bluuebunny/crossref_metadata_2025 huggingface dataset:
IOSR: Improving I/O Efficiency for Memory Swapping on Mobile Devices Via Scheduling and Reshaping
Authors: Li Wentong , Shi Liang , Li Hang , Li Changlong , Sha Edwin Hsing-Mean
Published: September 2023

Mobile systems and applications are becoming increasingly feature-rich and powerful, which constantly suffer from memory pressure, especially for devices equipped with limited DRAM. Swapping inactive DRAM pages to the storage device is a promising solution to extend the physical memory. However, existing mobile devices usually adopt flash memory as the storage device, where swapping DRAM pages to flash memory may introduce significant performance overhead. In this paper, we first conduct an in-depth analysis of the I/O characteristics of the flash-based memory swapping, including the I/O interference and swap I/O randomness in swap subsystem. Then an I/O efficiency optimization framework for memory swapping (IOSR) is proposed to enhance the performance of flash-based memory swapping for mobile devices. IOSR consists of two methods: swap I/O scheduling (SIOS) and swap I/O pattern reshaping (SIOR). SIOS is designed to schedule the swap I/O to reduce interference with other processes I/Os. SIOR is designed to reshape the swap I/O pattern with process-oriented swap slot allocation and adaptive granularity swap read-ahead. IOSR is implemented on Google Pixel 4. Experimental results show that IOSR reduces the application switching time by 31.7% and improves the swap-in bandwidth by 35.5% on average compared to the state-of-the-art.
WARM-tree: Making Quadtrees Write-efficient and Space-economic on Persistent Memories
Authors: Wu Shin-Ting , Chen Liang-Chi , Huang Po-Chun , Chang Yuan-Hao , Ho Chien-Chung , Shih Wei-Kuan
Published: September 2023

Recently, the value of data has been widely recognized, which highlights the significance of data-centric computing in diversified application scenarios. In many cases, the data are multidimensional, and the management of multidimensional data often confronts greater challenges in supporting efficient data access operations and guaranteeing the space utilization. On the other hand, while many existing index data structures have been proposed for multidimensional data management, however, their designs are not fully optimized for modern nonvolatile memories, in particular the byte-addressable persistent memories. As a result, they might undergo serious access performance degradation or fail to guarantee space utilization. This observation motivates the redesigning of index data structures for multidimensional point data on modern persistent memories, such as the phase-change memory. In this work, we present the
WARM-tree
, a
m
ultidimensional
t
ree for
r
educing the
w
rite
a
mplification effect, for multidimensional point data. In our evaluation studies, as compared to the bucket PR quadtree and R*-tree, the WARM-tree can provide any worst-case space utilization guarantees in the form of

\(\frac{m-1}{m}\)

(
m
∈ ℤ^+) and effectively reduces the write traffic of key insertions by up to 48.10% and 85.86%, respectively, at the price of degraded average space utilization and prolonged latency of query operations. This suggests that the WARM-tree is a potential multidimensional index structure for insert-intensive workloads.
A Self-Sustained CPS Design for Reliable Wildfire Monitoring
Authors: Tuncel Yigit , Basaklar Toygun , Carpenter-Graffy Dina , Ogras Umit
Published: September 2023

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. The proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.
Neural Abstraction-Based Controller Synthesis and Deployment
Authors: Majumdar Rupak , Salamati Mahmoud , Soudjani Sadegh
Published: September 2023

Abstraction-based techniques are an attractive approach for synthesizing correct-by-construction controllers to satisfy high-level temporal requirements. A main bottleneck for successful application of these techniques is the memory requirement, both during controller synthesis (to store the abstract transition relation) and in controller deployment (to store the control map).

We propose memory-efficient methods for mitigating the high memory demands of the abstraction-based techniques using
neural network representations
. To perform synthesis for reach-avoid specifications, we propose an on-the-fly algorithm that relies on compressed neural network representations of the forward and backward dynamics of the system. In contrast to usual applications of neural representations, our technique maintains soundness of the end-to-end process. To ensure this, we correct the output of the trained neural network such that the corrected output representations are sound with respect to the finite abstraction. For deployment, we provide a novel training algorithm to find a neural network representation of the synthesized controller and experimentally show that the controller can be correctly represented as a combination of a neural network and a look-up table that requires a substantially smaller memory.

We demonstrate experimentally that our approach significantly reduces the memory requirements of abstraction-based methods. We compare the performance of our approach with the standard abstraction-based synthesis on several models. For the selected benchmarks, our approach reduces the memory requirements respectively for the synthesis and deployment by a factor of 1.31× 10
5
and 7.13× 10
3
on average, and up to 7.54× 10
5
and 3.18× 10
4
. Although this reduction is at the cost of increased off-line computations to train the neural networks, all the steps of our approach are parallelizable and can be implemented on machines with higher number of processing units to reduce the required computational time.
BitSET: Bit-Serial Early Termination for Computation Reduction in Convolutional Neural Networks
Authors: Pan Yunjie , Yu Jiecao , Lukefahr Andrew , Das Reetuparna , Mahlke Scott
Published: September 2023

Convolutional Neural Networks (CNNs) have demonstrated remarkable performance across a wide range of machine learning tasks. However, the high accuracy usually comes at the cost of substantial computation and energy consumption, making it difficult to be deployed on mobile and embedded devices. In CNNs, the compute-intensive convolutional layers are usually followed by a ReLU activation layer, which clamps negative outputs to zeros, resulting in large activation sparsity. By exploiting such sparsity in CNN models, we propose a software-hardware co-design BitSET, that aggressively saves energy during CNN inference. The bit-serial BitSET accelerator adopts a prediction-based bit-level early termination technique that terminates the ineffectual computation of negative outputs early. To assist the algorithm, we propose a novel weight encoding that allows more accurate predictions with fewer bits. BitSET leverages the bit-level computation reduction both in the predictive early termination algorithm and in the non-predictive, energy-efficient bit-serial architecture. Compared to UNPU, an energy-efficient bit-serial CNN accelerator, BitSET yields an average 1.5× speedup and 1.4× energy efficiency improvement with no accuracy loss due to a 48% reduction in bit-level computations. Relaxing the allowed accuracy loss to 1% increases the gains to an average of 1.6× speedup and 1.4× energy efficiency improvement.
Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
Authors: Juang Tzung-Han , Schlaak Christof , Dubach Christophe
Published: September 2023

Traditional High-Level Synthesis (HLS) provides rapid prototyping of hardware accelerators without coding with Hardware Description Languages (HDLs). However, such an approach does not well support allocating large applications like entire deep neural networks on a single Field Programmable Gate Array (FPGA) device. The approach leads to designs that are inefficient or do not fit into FPGAs due to resource constraints.

This work proposes to shrink generated designs by coarse-grained resource control based on function sharing in functional Intermediate Representations (IRs). The proposed compiler passes and rewrite system aim at producing valid design points and removing redundant hardware. Such optimizations make fitting entire neural networks on FPGAs feasible and produce competitive performance compared to running specialized kernels for each layer.
Consistency vs. Availability in Distributed Cyber-Physical Systems
Authors: Lee Edward A. , Akella Ravi , Bateni Soroush , Lin Shaokai , Lohstroh Marten , Menard Christian
Published: September 2023

In distributed applications, Brewer’s CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We extend consistency and availability to refer to cyber-physical properties such as the state of the physical system and delays in actuation. We have further extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design cyber-physical systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on the
Lingua Franca
coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable embedded software.
DaCapo: An On-Device Learning Scheme for Memory-Constrained Embedded Systems
Authors: Khan Osama , Park Gwanjong , Seo Euiseong
Published: September 2023

The use of deep neural network (DNN) applications in microcontroller unit (MCU) embedded systems is getting popular. However, the DNN models in such systems frequently suffer from accuracy loss due to the dataset shift problem. On-device learning resolves this problem by updating the model parameters on-site with the real-world data, thus localizing the model to its surroundings. However, the backpropagation step during on-device learning requires the output of every layer computed during the forward pass to be stored in memory. This is usually infeasible in MCU devices as they are equipped only with a few KBs of SRAM. Given their energy limitation and the timeliness requirements, using flash memory to store the output of every layer is not practical either. Although there have been proposed a few research results to enable on-device learning under stringent memory conditions, they require the modification of the target models or the use of non-conventional gradient computation strategies. This paper proposes DaCapo, a backpropagation scheme that enables on-device learning in memory-constrained embedded systems. DaCapo stores only the output of certain layers, known as checkpoints, in SRAM, and discards the others. The discarded outputs are recomputed during backpropagation from the nearest checkpoint in front of them. In order to minimize the recomputation occurrences, DaCapo optimally plans the checkpoints to be stored in the SRAM area at a particular phase of the backpropagation and thus replaces the checkpoints stored in memory as the backpropagation progresses. We implemented the proposed scheme in an STM32F429ZI board and evaluated it with five representative DNN models. Our evaluation showed that DaCapo improved backpropagation time by up to 22% and saved energy consumption by up to 28% in comparison to AIfES, a machine learning platform optimized for MCU devices. In addition, our proposed approach enabled the training of MobileNet, which the MCU device had been previously unable to train.
Probabilistic Black-Box Checking via Active MDP Learning
Authors: Shijubo Junya , Waga Masaki , Suenaga Kohei
Published: September 2023

We introduce a novel methodology for testing stochastic black-box systems, frequently encountered in embedded systems. Our approach enhances the established
black-box checking (BBC)
technique to address stochastic behavior. Traditional BBC primarily involves iteratively identifying an input that breaches the system’s specifications by executing the following three phases: the
learning
phase to construct an automaton approximating the black box’s behavior, the
synthesis
phase to identify a candidate counterexample from the learned automaton, and the
validation
phase to validate the obtained candidate counterexample and the learned automaton against the original black-box system. Our method, ProbBBC, refines the conventional BBC approach by (1) employing an active Markov Decision Process (MDP) learning method during the learning phase, (2) incorporating probabilistic model checking in the synthesis phase, and (3) applying statistical hypothesis testing in the validation phase. ProbBBC uniquely integrates these techniques rather than merely substituting each method in the traditional BBC; for instance, the statistical hypothesis testing and the MDP learning procedure exchange information regarding the black-box system’s observation with one another. The experiment results suggest that ProbBBC outperforms an existing method, especially for systems with limited observation.
Methods to Realize Preemption in Phased Execution Models
Authors: Thilakasiri Thilanka , Becker Matthias
Published: September 2023

Phased execution models are a good solution to tame the increased complexity and contention of commercial off-the-shelf (COTS) multi-core platforms, e.g., Acquisition-Execution-Restitution (AER) model, PRedictable Execution Model (PREM). Such models separate execution from access to shared resources on the platform to minimize contention. All data and instructions needed during an execution phase are copied into the local memory of the core before starting to execute. Phased execution models are generally used with non-preemptive scheduling to increase predictability. However, the blocking time in non-preemptive systems can reduce schedulability. Therefore, an investigation of preemption methods for phased execution models is warranted. Although, preemption for phased execution models must be carefully designed to retain its execution semantics, i.e., the handling of local memory during preemption becomes non-trivial.

This paper investigates different methods to realize preemption in phased execution models while preserving their semantics. To the best of our knowledge, this is the first paper to explore different approaches to implement preemption in phased execution models from the perspective of data management. We introduce two strategies to realize preemption of execution phases based on different methods of handling local data of the preempted task. Heuristics are used to create time-triggered schedules for task sets that follow the proposed preemption methods. Additionally, a schedulability-aware preemption heuristic is proposed to reduce the number of preemptions by allowing preemption only when it is beneficial in terms of schedulability. Evaluations on a large number of synthetic task sets are performed to compare the proposed preemption models against each other and against a non-preemptive version. Furthermore, our schedulability-aware preemption heuristic has higher schedulability with a clear margin in all our experiments compared to the non-preemptive and fully-preemptive versions.
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment
Authors: Odema Mohanad , Bouzidi Halima , Ouarnoughi Hamza , Niar Smail , Al Faruque Mohammad Abdullah
Published: September 2023

Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between various parts of an image frame. On another front, the rising popularity of deep vision-based applications at the edge has been facilitated by the recent advancements in heterogeneous multi-processor Systems on Chips (MPSoCs) that enable inference under real-time, stringent execution requirements. By extension, GNNs employed for vision-based applications must adhere to the same execution requirements. Yet contrary to typical deep neural networks, the irregular flow of graph learning operations poses a challenge to running GNNs on such heterogeneous MPSoC platforms. In this paper, we propose a novel unified
design-mapping
approach for efficient processing of vision GNN workloads on heterogeneous MPSoC platforms. Particularly, we develop MaGNAS, a mapping-aware Graph Neural Architecture Search framework. MaGNAS proposes a GNN architectural design space coupled with prospective mapping options on a heterogeneous SoC to identify model architectures that maximize on-device resource efficiency. To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal
GNNs
and
mapping
pairings that yield the best performance trade-offs. Through designing a supernet derived from the recent Vision GNN (ViG) architecture, we conducted experiments on four (04) state-of-the-art vision datasets using both (
i
) a real hardware SoC platform (NVIDIA Xavier AGX) and (
ii
) a performance/cost model simulator for DNN accelerators. Our experimental results demonstrate that MaGNAS is able to provide
1.57
× latency speedup and is
3.38
× more energy-efficient for several vision datasets executed on the Xavier MPSoC vs. the GPU-only deployment while sustaining an average
0.11%
accuracy reduction from the baseline.
ANV-PUF: Machine-Learning-Resilient NVM-Based Arbiter PUF
Authors: Nassar Hassan , Bauer Lars , Henkel Jörg
Published: September 2023

Physical Unclonable Functions (PUFs) have been widely considered an attractive security primitive. They use the deviations in the fabrication process to have unique responses from each device. Due to their nature, they serve as a DNA-like identity of the device. But PUFs have also been targeted for attacks. It has been proven that machine learning (ML) can be used to effectively model a PUF design and predict its behavior, leading to leakage of the internal secrets.

To combat such attacks, several designs have been proposed to make it harder to model PUFs. One design direction is to use Non-Volatile Memory (NVM) as the building block of the PUF. NVM typically are multi-level cells, i.e, they have several internal states, which makes it harder to model them. However, the current state of the art of NVM-based PUFs is limited to ‘weak PUFs’, i.e., the number of outputs grows only linearly with the number of inputs, which limits the number of possible secret values that can be stored using the PUF. To overcome this limitation, in this work we design the Arbiter Non-Volatile PUF (ANV-PUF) that is exponential in the number of inputs and that is resilient against ML-based modeling. The concept is based on the famous delay-based Arbiter PUF (which is not resilient against modeling attacks) while using NVM as a building block instead of switches. Hence, we replace the switch delays (which are easy to model via ML) with the multi-level property of NVM (which is hard to model via ML). Consequently, our design has the exponential output characteristics of the Arbiter PUF and the resilience against attacks from the NVM-based PUFs. Our results show that the resilience to ML modeling, uniqueness, and uniformity are all in the ideal range of 50%. Thus, in contrast to the state-of-the-art, ANV-PUF is able to be resilient to attacks, while having an exponential number of outputs.
iAware: Interaction Aware Task Scheduling for Reducing Resource Contention in Mobile Systems
Authors: Zheng Yongchun , Li Changlong , Xiong Yi , Liu Weihong , Ji Cheng , Zhu Zongwei , Yu Lichen
Published: September 2023

To ensure the user experience of mobile systems, the foreground application can be differentiated to minimize the impact of background applications. However, this article observes that system services in the kernel and framework layer, instead of background applications, are now the major resource competitors. Specifically, these service tasks tend to be quiet when people rarely interact with the foreground application and active when interactions become frequent, and this high overlap of busy times leads to contention for resources. This article proposes iAware, an interaction-aware task scheduling framework in mobile systems. The key insight is to make use of the previously ignored idle period and schedule service tasks to run at that period. iAware quantify the interaction characteristic based on the screen touch event, and successfully stagger the periods of frequent user interactions. With iAware, service tasks tend to run when few interactions occur, for example, when the device’s screen is turned off, instead of when the user is frequently interacting with it. iAware is implemented on real smartphones. Experimental results show that the user experience is significantly improved with iAware. Compared to the state-of-the-art, the application launching speed and frame rate are enhanced by 38.89% and 7.97% separately, with no more than 1% additional battery consumption.
Equation-Directed Axiomatization of Lustre Semantics to Enable Optimized Code Validation
Authors: Brun Lélio , Garion Christophe , Garoche Pierre-Loïc , Thirioux Xavier
Published: September 2023

Model-based design tools like SCADE Suite and Simulink are often used to design safety-critical embedded software. Consequently, generating correct code from such models is crucial. We tackle this challenge on Lustre, a dataflow synchronous language that embodies the concepts that base such tools. Instead of proving correct a whole code generator, we turn an existing compiler into a certifying compiler from Lustre to C, following a translation validation approach.

We propose a solution that generates both C code and an attached specification expressing a correctness result for the generated and optionally optimized code. The specification yields proof obligations that are discharged by external solvers through the Frama-C platform.
An Empirical Analysis of Web Storage and Its Applications to Web Tracking
Authors: Ahmad Zubair , Casarin Samuele , Calzavara Stefano
Published: September 2023

In this article, we present a large-scale empirical analysis of the use of web storage in the wild.By using dynamic taint tracking at the level of JavaScript and by performing an automated classification of the detected information flows, we shed light on the key characteristics of web storage uses in the Tranco Top 10k. Our analysis shows that web storage is routinely accessed by third parties, including known web trackers, who are particularly eager to have both read and write access to persistent web storage information. We then deep dive in web tracking as a prominent case study: our analysis shows that web storage is not yet as popular as cookies for tracking purposes; however, taint tracking is useful to detect potential new trackers not included in standard filter lists. Moreover, we observe that many websites do not comply with the General Data Protection Regulation directives when it comes to their use of web storage.
An Overview of Structured Lipid in Food Science: Synthesis Methods, Applications, and Future Prospects
Authors: Hong Chi Rac , Jeon Byeong Jun , Park Kyung-Min , Lee Eun Ha , Hong Sung-Chul , Choi Seung Jun
Published: September 2023

Structured lipids have attracted significant interest in industries, such as food, pharmaceuticals, and cosmetics, because they can be alternatives to traditional lipids. Enzymatic synthesis techniques utilizing lipases have gained attention for structured lipids synthesis because of their site and substrate specificity. However, most lipases discovered to date exhibit sn-1,3 regiospecificity or nonspecificity, limiting the selective range of natural oils and fats that can be used as raw materials. In this review, we provide an overview of the current synthesis methods and applications of structured lipids and the limitations of existing lipases in producing structured lipids. In addition, we discuss innovative approaches, including metagenomics, and machine learning, to discover, and classify new lipases and the use of gene editing technologies for lipase engineering. These efforts aim to overcome the limitations of existing lipases and expand the range of natural oils and fats that can be used for the production of structured lipids. Therefore, this study aims to promote a better understanding of structured lipids as a material with high-added value and provides insight into ongoing research efforts in the field.
Assessing the Return Periods and Hydroclimatic Parameters for Rainwater Drainage in the Coastal City of Cotonou in Benin under Climate Variability
Authors: Badou Djigbo Félicien , Hounkanrin José , Hounkpè Jean , Sintondji Luc Ollivier , Emmanuel Lawin Agnidé
Published: September 2023

Cotonou, the economic capital of Benin, is suffering from the impacts of climate change, particularly evident through recurrent floods. To effectively manage these floods and address this issue, it is crucial to have a deep understanding of return periods and hydroclimatic parameters (such as intensity-duration-frequency (IDF) curves and related coefficients), which are essential for designing stormwater drainage structures. Determining return periods and these parameters requires statistical analysis of extreme events, and this analysis needs to be regularly updated in response to climate change. The objective of this study was to determine the necessary return periods and hydroclimatic parameters to improve stormwater drainage systems in the city and its surroundings areas. This required annual maximum precipitation series of 1, 2, 3, 6, 12, and 24 h for 20 years length (1999–2018) as well as flood record data. The intensity series, derived by dividing the amount of rainfall by its duration, was adjusted using Gumbel’s law. IDF curves were constructed based on Montana and Talbot models, and their coefficients were determined according to the corresponding return periods. In 2010, which witnessed devastating floods in the country, the return period for the most intense rainfall events was 40 years, followed by 2013 with a return period of 13.4 years. Consequently, the commonly used 10-year return period for the design of stormwater drainage structures in Cotonou is insufficient. The Talbot model produced the lowest mean square errors for each quantile series and coefficients of determination closest to one, indicating that the parameters obtained from this model are well suited for designing hydraulic structures in Cotonou. The hydroclimatic parameters presented in this study will contribute to the improved design of hydraulic structures in the city of Cotonou.
Evaluation of CD47 in the Suppressive Tumor Microenvironment and Immunotherapy in Prostate Cancer
Authors: Wang Qianqian , Feng Chunxaing , Chen Yuchun , Peng Tianming , Li Yong , Wu Kunlin , Pu Xiaoyong , Chen Hanzhong , Liu Jiumin
Published: September 2023

Background. CD47 has high levels of expression in malignant cancer cells, which binds to SIRP-α to release the “don’t eat me” signal and prevents mononuclear macrophages from phagocytosing the cells. Resistance to drugs and metastases are potential barriers for prostate cancer endocrine therapy. Although immunotherapy for tumors has developed rapidly in the last few decades, its effectiveness in treating prostate cancer is unsatisfactory. Prostate cancer has a high-expression level of CD47. Therefore, a novel approach for potential immunotherapy may be provided by investigating the relationship among CD47 and the infiltration of immune cells in the prostate carcinoma. Methods. The GEPIA database was utilized to compare the abundance of CD47 in malignant tissues with tissues that were normal. Furthermore, the function of CD47 in prostate carcinoma was assessed by CancerSEA. The association among CD47 and the tumor microenvironment was assessed utilizing the TISCH single cell data database. By using TIMER, the connection among CD47 and immunological invasion of prostate cancer was explored. Moreover, macrophages were cocultured with mouse prostate cancer cell RM-1 blocked by CD47 antibody to observe the changes in phagocytosis efficiency in vitro. Results. Expression level of CD47 is upregulated in prostate carcinoma, and it is closely connected with prostate cancer’s inadequate immune invasion. CD47 antibody blocking promotes macrophage phagocytosis of RM-1. Conclusion. Our research demonstrates a closely relationship among CD47 and the immunological microenvironment of prostate cancer, and blocking CD47 can promote macrophages to phagocytosis of prostate cancer cells. Therefore, CD47 may provide novel strategies for potential immunotherapy of prostate cancer.
Effect of Different Feeding Rates for the Growth and Body Composition of Manchurian Trout, Brachymystax lenok
Authors: Liu Enhui , Huang Tianqing , Gu Wei , Wang Gaochao , Wang Bingqian , Shi Xiulan , Xu Gefeng
Published: September 2023

A 50-day feeding trial was conducted to investigate the effects of feeding rates (FRs) on the growth, feed utilisation, and body composition of Brachymystax lenok. Juveniles of B. lenok (initial body weight of 7.6 ± 0.2 g) were fed a commercial diet at the different FRs of 1, 2, 3, 4, and 5% of their body weight. The results indicated that weight gain (WG) and specific growth rate (SGR) were first increased and then decreased with an increase in the FR. The WG and SGR of the 4% group were 299.78% and 3.96%, respectively. The feed conversion ratio (FCR) and protein efficiency ratio (PER) were also significantly affected by the FR (

P
<
0.05

). Furthermore, the condition factor (CF) did not differ significantly among the treatment groups (

P
>
0.05

). However, the FR significantly affected the hepatosomatic index (HSI) and visceral somatic index (VSI) (

P
<
0.05

). With an increase in the FR, the moisture content of the fish decreased, but the ash content changed irregularly. The protein content increased significantly with increasing FR (

P
<
0.05

). The lipid content showed a trend similar to protein in the body. Based on the SGR and FCR data, the optimal FR range was 2.4 to 3.1% for B. lenok (7−30 g body weight) at a water temperature of 16°C.
Study on the Bearing Plate of Steel Clarification Tank under Earthquake
Authors: Cheng Xiwei , Cheng Chuyun , Zhang Wanlin , Hu Lankai , Cheng Xuansheng
Published: September 2023

The stress at the support structure of the clarification tank is large, which will have a certain impact on the whole clarification tank structure. The typical stress concentration phenomenon will cause structural damage and serious harm to the steel clarification tank itself. In order to study the influence of the bearing plate on the seismic response of steel clarification tank, this paper analyzes the dynamic response of the clarification tank by setting different thickness bearing plates at the connection between the steel column and the structure body of the steel clarification tank. Based on the displacement, stress, and liquid sloshing height of the steel clarification tank, the thickness of the bearing plate is obtained. The results show that the maximum stress of the steel clarification tank under different seismic waves decreases with the increase of the thickness of the bearing plate. The thickness of the bearing plate has a certain influence on the maximum displacement of the clarification tank structure, but the increase in the thickness of the bearing plate does not necessarily reduce the maximum displacement. Different bearing plate thicknesses have no effect on the position of displacement, and the maximum displacement occurs at the top of the outer wall.
The Potential Dual Role of H2.0-like Homeobox in the Tumorgenesis and Development of Colorectal Cancer and Its Prognostic Value
Authors: Chen Shuo , Zhang Lin , Wang Kai , Huo Jizhen , Zhang Siqi , Zhang Xipeng
Published: September 2023

Background. H2.0-like homeobox (HLX) is highly expressed in several hematopoietic malignancies. However, the role of HLX in the carcinogenesis and progression of colorectal cancer (CRC) patients has rarely been reported. Methods. In this study, the data were collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. The diagnostic value of HLX was analyzed by the R package “pROC.” The overall survival was estimated using the “survival” and “survminer” packages. A nomogram was established to predict 1-, 3-, and 5-year overall survival of CRC patients. The CIBERSORT software was employed to calculate the relative proportions of 22 immune cells. Results. HLX expression was downregulated in CRC patients. Remarkably, HLX expression was increased with stage (stage I–stage III) of CRC, and the CRC patients with high HLX expression exhibited a poor prognosis. The promoter methylation level of HLX was prominently increased in CRC samples compared to paracancerous samples. We also found that the six miRNAs target HLX genes, leading to its downregulation, and HLX expression had a negative correlation with its downstream target gene BRI3BP in both CRC and normal samples. Finally, we found that the 12 immune infiltrating cells were observably different between high and low HLX expression groups. The HLX had a significant positive correlation with 8 immune checkpoints (PD-1 (PDCD1), CTLA4, PDL-1 (CD274), PDL-2 (PDCD1LG2), CD80, CD86, LAG3, and TIGIT) expressions. Conclusion. HLX probably played a carcinostasis role in the early stages of CRC but exhibited a cancer-promoting effect in the advanced stages. Meanwhile, HLX could serve as a reliable prognostic indicator for CRC.
Damage Identification of Rotating Blades Based on Fuzzy C-Means and Frequency Response Function Curvature Methods
Authors: Cui Ruize , Wang Zefeng , Song Zhiguang
Published: September 2023

Rotor blades are one of the key components of helicopter. If the blades are damaged, the safety and reliability of the helicopter will be seriously affected. Therefore, it is necessary to investigate the damage identification of the rotating blades. In this paper, a rotating cantilever beam is used to model the rotor blade. Based on the assumed mode method and Hamilton’s principle, the equation of motion is formulated, and the correctness of the model is verified by numerical and experimental studies. Altogether, two methods are used to identify the damages on the blade. The first one is the cluster analysis method based on the fuzzy C-mean theory. In order to reduce the dimension of the signal features, the singular value decomposition is introduced. The second method is the curvature of frequency response function method that can be used to determine the exact position of damages. Simulation results show that one can use fuzzy C-mean method to determine whether there is damage on the blade firstly and then determine the exact position of the damage through the curvature of frequency response function method.
Automatic Landing System Design for Unmanned Fixed-Wing Vehicles via Multivariable Active Disturbance Rejection Control
Authors: Sun Zonghua , Wu Liaoni , You Yancheng
Published: September 2023

Landing control of unmanned aerial vehicles (UAVs) is challenging because of the strong nonlinear dynamics, multivariable, model uncertainties, wind variations, and sensor noise. Motivated by this fact, this paper investigates an automatic landing system (ALS) that includes trajectory generation and guidance law for the first flight test of a turbine-based combined cycle technology demonstrator. Specifically, the control scheme increases the original model’s order to generate a reasonable monotone-decreasing throttle reference flare trajectory by the pseudospectral method. Subsequently, the guidance law based on innovative multivariable active disturbance rejection control is designed to robustly track the reference altitude and velocity simultaneously with high accuracy. The multivariable extended state observer (ESO) incorporated decoupling algorithm enhances the estimation capability and accuracy of potential problem in cross-coupling dynamics compared to the traditional ESO. It is proven that the closed-loop error dynamic has bounded-input bounded-output stability and an explicit upper bound is given. Numerical simulation verifies that the presented approach has better robustness and higher tracking accuracy for external disturbances and parametric uncertainties than the existing benchmark autolanding controller. Finally, flight tests show that the proposed ALS can land the vehicle effectively and safely under severe wind conditions.
Farmers versus Technocrats: A Comparative Analysis of A. D. Gordon and Theodor Herzl on Nature and Technology
Authors: Shamis Asaf J.
Published: September 2023

This paper analyzes the treatment of nature and technology in the writings of two prominent early Zionist thinkers, A. D. Gordon and Theodor Herzl. At the heart of Herzl’s vision, we find technocrats applying industrial systems to dominate the naked nature that Gordon is committed to preserve. Gordon, in contrast, describes Jewish national revival as triggered by farmers utilizing Eretz Israel’s natural world to extract Jews from industrial society, underwriting Herzl’s Zionist vision. Expanding the analysis to the domains of nature and technology reveals the two thinkers’ creeds to be rival Zionist ideologies in which nature and technology serve as catalysts for competing visions of national revival.
A Hasidic Commentary on the Passover Haggadah for the New World
Authors: Wiskind Ora
Published: September 2023

Todat Yehoshua (1935), a Hasidic commentary on the Passover Haggadah by Rabbi Yehoshua Heschel Rabinowitz of Monastyrishche, Ukraine, later of Brownsville, New York, offers an important perspective on Orthodox experience in North America in the interwar period. On his reading, the Haggadah invites an understanding of history that recognizes and contends with all that is radically unholy: from secularism, enlightenment, and Zionism in the Jewish camp, to Marxism, communism, anarchy, Nazism, and contemporary antisemitism. As a Hasidic tsadik and émigré rabbi, R. Yehoshua Heschel sought to revitalize religion as an existentially vital facet of being, while encouraging those around him to forge a Jewish identity loyal to the past and empowered to rise to the challenges of the present.
Entering Anthologies with Names: The First Generation of Courtesan Poets in Ming Dynasty Nanjing
Authors: Chen Jiani
Published: September 2023

This article traces the first generation of Ming courtesan poets and the earliest extant anthology recording both their poems and full names. These pioneering courtesan poets emerged from anonymity in the city of Nanjing during the late fifteenth and early sixteenth centuries. They represented a new mode of literary production and circulation in the context of social, economic, and cultural transformation which began in the mid-Ming. To better situate courtesan poets within the Ming world of letters, we examine the various sources recording their biographical information and literary/artistic creations.
Bathing through Time and Landscape: Everyday Encounters and Reconstructions at Huaqing Hot Springs (1000–1900)
Authors: Huang Fei
Published: September 2023

Despite the long-standing interest in hot springs in China, this subject has remained understudied. This article explores how the most famous hot springs in China, the Huaqing Hot Springs 華清池 at Mount Li 驪山, were selectively reused and negotiated by different social groups from the tenth to the nineteenth century. It retraces the historical process of reconstructions at the Huaqing Hot Springs by local officials, religious practitioners, literati, and local inhabitants after the Huaqing palaces were abandoned by the Tang central court. In the post-Tang life of the Huaqing Hot Springs, the long-term co-existence of activities between various social classes and across the gender divide are deeply rooted in the local everyday usage of the hot springs. This article reveals multiple transformations within continuity in reimaging and reconstructing the hot springs landscape in Chinese history.
Transmitted Sayings and Their Transmissibility: Patterns and Meanings of Zhuan yue 傳曰 Quotations in Early Chinese Texts
Authors: Gu Yixin
Published: September 2023

This article investigates the introductory formula zhuan yue 傳曰 (“a transmitted source says”) in early China, together with its “quotations” and their parallel texts. Rarely found in pre-imperial texts, the zhuan yue formula gained prominence especially from the mid-Western Han onward. As a vague, unspecific gesture of reference, the formula was flexibly applied to varying functions but also used with limited patterns and regularity. Both the formula and the quoted material are almost never found in the core texts of the “Five Classics” (wujing 五經); instead, zhuan yue appears to indicate sources that existed as an open repository outside of the emerging canon. Thus, in marking the non-canonical body, zhuan yue also helped define the canonical one. Finally, another usage of zhuan yue—which emerged in early Han times if not earlier—as an introduction to specific zhuan-commentaries of the classics is clearly a separate function altogether.
Democracy Under Occupation: Coalition Government Formation and Survival in Iraq and Palestine
Authors: Stapleton Stephanie , Biygautane Taib , Bhasin Tavishi , Hallward Maia Carter
Published: September 2023

While existing research on coalition government formation and durability has significantly enhanced our understanding of coalition processes, it remains heavily focused on (1) the experiences of established democracies in Europe and on (2) the various roles of domestic institutions and actors. In this article, we examine the interplay of external interference and domestic actors to explain the success and failure of coalition government formation in the context of fragile democracies. We ask: How does external interference impact government coalition formation? Drawing on Domestic Opposition Theory (dot), we explore two cases of coalition government efforts in the Middle East: post-elections in Iraq in 2018 and in Palestine in 2006. Findings show that, in the case of Palestine, external interference – financial boycott of Hamas-led government and support for Fatah against Hamas – played a key role in the eventual failure of the governing coalition. In Iraq’s case, widespread public domestic opposition to continued external influence, particularly from Iranian-backed interests, played a defining role in coalition formation processes.
Coalition Maintenance during Democratization: Comparative Insights from Tunisia and Yemen
Authors: Heibach Jens , Jermanová Tereza
Published: September 2023

In light of the oft-cited benefits of power sharing, this paper compares two cases of coalition governments that emerged from domestically initiated transition processes in the Arab world: the Tunisian Troika (2011–2014) and the Yemeni Coalition Government (1993–1994). While the former facilitated Tunisia’s democratization, the latter’s disintegration contributed to the 1994 war and Yemen’s authoritarian backsliding. It asks why coalition cooperation endured in Tunisia but not in Yemen, exploring intra-elite relations, the power dynamics at play, as well as elites’ motivations in launching and maintaining cooperation. The paper reveals that both coalitions’ internal functioning was hampered by adverse power configurations and ideological differences. In Yemen, intra-elite relations were further impaired by mutual distrust. Crucially, a history of sustained partnership along with normative commitment to the idea of power sharing, existent in Tunisia and not in Yemen, explains why cooperation within the Troika remained solid amid unfavorable circumstances.
Creating the ‘New Fascist Man’
Authors: Borri Michelangelo
Published: September 2023

The creation of the Fascist New Man was one of the primary objectives of the Mussolini regime. This article aims to examine the theme by way of a specific case study of the physician and Fascist party official Giorgio Alberto Chiurco. A Fascist ‘of the first hour’ and faithful follower of Mussolini, Chiurco expressed his commitment to the project of creating a new Italian man with activity in various sectors. In medicine and sports, by promoting the training of new generations that would be healthy and athletic. In eugenics, by promoting the defense of the national genetic heritage from the ‘peril’ of contamination. In politics and culture, by promoting the so-called fascistization of schools and universities. All this activity shows, on the one hand, the important contribution made by single individuals to the totalitarian project of the regime and, on the other, the efficacy with which Fascism succeeded in orienting the activities of intellectuals and scientists on a national and international level.
Art de Déconcentration
Authors: Pelc Adrian
Published: September 2023

The paper attempts to establish a dialogue between advertising, the Yugoslav avant-garde and a theoretical discourse on the “death” of the avant-garde. In a first step, the trope of the avant-garde dying through its techniques being appropriated by advertising is briefly scrutinized. In a second step, the article demonstrates that the axes of exchange between Yugoslav avant-garde movements (Zenitism, Hypnism, Dadaism) and early advertising were multifold and irreducible to a simple matrix of originality and bad appropriation. Finally, the results of the analysis are considered from the perspective of “peripheral modernism” and their consequences for the grand death-discourse are scrutinized.