bluuebunny/crossref_metadata_2025 huggingface dataset:
P030 Management of rectal chlamydia in an urban sexual health centre
Authors: Goodall Lisa
Published: May 2024

Introduction

We reviewed management of rectal chlamydia in our clinic and adherence to 2015 BASHH audit standards.

Methods

Electronic patient records of 100 consecutive patients diagnosed with rectal chlamydia prior to 31 July 2016 were reviewed with respect to gender, sexuality, HIV status, symptoms, STI screening, treatment, test of cure (TOC) and partner notification (PN).

Results

64% were female (all heterosexual). 94% males were MSM; 18% were HIV positive. 1 male presented with rectal symptoms (pain). 23% patients had other genital symptoms. 76% were asymptomatic. 71% had concomitant STIs (including chlamydia at other sites). 90% received doxycycline 100mg bd for at least 1 week. 24% were treated with azithromycin before being recalled for doxycycline. Reasons included; not initially tested for rectal infection, attendance as a contact, initial treatment for presumed GC. All patients were advised to attend for TOC; 58% attended. All TOC were negative. All HIV positive patients were tested for LGV (1 positive). 1 MSM with rectal pain was not tested for LGV but subsequent TOC was negative. 36% received written information. PN was performed in 99% of cases with 81% of traceable contacts reported as attended and 47% of contacts being verified as attended.

Discussion

High numbers of patients were issued with azithromycin as initial treatment requiring recall for doxycycline. This is concerning, particularly in an era of increasing antibiotic resistance. Education sessions have been provided, highlighting the importance of sexual history taking and use of doxycycline as first line chlamydia treatment where rectal infection is possible.
P032 Characteristics of a high syphilis incidence cohort in an inner-city london clinic
Authors: Kirby Catherine , Allen Catherine , Kirby Rachel , Samuel Itty , Brady Michael
Published: May 2024

Introduction

Syphilis cases continue to increase in London. We aimed to investigate the characteristics and risk factors of patients diagnosed with syphilis at our centre.

Methods

Retrospective case note analysis of all syphilis cases diagnosed in our sexual health clinic in 2016.

Results

56 cases were identified; mean age was 42 (range 16–69 years), with 80% male. The two commonest ethnicities were Black Caribbean (20%) and White Other (20%). 18% were HIV positive, and 18% had concomitant STIs, with one new HIV diagnosis. 26% had been treated for syphilis previously.

Just under a third of patients were symptomatic, the rest being identified through routine screening in clinic or through online testing. Just over a fifth of the cases (12/56) were primary syphilis, with secondary syphilis diagnosed in 7% of patients. All primary and secondary syphilis cases occurred in MSM, and there was a correlation with reported chemsex, with 38% prevalence.

Two of the patients were vulnerable, one being a vulnerable child aged 16. One of the patients was on PREP.

There were 21 cases in heterosexual patients, all were late latent syphilis. Heterosexual men were older (mean 50 years); most heterosexual patients came from regions with high syphilis rates and endemic treponematoses.

Discussion

There is high ongoing transmission of syphilis in MSM in our cohort, linked to risky sexual practices and drug use. Increased awareness of syphilis symptoms might facilitate earlier presentation to clinics. As many patients were asymptomatic, there is a pressing need for regular screening in high risk groups.
P050 The uptake of larc’s in addressing subsequent unplanned pregnancies in young women
Authors: Hamilton Rochelle
Published: May 2024

Introduction

To decrease the number of subsequent unplanned pregnancies in young women presenting to one regional public health service and to note the barriers to the overall uptake of contraceptive options.

Methods

Young mothers referred for antenatal care received consistent and regular education about contraceptive options. The young women attending were up to the age of 21 years. Contraceptive options that were utilised at the time of becoming pregnant consisted of combined oral contraceptive pill (COCP), condoms, withdrawal or none. The use of the Choices Contraceptive Chart aided in the process to support the efficacy of long acting reversible contraceptive methods (LARC).

Results

Total number of births within this public hospital for 2012 – 13 combined was 4,494. Of this, 276 were to mothers 21 years and younger. Total number of births for 2014 – 15 combined were 5,488 with 240 to mothers 21 years and younger. Health care professionals (HCP) education and beliefs, along with provision of LARC prior to discharge needs addressing.

Discussion

Despite international evidence, which strongly suggests that the use of LARC’s reduce unintended pregnancies and subsequent abortions, their use in Australia remains low. There is a need to address the barriers to increasing the use of LARC’s in Australia, particularly by young women who are highly fertile & have unintended pregnancies. Use of COCP is higher in Australia than in other countries. Implants, injectable and IU devices, combined are still used by fewer than 10% of Australian women and their provision in general practice is low.
Hemodynamic management of congenital diaphragmatic hernia: the role of targeted neonatal echocardiography
Authors: Surak Aimann , Mahgoub Linda , Ting Joseph Y
Published: May 2024

Congenital diaphragmatic hernia (CDH) is a major congenital anomaly, resulting from the herniation of abdominal contents into the thoracic cavity, thereby impeding the proper development of the lungs and pulmonary vasculature. CDH severity correlates with a spectrum of pulmonary hypoplasia, pulmonary hypertension (PHT), and cardiac dysfunction, constituting the pathophysiological triad of this complex condition. The accurate diagnosis and effective management of PHT and cardiac dysfunction is pivotal to optimizing patient outcomes. Targeted neonatal echocardiography is instrumental in delivering real-time data crucial for the bespoke, pathophysiology-targeted hemodynamic management of CDH-associated PHT.
Cardiovascular and physiological risk factors in women at mid-life and beyond
Authors: Rodriguez de Morales Yenny A. , Abramson Beth L.
Published: May 2024

Cardiovascular disease (CVD) is the leading cause of death in women. After menopause, sex-specific and gender-specific factors may play an important role in increasing CVD risk, with changes in sex hormones, body fat distribution, lipid and metabolic profile, and structural and functional vascular modifications. Premature and early-onset menopause are detrimental to cardiovascular health due to the early cessation of the protective effect of endogenous estrogen. An independent association of menopause with an increased risk of CVD has been documented in early menopause (<45 years). Sex-related differences are relevant in pharmacokinetics and pharmacodynamics; different enzyme formations, drug compatibility, efficacy, and side effects vary for different sexes. Despite some progress in sex and gender research in CVD, disparities remain. Menopausal hormone therapy (MHT) is available at mid-life for symptoms of menopause and may impact cardiovascular risk. Taken early, MHT may reduce CVD morbimortality. However, this is balanced against the risk of increased thrombosis. This paper reviews physiologic changes that contribute to cardiovascular risk in postmenopausal women and discusses clinical implications. Specifically, it explores the atheroprotective effects of estrogen and MHT and the associations between menopause with lipid levels, hypertension, body composition, and diabetes for women at mid-life and beyond.
Using subtle variations in groundwater geochemistry to identify the proximity of individual geological structures: a case study from the Grimsel Test Site (Switzerland)
Authors: Stillings M. , Shipton Z. K. , Lord R. A. , Lunn R. J.
Published: May 2024

Understanding groundwater flow and the evolution of groundwater chemistry in networks of fractures in crystalline rock is of fundamental interest for geothermal projects, nuclear waste disposal, and groundwater resources. Groundwater chemistry at a given location is typically conceived of being of a specific ‘type’ (e.g. meteoric, juvenile, connate, marine), with associated chemical types controlled through water–rock interactions. Minor chemical variations between groundwater sample locations with the same chemical type are generally considered as ‘noise’ in the geochemical data. Here, we argue that this noise contains useful information on the mineral phases encountered by the groundwater as it travels through specific flow pathways. We analyse the spatial variability of groundwater chemistry around the Grimsel Test Site (GTS), Switzerland, where groundwater is hosted in two lithologies: the Central Aar Granite and the Grimsel Granodiorite, where flow occurs predominantly in a fracture network created by brittle reactivation of ductile shear zones. Groundwater chemistry is analysed using principal component and hierarchical cluster analyses, which identify two groundwater types based on their chemistry. The primary control on groundwater type is the host rock lithology (granite/granodiorite). While the spatial variability of groundwater chemistry within each of the two lithologies is small, statistical analysis of the data shows similar groundwater chemistry in borehole intervals that are crosscut by similar geological structures, implying a structural control on groundwater chemistry. Our research shows that subtle chemical variations in groundwater provide information on fracture network connectivity and the proximity of geological features, that specific volumes of groundwater has interacted with.
Integrating Scholarly Evidence and Personal Experience in Health Information Technology-Enabled Evidence-Based Patient-Care Practices: Challenges, Opportunities, and Outcomes
Authors: Raman Roopa , Sullivan Nicholas
Published: May 2024

Scholarly evidence and personal experience are two prominent sources of knowledge informing patient-care practices in hospitals. In evidence-based patient-care practices, jointly applying both forms of knowledge is challenging due to the divergent preferences of administrative personnel versus frontline providers towards these knowledge sources and the limited understanding of how to combine these knowledge sources effectively despite divergent preferences. Health information technologies (HITs) tend to influence this challenge by simultaneously imprinting a standardized practice based on scholarly evidence while also allowing workarounds based on personal experience. We apply a theoretical framework, integrating cognitive information processing theory and agency theory, to the context of HIT-enabled evidence-based patient-care practices to investigate the circumstances that enable joint consideration of scholarly evidence and personal experience in value-adding versus value-depleting ways in these practices. Our findings reveal the salience of outcome uncertainty and of the prior stored knowledge of administrative personnel versus frontline providers in enabling joint considerations of the two types of knowledge and the value-adding versus value-depleting outcomes that result.
FAST: Filter-Adapted Spatio-Temporal Sampling for Real-Time Rendering
Authors: Donnelly William , Wolfe Alan , Bütepage Judith , Valdés Jon
Published: May 2024

Stochastic sampling techniques are ubiquitous in real-time rendering, where performance constraints force the use of low sample counts, leading to noisy intermediate results. To remove this noise, the post-processing step of temporal and spatial denoising is an integral part of the real-time graphics pipeline. The main insight presented in this paper is that we can optimize the samples used in stochastic sampling such that the post-processing error is minimized. The core of our method is an analytical loss function which measures post-filtering error for a class of integrands --- multidimensional Heaviside functions. These integrands are an approximation of the discontinuous functions commonly found in rendering. Our analysis applies to arbitrary spatial and spatiotemporal filters, scalar and vector sample values, and uniform and non-uniform probability distributions. We show that the spectrum of Monte Carlo noise resulting from our sampling method is adapted to the shape of the filter, resulting in less noisy final images. We demonstrate improvements over state-of-the-art sampling methods in three representative rendering tasks: ambient occlusion, volumetric ray-marching, and color image dithering. Common use noise textures, and noise generation code is available at https://github.com/electronicarts/fastnoise.
Efficient Deformation Learning of Varied Garments with a Structure-Preserving Multilevel Framework
Authors: Li Tianxing , Shi Rui , Li Zihui , Kanai Takashi , Zhu Qing
Published: May 2024

Due to the highly nonlinear behavior of clothing, modelling fine-scale garment deformation on arbitrary meshes under varied conditions within a unified network poses a significant challenge. Existing methods often compromise on either model generalization, deformation quality, or runtime speed, making them less suitable for real-world applications. To address it, we propose to incorporate multi-source graph construction and pooling to achieve a novel graph learning scheme. We first introduce methods for extracting cues from different deformation correlations and transform the garment mesh into a comprehensive graph enriched with deformation-related information. To enhance the learning capability and generalizability of the model, we present structure-preserving pooling and unpooling strategies for the mesh deformation task, thereby improving information propagation across the mesh and enhancing the realism of deformation. Lastly, we conduct an attribution analysis and visualize the contribution of various vertices in the graph to the output, providing insights into the deformation behavior. The experimental results demonstrate superior performance against state-of-the-art methods.
SimLOD: Simultaneous LOD Generation and Rendering for Point Clouds
Authors: Schütz Markus , Herzberger Lukas , Wimmer Michael
Published: May 2024

LOD construction is typically implemented as a preprocessing step that requires users to wait before they are able to view the results in real time. We propose an incremental LOD generation approach for point clouds that allows us to simultaneously load points from disk, update an octree-based level-of-detail representation, and render the intermediate results in real time while additional points are still being loaded from disk. LOD construction and rendering are both implemented in CUDA and share the GPU's processing power, but each incremental update is lightweight enough to leave enough time to maintain real-time frame rates.

Our approach is able to stream points from an SSD and update the octree on the GPU at rates of up to 580 million points per second (~9.3GB/s) on an RTX 4090 and a PCIe 5.0 SSD. Depending on the data set, our approach spends an average of about 1 to 2 ms to incrementally insert 1 million points into the octree, allowing us to insert several million points per frame into the LOD structure and render the intermediate results within the same frame.
Collaborating with my Doppelgänger
Authors: Guo Siqi , Choi Minsoo , Kao Dominic , Mousas Christos
Published: May 2024

The research community has long been interested in human interaction with embodied virtual characters in virtual reality (VR). At the same time, interaction with self-similar virtual characters, or virtual doppelgängers, has become a prominent topic in both VR and psychology due to the intriguing psychological effects these characters can have on people. However, studies on human interaction with self-similar virtual characters are still limited. To address this research gap, we designed and conducted a 2 (appearance: self-similar vs. non-self-similar appearance) × 2 (voice: self-similar vs. non-self-similar voice) within-group study (N = 25) to explore how combinations of appearance and voice factors influence participants' perception of virtual characters. During the study, we asked participants to collaborate with a virtual character in solving a VR jigsaw puzzle. After each experimental condition, we had participants complete a survey about their experiences with the virtual character. Our findings showed that 1) the virtual characters' self-similarity in appearance enhanced the sense of co-presence and perceived intelligence, but it also elicited higher eeriness; 2) the self-similar voices led to higher ratings on the characters' likability and believability; however, they also induced a more eerie sensation; and 3) we observed an interaction effect between appearance and voice factors for ratings on believability, where the virtual characters were considered more believable when their self-similarity in appearance matched that of their voices. This study provided valuable insights and comprehensive guidance for creating novel collaborative experiences with self-similar virtual characters in immersive environments.
Efficient Visibility Approximation for Game AI using Neural Omnidirectional Distance Fields
Authors: Ying Zhi , Edwards Nicholas , Kutuzov Mikhail
Published: May 2024

Visibility information is critical in game AI applications, but the computational cost of raycasting-based methods poses a challenge for real-time systems. To address this challenge, we propose a novel method that represents a partitioned game scene as neural Omnidirectional Distance Fields (ODFs), allowing scalable and efficient visibility approximation between positions without raycasting. For each position of interest, we map its omnidirectional distance data from the spherical surface onto a UV plane. We then use multi-resolution grids and bilinearly interpolated features to encode directions. This allows us to use a compact multi-layer perceptron (MLP) to reconstruct the high-frequency directional distance data at these positions, ensuring fast inference speed. We demonstrate the effectiveness of our method through offline experiments and in-game evaluation. For in-game evaluation, we conduct a side-by-side comparison with raycasting-based visibility tests in three different scenes. Using a compact MLP (128 neurons and 2 layers), our method achieves an average cold start speedup of 9.35 times and warm start speedup of 4.8 times across these scenes. In addition, unlike the raycasting-based method, whose evaluation time is affected by the characteristics of the scenes, our method's evaluation time remains constant.
Bounded VNDF Sampling for the Smith-GGX BRDF
Authors: Tokuyoshi Yusuke , Eto Kenta
Published: May 2024

Sampling according to a visible normal distribution function (VNDF) is often used to sample rays scattered by glossy surfaces, such as the Smith-GGX microfacet model. However, for rough reflections, existing VNDF sampling methods can generate undesirable reflection vectors occluded by the surface. Since these occluded reflection vectors must be rejected, VNDF sampling is inefficient for rough reflections. This paper introduces an unbiased method to reduce the number of rejected samples for Smith-GGX VNDF sampling. Our method limits the sampling range for a state-of-the-art VNDF sampling method that uses a spherical cap-based sampling range. By using our method, we can reduce the variance for highly rough and low-anisotropy surfaces. Since our method only modifies the spherical cap range in the existing sampling routine, it is simple and easy to implement.
Perceptions of Hybrid Lighting for Virtual Reality
Authors: Dinkov Martin , Pattanaik Sumanta , McMahan Ryan P.
Published: May 2024

Virtual reality (VR) applications are computationally demanding due to high frame rate requirements, which precludes large numbers of realtime lights from being used. In this paper, we explore a hybrid lighting approach that combines the benefits of realtime and baked lights based on the importance of each source. First, we demonstrate that the hybrid approach affords better frames per second than realtime and mixed lighting. We then present the results of an online paired-comparison study, in which participants (n = 60) compared videos of the four lighting conditions (baked, mixed, realtime, and hybrid) in terms of preference. Our results indicate that the hybrid lighting approach is better than realtime lighting in terms of graphical performance and also yields better perceptions of quality for scenes with a small to moderately large number of lights.
Filtering After Shading With Stochastic Texture Filtering
Authors: Pharr Matt , Wronski Bartlomiej , Salvi Marco , Fajardo Marcos
Published: May 2024

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that applying the texture filter after evaluating shading generally gives more accurate imagery than filtering textures before BSDF evaluation, as is current practice. These benefits are not merely theoretical, but are apparent in common cases. We demonstrate that practical and efficient filtering after shading is possible through the use of stochastic sampling of texture filters.

Stochastic texture filtering offers additional benefits, including efficient implementation of high-quality texture filters and efficient filtering of textures stored in compressed and sparse data structures, including neural representations. We demonstrate applications in both real-time and offline rendering and show that the additional error from stochastic filtering is minimal. We find that this error is handled well by either spatiotemporal denoising or moderate pixel sampling rates.
ShaderPerFormer: Platform-independent Context-aware Shader Performance Predictor
Authors: Liu Zitan , Huang Yikai , Liu Ligang
Published: May 2024

The ability to model and predict the execution time of GPU computations is crucial for real-time graphics application development and optimization. While there are many existing methodologies for graphics programmers to provide such estimates, those methods are often vendor-dependent, require the platforms to be tested, or fail to capture the contextual influences among shader instructions. To address this challenge, we propose ShaderPerFormer, a platform-independent, context-aware deep-learning approach to model GPU performance and provide end-to-end performance predictions on a per-shader basis. To provide more accurate predictions, our method contains a separate stage to gather platform-independent shader program trace information. We also provide a dataset consisting of a total of 54,667 fragment shader performance samples on 5 different platforms. Compared to the PILR and SH baseline methods, our approach reduces the average MAPE across five platforms by 8.26% and 25.25%, respectively.
A Fast GPU Schedule For À-Trous Wavelet-Based Denoisers
Authors: Dolp Reiner , Hanika Johannes , Dachsbacher Carsten
Published: May 2024

Given limitations of contemporary graphics hardware, real-time ray-traced global illumination is only estimated using a few samples per pixel. This consequently causes stochastic noise in the resulting frame sequences which requires wide filter support during denoising for temporally stable estimates. The edge avoiding à-trous wavelet transform amortizes runtime cost by hierarchical filtering using a constant number of increasingly dilated taps in each iteration. While the number of taps stays constant, the runtime of each iteration increases in these usually memory-throughput bound shaders with increasing dilation, because the increasing non-locality negatively impacts cache hit rates. We present a scheduling approach that optimizes usage of the memory subsystem by permutating global invocation indices in such a way that each wavelet filter iteration is applied through undilated taps. In contrast to prior approaches, our method has identical performance characteristics in each iteration, effectively decreasing maintenance cost and improving performance predictability. Furthermore, we are able to leverage on-chip memory and hardware texture interpolation. Our permutation strategy is trivial to integrate into existing wavelet filters as a permutation before and after each level of the wavelet filter. We achieve speedups between 1.3 and 3.8 for usual wavelet configurations in Monte Carlo denoising and computational photography.
Light Field Display Point Rendering
Authors: Gavane Ajinkya , Watson Benjamin
Published: May 2024

Rendering for light field displays (LFDs) requires rendering of dozens or hundreds of views, which must then be combined into a single image on the display, making real-time LFD rendering extremely difficult. We introduce light field display point rendering (LFDPR), which meets these challenges by improving eye-based point rendering [Gavane and Watson 2023] with texture-based splatting, which avoids oversampling of triangles mapped to only a few texels; and with LFD-biased sampling, which adjusts horizontal and vertical triangle sampling to match the sampling of the LFD itself. To improve image quality, we introduce multiview mipmapping, which reduces texture aliasing even though compute shaders do not support hardware mipmapping. We also introduce angular supersampling and reconstruction to combat LFD view aliasing and crosstalk. The resulting LFDPR is 2-8x times faster than multiview rendering, with similar comparable quality.
Learning Crowd Motion Dynamics with Crowds
Authors: Talukdar Bilas , Zhang Yunhao , Weiss Tomer
Published: May 2024

Reinforcement Learning (RL) has become a popular framework for learning desired behaviors for computational agents in graphics and games. In a multi-agent crowd, one major goal is for agents to avoid collisions while navigating in a dynamic environment. Another goal is to simulate natural-looking crowds, which is difficult to define due to the ambiguity as to what is a natural crowd motion. We introduce a novel methodology for simulating crowds, which learns most-preferred crowd simulation behaviors from crowd-sourced votes via Bayesian optimization. Our method uses deep reinforcement learning for simulating crowds, where crowdsourcing is used to select policy hyper-parameters. Training agents with such parameters results in a crowd simulation that is preferred to users. We demonstrate our method's robustness in multiple scenarios and metrics, where we show it is superior compared to alternate policies and prior work.
Marathi to Indian Sign Language Machine Translation
Authors: Bhagwat Suvarna Rajesh. , Bhavsar R. P. , Pawar B. V.
Published: May 2024

Machine translation has been a prominent field of research, contributing significantly to human life enhancement. Sign language machine translation, a subfield, focuses on translating spoken language content into sign language and vice versa, thereby facilitating communication between the normal hearing and hard-of-hearing communities, promoting inclusivity.

This study presents the development of a ‘sign language machine translation system’ converting simple Marathi sentences into Indian Sign Language (ISL) glosses and animation. Given the low-resource nature of both languages, a phrase-level rule-based approach was employed for the translation. Initial encoding of translation rules relied on basic linguistic knowledge of Marathi and ISL, with subsequent incorporation of rules to address 'simultaneous morphological' features in ISL. These rules were applied during the ‘generation phase’ of translation to dynamically adjust phonological sign parameters, resulting in improved target sentence fluency.

The paper provides a detailed description of the system architecture, translation rules, and comprehensive experimentation. Rigorous evaluation efforts were undertaken, encompassing various linguistic features, and the findings are discussed herein.

The web-based version of the system serves as an interpreter for brief communications and can support the teaching and learning of sign language and its grammar in schools for hard-of-hearing students.
Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection
Authors: Zhang Dengyong , Zhu Wenjie , Liao Xin , Qi Feifan , Yang Gaobo , Ding Xiangling
Published: May 2024

With the rise of the metaverse, the rapid advancement of Deepfakes technology has become closely intertwined. Within the metaverse, individuals exist in digital form and engage in interactions, transactions, and communications through virtual avatars. However, the development of Deepfakes technology has led to the proliferation of forged information disseminated under the guise of users’ virtual identities, posing significant security risks to the metaverse. Hence, there is an urgent need to research and develop more robust methods for detecting deep forgeries to address these challenges. This article explores deepfake video detection by leveraging the spatiotemporal inconsistencies generated by deepfake generation techniques, thereby proposing the interactive spatiotemporal inconsistency learning and interactive fusion (ST-ILIF) detection method, which consists of phase-aware and sequence streams. The spatial inconsistencies exhibited in frames of deepfake videos are primarily attributed to variations in the structural information contained within the phase component of the Fourier domain. To mitigate the issue of overfitting the content information, a phase-aware stream is introduced to learn the spatial inconsistencies from the phase-based reconstructed frames. Additionally, considering that deepfake videos are generated frame by frame and lack temporal consistency between frames, a sequence stream is proposed to extract temporal inconsistency features from the spatiotemporal difference information between consecutive frames. Finally, through feature interaction and fusion of the two streams, the representation ability of intermediate and classification features is further enhanced. The proposed method, which was evaluated on four mainstream datasets, outperformed most existing methods, and extensive experimental results demonstrated its effectiveness in identifying deepfake videos. Our source code is available at
https://github.com/qff98/Deepfake-Video-Detection
.
Enhancing GUI Exploration Coverage of Android Apps with Deep Link-Integrated Monkey
Authors: Hu Han , Wang Han , Dong Ruiqi , Chen Xiao , Chen Chunyang
Published: May 2024

Mobile apps are ubiquitous in our daily lives for supporting different tasks such as reading and chatting. Despite the availability of many GUI testing tools, app testers still struggle with low testing code coverage due to tools frequently getting stuck in loops or overlooking activities with concealed entries. This results in a significant amount of testing time being spent on redundant and repetitive exploration of a few GUI pages. To address this, we utilize Android’s deep links, which assist in triggering Android intents to lead users to specific pages and introduce a deep link-enhanced exploration method. This approach, integrated into the testing tool Monkey, gives rise to Delm (Deep Link-enhanced Monkey). Delm oversees the dynamic exploration process, guiding the tool out of meaningless testing loops to unexplored GUI pages. We provide a rigorous activity context mock-up approach for triggering existing Android intents to discover more activities with hidden entrances. We conduct experiments to evaluate Delm’s effectiveness on activity context mock-up, activity coverage, method coverage, and crash detection. The findings reveal that Delm can mock up more complex activity contexts and significantly outperform state-of-the-art baselines with 27.2% activity coverage, 21.13% method coverage, and 23.81% crash detection.
Stripe-schedule Aware Repair in Erasure-coded Clusters with Heterogeneous Star Networks
Authors: Zhou Hai , Feng Dan
Published: May 2024

More and more storage systems use erasure code to tolerate faults. It takes pieces of data blocks as input and encodes a small number of parity blocks as output, where these blocks form a stripe. When reconsidering the recovery problem in the multi-stripe level and heterogeneous network clusters, quickly generating an efficient multi-stripe recovery solution that reduces recovery time remains a challenging and time-consuming task. Previous works either use a greedy algorithm that may fall into the local optimal and have low recovery performance or a meta-heuristic algorithm with a long running time and low solution generation efficiency.

In this article, we propose a
Stripe-schedule Aware Repair
(SARepair) technique for multi-stripe recovery in heterogeneous erasure-coded clusters based on Reed–Solomon code. By carefully examining the metadata of blocks, SARepair intelligently adjusts the recovery solution for each stripe and obtains another multi-stripe solution with less recovery time in a computationally efficient manner. It then tolerates worse solutions to overcome the local optimal and uses a rollback mechanism to adjust search regions to reduce recovery time further. Moreover, instead of reading blocks sequentially from each node, SARepair also selectively schedules the reading order for each block to reduce the memory overhead. We extend SARepair to address the full-node recovery and adapt to the LRC code. We prototype SARepair and show via both simulations and Amazon EC2 experiments that the recovery performance can be improved by up to 59.97% over a state-of-the-art recovery approach while keeping running time and memory overhead low.
Underwater Backscatter: What is its Practical Communication Distance?
Authors: Akbar Waleed , Allam Ahmed , Adib Fadel
Published: May 2024

Recent years have seen mounting interest in low-power, distributed subsea internet-of-things (IoT) networks because of their potential in environmental, industrial, and defense applications [1,2]. Energy-efficient ocean IoT networks can enable long-term sensing of ocean variables (temperature, pH, pressure, salinity, etc.) to create more accurate climate and weather prediction models and monitor the impact of climate change on the ocean [3]. Similarly, low-cost and efficient ocean IoT networks can help boost the growth of the world's blue economy by enabling active monitoring of marine infrastructures ranging from oil/gas pipelines to underwater tunnels [4]. Real-time distributed underwater sensor networks can help boost aquaculture (seafood farm) production by monitoring the farm vitals (water temperature, dissolved nutrients, pH, etc.) and detecting environmental hazards (such as harmful algae blooms) early [5].
Electrochemical Sensor Modified with Graphene Oxide Decorated with Copper Oxide Nanoparticles for Velpatasvir’s Monitoring in Spiked Human Plasma
Authors: Hesham Nada , Mahmoud Amr M. , Hegazy Maha A. , Wagdy Hebatallah A.
Published: May 2024

Velpatasvir, an antiviral agent co-formulated with sofosbuvir used to treat hepatitis C, has recently demonstrated beneficial therapeutic effects against COVID-19. Therefore, therapeutic drug monitoring of velpatasvir is essential to achieve the desired clinical outcomes. An electrochemical sensor modified with synthesized copper oxide nanoparticles on the surface of graphene oxide (CuO/GO-NPs) was fabricated for the analysis of velpatasvir for the first time. Characterization was carried out using Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy with energy-dispersive X-ray spectroscopy. The voltammetric determinations were conducted using differential pulse and cyclic voltammetry, where the modified electrode exhibited better sensitivity than the unmodified one. The method was validated according to the International Council for Harmonisation (ICH) guidelines, exhibiting linearity within a range of 1.0 × 10−7 – 1.0 × 10−5 M, covering velpatasvir’s maximum plasma concentration (Cmax), with a quantification limit of 2.89 × 10−7 M and a detection limit of 9.03 × 10−8 M. The developed sensor was successfully applied to spiked human plasma at velpatasvir’s Cmax level. The method’s greenness was assessed using the Analytical Eco-scale and the Green analytical procedure index tools. This method holds promise as a green simple approach to implemented in future velpatasvir’s therapeutic drug monitoring studies.
Reduced Order Electrochemical-Thermal Modeling of Cylindrical Cells Containing Graphite-Silicon Anodes Considering Thermal Gradients and Hysteresis
Authors: Du Xiaoniu , Park Junbeom , Choe Song-Yul , Garrick Taylor R. , Dix Sean T. , Zhang Han
Published: May 2024

Electrochemical thermal modeling of cylindrical cells presents unique challenges compared to other cell formats due to the effect of internal temperature gradients, which typically requires time-consuming simulations due to the number of mesh elements solved numerically. Adding to the difficulty, the emergence of silicon anodes induces voltage hysteresis that affects the cell behavior. In this paper, a reduced-order electrochemical-thermal model is developed for a 21700 cell, which is highlighted by three microcells considering the effects of internal temperature gradients, and an anodic stress model capturing the hysteresis effects caused by the silicon content. The electrochemical, thermal, and mechanical behaviors are investigated. During operations, a temperature gradient arises in the radial direction, resulting in a decrease in local resistance and an increase in reaction rate at the high-temperature core location. The presence of silicon causes a voltage hysteresis that is dominant in the low SOC range, which affects not only the irreversible but also the entropic heat generation. The proposed method achieves an 85% calculation time reduction compared with the existing literature method and a 95% reduction compared with the full order method, while maintaining the accuracy of the terminal voltage and heat generation rate predictions that are validated by experiments.
Application of Graphdiyne Confined Gold Nanoparticles Modified Electrode as A Robust Sensing Platform for The Detection of 7-Hydroxy-6-Methoxy-Hydrocoumarin
Authors: Yan Lijun , Liu Tao , Huang Yuhao , Feng Huajie , Bin Zhang  , Tao Yaping , Bai Li , Huang Lifang , Zhang Xiaoping , Sun Wei
Published: May 2024

A graphdiyne (gdy) and gold nanoparticles (aunps) nanocomposite was formed with aunps growing on the surface and between the layers of gdy, which was used as the sensing interface on carbon ionic liquid electrode (cile) to detect 7-hydroxy-6-methoxy-hydrocoumarin (7–6-hdc) that isolated from the noni juice. The GDY-Au nanocomposite was characterized by different spectroscopic techniques, which exhibited corrugated and folded morphology with large specific surface area. The GDY-Au nanocomposite modified electrode displayed excellent performance which is attributed to the highly conductive nanocomposite, where gdy structure with defects could immobilize more aunps to decrease the electron transfer resistance. In addition, the reaction mechanism of 7–6-hdc at GDY-Au surface was deduced based on the electrochemical kinetics investigations. Combined with density functional theory calculation, it was verified that the active site of 7–6-hdc and the presence of aunps enhanced the electrocatalytic capacity of GDY-Au nanocomposite. Under optimal conditions, the modified electrode displayed a wider linear range from 10.0 nm to 1.0 mm for 7–6-hdc analysis with a detection limit of 3.22 nm (3s0/s). This work provides a promising material to realize accurate analysis for 7–6-hdc, and the discovered sensing mechanism provides method guidance for the exploration of other novel electrochemical sensors.
Exploring Li-Ion Transport Properties of Li3TiCl6: A Machine Learning Molecular Dynamics Study
Authors: Selvaraj Selva Chandrasekaran , Koverga Volodymyr , Ngo Anh T.
Published: May 2024

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li3TiCl6 cathode at six different temperatures, ranging from 25°C to 100°C. In this work, deep neural network method and data generated by ab − initio molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven’s ratio to describe the Li-ion transport mechanism in Li3TiCl6. Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li3TiCl6 cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.
Adipocyte-specific disruption of the BBSome causes metabolic and autonomic dysfunction
Authors: Zhao Yuying , Guo Deng-Fu , Morgan Donald A. , Cho Young-Eun , Rahmouni Kamal
Published: May 2024

The current study show how genetic manipulation of fat cells impacts various functions of the body including sensitivity to the hormone insulin.
Evaluation of Energy Density in Hexadecane Phase Change Emulsions in Comparison to Water
Authors: Giordano Fabrizia , Gschwander Stefan
Published: May 2024

The development of energy‐dense, thermomechanically stable, and low‐viscous phase change emulsions (PCMEs) is proposed as an alternative thermal energy storage solution for building air conditioning. A set of oil‐in‐water (O/W) nanoemulsions with hexadecane concentration varied between 10, 20, 25, 30, 35, and 40 wt. % is prepared and characterized with respect to their physical, thermal, and rheological properties. The storage characteristics are evaluated in terms of storage density, phase transition behaviour, supercooling, and dynamic viscosity. A systematic comparison in terms of energy density between the PCMEs and water is carried out at different temperature conditions. For this purpose, the storage break‐even temperature TBE  is proposed as a novel parameter to determine suitable operating temperature ranges and cycling conditions. The cycle stability is evaluated by rheological measurements, applying thermomechanical loads to the samples for a high number of cycles. According to the results, the energy density of the PCMEs is always higher than that of water, when the minimum temperature used for the cycling is below the storage break‐even temperature. The emulsion with 30 wt. % hexadecane fraction is considered particularly promising, thanks to its high stability when exposed to thermomechanical stress, relatively low viscosity between 10 and 22 mPa s (0–30°C), and a storage density of 98 MJ/m3 within a cycling temperature range of 12 K.
Examination of Precast Concrete Movement Subjected to Vibration Employing Mass‐Spring Model with Two Convective Masses
Authors: Aktas Gultekin
Published: May 2024

This article investigates the movement of concrete subjected to vibration employing mass‐spring model. For this purpose, two different prefabricated concrete‐formworks, on which experimental work was done before, were analysed analytically. The theoretical modeling of precast formworks employed in experiments has been made by the three‐dimensional finite element method employing the SAP2000 program. Modeling of mortar is performed to resolve the interaction between the fresh concrete and formwork using the mass‐spring model. Considering the dynamic behavior of the fluid, it is possible to define multiple oscillation (convective) masses with different frequency values in addition to the impulse mass. Thus, one impulse mass and two convective masses were used in the mass‐spring model. This study is essentially theoretical, and its accuracy has been strengthened by experimental work. The time‐dependent results of concrete movement obtained from the dynamic mass‐spring model were compared with the measured ones. The matches indicate that the findings are consistent.
Removal of Complete Spontaneous Migration of an Intrauterine Contraceptive Device to the Bladder by Cystoscopy
Authors: Saputra Akbar Novan Dwi , Rahman Muhammad Nurhadi , Widyasari Anis
Published: May 2024

Intrauterine contraceptive devices (IUCD) are widely used as a reversible method of contraception. Although uterine perforation caused by an IUCD is rare, in some cases, the device may migrate to the pelvic/abdominal cavity or nearby structures. When the IUCD migrate to the bladder, it can lead to various symptoms. These include pain or discomfort in the lower abdomen, difficulties or pain during urination, frequent urinary tract infections, and the development of bladder stones. This article presents a case report of a 24-year-old woman with an intrauterine contraceptive device (IUCD) that had migrated completely into the bladder. She had an IUCD inserted by a midwife four years earlier and became pregnant a year later, unaware of the IUCD’s presence. She later presented with lower abdominal pain, hematuria, and dysuria three months before being admitted to our hospital. Imaging confirmed the intravesical location of the IUCD. She underwent successful cystoscopy treatment to remove the migrated IUCD. Prompt diagnosis and appropriate management are crucial in preventing complications and improving patient outcomes. Clinicians should be aware of this potential complication and consider it when patients present with symptoms or complications after IUCD insertion. Regular monitoring and timely intervention can help detect and address IUCD migration effectively.
FIGURE 6 from Exercise-induced β2-adrenergic Receptor Activation Enhances the Antileukemic Activity of Expanded γδ T-Cells via DNAM-1 Upregulation and PVR/Nectin-2 Recognition
Authors: Baker Forrest L. , Smith Kyle A. , Mylabathula Preetesh L. , Zúñiga Tiffany M. , Diak Douglass M. , Batatinha Helena , Niemiro Grace M. , Seckeler Michael D. , Pedlar Charles R. , O'Connor Daniel P. , Colombo Jamie , Katsanis Emmanuel , Simpson Richard J.
Published: May 2024

Interactions between DNAM-1 and PVR/Nectin-2 enhances the antileukemic activity of exercise expanded Vγ9Vδ2+ T-cells. A, Illustration of the receptors expressed on Vγ9Vδ2+ T-cells and their corresponding ligands expressed on cancer cells. B, Heat map of the surface expression and MFI of ligands and receptors on K562 cells. C and D, The specific lysis of K562 cells by expanded Vγ9Vδ2+ T-cells under two conditions: unstained control (−) and respective blocking antibody (+), DNAM-1, PVR, Nectin-2, TRAIL, TRAIL-R1, and TRAIL-R2 (n = 3). Significant differences between the between the unstained (−) and blocking conditions (+) were indicated by * and #, respectively. Data are represented as mean ± SEM; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001 by repeated measures two-way ANOVA with Bonferroni post hoc test (C–D).