Miracle-dz/UniFAR-FTUs huggingface dataset:
SPECTRAL L 2 -DISTANCE FOR INTRINSIC 3D SHAPE ANALYSIS

One of the main challenges in 3D shape analysis is the comparison of surfaces according to rigorous metrics that can capture their intrinsic differences. A well-deﬁned metric should be invariant to translation, rotation, scaling, articulation, and more generally, isometric variations. On the other hand, surfaces are typically represented as embedding manifolds in 3D where the explicit use of Euclidean coordinates makes it difﬁcult to achieve the above invariance. To overcome this difﬁculty, we propose in this work a novel metric, the spectral l 2 -distance, based on optimizing the surface Laplace-Beltrami spectral embedding. Because the Laplace-Beltrami eigen-system is isometry invariant, the proposed distance automatically satisﬁes the desired invariance. We also prove that the spectral l 2 -distance meets the conditions of a metric, which allows the rigorous comparison of intrinsic shape differences. One key advantage of this novel metric is that it enables us to perform robust analysis of global shape differences and local shape features. For global shape analysis, we apply it to surface classiﬁcation and show that it can achieve intuitively meaningful results. For local shape analysis, we develop a template matching approach in the optimal embedding space to tackle the challenging problem of identifying major sulci on vervet cortices. To further demonstrate its power in local analysis, we design a direct surface mapping algorithm based on the correspondence induced from the spectral l 2 -distance and illustrate its application on mapping cortical surfaces.
Awareness and Perception of Hospitalized Patients on Thromboembolism and Thromboprophylaxis: A Cross-Sectional Study in Sana’a-Yemen

Purpose Patients’ awareness toward VTE and thromboprophylaxis is critical for medication adherence. This study aimed to evaluate the patient’s awareness and perception towards VTE and its prophylaxis and to assess patient’s satisfaction towards the information given by the medical staff. Participants and Methods A cross-sectional survey was conducted among hospitalized patients who received VTE prophylaxis in public and private hospitals in Sana’a, Yemen. Besides sociodemographic variables, participants’ awareness and perception of VTE and its prophylaxis were evaluated. Variables’ association with VTE awareness were analyzed using univariable and multivariable logistic regression using SPSS. Results A total of 396 of the hospitalized patients agreed to participate in the study, with 31% (n = 121) have ever heard about VTE. The multivariable logistic regression findings revealed that the participants who did not have a profession had OR = 17.182 (P < 0.001) of being unaware of VTE compared with those who had one. Participants who did not have a personal history of VTE had OR = 7.580 (P < 0.001) of being unaware of DVT/PE compared with the participants who had a personal history of VTE. Male participants had OR = 2.839 (P < 0.005) of being unaware of DVT/PE compared with female participants. Participants who were illiterate had OR = 2.832 (P < 0.022) of being unaware of DVT/PE compared with participants with a higher level of education. Conclusion The study revealed lack of awareness and perception of VTE and its prophylaxis among hospitalized patients in Yemen. The patients who are unaware of the disease have wrong perception about their role in DVT prophylaxis. Patient’s education must be considered in healthcare setting to improve patient’s health outcomes.
Optimal Material Selection for Transducers

When selecting an active material for an application, it is tempting to rely upon prior knowledge or commercial products that fit the design criteria. While this method is time effective, it does not provide an optimal selection. The optimal material selection requires an understanding of the limitations of the active material, understanding of the function, constraints and objectives of the device, and rigorous decision making method to ensure rational and clear material selection can be performed. Therefore, this work looks into the three most researched active materials (piezoelectrics, magnetostrictives and shape memory alloys) and looks at how they work and their difficulties. The field of piezoelectrics is vast and contains ceramics, plastics and cellular structures that couple the mechanical and electrical domain. The difficulty with piezoelectric ceramics is their small strains and the dependence of their coefficients on the ferroelectric domains. Giant magnetostrictives materials couple the mechanical and magnetic domains. They are generally better suited for low-frequency operations since they properties deteriorate rapidly with heat. Shape memory alloys are materials that couple thermal and mechanical domains. They have large strain but are limited in their force output, fatigue life and cycle frequency. Optimal material selection requires a formalized material selection method. In mechanical material selection, the formal material selection method uses function, constraints and objectives of the designer to limit the parameter space and allow better decisions. Unfortunately, active materials figures of merit are domain dependent and therefore the mechanical material selection method needs to be adapted. A review of device selection of actuators, sensors and energy harvesters indicates a list of functions, constrains and objectives that the designer can use. Through the analysis of these devices figures of merit, it is realized that the issue is that the simplification that the figures of merit perform does not assist in decision making process. It is better to use decision making methods that have been developed in the field of operational research which assists complex comparative decision making. Finally, the decision making methods are applied to two applications: a resonant cantilever energy harvester and an ultrasound transducer. In both these cases, a review of selection methods of other designers provides guidance of important figures of merit. With these selection methods in consideration, figures of merit are selected and used to find the optimal material based upon the designer preference.
Maternal High-Fat Diet Reduces Type-2 Neural Stem Cells and Promotes Premature Neuronal Differentiation during Early Postnatal Development

Maternal obesity or exposure to a high-fat diet (HFD) has an irreversible impact on the structural and functional development of offspring brains. This study aimed to investigate whether maternal HFD during pregnancy and lactation impairs dentate gyrus (DG) neurogenesis in offspring by altering neural stem cells (NSCs) behaviors. Pregnant Sprague-Dawley rats were fed a chow diet (CHD) or HFD (60% fat) during gestation and lactation. Pups were collected on postnatal day 1 (PND 1), PND 10 and PND 21. Changes in offspring body weight, brain structure and granular cell layer (GCL) thickness in the hippocampus were analyzed. Hippocampal NSCs behaviors, in terms of proliferation and differentiation, were investigated after immunohistochemical staining with Nestin, Ki67, SOX2, Doublecortin (DCX) and NeuN. Maternal HFD accelerated body weight gain and brain structural development in offspring after birth. It also reduced the number of NSCs and their proliferation, leading to a decrease in NSCs pool size. Furthermore, maternal HFD intensified NSCs depletion and promoted neuronal differentiation in the early postnatal development period. These findings suggest that maternal HFD intake significantly reduced the amount and capability of NSCs via reducing type–2 NSCs and promoting premature neuronal differentiation during postnatal hippocampal development.
Human Resource Development of Lecturers and The Analysis of Influencing Factors Efforts to Increase Flowering and Fruiting of Siam Orange through Application of Calcium Fertilizer and Biopesticide

This study was intended to determine the human resource development efforts of lecturers. This research was done at UIN Syarif Kasim Sultan Riau with sample of 34 lecturers and all of faculty and department leaders. Methods used was explorative methods with qualitative descriptive approach. This research revealed that the development activities of lecturers are among others: motivating, proposing budget to support, providing permission for lecturer to continue next study and the training and development, to invite experts, to support scientific activities, to propose funding and encouraging for research and community service. The factors that influence the development of human resources of lecturers showed five variables were very influential for the development of human resources of lecturers of organizational development, organizational commitment, welfare system, development and evaluation of training and education, while the three other variables of work motivation, admissions system and career development system affected less.
Joint Uplink and Downlink Resource Allocation for UAV-enabled MEC Networks under User Mobility

This paper investigates a unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with stochastic user mobility, time-varying wireless channel, and computational task arrival. An optimization problem is formulated to minimize the weighted-sum energy consumption of the ground user and the UAV, subjected to constraints on the limited processing capability and transmit power of both sides, the total system bandwidth, and the stability of task queues. Local computation at the user and the volume of offloaded tasks are jointly optimized with the UAV's uplink/downlink bandwidth and remote processing power allocation to minimize the system's energy. Due to the time coupling of optimization variables, the Lyapunov framework is adopted to transform the original multistage problem into deterministic problems that can be solved in each time slot. The per-time-slot problem is then decomposed into three manageable sub-problems that are designed for different variables. An online algorithm is proposed to iteratively solve the problems. Numerical results show that the proposed approach outperforms benchmark schemes in terms of energy efficiency for the whole system while satisfying the queue stability.
Accelerating Electromigration Stress Analysis Using Low-Rank Balanced Truncation

Electromigration (EM) has become one of the most significant challenges considering longterm reliability in integrated circuit design. The problem is caused by the large current density in circuit interconnections. However, in most cases, we are interested in the EM stress at specific points of the interconnect, such as vias and boundaries. As a result, Model Order Reduction (MOR) techniques can provide attractive methodologies to reduce the complexity of the original systems. System-theoretic techniques like Balanced Truncation (BT) offer very reliable bounds for the approximation error, compared to moment-matching methods. In this paper, we apply a computationally efficient low-rank BT procedure based on the extended Krylov subspace method, that can handle large-scale models and significantly accelerate the EM stress analysis. Experimental results on the industrial IBM power grid benchmarks demonstrate that our method can achieve a speedup up to 238× over a standard transient analysis method and a speedup up to 15× over COMSOL, while exhibiting negligible error.
A new perspective on cooperative control of multi-agent systems through different types of graph Laplacians

In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matrix is an important representation of graph topology, and depending on the field of its entries, there are three types: ordinary Laplacian (non-negative diagonal entries and non-positive off-diagonal entries), signed Laplacian (arbitrary real entries), and complex Laplacian (arbitrary complex entries). Each type of graph Laplacian is useful in modeling and solving a different set of cooperative control problems. In particular, their algebraic properties are fundamental in characterizing stability and performance of the respective solution algorithms. To our best knowledge, organizing the literature on multi-agent cooperative control through the lens of different graph Laplacians is new. GRAPHICAL ABSTRACT
Is It Possible to Stably Manage Complexes of Unstable Aggregates?

: The transition to green energy is widely discussed by economists, journalists and politicians. As often happens, the engineers are almost inaudible. Meanwhile, there are problems. If green energy is based on wind turbines, as the most important and powerful power units, then the problem of sustaining such an energy system will be extremely acute, especially now that wind energy is being used as a substitute for the more stable nuclear and carbon energy. All criteria for the stability of complex multidimensional systems begin with the words “All elements of the system must be stable in autonomous modes of operation. ”. The report proposes several provisions that analyze the conditions for the operation of wind power complexes from the point of view of ensuring their sustainability as multidimensional, complex, dynamic systems and proposes theoretical approaches to solving the problem. Abstract: The transition to green energy is widely discussed by economists, journalists and politi-cians. As often happens, the engineers are almost inaudible. Meanwhile, there are problems. If green energy is based on wind turbines, as the most important and powerful power units, then the problem of sustaining such an energy system will be extremely acute, especially now that wind energy is being used as a substitute for the more stable nuclear and carbon energy. All criteria for the stability of complex multidimensional systems begin with the words “All elements of the system must be stabl e in autonomous modes of operation. ”. The report proposes several provisions that analyze the conditions for the operation of wind power complexes from the point of view of ensuring their sustainability as multidimensional, complex, dynamic systems and proposes theoretical approaches to solving the problem.
Data-driven material discovery for photocatalysis: a short review

In this short review, we introduce recent progress in the research field of data-driven material discovery and design for solar fuel generation. Construction of material databases under the materials genome initiative provides a great platform for material discovery and design by creating computational screening pipelines based on the materials' descriptors. In the field of solar water splitting, data-driven computational discovery approach has been effective in making material predictions. When combined with synergistic and complimentary experimental efforts, high-throughput computations based on density functional theory showed great predictive power for accelerated discovery of inorganic compounds as functional materials for solar fuel generation. As an example, we introduce the theory–experiment joint discovery of a large set of metal oxide photoanode materials that have been theoretically predicted to be efficient candidates and soon verified by synergistic experimental fabrication and characterization processes. In the field of two-dimensional materials, the application of data-driven approach has realized the prediction of many promising candidates with suitable direct band gaps and optimal band edges for the generation of chemical fuels from sunlight, greatly expanding the number of theoretically predicted 2D photoelectrocatalysts that are awaiting experimental verification. We discuss the challenges for the continued discovery and design of novel bulk and 2D compounds for photocatalysis via a data-driven approach. At the end of this review, we provide a brief outlook for future material discoveries in the field of solar fuel generation.
Predictive Estimations for Patients Who Have Suffered From Acute Cerebrovascular Accident and Have Undergone Rehabilitation

BACKGROUND: Acute cerebral circulation disorder is known to be one of the main causes of morbidity, mortality, long-term disability, and the overall so-called disability in society. Prevention of acute cerebral circulation disorder, mortality after acute cerebral circulation disorder, methods of rehabilitation after acute cerebral circulation disorder are studied all over the world, but there are so few studies in the literature on the relationship between rehabilitation and survival of patients after acute cerebral circulation disorder. AIM: The aim is to study the features of survival among patients who have suffered from acute cerebral circulation disorder (hereinafter referred to as ACCD) and have undergone rehabilitation. METHODS: Based on the register statistics on cases of acute cerebral circulation disorder among Almaty residents, the association of the fact of rehabilitation with a fatal outcome was studied and a survival analysis was performed using the Kaplan-Meyer method. RESULTS: In patients with acute cerebral circulation disorder who have not undergone rehabilitation, the chances of a fatal outcome increase by 3.830 times, in comparison with patients who have received an appropriate course of recovery. With the postponement of rehabilitation, the probability of death in patients with acute cerebral circulation disorder increased by 6−10%. The average survival rates in patients who did not receive a rehabilitation course are significantly lower compared to those who underwent rehabilitation: the average survival was 87 years (CI95% 87.0−87.0) and 82 years (CI95% 80.3 ÷ 83.7), respectively, (Log-rank test: test statistics χ2 = 7.916, for DF = 1, p = 0.005). CONCLUSIONS: The main conclusion that can be drawn is that the early rehabilitation care increases the probability of survival among patients who have undergone ACCD. At the same time, the predictive parameters of an unfavorable outcome are the sexual characteristic and the type of acute cerebral circulation disorder.
Cognitive and Affective Theory of Mind across Adulthood

Background: Theory of mind (ToM) is a fundamental aspect of social cognition. Previous studies on age-related changes in mentalizing processes have provided conflicting results. This study aims to investigate the age-related changes in the cognitive and affective components of ToM throughout adulthood. Methods: Two hundred and thirty-eight healthy participants divided into five age groups (18–40 years old; 41–50 years old; 51–60 years old; 61–70 years; 71–80 years old) underwent tasks assessing the cognitive (ToM Picture Sequencing Task, TMPS, and the Advanced Test of ToM, ATT) and affective (Reading the Mind in the Eyes Task, RMET, and the Emotion Attribution Task, EAT) components of ToM, in both verbal and nonverbal modality. Results: Regarding affective ToM, both the youngest- and middle-old adult groups (61 to 80 years) performed worse than the young and youngest-middle adult groups (18 to 50 years) in the RMET, but no significant differences were found in the EAT. Regarding cognitive ToM, the middle-old adult group (71 to 80 years) performed worse than the young adult group (18 to 40 years) only in the TMPS, but no significant differences were found in the ATT. Conclusion: Rather than a general decline in ToM, our results provide evidence regarding selective changes in ToM in older adults, further confirming the dissociation of cognitive and affective ToM.
Improving Cancer Metastasis Detection via Effective Contrastive Learning

The metastasis detection in lymph nodes via microscopic examination of H&E stained histopathological images is one of the most crucial diagnostic procedures for breast cancer staging. The manual analysis is extremely labor-intensive and time-consuming because of complexities and diversities of histopathological images. Deep learning has been utilized in automatic cancer metastasis detection in recent years. The success of supervised deep learning is credited to a large labeled dataset, which is hard to obtain in medical image analysis. Contrastive learning, a branch of self-supervised learning, can help in this aspect through introducing an advanced strategy to learn discriminative feature representations from unlabeled images. In this paper, we propose to improve breast cancer metastasis detection through self-supervised contrastive learning, which is used as an accessional task in the detection pipeline, allowing a feature extractor to learn more valuable representations, even if there are fewer annotation images. Furthermore, we extend the proposed approach to exploit unlabeled images in a semi-supervised manner, as self-supervision does not need labeled data at all. Extensive experiments on the benchmark Camelyon2016 Grand Challenge dataset demonstrate that self-supervision can improve cancer metastasis detection performance leading to state-of-the-art results.
Operation optimization of variable frequency pumps in compound series‐parallel heat transfer systems based on the power flow method

The optimal match of multiple operation parameters in heat transfer systems (HTSs) is the key to trade‐off heat transfer and flow resistance for energy conservation. For a compound series‐parallel HTS, this study applies the power flow model and the driving‐resistance model to build the heat transfer and fluid flow constraints of the whole system directly, instead of individual components. Utilizing these constraints together with the Lagrange multiplier method offers the optimal operating frequencies of each variable frequency pumps (VFP) with the minimum pumping power consumptions under different heat loads. Operating the experiment platform with the optimized parameters shows that the heat load increment in parallel branches only needs to increase the operating frequency of VFP in the related hot‐water loop, whereas the heat load increment in series branches needs to increases the operating frequencies of VSPs in both cold‐water and the corresponding hot‐water loops. When the heat load varies from 10 to 11 kW in the parallel branch, the downstream, and the upstream of the series branch, the total pumping power consumptions of all VFPs increase by 19.45%, 43.86%, and 39.99%, respectively. It means assigning the additional heat load in the parallel branch is more energy efficient.