bluuebunny/crossref_metadata_2025 huggingface dataset:
Enhanced surveillance of notifications of hepatitis C to Queensland Health up to 19 years previously
Authors: Fernando TS Mekala , Lambert Stephen B , Kemp Robert , Selvey Linda A
Published: October 2023

In this study we aimed to assess the utility of following up historical hepatitis C notifications for enhanced surveillance and linking cases to further testing and treatment. Queensland hepatitis C notifications from June 2018, 2013, 2008 and 2003 who were not incarcerated at the time of testing were followed up. The most recent identified clinicians for cases were contacted by telephone. When no information about a current clinician was available, the case was contacted via a letter or text message. Clinicians and cases were encouraged to pursue further testing and treatment and provide information about management. Following notification but prior to this study’s follow-up, a majority of cases (309/532; 58%) had a negative polymerase chain reaction (PCR) test or underwent treatment.Clinician follow-up was successful in 21% of eligible cases, with the proportion decreasing with increasing time since notification. In conclusion, contacting clinicians to link notified cases to further testing and treatment may increase testing and treatment in a small proportion of cases notified up to nine years post-notification. From our experience, the follow-up of notifications before this time is unlikely to result in improved outcomes.
Utilization of Tea Waste Activated by Using Hydrochloric Acid as An Adsorbent for Calcium (II) Ions in Hard Water
Authors: Wuryani Meizaristy Ananta , Taufiq Agus , Fathurrahman Muhammad
Published: October 2023

Water is an important natural resource for human survival. Groundwater in calcareous soil areas contains high levels of Ca(HCO3)2 and Mg(HCO3)2 salts and will have a negative impact if consumed continuously. In this study, an adsorption method using tea waste (Camellia sinensis) was conducted to reduce the concentration of Ca(II) ions that cause water hardness. Tea waste was activated with 0.1 N HCl for 24 hours. Tea waste was characterized by Fourier Transform Infrared (FTIR) and Scanning Electron Microscopy (SEM). Determination of optimum conditions was carried out at pH variations of 4, 6, and 8, with contact times of 30 minutes, 60 minutes and 90 minutes, and adsorbent weights were 0,5 grams, 1 gram, 1.5 grams and 2 grams. Tea waste with the best optimum conditions was tested on Ca(II) solutions of various concentrations from 30, 50, and 70 mg/L respectively to determine the maximum capacity for Ca(II) metal ions and the type of adsorption isotherm. Then applied to well water samples analyzed by Inductively Coupled Plasm (ICP) to determine the capacity of Ca(II) that can be absorbed by each adsorbent. The results showed that to utilize 0.1 N HCl-activated tea waste as an adsorbent for Ca(II) metal ions were optimum at pH 8, with a contact time of 30 minutes and an adsorbent weight of 0.5 grams. The maximum adsorption capacity was obtained at Ca(II) solution concentration of 70 mg/L with an adsorption capacity value of 6.704 mg/g. The adsorption isotherm corresponding to the tea waste adsorbent is the Freundlich isotherm (r2 = 0.9199). The highest absorption of Ca(II) metal ions in well water samples from the concentration of Ca(II) metal ions was 72.7702 mg/L to 4.877 mg/L, with adsorption efficiency of 93.3% and adsorption capacity of 6.7893 mg/g.
Anti-inflammatory Potential of Mangosteen Pericarp Extract (Garcinia mangostana L.)
Authors: Felicia Karnadi Auw Natasha , Kok Tjie
Published: October 2023

Mangosteen pericarp (Garcinia mangostana L.) has been used as an active ingredient in numerous traditional medicine formulations. However, their function as an anti-inflammatory agent has yet to be fully understood. Macrophage migration inhibitory factor (MIF) is a mediator that is essential for the development of pro-inflammatory reactions. Hence, MIF activity is often associated with inflammatory diseases and its inhibition could therefore be used as an approach to assess the potential of a material as anti-inflammatory agent. In this study, MIF was exposed with mangosteen pericarp extracts in order to assess the possible anti-inflammatory effect of the extract. The type of mangosteen pericarp extract that possesses the greatest potential as MIF inhibitor and its IC50 value were determined. Based on the results, n-hexane extract of mangosteen pericarp possesses the activity as MIF inhibitor with an IC50 of 4.24 mg/L, and therefore could potentially be used as an anti-inflammatory agent.
Cholera comes to Portugal: Myth and science during the 1833-1834 epidemic and beyond
Authors: Davis Ian
Published: October 2023

In 1833 William Lardner was a surgeon on the ship Rainha de Portugal stationed off Porto, and in 1834 a surgeon at the Royal Marine Hospital in Lisboa. Lardner wrote two letters to the prestigious British medical journal The Lancet (Lardner, 1833, 1834). He described the onset of cholera, first in Vigo, then in Porto during skirmishes between Liberals and the Miguelistas, then in Aveiro, and eventually in Lisboa (Thomas, 2006). Lardner states that neither Spain nor Portugal had experienced cholera until the London Merchant, sailing from Dover and Falmouth, made port in Vigo in late December 1832. Lardner’s ship, the Rainha, was berthed in Vigo then and proceeded to Porto, arriving on 2 January 1833. Lardner believed that the troops under General Jean-Baptiste Solignac had contracted cholera from persons on the London Merchant and transported the disease to Porto. Cholera progressed down the coast to Aveiro by 3 February 1833 and then to Lisboa in mid-June 1833. Lardner’s letters to The Lancet provide a wealth of information on the progress, diagnosis, and treatment of cholera in Portugal, as well as many scurrilous comments regarding the practice of medicine in Portugal at that time. Elements from his letters and additional context from other sources and how these elements might relate to the Covid-19 pandemic will be explored.
Heat Demand Modelling for a Sustainable Urban Development Project: A Case Study of Kopparlunden in Västerås, Sweden.
Authors: Krayem Alaa , Guezgouz Mohammed , Wallin Fredrik
Published: October 2023

As cities grow and develop, urban planners face an increasing challenge to create more sustainable and environment friendly communities. The Kopparlunden district in Västerås, Sweden, is no exception, with plans underway to transition the area to a more sustainable neighborhood. To assist this effort, this paper presents a simple grey box modeling approach to predict the heat demand of eight buildings in the area. As the city transforms from a historical industrial district to a mixed district with residential buildings, shops, and offices, the model will allow urban planners to predict their new heat demand. The model is calibrated using a genetic algorithm, then validated using real historical data. The results show a good accuracy of the model and highlight the importance of increasing the insulation efficiency of the walls in the modelled buildings. The model can be used to predict the heat demand variations, with minimum error of 2.49 kW and up to 16.6 kW for large buildings. The model highlights the importance of energy modeling for urban development projects and shows its significance as a tool to aid in decision-making towards sustainable and more efficient urban areas.
Response Surface Modelling to Reduce CO2 Capture Solvent Cost by Conversion of OZD to MEA
Authors: Mereu Federico , Wagaarachchige Jayangi D. , Idris Zulkifli , Jens Klaus-Joachim , Halstensen Maths
Published: October 2023

The increasing CO2 concentration in the atmosphere is the most urgent global challenge. The most mature CO2 abatement option is post-combustion CO2 capture employing Monoethanolamine (MEA) solvent. One challenge of using MEA is its in-service degradation to 2-oxazolidinone (OZD), a heterocyclic five-membered organic ring compound. Furthermore, OZD degrades more MEA leading to CO2 capture solvent loss and hence increased operational cost. It is therefore of interest to investigate methods to convert OZD back to MEA. This work reports the conversion of 2-oxazolidinone to MEA by heat treatment at an alkaline condition. Raman spectroscopy and Ion-Exchange chromatography were applied to qualify and quantify the reaction. The optimal reaction parameters were identified by an experimental design model using the Response Surface Methodology (RSM). A second-order model with three variables and five levels of focus was employed, with the OZD conversion percentage as the response. This methodology was chosen because such a model could estimate the main effects, interactions and quadratic terms by relying on a relatively small number of experiments. 17 experimental runs were designed by the software using this method. At a reaction time of 35 minutes, reaction temperature of 100°C, and 2.5 mole of hydroxide per mole of OZD resulted in a complete conversion of OZD to MEA.
Economic investigation of heat pumps for heat recovery from data center
Authors: Zangeneh Vahid , Øi Lars Erik
Published: October 2023

The rapid growth of technology and digitalization lead to an increase in the number of data centers around the world. Data centers produce a considerable amount of heat because of their servers and a large number of electric components. The heat generated by the data centers can be used as a potential source of heating, but the quality (temperature level) of the heat is normally low. In this work, the temperature of the excess (cooling) water from a data center is 45 °C. Generally, there is a possibility to use heat pumps to improve the quality of the heat. To obtain a district heating temperature of 60 °C, 70 °C and 80 °C, the coefficient of performance (COP) was calculated to 5.5, 4.3 and 3.5, respectively. This work is about utilization of the excess heat from a data center with three alternative heat pump solutions with a payback period and economic potential for 10 and 20 years. The simulation process was done by Aspen HYSYS. It was observed that the payback period as expected increases with decreasing COP. The payback period was calculated to values between 2.6 and 5.5 years, depending on the market situation and the delivery temperature. In this work, it is shown that Aspen HYSYS is a reasonable tool to calculate alternatives for heat recovery from data centers based on heat pumps.
Approaching simulation-based controller design: heat exchanger case study
Authors: Waller Matias , Leal Leonardo Espinosa
Published: October 2023

Supported by an identification experiment using random-phase multisines, uncertain parameters in a grey-box model for a multiple-input multiple-output laboratory-scale heat exchanger are fitted to experimental data. By defining desired trajectories for the controlled system concerning setpoint changes, simulations and a cost function taking control signal activity into account, we determine both a linear and a nonlinear PI-controller. The resulting control systems are evaluated through practical experiments and analysis with encouraging results. The approach to modelling and controller design raises questions about what is needed from an educational point of view, e.g., what skills are needed for simulation-based control design and analysis?
Visualization of Industrial Production Processes using 3D Simulation Software for Enhanced Decision-Making
Authors: Goyal Akshay
Published: October 2023

This paper explores the use of 3D simulation software for visualizing industrial production processes and its potential to enhance decision-making for improved production efficiency, quality, and profitability. Industrial production processes are complex and involve many variables and factors that can interact in unpredictable ways. Visualization helps to simplify these complex interactions, identify patterns and relationships, and enable more informed decision-making.The research question that guides this paper is: How can the use of 3D simulation software for visualization of industrial production processes improve decision-making and optimize production efficiency, quality, and profitability? This paper will investigate the benefits and challenges of using 3D simulation software for visualizing industrial production processes, including the ability to identify bottlenecks, and optimize the production process. Further, the paper examines the role of visualization in enabling more informed decision-making, including the ability to analyze production data and make data-driven decisions. To illustrate this, an industrial automation case study consisting of a manufacturing industry modelled in a 3D simulation software has been presented.The results of this 3D-simulation model provide insights into the advantages and disadvantages of utilizing 3D simulation software to visualize industrial manufacturing processes. The article further presents the significance of these findings for production managers, engineers, and decision-makers. Thus, the purpose of this study is to help readers understand how using 3D simulation software for visualization of industrial production processes can improve decision-making and optimize production efficiency, quality, and profitability.
Information extraction from operator interface images using computer vision and machine learning
Authors: Illing Eirik , Skeie Nils-Olav , Brastein Ole Magnus
Published: October 2023

In the process of system upgrades or migrations, the utilization of existing layouts and object structures for designing new Human Machine Interfaces (HMI) can significantly save time and effort. Operator interface images, commonly referred to as HMI´s, contain valuable information crucial to industrial operations, but access to source code or design files can be limited. Modern frameworks for object detection and text recognition offer a solution by extracting information directly from images. However, these methods require time-consuming data acquisition and manual effort to initiate. This paper proposes a novel approach utilizing traditional Computer Vision (CV) and Machine Learning (ML) techniques to extract objects from images. The extracted objects are used as training data to transfer learn a ResNet model for multi-label image classification. The combination of this model with techniques such as sliding window, pyramid scaling, and non-maximum suppression forms the basis for a semi-automated annotation tool. This tool generates training data for more optimized object detection methods, specifically the YOLO (You Only Look Once) one-stage object detector. The semi-automated annotation tool allows engineers to manually refine the training data and export state-of-the-art training images for YOLO. The YOLO model achieves an impressive mean Average Precision at IoU 50% (mAP50) score of 95.5% when transfer learned on the annotated data. Additionally, an Optical Character Recognition (OCR) engine is utilized to extract text information from preprocessed images, followed by postprocessing to filter tag data. An algorithm is then employed to link objects and tags together. The final solution is implemented in software designed to optimize user interaction, resulting in an analysis document in Excel format, which can be easily exported for end-user access. With the novel use of this software to automate image analysis, the time required to analyze HMI images prior to migration or rebuild can be reduced by an estimate of 90%.
The Impact of Autonomous Inflow Control Valve on Enhanced Oil Recovery in SAGD Application
Authors: Alam Farhan Hasin , Tahami Amin , Furuvik Nora C.I. , Moldestad Britt M.E. , Taghavi Soheila
Published: October 2023

The demand for non-conventional oil has increased globally. Non-conventional oil is categorized as extra heavy oil and bitumen. In reservoirs with extra heavy oil and bitumen, thermal methods are used to reduce the oil viscosity. Steam assisted gravity drainage (SAGD) is a thermal recovery method to enhance the bitumen recovery. In this method, steam is injected to bitumen and heavy oil to reduce the viscosity and make the oil mobile and extractable. To obtain an efficient SAGD process, the residence time for steam in the reservoir must be long enough for the steam to condense and release the latent energy to be transferred to the cold bitumen. Early breakthrough of steam in some parts of the well will eventually limit the oil production and must be avoided. Autonomous inflow control valve (AICV) can prevent the steam breakthrough and restrict the excessive production of steam. The objective of this paper is to investigate the performance of AICV and its impacts on increased oil production in a SAGD production well. This is achieved by focusing on the implementation, and performance evaluation of inflow control devices (ICDs) and AICVs compared with standard well perforations. CMG STARS, a multi-phase, multi-component thermal reservoir simulator, is used to perform numerical simulation studies. The simulation results demonstrate the significant benefit of AICV in steam reduction compared to ICD and well perforations. The simulation results demonstrate that utilizing AICV in a SAGD reservoir will lead to higher oil production, less steam production, and a more uniform temperature distribution, and steam chamber conformance. Reduction in steam production, will improve the overall SAGD operation performance. This will also result in more cost-effective oil production, as less steam is needed to be generated for production of each barrel of oil.
Data-driven reinforcement learning-based parametrization of a thermal model in induction traction motors
Authors: Fattouh Anas , Sahoob Smrutirekha
Published: October 2023

Monitoring the temperature of induction traction motors is crucial for the safe and efficient operation of railway propulsion systems. Several thermal models were developed to capture the thermal behaviour of the induction motors. With proper calibrating of the thermal model parameters, they can be used to predict the motor’s temperature. Moreover, calibrated thermal models can be used in simulation to evaluate the motor’s performance under different operating conditions and find the optimal control strategies.Parameterization of the thermal model is usually performed in dedicated labs where the induction motor is operated under predefined operating conditions and calibrating algorithms are then used to find the model’s parameters. With the development of digital tools, including smart sensors, Internet of Things (IoT) devices, software applications, and various data collection platforms, operational data can be collected and used later to calibrate the parameters of the thermal model. Nevertheless, calibrating the model’s parameters from operational data collected from different driving cycles is challenging as the model has to capture the thermal behaviour from all driving cycles’ data.In this paper, a data-driven reinforcement learning-based parametrization method is proposed to calibrate a thermal model in induction traction motors. First, the thermal behaviour of the induction motor is modelled as a thermal equivalent network. Second, a reinforcement learning (RL) agent is designed and trained to calibrate the model parameters using the data collected from multiple driving cycles. The proposed method is validated by numerical simulation results. The results showed that the trained RL agent came up with a policy that adeptly handles diverse driving cycles with different performance characteristics.
Thermodynamics analysis of a novel compressed air energy storage (CAES) system combined with SOFC-MGT and using low grade waste heat as heat source
Authors: Yang Chen , Sun Li
Published: October 2023

As modern societies face increasing energy demands and a complex smart grid with multiple inputs of traditional and intermittent renewable energy power generation systems, the need for energy storage systems has become a general trend. Among these systems, compressed air energy storage (CAES) has received extensive attention due to its low cost and high efficiency. This study proposes a novel design framework for a hybrid energy system comprised of CAES system, gas turbine, and high-temperature solid oxide fuel cells, aiming for power generation and energy storage solutions. The overall model of the hybrid power generation system was constructed in Aspen Plus, and the mass balance, energy balance, and thermodynamic properties of the thermal system were simulated and analyzed. The results demonstrate that the hybrid system utilizes the functional complementarity of CAES and solid oxide fuel cells (SOFC), resulting in the cascade utilization of energy, flexible operation mode and increased efficiency. The overall round trip efficiency of the system is 63%, and the overall exergy efficiency is 67%, with a design net power output of 12.5 MW. Additionally, thermodynamic analysis shows that it is advisable to operate the system under higher compressor and turbine isentropic efficiencies, and optimal SOFC/MGT (Micro Gas Turbine) split air flow rates. The results of this article provide guidance for designing innovative hybrid systems and system optimization.
Mapping Simulation optimization requirements for construction sites: A study in heavy-duty vehicles industry
Authors: Habbab Abdulkarim , Fattouh Anas , Frank Bobbie , Chirumalla Koteshwar , Bohlin Markus
Published: October 2023

The Construction and mining Industry comprises complex operations and interactions between various actors at different levels. Simulation has emerged as a valuable tool in this domain to better understand the site's behavior and optimize its operation. However, developing a simulation platform that can handle all the operations on the site is challenging due to the computational cost of the digital representation of reality along with the required accuracy level.This paper aims at extracting and mapping the optimization requirements of construction sites at three main levels: site level, operational level and dynamics level. More precisely, this work seeks to define and map the most important requirements between these levels that ensure simulation credibility and reliability.Based on interviews with experts in the domain, both from academia and industry, several key insights and recommendations emerged: at the site level, the layout and the key performance indicators, such as productivity, time, cost, number of machines and workers, need to be modeled and simulated. At the operational level, the simulation platform must include the main activities, such as loading, excavating, transporting and dumping. Moreover, the dynamics level should involve machine models and their interactions with the site's environment, such as earthmoving, drilling, excavating and blasting.
Main guidelines in road safety audits: a literature review
Authors: Calderón Ramírez Julio Alberto , Núñez López Itzel , García Gómez Leonel Gabriel , Montoya Alcaraz Marco Antonio
Published: October 2023

Road safety is a global issue, as each year more than 1.25 million people die in traffic accidents, and between 20 and 50 million suffer non-fatal injuries. Road Safety Audits (RSA) are implemented in different countries around the world. These audits involve a systematic process that includes an analysis to ensure that existing or future roads meet optimal safety criteria. In this context, the objective of this research is to identify the main guidelines applied in RSA through a literature review conducted using the PRISMA-ScR methodology. After grouping the guidelines, the ten most used worldwide are obtained, with four of them related to the human factor and six related to the road factor. The guidelines with the highest usage are those related to geometric design, signage, and safety devices. Finally, it is concluded that despite road accidents being an international issue faced by all countries, there is no clear or standardized methodology for the application of these audits. In other words, each country adopts its own method or checklist as a support tool for the team of auditors conducting RSA.
Pathophysiological impact of CXC and CX3CL1 chemokines in preeclampsia and gestational diabetes mellitus
Authors: Ullah Amin , Zhao Jing , Singla Rajeev K. , Shen Bairong
Published: October 2023

Diabetes-related pathophysiological alterations and various female reproductive difficulties were common in pregnant women with gestational diabetes mellitus (GDM), who had 21.1 million live births. Preeclampsia (PE), which increases maternal and fetal morbidity and mortality, affects approximately 3%–5% of pregnancies worldwide. Nevertheless, it is unclear what triggers PE and GDM to develop. Therefore, the development of novel moderator therapy approaches is a crucial advancement. Chemokines regulate physiological defenses and maternal-fetal interaction during healthy and disturbed pregnancies. Chemokines regulate immunity, stem cell trafficking, anti-angiogenesis, and cell attraction. CXC chemokines are usually inflammatory and contribute to numerous reproductive disorders. Fractalkine (CX3CL1) may be membrane-bound or soluble. CX3CL1 aids cell survival during homeostasis and inflammation. Evidence reveals that CXC and CX3CL1 chemokines and their receptors have been the focus of therapeutic discoveries for clinical intervention due to their considerable participation in numerous biological processes. This review aims to give an overview of the functions of CXC and CX3CL1 chemokines and their receptors in the pathophysiology of PE and GDM. Finally, we examined stimulus specificity for CXC and CX3CL1 chemokine expression and synthesis in PE and GDM and preclinical and clinical trials of CXC-based PE and GDM therapies.
Interactions between gold nanoparticles with different morphologies and human serum albumin
Authors: Dai Jiahui , Chen Chao , Yin Man , Li Huixing , Li Wenbo , Zhang Zhaowei , Wang Qian , Du Zhongyu , Xu Xiangyu , Wang Yunfei
Published: October 2023

Introduction: Three different shapes of gold nanoparticles were synthesized in this experiment. At the same time, studies compared their effects with human serum albumin (HSA).

Methods: Gold nanoparticles (AuNPs) with three different morphologies, such as, nanospheres (AuNSs), nanorods (AuNRs), and nanoflowers (AuNFs) were synthesized via a seeding method and their characteristic absorption peaks were detected using ultraviolet-visible (UV-vis) absorption spectroscopy, Telectron microscopy (TEM), Dynamic Light Scattering (DLS) and Zeta potential measurements, circular dichroism (CD), and Fourier transform infrared spectroscopy (FTIR) to study the interactions between them and HSA. By comparing the thermodynamic parameters and quenching mechanism of the three materials, similarities and differences were determined in their interactions with HSA.

Results: The results showed that with an increase in the concentration of the AuNPs with the three different morphologies, the UV-vis absorption peak intensity of the mixed solution increased, but its fluorescence intensity was quenched. This indicates that the three types of AuNPs interact with HSA, and that the interactions between them represent a static quenching process, which is consistent with the conclusions derived from three-dimensional fluorescence experiments. Through variable-temperature fluorescence experiments, the binding constants, number of binding sites, and thermodynamic parameters of the interactions between the three types of AuNPs and HSA were determined. The Gibbs free energy changes were <0, indicating that the reactions of the three types of AuNPs with HSA are spontaneous, resulting in associated matter. Binding constant measurements indicated that the strongest binding took place between the AuNFs and HSA. In addition, the results of fluorescence, CD spectroscopy, and FTIR showed that three different shapes of AuNPs can induce conformational changes in HSA and reduce the α-helix content. Among them, AuNFs have the smallest ability to induce conformational changes.

Discussion: According to studies, AuNFs interact more favorably with HSA. This can be used as a reference for the administration of drugs containing AuNPs.
Omicron related COVID-19 prevention and treatment measures for patients with hematological malignancy and strategies for modifying hematologic treatment regimes
Authors: Guo Wenjing , Zheng Yizhou , Feng Sizhou
Published: October 2023

The Omicron variant of SARS-CoV-2 has rapidly become the dominant strain worldwide due to its high transmissibility, although it appears to be less pathogenic than previous strains. However, individuals with hematological malignancy (HM) and COVID-19 remain susceptible to severe infection and mortality, especially those with chronic lymphocytic leukemia (CLL) and those undergoing chimeric antigen receptor T-cell (CAR-T) treatment. Hematologists should thoroughly assess the severity of the patient’s hematological disease and the potential risk of SARS-CoV-2 infection before initiating chemotherapy or immunosuppressive treatment. Vaccination and booster doses are strongly recommended and patients with a poor vaccine response may benefit from long-acting COVID-19 neutralizing monoclonal antibodies (such as Evusheld). Early use of small molecule antiviral drugs is recommended for managing mild COVID-19 in HM patients and those with severe immunodeficiency may benefit from SARS-CoV-2 neutralizing monoclonal antibody therapy and high-titer COVID-19 convalescent plasma (CCP). For moderate to severe cases, low-dose glucocorticoids in combination with early antiviral treatment can be administered, with cytokine receptor antagonists or JAK inhibitors added if the condition persists or worsens. In the treatment of hematological malignancies, delaying chemotherapy is preferable for CLL, acute leukemia (AL), and low-risk myelodysplastic syndrome (MDS), but if the disease progresses, appropriate adjustments in dosage and frequency of treatment are required, with the avoidance of anti-CD20 monoclonal antibody, CAR-T and hematopoietic stem cell transplantation (HSCT). Patients with chronic myelocytic leukemia (CML) and myeloproliferative neoplasms (MPNs) can continue current treatment. What’s more, non-drug protective measures, the development of new vaccines and antiviral drugs, and monitoring of mutations in immunocompromised populations are particularly important.
Quantum image denoising: a framework via Boltzmann machines, QUBO, and quantum annealing
Authors: Kerger Phillip , Miyazaki Ryoji
Published: October 2023

We investigate a framework for binary image denoising via restricted Boltzmann machines (RBMs) that introduces a denoising objective in quadratic unconstrained binary optimization (QUBO) form well-suited for quantum annealing. The denoising objective is attained by balancing the distribution learned by a trained RBM with a penalty term for derivations from the noisy image. We derive the statistically optimal choice of the penalty parameter assuming the target distribution has been well-approximated, and further suggest an empirically supported modification to make the method robust to that idealistic assumption. We also show under additional assumptions that the denoised images attained by our method are, in expectation, strictly closer to the noise-free images than the noisy images are. While we frame the model as an image denoising model, it can be applied to any binary data. As the QUBO formulation is well-suited for implementation on quantum annealers, we test the model on a D-Wave Advantage machine, and also test on data too large for current quantum annealers by approximating QUBO solutions through classical heuristics.
Extraction of building from remote sensing imagery base on multi-attention L-CAFSFM and MFFM
Authors: Jin Huazhong , Fu Wenjun , Nie Chenhui , Yuan Fuxiang , Chang Xueli
Published: October 2023

Building extraction from high-resolution remote sensing images is widely used in urban planning, land resource management, and other fields. However, the significant differences between categories in high-resolution images and the impact of imaging, such as atmospheric interference and lighting changes, make it difficult for high-resolution images to identify buildings. Therefore, detecting buildings from high-resolution remote sensing images is still challenging. In order to improve the accuracy of building extraction in high-resolution images, this paper proposes a building extraction method combining a bidirectional feature pyramid, location-channel attention feature serial fusion module (L-CAFSFM), and meticulous feature fusion module (MFFM). Firstly, richer and finer building features are extracted using the ResNeXt101 network and deformable convolution. L-CAFSFM combines feature maps from two adjacent levels and iteratively calculates them from high to low level, and from low to high level, to enhance the model’s feature extraction ability at different scales and levels. Then, MFFM fuses the outputs from the two directions to obtain building features with different orientations and semantics. Finally, a dense conditional random field (Dense CRF) improves the correlation between pixels in the output map. Our method’s precision, F-score, Recall, and IoU(Intersection over Union) on WHU Building datasets are 95.17%、94.83%、94.51% and 90.18%. Experimental results demonstrate that our proposed method has a more accurate effect in extracting building features from high-resolution image.
Analysis of the movement pattern of overburden and the form of spatial development of separation after mining in a fully mechanized caving face
Authors: Huang Wanpeng , Yang Mingfeng , Liu Yaxin , Sui Le , Gao Mingtao , Wang Xiao , Gao Haibin
Published: October 2023

For the fully mechanized caving face, it is easy to cause significant surface subsidence and other related problems after large-scale mining of coal seams, we should take some measures to solve them. In this study, in order to further explore the movement pattern of overburden and the form of spatial development of separation after mining in a fully mechanized caving face, we combined the engineering practice of Tangshan mining area, took the T2294 and T2291 working faces as the engineering background and used the three methods of similar simulation, numerical simulation and field measurement to comprehensively study. The results show that in the first stage of working face mining, the separation can generally reach 0.31 times the mining thickness of the coal seam, and the maximum can reach 0.58 times the mining thickness; in the second stage, the width of the separation seam is narrow, and the separation is small. It generally takes 20–30 days for the separation to reach its maximum from initiation, which is equivalent to the working face advancing 70–100 m, and the corresponding horizon height is 200 m. The research results provide theoretical guidance and a basis of engineering practice for the safe mining of multiple working faces under the Jingshan railway. This study even provides a basic theoretical reference for the safe mining of a thick coal seam working face under similar engineering geological conditions.
Dynamic learning spaces—dynamic pedagogy. Students’ voices from a master’s program focusing on student active learning in a cross-institution two-campus organization
Authors: De Caro-Barek Veruska , Lysne Dag Atle , Støckert Robin , Solbjørg Ole Kristen , Røren Kari Anne Flem
Published: October 2023

These past few years have shown the importance of successfully designing cross-campus and multi-campus hybrid and fully digital learning environments to sustain and guarantee continuity in learners’ higher education - an aspect that has become pivotal to ensure the survival of Higher Educational Institutions in an increasingly digitalized world. Such learning environments, and related pedagogical practices, seem to contribute to promoting the development of the learners’ critical skills to meet future work-life challenges and possible new crises. It is therefore increasingly important to include students’ experiences and feedback to help develop and define standards and frameworks that can guide educators and other stakeholders in their work. This article presents the results from an exploratory case study within the frame of a larger research project closely linked to a cross-campus and cross-institution master’s degree program at the Norwegian University of Science and Technology (NTNU). The master’s program is a collaboration between NTNU and the University of Oslo (UiO) and is co-located at both campuses through one shared hybrid, physical and virtual, learning space called the Portal. The genesis of the Portal is informed by Radcliffe’s Pedagogy-Space-Technology framework for the design and evaluation of learning spaces. The scope of our research focuses on the “user experiences”, particularly on how students experience their learning space as an arena for student active learning and collaborations in a cross-campus (and cross-institution) organization. With this article, we hope to contribute to the research field in higher education by bringing in newer insight and, hopefully, a fresh point of view.
Understanding the socio-economic causes of deforestation: a global perspective
Authors: Prochazka Petr , Abrham Josef , Cerveny Jaroslav , Kobera Lukas , Sanova Petra , Benes Daniel , Fink Julia-Maria , Jiraskova Eliska , Primasova Simona , Soukupova Jana , Smutka Lubos
Published: October 2023

Introduction

This paper investigates the links between deforestation and key economic, social, environmental, and geographical variables. We focus on per capita GDP, total forest cover, and the population across a diverse sample of countries from five continents for the last three decades.

Methods

This study utilizes a regression model using panel data to show the impact of key economic, and social variables on deforestation. Also, set of dummy variables is introduced in the paper. To enable the investigation, we use a set of dummies to capture their influence. The random effect specifications are used in this investigation. The research focuses on a period ranging from 1990 to 2020.

Results

Results show how different socio-economic variables influence deforestation. For example, disruptive events like the COVID-19 pandemic and the financial crisis had a negative effect on forest area development across all models. GDP per capita has different impact depending on the size of a country. Former colonies seem to have more deforestation.

Conclusions

The global environmental challenges posed by human activities and their impact on the state of forest have become increasingly evident. It is necessary to undertake policy and governance reforms to establish a solid legal framework, strengthen enforcement mechanisms, and foster transparency and accountability. The promotion of sustainable agriculture and agroforestry practices can substantially alleviate pressure on forests. Furthermore, it is necessary to mitigate disruptive events like pandemics by establishing specific strategies and creating contingency plans.
Identification and functional characterization of de novo variant in the SYNGAP1 gene causing intellectual disability
Authors: Li Boxuan , Wang Yu , Hou Dong , Song Zhen , Zhang Lihua , Li Na , Yang Ruifang , Sun Ping
Published: October 2023

Background: Intellectual disability (ID) is defined by cognitive and social adaptation defects. Variants in the SYNGAP1 gene, which encodes the brain-specific cytoplasmic protein SYNGAP1, are commonly associated with ID. The aim of this study was to identify novel SYNGAP1 gene variants in Chinese individuals with ID and evaluate the pathogenicity of the detected variants.

Methods: Whole exome sequencing (WES) was performed on 113 patients diagnosed with ID. In the study, two de novo variants in SYNGAP1 were identified. Sanger sequencing was used to confirm these variants. Minigene assays were used to verify whether the de novo intronic variant in SYNGAP1 influenced the normal splicing of mRNA.

Results: Two de novo heterozygous pathogenic variants in SYNGAP1, c.333del and c.664-2A>G, were identified in two ID patients separately. The c.333del variant has been reported previously as a de novo finding in a child with ID, while the c.664-2A>G variant was novel de novo intronic variant, which has not been reported in the literature. Functional studies showed that c.664-2A>G could cause aberrant splicing, resulting in exon 7 skipping and a 16bp deletion within exon 7.

Conclusion: We identified two de novo pathogenic heterozygous variants in SYNGAP1 in two patients with ID, among which the c.664-2A>G variant was a novel de novo pathogenic variant. Our findings further enrich the variant spectrum of the SYNGAP1 gene and provide a research basis for the genetic diagnosis of ID.
Computational prediction of allergenic proteins based on multi-feature fusion
Authors: Liu Bin , Yang Ziman , Liu Qing , Zhang Ying , Ding Hui , Lai Hongyan , Li Qun
Published: October 2023

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.
NK cell subsets and dysfunction during viral infection: a new avenue for therapeutics?
Authors: Bjorgen Jacob C. , Dick Jenna K. , Cromarty Ross , Hart Geoffrey T. , Rhein Joshua
Published: October 2023

In the setting of viral challenge, natural killer (NK) cells play an important role as an early immune responder against infection. During this response, significant changes in the NK cell population occur, particularly in terms of their frequency, location, and subtype prevalence. In this review, changes in the NK cell repertoire associated with several pathogenic viral infections are summarized, with a particular focus placed on changes that contribute to NK cell dysregulation in these settings. This dysregulation, in turn, can contribute to host pathology either by causing NK cells to be hyperresponsive or hyporesponsive. Hyperresponsive NK cells mediate significant host cell death and contribute to generating a hyperinflammatory environment. Hyporesponsive NK cell populations shift toward exhaustion and often fail to limit viral pathogenesis, possibly enabling viral persistence. Several emerging therapeutic approaches aimed at addressing NK cell dysregulation have arisen in the last three decades in the setting of cancer and may prove to hold promise in treating viral diseases. However, the application of such therapeutics to treat viral infections remains critically underexplored. This review briefly explores several therapeutic approaches, including the administration of TGF-β inhibitors, immune checkpoint inhibitors, adoptive NK cell therapies, CAR NK cells, and NK cell engagers among other therapeutics.
13C tracer analysis reveals the landscape of metabolic checkpoints in human CD8+ T cell differentiation and exhaustion
Authors: Kirchmair Alexander , Nemati Niloofar , Lamberti Giorgia , Trefny Marcel , Krogsdam Anne , Siller Anita , Hörtnagl Paul , Schumacher Petra , Sopper Sieghart , Sandbichler Adolf , Zippelius Alfred , Ghesquière Bart , Trajanoski Zlatko
Published: October 2023

Introduction

Naïve T cells remain in an actively maintained state of quiescence until activation by antigenic signals, upon which they start to proliferate and generate effector cells to initiate a functional immune response. Metabolic reprogramming is essential to meet the biosynthetic demands of the differentiation process, and failure to do so can promote the development of hypofunctional exhausted T cells.

Methods

Here we used 13C metabolomics and transcriptomics to study the metabolism of CD8+ T cells in their complete course of differentiation from naïve over stem-like memory to effector cells and in exhaustion-inducing conditions.

Results

The quiescence of naïve T cells was evident in a profound suppression of glucose oxidation and a decreased expression of ENO1, downstream of which no glycolytic flux was detectable. Moreover, TCA cycle activity was low in naïve T cells and associated with a downregulation of SDH subunits. Upon stimulation and exit from quiescence, the initiation of cell growth and proliferation was accompanied by differential expression of metabolic enzymes and metabolic reprogramming towards aerobic glycolysis with high rates of nutrient uptake, respiration and lactate production. High flux in anabolic pathways imposed a strain on NADH homeostasis, which coincided with engagement of the proline cycle for mitochondrial redox shuttling. With acquisition of effector functions, cells increasingly relied on glycolysis as opposed to oxidative phosphorylation, which was, however, not linked to changes in mitochondrial abundance. In exhaustion, decreased effector function concurred with a reduction in mitochondrial metabolism, glycolysis and amino acid import, and an upregulation of quiescence-associated genes, TXNIP and KLF2, and the T cell suppressive metabolites succinate and itaconate.

Discussion

Overall, these results identify multiple metabolic features that regulate quiescence, proliferation and effector function, but also exhaustion of CD8+ T cells during differentiation. Thus, targeting these metabolic checkpoints may be a promising therapeutic strategy for both prevention of exhaustion and promotion of stemness of anti-tumor T cells.