigated on Ag@MP micropatterns. Therefore, the proposed protocol to prepare hydrogel core-shell micropattern by the home-built Fs-MOPL technique is prospective for potential applications in the biomedical and biotechnological fields, such as biosensors, cell imaging, and antimicrobial.
Tailoring adhesive bonding strength: the role of mechanical abrasion and cure time in aluminum-stainless steel joints
Authors: Abid Jawad , Ahmad Arslan , Raza Yasir , Ayub Azeem , Yasir Hammad , Raza Hasan , Abid Muhammad , Murtaza Mubashir Ali , Masood Wajeeh , Janjua Ajaz Bashir 
Published: January 2025

Abstract
Adhesive bonding is an efficient strategy for fabricating lightweight structural components owing to its superior toughness and fatigue resistance. Major factors affecting the adhesion of various materials encompass the elimination of impurities, adhesive wettability, and surface roughness. This study examines the effects of surface roughness and curing duration on the adhesive bonding of AL5083 and SS 304 substrates. Impurities are eliminated from the substrates utilizing laboratory-grade acetone, succeeded by pre-treatment with P30, P40, and P60 grades of silicon carbide sheets. Surface roughness is quantified using a profilometer Surftest (SJ-210). Morphologies were analyzed by scanning electron microscopy (SEM) and x-ray diffractometry (XRD) were employed to validate the specimen fabrication. The adhesively bonded specimens, exhibiting different surface roughness, undergo lap shear tensile tests utilizing a Universal Testing Machine (UTM), and tensile strength is determined. The results demonstrate that the surface treated with silicon carbide P30 and cured for 48 h attained the maximum tensile strength for both adherend materials: 129.00 MPa for SS 304 with a surface roughness of Ra = 3.20 μm, and 44.65 MPa for AL5083 with a surface roughness of Ra = 5.00 μm. This research will pave the way to optimize the surface preparation for bonded joints.
Temporal convolution long short-term memory network with multiple attention for remaining useful life prediction of rolling bearings
Authors: Zhang Jiashuo , He Deqiang , Wu Jinxin , Jin Zhenzhen , Xiang Weibin , Shan Sheng 
Published: January 2025

Abstract
Remaining useful life (RUL) prediction of rolling bearings prevents sudden mechanical failures and reduces equipment maintenance costs. Due to the strong performance in time series forecasting tasks, the temporal convolutional network (TCN) has become a mainstream model for RUL prediction. However, existing TCN-based prediction models struggle to fully capture both long-term and global dependencies in complex data. To address these issues, a temporal convolutional long short-term memory network integrated with multi-head attention mechanism (TCLSTM-MA) is proposed to predict the rolling bearings’ RUL. Firstly, the time-domain and frequency-domain features are extracted from the acquired raw vibration signals to form a complete degradation feature. Secondly, we enhance the traditional TCN by combining it with LSTM and introducing a multi-head attention mechanism. This integration allows the model to effectively capture both global degradation information and local context information. Additionally, a time-weighted t-MSE loss function is employed throughout training to make the model focus more on data close to failure points. Finally, the trained TCLSTM-MA model is used for RUL prediction. Extensive experiments were conducted on two authoritative rolling bearing datasets and compared with other methods. The experimental results demonstrate that the proposed method exhibits good accuracy and generalization capability.
Full space scanning leaky wave antenna with wide angle beam steering based on SSPPs
Authors: Rajaparthiban Mani , Karthikeyan Madurakavi 
Published: January 2025

Abstract
We proposed a leaky wave antenna based on spoof surface plasmon polariton (SSPP) transmission line (TL) with unit cell of X-Shaped orthogonal metal patches. These patches are arranged in a couple of metallic patches along with semi-elliptical patches on both sides of the slot to attain the full space scanning (−90° to +90°) between the frequency range of 2.53 GHz and 5 GHz. The designed LWA shows that the continuous beam scanning range from −94° to + 2° (96°), with an average gain of 8.70 dBi, a radiation efficiency of 79.09%, and a scanning rate of 2.44°/% has been achieved over the frequency range of 5 GHz to 7.45 GHz. Furthermore, the antenna achieves a 174° scanning range from −172 to +2° between the frequency range of 100 MHz and 7.45 GHz, with an average gain of 8.33dBi, a radiation efficiency of 69.28%, a scanning rate of 0.88°/%, and obtains a lower side lobe level of less than −10 dB. The proposed antenna has a beam coverage range of 357° over the frequency range of 5 MHz-3.1 GHz. The antenna was fabricated using Rogger RT 5880 substrate with εr = 2.2. The total size of the structure is 180 × 30 × 0.508. With these features, this antenna is suitable for continuous beam scanning and full space scanning applications in mobile/wireless communication and microwave radar communication.
Satellite image classification with neural quantum kernels
Authors: Rodriguez-Grasa Pablo , Farzan-Rodriguez Robert , Novelli Gabriele , Ban Yue , Sanz Mikel 
Published: January 2025

Abstract
Achieving practical applications of quantum machine learning (QML) for real-world scenarios remains challenging despite significant theoretical progress. This paper proposes a novel approach for classifying satellite images, a task of particular relevance to the earth observation industry, using QML techniques. Specifically, we focus on classifying images that contain solar panels, addressing a complex real-world classification problem. Our approach begins with classical pre-processing to reduce the dimensionality of the satellite image dataset. We then apply neural quantum kernels-quantum kernels derived from trained quantum neural networks-for classification. We evaluate several strategies within this framework, demonstrating results that are competitive with the best classical methods. Key findings include the robustness of or results and their scalability, with successful performance achieved up to 8 qubits.
Nanonization of ethanolic tulsi leaf extract and its major compound eugenol and evaluation of their inhibitory potential against calcium oxalate crystal (a kidney stone component)
Authors: Ghosh Sourav , Basu Tarakdas 
Published: January 2025

Abstract
The aim of this study was a) to synthesize nanoforms of the whole leaf extract of the flowering herb ‘Tulsi—Ocimum sanctum’ (regarded as the ‘queen of herbs’ having diverse healing and health-improving properties) and its major constituent eugenol, b) to characterize some of their physico-chemical properties and c) to explore their inhibitory role on formation and disintegration of the kidney stone-former calcium oxalate crystals, through in vitro investigation. Colloidal suspensions of Tulsi-leaf-extract nanoparticles (TLE-NPs) and eugenol nanoparticles (ENPs) were synthesized separately by ultrasonication method through emulsification of hydrophobic TLE/eugenol into hydrophilic protein gelatin. Due to nanonization, water-insoluble TLE/eugenol became water-soluble and thus bioavailable. The average size of the particles was about 35nm for TLE-NPs and 25 nm for ENPs, entrapment efficiency of TLE and eugenol within gelatin was about 20 and 80% respectively, and the release rate of TLE/eugenol from gelatin cap was slow and sustained throughout 72 h for TLE-NPs and 120 h for ENPs. When spectroscopic and microscopic studies were performed on the formation and disintegration of calcium oxalate crystals in presence of TLE-NP and ENP, both types of particles depicted significant inhibitory potential on crystal formation and disintegrating potential on pre-formed crystals, compared to the same doses of their bulk forms i.e., bare TLE and eugenol; ENP was more potent than TLE-NP with respect to crystallization inhibitory activity, whereas they were comparable in crystal disintegration potency. Future in vivo pharmacological and toxicological studies will surely lead to the development of the TLE-NPs and ENPs as potential therapeutic agents against calcium oxalate pathogenesis.
Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware
Authors: Cotteret Madison , Greatorex Hugh , Renner Alpha , Chen Junren , Neftci Emre , Wu Huaqiang , Indiveri Giacomo , Ziegler Martin , Chicca Elisabetta 
Published: January 2025

Abstract
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly nonideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. Moreover, it demonstrates that distributed symbolic representations serve as a highly capable representation-invariant language for cognitive algorithms in neuromorphic hardware.
Asymptotic behavior of an integral equation of Allen-Cahn type
Authors: Lei Yutian 
Published: January 2025

In this paper, we are concerned with the asymptotic behavior of solutions of an integral equation of the Allen-Cahn type

u
(
x
)
=

l
→

+
K
(
x
)

W

β
,
p

[
u

|

u

|

q
1

−
2

(
1
−

|

u

|

q
1

)

|

1
−

|

u

|

q
1

|

q
2

−
2

]
(
x
)

\begin{equation*} u(x)=\overrightarrow {l} +K(x)W_{\beta ,p}[u|u|^{q_1-2}(1-|u|^{q_1})|1-|u|^{q_1}|^{q_2-2}](x) \end{equation*}

when

|

x

|

→
∞

|x| \to \infty

. Here

u
:

R

n

→

R

k

u:{\mathbb {R}}^n\rightarrow {\mathbb {R}}^k

is uniformly continuous, and

k
≥
1

k \geq 1

,

n
≥
2

n \geq 2

,

p
>
1

p>1

,

β
>
0

\beta >0

,

p
β
>
n

p\beta >n

, and

min
{

q
1

,

q
2

}
>
2

\min \{q_1, q_2\} >2

. In addition,

l
→

∈

R

k

\overrightarrow {l} \in {\mathbb {R}}^k

is a constant vector and

K
(
x
)

K(x)

is a nonnegative bounded function. We prove that if

u
u

or

1
−

|

u

|

q
1

1-|u|^{q_1}

has some integrability, then

|

l
→

|

∈
{
0
,
1
}

|\overrightarrow {l}| \in \{0,1\}

and

u
→

l
→

u \to \overrightarrow {l}

when

|

x

|

→
∞

|x| \to \infty

. Moreover, if

u
u

or

1
−

|

u

|

q
1

1-|u|^{q_1}

has some better integrability, then

|

|

u
(
x
)

|

−

|

l
→

|

|

=
O
(

|

x

|

(
p
β
−
n
)

/

(
p
−
1
)

)

| |u(x)|-|\overrightarrow {l}| |=O(|x|^{(p\beta -n)/(p-1)})

when

|

x

|

→
∞

|x| \to \infty

. Here a regularity lifting lemma comes into play in the proof.