Partial Segment Research Articles

What drives stock returns across countries? Insights from machine learning models

We employ machine learning techniques to examine cross-sectional variation in country equity returns by aggregating information across multiple market characteristics. Our models reveal significant return predictability, which translates into discernible patterns in portfolio performance. In addition, variable importance analysis uncovers a sparse factor structure that varies across forecast horizons. A handful of critical predictors—such as long-term reversal, momentum, earnings yield, and market size—capture most of the return differences, while country risk measures play a minor role. Consistent with the partial segmentation perspective, return predictability persists in small, illiquid, and unintegrated markets and weakens over time as the constraints on capital mobility diminish. As a result, attempts to forge them into profitable strategies can be challenging at best.

Capacity estimation of lithium-ion battery based on soft dynamic time warping, stratified random sampling and pruned residual neural networks

Accurate battery capacity estimation is crucial for ensuring battery management systems' safe and reliable operation. Although deep learning algorithms have been widely applied in the field of image recognition, their application in battery diagnosis is relatively limited. Besides, obtaining complete cycling data during charging/discharging processes can be challenging in practical applications. This paper proposes a battery capacity estimation method based on partial charge curves and pruned residual neural networks. To leverage the aging information inherent in partial charge curves, the partial segment battery data is transformed into images using wavelet denoising and soft dynamic time warping algorithms. Subsequently, we present a stratified random sampling method to address the issue of imbalanced sample distributions in battery dataset partitioning, and employ residual neural networks to mitigate performance degradation caused by stacking multiple network layers. To validate the effectiveness of the proposed method, experiments are conducted on three types of battery data. During the experimental phase, the root mean square error and mean absolute error of estimated battery capacity are both below 0.79% and 0.54%. Furthermore, a hybrid-pruning algorithm is proposed to enable the deployment of the model on the battery management system, which reduces the model size and computational complexity without compromising accuracy. Compared to other pruning algorithms, the proposed hybrid-pruning algorithm outperforms diagnostic accuracy and computational efficiency after fine-tuning, reducing the model size by 77.40% and creating a more compact structure.

Strategic data sales with partial segment profiling

We analyse the incentives of a data broker to sell consumer-level data that enable personalised pricing to compete with firms when only a fraction of consumers — centred around one firm that we label “central” — are profiled. The central firm can potentially benefit from the data more than the rival ones (“peripheral”). We show that the data broker may decide not to sell the dataset to the central firm and instead trade with its peripheral competitors. In particular, we identify a strategic reaction of competitors that want to prevent that data increase competition.

Spatial arrangements and types of dislocations in interfacial networks obtained by Si(001) wafer bonding at low twist angle: A TEM characterization

Two interfacial dislocation networks were obtained in a Si–Si material system by wafer bonding process for low twist angles around 0.2°. For one sample, one of the two initial wafers was rotated with an additional angle of 90° before the bonding. TEM analyses of samples prepared for planar and transverse views show different characteristics in terms of spatial arrangement and dislocation types. Long climbing segments slightly deviated from the [100] axis and short segments a/2<101> are arranged in the dislocation network obtained with an additional rotation and at the lowest twist angle (Ψ = 0.18°) from a <110> axis. Long screw segments a/2<110> and short partial Shockley segments are arranged in the dislocation network obtained without any additional rotation at the highest twist angle (Ψ = 0.27°) from a <110> axis. In both cases, the long and short segments form elongated hexagonal patterns, which are accurately ordered in one set paving the <100> directions or randomly ordered in two perpendicular sets paving the <110> directions, respectively. The results are discussed taking into account the arangement of the surface miscut steps in the initial wafers before the bonding.

Morphological and molecular evidence support four new Liberonautes Bott, 1955 (Decapoda, Potamonautidae, Liberonautinae) species of freshwater crabs from Sierra Leone, West Africa

Abstract Four new Liberonautes species of freshwater crabs are described from material collected during an expedition to the rainforests of Sierra Leone, West Africa. Liberonautes kasonko sp. nov., L. njala sp. nov., L. schubarti sp. nov. and L. sugarloaf sp. nov. are recognized based on a combination of morphological characters including the carapace, adult male chelipeds, and first gonopods, and on genetic evidence involving analyses of partial segments of the mitochondrial genes of COI, 16S rRNA and 12S rRNA. Diagnoses, illustrations, and a tree of phylogenetic relationships based on mtDNA sequences, as well as a map showing the distributions of the new species are provided.

PCR-Based Microarray Enhances Diagnosis of Culture-Negative Biopsied Tissue in Patients with Invasive Mold Infections: Real-World Experience in a Tertiary Medical Center.

A fungal polymerase chain reaction (PCR) amplifies conserved genes across diverse species, combined with the subsequent hybridization of amplicons using a specific oligonucleotide microarray, allowing for the rapid detection of pathogens at the species level. However, the performance of microarrays in diagnosing invasive mold infections (IMI) from infected tissue samples is rarely reported. During the 4-year study period, all biopsied tissue samples from patients with a suspected IMI sent for microarray assays were analyzed. A partial segment of the internal transcribed spacer (ITS) region was amplified by nested PCR after DNA extraction. Amplicons were hybridized with specific probes for a variety of mold species using an in-house oligonucleotide microarray. A total of 80 clinical samples from 74 patients were tested. A diagnosis of an IMI was made in 10 patients (4 proven, 1 probable, 3 possible, 2 clinical suspicion). The PCR/microarray test was positive for three out of four proven IMIs, one probable IMI, and one out of three possible IMIs. Two patients with positive PCR/microarray findings were considered to have clinical suspicion of an IMI, and their responsible physicians initiated antifungal therapy despite the absence of supporting microbiological and histological evidence. Clinical diagnoses were categorized into non-IMI and IMI groups (including proven, probable, possible, and clinical suspicion). The sensitivity and specificity of the microarray in diagnosing the IMIs were 70% and 95.7%, respectively, while the sensitivity and specificity of the culture and histological findings were 10%/96.3% and 40.0%/100%, respectively. PCR-based methods provide supportive microbiological evidence when culture results are inconclusive. The combination of a microarray with fungal culture and histology promotes the precise diagnosis of IMIs in difficult-to-diagnose patients.

Recent trends and new developments in liver transplantation.

Liver transplantation (LT) has been an established treatment for end-staged liver disease for acute, chronic, metabolic diseases and liver cancer. Advanced surgical techniques, refined indications and contraindications for LT, improvements of donor selection, prognostic scorings system and immunosuppressive regimens have contributed to the improved outcomes of liver transplantation. The etiologies of cirrhosis have been shifting from viral hepatitis to metabolic associated fatty liver disease. New indications include peripheral or mass forming bile duct cancer, metastases from bowel cancers or neuroendocrine tumors. Resection and partial liver segments 2-3 transplantation with delayed total hepatectomy has been performed to the limited cases, which was the explored technique of auxiliary partial orthotopic LT. Minimally invasive donor hepatectomy (laparoscopic or robotic) has been increasingly done. In this review are described the recent pressing topics in LT.

Hard ticks (Ixodida: Ixodidae) in the Colombian Caribbean harbor the Jingmen tick virus: an emerging arbovirus of public health concern

BackgroundTicks are obligate hematophagous ectoparasites involved in transmitting viruses of public health importance. The objective of this work was to identify the Jingmen tick virus in hard ticks from the Colombian Caribbean, an arbovirus of importance for public health.MethodsTicks were collected in rural areas of Córdoba and Cesar, Colombia. Taxonomic identification of ticks was carried out, and pools of 13 individuals were formed. RNA extraction was performed. Library preparation was performed with the MGIEasy kit, and next-generation sequencing (NGS) with MGI equipment. Bioinformatic analyses and taxonomic assignments were performed using the Galaxy platform, and phylogenetic analyses were done using IQ-TREE2.ResultsA total of 766 ticks were collected, of which 87.33% (669/766) were Rhipicephalus microplus, 5.4% (42/766) Dermacentor nitens, 4.2% (32/766) Rhipicephalus linnaei, and 3.0% (23/766) Amblyomma dissimile. Complete and partial segments 1, 2, 3, and 4 of Jingmen tick virus (JMTV) were detected in the metatranscriptome of the species R. microplus, D. nitens, and A. dissimile. The JMTVs detected are phylogenetically related to JMTVs detected in Aedes albopictus in France, JMTVs detected in R. microplus in Trinidad and Tobago, JMTVs in R. microplus and A. variegatum in the French Antilles, and JMTVs detected in R. microplus in Colombia. Interestingly, our sequences clustered closely with JMTV detected in humans from Kosovo.ConclusionsJMTV was detected in R. microplus, D. nitens, and A. dissimile. JMTV could pose a risk to humans. Therefore, it is vital to establish epidemiological surveillance measures to better understand the possible role of JMTV in tropical diseases.Graphical

Review: Binocular double vision in the presence of visual field loss.

Binocular double vision in strabismus is marked by diplopia (seeing the same object in two different directions) and visual confusion (seeing two different objects in the same direction). In strabismus with full visual field, the diplopia coexists with visual confusion across most of the binocular field. With visual field loss, or with use of partial prism segments for field expansion, the two phenomena may be separable. This separability is the focus of this review and offers new insights into binocular function. We show that confusion is necessary but is not sufficient for field expansion. Diplopia plays no role in field expansion but is necessary for clinical testing of strabismus, making such testing difficult in field loss conditions with confusion without diplopia. The roles of the three-dimensional structure of the real world and the dynamic of eye movements within that structure are considered as well. Suppression of one eye's partial view under binocular vision that develops in early-onset (childhood) strabismus is assumed to be a sensory adaption to diplopia. This assumption can be tested using the separation of diplopia and confusion.

Capacity estimation for series-connected battery pack based on partial charging voltage curve segments

Accurate estimation of battery pack capacity is crucial in determining electric vehicle driving range and providing valuable suggestions for battery health management. This article proposes an improved capacity co-estimation framework for cells and battery pack using partial charging process. The transformation characteristics of cell capacity difference within the battery pack on the external voltage curve are discussed. The charging voltage curve is segmented according to the feature points extracted from battery incremental capacity curve, and the linear correlation between the voltage curve segments and capacity decline is analyzed to obtain the optimal transformation part, which is verified through a series of cycle aging data. Subsequently, by optimizing the transformation coefficients, we achieve capacity estimation for each cell within the battery pack utilizing only a partial charging voltage profile at approximately 30 % SOC. We further establish a connection between the battery pack and its series cells to enable pack capacity estimation. The proposed method is verified based on two sets of battery pack tests comprising 60 cells in series and with severe capacity inconsistency. The results show that the estimation errors of cells are <2 % and the errors of battery packs are <1 %.

Transfer learning from synthetic data for open-circuit voltage curve reconstruction and state of health estimation of lithium-ion batteries from partial charging segments

Data-driven models for battery state estimation require extensive experimental training data, which may not be available or suitable for specific tasks like open-circuit voltage (OCV) reconstruction and subsequent state of health (SOH) estimation. This study addresses this issue by developing a transfer-learning-based OCV reconstruction model using a temporal convolutional long short-term memory (TCN-LSTM) network trained on synthetic data from an automotive nickel cobalt aluminium oxide (NCA) cell generated through a mechanistic model approach. The data consists of voltage curves at constant temperature, C-rates between C/30 to 1C, and a SOH-range from 70% to 100%. The model is refined via Bayesian optimization and then applied to four use cases with reduced experimental nickel manganese cobalt oxide (NMC) cell training data for higher use cases. The TL models’ performances are compared with models trained solely on experimental data, focusing on different C-rates and voltage windows. The results demonstrate that the OCV reconstruction mean absolute error (MAE) within the average battery electric vehicle (BEV) home charging window (30% to 85% state of charge (SOC)) is less than 22mV for the first three use cases across all C-rates. The SOH estimated from the reconstructed OCV exhibits an mean absolute percentage error (MAPE) below 2.2% for these cases. The study further investigates the impact of the source domain on TL by incorporating two additional synthetic datasets, a lithium iron phosphate (LFP) cell and an entirely artificial, non-existing, cell, showing that solely the shifting and scaling of gradient changes in the charging curve suffice to transfer knowledge, even between different cell chemistries. A key limitation with respect to extrapolation capability is identified and evidenced in our fourth use case, where the absence of such comprehensive data hindered the TL process.

Genomic characterization of infectious bursal disease virus in Argentina provides evidence of the recent transcontinental spread of Chinese genotype A2dB1b

ABSTRACT The infectious bursal disease virus (IBDV) is a significant pathogen affecting the poultry industry worldwide. Its epidemiological history has been marked by the emergence of strains with different antigenic, pathogenic, and genetic features, some of which have shown notable spread potential. The A2dB1b genotype, also known as novel variant, has become widespread and gained increased relevance in IBDV epidemiology. This genotype was described in China in the 2010s and rapidly spread in Asia and Africa. The present study describes the circulation of the A2dB1b genotype in Argentina. Applying a next-generation sequencing approach, we obtained the complete coding sequence of 18 Argentine viruses. The high level of genomic homogeneity observed amongst these viruses, their monophyletic clustering in both partial and complete segments A and B derived phylogenies, and their close relatedness to some Chinese strains suggest that a unique transcontinental spread event from China to Argentina occurred recently. The apparent success of the A2dB1b genotype spreading throughout Asia, Africa, and South America may partially be due to specific amino acid characteristics. Novel residues in the hypervariable region of VP2 may help A2dB1b IBDVs evade the protection elicited by the applied commercial vaccines. Our findings underscore the importance of continuous characterization of field samples and evaluation of the control measures currently applied to fight against this specific IBDV genotype.

Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression

Reliable capacity estimation is crucial for safe operation of lithium-ion batteries (LIBs). This work combines the temporal convolutional network (TCN) and Gaussian process regression (GPR) to establish a novel probabilistic capacity estimation method. The proposed TCN-GPR method can not only provide accurate capacity estimation but also quantify the uncertainty of the estimation. Besides, the TCN-GPR method can automatically extract degradation features from partial charging segments, overcoming the limitations of manual experience. In addition, the TCN-GPR method can be applied to different types of LIBs through transfer learning using only a small amount of training data. For validation, the Oxford battery dataset is used to demonstrate the accuracy and robustness of the TCN-GPR method, where a mean absolute percentage error (MAPE) of less than 0.3% can be achieved with only a 15-min partial charging segment. Furthermore, our own experimental dataset is used to demonstrate the generalization ability of the TCN-GPR method through transfer learning, where a MAPE of less than 0.7% can be achieved by using only one battery cell as the training sample.

GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation

Offline imitation learning (IL) refers to learning expert behavior solely from demonstrations, without any additional interaction with the environment. Despite significant advances in offline IL, existing techniques find it challenging to learn policies for long-horizon tasks and require significant re-training when task specifications change. Towards addressing these limitations, we present GO-DICE an offline IL technique for goal-conditioned long-horizon sequential tasks. GO-DICE discerns a hierarchy of sub-tasks from demonstrations and uses these to learn separate policies for sub-task transitions and action execution, respectively; this hierarchical policy learning facilitates long-horizon reasoning.Inspired by the expansive DICE-family of techniques, policy learning at both the levels transpires within the space of stationary distributions. Further, both policies are learnt with goal conditioning to minimize need for retraining when task goals change. Experimental results substantiate that GO-DICE outperforms recent baselines, as evidenced by a marked improvement in the completion rate of increasingly challenging pick-and-place Mujoco robotic tasks. GO-DICE is also capable of leveraging imperfect demonstration and partial task segmentation when available, both of which boost task performance relative to learning from expert demonstrations alone.

Effect of Liver Segments and Hepatic Fibrosis Grade on Repeatability, Reliability, and Diagnostic Efficiency of Intravoxel Incoherent Motion

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is considered a potential marker of hepatic fibrosis (HF). To explore the influencing factors of repeatability and reliability in IVIM-DWI parameters of ROI-based liver segments in participants with HF and healthy volunteers (HV) and to assess the diagnostic efficiency of these parameters in HF. Participants with early HF (EHF, n=59) or advanced HF (AHF, n=38) and HV (n=48) were recruited. Two examiners measured IVIM data using mono-, bi-exponential and stretched exponential models. The results and influencing factors of repeatability and reliability of IVIM-DWI, and the diagnostic efficiency were analyzed. The repeatability of D* (CV: 26.62-41.47%) and DDC (CV: 18.01-34.40%) was poor, the repeatability of ADC (CV: 4.95-9.76%), D (CV: 7.09-15.52%), f (CV: 9.35-17.15%), and α (CV: 7.48-13.81%) was better; ordered logistic regression showed statistically significant results of IVIM-derived parameters; the reliability showed no obvious trend, and ordered logistic regression showed statistically significant results of IVIMderived parameters, groups, and partial hepatic segments (all p<0.001). IVIM-derived parameters with relatively good repeatability (CV<20%) and reliability (ICC>0.4) were used to establish regression models for differential diagnosis. The AUC of regression models was 0.744-0.783 (EHF vs. AHF), but no statistically significant parameters were found in the HV vs EHF comparison. IVIM-derived parameters were the most important factors affecting the repeatability and reliability, while staging of HF and hepatic segments may be the influencing factors of reliability. IVIM-derived parameters showed medium diagnostic efficiency in distinguishing between EHF and AHF.

Deep Learning Powered Online Battery Health Estimation Considering Multitimescale Aging Dynamics and Partial Charging Information

Online accurate battery state-of-health (SOH) estimation is crucial for ensuring safe and reliable operations of electric vehicles (EVs). Yet, such estimation problem remains a challenge in reality due to complex battery degradation behaviors and dynamic EV operations. This paper proposes a novel deep learning-based framework, a bilateral branched Visual Transformer with Dilated Self-Attention (Bi-ViT-DSA), for online SOH estimation. The proposed framework considers partial charging segments during incomplete charging based on two mainstream charging modes, the multi-stage fast-charging and CCCV charging. To incorporate multi-timescale battery ageing dynamics into SOH estimation, a novel bi-party input structure is developed to convey both inner-cycle and intra-cycle degradation information from raw data. The proposed Bi-ViT-DSA is developed to learn multi-timescale high-level latent features from the bi-party input in parallel for SOH estimation. A dilated self-attention (DSA) mechanism is developed to reduce redundant operations in modeling. Computational studies are conducted on datasets of batteries under different chemistries and test conditions. Results validate the feasibility and robustness of the proposed method and its superior performance over a set of state-of-the-art benchmarks.

A new species of Pseudohelicoon (Tubeufiales, Dothideomycetes) from Taiwan

Pseudohelicoon ellipsoideum sp. nov. from a decaying rachis of an unidentified palm submerged in a freshwater stream at Linmei-Shipan Trail, Taiwan, is described and illustrated. The phylogeny of Pseudohelicoon and other Helicoon species (sensu lato) was inferred by a concatenated dataset of the ITS and partial LSU segments of the nuc rDNA. The new species is phylogenetically positioned among the other two previously described species of Pseudohelicoon, but morphologically it differs from them in the shape and size of conidia.

Comparison of bone mineral density of runners with inactive males: A cross-sectional 4HAIE study.

The purpose of the study was to determine whether running is associated with greater bone mineral density (BMD) by comparing the BMD of regularly active male runners (AR) with inactive nonrunner male controls (INC). This cross-sectional study recruited 327 male AR and 212 male INC (aged 18-65) via a stratified recruitment strategy. BMD of the whole body (WB) and partial segments (spine, lumbar spine (LS), leg, hip, femoral neck (FN), and arm for each side) were measured by dual-energy x-ray absorptiometry (DXA) and lower leg dominance (dominant-D/nondominant-ND) was established by functional testing. An ANCOVA was used to compare AR and INC. The AR had greater BMD for all segments of the lower limb (p<0.05), but similar BMD for all segments of the upper limb (p>0.05) compared with INC. Based on the pairwise comparison of age groups, AR had greater BMD of the ND leg in every age group compared with INC (p<0.05). AR had grater BMD of the D leg in every age group except for (26-35 and 56-65) compare with INC (p<0.05). In the youngest age group (18-25), AR had greater BMD in every measured part of lower extremities (legs, hips, femoral necks) compared with INC (p<0.05). In the 46-55 age group AR had greater BMD than INC (p < 0.05) only in the WB, D Leg, D neck, and ND leg. In the 56-65 age group AR had greater BMD than INC (p<0.05) only in the ND leg. Overall, AR had greater BMD compared with INC in all examined sites except for the upper limbs, supporting the notion that running may positively affect bone parameters. However, the benefits differ in the skeletal sites specifically, as the legs had the highest BMD difference between AR and INC. Moreover, the increase in BMD from running decreased with age.

Soft Wearable Body-Powered Hydraulic Actuation System for a Prosthetic Finger Design.

Finger and fingertip loss is the most common form of upper-limb amputation. With a focus on amputations involving the loss of distal and/or partial middle finger segments, this paper outlines the design and development of a novel soft body-powered hydraulically-driven actuation system for a prosthetic finger, while offering an in-depth examination of its subsystems. The proposed device utilises a soft wearable hydraulic mechanism to transfer pressure from the proximal interphalangeal (PIP) joint of the human finger to the distal interphalangeal (DIP) joint of the prosthetic finger, enabling movement of the soft prosthetic DIP joint. The design parameters of the soft actuator, such as its configuration, constituent material, and volume were analysed through experiments with able-bodied participants. Each participant tried 42 different actuators while flexing their index finger, repeating the task four times, yielding 168 trials per participant. The human and prosthetic finger flexion angles and resultant pressures were measured using an Aurora electromagnetic sensor and a fluid pressure transducer. All data was segmented and analysed. Soft actuator designs were selected through statistical analysis of the material (Agilus 30 and Dragon Skin 30), configuration (chambers located underside or around the PIP joint), and volume. The study demonstrated that the selected soft wearable hydraulic mechanism transferred generated pressure from the participant's PIP joint effectively, enabling movement of the prosthetic digit. Our research contributes to current developments in versatile body-powered prosthetic devices, laying the foundations for broad applications in affordable healthcare devices.

Automatic tooth segmentation for patients with alveolar clefts guided by tooth descriptors

Tooth instance segmentation, which separates teeth from surrounding tissues and structures, provides the basis for preoperative planning and research. Existing methods have demonstrated breakthrough results on partial dental segmentation tasks. However, tooth instance segmentation based on cone-beam computed tomography in patients with alveolar clefts is still challenging due to tooth shape variation, high interdental similarity and adjacent tooth occlusion issues. In this study, we propose a new indicator called the tooth descriptor to assist in localizing tooth instances and guide segmentation tasks. The proposed two-stage network first predicts the tooth descriptors and the centroids used to generate the tooth cubes, and then the segmentation network extracts the complete tooth from the tooth cube guided by the morphology of the tooth descriptor. The experimental results demonstrate that the proposed algorithm exhibits excellent robustness in the task of segmenting CBCT (Cone-Beam Computed Tomography) tooth instances of patients with alveolar clefts, with a Dice accuracy of 94.4%, which outperforms state-of-the-art dental segmentation methods.