Miracle-dz/UniFAR-FTUs huggingface dataset:
Permeability Tests and Numerical Simulation of Argillaceous Dolomite in the Jurong Pumped-Storage Power Station, China

Due to its poor hydro-physical properties and other characteristics, argillaceous dolomite is susceptible to seepage failure under high water pressure, affecting the seepage stability of a rock mass. To ensure the safety of the project, when the argillaceous dolomite is present, it is necessary to study the conditions pertaining to its seepage failure. Taking the argillaceous dolomite of Jurong Pumped Storage Power Station as the research object, the spatial distribution, occurrence, scale, degree of weathering, and mechanical and hydrogeological characteristics of the argillaceous dolomite were studied. Through on-site water pressure tests and laboratory variable head tests, the permeability characteristics of argillaceous dolomite were analyzed, and the hydraulic conductivity of the argillaceous dolomite in the upper reservoir and underground powerhouse areas was quantified. The argillaceous dolomite specimens were collected, and seepage failure tests were conducted to determine the critical water pressure for its seepage failure. Based on the results of the laboratory tests, a numerical model of groundwater flow was established. By changing the water level of the upper reservoir and the measures of the anti-seepage and drainage, the seepage stability of the argillaceous dolomite was discussed. The actual water pressure of argillaceous dolomite in the underground powerhouse area was identified during the operation of the Jurong pumped-storage power station. The calculations show that when fully enclosed anti-seepage and drainage measures are taken for the underground powerhouse, the maximum head of water is 98 m, which is lower than the critical water pressure of seepage failure for the argillaceous dolomite. Therefore, no seepage failure will occur. The results provide a scientific basis for the anti-seepage and drainage design of the underground powerhouse area.
De Novo Assembly of Venom Gland Transcriptome of Tropidolaemus wagleri (Temple Pit Viper, Malaysia) and Insights into the Origin of Its Major Toxin, Waglerin

The venom proteome of Temple Pit Viper (Tropidolaemus wagleri) is unique among pit vipers, characterized by a high abundance of a neurotoxic peptide, waglerin. To further explore the genetic diversity of its toxins, the present study de novo assembled the venom gland transcriptome of T. wagleri from west Malaysia. Among the 15 toxin gene families discovered, gene annotation and expression analysis reveal the dominating trend of bradykinin-potentiating peptide/angiotensin-converting enzyme inhibitor-C-type natriuretic peptide (BPP/ACEI-CNP, 76.19% of all-toxin transcription) in the transcriptome, followed by P-III snake venom metalloproteases (13.91%) and other toxins. The transcript TwBNP01 of BPP/ACEI-CNP represents a large precursor gene (209 amino acid residues) containing the coding region for waglerin (24 residues). TwBNP01 shows substantial sequence variations from the corresponding genes of its sister species, Tropidolaemus subannulatus of northern Philippines, and other viperid species which diversely code for proline-rich small peptides such as bradykinin-potentiating peptides (BPPs). The waglerin/waglerin-like peptides, BPPs and azemiopsin are proline-rich, evolving de novo from multiple highly diverged propeptide regions within the orthologous BPP/ACEI-CNP genes. Neofunctionalization of the peptides results in phylogenetic constraints consistent with a phenotypic dichotomy, where Tropidolaemus spp. and Azemiops feae convergently evolve a neurotoxic trait while vasoactive BPPs evolve only in other species.
Nrf2 signaling pathway: current status and potential therapeutic targetable role in human cancers

Cancer is a borderless global health challenge that continues to threaten human health. Studies have found that oxidative stress (OS) is often associated with the etiology of many diseases, especially the aging process and cancer. Involved in the OS reaction as a key transcription factor, Nrf2 is a pivotal regulator of cellular redox state and detoxification. Nrf2 can prevent oxidative damage by regulating gene expression with antioxidant response elements (ARE) to promote the antioxidant response process. OS is generated with an imbalance in the redox state and promotes the accumulation of mutations and genome instability, thus associated with the establishment and development of different cancers. Nrf2 activation regulates a plethora of processes inducing cellular proliferation, differentiation and death, and is strongly associated with OS-mediated cancer. What’s more, Nrf2 activation is also involved in anti-inflammatory effects and metabolic disorders, neurodegenerative diseases, and multidrug resistance. Nrf2 is highly expressed in multiple human body parts of digestive system, respiratory system, reproductive system and nervous system. In oncology research, Nrf2 has emerged as a promising therapeutic target. Therefore, certain natural compounds and drugs can exert anti-cancer effects through the Nrf2 signaling pathway, and blocking the Nrf2 signaling pathway can reduce some types of tumor recurrence rates and increase sensitivity to chemotherapy. However, Nrf2’s dual role and controversial impact in cancer are inevitable consideration factors when treating Nrf2 as a therapeutic target. In this review, we summarized the current state of biological characteristics of Nrf2 and its dual role and development mechanism in different tumor cells, discussed Keap1/Nrf2/ARE signaling pathway and its downstream genes, elaborated the expression of related signaling pathways such as AMPK/mTOR and NF-κB. Besides, the main mechanism of Nrf2 as a cancer therapeutic target and the therapeutic strategies using Nrf2 inhibitors or activators, as well as the possible positive and negative effects of Nrf2 activation were also reviewed. It can be concluded that Nrf2 is related to OS and serves as an important factor in cancer formation and development, thus provides a basis for targeted therapy in human cancers.
Full-Vectorial Light Propagation Simulation of Optimized Beams in Scattering Media

Volumetric scattering prevents imaging modalities in biomedical optics from imaging deep inside tissue. The optimization of the incident wavefront has the potential to improve these imaging modalities. To investigate the optimization and light propagation of such beams inside scattering media rigorously, full-vectorial simulations based on solutions of Maxwell’s equations are necessary. In this publication, we present a versatile two-step beam synthesis method to efficiently simulate the scanning and phase optimization of a focused beam inside a static scattering medium. We present four different approaches to the phase optimization of the energy density and the absolute value of the Poynting vector. We find that these quantities have two regions with different, almost exponential decays over depth for a non-optimized beam. Optimization by conjugating the phase of the projected electric field in various directions at the focus shows an improvement below a certain penetration depth. Seeking global solutions to the optimization problems reveals an even better enhancement in the energy density and the absolute value of the Poynting vector in the focus. For Poynting vector optimization, the differences between the presented optimization approaches are more significant than for the energy density. With the presented method, it is possible to efficiently simulate different imaging methods improved by wavefront shaping to investigate their possible penetration depths.
Exploring the Use of Lung Ultrasonography to Assess Cardiac Surgery Patients: A Scoping Review

Objective: Lung ultrasonography (LUS) is a safe, quick, and portable diagnostic tool, which can accurately detect postoperative pulmonary complications, postsurgically, without ionizing radiation. The aim of this scoping review was to map the evidence base regarding the use of LUS to assess cardiac surgery patients. Materials and Methods: The JBI methodology was used to conduct this particular scoping review. Results: In total, 90 publications were identified and of those, 73 were research studies, six were narrative reviews, and 11 were narrative, opinion, and text articles. The studies that were included were predominantly observational cohorts and aimed to determine or compare LUS diagnostic ability, prognostic ability, or both. The LUS methods used with patients were heterogeneous and variably reported. Conclusion: Despite an increasing number of studies since 2014, standardized protocols for the use of LUS are yet to be widely adopted and remain an important area for further work. Future research should consider exploring perceptions and experiences of LUS, the use of LUS in treatment outcome measurement, and use by nonphysician health care professionals.
Hypersphere-Based Remote Sensing Cross-Modal Text–Image Retrieval via Curriculum Learning

Remote sensing cross-modal text–image retrieval (RSCTIR) is a flexible and human-centered approach to retrieving rich information from different modalities, which has attracted plenty of attention in recent years. It remains challenging because the current methods usually ignore the varying difficulty levels of different sample pairs, stemming from the large image distribution difference and the high text similarity in the remote sensing (RS) field. Therefore, in this article, we propose an innovative hypersphere-based visual semantic alignment (HVSA) network via curriculum learning (CL). Specifically, we first design an adaptive alignment strategy based on CL, which aligns RS image–text pairs from easy to hard. Sample pairs with different levels of difficulty are treated unequally, and we obtain a better embedding representation when projecting the features onto the unit hypersphere. Then, to measure the robustness of cross-modal feature alignment on the unit hypersphere, we introduce the feature uniformity strategy. It reduces the occurrence of mismatching cases and improves generalization performance. Finally, we design the key-entity attention (KEA) mechanism to alleviate the problem of information imbalance among different modalities. KEA has the ability to extract information about the key entity which is aligned with textual information. Despite its conciseness, our framework achieves state-of-the-art performance on classical datasets of RSCTIR tasks while enjoying faster inference. The summed recall of HVSA on the RISCD and RSITMD is 120.97 and 198.94, 2.50 and 10.49 points ahead of the current best methods, respectively. Extensive experiments demonstrate the competitiveness of our method. The code has been released at https://github.com/ZhangWeihang99/HVSA.
Metaphor drawing as decolonial research and feminist care among Black women academics in selected South African higher education institutions in times of crises

abstract The global COVID-19 pandemic and devastating floods in parts of Southern Africa in 2022 intensified the competing gender role expectations for women academics in the home and workplace, with negative consequences on their effective participation and success in their institutions. This article explores the value of participatory visual methodology generally, and metaphor drawing as feminist decolonial praxis and a research as social change approach to develop a community of practice among a group of 20 South African Black women academics. Participants drew animal metaphors to reflect on their experiences of COVID-19 and the gendered impacts of the neoliberal policies and processes in universities during crises. The participants exhibited and shared their reflections in a workshop format. These were audio-recorded, transcribed and thematically analysed using decolonial feminist care as a conceptual lens. Two main themes highlighting women’s vulnerabilities and strengths emerged. Metaphor drawing as a decolonial method has the potential to generate counter-narratives that disrupt pathologising discourses about women academics’ experiences and capacities. The drawing workshop (and previous ones) contributed to generating deep relationality and sociality in the group, with care embedded deeply in the various interactions and formations that arose throughout the workshop and the project.
Multi-Document Summarization Using Selective Attention Span and Reinforcement Learning

Abstractive text summarization systems using recently improved RNN-based sequence-to-sequence architecture have shown great promise for single-document summarization. However, such neural models fail to perpetuate the performance in the multi-document summarization setting owing to the long-range dependencies within the documents, overlapping/contradicting facts and extrinsic model hallucinations. These shortcomings augment the model to generate inconsistent, repetitive and non-factual summaries. In this work, we introduce <monospace>REISA</monospace>, a sequence-to-sequence model with a novel <italic>reinforced selective attention span</italic> that attends over the input and recalibrates the local attention weights to focus on important segments while generating output at each time step. <monospace>REISA</monospace> utilizes a reinforcement learning-based policy gradient algorithm to reward the model and formulate attention distributions over the encoder input. We further benchmark <monospace>REISA</monospace> on two widely-used multi-document summarization corpora – Multinews and CQASumm, and observe an improvement of <inline-formula><tex-math notation="LaTeX">$+2.91$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$+6.64$</tex-math></inline-formula> ROUGE-L scores, respectively. The qualitative analyses on semantic similarity by BERTScore, faithfulness by question-answer evaluation and human evaluation show significant improvement over the baseline-generated summaries.
Resonant dynamics for the quintic non linear Schr

We consider the quintic nonlinear Schr\"odinger equation (NLS) on the circle. We prove that there exist solutions corresponding to an initial datum built on four Fourier modes which form a resonant set, which have a non trivial dynamic that involves periodic energy exchanges between the modes initially excited. It is noticeable that this nonlinear phenomena does not depend on the choice of the resonant set. The dynamical result is obtained by calculating a resonant normal form up to order 10 of the Hamiltonian of the quintic NLS and then by isolating an effective term of order 6. Notice that this phenomena can not occur in the cubic NLS case for which the amplitudes of the Fourier modes are almost actions, i.e. they are almost constant.
Photonic Tensor Processing Unit With Single Dataflow and Programmable High-Precision Weighting Control

With the explosive growth of data, tensor processing has emerged as a pivotal component of the next generation of artificial intelligence (AI) algorithms. Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), which follows the path of electronic counterparts, leading to data replication and hardware complexity. In this study, we experimentally and theoretically demonstrate a photonic tensor processing unit (PTPU) with a single modulator, which offers a more concise approach for multi-channel standard tensor convolution processing, different from the channel-wise convolution method. By executing multi-tensor parallel computing instead of multi-channel parallel computing, PTPU can directly produce feature tensors without clock synchronization and delay compensation between multiple channels and allows more release of physical hardware resources. Furthermore, an integrated array of semiconductor optical amplifiers (SOAs) are used to be photonic synapses for programmable weight bank, demonstrating record-high precision of 9.2 bits for weights. In the proof-of-concept experiment, we extracted features from a 3-channel (RGB) image in horizontal and vertical directions, using integrated multi-wavelength photonic tensor kernels. We then built a 3D convolutional neural network to predict the presence of COVID-19 based on computer tomography (CT) scan data consisting of 64-channel tensors.
Geography, topography, landscape : configurations of space in Greek and Roman epic

This collection of essays explores how epic narratives negotiate, define, and transform genre-specific geographical configurations. A team of international scholars engages in an interdisciplinary discussion about how Greek and Roman epic poetry interacts with the historical and cultural dynamics of geography. The book brings together the world of Classical literature with current trends in examining the politics of spatial constructions.
A Hybrid Mutual Authentication Approach for Artificial Intelligence of Medical Things

Artificial Intelligence of Medical Things (AIoMT) is a hybrid of the Internet of Medical Things (IoMT) and artificial intelligence to materialize the acquisition of real-time data via the smart wearable devices. Due to a diverse geographical environment of IoMT, secure, and reliable communication among these devices is a challenging task that needs to be resolved on priority basis. For this purpose, numerous device-focused authentication approaches have been proposed in the literature, however, the problem still persists. This article introduces an advanced, secured, and efficient solution for the IoMT by leveraging a lightweight mutual authentication scheme as well as facilitating AI-enabled Big Data analytics and predictive modeling. The proposed approach is specifically designed to establish secured communication between wearable sensing devices and servers within IoMT by exploiting the desirable features of cloud–edge paradigm. In this approach, every device needs to verify whether the requesting wearable device is legitimate or not and this process needs to be carried out prior to the actual communication. Our proposed approach employs a hybrid of Advanced Encryption Standard, i.e., AES-128 bit and medium access control (MAC) for the establishment of secured communication sessions. In addition, the proposed approach utilizes real-time data collection from wearable devices, enabling predictive modeling for the early detection of health anomalies, thereby, enhancing the patient outcomes of a specific disease. This continuously adaptive approach excels in real-time decision making, promptly alerting healthcare professionals of potential risks. Simulation results have verified that the proposed approach serves an ideal solution for the resource-constrained devices by achieving the expected level of authenticity through minimum possible communication and processing overhead. Additionally, this scheme is prune against well-known security attacks in the AIoMT infrastructures.
Time-Dependent ROC Curve Analysis for Assessing the Capability of Radiation-Induced CD8 T-Lymphocyte Apoptosis to Predict Late Toxicities after Adjuvant Radiotherapy of Breast Cancer Patients

Simple Summary Intrinsic radiosensitivity has been found to increase the risk of radiation-induced toxicities. Identifying individual characteristics that can predict the risk of late fibrosis in breast cancer patients is essential to better adapt the irradiation dose to be delivered. Previous studies found radiation-induced CD8 T-lymphocyte apoptosis (RILA) to be associated with grade ≥2 late toxicities, as well as tobacco smoking status and adjuvant hormonotherapy. In this article we evaluate the predictive performance of the RILA, alone and in association with the other factors, using a recent ROC curve approach suitable to the dynamic nature of fibrosis occurrence. Our analysis confirmed the RILA predictive ability, which was not necessarily improved by the others factors. This article also illustrates the underused time-dependent ROC curve method. Abstract Late fibrosis can occur in breast cancer patients treated with curative-intent radiotherapy. Predicting this toxicity is of clinical interest in order to adapt the irradiation dose delivered. Radiation-induced CD8 T-lymphocyte apoptosis (RILA) had been proven to be associated with less grade ≥2 late radiation-induced toxicities in patients with miscellaneous cancers. Tobacco smoking status and adjuvant hormonotherapy were also identified as potential factors related to late-breast-fibrosis-free survival. This article evaluates the predictive performance of the RILA using a ROC curve analysis that takes into account the dynamic nature of fibrosis occurrence. This time-dependent ROC curve approach is also applied to evaluate the ability of the RILA combined with the other previously identified factors. Our analysis includes a Monte Carlo cross-validation procedure and the calculation of an expected cost of misclassification, which provides more importance to patients who have no risk of late fibrosis in order to be able to treat them with the maximal irradiation dose. Performance evaluation was assessed at 12, 24, 36 and 50 months. At 36 months, our results were comparable to those obtained in a previous study, thus underlying the predictive power of the RILA. Based on specificity and cost, RILA alone seemed to be the most performant, while its association with the other factors had better negative predictive value results.
Electrical characteristics of the 2 × n and □ × n circuit network

This paper presents two new fundamentals of the 2 × n and □ × n circuit network. The results of a plane 2 × n resistor network can be applied to a □ × n circuit network, which has not been studied before. We first study the 2 × n resistor network by modeling a differential equation and obtain two equivalent resistances between two arbitrary nodes of the 2 × n network. Next, the □ × n cube network is transformed to the 2 × n plane network equivalently to achieve two resistance formulae between two arbitrary nodes of the □ × n cube network. By applying the resistance results to the □ × n LC cube network, the complex impedance characteristics of the LC network, which includes oscillation characteristics and resonance properties, are discovered.
Evaluating the Temperature Dependence of Heat-Transfer Based Detection: A Case Study with Caffeine and Molecularly Imprinted Polymers as Synthetic Receptors

: Molecularly Imprinted Polymers (MIPs) are synthesized for the selective detection of caffeine. The polymerization process, monomer and crosslinker monomer composition are varied to determine the optimal synthesis procedure via batch rebinding experiments evaluated with optical detection. The selectivity is tested by comparing the response of caffeine to compounds with similar chemical structures (theophylline and theobromine) and dopamine, another neurotransmitter. Subsequently, the MIP polymer particles are integrated into bulk modified MIP screen-printed electrodes (MIP-modified SPEs). The sensors are used to measure caffeine content in various samples employing the Heat-Transfer Method (HTM), a low-cost and simple thermal detection method that is based on differences in thermal resistance at the solid-liquid interface. At first, the noise is minimized by adjusting the settings of temperature feedback loop. Second, the response of the MIP-modified SPE is studied at various temperatures ranging Using caffeine as proof-of-concept, it is demonstrated that at 85 °C the detection limit is significantly enhanced due to higher signal to noise ratios and enhanced diffusion of the biomolecule. Thermal wave transport analysis (TWTA) is also optimized at 85 °C producing a limit of detection of ~1 nM. Next, MIP-modified SPEs are used to measure the caffeine concentration in complex samples including caffeinated beverages, spiked tap water and waste water samples. The use of MIP-modified SPEs combined with thermal detection provides sensors that can be used for fast and low-cost detection performed on-site, which holds great potential for the determination of contaminants in environmental samples. The platform is generic and by adapting the MIP layer, we can expand to this a range of relevant targets.