bluuebunny/crossref_metadata_2025 huggingface dataset:
Clean Energy Stocks: Resilient Safe Havens in the Volatility of Dirty Cryptocurrencies
Authors: Dias Rui , Alexandre Paulo , Teixeira Nuno , Chambino Mariana
Published: June 2023

Green investors have expressed concerns about the environment and sustainability due to the high energy consumption involved in cryptocurrency mining and transactions. This article investigates the safe haven characteristics of clean energy stock indexes in relation to 3 cryptocurrencies, taking into account their respective levels of "dirty" energy consumption from May 16, 2018, to May 15, 2023. By virtue of analyzing a tumultuous era in the global economy, the level of integration between clean energy stock indexes and cryptocurrencies will be inferred by using Gregory and Hansen's methodology. Furthermore, to assess the presence of a volatility spillover effect between clean energy stock indexes and “dirty-classified” cryptocurrencies, the ?-test of heteroscedasticity of two samples from Forbes and Rigobon will be employed. The empirical findings show that clean energy stock indexes may offer a viable safe haven for dirty energy cryptocurrencies. However, the precise associations differ depending on the cryptocurrency under examination. The implications of the study's results are significant for investment strategies, this knowledge can inform decision-making procedures and facilitate the adoption of sustainable investment practices. Investors and policymakers can gain a deeper understanding of the interplay between investments in renewable energy and the cryptocurrency market.
Comparison of Drying Techniques for Extraction of Bioactive Compounds from Olive-Tree Materials
Authors: Castillo-Luna A. , Miho H. , Ledesma-Escobar C. A. , Priego-Capote F.
Published: June 2023

Olive tree vegetal materials are considered a powerful source for isolation of bioactive compounds, mainly, phenols and triterpenic acids. However, the high humidity content of them reduces their preservation and extractability to a liquid solvent. Accordingly, a drying step is crucial to homogenize the material and to obtain an efficiency extraction. We studied the influence of the drying process on the extraction efficiency of bioactive compounds from olive vegetal material. For this purpose, we evaluated the effects of four drying processes on the solid–liquid extraction of bioactive compounds from two by-products, olive leaves and pomace, and olive fruits harvested from two cultivars, Alfafara and Koroneiki. Infrared-assisted drying (IAD) was the most suited approach to obtain extracts enriched in oleuropein from leaves. In the case of pomace, lyophilization and microwaves-assisted drying led to extracts concentrated in oleacein and oleuropein aglycone, whereas IAD and oven-drying led to extracts with enhanced content in hydroxytyrosol glucoside and hydroxytyrosol, respectively. The drying process affected considerably to the chemical composition of extracts obtained from fruits. Changes in the composition of extracts were explained essentially by the drying process conditions using auxiliary energies, temperature and time, which promoted chemical alterations and increased the extractability of compounds.
Outcomes of Keratoplasty in a Cohort of Pythium insidiosum Keratitis Cases at a Tertiary Eye Care Center in India
Authors: Acharya Manisha , Singh Aastha , Nidhi Vatsala , Tiwari Anil , Gandhi Arpan , Chaudhari Isha
Published: June 2023

Pythium insidiosum(PI) can cause sight threatening keratitis which is managed commonly by performing penetrating keratoplasty. This article is a retrospective review to assess outcomes of keratoplasty performed in patients diagnosed with PI keratitis. (2) Methods: Pre-operative, intra operative and post-operative data of patients diagnosed with PI keratitis and who underwent keratoplasty for their condition from January 2020 to December 2021 were col-lected from the central patient database of a tertiary eye care hospital in India. The data were an-alyzed for anatomic success, elimination of infection, graft survival, incidence of repeat kerato-plasty, final visual acuity and varied complications. (3) Results: In total, 16 eyes underwent pene-trating keratoplasty for PI keratitis during the study period. Mean time to keratoplasty from on-set of symptoms was 31.3 days and mean graft size was 10.4 mm. Nine out of the 16 cases had recurrence of infection following surgery, six of which required a repeat keratoplasty for elimina-tion of infection. Out of these 6 patients, one patient underwent 2 repeat keratoplasties. Mean graft size for repeat keratoplasty performed in recurrent cases was 11.7 mm. Globe was successfully salvaged in 14 out of 16 patients (87.5 %). Endo-exudates, graft infiltration, graft dehiscence, secondary glaucoma and retinal detachment were the various complications noted after kerato-plasty. (4) Conclusion: Keratoplasty remains the choice of treatment in PI keratitis, however re-currence of disease and graft failure are common. Large sized grafts, meticulous per-operative removal of infection, adjuvant cryotherapy, and intraoperative and post-operative use of antibi-otics can help in improving outcome of keratoplasty in these patients.
Nanomedicine and Hyperthermia for the Treatment of Gastrointestinal Cancer: A Systematic Review
Authors: Gago Lidia , Quiñonero Francisco , Perazzoli Gloria , Melguizo Consolacion , Prados Jose , Ortiz Raul , Cabeza Laura
Published: June 2023

The incidence of gastrointestinal cancers has seen a significant increase in recent years. Current treatments present numerous challenges, including drug resistance, non-specificity, and severe side effects, among others. These issues have necessitated the exploration of new therapeutic strategies. One promising avenue is the use of magnetic nanoparticles, which have gained considerable interest due to their ability to generate heat in tumor regions upon the application of an external alternating magnetic field, a process known as hyperthermia. This review conducted a systematic search of in vitro and in vivo studies published in the last decade that employ hyperthermia therapy mediated by magnetic nanoparticles for treating gastrointestinal cancers. After applying various inclusion and exclusion criteria, a total of 40 articles were analyzed. The results revealed that iron oxide is the preferred material for magnetism generation in the nanoparticles, and colorectal cancer is the most studied gastrointestinal cancer. Interestingly, novel therapies employing nanoparticles loaded with chemotherapeutic drugs demonstrated an excellent antitumor effect. In conclusion, hyperthermia treatments mediated by magnetic nanoparticles appear to be an effective approach for the treatment of gastrointestinal cancers, offering advantages over traditional therapies.
The Effect of Dual-Modification by Heat-Moisture Treatment and Octenylsuccinylation on Physicochemical and Pasting Properties of Arrowroot Starch
Authors: Marta Herlina , Rismawati Ari , Soeherman Giffary Pramafisi , Cahyana Yana , Djali Mohamad , Yuliana Tri , Sondari Dewi
Published: June 2023

Starch is widely applied in various industrial sectors, including the food industry. Starch is used as a thickener, stabilizer, or emulsifier. However, arrowroot starch generally has weaknesses, such as unstable under heating and acidic conditions, which are generally applied to processing in the food industry. Modifications were applied to improve the characteristics of native arrowroot starch. In this study, arrowroot starch was modified by heat-moisture treatment (HMT), octenylsuccinylation (OSA), and dual modification between OSA and HMT in a different sequence, namely HMT followed by OSA, and OSA followed by HMT. This study aims to determine the effect of different modification methods on the physicochemical and functional properties of native arrowroot starch. The result shows that both single HMT and dual modification caused damage to native starch granules, such as the formation of cracks and roughness. Especially for single OSA treatment, there is no significant change in granule morphology after modification. All modification treatments did not change the crystalline type of starch but reduced the RC of native starch. Both single HMT and dual modifications (HMT-OSA, OSA-HMT) increased pasting temperature and setback, conversely decreasing the peak and breakdown viscosity of native starch. Whereas, single OSA has the opposite trend compared with the other modifications. HMT played a greater role in increasing the thermal stability and retrogradation ability of arrowroot starch. Both single modifications (HMT and OSA) increase the hardness and gumminess of native starch, conversely for dual modifications. HMT has a greater effect on color characteristics, where decreased the lightness and whiteness index of native arrowroot starch. Single OSA modification increased swelling volume higher than dual modification. Both single HMT and dual modifications increase water absorption capacity and decrease the oil absorption capacity of native arrowroot starch.
18 Months Follow-Up of Deep Molecular Response 4.5 (MR4.5) with Nilotinib in Patients with Newly Diagnosed Chronic-Phase Chronic Myeloid Leukemia: A Prospective, Multi-Center Study in China
Authors: Wen Bingbing , Zhang Yuming , Lin Haiqing , Lou Jin , Tu Chuangqing , Jiang Yirong , Liu Xiaolian , Chen Yan , He Huiqing , Liu Zelin , Xie Xiaoling , Huang Wangxiang , Pang Liping , Du Xin
Published: June 2023

Early stable deep molecular response (DMR) of nilotinib is associated with goal of treatment-free remission (TFR) for patients with chronic-phase chronic myeloid leukemia (CML-CP). It is significant to early distinguish patients who can obtain DMR and fit for TFR. We performed a multicenter study to explore early cumulative MR4.5 rate by 18 months with nilotinib in patients with newly diagnosed CML-CP (ND-CML-CP) in China. From 29 institutes, 106 ND-CML-CP patients received nilotinib (300mg BID). The cumulative MR4.5 rate of nilotinib by 18 months was 69.8% (74/106). The cumulative MMR and MR4.0 rates of nilotinib by 18 months were 94.3% (100/106) and 84.9% (90/106), respectively. Patients with ultra-early molecular response (u-EMR) at 6 weeks were not significantly different to obtain DMR or MMR by 24 months compared to those without u-EMR (P=0.7584, P=0.9543, respectively). Our study demonstrated that treatment with nilotinib in patients with ND-CML-CP contributed to obtain high early MR4.5.
Bayes Estimation For The Rayleigh Weibull Distribution Based On Progressive Type-II Censored Samples For Cancer Data In Medicine
Authors: Akdam Neriman
Published: June 2023

The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh Weibull (RW) distribution based on progres-sive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley and Tierney-Kadane's approximation method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes esti-mates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. In addition, the coverage probabilities of the parametric bootstrap estimates are calculated. Real lifetime data sets belonging to the cancer types as bladder cancer, head and neck cancer, and leukemia are used to illustrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.
Language-led Visual Grounding for Human Computer Interaction
Authors: Zhou Mian , Stefanidis Angelos , Jiang Nan , Sui Zezhou , Feng Zhikun
Published: June 2023

In recent years, with the rapid development of computer vision technology and the popularization of intelligent hardware, as well as people’s increasing demand for intelligent products for human-computer interaction, visual grounding technology can help machines and humans identify and locate objects, thereby promoting human-computer interaction and intelligent manufacturing. At the same time, human-computer interaction is constantly evolving and improving, becoming increasingly intelligent, humane, and efficient. This paper proposes a new VG model and designs a language verification module that uses language information as the main information to increase the model’s interactivity. Additionally, we propose the combination of visual grounding and human-computer interaction, aiming to explore the research status and development trends of visual grounding and human-computer interaction technology, as well as their application in practical scenarios and optimization directions, to provide references and guidance for relevant researchers and promote the development and application of visual grounding and human-computer interaction technology.
Complete Feed Based on Fermented Sago Waste Against the Consumption and Digestion of Goat
Authors: Fadly Fadly Hidayat Ilyas , Syahrir Syahriani , Islamiyati Rohmiyatul
Published: June 2023

A common method used to improve the nutritional quality of feed is by fermentation method.  The working principle is that fermentation activates the growth and metabolism of microorganism so that it can increase the digestibility of feed and produce the preferred aroma of livestock.  This study aims to find out the effect of giving fermented sago waste on the consumption and digestibility of goat and cattle. Two feed treatments were tested on 12 goats.  The results showed that the consumption and digestibility value of Dry Matter (DM) and Crude Protein (CP) showed no noticeable difference in goat cattle. The study concluded that complete feed-based fermented sago waste did not affect the consumption and digestibility of CP and DM of goat.
Keywords: Sago waste, completed feed, consumption, digestion, dry matter, crude protein
MICROWAVE TECHNOLOGY OF LIQUID-GLASS HEAT-INSULATING MATERIALS BASED ON A GRANULAR PRODUCT
Authors: Rymar Tatyana
Published: June 2023

The paper investigates the properties of liquid glass thermal insulation products obtained on the basis of granular products under the influence of microwave radiation. Due to the volumetric heat release in the thickness of the liquid glass composition, it is possible to obtain homogeneously structured materials by applying the technology of simultaneous porosization with volumetric expansion of both granules and binder. Thanks to volumetric heating and the mechanism of "non-thermal" effect of microwave radiation on the processing objects, the duration and temperature of their heating is significantly reduced, as well as the operational properties of the products are improved. The properties of materials obtained by this technology are compared with the properties of materials obtained by binding pre-swollen granules. It was proved that the simultaneous swelling of granules with a binder made it possible to obtain materials of a monolithic, uniformly porous structure with high physical and mechanical parameters and an order of magnitude lower indicators of water absorption and sorption moisture due to the dense packing of the granules and the uniform distribution of the binder in the intergranular space. The developed technology for the production of composite materials in the form of heat-insulating products of various configurations by means of simultaneous porosization with volume expansion of granules and binding under the action of microwave radiation allows to obtain materials with a better set of operational characteristics than in the case of binding pre-swollen granules at lower energy costs for their production because it excludes the stage of separate swelling of the granules. The resulting composite thermal insulation materials do not deform and have a fairly high strength for their average density. They are practically incompressible, and this is a very important fact, since the compression of heat insulating materials over time leads to an increase in its thermal conductivity and a decrease in the heat-shielding properties of the structure.
The method of assessing and improving the user experience of subscribers in software-configured networks based on the use of machine learning
Authors: Aqeel Abdulhussein M Al-Mudhafar , Smirnova Tetiana , Buravchenko Kostiantyn , Smirnov Oleksii
Published: June 2023

Evolutionary processes, which primarily affected computer technologies, led to the appearance of several types of computer networks, representing a set of computer devices combined into one system. The main purpose of such a system is user access to shared resources and the ability to exchange data between subscribers during work. Such networks are called software-configurable networks - SDN. SDN networks have long been the basis for building carrier-class telecommunication networks. However, they have a certain number of shortcomings that must be eliminated. The object of the study is the process of evaluating and improving the user experience of subscribers in software-configured networks. The subject of the study is a method of evaluating and improving the user experience of subscribers in software-configured networks based on the use of machine learning. The purpose of the work is to develop a model and a corresponding method for assessing the quality of the user experience of subscribers of SDN networks. As a result of the research, for the first time, a method of evaluating and improving the user experience of subscribers of SDN networks was developed based on the use of machine learning. The method consists in sequentially conducting an automated survey of users, measuring indicators of subscriber service quality, selecting and building a regression model from a set of defined models, and managing the user experience according to the measured parameters of SDN subscriber service quality. The developed method, in contrast to the known ones, makes it possible to improve the quality of the user experience in real time. Conclusions. The research of existing mechanisms for managing the user experience of subscribers and the analysis of regression models for the possibility of using them to establish the relationship between network parameters and user experience allowed to develop a generalized model for evaluating and improving the user experience of subscribers of SDN networks, based on the use of machine learning, and to develop an algorithm works of the method. The developed method makes it possible to build accurate models of the relationship of QoE and QoS parameters and increases the quality of the user experience of subscribers of SDN networks by up to 10%.
GENERATING CURRENCY EXCHANGE RATE DATA BASED ON QUANT-GAN MODEL
Authors: Dun Bao , Zakovorotnyi Oleksandr , Kuchuk Nina
Published: June 2023

The aim of the research. This paper discusses the use of machine learning algorithms to generate data that meets the demands of academia and industry in the context of exchange rate fluctuations. Research results. The paper builds a Quant-GAN model using temporal convolutional neural networks (CNN) and trains it on end-of-day and intraday high-frequency rates of currency pairs in the global market. The generated data is evaluated using various statistical methods and is found to effectively simulate the real dataset. Experimental results show that data generated by the model effectively fits statistical characteristics and typical facts of real training datasets with good overall fit. The results provide effective means for global FX market participants to carry out various tasks such as stress tests and scenario simulations. Future work includes accumulating data and increasing computing power, optimizing and improving GAN models, and establishing evaluation standards for generating exchange rate price data. As computing power continues to grow, the GAN model’s ability to process ultra-large-scale datasets is expected to improve.
Women Are Underrepresented Among Principal Investigators of Hip and Knee Arthroplasty Clinical Trials in the United States
Authors: Silvestre Jason , Walker Jasmine J. , LaPorte Dawn M. , Nelson Charles L.
Published: June 2023

Background:

Leadership of clinical trials confers national recognition and promotes academic advancement. We hypothesized that women would be underrepresented among principal investigators (PIs) of hip and knee arthroplasty clinical trials in the United States.

Methods:

A query of hip and knee arthroplasty clinical trials from 2015 to 2021 was performed on ClinicalTrials.gov. Clinical trials were included if they involved a U.S.-based orthopaedic-surgeon PI. We analyzed the sex representation of arthroplasty PIs among junior-level (assistant professor) and senior-level (associate or full professor) faculty. Participation-to-prevalence ratios (PPRs) were calculated by comparing the sex representation among arthroplasty PIs with that among academic arthroplasty faculty at institutions conducting hip and knee arthroplasty clinical trials. A PPR of <0.8 indicated underrepresentation and a PPR of >1.2 indicated overrepresentation.

Results:

A total of 157 clinical trials involving 192 arthroplasty PIs were included. Of these PIs, only 2 (1.0%) were women. PIs were mostly funded by academic institutions (66%) and industry (33%). U.S. federal sources funded a minority (1%) of PIs. Of the 243 male arthroplasty faculty who were eligible, 190 men (78.2%) served as PIs. In contrast, of the 17 female arthroplasty faculty who were eligible, only 2 women (11.8%) served as PIs (p < 0.001). Across the entire cohort of arthroplasty PIs, women were underrepresented (PPR = 0.16), whereas men were equitably represented (PPR = 1.06). Women were underrepresented at the assistant professor (PPR = 0.0), associate professor (PPR = 0.52), and full professor (PPR = 0.58) levels.

Conclusions:

Women were underrepresented among PIs of hip and knee arthroplasty clinical trials, which may lead to disparities in academic promotion and advancement. More research is needed to understand the potential barriers to female leadership of clinical trials. Greater awareness and engagement are needed to create sex equity in clinical trial leadership for hip and knee arthroplasty research.

Clinical Relevance:

The underrepresentation of women among arthroplasty PIs may lead to fewer options in surgical providers for patients and may limit access to musculoskeletal care for certain patient populations. A diverse arthroplasty workforce can promote attention to issues that disproportionately affect historically underrepresented and vulnerable patient populations.
Chronic Lower Back Pain in Weight Lifters
Authors: Ross Ruby , Han Julie , Slover James
Published: June 2023

» Chronic lower back pain (LBP) is common in both nonathletes and weight lifters, but the diagnosis and treatment should be approached differently in these 2 populations based on the unique movement patterns causing the pain.

» Injury rates of weight lifters are far less than those of contact sports, ranging from 1.0 to 4.4 injuries per 1,000 workout hours. However, the lower back was consistently one of the top 2 injury sites for weight lifters, accounting for anywhere from 23% to 59% of all injuries. LBP was most often associated with the squat or deadlift.

» Guidelines for evaluating general LBP are applicable to weight lifters, including a thorough history and physical examination. However, the differential diagnosis will change based on the patient's lifting history. Of the many etiologies of back pain, weight lifters are most likely to be diagnosed with muscle strain or ligamentous sprain, degenerative disk disease, disk herniation, spondylolysis, spondylolisthesis, or lumbar facet syndrome.

» Traditional recommended therapies include nonsteroidal anti-inflammatory drugs, physical therapy, and activity modification, which are often insufficient to resolve pain and prevent injury recurrence. Because most athletes will want to continue to lift weights, lifting-specific behavior modifications focused on improved technique and correcting mobility and muscular imbalances are important aspects of management in this patient population.
Resilience in Residency and Beyond
Authors: Dove James H. , Kutschke Michael J. , Fadale Paul D. , Akelman Edward
Published: June 2023

» Distinct from the burnout and wellness continuum, resilience is a developed and refined characteristic that propels an individual toward personal and professional success.

» We propose a clinical resilience triangle consisting of 3 components that define resilience: grit, competence, and hope.

» Resilience is a dynamic trait that should be built during residency and constantly fortified in independent practice so that orthopaedic surgeons may acquire and hone the skills and mental fortitude required to take on the overwhelming challenges that we all inevitably face.
Recent Advances and Challenges of the Application of Artificial Intelligence to Predict Wellbore Instabilities during Drilling Operations
Authors: Kamgue Lenwoue Arnaud Regis , Li Zhonghui , Tang Chuanfu , Zhang Wuchang , Ding Shi , Hu Pengjie , Sun Wentie
Published: June 2023

Artificial intelligence (AI) is revolutionizing several businesses across the world, and its implementation in drilling engineering has enhanced the performance of oil and gas companies. This paper reviews and analyzes the successful application of AI techniques to predict wellbore instabilities during drilling operations. First, a summary of the implementation of AI for the prediction of loss circulation, pipe stuck, and mud window is highlighted. Then, the recent innovations and challenges of the AI adoption in major drilling companies is presented. Finally, recommendations are provided to improve the integration of AI in the drilling industry. This analysis gives deep insight into the main publications and recent advances of the application of AI in drilling engineering and is expected to contribute to the further development of the drilling industry.
Atrial fibrillation and cardioversion in the critically ill – a post-hoc analysis of emergency department patients
Authors: Gupta Sophie , Schnaubelt Sebastian , Oppenauer Julia , Lutnik Martin , Cacioppo Filippo , Spiel Alexander , Domanovits Hans , Sulzgruber Patrick , Niederdöckl Jan
Published: June 2023

Objective:
In critically ill patients with atrial fibrillation (AF) standard treatment algorithms might not be applicable. Emergency departments (ED) play a crucial role in implementing individualized treatment approaches. The aim of this study was to evaluate the effectiveness of ED cardioversion algorithms based on lactate levels as a surrogate for severity of illness.
Design, Setting and Patients:
This retrospective study was conducted at the ED of the Medical University of Vienna, Austria, between 2012 and 2022. A consecutive series of 3,535 AF episodes were analyzed.
Measurement and main results:
The main outcome was cardioversion success depending on serum lactate levels [mmol/L]. Lactate levels were divided into quintiles (lac <1.1, 1.1 - 1.3, 1.4 - 1.7, 1.8 - 2.3 and > 2.3mmol/L) and into two groups using a cut-off of 4mmol/L. Overall CV success decreased with rising lactate levels (79% in lac < 1.1mmol/L vs. 61% in lac > 2.3mmol/L; p < 0.001). For electrical cardioversion [eCV] (n=1,119 cases; sinus rhythm [SR] achieved in 86%) success, no significant difference was seen in relation to lactate levels. However, for medical cardioversion [mCV] (n=759; 67% SR), conversion success decreased with increasing lactate levels (SR: lac < 1.1mmol/L 84% vs. lac > 2.3mmol/L 59%; p < 0.001).
Conclusion:
Overall cardioversion success was less likely with rising lactate levels; especially medical cardioversion success rates decreased. Therefore, AF in the critically ill may benefit from either electrical cardioversion, treatment of the underlying condition or primary rate control, rather than medical cardioversion attempts.
Longitudinal plasma proteomics reveals alveolar-capillary barrier disruption in critically ill COVID-19 patients
Authors: Duijvelaar Erik , Gisby Jack , Peters James , Bogaard Harm Jan , Aman Jurjan
Published: June 2023

The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between direct viral cytopathic effects and a dysregulated host immune response. In a randomised clinical trial, imatinib treatment improved clinical outcomes associated with respiratory failure. Here, we performed longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predicted critical illness development. Next to dysregulation of inflammation, critical illness was characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuated protein perturbations associated with inflammation and extracellular matrix turnover. External RNA-sequencing data from the lungs of SARS-CoV-2 infected hamsters validated that imatinib exerts these effects in the pulmonary compartment. These findings implicate that the plasma proteome reflects alveolar capillary barrier disruption in critical COVID-19 which was attenuated with imatinib treatment.
Impacts of an abbreviated personal agency training with refugee women and their male partners on economic empowerment, gender-based violence, and mental health: a randomized controlled trial in Rwanda
Authors: Kalra Naira , Habumugisha Lameck , Shankar Anita
Published: June 2023

Introduction We assessed the impact of a personal agency-based training for refugee women and their male partners on their economic and social empowerment, rates of intimate partner violence (IPV), and non-partner violence (NPV).
Methods We conducted an individually randomized controlled trial with 1061 partnered women (aged 18–45) living in a refugee camp in Rwanda. Women received two days of training, and their partners received one day of training. The follow-up survey where all relevant outcomes were assessed was carried out at 6–9 months post-intervention.
Results At follow up, women in the intervention arm were more likely to report partaking in income generating activities (aIRR 1.27 (1.04–1.54), p < 0.05) and skill learning (aIRR 1.59 (1.39–1.82), p < 0.001) and reported a reduction in experience of physical or sexual NPV in the past six months (aIRR 0.65 (0.39–1.07), p < 0.09). While improved, no statistically significant impacts were seen on physical or sexual IPV (aIRR 0.80 (0.58–1.09), p = 0.16), food insecurity (β 0.98 (0.93 to 1.03), p = 0.396), or clean cookstove uptake (aIRR 0.95 (0.88 to 1.01), p = 0.113) in the past six months. We found statistically significant reduction in physical and sexual IPV amongst those experiencing IPV at baseline (aIRR 0.72 (0.50 to 1.02), p < 0.07). Small improvements in self-efficacy scores and our indicator of adapting to stress were seen in the intervention arm. Some challenges were also seen, such as higher prevalence of probable depression and/or anxiety (aIRR 1.79 (1.00-3.22), p = 0.05) and PTSD (aIRR 2.07 (1.10–3.91), p < 0.05) in the intervention arm compared to the control arm.
Conclusion Our findings echo previous research showing personal agency training can support economic well-being of women. We also find potentially promising impacts on gender-based violence. However, there is some evidence that integration of evidence-based mental health support is important when enhancing agency amongst conflict-affected populations.
Trial registration number
The trial was registered with ClinicalTrials.gov, Identifier: NCT04081441 on 09/09/2019.
Structure-Dynamics Correlation in Metallic Glass Revealed by 5-Dimensional Scanning Transmission Electron Microscopy
Authors: Mitsuishi Kazutaka , Iakoubovskii Konstantin , Kohara Shinji , Tsuchiya Koichi , Nakazawa Katsuaki
Published: June 2023

Dynamic and structural heterogeneities play an important role in glass transition phenomena and in the formation of amorphous structures. Since structure and dynamics are mutually related, it is expected that there exists some relation between them; however, this relation has not been characterized by a direct experiment. Elucidation of this relation is the key to identifying the structure responsible for the rapid freezing of atomic motion during the glass transition. In this study, we simultaneously observed the dynamic and structural heterogeneities near the glass transition temperature in Zr50Cu40Al10 using five-dimensional scanning transmission electron microscopy, which is capable of recording the spatiotemporal distribution of electron diffraction pattern. Dynamic and structural heterogeneities were visualized with sub-nanometer resolution upon heating in situ, and a spatial correlation between them was observed up to the glass transition temperature. Simultaneous measurements of dynamic and structural heterogeneities directly revealed that the ordered atomic structure had slow dynamics and that the order decreased with temperature.
Ensemble Model for Educational Data Mining Based on Synthetic Minority Oversampling Technique
Authors: R Manoharan , Stalin M.Subi , Loganathan Ganesh Babu , s Deepa , k Venkateswaran
Published: June 2023

Data mining in the classroom is a well-known field that involves data mining concepts, statistical analysis, and machine learning concepts, all of which are applied to educational data. These EDM processed data are frequently used to analyse various aspects of the business and process model. Existing and traditional process models involve the use of traditional statistical techniques to process data, which necessitates a significant amount of manual intervention for data modelling and pre-processing. To address the issues raised above, this paper proposes a novel technique that combines a machine learning model with statistical approaches. This machine learning combination combines various classifiers such as Decision Tree Logistic Regression, Random Forest, Multiplayer Perceptron, K Nearest Neighbor, Decision Tree, and so on. Because of the limited data availability, the information used in the observation is highly imbalanced. As a result, the above-mentioned technique is combined with the universally benchmarked model known as Synthetic Minority Oversampling Technique (SMOTE) to discuss issues related to class imbalance. In addition, the performance evaluation is statistically performed to demonstrate the efficacy of the suggested strategies. A technological college in India obtained the main student data collection, which included information on 6,807 students with characteristics. A synthetic minority oversampling approach sensor is used to handle the imbalanced data set. The model is calibrated using eight methodologies, which are then evaluated to determine the dimensions that will help produce the best suitable model to categorise a student based on his achievements.
Identification of anoikis-related lncRNAs and analysis of immune infiltration in patients with breast cancer
Authors: Deng Hao , Jin Yao , Luo Meng , Chen Huihui , Chen Yiding
Published: June 2023

Background
Breast cancer is a significant worldwide health concern with a complex etiology and multiple treatment approaches. Anoikis, a programmed cell death mechanism, has emerged as a critical regulatory pathway in biological processes like proliferation and migration in breast cancer. Long non-coding RNAs (lncRNAs), specifically those related to anoikis (arLncRNAs), have been linked to breast cancer development. Therefore, investigating the role of arLncRNAs in breast cancer is of utmost importance.
Methods
In this study, we collected RNA sequencing data from The Cancer Genome Atlas (TCGA) database and identified anoikis-related genes from GeneCards, which we then compared with the TCGA dataset. Using differential analysis, functional enrichment analysis, and single-factor Cox regression, we identified arLncRNAs that are significantly associated with breast cancer prognosis and constructed a reliable risk model.
Results
We identified eight arLncRNAs that are significantly associated with breast cancer prognosis: LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2. Among these, LINC01235 and AC091182.2 were identified as unfavorable prognostic factors, while the remaining six (AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1) were identified as favorable prognostic factors. Furthermore, we analyzed the relationship between cancer-related immune infiltration and the arLncRNAs risk model. Our results showed that the degree of immune infiltration was higher in the low-risk group.
Conclusions
We have identified eight arLncRNAs, namely LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2, which possess significant predictive value for breast cancer prognosis and response to immunotherapy. These arLncRNAs could be potentially exploited as clinical targets for the treatment of breast cancer associated with anoikis.
On the estimation of landslide intensity, hazard and density via data-driven models
Authors: Napoli Mariano Di , Tanyas Hakan , Castro-Camilo Daniela , Calcaterra Domenico , Cevasco Andrea , Martire Diego Di , Pepe Giacomo , Brandolini Pierluigi , Lombardo Luigi
Published: June 2023

Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in fifty years, the geomorphological community addressed and still addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further as we use the relation between landslide intensity and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.
Interacting Topological Quantum Chemistry in 2D: Many-body Real Space Invariants
Authors: Bernevig B. Andrei , Song Zhida , Herzog-Arbeitman Jonah
Published: June 2023

The topological phases of non-interacting fermions have been classified by their symmetries, culminating in a modern electronic band theory where wavefunction topology can be obtained from momentum space. Recently, Real Space Invariants (RSIs) have provided a spatially local description of the global momentum space indices. The present work generalizes this real space classification to interacting 2D states. We construct many-body local RSIs as the quantum numbers of a set of symmetry operators on open boundaries, but which are independent of the choice of boundary. Using the U(1) particle number, they yield many-body fragile topological indices, which we use to identify which single-particle fragile states are many-body topological or trivial at weak coupling. To this end, we construct an exactly solvable Hamiltonian with single-particle fragile topology that is adiabatically connected to a trivial state through strong coupling. We then define global many-body RSIs on periodic boundary conditions. They reduce to Chern numbers in the band theory limit, but also identify strongly correlated stable topological phases with no single-particle counterpart. Finally, we show that the many-body local RSIs appear as quantized coefficients of Wen-Zee terms in the topological quantum field theory describing the phase.
Textural Image-Based Feature Prediction Model for Stochastic Streamflow Synthesis
Authors: Piran Shirin , Panu Umed
Published: June 2023

To address the challenge of obtaining reliable streamflow data for water resource management, this paper develops an encoding scheme to transform a streamflow time series into an 8-bit grayscale image where it is feasible to develop a correlation between neighbouring pixels to reveal patterns that are not easily discernable in traditional time series analysis. To extract relevant information from an encoded streamflow image, a novel textural feature extraction approach has been developed for synthesizing streamflow data. The developed textural feature extraction model can capture the simultaneous correlation in two dimensions of an encoded streamflow image, which is then transformed into the frequency domain using a discrete Fourier transform. The use of the power spectrum of the Fourier coefficients facilitates the synthesis of encoded streamflow images. The effectiveness of the model is evaluated using three case studies across Canada by comparing the properties of synthesized streamflow with the historical streamflow using the structural similarity (SSIM) index. Results show that the proposed model effectively synthesizes encoded streamflow with high SSIM values for the Fraser, Black, and South Saskatchewan Rivers. The results also affirm that the model reproduces the temporal dependence and correlation structure of the historical streamflow and the average of 30 synthesized realizations up to more than 100 lags. The null hypothesis tests support the conclusion that there is statistically no significant difference between the synthesized monthly streamflow time series and the historical time series. In addition, the entropy-based test also emphasizes that synthesized and historical streamflow are indistinguishable.
A New Racial Disparity in Traffic Fatalities
Authors: Massenkoff Maxim , Chalfin Aaron
Published: June 2023

Motor vehicle accidents kill approximately 40,000 people annually in the United States and are the second largest cause of preventable deaths after overdoses. In 2015, for the first time in nearly forty years, the rate of motor vehicle fatalities for Black Americans exceeded that of white Americans. By 2020, the gap in death rates stood at 34%, accounting for approxi- mately 4,000 excess deaths between 2014 and 2020. This paper is the first to document the emergence of this disparity. Using data on fatal traffic accidents from the Fatality Analysis Reporting System and the Centers for Disease Control and other administrative and survey data, we study the evolution of race-specific rates of fatal vehicle crashes. We document two potential causes: increases in time spent driving and drug use. Several other common causes of accidents, such as speeding and driving without a seat belt, do not exhibit race-specific increases.
Robust and Efficient Claim Retrieval for Online Fact-Checking Applications
Authors: Boland Katarina , Hövelmeyer Alica , Fafalios Pavlos , Todorov Konstantin , Mazhar Usama , Dietze Stefan
Published: June 2023

Understanding the veracity of statements is important when consuming information on the Web. Whereas fact-checking sites have provided a large corpus of already verified claims, matching a given utterance to already fact-checked claims remains a challenging task. Verified claim retrieval has been approached through a variety of different methods, among them approaches relying on supervised neural models. Whereas such models tend to perform strongly, they require significant training effort and their robustness towards unseen data distributions may vary heavily. Also, prior works demonstrate the capability of unsupervised models to provide state-of-the-art performance.In this paper, we assess established claim retrieval benchmark datasets and experimentally evaluate and compare different state-of-the-art supervised and unsupervised methods with regard to performance, but also computational effort and run time. We show that unsupervised approaches outperform supervised ones with respect to robustness. While the best state-of-the-art method relies on supervised deep neural networks, its high computational costs make it difficult to use in online fact-checking applications. The best unsupervised method reaches a similar performance and meets efficiency requirements of online application scenarios due to low hardware requirements. Our experiments verify that, due to the nature of the task and data, the choice of pre-trained language models is more important than fine-tuning and that training supervised models on the target data may not be cost-efficient in online claim retrieval applications.
Exploring patterns of taxonomic, functional and phylogenetic β-diversity variation of Neotropical small mammals in a highly fragmented landscape
Authors: Hannibal Wellington , Cunha Nicolay Leme da
Published: June 2023

Diversity can be partitioned into several components and dimensions that are affected in different ways by habitat loss and fragmentation. However, these partitions and dimensions are rarely investigated in human-modified landscapes. In this study, we investigated different partitions (Hill numbers) and dimensions (taxonomic [TβD], functional [FβD] and phylogenetic [PβD]) of small mammal β-diversity in a fragmented landscape of central Brazil using a multi-scale approach. TβD was estimated considering rare, common and abundant species. Tolerance to disturbed habitat, assessed via the traits “habitat use”, “tail length” and “use of vertical strata”, and trophic guild, defined by “diet”, were used to estimate FβD. PβD was based on phylogenetic relatedness of the sampled species. The association between different partitions and dimensions of β-diversity and habitat and landscape attributes was investigated using Mantel tests. We found a significant positive effect of geographical distance on all partitions and dimensions of β-diversity. Canopy cover were positively associated with abundance-based TβD, FβD and PβD. While, forest area and normalized difference vegetation index (NDVI) were associated with PβD and FβD, respectively. Our findings support the hypothesis that even in a highly modified landscape, small mammal’s β-diversity is determined by different environmental factors and geographical distance of forest patches. However, the relatively higher importance of distance appears to be related to dispersal limitation of this group.