bluuebunny/crossref_metadata_2025 huggingface dataset:
A Spatiotemporal Deep Learning-Based Smart Discovery Approach for Marine Pollution Incidents from the Data-Driven Perspective
Authors: Zheng Jinjin , Li Ning , Ye Song
Published: January 2024

Marine pollution incidents (MPI) are often a dynamic process of time and space interaction. Currently, the monitoring of MPI is basically realized by manual analysis from expert experience. Such working mode has an obvious time lag, and is not useful for timely disposal. As a result, intelligent algorithms that can make quick discovery for MPI from massive monitoring data remain a practical demand in this field. Considering that monitoring elements generally have multi-dimensional characteristics and spatiotemporal causal relationships, this work develops a spatiotemporal deep learning-based smart discovery approach for MPI from the data-driven perspective. In particular, a systematic preprocessing workflow is developed for the spatiotemporal monitoring data, which facilitates following feature extraction. Then, a spatiotemporal convolution neural network structure is developed to extract features from original spatiotemporal monitoring data. On this basis, the discovery results of MPI can be output via neural computing structures. Taking the polluting marine oil spill incident in the Bohai Sea in eastern China as a case study, this work carries out a simulation application and its result analysis. The obtained simulation results can reveal the proper performance of the proposal.
Development of boundary layers in Euler fluids that on “activation” respond like Navier–Stokes fluids
Authors: Gazca-Orozco P. A. , Málek J. , Rajagopal K. R.
Published: January 2024

We consider the flow of a fluid whose response characteristics change due the value of the norm of the symmetric part of the velocity gradient, behaving as an Euler fluid below a critical value and as a Navier–Stokes fluid at and above the critical value, the norm being determined by the external stimuli. We show that such a fluid, while flowing past a bluff body, develops boundary layers which are practically identical to those that one encounters within the context of the classical boundary layer theory propounded by Prandtl. Unlike the classical boundary layer theory that arises as an approximation within the context of the Navier–Stokes theory, here the development of boundary layers is due to a change in the response characteristics of the constitutive relation. We study the flow of such a fluid past an airfoil and compare the same against the solution of the Navier–Stokes equations. We find that the results are in excellent agreement with regard to the velocity and vorticity fields for the two cases.
Retail Demand Forecasting: A Multivariate Approach and Comparison of Boosting and Deep Learning Methods
Authors: Theodoridis Georgios , Tsadiras Athanasios
Published: January 2024

Retail demand forecasting is a highly intricate and multilevel problem as it may include numerous products, under multiple categories, and potentially sold in many different stores. Forecasts may also process products in isolation or collectively so as to detect mutual correlations. In technical terms, retail demand forecasting may be broken down to a multivariate time series forecasting problem where the time axis is prevalent and introduces added complexity which necessitates proper usage of advanced methodologies with machine/deep learning backgrounds. Popular boosting regressors, such as XGBoost and LightGBM, are excellent machine learning candidates with extensive bibliographic backgrounds covering multiple scientific fields and applications including generic multivariate time series forecasts. The Temporal Convolutional Network and the Temporal Fusion Transformer are recently proposed deep neural network architectures that found success in specific multivariate scenarios but are yet to be tested in a variety of related fields such as retail demand forecasting. Therefore, within this paper, these novel in-the-field models are compared to the aforementioned boosting approaches alongside popular statistical univariate methods, namely Exponential Smoothing and SARIMA. To properly compare the selected models and attempt to generalize their usability, two separate datasets are analyzed and forecasted; an item sales dataset with 500 time series and a category sales dataset with 540 time series. The findings suggest that the deep learning solutions are the better predictors, with the Transformer model surpassing the Boosting solutions by up to 16.8% sMAPE decrease and the statistical approaches by up to 28.6% decrease, further substantiating the notion that deep learning techniques are exceptionally promising in handling large-scale, non-linear and outlier data whilst suggesting that retail demand forecasting does benefit from multivariate approaches and the application of advanced machine learning methods.
A Unified Model for Investigating the Propagation of SH Surface Waves in a Piezoelectric Layered Medium
Authors: Fang Xun , Wang Linyao , Lou Jia , Fan Hui , Zhang Aibing , Du Jianke
Published: January 2024

In this paper, a thorough analysis is conducted to examine the propagation characteristics of SH surface waves in a layered medium with a nanoscale piezoelectric guiding layer deposited on an isotropic elastic substrate. Specifically, a two-dimensional analytical model is established, within which the effects of strain gradient, electric field gradient, inertia gradient, and flexoelectricity are considered, as well as interfacial imperfections at the interface between the piezoelectric guiding layer and the elastic substrate, which are characterized by spring models. Within the framework of the variational principle, the governing equations, boundary conditions, and continuity conditions at the interface are derived. Based on these equations, the dispersion relations for SH surface waves are deduced and numerically solved for both the electrically open-circuit and electrically short-circuit cases. A comprehensive investigation of the dispersion relations for the fundamental mode of SH surface waves is subsequently provided, with a detailed discussion on the influence of critical factors. The developed theoretical model, encompassing various size effects observed in nano-scale structures, enables a more precise prediction of surface wave propagation behavior, thereby enhancing the design and application of surface acoustic wave devices.
System Fragility Analysis of Bridges Based on Response Variables Dimensionality Reduction
Authors: Zhu Boyang , Liu Jinlong , Lin Junqi
Published: January 2024

Earthquake resistance of bridge structures is an important part of ensuring the functionality of transportation network systems. The structural system of a bridge is composed of multiple components, and the damage of different components may affect the function of the bridge, while the system seismic fragility analysis provides an effective way to evaluate the overall seismic performance of the bridge. In the past, the failure probability of a system is often estimated by the correlation between components, but the complex correlation between components brings certain difficulties to the actual calculation. In this paper, an efficient, accurate and universal method of system fragility analysis is proposed. This method decouples the correlation between components from the perspective of relative damage, adopts the critical demand-to-capacity ratio as the response variable of the system, realizing the dimensionality reduction of the response variables and simplifying the system fragility analysis from high-dimensional fragility analysis to one-dimensional fragility analysis. This methodology offers a viable solution to the challenges posed by the wide estimation of fragility boundaries in the boundaries estimation method and the complexities involved in analyzing correlation of components in the joint probability method. Comparison with traditional methods indicates that the proposed method exhibits high efficiency and convenience, making it suitable for practical engineering applications in system fragility computation. The proposed method holds great potential for future applications in the field of complex bridge structure fragility analysis, large-scale regional seismic damage prediction, rapid assessment, and beyond.
On finite groups with some Hall normally embedded subgroups
Authors: Meng Wei , Lu Jiakuan
Published: January 2024

Let [Formula: see text] be a finite group. A subgroup [Formula: see text] of [Formula: see text] is called Hall normally embedded in [Formula: see text] if [Formula: see text] is a Hall subgroup of [Formula: see text], where [Formula: see text] is the normal closure of [Formula: see text] in [Formula: see text], that is, the smallest normal subgroup of [Formula: see text] containing [Formula: see text]. A group [Formula: see text] is called a [Formula: see text]-group if its all minimal subgroups and cyclic subgroup of order [Formula: see text] are Hall normally embedded in [Formula: see text]. In this paper, we give the classification of minimal non-[Formula: see text]-groups. Furthermore, we investigate the structure of finite group all of whose maximal subgroups of even order are [Formula: see text]-groups.
On S-valuation domains
Authors: Kim Dong Kyu , Lim Jung Wook
Published: January 2024

Let [Formula: see text] be an integral domain and let [Formula: see text] be a multiplicative subset of [Formula: see text]. In this paper, we study integral domains whose quotient rings are valuation domain. To do this, we introduce the concept of [Formula: see text]-valuation domains. We define [Formula: see text] to be an [Formula: see text]-valuation domain if for each nonzero [Formula: see text], there exists an element [Formula: see text] such that [Formula: see text] divides [Formula: see text] or [Formula: see text] divides [Formula: see text]. Among other things, we show that [Formula: see text] is an [Formula: see text]-valuation domain if and only if [Formula: see text] is a valuation domain. By using this result, we give several valuation-like properties.
Accelerating SPH-Fatigue Computation by Using Single Precision Program on GPU
Authors: Tazoe Koki , Yamada Tomonori , Yagawa Genki
Published: January 2024

The fatigue crack propagation analysis based on the SPH framework “SPH-Fatigue” is accelerated by using the single precision program on GPU. It is found that the single precision program slightly disorders the stress wave but doubles the computational speed. In addition, the computational results of the fatigue crack propagation by the single precision program show almost the same crack shapes as the experimental ones. Accordingly, it is concluded that the use of the single precision program is one of the choices to accelerate the SPH-Fatigue computation.
The Potential of Technological Innovation in Transforming the Economy: A Framework and its Application to Assess Academic Patents
Authors: Silva Sérgio Evangelista , Silva André Luís , Reis Luciana Paula , Fernandes June Marques , De Castro Eloiza Luzia Boanerges
Published: January 2024

Technological innovation has played a fundamental role in the economic prosperity of firms and countries. Despite several studies on their effects on firms and markets, it is still necessary to assess how introducing technological innovation as innovative products, processes, and services will face extant market forces and transform them. This paper introduces a model for evaluating the potential economic effects of technological innovations based on three dimensions: technological paradigm, market value, and impact on extant industries. Based on the combination of these three dimensions, eight configurations of technological innovations are presented from an intertemporal perspective, considering their impact on the economy. This model has been validated by assessing several academic patents and helps evaluate the market potential of new technologies.
WHAT MAKES A FIRM INNOVATIVE? AN INTER-ORGANISATIONAL SOCIAL CAPITAL PERSPECTIVE ON EXTERNAL PARTNERS’ INFLUENCE ON A FIRM’S INTELLECTUAL CAPITAL
Authors: WAGNER HEINZ-THEO , BEIMBORN DANIEL , MOOS BERNHARD
Published: January 2024

This research investigates how and to which extent the social capital inherent in a firm’s external relationships to diverse business partners contributes to the firm’s intellectual capital and in turn fosters its innovativeness. We apply the Social Capital Theory to an inter-organisational context and show how a firm’s social relationships with its various external partners contribute to its intellectual capital, and how these contributions differ between different types of partners (e.g., customers vs. suppliers). In contrast to intra-organisational contexts, we show that the association between social capital and intellectual capital is positive, neutral, or even negative depending on the external partner’s position vis-à-vis a firm’s supply chain and the type of knowledge. Using data from 153 German manufacturing firms, the results of a PLS-based analysis provide important insights into how and through which mechanisms firms can become successful innovators.
Laplacian Support Vector Machines as Data Classifier in Machine Learning Approaches of Structural Health Monitoring
Authors: Fazeli Hassan , Hassani Nemat , Safi Mohammad
Published: January 2024

With the progress of technologies in data collection systems, structural engineers are faced with a large amount of unlabeled data gathered from various states of different structures, directing studies toward using improved classifiers in machine learning approaches of structural health monitoring. In this research, Laplacian Support Vector Machines (LapSVMs) are used as a semi-supervised learning algorithm to classify mentioned data. Using vibrational data of structure, dynamic properties of the structure are extracted. Modal strain energy is used as damage sensitive features (DSF) to perform damage assessment in a statistical pattern recognition framework by a semi-supervised learning algorithm using LapSVMs to classify unlabeled and labeled data. Also, Support Vector Machines (SVMs) and Regularized Least Square Classifier (RLSC) are used as classifiers to compare results. To compare the effectiveness of the proposed algorithm, different states of structural response are determined by labeled and unlabeled data. These results show high accuracy of LapSVM methods compared to others in cases where the labeled dataset is small.
AAGAN: Android Malware Generation System Based on Generative Adversarial Network
Authors: Trung Doan Minh , Khoa Nghi Hoang , Duy Phan The , Pham Van-Hau , Cam Nguyen Tan
Published: January 2024

With the rapid evolution of mobile malware, especially Android malware, machine learning (ML)-based Android malware detection systems have drawn massive attention. Although ML algorithms have recently led to many vital breakthroughs in malware detection, they are still particularly vulnerable to adversarial example (AE) attacks. By applying small random perturbations (e.g. simply modifying different kinds of features from the application’s manifest file), an AE attack can cause the misclassification of legitimate applications. This paper proposes AAGAN, an automated Android malware generation system based on Generative Adversarial Networks (GAN) that can successfully deceive current ML detectors. Our experiment results indicate that AEs generated by our system can flip the prediction of the state-of-the-art detection algorithms in 99% of cases using a real-world dataset. To defend against AE attacks, we improve the robustness of our detection system by alternatively retraining with these newly generated AEs. Surprisingly, after retraining five times, AAGAN can achieve an 89% success rate in bypassing our malware detection system.
A Grave Concern: Water-Intensive Crops and Subsidized Electricity Amplifying Groundwater Depletion in India
Authors: Sahu Harish , Pradhan Kalandi Charan
Published: January 2024

This study investigates the consequences of agricultural activities on groundwater depletion, with a specific focus on the overproduction of water-intensive crops. Addressing this existing research gap, this study challenges the prevailing notion that groundwater depletion reduces agricultural activity. Instead, our study reveals a significant relationship between intensified cropping practices, particularly the production of water-intensive crops (Paddy, Sugarcane and Wheat) and the depletion of groundwater. Further, we identify how subsidized electricity helps to accelerate groundwater depletion. Therefore, we use variables related to groundwater and agricultural practices for the period 2004–2020. In doing so, we find that the Northern states of India are most vulnerable in terms of groundwater depletion (PNB: 59.92, SGD: 106.3), followed by southern, central, western, eastern and northeastern states. Additionally, the regression results suggest that our main variables, magnitude of electricity consumption in agriculture and irrigation intensity of water-intensive crops, have a positive and significant impact on groundwater depletion. In fact, our study found that other variables such as cropping intensity (CI), state population (SP), agriculture credit (AC) and total electric pumps (TP) are also significant and positively associated with groundwater depletion while the magnitude of annual rainfall (AR) shows a negative impact on groundwater depletion. Based on the findings, this study presents an alternative perspective that underscores the contribution of ineffective government policies in regulating subsidize electricity and the cultivation of water-intensive crops, leading to the exploitation of groundwater resources in Indian states with regional variations. These findings emphasize the critical and immediate need for effective regulations on the production of water-intensive crops to address this pressing issue.
Cretaceous planktonic foraminiferal biostratigraphy of the Umbria–Marche Basin (Central Italy)
Authors: Coccioni Rodolfo , Frontalini Fabrizio
Published: January 2024

The Umbria–Marche Basin of central Italy includes some of the most studied, continuous and well-exposed sequences of Cretaceous pelagic sediments known from the Tethyan Realm. A complete and well-preserved Cretaceous pelagic composite succession of this area is here presented. It has been constructed through the integration of the well-established magnetostratigraphy with the planktonic foraminiferal biostratigraphy based on previous and newly identified events from six key sections and one drill core. The recovery of planktonic foraminifera successfully disaggregated from the hard lithologies allows the identification and precise placement of several primary and secondary bioevents. In particular, the record of the identified bioevents counts 35 primary biohorizons, which allow the subdivision of the studied succession into 34 zones and 4 subzones. Following this study and previous findings on Cretaceous planktonic foraminifera, an updated planktonic foraminiferal zonation for the Cretaceous Period is proposed. This refined magnetobiostratigraphical framework, which in this study is integrated with the Cretaceous inoceramid, anoxic and carbon-isotope events recognized in the Umbria–Marche Basin, may provide an invaluable tool for improving Cretaceous correlations at low to middle latitudes, as well as palaeoenvironmental, palaeoclimatic and palaeoceanographical interpretations.
HyperTracking
Authors: Xu Xiaoqiang , Meng Xuanqi , Tong Xinyu , Liu Xiulong , Xie Xin , Qu Wenyu
Published: January 2024

Wireless sensing technology allows for non-intrusive sensing without the need for physical sensors worn by the target, enabling a wide range of applications, such as indoor tracking, and activity identification. To theoretically reveal the fundamental principles of wireless sensing, the Fresnel zone model has been introduced in the field of Wi-Fi sensing. While the Fresnel zone model is effective in explaining the sensing mechanism in line-of-sight (LoS) scenarios, achieving accurate sensing in non-line-of-sight (NLoS) situations continues to pose a significant challenge. In this paper, we propose a novel theoretical model called the Hyperbolic zone to reveal the fundamental sensing mechanism in NLoS scenarios. The main principle is to eliminate the complex NLoS path shared among different transmitter-receiver pairs, which allows us to obtain a series of simple "virtual" reflection paths among receivers. Since these "virtual" reflection paths satisfy the properties of the hyperbola, we propose the hyperbolic tracking model. Based on the proposed model, we implement the HyperTracking system using commercial Wi-Fi devices. The experimental results show that the proposed hyperbolic model is suitable for accurate tracking in both LoS and NLoS scenarios. We can reduce 0.36 m tracking error in contrast to the Fresnel zone model in NLoS scenarios. When we utilize the proposed hyperbolic model to train a typical LSTM neural network, we are able to further reduce the tracking error by 0.13 m and save the execution time by 281% with the same data. As a whole, our method can reduce the tracking error by 54% in NLoS scenarios compared with the Fresnel zone model.
Optimization-Free Test-Time Adaptation for Cross-Person Activity Recognition
Authors: Wang Shuoyuan , Wang Jindong , Xi Huajun , Zhang Bob , Zhang Lei , Wei Hongxin
Published: January 2024

Human Activity Recognition (HAR) models often suffer from performance degradation in real-world applications due to distribution shifts in activity patterns across individuals. Test-Time Adaptation (TTA) is an emerging learning paradigm that aims to utilize the test stream to adjust predictions in real-time inference, which has not been explored in HAR before. However, the high computational cost of optimization-based TTA algorithms makes it intractable to run on resource-constrained edge devices. In this paper, we propose an Optimization-Free Test-Time Adaptation (OFTTA) framework for sensor-based HAR. OFTTA adjusts the feature extractor and linear classifier simultaneously in an optimization-free manner. For the feature extractor, we propose Exponential Decay Test-time Normalization (EDTN) to replace the conventional batch normalization (CBN) layers. EDTN combines CBN and Test-time batch Normalization (TBN) to extract reliable features against domain shifts with TBN's influence decreasing exponentially in deeper layers. For the classifier, we adjust the prediction by computing the distance between the feature and the prototype, which is calculated by a maintained support set. In addition, the update of the support set is based on the pseudo label, which can benefit from reliable features extracted by EDTN. Extensive experiments on three public cross-person HAR datasets and two different TTA settings demonstrate that OFTTA outperforms the state-of-the-art TTA approaches in both classification performance and computational efficiency. Finally, we verify the superiority of our proposed OFTTA on edge devices, indicating possible deployment in real applications. Our code is available at https://github.com/Claydon-Wang/OFTTA.
TouchEditor
Authors: Zhan Lishuang , Xiong Tianyang , Zhang Hongwei , Guo Shihui , Chen Xiaowei , Gong Jiangtao , Lin Juncong , Qin Yipeng
Published: January 2024

A text editing solution that adapts to speech-unfriendly (inconvenient to speak or difficult to recognize speech) environments is essential for head-mounted displays (HMDs) to work universally. For existing schemes, e.g., touch bar, virtual keyboard and physical keyboard, there are shortcomings such as insufficient speed, uncomfortable experience or restrictions on user location and posture. To mitigate these restrictions, we propose TouchEditor, a novel text editing system for HMDs based on a flexible piezoresistive film sensor, supporting cursor positioning, text selection, text retyping and editing commands (i.e., Copy, Paste, Delete, etc.). Through literature overview and heuristic study, we design a pressure-controlled menu and a shortcut gesture set for entering editing commands, and propose an area-and-pressure-based method for cursor positioning and text selection that skillfully maps gestures in different areas and with different strengths to cursor movements with different directions and granularities. The evaluation results show that TouchEditor i) adapts to various contents and scenes well with a stable correction speed of 0.075 corrections per second; ii) achieves 95.4% gesture recognition accuracy; iii) reaches a considerable level with a mobile phone in text selection tasks. The comparison results with the speech-dependent EYEditor and the built-in touch bar further prove the flexibility and robustness of TouchEditor in speech-unfriendly environments.
RimSense
Authors: Xie Wentao , Chen Huangxun , Wei Jing , Zhang Jin , Zhang Qian
Published: January 2024

Smart eyewear's interaction mode has attracted significant research attention. While most commercial devices have adopted touch panels situated on the temple front of eyeglasses for interaction, this paper identifies a drawback stemming from the unparalleled plane between the touch panel and the display, which disrupts the direct mapping between gestures and the manipulated objects on display. Therefore, this paper proposes RimSense, a proof-of-concept design for smart eyewear, to introduce an alternative realm for interaction - touch gestures on eyewear rim. RimSense leverages piezoelectric (PZT) transducers to convert the eyeglass rim into a touch-sensitive surface. When users touch the rim, the alteration in the eyeglass's structural signal manifests its effect into a channel frequency response (CFR). This allows RimSense to recognize the executed touch gestures based on the collected CFR patterns. Technically, we employ a buffered chirp as the probe signal to fulfil the sensing granularity and noise resistance requirements. Additionally, we present a deep learning-based gesture recognition framework tailored for fine-grained time sequence prediction and further integrated with a Finite-State Machine (FSM) algorithm for event-level prediction to suit the interaction experience for gestures of varying durations. We implement a functional eyewear prototype with two commercial PZT transducers. RimSense can recognize eight touch gestures on the eyeglass rim and estimate gesture durations simultaneously, allowing gestures of varying lengths to serve as distinct inputs. We evaluate the performance of RimSense on 30 subjects and show that it can sense eight gestures and an additional negative class with an F1-score of 0.95 and a relative duration estimation error of 11%. We further make the system work in real-time and conduct a user study on 14 subjects to assess the practicability of RimSense through interactions with two demo applications. The user study demonstrates RimSense's good performance, high usability, learnability and enjoyability. Additionally, we conduct interviews with the subjects, and their comments provide valuable insight for future eyewear design.
Modeling and Analyzing Evaluation Cost of CUDA Kernels
Authors: Muller Stefan K. , Hoffmann Jan
Published: January 2024

Motivated by the increasing importance of general-purpose Graphic Processing Units (GPGPU) programming, exemplified by NVIDIA’s CUDA framework, as well as the difficulty, especially for novice programmers, of reasoning about performance in GPGPU kernels, we introduce a novel quantitative program logic for CUDA kernels. The logic allows programmers to reason about both functional correctness and resource usage of CUDA kernels, paying particular attention to a set of common but CUDA-specific performance bottlenecks: warp divergences, uncoalesced memory accesses, and bank conflicts. The logic is proved sound with respect to a novel operational cost semantics for CUDA kernels. The semantics, logic, and soundness proofs are formalized in Coq. An inference algorithm based on LP solving automatically synthesizes symbolic resource bounds by generating derivations in the logic. This algorithm is the basis of RaCUDA, an end-to-end resource-analysis tool for kernels, which has been implemented using an existing resource-analysis tool for imperative programs. An experimental evaluation on a suite of benchmarks shows that the analysis is effective in aiding the detection of performance bugs in CUDA kernels.
Is There Another System?
Authors: Neville-Neil George V.
Published: January 2024

One of the easiest tests to determine if you are at risk is to look hard at what you do every day and see if you, yourself, could code yourself out of a job. Programming involves a lot of rote work: templating, boilerplate, and the like. If you can see a way to write a system to replace yourself, either do it, don't tell your bosses, and collect your salary while reading novels in your cubicle, or look for something more challenging to work on.
Security and Privacy Issues in Deep Reinforcement Learning: Threats and Countermeasures
Authors: Mo Kanghua , Ye Peigen , Ren Xiaojun , Wang Shaowei , Li Wenjun , Li Jin
Published: January 2024

Deep Reinforcement Learning (DRL) is an essential subfield of Artificial Intelligence (AI), where agents interact with environments to learn policies for solving complex tasks. In recent years, DRL has achieved remarkable breakthroughs in various tasks, including video games, robotic control, quantitative trading, and autonomous driving. Despite its accomplishments, security and privacy-related issues still prevent us from deploying trustworthy DRL applications. For example, by manipulating the environment, an attacker can influence an agent’s actions, misleading it to behave abnormally. Additionally, an attacker can infer private training data and environmental information by maliciously interacting with DRL models, causing a privacy breach. In this survey, we systematically investigate the recent progress of security and privacy issues in the context of DRL. First, we present a holistic review of security-related attacks within DRL systems from the perspectives of single-agent and multi-agent systems and review privacy-related attacks. Second, we review and classify defense methods used to address security-related challenges, including robust learning, anomaly detection, and game theory approaches. Third, we review and classify privacy-preserving technologies, including encryption, differential privacy, and policy confusion. We conclude the survey by discussing open issues and possible directions for future research in this field.
Plant Cryopreservation: Principles, Applications, and Challenges of Banking Plant Diversity at Ultralow Temperatures
Authors: Nagel Manuela , Pence Valerie , Ballesteros Daniel , Lambardi Maurizio , Popova Elena , Panis Bart
Published: January 2024

Progressive loss of plant diversity requires the protection of wild and agri-/horticultural species. For species whose seeds are extremely short-lived, or rarely or never produce seeds, or whose genetic makeup must be preserved, cryopreservation offers the only possibility for long-term conservation. At temperatures below freezing, most vegetative plant tissues suffer severe damage from ice crystal formation and require protection. In this review, we describe how increasing the concentration of cellular solutes by air drying or adding cryoprotectants, together with rapid cooling, results in a vitrified, highly viscous state in which cells can remain viable and be stored. On this basis, a range of dormant bud–freezing, slow-cooling, and (droplet-)vitrification protocols have been developed, but few are used to cryobank important agricultural/horticultural/timber and threatened species. To improve cryopreservation efficiency, the effects of cryoprotectants and molecular processes need to be understood and the costs for cryobanking reduced. However, overall, the long-term costs of cryopreservation are low, while the benefits are huge.
Immunology of Kidney Disease
Authors: Foresto-Neto Orestes , Menezes-Silva Luísa , Leite Jefferson Antônio , Andrade-Silva Magaiver , Câmara Niels Olsen Saraiva
Published: January 2024

The immune system and the kidneys are closely related. Immune components mediate acute kidney disease and are crucial to the progression of chronic kidney disease. Beyond its pathogenic functions, the immune system supports immunological homeostasis in healthy kidneys. The kidneys help maintain immune equilibrium by removing metabolic waste products and toxins, thereby limiting local and systemic inflammation. In this review, we describe the close relationship between the immune system and the kidneys. We discuss how the imbalance in the immune response can be deleterious to the kidneys and how immunomodulation can be important in preventing end-stage renal disease. In addition, recent tools such as in silico platforms and kidney organoids can help unveil the relationship between immune cells and kidney homeostasis.
Accelerating FEM-Based Corrosion Predictions Using Machine Learning
Authors: Montes de Oca Zapiain David , Maestas Demitri , Roop Matthew , Noel Philip , Melia Michael , Katona Ryan
Published: January 2024

Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The Finite Element Method (FEM) is a computational technique capable of providing accurate estimates of corrosion rate by numerically solving complex differential Eqs. characterizing this phenomena. Nevertheless, given the iterative nature of FEM and the computational expense required to solve these complex equations, FEM is ill-equipped for an efficient exploration of the design space to identify factors that accelerate or deter corrosion, despite its accuracy. In this work, a machine learning based surrogate model capable of providing accurate predictions of corrosion with significant computational savings is introduced. Specifically, this work leverages AdaBoosted Decision trees to provide an accurate estimate of corrosion current per width given different values of temperature, water layer thickness, molarity of the solution, and the length of the cathode for a galvanic couple of aluminum and stainless steel.
Surface Modification Improves Spinel LiCoO2 Li-Ion Battery Cathode Materials Grown by Low Temperature Solvothermal Flow Reaction
Authors: Zhang Yan , Gulzar Umair , Lonergan Alex , Grant Alex , Carroll Aoife , Roy Ahin , Nicolosi Valeria , Keene Tony D. , O’Dwyer Colm
Published: January 2024

Methods that provide routes to LiCoO2 growth with lower energy requirements from recycled battery cathode ashes are important for sustainable Li-ion battery technology . Here, a low temperature route to a stable, coated spinel-phase LT-LCO material with secondary Co3O4 phase can be achieved at 300 °C directly from the layered double hydroxide [Li2(ox)2][Co5(OH)8] product of solvothermally synthesized LiOH and CoCl2. The low-temperature LiCoO2 materials (known as LT-LCO) consist of spinel-phase LCO and secondary Co3O4 phase. As a cathode in lithium batteries, we used a solution-based method of coating with an ionic conductor LiAlO2 with AlF3 to mitigate sluggish reversible lithiation kinetics and the poor cycling and rate performance of as-synthesized spinel LT-LCO. The coating modification promotes reversible lithium ion transfer and stabilizes the spinel structure. The modified LT-LCO cathode has significantly better overall capacity and rate performance, with a capacity retention of ∼80 mAh g−1 after 150 cycles (factoring the LT-LCO and Co3O4 mass). The initial first cycle coulombic efficiency significantly improves to >95%. The data show that even spinel phase LCO grown by this solvothermal route cycles stably with a useful specific capacity and rate response in the voltage range 2.0–4.2 V.
Impact of arterial stiffness on cerebrovascular function: a review of evidence from humans and preclinical models
Authors: Reeve Emily H. , Barnes Jill N. , Moir M. Erin , Walker Ashley E.
Published: January 2024

With advancing age, the cerebral vasculature becomes dysfunctional, and this dysfunction is associated with cognitive decline. However, the initiating cause of these age-related cerebrovascular impairments remains incompletely understood. A characteristic feature of the aging vasculature is the increase in stiffness of the large elastic arteries. This increase in arterial stiffness is associated with elevated pulse pressure and blood flow pulsatility in the cerebral vasculature. Evidence from both humans and rodents supports that increases in large elastic artery stiffness are associated with cerebrovascular impairments. These impacts on cerebrovascular function are wide-ranging and include reductions in global and regional cerebral blood flow, cerebral small vessel disease, endothelial cell dysfunction, and impaired perivascular clearance. Furthermore, recent findings suggest that the relationship between arterial stiffness and cerebrovascular function may be influenced by genetics, specifically APOE and NOTCH genotypes. Given the strength of the evidence that age-related increases in arterial stiffness have deleterious impacts on the brain, interventions that target arterial stiffness are needed. The purpose of this review is to summarize the evidence from human and rodent studies, supporting the role of increased arterial stiffness in age-related cerebrovascular impairments.
Sleep disturbance and sympathetic neural reactivity in postmenopausal females
Authors: Tahsin Chowdhury Tasnova , Anselmo Miguel , Lee Emma , Stokes William , Fonkoue Ida T. , Vanden Noven Marnie L. , Carter Jason R. , Keller-Ross Manda L.
Published: January 2024

This is the first study to demonstrate that muscle sympathetic nerve activity (MSNA) to a cold pressor test is augmented in postmenopausal females with perceived sleep disturbance. The more rapid increase in MSNA reactivity during the cold pressor test in the sleep disturbance group was present despite similar increases in the perceived pain levels between groups. Baseline MSNA burst incidence and burst frequency, as well as blood pressure and heart rate, were similar between the sleep disturbance and nonsleep disturbance groups.
Delayed Diagnosis of Perrault Syndrome: A Rare Genetic Disorder
Authors: Bayanova Mirgul , Abilova Aigerim , Nauryzbayeva Alisa , Turarbekova Zhibek
Published: January 2024

Perrault syndrome (PRLTS) is a rare autosomal recessive disorder which is associated with pathogenic variants in HSD17B4, HARS2, CLPP, LARS2, GGPS1, RMND1, TWNK, ERAL1, and PRORP genes. The disease is characterized by sensorineural hearing loss, sometimes with neurological signs, including progressive sensory and motor peripheral neuropathy, cerebellar ataxia, mild mental retardation, and ovarian dysgenesis in females. In this article, we report a case of a child diagnosed with spastic diplegic cerebral palsy. Determination of the segregation status of the parents of a proband with a rare compound heterozygote in the gene HSD17B4 will help the genetic counselling for the prognosis of Perrault syndrome in the family.
Research Status in the Use of Surface-Enhanced Raman Scattering (SERS) to Detect Pesticide Residues in Foods and Plant-Derived Chinese Herbal Medicines
Authors: Chu Bing-Yan , Lin Chi , Nie Peng-Cheng , Xia Zheng-Yan
Published: January 2024

Surface-enhanced Raman scattering (SERS) technology has unique advantages in the rapid detection of pesticides in plant-derived foods, leading to reduced detection limits and increased accuracy. Plant-derived Chinese herbal medicines have similar sources to plant-derived foods; however, due to the rough surfaces and complex compositions of herbal medicines, the detection of pesticide residues in this context continues to rely heavily on traditional methods, which are time consuming and laborious and are unable to meet market demands for portability. The application of flexible nanomaterials and SERS technology in this realm would allow rapid and accurate detection in a portable format. Therefore, in this review, we summarize the underlying principles and characteristics of SERS technology, with particular focus on applications of SERS for the analysis of pesticide residues in agricultural products. This paper summarizes recent research progress in the field from three main directions: sample pretreatment, SERS substrates, and data processing. The prospects and limitations of SERS technology are also discussed, in order to provide theoretical support for rapid detection of pesticide residues in Chinese herbal medicines.
Biofloc Technology (BFT) in Aquaculture: What Goes Right, What Goes Wrong? A Scientific‐Based Snapshot
Authors: Khanjani Mohammad Hossein , Sharifinia Moslem , Emerenciano Maurício Gustavo Coelho
Published: January 2024

Aquaculture is a crucial industry that can help meet the increasing demand for aquatic protein products and provide employment opportunities in coastal areas and beyond. If incorrectly manage, traditional aquaculture methods can have negative impacts on the environment and natural resources, including water pollution and overuse of wild fish stocks as aquafeed ingredients. Biofloc technology (BFT) may offer a promising solution to some of these challenges by promoting a cleaner and sustainable production system. BFT converts waste into bioflocs, which serve as a natural food source for fish and shrimp within the culture system, reducing the need for external inputs, such as feed and chemicals. Moreover, BFT has the potential to improve yields and economic performance while promoting efficient resource utilization, such as water and energy. Despite its numerous advantages, BFT presents several challenges, such as high energy demand, high initial/running costs, waste (effluent, suspended solids, and sludge) management, opportunistic pathogens (vibrio) spread, and a lack of understanding of operational/aquatic/microbial dynamics. However, with further training, research, and innovation, these challenges can be overcome, and BFT can become a more widely understood and adopted technique, acting as an effective method for sustainable aquaculture. In summary, BFT offers a cleaner production option that promotes circularity practices while enhancing performance and economic benefits. This technique has the potential to address several challenges faced by the aquaculture industry while ensuring its continued growth and protecting the environment. A more broad BFT adoption can contribute to meeting the increasing demand for aquaculture products while reducing the industry’s negative impact on the environment and natural resources. In this context, this review provides an overview of the advantages and challenges of BFT and highlights key technical, biological, and economic aspects to optimize its application, promote further adoption, and overcome the current challenges.
Development and Validation of an Improved Quasisteady Flow Model with Additional Parasitic Loss Effects for Stirling Engines
Authors: Kim Dong-Jun , Park Young-Chae , Sim Kyuho
Published: January 2024

This paper presents the development and validation of an improved quasisteady flow model (iQSFM) that applies comprehensive parasitic losses to the quasisteady flow model (QSFM) considering an oscillating flow, which is the actual type of flow occurring in a regenerator. Validation of iQSFM was evaluated by comparing it with a QSFM based on the experimental results of a RE‐1000 regenerator. Compared to QSFM, iQSFM improved the prediction accuracy by reducing the indicated power error from 66.7% to 24.9% and the efficiency error from 35.3% to 9.4%. In addition, the prediction accuracy of iQSFM was compared when the oscillating flow and the steady flow correlation were applied to a regenerator. When iQSFM applied an oscillating flow correlation to the regenerator, it predicted the experimental results of RE‐1000 slightly more accurately than in a steady flow correlation. Finally, the engine performance and parasitic losses were analyzed through a parameter study of RE‐1000 using iQSFM. Through this, it was confirmed with iQSFM that the RE‐1000 is designed to maximize the engine performance by minimizing the parasitic losses.
Extraosseous Calcifying Odontogenic Cyst Initially Interpreted as a Parulis
Authors: Santana-Arenas Karla Lizeth , Guardado-Luevanos Israel , Padilla-Rosas Miguel , Nava-Villalba Mario
Published: January 2024

Extraosseous variants of odontogenic cysts are an infrequent clinical finding, although they have a relatively indolent biological behavior compared to their intraosseous counterparts; due to their nature and clinical appearance, these lesions can be confused with multiple entities that affect soft tissues, so their diagnosis can only be achieved through surgical removal and subsequent histopathological analysis. The extraosseous/peripheral variant of calcifying odontogenic cyst (E/PCOC) has a heterogeneous clinical presentation mainly in terms of size and involvement or not of adjacent anatomical structures such as bone and teeth; in addition, there are few cases reported to date; thus, there are still clinical features to be elucidated. This report presents a child affected by E/PCOC in an unusual location, as well as its therapeutic management, which at first time was suspected of endodontic nature, due to a history of dental trauma.
Supplementary Figure S1 from First-in-Human Study of the Reversible BTK Inhibitor Nemtabrutinib in Patients with Relapsed/Refractory Chronic Lymphocytic Leukemia and B-Cell Non–Hodgkin Lymphoma
Authors: Woyach Jennifer A. , Stephens Deborah M. , Flinn Ian W. , Bhat Seema A. , Savage Ronald E. , Chai Feng , Eathiraj Sudharshan , Reiff Sean D. , Muhowski Elizabeth M. , Granlund Lindsey , Szuszkiewicz Lyndsey , Wang Wayne , Schwartz Brian , Ghori Razi , Farooqui Mohammed Z.H. , Byrd John C.
Published: January 2024

Supplementary Figure S1 shows mean (+SD) plasma concentrations of nemtabrutinib vs time following single (C1D1) oral doses of MK-1026 (linear and semi-log plots) nM
Supplementary Fig 3 from Vitamin B6 Competition in the Tumor Microenvironment Hampers Antitumor Functions of NK Cells
Authors: He Chunbo , Wang Dezhen , Shukla Surendra K. , Hu Tuo , Thakur Ravi , Fu Xiao , King Ryan J. , Kollala Sai Sundeep , Attri Kuldeep S. , Murthy Divya , Chaika Nina V. , Fujii Yuki , Gonzalez Daisy , Pacheco Camila G. , Qiu Yudong , Singh Pankaj K. , Locasale Jason W. , Mehla Kamiya
Published: January 2024

Supplementary Figure S3 shows that glycogenolysis is required for NK cell activation under acute stimulation, while PDAC cells induce impairment of NK cell glycogenolysis by reducing VB6 level.
Supplementary Methods from Xaluritamig, a STEAP1 × CD3 XmAb 2+1 Immune Therapy for Metastatic Castration-Resistant Prostate Cancer: Results from Dose Exploration in a First-in-Human Study
Authors: Kelly William K. , Danila Daniel C. , Lin Chia-Chi , Lee Jae-Lyun , Matsubara Nobuaki , Ward Patrick J. , Armstrong Andrew J. , Pook David , Kim Miso , Dorff Tanya B. , Fischer Stefanie , Lin Yung-Chang , Horvath Lisa G. , Sumey Christopher , Yang Zhao , Jurida Gabor , Smith Kristen M. , Connarn Jamie N. , Penny Hweixian L. , Stieglmaier Julia , Appleman Leonard J.
Published: January 2024

Supplementary Methods: Additional methodology including full inclusion criteria, full exclusion criteria, and definition of dose-limiting toxicities.
Supplementary Table 6 from SUV39H1 Ablation Enhances Long-term CAR T Function in Solid Tumors
Authors: López-Cobo Sheila , Fuentealba Jaime R. , Gueguen Paul , Bonté Pierre-Emmanuel , Tsalkitzi Kyriaki , Chacón Irena , Glauzy Salomé , Bohineust Armelle , Biquand Ariane , Silva Lisseth , Gouveia Zelia , Goudot Christel , Perez Franck , Saitakis Michael , Amigorena Sebastian
Published: January 2024

Reagents or resources used in the study. Numbers of donors and CAR-T productions for in vitro experiments. Donor information for in vivo experiments.