bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discrete Particle Swarm Optimization for Coordinated Robust Configuration of Photovoltaic Energy Storage in Distribution Network
Authors: Yin Zhi , Li Chao , Ouyang Lilin , Liu Chensong 
Published: July 2024

Abstract
This paper focuses on examining the coordination and optimization of photovoltaic energy storage systems within distribution networks. It introduces a configuration approach utilizing the Discrete Particle Swarm Optimization (DPSO) algorithm and validates its efficacy through simulation experiments. Initially, the research establishes a coordinated optimization framework for photovoltaic energy storage, accounting for uncertainties like light intensity fluctuations and load variations. The primary objective is to minimize the system’s overall operational costs. To address this optimization challenge, the study employs the DPSO algorithm. By seeking out the best configuration of photovoltaic generation and energy storage units, it achieves a multi-faceted optimization encompassing economic efficiency, reliability, and sustainability. Simulation outcomes reveal that the proposed DPSO algorithm exhibits notable strengths in solution quality, convergence rate, robustness, and adaptability. When benchmarked against other algorithms, the DPSO method demonstrates a shorter runtime, improved by approximately 32%. Moreover, it attains a high solution quality, exceeding 92%. These findings underscore the algorithm’s proficiency in swiftly identifying superior solutions. Overall, this research offers valuable insights for optimizing the design and operation of photovoltaic energy storage systems within distribution networks.
Design and optimization of a circuit for ECG signal processing using 180nm technology
Authors: Lu Jinshan , Sun Yimeng , Yuan Tiancan 
Published: July 2024

Abstract
In the medical industry, electrocardiogram (ECG) plays a decisive role in helping people diagnose different diseases. This article applies the designed high-performance differential amplifier to the amplification and denoising of ECG signals, using TSMC 180nm CMOS process and a 1.8V power supply. The differential pair is used to suppress common-mode signals and amplify differential effective signals. The tail current stabilizes the working status of the device and helps achieve a better common mode rejection ratio and linearity. Considering actual manufacturing, the ideal current source in the circuit is replaced with a proportional current mirror (composed using MOS transistors) and a linear resistor, even though the ideal current source works better. This article uses CoolSpice to design schematics and simulations and build different circuits to test various parameters, including gain, input common mode range, DC output swing, etc. What is unique is that the circuit is finally connected to the human body model to observe the ECG results. When optimizing, it is very efficient to use Excel to list and process the data obtained from the simulation in CoolSpice into a line chart. After this, the aspect ratios of transistors and tail current are modified to find excellent differential mode gain, common-mode gain, common-mode rejection ratio, power consumption and bandwidth, which are 46.14dB, −45.46dB, 91.6, 36μW and 77.45MHz respectively. For application scenarios with wider bandwidth and lower power consumption, these parameters are also found, which are 42.74dB, −47.07dB, 89.81, 27μW and 144.36MHz respectively.
Optimization of Reinforcement Learning Algorithms for Addressing the Linear Function Fitting Challenge in the Electric Power Internet of Things
Authors: Wang Chunxin , Lu Yang , Shan Guodong , Qu Wenyu , Xu Jun 
Published: July 2024

Abstract
With the rapid development of the Electric Power Internet of Things (EPIoT), effectively utilizing reinforcement learning algorithms for optimizing energy management and distribution has become crucial. This paper aims to explore and address one of the primary challenges when applying reinforcement learning algorithms in the context of EPIoT: the issue of linear function fitting. Firstly, we analyze the complexity of data processing in the EPIoT and why standard reinforcement learning algorithms perform poorly in this environment. Subsequently, this research proposes an improved reinforcement learning framework that enhances the accuracy and efficiency of the algorithm in handling large-scale, high-dimensional data by optimizing the linear function fitting process. We validate the performance of the proposed algorithm through a series of experiments using real-world power data. The experimental results demonstrate significant improvements in accuracy and computational speed compared to traditional methods. Finally, this paper discusses the limitations of the study and future research directions, providing new perspectives and ideas for further research in the Electric Power Internet of Things.
Design and optimization of 1 × 8 PLC splitter with backpropagation neural networks and genetic algorithm
Authors: Wan Qiangwei , Zheng Yu , Ouyang Xinyu , Li Zetong , Duan Ji’an 
Published: July 2024

Abstract
The planar lightwave circuit (PLC) splitter serves as a crucial component in fiber-to-the-home. Diverse applications in complex scenarios necessitate a variety of PLC splitters, thereby escalating the complexity of their design. A novel optimization method based on orthogonal experimental design integrating a backpropagation neural network with a genetic algorithm is proposed. The optimization results demonstrate that for the 1 × 8 PLC splitter example, the insertion loss (IL), wavelength-dependent loss (WDL), and uniformity loss (UL) of the designed device are 9.2202 dB, 0.0915 dB, and 0.0914 dB, respectively. Compared to the previous results obtained using a single-parameter optimization method, which yielded an IL, WDL, and UL of 9.5 dB, 0.35 dB, and 0.30 dB respectively, there is a significant performance improvement.
Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status
Authors: Schmitz Erich , Guo Yunhui , Wang Jing 
Published: July 2024

Abstract

Background. Glioblastoma Multiforme (GBM) is an aggressive form of malignant brain tumor with a generally poor prognosis. O
6-methylguanine-DNA methyltransferase (MGMT) promoter methylation has been shown to be a predictive bio-marker for resistance to treatment of GBM, but it is invasive and time-consuming to determine methylation status. There has been effort to predict the MGMT methylation status through analyzing MRI scans using machine learning, which only requires pre-operative scans that are already part of standard-of-care for GBM patients. Purpose. To improve the performance of conventional transfer learning in the identification of MGMT promoter methylation status, we developed a 3D SpotTune network with adaptive fine-tuning capability. Using the pretrained weights of MedicalNet with the SpotTune network, we compared its performance with a randomly initialized network for different combinations of MR modalities. Methods. Using a ResNet50 as the base network, three categories of networks are created: (1) A 3D SpotTune network to process volumetric MR images, (2) a network with randomly initialized weights, and (3) a network pre-trained on MedicalNet. These three networks are trained and evaluated using a public GBM dataset provided by the University of Pennsylvania. The MRI scans from 240 patients are used, with 11 different modalities corresponding to a set of perfusion, diffusion, and structural scans. The performance is evaluated using 5-fold cross validation with a hold-out testing dataset. Results. The SpotTune network showed better performance than the randomly initialized network. The best performing SpotTune model achieved an area under the Receiver Operating Characteristic curve (AUC), average precision of the precision-recall curve (AP), sensitivity, and specificity values of 0.6604, 0.6179, 0.6667, and 0.6061 respectively. Conclusions. SpotTune enables transfer learning to be adaptive to individual patients, resulting in improved performance in predicting MGMT promoter methylation status in GBM using equivalent MRI modalities as compared to a randomly initialized network.
Perception, Vision and Causation
Authors: Snowdon Paul F 
Published: July 2024

Abstract
The causal theory of perception advanced by H. P. Grice, P. F. Strawson, and others affirms three claims: (1) if a subject (S) sees a public object (O) then O causally affects S; (2) if S sees O then O produces in S a state reportable in a sentence beginning ‘It looks to S as if … ’ (looks-state), where these words are interpreted phenomenologically; and (3) claims (1) and (2) represent requirements of our concept of vision. Against this theory, it is argued here that visual perception does not depend on a casual relation between looks-states and objects perceived. This, it is argued, is because to be in a looks-state is either to be in a state that is independent of surrounding objects or to be in a state that involves the surrounding objects. If this disjunctivism about visual perception is true, then the causal theory is false.
How to Interpret ‘Direct Perception’
Authors: Snowdon Paul F 
Published: July 2024

Abstract
Do our perceptual experiences enable us to directly perceive external objects? What is it to perceive something “directly”? Some philosophers have failed to avoid running together an epistemological with a non-epistemological way of understanding the phrase ‘direct perception’ (or ‘directly perceives’). The present essay explores a non-epistemological interpretation which identifies the direct object of perception as the direct object of demonstrative thought. Roughly, on the view offered here, a subject, S, perceives an object, O, if and only if S can demonstratively identify O. Against this backdrop, an interpretation of the phrase ‘naïve realism’ and an answer to the argument from illusion are proposed.
The Formulation of DisjunctivismA Response to Fish
Authors: Snowdon Paul F 
Published: July 2024

Abstract
William Fish proposes that we need to elucidate what ‘disjunctivism’ stands for, and he also proposes that it stands for the rejection of a principle about the nature of experience that he calls the decisiveness principle. The present chapter argues that his first proposal is reasonable, but then argues, in §II, that his positive suggestion does not draw the line between disjunctivism and non-disjunctivism in the right place. In §III, it is argued that disjunctivism is a thesis about the special nature of perceptual experience, and the thesis as elucidated here is then distinguished from and related to certain other ideas about perception, namely, direct realism and also McDowell’s epistemological disjunctivism.
Perceptual Concepts as Non-Causal Concepts
Authors: Snowdon Paul F 
Published: July 2024

Abstract
The question discussed is whether our perceptual concepts should be thought of as causal concepts. The notion of a causal concept is clarified, as is the nature of the question, and it is argued that there is insufficient evidence to support the claim that perceptual concepts are causal concepts. It is also argued that in the nature of the case it is very hard to demonstrate that the concepts are non-causal. Part of the evidence considered is that provided in earlier publications by Bill Child. An alternative, although partial, treatment of these concepts is sketched.
Rule-Following, Meaning, and Content
Authors: Miller Alexander 
Published: July 2024

Abstract
This chapter explores the relations between the notions of rule-following, linguistic meaning, and intentional content. It first outlines Paul Boghossian’s intuitive ACER model of rule-following, according to which following a rule R involves accepting R such that one’s behaviour counts as correct or incorrect relative to R and is sometimes rationally explained in terms of that acceptance. It extends the ACER model to meaningful language use and intentional action generally. It then argues that meaningful use of language is analogous to a rule-guided activity to the extent that arguments about the possibility and nature of rule-following can deliver conclusions about the possibility and nature of linguistic understanding; it also argues that the life of the mind is analogous to a rule-guided activity to the extent that arguments about the possibility and nature of rule-following are capable of delivering conclusions about the possibility and nature of intentional content. It responds to a recent argument that the later Wittgenstein rejects the idea that speaking a language is a rule-guided activity.
Appetite (Epithumia)
Authors: Pearson Giles 
Published: July 2024

Abstract
This chapter considers the puzzling status of appetite (epithumia) for Aristotle’s account of the emotions. This appears on several of Aristotle’s lists of emotions, but does not receive separate discussion in his catalogue of emotions in Rhetoric II. Also, elsewhere, Aristotle seems to view appetite as one of his three species of desire (alongside, thumos (spirit), and boulēsis (rational desire or wish)). It is argued that, appearances to the contrary notwithstanding, there is good evidence that Aristotle thinks appetite can count as an emotion in the Rhetoric. And it is argued that we can make sense of this if we envisage him as holding that (at least one category of) appetites can be construed along the lines of anger, viz. as involving both a representational distress (an emotion) and a desire. In appetite’s case, the representational distress concerns not a perceived slight, but a perceived lack, discomfort, or bodily pain.
Truce, Negotiations, and Damietta’s Fall, September–November 1219
Authors: Marvin Laurence W 
Published: July 2024

Abstract
Chapter 5 covers the truce requested by al-Kamil after Fariskur from early September 1219, Damietta’s fall, and the taking of Tinnīs. The sultan wished to end the war as he anticipated famine because the Nile’s annual flood failed. He also expected crusader reinforcements, though many veterans also departed. Al-Kamil broke the truce by attacking the Latin Christians’ west bank defenses. The two sides negotiated throughout to end the fighting, though both dissembled. Eventually al-Kamil offered to return the True Cross, restore most of the Latin Kingdom of Jerusalem, including Jerusalem, and pay tribute for fortresses he could not return. Crusader opinion was divided. Some thought accepting the terms would achieve the grand strategic goal of Jerusalem’s return to Latin Christian hands. Others believed it was a ploy to weaken their resolve and that a diplomatic solution discounted the massive casualties and effort the crusaders had expended. By late October Damietta was teetering, and doubts as to a diplomatic solution’s efficacy caused the crusaders to reject al-Kamil’s offer. Al-Kamil unsuccessfully attempted to sneak several hundred men into Damietta. This strengthened crusader resolve to continue until Damietta fell. Before the crusaders mounted one final storm, Pelagius dispatched a small force through an unguarded gate at night and seized the city. No massacre ensued, but the siege had caused thousands of deaths and massive starvation among the population. The crusaders discovered much wealth and foodstuffs, and squabbles broke out over this. In late November the crusaders seized the island city of Tinnīs.
Chromosome-scale genome assembly of the hunt bumble bee, Bombus huntii Greene, 1860, a species of agricultural interest
Authors: Koch Jonathan Berenguer Uhuad , Sim Sheina B , Scheffler Brian , Lozier Jeffrey D , Geib Scott M 
Published: July 2024

Abstract
The Hunt bumble bee, Bombus huntii, is a widely distributed pollinator in western North America. The species produces large colony sizes in captive rearing conditions, experiences low parasite and pathogen loads, and has been demonstrated to be an effective pollinator of tomatoes grown in controlled environment agriculture systems. These desirable traits have galvanized producer efforts to develop commercial Bombus huntii colonies for growers to deliver pollination services to crops. To better understand Bombus huntii biology and support population genetic studies and breeding decisions, we sequenced and assembled the Bombus huntii genome from a single haploid male. High-fidelity sequencing of the entire genome using PacBio, along with HiC sequencing, led to a comprehensive contig assembly of high continuity. This assembly was further organized into a chromosomal arrangement, successfully identifying 18 chromosomes spread across the 317.4 Mb assembly with a BUSCO score indicating 97.6% completeness. Synteny analysis demonstrates shared chromosome number (n = 18) with Bombus terrestris, a species belonging to a different subgenus, matching the expectation that presence of 18 haploid chromosomes is an ancestral trait at least between the subgenera Pyrobombus and Bombus sensu stricto. In conclusion, the assembly outcome, alongside the minimal tissue sampled destructively, showcases efficient techniques for producing a comprehensive, highly contiguous genome.
QTL discovery for agronomic and quality traits in diploid potato clones using PotatoMASH amplicon sequencing
Authors: Vexler Lea , Leyva-Perez Maria de la O , Konkolewska Agnieszka , Clot Corentin R , Byrne Stephen , Griffin Denis , Ruttink Tom , Hutten Ronald C B , Engelen Christel , Visser Richard G F , Prigge Vanessa , Wagener Silke , Lairy-Joly Gisele , Driesprong Jan-David , Riis Sundmark Ea Høegh , Rookmaker A Nico O , van Eck Herman J , Milbourne Dan 
Published: July 2024

Abstract
We genotyped a population of 618 diploid potato clones derived from six independent potato-breeding programmes from NW-Europe. The diploids were phenotyped for 23 traits, using standardized protocols and common check varieties, enabling us to derive whole population estimators for most traits. We subsequently performed a genome-wide association study (GWAS) to identify quantitative trait loci (QTL) for all traits with SNPs and short-read haplotypes derived from read-backed phasing. In this study, we used a marker platform called PotatoMASH (Potato Multi-Allele Scanning Haplotags); a pooled multiplex amplicon sequencing based approach. Through this method, neighboring SNPs within an amplicon can be combined to generate multiallelic short-read haplotypes (haplotags) that capture recombination history between the constituent SNPs and reflect the allelic diversity of a given locus in a different way than single bi-allelic SNPs. We found a total of 37 unique QTL across both marker types. A core of 10 QTL was detected with SNPs as well as with haplotags. Haplotags allowed to detect an additional 14 QTL not found based on the SNP set. Conversely, the bi-allelic SNP set also found 13 QTL not detectable using the haplotag set. We conclude that both marker types should routinely be used in parallel to maximize the QTL detection power. We report 19 novel QTL for nine traits: Skin Smoothness, Sprout Dormancy, Total Tuber Number, Tuber Length, Yield, Chipping Color, After-cooking Blackening, Cooking Type, and Eye depth.
Overlapping coactivator function is required for transcriptional activation by the Candida glabrata Pdr1 transcription factor
Authors: Conway Thomas P , Simonicova Lucia , Moye-Rowley W Scott 
Published: July 2024

Abstract
Azole resistance in the pathogenic yeast Candida glabrata is a serious clinical complication and increasing in frequency. The majority of resistant organisms have been found to contain a substitution mutation in the Zn2Cys6 zinc cluster-containing transcription factor Pdr1. These mutations typically lead to this factor driving high, constitutive expression of target genes like the ATP-binding cassette transporter-encoding gene CDR1. Overexpression of Cdr1 is required for the observed elevated fluconazole resistance exhibited by strains containing one of these hyperactive PDR1 alleles. While the identity of hyperactive PDR1 alleles has been extensively documented, the mechanisms underlying how these gain-of-function (GOF) forms of Pdr1 lead to elevated target gene transcription are not well understood. We have used a tandem affinity purification-tagged form of Pdr1 to identify coactivator proteins that biochemically purify with the wild-type and 2 different GOF forms of Pdr1. Three coactivator proteins were found to associate with Pdr1: the SWI/SNF complex Snf2 chromatin remodeling protein and 2 different components of the SAGA complex, Spt7 and Ngg1. We found that deletion mutants lacking either SNF2 or SPT7 exhibited growth defects, even in the absence of fluconazole challenge. To overcome these issues, we employed a conditional degradation system to acutely deplete these coactivators and determined that loss of either coactivator complex, SWI/SNF or SAGA, caused defects in Pdr1-dependent transcription. A double degron strain that could be depleted for both SWI/SNF and SAGA exhibited a profound defect in PDR1 autoregulation, revealing that these complexes work together to ensure high-level Pdr1-dependent gene transcription.
spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics
Authors: Zhang Chao , Liu Lin , Zhang Ying , Li Mei , Fang Shuangsang , Kang Qiang , Chen Ao , Xu Xun , Zhang Yong , Li Yuxiang 
Published: July 2024

Abstract

Background
Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times.

Findings
We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space.

Conclusions
In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.