bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine learning based mechanical fault diagnosis and detection methods: a systematic review
Authors: Xin Yuechuan , Zhu Jianuo , Cai Mingyang , Zhao Pengyan , Zuo Quanzhi
Published: October 2024

Mechanical fault diagnosis and detection (FDD) are crucial for enhancing equipment reliability, economic efficiency, production safety, and energy conservation. In the era of Industry 4.0, artificial intelligence (AI) has emerged as a significant tool for mechanical FDD, attracting considerable attention from both academia and industry. This review focuses on the application of AI techniques in mechanical FDD using artificial intelligence techniques based on the existing research. It examines various AI algorithms including k-nearest neighbors, support vector machine, artificial neural network, deep learning, reinforcement learning, computer vision, and transformer algorithm integrating theoretical foundations with practical applications in industrial production. Furthermore, a comprehensive overview of these algorithms applications in mechanical FDD is provided. Finally, a critical assessment highlights the advantages and limitations of these techniques, while forecasting the developmental trajectories of future intelligent diagnostic technologies based on machine learning. This review serves to bridge the gap between researchers in AI and fault diagnosis, contributing significantly to the field.
Study on enhancing the quantitative performance of LIBS for Cu element in liquids based on the chitosan-parafilm enrichment method
Authors: Wang Hongbao , Li Honglian , Yao Zhichao , Sun Jiaxing , Pang Lili
Published: October 2024

To enhance the accuracy of laser-induced breakdown spectroscopy (LIBS) in detecting heavy metal elements in solutions, a chitosan (CS)-Parafilm (PM) enrichment method is proposed. This method involves drying chitosan-heavy metal complexes on a Parafilm substrate. During the drying of droplets, electrostatic attraction and chelation by CS effectively concentrate analytes. Additionally, the hydrophobic effect of the PM substrate induces Marangoni flow, which drags analytes from the bottom edge to the central top of the droplet surface, effectively suppressing the coffee ring effect (CRE). Using LIBS technology, spectra were uniformly collected in an array on the surface of sediments. Through the analysis of cumulative excitation spectra and measurement repeatability, the results showed that the RSD of the emission lines Cu I 324.754 nm and Cu I 327.396 nm was reduced to 3.85% and 3.78%, respectively. This indicates that the CS-PM enrichment method allows for uniform deposition of analytes within the samples, effectively suppressing the CRE. Quantitative analysis of Cu elements using the CS-PM enrichment method was conducted using PSO-SVM, PSO-BPNN, and random forest (RF) algorithms. The RF algorithm demonstrated the best predictive performance with Rp
2
of 0.977, root mean square error of prediction of 3.86 mg l−1, mean absolute error of 3.0 mg l−1, and RPD of 5.52. Thus, it can be seen that CS-PM effectively improves the repeatability of spectral measurements and the accuracy of quantitative analysis predictions, providing a technical reference for enhancing the stability and quantitative performance of element analysis in liquids using LIBS.
Dual-feature enhanced hybrid convolutional network for imbalanced fault diagnosis of rolling bearings
Authors: Zhao Yingjie , Yan Changfeng , Liu Bin , Kang Jianxiong , Li Shengqiang
Published: October 2024

Deep learning has been extensively applied in the rolling bearing fault diagnosis domain due to its superior data analysis and feature extraction capabilities. However, in practical applications, the normal operating state occupies most of the service life of equipment, and the occurrence probability of each kind of fault is different, leading to imbalanced data that significantly degrades the performance of the neural network. In order to solve this problem, a dual-feature enhanced hybrid convolutional network (DEHCNet) is proposed. Firstly, an impulse segment enhancement module is constructed to enhance impulse segment features in the raw data, helping the network to learn fault features more accurately. Then, a hybrid convolutional module is designed to fully mine discriminant fault features of minority classes from imbalanced data. In addition, a feature-enhanced combinational pooling module is devised to guide the network to focus more on the critical features and maximize the retention of key features in dimensionality reduction operations, thereby reducing the influence of data imbalance on the classifier. Finally, three distinct datasets are used to verify the DEHCNet. Experimental results show that this network has better diagnostic accuracy and robustness under conditions of data imbalance.
Stochastic Cahn–Hilliard and conserved Allen–Cahn equations with logarithmic potential and conservative noise*
Authors: Primio Andrea Di , Grasselli Maurizio , Scarpa Luca
Published: October 2024

We investigate the Cahn–Hilliard and the conserved Allen–Cahn equations with logarithmic type potential and conservative noise in a periodic domain. These features ensure that the order parameter takes its values in the physical range and, albeit the stochastic nature of the problems, that the total mass is conserved almost surely in time. For the Cahn–Hilliard equation, existence and uniqueness of probabilistically-strong solutions is shown up to the three-dimensional case. For the conserved Allen–Cahn equation, under a restriction on the noise magnitude, existence of martingale solutions is proved even in dimension three, while existence and uniqueness of probabilistically-strong solutions holds in dimension one and two. The analysis is carried out by studying the Cahn–Hilliard/conserved Allen–Cahn equations jointly, that is a linear combination of both the equations, which has an independent interest.
Stage-IV cosmic shear with Modified Gravity and model-independent screening
Authors: Tsedrik M. , Bose B. , Carrilho P. , Pourtsidou A. , Pamuk S. , Casas S. , Lesgourgues J.
Published: October 2024

We forecast constraints on minimal model-independent parametrisations of several Modified
Gravity theories using mock Stage-IV cosmic shear data. We include nonlinear effects and
screening, which ensures recovery of General Relativity on small scales. We introduce a power
spectrum emulator to accelerate our analysis and evaluate the robustness of the growth index
parametrisation with respect to two cosmologies: ΛCDM and the normal branch of the DGP
model. We forecast the uncertainties on the growth index γ to be of the order ∼
10%. We find that our halo-model based screening approach demonstrates excellent performance,
meeting the precision requirements of Stage-IV surveys. However, neglecting the screening
transition results in biased predictions for cosmological parameters. We find that the screening
transition shows significant degeneracy with baryonic feedback, requiring a much better
understanding of baryonic physics for its detection. Massive neutrinos effects are less prominent
and challenging to detect solely with cosmic shear data.
A model-independent precision test of General Relativity using LISA bright standard sirens
Authors: Afroz Samsuzzaman , Mukherjee Suvodip
Published: October 2024

The upcoming Laser Interferometer Space Antenna (LISA), set for launch in the mid-2030s, will enhance our capability to probe the universe through gravitational waves (GWs) emitted from binary black holes (BBHs) across a broad range of cosmological distances. LISA is projected to observe three classes of BBHs: massive BBHs (MBBHs), extreme mass-ratio inspirals (EMRIs), and stellar mass BBHs. This study focuses on MBBHs, which are anticipated to occur in gas-rich environments conducive to producing powerful electromagnetic (EM) counterparts, positioning them as excellent candidates for bright sirens. By combining GW luminosity distance measurements from these bright sirens with Baryon Acoustic Oscillation (BAO) measurements derived from galaxy clustering and sound horizon measurements from the Cosmic Microwave Background (CMB), and spectroscopic redshift measurements from observations of the electromagnetic (EM) counterpart, we propose a data-driven model-independent method to reconstruct deviations in the variation of the effective Planck mass (in conjunction with the Hubble constant) as a function of cosmic redshift. Using this multi-messenger technique, we achieve precise measurements of deviations in the effective Planck mass variation with redshift (z), with a precision ranging from approximately 2.4% to 7.2% from redshift z=1 to z=6 with a single event. Additionally, we achieved a measurement of the Hubble constant with a precision of about 1.3%, accounting for variations in the effective Planck mass over 4 years of observation time (T
obs). This assumes that EM counterparts are detected for 75% of the events. This precision improves with observation time as T
obs
-1/2. This approach not only has the potential to reveal deviations from General Relativity but also to significantly expand our understanding of the universe's fundamental physical properties.
A gentle introduction to gradient-based optimization and variational inequalities for machine learning
Authors: Wadia Neha S , Dandi Yatin , Jordan Michael I
Published: October 2024

The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. In these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. Gradient-based methods remain essential—given the high dimensionality and large scale of machine-learning problems—but simple gradient descent is no longer the point of departure for algorithm design. We provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. While we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.
Real Time Prediction and Modelling of Power Grid System Stability and Load Balancing
Authors: Wu Xinhua , Jiang Shuting , Li Zuxin , Liao Ruifeng
Published: October 2024

The extensive geographical distribution of the power system determines its highly complex structure. The system includes key components such as generators, substations, transmission and distribution grids, and users. This system has characteristics such as nonlinearity, time-varying, and parameter uncertainty. Power system stability is inherently linked to its regular operational performance. This article uses Simulink models to simulate the working state of generators and circuit breakers during faults. Under the conditions of a synchronous generator with a rated power of 350MW, a voltage of 10.5KV, a three-phase parallel RLC load rated voltage of 115KV, and a power consumption of 300MW, when a short occurs on line, the speed and relative angle of the generator tend to a fixed value, and the system remains stable. When a fault occurs in the circuit within 0.1 seconds, the circuit breaker will cut off the faulty line within 0.6 seconds. Avoiding the occurrence of oscillations and unstable states in typical dynamic systems.
Enhanced Vehicle Recognition and Collision Alert System Using Yolo+DeepSORT Algorithm
Authors: Wang Xinchi , Wei Huimei , Wang Lijuan , Zhao Chenrui , Wu Siheng
Published: October 2024

This paper sets out to develop and deploy a deep learning-driven vehicle collision avoidance warning system. Initially, it delves into the foundational principles of traditional collision avoidance warning systems, alongside key safety driving concepts. It then elaborates on leveraging deep learning technology to enhance the precision and reliability of target tracking. This includes detailed discussions on techniques such as appearance feature extraction, Kalman filters, the Hungarian algorithm, and more. In the technical section, the paper outlines the specific implementation of utilizing the YOLO algorithm for target detection and the DeepSORT algorithm for target tracking within the system architecture. The experimental section showcases the system’s performance and stability through formatted experimental results, followed by a comprehensive analysis of the data obtained. Concluding, the paper summarizes its research findings, underscoring the significance and feasibility of a deep learning-based vehicle collision avoidance warning system.
Adaptive Style Normalization and Similarity Grouping for Unsupervised Cross-Domain Person Re-Identification
Authors: Wang Mingxuan , Hui Yan , Hu Xiuhua , Yang Ruiying
Published: October 2024

In unsupervised cross-domain person re-identification, feature extraction often includes identity-irrelevant information, reducing model generalization. Additionally, ignoring potential similarities within the target domain leads to noisy pseudo-labels. To address these issues, this paper introduces a method for cross-domain person re-identification that combines adaptive style normalization with similarity grouping. First, adaptive style normalization is applied on the backbone network to mitigate loss of information caused by instance normalization, thus improving the generalization ability and identity recognition accuracy of the model. Second, similarity grouping is employed to uncover potential similarities in target domain, overcoming the problem of loud pseudo-labels and further improve model performance. Using this method, mAP accuracy increased by 2.27% on the Market-1501 dataset and by 4.26% on the DukeMTMC-ReID dataset, enhancing its application in the real world.
RFA-YOLOv8: Steel Plate Surface Defect Detection Algorithm Based On Improved YOLOv8
Authors: Bai Jiangfeng , Zhang Shifang
Published: October 2024

To address the issues in existing steel plate surface defect detection algorithms, such as low accuracy, numerous model parameters, and high computational complexity, this paper introduces a novel algorithm named RFA-YOLOv8, which is built on an enhanced version of YOLOv8n. The approach enhances the C2f module within the backbone network and incorporates a receptive field attention (RFA) mechanism to refine the convolution layer, leading to the development of the C2f_RFAConv structure. This advancement significantly boosts feature extraction capabilities in complex scenarios. Experimental results demonstrate that RFA-YOLOv8 achieves a 74.9% accuracy on the NEU-DET dataset, surpassing YOLOv8n by 1.8%, with minimal changes in model parameters and floating-point operations. Moreover, improvements in both recall and precision rates confirm that RFA-YOLOv8 delivers high detection accuracy and efficiency, making it valuable for industrial quality inspections.
YOLO-Based Design and Optimization of Weld Seam Detection Model
Authors: Zhou Zhaoxuan , Cao Yan
Published: October 2024

The traditional welding seam inspection efficiency is low, the model possesses a significant amount of computational parameters, and it is not suitable for the application of small and medium-sized welding seam recognition machines. In view of the wide application and remarkable effect of deep learning in the domain of machine vision, a welding seam defect detection model designed by G-Efficientnet-CA neural network was proposed and optimized. EfficientNet was employed as the core architecture for extracting features to greatly reduce calculation parameters and model volume. The CA attention mechanism is incorporated to enhance the model’s capacity for focusing attention on the weld image, thereby improving its ability to discern and analyze relevant features, and the accuracy is improved. The Generalized Intersection over Union (GIoU) loss function is changed from the original loss function to optimize the calculation of the coincidence degree between the real frame and the predicted frame. The efficient K-Means++ clustering algorithm is utilized to calculate the initial anchor frame which is more suitable for different weld data sets. The experimental outcomes demonstrate that the optimized model, when compared to the YOLOv3 model, exhibits superior effectiveness in detecting the VOC2007 dataset, as evidenced by a notable 12.7% increase in the average precision (mAP) and a significant reduction of 88% in the number of parameters.
Optical 3D Surface Vertical Measurement Based on 2D Generalized S-Transform
Authors: Zhong Min , Duan Peng , Hu Zhengyan , Chen Xiaolong , Cao Weiwei
Published: October 2024

The optical 3D surface measurement technique aligns the optical axis of the projector with that of the camera, enabling the measurement of objects with significant height variations, such as deep holes and grooves. This technique encodes the height of the object into the modulation of the fringe pattern, eliminating the requirement for phase unwrapping and avoiding issues such as shadow occlusion. To further enhance the noise reduction capability of fringe analysis and reconstruct objects with high-frequency detail, this paper introduces the two-dimensional generalized S-transform (2D-GST) method for modulation extraction. By incorporating two additional parameters px
and py
to adjust the resolution in the time domain/frequency domain, 2D-GST can provide higher reconstruction precision. The root mean square (RMS) for tested plane with 2D-GST is 4.35 μm, whereas for the traditional 1D S-transform (1D-ST), the RMS is 4.97 μm.
Dual Modal Face Recognition Based on Visible Light and Thermal Infrared
Authors: Wei Yongchao , Yue Yuchen , Zhu Zihan
Published: October 2024

In recent years, face recognition has received widespread attention, in the use of visible light for face recognition, there are a series of problems that require auxiliary light sources, may be attacked by photos, and camouflage is difficult to penetrate. Compared with visible light recognition, thermal infrared face recognition shows usability, and it can still be stable in low light or dark extreme environmental conditions, but due to low resolution, it can not capture rich face feature information. In view of this paper proposes that dual mode face recognition can effectively integrate two single mode information and make up for the deficiency of single feature. Using a Bimodal Face network model based on visible light and thermal infrared, the idea of feature decoupling is introduced into image fusion. The cross-modal information is decomposed into common information and unique information, and the common information and unique information of different modes are fused respectively according to the feature fusion method. It is tested on the self-built dual modal dataset that this fusion strategy can achieve the optimal effect, and the accuracy rate can reach 97.08%, which is better than other five face recognition network models.
Microstructural Regulation of Co TCPP by Acetylene Black: Enhanced Electrochemical Performance for Wearable Supercapacitors
Authors: Diao Binxuan , Liu Jianzheng , Wang Shilin , Xie Wenjun , Cong Chenhao , Li Xinlin
Published: October 2024

With the rapid development of wearable devices, flexible supercapacitors have become an important energy storage prototype due to their inherent flexibility and high power density. Two-dimensional metal-organic framework (2D MOF) nanosheets are widely used in energy storage systems due to their structural tunability and anisotropy. However, their high surface energy tends to lead to aggregation, which results in poor dispersion and rheology of formulated inks and inhomogeneity of printed electrodes. Existing methods involve surface modulation by capped organic ligands, yet ignore the inherent conductivity and stability of MOFs. To overcome the low conductivity and aggregation problems of 2D MOFs, we adopted a novel approach combining mechanical intercalation and surface functionalization of cobalt-based porphyrin frameworks (Co TCPP) with acetylene black (AB). The unique microstructure of the composite material leads to favorable changes in the electronic properties of AB, which reduces the overall internal resistance and promotes rapid charge transfer. For this purpose, we have prepared micro-supercapacitors (MSCs) using dispensing printing technology. This integrated material has excellent mechanical flexibility and maintains 92% specific capacitance even under extreme bending and torsion. This study highlights the potential of integrating 2D MOFs with carbon-based materials (e.g., acetylene black) for a variety of applications in wearable electronics, and provides a guiding scheme for future micro-modulation of 2D MOFs using conductive materials.
Consumer perspectives towards aquaponics: Products and native fish cultivation
Authors: Colin Kiu Qi-Song , Ooi Ai-Lin , Teoh Chaiw-Yee
Published: October 2024

Aquaponics is a farming system that integrates hydroponics and aquaculture. Many questions and concerns about the feasibility of aquaponics to sustain food productivity and its potential market remain unknown. One of them is the consumer’s perception and willingness to pay for aquaponic products. This study presents findings and analysis regarding consumer perception, knowledge and acceptance of aquaponic products in Malaysia, particularly native fish species cultivated within aquaponic system. A total of 385 respondents participated in this study from all regions (northern region, central region, east coast, southern region, Sabah, and Sarawak) in Malaysia. Overall, a significant proportion of consumers (84%) had not practised aquaponics, and 63% had not made purchases of any aquaponic products before. Consumers’ decisions to purchase aquaponic products is primarily influenced by price (79.2%) and food safety (67.8%). Other significant factors include food nutrition (52.7%), cultivation methods used in production (39.7%), taste and texture of aquaponic products (33%), accessibility to these products (0.5%) and concerns related to zoonotic diseases in aquaponic products (0.3%). Overall, consumers had a positive outlook on the benefits of aquaponics and its products. However, they had a neutral attitude towards purchasing and were willing to spend more on aquaponic products compared to products produced using conventional farming. Consumers also had a positive attitude towards culturing native fish species in aquaponics systems and had a neutral attitude when asked to buy native fish species cultivated in aquaponics systems. The study’s findings show a potential market for aquaponic products in Malaysia. However, more efforts are needed to increase awareness and promote aquaponics systems in Malaysia.
Mitochondrial COX2 primer for bovine identification using qPCR: Design and validation
Authors: Sintia R S , Barido F H , Volkandari S D , Anam C , Pramono A , Utama D T , Erwanto Y , Cahyadi M
Published: October 2024

Beef is a prime livestock product that is prone to adulteration with other relatively lower price species. The occurrence causes losses to consumers and also violates consumer protection law. Quantitative PCR (qPCR) is a molecular approach technology that is suitable for detecting the presence of a broad spectrum of DNA in raw and processed animal-based foods. Mitochondrial DNA (mtDNA) is well known having unique nucleotide sequence which is able to be used as genetic markers in living things. The cytochrome C oxidase subunit II (COX2), a part of mtDNA, can be explored to be biomarker candidate for detection of contamination in various animal products. Therefore, this study was conducted to design and validate specific primer targeting mtDNA COX2 for bovine identification using qPCR. This study designed forward primer (5’-CATGAGCTGTGCCCTCTCTAG-3’) and reverse primer (5’-TAATATAAGCCTGGACGGGA-3’) using the National Center for Biotechnology Information (NCBI) website with accession number GU947020. Moreover, those primers were tested in vitro to optimize reaction condition, and component, and also to evaluate repeatability and sensitivity of the qPCR. Three species DNA, bovine, pig, and chicken were utilized in this study. This study found that the designed primer pair successfully amplified the DNA target in 32 cycles of qPCR, Cq value at 22.48±0.38, Tm at 82.55°C, and 20 ng/μL of bovine DNA. In addition, the limit of detection (LOD) was 2.08 ng/μL and the primer pair specifically attached to bovine DNA. This study concluded that the primer pair targeting mtDNA COX2 can be a strong biomarker candidate for the identification of bovine DNA in various food products.
Enzymatically Pre-treated Cordyceps militaris Mushroom Extracts Influences Spent Hen Chicken Meat Stability during Cold Storage
Authors: Barido F H , Kartikasari L R , Hertanto B S , Nuhriawangsa A M P , Swastike W , Kim J , Lee S K
Published: October 2024

Evaluations were conducted on storage stability of old laying hen chicken breast following treatment using Cordyceps militaris (CM) mushroom extracts and hydrolyzed CM mushroom extract produced with a bromelain eznyme from pineapple stem. A total of Forty-two spent hen breast samples that had their fat removed were subjected to each of the four treatments: (1) no addition as a negative control (NC); (2) Butylated hydroxytoluene (BHT) as positive control; (3) CM extract solution (CME); and (4) hydrolyzed mushroom extract prepared with the bromelain enzyme (STB). The pH value of all treatment groups maintained a significantly lower level across storage days, except treatment with STB resulted in an acceptable (6.09) pH value up to 9 days of cold storage. Subsequently, breast samples treated with STB (41.23%) possessed the highest antioxidant activity, followed by CME (32.17%), BHT (32.97%), and NC (6.11%) after the initial storage day. These results displayed a limited effect from treatment using enzymolyzed CM on meat’s instrumental surface color. This study recommended that immersing breast meat into hydrolyzed CM mushroom prior application using (STB) could be a robust alternative for improvement of spent hen breast meat during storage.
DNA Barcoding using Cytochrome C Oxidase Subunit I (COI) Reveals Wildlife Consumption of Malayan Tapir (Tapirus indicus) and Dusky Leaf Monkey (Trachypithecus obscurus) by the Senoi (Semoq Beri) Tribe in Peninsular Malaysia
Authors: Akmal Fawwazah Abdullah-Fauzi Nurfatiha , Abu Bakar Abdul-Latiff Muhammad
Published: October 2024

Malaysia, a culturally diverse nation, encompasses various ethnicities, including the Orang Asli, the region’s aboriginal people. Among the Orang Asli tribes in Peninsular Malaysia, the Senoi constitute the largest demographic, followed by the Proto-Malay and Negrito groups, each comprising six distinct subtribes. Traditional methods, such as surveys and interviews, have been instrumental in understanding wildlife consumption practices among indigenous communities like the Senoi. However, they may not always provide accurate and comprehensive data. The adoption of molecular techniques, such as DNA barcoding, presents a promising avenue for enhancing the accuracy and reliability of wildlife consumption studies. This study investigates wildlife consumption practices among the Senoi tribe, particularly the Semoq Beri subgroup in Maran, Pahang, using DNA barcoding that focuses on the Cytochrome C Oxidase Subunit I (COI) gene. Following the collection of biological samples with consent from Semoq Beri villagers, DNA extraction and PCR amplification were performed using established protocols. Subsequently, phylogenetic analysis was conducted to identify wildlife species consumed by the tribe. From the DNA barcoding analysis, two wildlife species were successfully detected: including the Malayan Tapir (Tapirus indicus) and the dusky leaf monkey (Trachypithecus obscurus), commonly consumed by the Semoq Beri tribe. Despite their status conservation, the findings suggest that the Semoq Beri tribe’s hunting practices are relatively sustainable, as they hunt only for subsistence purposes and in small quantities. The study emphasizes the significance of incorporating molecular techniques alongside conventional methods to accurately assess wildlife consumption patterns among indigenous communities. The findings highlight the necessity of collaborative approaches involving indigenous peoples, government agencies, and conservation organizations to preserve traditional knowledge, safeguard wildlife resources, and promote the sustainable coexistence of indigenous communities and biodiversity.
Spin angular momentum and optical chirality of Poincaré vector vortex beams
Authors: Forbes Kayn A
Published: October 2024

The optical chirality and spin angular momentum of structured scalar vortex beams has been intensively studied in recent years. The pseudoscalar topological charge

ℓ

of these beams is responsible for their unique properties. Constructed from a superposition of scalar vortex beams with topological charges

ℓ
A

and

ℓ
B

, cylindrical vector vortex beams are higher-order Poincaré modes which possess a spatially inhomogeneous polarization distribution. Here we highlight the highly tailorable and exotic spatial distributions of the optical spin and chirality densities of these higher-order structured beams under both paraxial (weak focusing) and non-paraxial (tight focusing) conditions. Our analytical theory can yield the spin angular momentum and optical chirality of each point on any higher-order or hybrid-order Poincaré sphere. It is shown that the tunable Pancharatnam topological charge

ℓ

P

=
(

ℓ
A

+

ℓ
B

)

/

2

and polarization index

m
=
(

ℓ
B

−

ℓ
A

)

/

2

of the vector vortex beam plays a decisive role in customizing their spin and chirality spatial distributions. We also provide the correct analytical equations to describe a focused, non-paraxial scalar Bessel beam.
A crawling robot with temperature-controlled variable stiffness feet: strong surface adaptability
Authors: Yang Bing-Ying , Dong Bu-Sheng , Chen Hao , Ning Hong-Yang , Zhang Zhong-Qiang
Published: October 2024

Existing soft robots have not shown the same level of adaptability on diverse terrains when compared to natural animals that possess variable stiffness characteristics. In this paper, utilizing the mutual transformation between the glassy and molten states of rosin materials, temperature-controlled variable stiffness feet (TVSF) are achieved by filling the rosin into an array of soft cavity villi. A single motor is utilized as the vibration-driven actuator of the robot. It is found that the feet stiffness can be changed ranging from 0.6227 N/mm to 0.9611 N/mm, which has a significant impact on the robot’s velocity and follows a nonlinear relationship. A smooth motion surface requires a high-stiffness foot to provide support and stability for the robot, while a rough motion surface necessitates a low-stiffness foot to reduce friction and resistance allowing for quick movement for the driving voltage of 1.6 V. Furthermore, the TVSF enable stable operation on complex surfaces, including a 30° slope, pipes, and pothole surfaces. This research provides a new technological approach to designing and manufacturing variable stiffness robots with enhanced surface adaptability.
Underestimation of meteorological drought intensity due to lengthening of the drought season with climate change
Authors: Dullaart Job , van der Wiel Karin
Published: October 2024

Meteorological drought may lead to water shortages, which has negative impacts on water-dependent sectors. Whilst there is a wealth of studies on changing drought intensity or frequency due to climate change, much less is known regarding potential shifts in the timing of drought. The purpose of this study is to analyze the timing of the drought season in the Netherlands and climatic changes therein, with a special focus on the onset of the drought season. Based on an analysis of meteorological observations in the Netherlands over the period 1965–2023, we conclude that the Dutch meteorological drought season has extended forward in time. On average, the drought season starts 16 d earlier in the period 1994–2023 compared to 1965–1993. This is mostly the result of an increase in potential evapotranspiration, while the amount of precipitation does not show a clear change at the start of the growing season. Using three climate model ensembles, we show that a forced climate change signal exists, but that natural variability also plays a role. Following this assessment of trends in meteorological variables, we analyze the consequences for the operational monitoring of meteorological drought. In the Netherlands, this is done by means of the ‘precipitation deficit’-indicator, based on a fixed-in-time starting point (1 April) of the drought season. The combination of this fixed starting point and the observed earlier onset of the drought season, means that in some years the indicator underestimates drought intensity, and that climatic trends are underestimated. We therefore advocate for an update of the operational drought indicator, such that meteorological drought occurring before 1 April will not be missed.
Analysis of divergence-preserving unfitted finite element methods for the mixed Poisson problem
Authors: Lehrenfeld Christoph , van Beeck Tim , Voulis Igor
Published: October 2024

In this paper we present a new

H
(
div
)

H(\operatorname {div})

-conforming unfitted finite element method for the mixed Poisson problem which is robust in the cut configuration and preserves conservation properties of body-fitted finite element methods. The key is to formulate the divergence-constraint on the active mesh, instead of the physical domain, in order to obtain robustness with respect to cut configurations without the need for a stabilization that pollutes the mass balance. This change in the formulation results in a slight inconsistency, but does not affect the accuracy of the flux variable. By applying post-processings for the scalar variable, in virtue of classical local post-processings in body-fitted methods, we retain optimal convergence rates for both variables and even the superconvergence after post-processing of the scalar variable. We present the method and perform a rigorous a priori error analysis of the method and discuss several variants and extensions. Numerical experiments confirm the theoretical results.
Unconditional Cesàro convergence of sequences of super-reflexive valued random variables
Authors: Dehaj Abdessamad , Guessous Mohamed
Published: October 2024

We establish that if

X
X

is a super-reflexive Banach space, every

L

X

1

L_{X}^{1}

-bounded sequence has a subsequence for which any subsequences Cesàro converges unconditionally a.e. in

X
X

to the same limit, generalizing a similar result for a Hilbert space.
Luther, the “Most German of All Germans”
Authors: Hayden-Roy Patrick
Published: October 2024

The study begins by tracing the development of Luther’s image in the course of the nineteenth century, starting with the celebration in 1817 of the three-hundredth anniversary of the Reformation and the new image German intellectuals projected of Luther at that time, considering significant philosophical and literary treatments by Hegel, Heinrich Heine, and Leopold von Ranke that mark the development of his nationalist image in the course of the century. At its center is a speech delivered by Heinrich von Treitschke in 1883, “Luther und die Deutschen,” which projects a newly imagined understanding of a völkisch Luther, whose resonance is then traced by considering the explosion of public monuments and other Luther-related public memorials, especially in the era after German unification. It establishes the völkisch image that will be passed down into the era of the First World War.
Luther the Heathen
Authors: Hayden-Roy Patrick
Published: October 2024

In 1937 Arno Deutelmoser published a work, Luther Staat und Glaube, which purported to demonstrate that Luther, far from being the Reformer of the Church, actually developed the theological underpinnings for an anti-Christian “Germanic” faith. The study itself would be nothing but an oddity if not for the furious response it elicited, and the debate it generated about what was the “real” Luther. Deutelmoser’s study, I argue, despite its provocative thesis, in fact represents something of an apotheosis for the image of Luther as it has developed since World War I. In that sense, it represents the fundamental cultural dissonance that surrounded the figure of Luther at the end of the 1930s and the onset of the Second World War.
Wastewater surveillance of antibiotic resistant bacteria for public health action: Potential and Challenges
Authors: Foxman Betsy , Salzman Elizabeth , Gesierich Chelsie , Gardner Sarah , Ammerman Michelle , Eisenberg Marisa , Wigginton Krista
Published: October 2024

Antibiotic resistance is an urgent public health threat. Actions to reduce this threat include requiring prescriptions for antibiotic use, antibiotic stewardship programs, educational programs targeting patients and healthcare providers, and limiting antibiotic use in agriculture, aquaculture, and animal husbandry. Wastewater surveillance might complement clinical surveillance by tracking time/space variation essential for detecting outbreaks and evaluating efficacy of evidence-based interventions; identifying high-risk populations for targeted monitoring; providing early warning of the emergence and spread of antibiotic resistant bacteria and identifying novel antibiotic resistant threats. Wastewater surveillance was an effective early warning system for SARS-CoV-2 spread and detection of the emergence of new viral strains. In this data-driven commentary we explore whether monitoring wastewater for antibiotic resistant genes and/or bacteria resistant to antibiotics might provide useful information for public health action. Using carbapenem resistance as an example, we highlight technical challenges associated with using wastewater to quantify temporal/spatial trends in antibiotic resistant bacteria (ARBs) and antibiotic resistant genes (ARGs) and compare with clinical information. While ARGs and ARBs are detectable in wastewater enabling early detection of novel ARGs, quantitation of ARBs and ARGs with current methods is too variable to reliably track space/time variation.
Inverting estimating equations for causal inference on quantiles
Authors: Cheng Chao , Li Fan
Published: October 2024

The causal inference literature frequently focuses on estimating the mean of the potential outcome, whereas quantiles of the potential outcome may carry important additional information. We propose an inverse estimating equation framework to generalize a wide class of causal inference solutions from estimating the mean of the potential outcome to its quantiles. We assume that a moment function is available to identify the mean of the threshold-transformed potential outcome, based on which a convenient construction of the estimating equation of the quantiles of the potential outcome is proposed. In addition, we give a general construction of the efficient influence functions of the mean and quantiles of potential outcomes, and establish their connection. We motivate estimators for the quantile estimands with the efficient influence function, and develop their asymptotic properties when either parametric models or data-adaptive machine learners are used to estimate the nuisance functions. A broad implication of our results is that one can rework the existing result for mean causal estimands to facilitate causal inference on quantiles. Our general results are illustrated by several analytical and numerical examples.
Mycosis fungoides: differentiation from inflammation and detection of circulating tumour cells with the EuroClonality next-generation sequencing assay
Authors: Wehkamp Ulrike , Pietzka Sophie , Kotrová Michaela , Jost Marion , Oschlies Ilske , Schwarz Agatha , Baldus Claudia , Darzentas Nikos , Brüggemann Monika
Published: October 2024

Background

Mycosis fungoides (MF) is a rare malignancy that is characterized by the presence of circulating tumour cells (CTCs) in a subgroup of patients. Reliably distinguishing MF from inflammatory skin conditions is challenging.

Objectives

To evaluate the potential benefits of next-generation sequencing (NGS)-based T-cell receptor rearrangement repertoire analysis in detecting clonal rearrangements in MF and inflammatory skin conditions.

Methods

Skin biopsies and blood samples from 33 patients with MF and 10 patients with inflammatory skin conditions were analysed using TRB and TRG NGS. Twenty-seven patients had early-stage IA (n = 19) and IB (n = 8) MF, and six had advanced-stage disease (IIB, n = 5; IIIA, n = 1).

Results

Analysis applying standard abundance thresholds identified at least one clonal rearrangement in the skin DNA of 97% (n = 32/33) of patients with MF and in 90% (n = 9/10) of those with inflammatory skin conditions. To enhance specificity, an abundance and distribution-based approach was applied, which considered only rearrangements that significantly stood out from the physiological background as clonal (MF, n = 29/33; inflammatory skin conditions, n = 1/10), allowing for highly sensitive (88%) and specific (90%) discrimination between MF and other inflammatory skin conditions. CTCs were detected in 46% (n = 11/24) of patients with early-stage MF and in 60% (n = 3/5) of those with late-stage MF.

Conclusions

NGS-based T-cell receptor repertoire analysis is a highly sensitive and specific method for the differential diagnosis of early-stage MF vs. inflammatory skin conditions, and for the sensitive molecular detection of CTCs.
Reinforced tension-line suture after laparotomy: early results of the Rein4CeTo1 randomized clinical trial
Authors: Wenzelberg Charlotta L , Rogmark Peder , Ekberg Olle , Petersson Ulf
Published: October 2024

Background

The aim was to investigate whether adding a reinforced tension-line (RTL) suture to a standard 4 : 1 small-bite closure would reduce the incidence of incisional hernia after colorectal cancer surgery.

Methods

Patients aged at least 18 years, who were scheduled for elective colorectal cancer surgery through a midline incision at two Swedish hospitals (2017–2021), were randomized in a 1 : 1 ratio to either fascial closure with RTL and 4 : 1 small-bite closure with polypropylene sutures (RTL group) or 4 : 1 small-bite closure with polydioxanone suture alone (PDS group). The primary outcome was CT-detected incisional hernia 1 year after surgery. CT interpreters were blinded regarding treatment group.

Results

In all, 160 patients were randomized, 80 in each group. The study closed early to recruitment and follow-up. Some 134 patients were analysed at 1 year: 63 in the RTL group and 71 in the PDS group. Nineteen patients were found to have an incisional hernia: 4 (6%) in the RTL group and 15 (21%) in the PDS group (OR 3.95, 95% c.i. 1.24 to 12.60; P = 0.014). No unintended effects were found in either group.

Conclusion

Adding an RTL suture at fascial closure decreased the incidence of incisional hernia in patients undergoing surgery for colorectal cancer. Trial registration: NCT03390764 (https://clinicaltrials.gov).
Controlled connection: Substack and writers of color
Authors: Hewa Nelanthi , Cohen Nicole S
Published: October 2024

Substack, a self-described “subscription network for independent writers and creators,” launched in 2017. Substack claims to be part of a better future for news workers, yet it has been embroiled in controversy and questions, particularly in recent years. In this article, we draw on interviews we conducted with journalists and writers of color who produce newsletters to better understand the opportunities and limitations of contemporary newsletter platforms. We find that studying the experiences of journalists of color reveals a nuanced and somewhat ambivalent picture of the journalism industry and people of color’s place within it. We find that the Substack models favors the status quo, a reality that further entrenches the success of White journalists. Yet writers of color value the safety enabled by sending writing directly to a small, defined, private audience, a contrast with social media logics and their compulsory visibility.
10 tips on how to use dynamic risk assessment and alerts for AKI
Authors: Quickfall Danica , La Ashley M , Koyner Jay L
Published: October 2024

Acute kidney injury (AKI) is a common syndrome in hospitalized patients and is associated with increased morbidity and mortality. The focus of AKI care requires a shift away from strictly supportive management of established injury to the early identification and timely prevention of worsening renal injury. Identifying patients at risk for developing or progression of severe AKI is crucial for improving patient outcomes, reducing the length of hospitalization and minimizing resource utilization. Implementation of dynamic risk scores and incorporation of novel biomarkers show promise for early detection and minimizing progression of AKI. Like any risk assessment tools, these require further external validation in a variety of clinical settings prior to widespread implementation. Additionally, alerts that may minimize exposure to a variety of nephrotoxic medications or prompt early nephrology consultation are shown to reduce the incidence and progression of AKI severity and enhance renal recovery. While dynamic risk scores and alerts are valuable, implementation requires thoughtfulness and should be used in conjunction with the overall clinical picture in certain situations, particularly when considering the initiation of fluid and diuretic administration or renal replacement therapy. Despite the contemporary challenges encountered with alert fatigue, implementing an alert-based bundle to improve AKI care is associated with improved outcomes, even when implementation is incomplete. Lastly, all alert-based interventions should be validated at an institutional level and assessed for their ability to improve institutionally relevant and clinically meaningful outcomes, reduce resource utilization and provide cost-effective interventions.