bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimal finite-differences discretization for the diffusion equation from the perspective of large-deviation theory
Authors: Smith Naftali R
Published: April 2024

When applying the finite-differences method to numerically solve the one-dimensional diffusion equation, one must choose discretization steps Δx, Δtin space and time, respectively. By applying large-deviation theory on the discretized dynamics, we analyze the numerical errors due to the discretization, and find that the (relative) errors are especially large in regions of space where the concentration of particles is very small. We find that the choiceΔt=Δx2/(6D), whereDis the diffusion coefficient, gives optimal accuracy compared to any other choice (including, in particular, the limitΔt→0), thus reproducing the known result that may be obtained using truncation error analysis. In addition, we give quantitative estimates for the dynamical lengthscale that describes the size of the spatial region in which the numerical solution is accurate, and study its dependence on the discretization parameters. We then turn to study the advection–diffusion equation, and obtain explicit expressions for the optimal Δtand other parameters of the finite-differences scheme, in terms of Δx,Dand the advection velocity. We apply these results to study large deviations of the area swept by a diffusing particle in one dimension, trapped by an external potential∼|x|. We extend our analysis to higher dimensions by combining our results from the one dimensional case with the locally one-dimension method.
Military Unmanned Equipment Image Target Recognition Method based on Improved Deep Learning
Authors: Gao Liming , Li Chao , Wei Zhuo , Han Xiangdong , Dang Feng , Wei Xuemei
Published: April 2024

Military unmanned equipment target recognition is currently a research hotspot and trend in the field of military intelligence. The small sample size and complex recognition scenarios of military unmanned equipment image datasets result in low recognition accuracy. It is proposed a military unmanned equipment image target recognition method based on improved deep learning, which set Faster R-CNN as the network framework for target recognition, used the Kmeans++ algorithm to label boxes on customized datasets, and then added OHEM to the framework to improve the network’s recognition accuracy for difficult to recognize samples. The accuracy of the algorithm proposed in this article reaches 93.8%, which is 2.8% higher than the YOLOv5 algorithm, providing an improved deep learning method for military unmanned equipment image target recognition.
Effect of Door Openings on Train Fire Scenarios within a Subway Depot
Authors: Yang Xin , Li Yanfeng , Liu Huiqiang , Su Zhihe
Published: April 2024

It is challenging to control smoke in the case of a train fire occurred in the subway depot with a complex internal structure. In this paper, the effect of door opening state on smoke behavior characteristics induced by subway depot fire scenario was investigated. A series of numerical simulation were conducted by Fire Dynamics Simulator (FDS) software. Three key parameters were analyzed corresponding to the temperature, visibility and CO concentration for evaluating smoke propagation respectively. Results show that temperature distribution and CO concentration at 2 m height inside the train with train door opening state are lower than those with the train door closed. However, visibility at 2 m height inside the train shows the opposite trend. The results can provide reference in the ventilation system design and emergency evacuation scheme for the subway depot.
Evaluating the Feasibility of Low-Carbon Technology in the Sustainable Economic Development of Electricity Using Generative Adversarial Networks
Authors: Wang Guangming
Published: April 2024

Electricity occupies a central position in energy strategy, and the low-carbon development of the power industry is an important guarantee for achieving sustainable development and carbon dioxide reduction goals. Orderly carrying out low-carbon technology transformation and innovation in electricity, ultimately achieving the goal of sustainable development of the power industry, has strong practical significance. Therefore, this article conducts a feasibility analysis of using generative adversarial networks to evaluate low-carbon technologies in the sustainable economic development of electricity. Firstly, the background of the theory of technological and economic evaluation was analysed and used as a theoretical basis. Secondly, an adversarial network was generated for the study of low-carbon emissions in the power industry, and an evaluation system for low-carbon technology in the sustainable economic development of electricity was constructed. The study showed that the preliminary economic evaluation index system and comprehensive benefit evaluation model for low-carbon technology transformation projects in electricity established in this article have feasibility and reliability for systematic analysis and evaluation of actual projects.
Efficiency Evaluation and Performance Governance of Infrastructure Construction Based on DEA and Improved Neural Networks
Authors: Wang Caichuan
Published: April 2024

Infrastructure construction is the carrier of regional economic development, and the rational use of urban construction land is an important basis that largely depends on the efficiency of urban infrastructure construction. However, due to the lack of effective integration between urban evaluation and management in infrastructure, the infrastructure performance evaluation system is not yet mature enough. Therefore, this article conducts efficiency evaluation and performance governance of infrastructure construction based on DEA and improved neural networks. Firstly, based on a comprehensive analysis of performance evaluation, the advantages and disadvantages of infrastructure construction were explored. Secondly, in the study of using DEA and improved neural networks to evaluate the efficiency of infrastructure construction, a performance evaluation index system for urban infrastructure construction was constructed from four aspects: input, output, effect, and activity. A systematic performance evaluation of urban infrastructure was conducted, and optimization suggestions were proposed accordingly.
Research on Joint Sensing Technology for Vibration and Dielectric Spectrum of Power Equipment in the Context of New Power Systems
Authors: He Yongsheng , Zhang Shiling , Lu Zongxiang , Liangjun Dai
Published: April 2024

This paper has carried out the test results of SF6 gas decomposition products and the dielectric spectrum detection, proposed method for the identifying equipment defects based on decomposition products, and the partial discharge detection and diagnosis method for the equipment discharge defects. Combining the above two methods, the GIS discharge defects are comprehensively evaluated by decomposition products and partial discharge detection, achieving the type prediction of latent faults in operating switch-gear. In the aspect of vibration signal analysis, this paper conducts the wavelet analysis of the vibration signals and obtains frequency spectrum results under multi-layer wavelet decomposition. In the aspect of dielectric spectrum analysis, a model of three HN function superposition was used to fit the experimental data to verify the validity of the model, and the characteristic parameters such as the DC conductivity were extracted. It was found their dependence on temperature basically conformed to the Arrhenius formula. On this basis, the thermal aging test platform for the epoxy resin samples was established, and an accelerated thermal aging experiment was conducted at the temperature of 115 °C. The dielectric response test system was used to measure dielectric spectra of the samples with different aging times, compare the dielectric spectra at different aging times, and analyze the reasons for the differences.
Conceptual Design of a 3D Visualization Platform for Ships Based on Digital Twin Technology
Authors: Li Li , Li Guangnian , Du Lin , Wu Qin , Yang Yizheng
Published: April 2024

Addressing the challenges in the current ship’s lifecycle design process, such as high difficulty in information sharing and complex installation of design environments, this study proposes a concept design of a ship 3D visualization platform based on digital twin technology. It constructs a web-based ship 3D visualization platform with an interactive interface of realism. The aim is to avoid cumbersome client-side programs and provide a method to showcase and interact with ship 3D models on web pages. This method not only reduces the threshold for ordinary users but also significantly enhances the work efficiency of professional designers. Following the model construction theory of digital twin platforms, it utilizes WebGL technology and the Three.js library to build ship 3D scenes, achieving ship 3D visualization. Functional interaction among modules is implemented using JavaScript, establishing the digital twin of the ship. Test results indicate that the proposed concept design of the ship 3D visualization platform can preliminarily fulfill the basic requirements of achieving ship 3D display, rendering, and interaction on web platforms. The proposed method can support the development of ship online collaborative design systems based on front-end internet technologies.
Optimization of Circularly Polarized Omnidirectional Base Station Antenna by Machine Learning
Authors: Tian Yubo , Sun Jinlong , Lai Zhanjun , Mei Zhilin , Meng Fei
Published: April 2024

Machine learning (ML) is a scientific technology related to data learning that can help machines learn patterns from existing complex data to predict future behavioral outcomes and trends. In this paper, we use 16 different ML algorithms and 5 different hyper-parameters optimal methods to model a circularly polarized omnidirectional base station antenna. The mean absolute error is 8.431, the root mean squared error is 11.100, the coefficient of determination is 0.967, and the adjusted coefficient of determination is 0.944, which means evaluation metrics are excellent.
The diversity of stingless bee (Apidae: Meliponini) in Batusangkar, West Sumatra: the current meliponiculture status
Authors: Puteri G , Herwina H , Mairawita  , Janra M N
Published: April 2024

The study on the diversity of stingless bees (Apidae: Meliponini) in Batusangkar was conducted from September until December 2020. This study aimed to inventory stingless bee species at meliponicultures in three subdistricts of Batusangkar City in Tanah Datar Regency, West Sumatra. The study surveyed the meliponiculture sites by purposive sampling and directly collecting, by hand, the workers of the stingless bee at their colony entrances. A total of 5 species of stingless bees from 44 colonies were collected. The species with the most abundant population observed was Heterotrigona itama (23 colonies). Other four species shortly followed i.e., Geniotrigona thoracica (10 colonies), Tetragonula laeviceps (seven colonies), Tetragonula fuscobalteata (three colonies), and Homotrigona fimbriata (one colony). The diversity of stingless bees in this study was thought to be influenced by species’ productivity and behavior that work in their favor to be included by the farmers in their meliponiculture. This study presents the most suitable species of stingless bees to breed in the meliponiculture environment in Batusangkar.
Hydrology Balance in Sustainable Land Development
Authors: Rasyidin A
Published: April 2024

Several cities in West Sumatra are in an alluvial system formation even though they are at an altitude above 350 m msl, for example, Kota Solok 350 m, Bukittinggi 900 m, and Padang 25 m msl. The growth of this city was initially safe from floods, but lately, it has often experienced flood disasters or disturbed the hydrological balance. This is presumably due to changes in land use. The city of Solok has lost 385 ha of rice fields so far, the city of Padang has lost 35 ha of rice fields/year, and Bukittinggi has lost nearly 200 ha of its total rice field area. When there is a land use change to a settlement, the amount of water required during rice cultivation becomes water that has to be disposed of, and this should affect the expansion of the drainage channel. If the water requirement for one growing season is at a value of 1,300 mm, with a length of time from soil processing to harvesting is 4 months, meaning that every month the rice fields need water as much as 1,251,250 m3, which is equivalent to 0.48m3/sec.
Tracing sulphur dioxide in volcanic deposits and ash emission during the 2019 Sinabung eruptions
Authors: Fiantis D , Zulhakim H , Yulanda N , Ginting F I , Gusnidar  , Yasin S
Published: April 2024

Sulphur dioxide (SO2) emissions from Mt. Sinabung eruption were quantified in time series for 2019. Both pyroclastic materials and gas or aerosol ejected during volcanic eruption contain sulphur as sulphate salt deposits coating volcanic ash grains or gasses. Sulphur dioxide from the eruption will directly impact the surrounding area. Spectral from satellite optical sensors can be used to monitor and measure SO2 gas near real-time after an eruption. The distribution of SO2 column density in the atmosphere was tracked using the Sentinel-5P satellite. Regression kriging (RK) is applied to predict the spatial distribution of sulphur. The area under study is located in a radius of 3 to 7 km from the eruptive center, covering an area of about 4,517 ha. A total of 51 soil samples and volcanic ash were collected from 0- 20 cm soil depth based on a 1x1 km grid interval. All samples were air- dried, sieved, and analyzed for pH, sulphate, and total SO3 using XRF. The Google Earth Engine (GEE) platform was also used to process Sentinel-5P satellite imagery to determine the number and distribution of SO2 column density in the atmosphere during 2019. The pH of the ash is very acidic to neutral (3.56 - 6.55), while soils are considered acidic to neutral (4.67 - 6.52). The available sulphate content in soil ranges from 0 to 303.39 ppm and 0 to 142.47 ppm in volcanic ash samples. SO2 content in ash ranges from 0 to 16.53% and 0 to 3.71% in soils. Sentinel-5P satellite image spectral data shows that SO2 is concentrated mainly in the southern region, with the highest level occurring in August 2019. This study can serve as one of the volcanic mitigation programs and forecast the distribution of SO2 in an active volcanic region of Indonesia.
Application of Clay and Rice Husk Biochar and its effect on Soil Pore Distribution of Psamment and Corn Yield
Authors: Adrinal  , Gusmini  , Putri E L , Delind N M F
Published: April 2024

The effect of clay and rice husk biochar application under minimum soil tillage on soil pore distribution of psamment and corn yield was conducted in Batang Anai District, Padang Pariaman Regency. Plot experiment consisting of four treatment (without clay or biochar; 20 ton clay/ha; 20 ton biochar/ha; and 20 ton biochar/ha + 20 ton clay/ha) with 3 replications. Soil samples were analyzed at the soil laboratory, Faculty of Agriculture, Andalas University Padang and Soil Laboratory, Bogor Soil Research Institute. Soil physical properties were categorized by soil criteria, while crop growth and yield data were statistically analyzed using F test and DNMRT at 5% level. Results showed that application of 20 ton/ha of clay and rice husk biochar improved soil physical properties of psamment namely: decreasing soil bulk density, increasing total soil porosity, aeration pores, and available water pores. The application of biochar and clay has also increased the growth and yield of corn.
Effect of sustainable leadership development and employee empowerment in MSME industry in India
Authors: Luthra R K , Senthilkumar
Published: April 2024

In the 21st Century, The Micro Small, and Medium Enterprises (MSME) in India have established their credibility of mass employment and contributing to around 30% of India’s GDP in 2020. As of 2020, India is a house of 2.5 million units and proved the backbone of large sectors such as Manufacturing, Agriculture, Aviation, IT & ITeS, Pharma, Cement, and Automobile to name a few. India Government initiatives helped MSME to grow at the rate of 18.5% CAGR in 2019-2020. Indian Government is committed to bringing sustainable growth to the MSME sector. This brings the point to look into scalability issues often faced by these industries due to typical Owner and employee organization structure with lack of knowledge in recent HR practices. All the management and operational decisions are factored into top leadership (the business owner and his son’s). With the young entrepreneurs stepping into the family business, the face of this industry is bound to change further. Indian Government has doubled the budget for the MSME sector in FY22 that is $ 1.03 billion in FY 21 to $ 2.14 billion in FY22. With the Industrial revolution 5.0, India has witnessed multiple Unicorns in the last decade; therefore, it is bound to see maturity in leadership sustainability and focus on employees’ empowerment. The study goal is to look into labour law barriers faced by the MSME industry which is not covered in the FY22 vision document for the early adoption of a modern HRM approach to meet 5 trillion dollars business demands by 2025.
IoT Enabled Smart Homes in Tropical Regions as a Means of Sustainable Development
Authors: Geddam Samuel Mores , Gowda Anil B , Hussain Ameer
Published: April 2024

Internet of Things (IoT) in the context of Smart Homes has been an emerging area of study. IoT technology-enabled provides Integrated and centralized monitoring and control system where the inhabitants can remotely adjust access in the house, temperature and lighting etc., but also functions of the smart TV, refrigerator, oven etc. A step forward to this, Smart 2.0 can be achieved by integrating greeneries maintenance atomization, energy and water conservation, etc., with the IoT technologies to maintain a healthy indoor environment. The study focus on how IoT enabled Smart Homes can contribute, enabling a new era of development and growth aimed at achieving Sustainability. A critical literature review and factors are identified that will align the development catering to the 2030 Agenda for Sustainable Development, 2015 and the UN-Habitat New Urban Agenda, 2016 which are today’s International Agendas aimed at achieving Sustainable Development. Well-planned and well-managed IoT enabled Smart Homes in the context of urbanization, can be a powerful tool for sustainable development for both developing and developed nations. Achievement of SDGs especially, Goal 3, 6, 7 and 11 is impossible without the role of technology. With the proper delivery and implementation of IoT enabled Smart Homes, safeguarding the sustainability of Cities and Communities is possible. IoT in Smart Home keep the power, water, temperature, ventilation systems, solar power, greeneries and waste in check which in turn leads to sustainability. An IoT enabled Smart Home model for Sustainability has been proposed.
Modeling and characterization of deeply etched multilayer resonators under partial coherent excitation via multimode optical fibers
Authors: A Selim Mahmoud , Sabry Yasser M , Marty Frédéric , Bourouina Tarik , Khalil Diaa
Published: April 2024

Recently, there has been a resurgence of interest in multimode optical fibers illuminated by a white light source. Largely, in anticipation of many integrated applications in the biomedical domain and spectral sensing benefiting from the broad spectral range and high numerical aperture (NA). Along these lines, the output light from these fibers can be captured by the physics of partially coherent sources. While the Gaussian Schell model has provided a framework for studying partial coherence, to our knowledge, its impact on microstructures remains unexplored. As the sheer complexity arising from the interplay between partial coherence and microstructures transfer function has posed fundamental challenges in deciphering their response. In this work, we introduce a comprehensive numerical model paired with experimental validation to assess the performance of multilayer optical resonators, which are meticulously crafted through high aspect ratio silicon etching under the influence of a partially coherent optical source. The model studies the effects of optical fiber NA, Bragg mirror order, cavity length, and surface roughness of the microstructures on the output of the resonator. The results show that the response under standard multimode fiber (MMF, partial coherent source) has lower insertion loss, more asymmetry versus wavelength, and larger full width at half maximum than the standard single mode fiber (full coherent source). A silicon-on-insulator chip is fabricated using 130 µm deep etching of silicon for Bragg mirrors with 2.25, 3, and 3.25 µm silicon layer widths and a different number of layers. The structures are characterized using a MMF of 62.5 µm core diameter illuminated by an infrared white light source. The theoretical results have been compared with the experimental results and a good agreement has been obtained.
Research on tribological performance of textured thrust bearing using gray relational degree and improved multi-objective water circulation algorithm
Authors: Yu Xiaodong , Shi Guangqiang , Yang Xinyi , Gao Weicheng
Published: April 2024

To enhance the loading capacity of the oil film, reduce the lubricating oil temperature, and minimize the deformation of the friction pairs, three types of concave textures were proposed. The tribological properties of the thrust bearing underwent multi-objective optimization analysis using the two different methods. By comparing the two methods, it was found that the improved algorithm (MOWCA) demonstrated better optimization performance. Under this algorithm, when the texture shape was triangular, the number of textures was 18n, the size was approximately 0.135 mm, and the spacing between textures was approximately 4.6 mm, the loading capacity of the oil film increased by approximately 26.8%, and the temperature decreased by approximately 0.46%.
Event identification in acoustic emission from wire breaks in pre-stressing/post-tensioning cables
Authors: Mahmoudkhani Sadegh , Zhao Junhui , Cochingco Jasmin , Mufti Aftab , Thomson Douglas
Published: April 2024

Steel tendons commonly used in pre-stressed/post-tensioned concrete structural systems can lose cross-section due to corrosion, eventually leading to acoustic emission (AE) events when the stress exceeds the breaking strength of the wires that make up the tendons. Reliable differentiation of wire break AE events from traffic or grout crack events is critical for monitoring large structures, even where the distance between sensors may produce highly attenuated signals. In this paper, the Fuzzy c-means clustering algorithm was employed to differentiate AEs released from breaking wires of steel tendons from a database of 13464 AEs, including wire breaks, environmental and grout crack AEs. Wire breaks and grout crack AEs were collected from axial loading tests of grouted tendons in which the load increased until a wire broke. Environmental acoustic signals were collected from a bridge. Then all the collected AEs were gathered in a database and post-processed to simulate attenuation of up to 20 m from source to sensor. To optimize the speed and reliability of the Fuzzy c-means clustering algorithm, a non-dominated sorting genetic algorithm-II (NSGA-II) was used to find the minimum number of acoustic features needed. The NSGA-II algorithm started with 201 possible acoustic features and found 12 combinations of features that resulted in more than 80% wire break detection accuracy. In contrast, less than 3% of grout cracks and 0% of environmental signals were detected as wire breaks. The proposed method is suitable for deployment in a large sensor network and has sufficiently low-computational requirements for at-the-sensor processing, eliminating the need to send high-frequency sampled data outside the sensor node.
Scaling neural simulations in STACS
Authors: Wang Felix , Kulkarni Shruti , Theilman Bradley , Rothganger Fredrick , Schuman Catherine , Lim Seung-Hwan , Aimone James B
Published: April 2024

As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited.

In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends.

We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.
Assessment of the impact of Uber system implementation on mortality from traffic injuries in Brazilian capital cities
Authors: de Aquino Érika Carvalho , Silva Renato Rodrigues , de Morais Neto Otaliba Libânio
Published: April 2024

The rapid expansion of Uber Technologies, Inc.’s ride-sharing, courier service, and food delivery system and e-hailing applications has been transforming the logistics network and urban mobility around the world. We aimed to evaluate the impact of the Uber system on traffic injury (TI) mortality during its implementation in Brazilian capital cities. A quasiexperimental design of interrupted time series was used. The monthly mortality rates for TI standardized by age were analyzed. The date of availability of the Uber app, specific to each capital, was considered the start date. Data from the Brazilian Mortality Information System and the Brazilian Institute of Geography and Statistics were used. For the data analysis, from an interrupted time-series design, autoregressive integrated moving average (ARIMA) models with a transfer function were fitted. In 92.6% (n = 25) of Brazilian capitals, there was no impact of Uber system implementation, 12 months after the start of its activities, on TI mortality. A reduction in mortality from this cause was observed after the system was implemented in Belo Horizonte and Rio de Janeiro. The impact on TI mortality was progressive and continuous in both. More studies are needed to establish the factors associated with the inequalities observed in the impact of Uber system implementation between different locations and the heterogeneity of effects.
Revisiting the Construct Validity of Self-Regulating Capacity in Vocabulary Learning Scale: The Confirmatory Composite Analysis (CCA) Approach
Authors: Alamer Abdullah , Teng Mark Feng , Mizumoto Atsushi
Published: April 2024

Research into self-regulating capacity in vocabulary learning is recognized as a significant topic within the second language domain. The self-regulating capacity in vocabulary learning scale (SRCvoc; Tseng et al. 2006) is arguably the most widely used tool for assessing this construct. The common factor model, which is applied through confirmatory factor analysis and exploratory factor analysis, has been the primary methods for validating the SRCvoc. However, previous studies have encountered difficulties in obtaining good model fit and generated results that deviate from supporting the theory. We argue the constructs of the SRCvoc are inherently composites, rather than common factors, and should therefore be evaluated using confirmatory composite analysis. In this study, we compared the results of CFA and CCA, highlighting the shortcomings of CFA in supporting the construct validity of composite constructs such as SRCvoc. CFA failed to satisfy the conceptual and empirical perspectives of SRCvoc. In contrast, our data supported the construct validity of SRCvoc solely through CCA. We also evaluated the criterion-related validity of SRCvoc via the composite model and showed that SRCvoc constructs were positively and moderately associated with L2 vocabulary achievement.
Individualized dynamic latent factor model for multi-resolutional data with application to mobile health
Authors: Zhang J , Xue F , Xu Q , Lee J , Qu A
Published: April 2024

Mobile health has emerged as a major success for tracking individual health status, due to the popularity and power of smartphones and wearable devices. This has also brought great challenges in handling heterogeneous, multi-resolution data that arise ubiquitously in mobile health due to irregular multivariate measurements collected from individuals. In this paper, we propose an individualized dynamic latent factor model for irregular multi-resolution time series data to interpolate unsampled measurements of time series with low resolution. One major advantage of the proposed method is the capability to integrate multiple irregular time series and multiple subjects by mapping the multi-resolution data to the latent space. In addition, the proposed individualized dynamic latent factor model is applicable to capturing heterogeneous longitudinal information through individualized dynamic latent factors. Our theory provides a bound on the integrated interpolation error and the convergence rate for B-spline approximation methods. Both the simulation studies and the application to smartwatch data demonstrate the superior performance of the proposed method compared to existing methods.
Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population
Authors: Zhang Ning , Chen Shuning , Jiang Keying , Ge Wei , Im Hohjin , Guan Shunping , Li Zixi , Wei Chuqiao , Wang Pinchun , Zhu Ye , Zhao Guang , Liu Liqing , Chen Chunhui , Chang Huibin , Wang Qiang
Published: April 2024

Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms—ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression—to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.
Common mistakes and evidence-based approaches in goals-of-care conversations for seriously ill older adults in cardiac care unit
Authors: Manning Katharine A , Bowman Jason , Nakagawa Shunichi , Ouchi Kei
Published: April 2024

For older adults with serious, life-limiting illnesses near the end of life, clinicians frequently face difficult decisions about the medical care they provide because of clinical uncertainty. This difficulty is further complicated by unique challenges and medical advancements for patients with advanced heart diseases. In this article, we describe common mistakes encountered by clinicians when having goals-of-care conversations (e.g. conversations between clinicians and seriously ill patients/surrogates to discuss patient’s values and goals for clinical care near the end of life.). Then, we delineate an evidence-based approach in goals-of-care conversations and highlight the unique challenges around decision-making in the cardiac intensive care unit.
Duration of adenosine-induced myocardial hyperaemia: insights from quantitative 13N-ammonia positron emission tomography myocardial perfusion imaging
Authors: Garefa Chrysoula , Sager Dominik F , Heiniger Pascal S , Markendorf Susanne , Albertini Tobia , Jurisic Stjepan , Gajic Marko , Gebhard Catherine , Benz Dominik C , Pazhenkottil Aju P , Giannopoulos Andreas A , Kaufmann Philipp A , Slomka Piotr J , Buechel Ronny R
Published: April 2024

Aims

This study aimed to assess the impact of adenosine on quantitative myocardial blood flow (MBF) in a rapid stress–rest protocol compared with a rest–stress protocol using 13N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) and to gain insights into the time dependency of such effects.

Methods and results

Quantitative MBF at rest (rMBF) and during adenosine-induced stress (sMBF) and myocardial flow reserve (MFR) were obtained from 331 retrospectively identified patients who underwent 13N-ammonia PET MPI for suspected chronic coronary syndrome and who all exhibited no perfusion defects. Of these, 146 (44.1%) underwent a rapid stress–rest protocol with a time interval (Δtstress–rest) of 20 ± 4 min between adenosine infusion offset and rest imaging, as per clinical routine. The remaining 185 (55.9%) patients underwent a rest–stress protocol and served as the reference. Groups did not differ regarding demographics, risk factors, medication, left ventricular function, and calcium scores. rMBF was significantly higher in the stress–rest vs. the rest–stress group [0.80 (interquartile range 0.66–1.00) vs. 0.70 (0.58–0.83) mL·min−1·g−1, P < 0.001], and, as sMBF was identical between groups [2.52 (2.20–2.96) vs. 2.50 (1.96–3.11), P = 0.347], MFR was significantly lower in the stress–rest group [3.07 (2.43–3.88) vs. 3.50 (2.63–4.10), P = 0.007]. There was a weak correlation between Δtstress–rest and rMBF (r = −0.259, P = 0.002) and between Δtstress–rest and MFR (r = 0.163, P = 0.049), and the proportion of patients with abnormally high rMBF was significantly decreasing with increasing Δtstress–rest.

Conclusion

Intravenously applied adenosine induces a long-lasting hyperaemic effect on the myocardium. Consequently, rapid stress–rest protocols could lead to an overestimation of rMBF and an underestimation of MFR.
Contemporary trends in incident ischaemic stroke, intracranial haemorrhage, and mortality in individuals with atrial fibrillation
Authors: Noubiap Jean Jacques , Tang Janet J , Dewland Thomas A , Marcus Gregory M
Published: April 2024

Background

The prognosis for atrial fibrillation (AF) patients is based on data that is decades old. Given evolving standards of clinical practice, we sought to evaluate temporal trends in clinically important outcomes among patients with AF.

Methods and results

California's Department of Health Care Access and Information databases were used to identify adults aged ≥18 years with AF receiving hospital-based care in California. We compared three time-periods: 2005–2009, 2010–2014, and 2015–2019. International Classification of Diseases codes were used to identify chronic diseases and acute events. The outcomes were incident ischaemic stroke, intracranial haemorrhage, and overall mortality. We included 2 009 832 patients with AF (52.7% males, 70.7% Whites, and mean age of 75.0 years), divided in three cohorts: 2005–2009 (n = 738 954), 2010–2014 (n = 609 447), and 2015–2019 (n = 661 431). Each outcome became substantially less common with time: compared with 2005–2009, AF patients diagnosed in 2015–2019 experienced a 34% (adjusted hazard ratio [HR] 0.66, 95% confidence interval 0.64–0.69), 22% (HR 0.78, 0.75–0.82), and 24% (HR 0.76, 0.75–0.77) reduction in risk of incident ischaemic stroke, intracranial haemorrhage, and mortality, respectively. Between 2005–2009 and 2015–2019, patients aged ≥65 years experienced more reductions in each outcome compared with younger patients (P < 0.001 for all), and declines in each outcome were significantly lower for Hispanics and Blacks compared with white patients.

Conclusion

The risks of stroke, intracranial haemorrhage, and death have significantly declined among AF patients, although differences in the magnitude of improvement of these outcomes by demographic groups were observed. Commonly described estimates of the prognosis for AF patients should be updated to reflect contemporary care.
A novel mutation in the NR3C1 gene associated with reversible glucocorticoid resistance
Authors: Laulhé Margaux , Kuhn Emmanuelle , Bouligand Jérôme , Amazit Larbi , Perrot Julie , Lebigot Elise , Kamenickỷ Peter , Lombès Marc , Fagart Jérôme , Viengchareun Say , Martinerie Laetitia
Published: April 2024

Objective

Glucocorticoid resistance is a rare endocrine disease caused by variants of the NR3C1 gene encoding the glucocorticoid receptor (GR). We identified a novel heterozygous variant (GRR569Q) in a patient with uncommon reversible glucocorticoid resistance syndrome.

Methods

We performed ex vivo functional characterization of the variant in patient fibroblasts and in vitro through transient transfection in undifferentiated HEK 293T cells to assess transcriptional activity, affinity, and nuclear translocation. We studied the impact of the variant on the tertiary structure of the ligand-binding domain through 3D modeling.

Results

The patient presented initially with an adrenal adenoma with mild autonomous cortisol secretion and undetectable adrenocorticotropin hormone (ACTH) levels. Six months after surgery, biological investigations showed elevated cortisol and ACTH (urinary free cortisol 114 µg/24 h, ACTH 10.9 pmol/L) without clinical symptoms, evoking glucocorticoid resistance syndrome. Functional characterization of the GRR569Q showed decreased expression of target genes (in response to 100 nM cortisol: SGK1 control +97% vs patient +20%, P < .0001) and impaired nuclear translocation in patient fibroblasts compared to control. Similar observations were made in transiently transfected cells, but higher cortisol concentrations overcame glucocorticoid resistance. GRR569Q showed lower ligand affinity (Kd GRWT: 1.73 nM vs GRR569Q: 4.61 nM). Tertiary structure modeling suggested a loss of hydrogen bonds between H3 and the H1–H3 loop.

Conclusion

This is the first description of a reversible glucocorticoid resistance syndrome with effective negative feedback on corticotroph cells regarding increased plasma cortisol concentrations due to the development of mild autonomous cortisol secretion.
On the variability of dynamic functional connectivity assessment methods
Authors: Torabi Mohammad , Mitsis Georgios D , Poline Jean-Baptiste
Published: April 2024

Background

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.

Methods

We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.

Results

Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.

Conclusions

Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.
euAP2a, a key gene that regulates flowering time in peach (Prunus persica) by modulating thermo-responsive transcription programming
Authors: Liu Jianyang , Bennett Dennis , Demuth Mark , Burchard Erik , Artlip Tim , Dardick Chris , Liu Zongrang
Published: April 2024

Frequent spring frost damage threatens temperate fruit production, and breeding of late-flowering cultivars is an effective strategy for preventing such damage. However, this effort is often hampered by the lack of specific genes and markers and a lack of understanding of the mechanisms. We examined a Late-Flowering Peach (LFP) germplasm and found that its floral buds require a longer chilling period to release from their dormancy and a longer warming period to bloom than the control cultivar, two key characteristics associated with flowering time. We discovered that a 983-bp deletion in euAP2a, an APETALA2 (AP2)-related gene with known roles in regulating floral organ identity and flowering time, was primarily responsible for late flowering in LFP. This deletion disrupts an miR172 binding site, resulting in a gain-of-function mutation in euAP2a. Transcriptomic analyses revealed that at different stages of floral development, two chilling-responsive modules and four warm-responsive modules, comprising approximately 600 genes, were sequentially activated, forming a unique transcription programming. Furthermore, we found that euAP2a was transiently downregulated during the activation of these thermal-responsive modules at various stages. However, the loss of such transient, stage-specific downregulation of euAP2a caused by the deletion of miR172 binding sites resulted in the deactivation or delay of these modules in the LFP flower buds, suggesting that euAP2a acts as a transcription repressor to control floral developmental pace in peaches by modulating the thermo-responsive transcription programming. The findings shed light on the mechanisms behind late flowering in deciduous fruit trees, which is instrumental for breeding frost-tolerant cultivars.
HortGenome Search Engine, a universal genomic search engine for horticultural crops
Authors: Wang Sen , Wei Shangxiao , Deng Yuling , Wu Shaoyuan , Peng Haixu , Qing You , Zhai Xuyang , Zhou Shijie , Li Jinrong , Li Hua , Feng Yijian , Yi Yating , Li Rui , Zhang Hui , Wang Yiding , Zhang Renlong , Ning Lu , Yao Yuncong , Fei Zhangjun , Zheng Yi
Published: April 2024

Horticultural crops comprising fruit, vegetable, ornamental, beverage, medicinal and aromatic plants play essential roles in food security and human health, as well as landscaping. With the advances of sequencing technologies, genomes for hundreds of horticultural crops have been deciphered in recent years, providing a basis for understanding gene functions and regulatory networks and for the improvement of horticultural crops. However, these valuable genomic data are scattered in warehouses with various complex searching and displaying strategies, which increases learning and usage costs and makes comparative and functional genomic analyses across different horticultural crops very challenging. To this end, we have developed a lightweight universal search engine, HortGenome Search Engine (HSE; http://hort.moilab.net), which allows for the querying of genes, functional annotations, protein domains, homologs, and other gene-related functional information of more than 500 horticultural crops. In addition, four commonly used tools, including ‘BLAST’, ‘Batch Query’, ‘Enrichment analysis’, and ‘Synteny Viewer’ have been developed for efficient mining and analysis of these genomic data.
Clinical outcomes after hip arthroscopy in acetabular dysplastic patients, previously treated with periacetabular osteotomy: a minimum of two-year follow-up data from the Danish Hip Arthroscopy Registry
Authors: Mygind-Klavsen Bjarne , Lund Bent , Nielsen Torsten Grønbech , Lind Martin
Published: April 2024

Periacetabular osteotomy (PAO) is the treatment of choice in dysplastic acetabulum. Due to continued symptoms, 2–11% of these patients require an additional hip arthroscopy. The purpose of this study was to report clinical outcome after a minimum of 2-year follow-up of additional hip arthroscopy after PAO with data from Danish Hip Arthroscopy Registry. Inclusion criteria in the study cohort were PAO surgery resulting in an additional hip arthroscopy procedure. The cohort was evaluated according to the surgical findings and patient-related outcome measures (PROMs) pre-operatively and at 2-year follow-up. A total of 287 patients were included in the study cohort. PROMs improved significantly in all subscales from pre-operatively to 2-year follow-up in the study cohort. According to PROM subscales, 47.8–57.6% and 25.2–38.2% achieved Minimal Clinical Important Difference and Patient Acceptable Symptom State, respectively. This study demonstrates, in PAO-treated patients, significant PROM improvements after additional hip arthroscopy. Unfortunately, only ∼50% and 30% achieved Minimal Clinical Important Difference and Patient Acceptable Symptom State, respectively.