Miracle-dz/UniFAR-FTUs huggingface dataset:
Carbon Management behind the Ambitious Pledge of Net Zero Carbon Emission—A Case Study of PepsiCo

Since the industrial revolution, greenhouse gas emissions caused by human activities have posed an unprecedented global challenge to social development and impact on the natural environment. With the growing awareness of environmental protection and the promotion of international cooperation mechanisms, there is a global consensus to control greenhouse gases. In order to avoid irreversible and catastrophic climate change, there is an urgent need for more companies to take action and make credible commitments to combat climate change and carbon reduction goals aligned with the Paris Agreement and the UN Sustainable Development Goals. As one of the largest and most influential international food and beverage companies with a range of well-known brands, PepsiCo has made ambitious commitments to science-based climate goals, including reducing GHG emissions from its direct operations by 75% against the 2015 baseline and reducing GHG emissions across its indirect value chain by 40% by 2030, as well as setting an ambitious new target to achieve net-zero emissions by 2040. PepsiCo has incorporated carbon reduction and climate strategies in all focus areas across its value chain, accelerating its work on broadening the scale of sustainable agriculture and regenerative farming practice; reducing plastic use and increasing the use of recycle and renewable materials as well as adopting low-carbon alternatives; developing efficient and alternative solutions in transportation and distribution; shifting to renewable electricity and fuels in manufacturing and fleet. Up to 2021, PepsiCo has achieved a 23% of the absolute emissions target of reducing Scope 1 and Scope 2 emissions and 7.9% of the absolute emissions target of reducing Scope 3 emissions. This research aims to evaluate the performance of PepsiCo on achieving their carbon reduction targets based on the analysis of the reported carbon estimates and reduction strategies, and also provides future strategic suggestions and guidance by adopting case study analysis. Although PepsiCo has reported great progress in reducing carbon emissions, further efforts are needed to achieve these goals.
RPkNN: An OpenCL-Based FPGA Implementation of the Dimensionality-Reduced kNN Algorithm Using Random Projection

Due to the so-called curse of dimensionality and increase in the size of databases, there is an ever-increasing demand for computing resources and memory bandwidth when performing the k-nearest neighbors (kNNs) algorithm, resulting in a slow-down to process large datasets. This work presents an OpenCL-based framework for accelerating the kNN algorithm on field-programmable gate arrays (FPGAs) benefiting from the random projection dimensionality reduction. The proposed RPkNN framework includes two compute modules implementing a throughput-optimized hardware architecture based on random projection and the kNN algorithm and a host program facilitating easy integration of the compute modules in the existing applications. RPkNN also utilizes a new buffering scheme tailored to random projection and the kNN algorithm. The proposed architecture enables parallel kNN computations with a single memory channel and takes advantage of the sparsity features of the input data to implement a highly optimized and parallel implementation of random projection. We employ a computation storage device (CSD) to directly access the high-dimensional data on non-volatile memory express (NVMe) solid state drive (SSD) and store and reuse the compressed and low-dimensional data on the FPGA dynamic random access memory (DRAM), hence eliminating data transfers to the host DRAM. We compare RPkNN implemented on the Samsung SmartSSD CSD with the kNN implementation of the scikit-learn library running on an Intel Xeon Gold 6154 CPU. The experimental results show that the proposed RPkNN solution achieves, on average, <inline-formula> <tex-math notation="LaTeX">$26\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$46\times $ </tex-math></inline-formula> higher performance across different dimensions per a single kNN computation for the SIFT1M and GIST1M databases, respectively. Finally, RPkNN is <inline-formula> <tex-math notation="LaTeX">$1.7\times $ </tex-math></inline-formula> faster than the similar FPGA-based reference method.
Perspectives on Additive Manufacturing Enabled Beta-Titanium Alloys for Biomedical Applications

“Stress shielding” caused by the mismatch of modulus between the implant and natural bones, is one of the major problems faced by current commercially used biomedical materials. Beta-titanium (β-Ti) alloys are a class of materials that have received increased interest in the biomedical field due to their relatively low elastic modulus and excellent biocompatibility. Due to their lower modulus, β-Ti alloys have the potential to reduce “stress shielding. ” Powder bed fusion (PBF), a category of additive manufacturing, or more commonly known as 3D printing techniques, has been used to process β-Ti alloys. In this perspective article, the emerging research of PBF of β-Ti alloys is covered. The potential and limitations of using PBF for these materials in biomedical applications are also elucidated with focus on the perspectives from processes, materials, and designs. Finally, future trends and potential research topics are highlighted.
Insulators Icing and Electrical Tests Methods

Insulators icing and electrical tests methods, which is not proposed at any standards by now ,are important for the external insulation design of HV transmission lines in severe icing districts. Based on the used methods by researchers and referenced on IEC507 and IEEE Std 4, This paper indicates the characteristic parameters specialized insulators icing. It proposes that the insulators icing can be divided into natural icing and artificial icing which includes energized icing and non-energized icing, that the electrical tests methods can be described as a withstand method, an average flashover voltage method and a U-type method, the former two can be used in icing or ice-melting, but the last can only be used in the ice-melting period, and that the test power supply used for iced insulator is according with the polluted insulator.
Virus-like Particles: Measures and Biological Functions

Virus-like particles resemble infectious virus particles in size, shape, and molecular composition; however, they fail to productively infect host cells. Historically, the presence of virus-like particles has been inferred from total particle counts by microscopy, and infectious particle counts or plaque-forming-units (PFUs) by plaque assay; the resulting ratio of particles-to-PFUs is often greater than one, easily 10 or 100, indicating that most particles are non-infectious. Despite their inability to hijack cells for their reproduction, virus-like particles and the defective genomes they carry can exhibit a broad range of behaviors: interference with normal virus growth during co-infections, cell killing, and activation or inhibition of innate immune signaling. In addition, some virus-like particles become productive as their multiplicities of infection increase, a sign of cooperation between particles. Here, we review established and emerging methods to count virus-like particles and characterize their biological functions. We take a critical look at evidence for defective interfering virus genomes in natural and clinical isolates, and we review their potential as antiviral therapeutics. In short, we highlight an urgent need to better understand how virus-like genomes and particles interact with intact functional viruses during co-infection of their hosts, and their impacts on the transmission, severity, and persistence of virus-associated diseases.
THE PREDICTION MODELS OF MOTORCYCLE ACCIDENTS ON SURABAYA ARTERIAL ROADS USING GENERALIZED LINEAR MODELS

: In Indonesia, traffic accident grows 11.64 % per-year, which is dominated by motorcycles traffic accidents. Similarly in Surabaya, the second biggest city in Indonesia, traffic accident increased by 17.71% per-year and motorcycles traffic accident occupied 70.7 % of the total traffic accident. This paper aims to develop a predictive model of the accident, in order to overcome motorcycle accidents on arterial roads in Surabaya, Indonesia. In developing the model, the researchers used generalized linear models approach, with a Poisson distribution and logarithmic link function. The results of this study indicate that the traffic volume, length of roads, number of access points and the speed of traffichave a significant effect in describing the motorcycle accidents on arterial roads, where an increasing value of those variables lead to the increasing risk of accidents. These findings can help many stakeholders in traffic engineering solve the problems related to motorcycle accidents on arterial roads in Surabaya and many other cities.
STUDY ON IN-SITU TESTS OF MECHANICAL CHARACTERISTICS ON SOIL-ROCK AGGREGATE

Soil-rock aggregate(RSA)is a special kind of geomaterial composed of soils and irregular rock blocks.The mechanical properties of RSA are rarely known although RSA distributes here and there in the mountainous area in China.In-situ large-scale test can explore the mechanical properties of RSA effectively since RSA is described as a kind of heterogeneous and nonuniform material.Here,the method of in-situ test for soils and rocks is referred;and a practical procedure of in-situ test for RSA is proposed and normalized including the method of push-shear test and press-shear test,equipments,steps,and some key points.Combined with the typical RSA at the Baiyian Landslide in the Three Gorges Reservoir area,23 large-scale in-situ tests(including push-shear and press-shear)are carried out.The curves of shear stress-displacement,curves of shear strength,failure mode,and shear strength parameters for RSA with different rock contents,sample sizes and stress states are obtained from tests.The results show that RSA has typical complete curves of stress-strain,strain softening,and high internal friction angle,which are caused by the special structure of RSA.It is also indicated that rock content is an important factor for strength and failure mode of RSA and that dimension effect is observed in the in-situ tests of RSA.The procedure of in-situ tests and results obtained from the tests can provide scientific data for further study of RSA.
Evaluating the Optimal Management of Inoperable Giant Cell Tumors of the Spine: A Systematic Review and Meta-Analysis

Simple Summary Spine giant cell tumors (SGCTs) are intermediate malignant bone tumors, sometimes aggressive and responsible for debilitating axial pain and sensorimotor impairments. Non-surgical therapies, including denosumab, radiotherapy, and selective arterial embolization (SAE), have shown promising results in the treatment of patients with inoperable SGCTs. In this systematic review, we aimed to comprehensively analyze the current literature on denosumab, radiotherapy, and SAE for inoperable SGCTs, comparing treatment outcomes and complications using a random-effect model meta-analysis. We found that all three treatments were equally effective in providing symptom improvement and radiological tumor response, also showing low and comparable rates of treatment-related complications. Patients treated with denosumab showed lower rates of local recurrences and distant metastases. Abstract Background: Surgical resection remains the preferred treatment in spine giant cell tumors (SGCTs), but it is not always feasible. Conservative strategies have been studied for inoperable cases. We systematically reviewed the literature on inoperable SGCTs treated with denosumab, radiotherapy or selective arterial embolization (SAE). Methods: PubMed, Scopus, Web-of-Science, Ovid-EMBASE, and Cochrane were searched following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to include studies of inoperable SGCTs treated with denosumab, radiotherapy or SAE. Treatment outcomes were analyzed and compared with a random-effect model meta-analysis. Results: Among the 17 studies included, 128 patients received denosumab, 59 radiotherapy, and 43 SAE. No significant differences in baseline patient characteristics were found between the three groups. All strategies were equally effective in providing symptom improvement (p = 0.187, I2 = 0%) and reduction in tumor volume (p = 0.738, I2 = 56.8%). Rates of treatment-related complications were low (denosumab: 12.5%; radiotherapy: 8.5%; SAE: 18.6%) and comparable (p = 0.311, I2 = 0%). Patients receiving denosumab had significantly lower rates of local tumor recurrence (10.9%) and distant metastases (0%) compared to patients receiving radiotherapy (30.5%; 8.5%) or SAE (35.6%; 7%) (p = 0.003, I2 = 32%; p = 0.002, I2 = 47%). Denosumab was also correlated with significantly higher overall survival rates at 18 months (99.2%) and 24 months (99.2%) compared to radiotherapy (91.5%; 89.6%) and SAE (92.5%; 89.4%) (p = 0.019, I2 = 8%; p = 0.004, I2 = 23%). Mortality was higher in patients receiving SAE (20.9%) or radiotherapy (13.6%) compared to denosumab (0.8%) (p < 0.001), but deaths mostly occurred for unrelated diseases. Conclusions: Denosumab, radiotherapy, and SAE are safe and effective for inoperable SGCTs. Clinical and radiological outcomes are mostly comparable, but denosumab may provide superior tumor control.
SOH prediction for lithium-ion battery based on improved support vector regression

ABSTRACT As a good energy storage element, lithium-ion batteries are used widely in electric vehicles and electric energy storage systems now, but its own State of Health (SOH) is difficult to measure directly. The Support Vector Regression (SVR) method can use the cell characteristics extracted from the experiment to predict the SOH of the cell.However,the method itself is difficult to determine the hyperparameters and has a large impact on the prediction accuracy.For this reason, this paper proposes the joint algorithm of Bat Algorithms (BA) and Support Vector Regression to predict the SOH of lithium-ion batteries, uses the BA algorithm to find the optimal SVR hyperparameters and uses Grey Relationship Analysis (GRA) to verify their high correlation with the battery SOH correlation.The proposed method was validated using a publicly available experimental dataset, and the results showed that the method has higher prediction accuracy compared with the conventional GA-SVR and PSO-SVR.
The chemokine receptor CXCR3 promotes CD8+ T cell-dependent lung pathology during influenza pathogenesis

While the role of CD8+ T cells in influenza clearance is established, their contribution to pathological lung injury is increasingly appreciated. To explore if protective versus pathological functions can be linked to CD8+ T cell subpopulations, we dissected their responses in influenza-infected murine lungs. Our single-cell RNASeq (scRNAseq) analysis revealed significant diversity in CD8+ T cell subpopulations during peak viral load vs. infection-resolved state. While enrichment of Cxcr3hi CD8+ T effector (Teff) subset was associated with a more robust cytotoxic response, both CD8+ Teff and CD8+ T central memory (TCM) exhibited equally potent effector potential. The scRNAseq analysis identified unique regulons regulating the cytotoxic response in CD8+ T cells. The neutralization of CXCR3 mitigated lung injury without affecting viral clearance. IFN-γ was dispensable to regulate the cytotoxic response of Cxcr3hi CD8+ T cells. Collectively, our data imply that CXCR3 interception could have a therapeutic effect in preventing influenza-linked lung injury. TEASER The CXCR3 expressing CD8+ T cell subset causes severe lung pathology and exacerbates disease severity without affecting viral clearance during influenza infection
A new type of prestressed concrete hollow girder design for overcoming the typical diseases

This paper proposes a new type of prestressed concrete hollow girder based on the summaries of the current studies on longitudinal cracks and hinge cracks.The match prefabricated method is used with shear key at interface to solve the problem of hingle cracks,and the two-way prestressing strand techniques are used to assure the longitudinal load-bearing capacity and to prevent the emergence of longitudinal cracks because of pressure existing at the match interface.In order to reduce the temperature difference between the internal and external of the prestressed concrete hollow girder,drilling holes are carried out at the bottom slab of girder every distance.To ensure the adhesive effect between the girder and deck pavement,we designed grooves at the top slab of girders.The finite element analysis results of a design example show that the pressure stress of bottom slab of girder is about 2.58 MPa and 0.16 MPa,which are effective to avoid the appearance of longitudinal cracks and hinge cracks.
A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzy and Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzy neural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.
Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm.
Stability and Accuracy Analysis of Baumgartes Constraint Violation Stabilization Method

When Baumgarte's Constraint Violation Stabilization Method (CVSM) is used in the simulation of Lagrange equations of motion with holonomic constraints, it is shown that, with suitable assumptions on the integration step size h and the eigen-values (λ's) of the linearized system, the constraint variables are effectively integrated by the same algorithm as that used for the state variables. A numerical stability analysis of the constraint violations can be performed using this so-called pseudo-integration equation. A study is also made of truncation errors and their modeling in the continuous time domain. This model can be used to determine the effectiveness of various constraint controls and integration methods in reducing the errors in the solution due to truncation errors. Examples are presented to illustrate the use of a higher-order truncation error model which leads to an accurate quantitative steady-state analysis of the constraint violations.
Hemostatic Responses to Multiple Bouts of Firefighting Activity: Female vs. Male Differences in a High Demand, High Performance Occupation

While the fire service has long been a male-dominated occupation, women’s participation in this strenuous, high risk, high performance activity has increased in recent years. Firefighting induces significant cardiovascular strain, including hemostatic disruption; however, the effect of sex on hemostatic responses has not been investigated despite evidence that there are sex-related differences in hemostatic variables at rest and following exercise. Thus, we investigated hemostatic responses in age- and BMI-matched male and female firefighters who performed 3–4 evolutions of firefighting drills over a 3 h period. Venous blood samples were collected before and after the firefighting training drills and hemostatic variables were assessed. Firefighting significantly increased platelet count and factor VIII, tissue plasminogen activator (t-PA) antigen, and t-PA activity, and decreased activated partial thromboplastin time and plasminogen activator inhibitor (PAI-1) activity. Females had lower values for epinephrine-induced platelet closure time, antithrombin III, PAI-1 activity, and PAI-1 antigen. There were no interactions between sex and time for any variables assessed. In conclusion, multiple bouts of firefighting activity resulted in a procoagulatory state. Although there were sex differences for several hemostatic variables, male and female firefighters did not differ in their hemostatic response to multiple bouts of firefighting.
Oceanic drivers of juvenile sea turtle strandings in the UK

: Juvenile sea turtles can disperse thousands of kilometers from nesting beaches to oceanic development habitats, aided by ocean currents. In the North Atlantic, turtles dispersing from American beaches risk being advected out of warm nursery grounds in the North Atlantic Gyre into lethally cold northern European waters (e.g. around the UK). We used an ocean model simulation to compare simulated numbers of turtles that were advected to cold waters around the UK with observed numbers of turtles reported in the same area over ~5 decades. Rates of virtual turtles predicted to encounter lethal (10°C) or detrimental (15°C) temperatures (mean 19% ± 2.7 SD) and reach the UK were consistently low (median 0.83%, lower quartile 0.67%, upper quartile 1.02%), whereas there was high inter-annual variability in the numbers of dead or criti-cally ill turtles re ported in the UK. Generalized additive models suggest inter-annual variability in the North Atlantic Oscillation (NAO) index to be a good indicator of annual numbers of turtle strandings reported in the UK. We demonstrate that NAO variability drives variability in the dispersion scenarios of juvenile turtles from key nesting regions into the North Atlantic. Coastal effects, such as the number of storms and mean sea surface temperatures in the UK, were significant but weak predictors, with a weak effect on turtle strandings. Further understanding how changing environmental conditions such as NAO variability and storms affect the fate of juvenile turtles is vital for understanding the distribution and population dynamics of sea turtles.