Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Factors of Anxiety, Depression, and Health-Related Quality of Life in Community-Dwelling and Institutionalized Elderly during the COVID-19 Pandemic

The COVID-19 health emergency and restrictive measures have increased psychological problems, particularly anxiety and depression, in the general population. However, little is known about mental health conditions and the possible risk and protective factors of specific population groups, such as institutionalized vs. community-dwelling elderly. We investigated the abovementioned aspects in a sample of 65–89-year-old people during the third wave of COVID-19 in Italy. We employed a sociodemographic survey and four questionnaires on health-related quality of life (SF-36), loneliness (UCLA), spirituality (FACIT-Sp), and anxiety/depression (HADS). Our findings suggest that the physical, psychological, and spiritual well-being of the elderly had not been seriously impaired by the events related to the pandemic, although most of the participants reported a worsening of their social life and a moderate/high fear of COVID-19. In regression analyses, these two latter aspects turned out to be predictors of higher anxiety, while spiritual well-being and the possibility to get out of the house/institution emerged as protective factors against anxiety and for preserving quality of life, respectively. Our findings help refine the picture of the condition of the elderly in the aftermath of the pandemic, giving some hints about how to continue supporting their well-being and quality of life.
A Bayesian Approach to Network Monitoring for Progressive Failure Localization

Boolean Network Tomography (BNT) aims at identifying failures of internal network components by means of end-to-end monitoring paths. However, when the number of failures is not known a priori, failure identification may require a huge number of monitoring paths. We address this problem by designing a Bayesian approach that progressively selects the next path to probe on the basis of its expected information utility, conditioned on prior observations. As the complexity of the computation of posterior probabilities of node failures is exponential in the number of failed paths, we propose a polynomial-time greedy strategy which approximates these values. To consider aging of information in dynamic failure scenarios where node states can change during a monitoring period, we propose a monitoring technique based on a sliding observation window of adaptive length. By means of numerical experiments conducted on real network topologies we demonstrate the practical applicability of our approach, and the superiority of our algorithms with respect to state of the art solutions based on classic BNT as well as sequential group testing.
CCUBI: A cross‐chain based premium competition scheme with privacy preservation for usage‐based insurance

Usage‐based insurance (UBI) provides reasonable vehicle insurance premiums based on vehicle usage and driving behavior. In general, there are three major issues in realizing intelligent UBI systems. First, UBI evaluation mechanisms are not auditable to drivers. Insurers may thus deliberately adjust the UBI premiums. Second, the process of collecting driving data by insurers may lead to serious privacy breaches. Third, forging safer driving data for reducing insurance premiums may cause economic losses for insurers. To address these challenges, in this study, we propose CCUBI, a cross‐chain‐based premium competition scheme with privacy preservation for intelligent UBI systems. We introduce tamper‐resistant blockchain and smart contracts to construct credible insurance mechanisms. The cross‐chain technology connects these blockchains in the entire network to form an open premium competition scheme. Vehicle owners can assess designated insurers by sharing historical data with them to get a suitable CCUBI plan. In addition, we propose a data aggregation method used for CCUBI analysis with privacy preservation. Vehicle owners only publish proofs of the driving data. Proofs can still maintain privacy and computability in cross‐chain flows. Finally, we adopt roadside units to detect forged driving data. We conduct a detailed security analysis. Experimental results also demonstrate the efficiency of CCUBI.
DA-R-EPOCH versus R-CHOP in intermediate and high risk IPI diffuse large B-cell lymphoma, a randomized controlled trial

Background: Diffuse large B-cell lymphoma is a challenging disease in management. Although most patients are cured with the standard R-CHOP, one third of them remains refractory to this regimen. As prognosis of refractory disease is worse than primarily responding one, several trials investigated other more intense frontline regimens tailored based upon risk and biological characteristics. Patients & Methodology: This is a prospective randomized trial investigating the more intensive DA-R-EPOCH regimen as frontline therapy in intermediate and high risk DLBCL patients in comparison to the standard R-CHOP regimen. We compared both regimens in these risk categories as well as undergoing a subgroup analysis according to cell of origin (Germinal center versus activated B cell) and according to BCL2 and C-myc expression (double expressor lymphoma). Toxicities in both arms have been analyzed according to common terminology criteria for adverse events (CTCAE). Results: In spite of being more toxic and complex, there was no significant improvement in DFS or response rate with DA-R-EPOCH. No significant benefit for DA-R-EPOCH over R-CHOP in both germinal center and activated B-cell DLBCL. Conclusion: Tailoring upfront treatment of DLBCL based upon risk classification or BCL2/c-myc expression remains an area of unanswered questions and warrants further investigations.
Correlating Machine Learning Classi cation of Traf c Camera Images with Snow-related Vehicular Crashes in New York State

Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road froma traf c camera image. This information is coupled with the number of coincident vehicular crashes to provide detailedconsideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was foundthat, during meteorological winter, when the ML model determined there to be snow on the road in a traf c camera image, the chance of a vehicular crash pairing with that traf c camera increased by 61%. The systems developed as part of this research have potential to assist roadway of cials in assessing risk in real time and making informed decisionsabout snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.
The Effect of Acid Hydrolysis Parameters on the Properties of Nanocellulose Extracted from Almond Shells

ABSTRACT The effects of acid hydrolysis parameters (acid type, acid concentration, reaction time, and temperature) on the properties of nanocellulose (NC) extracted from almond shells were investigated. The highest percentage yield of NC obtained from each type of acid was 71.8% using 25% w/v sulfuric acid at 50°C for 60 min, followed by 71.1% with 50% w/v perchloric acid at 35°C for 15 min, and finally 67% with 50% w/v methane sulfonic acid at 25°C for 10 min. The hydrolysis treatment using sulfuric and perchloric acids greatly affect the NC particle size and length. The extracted NCs show different thermal degradation profiles due to the difference in the crystallinity index and types of acid used. Hydrolysis with strong acids (sulfuric and perchloric acids) produce NC with lower crystallinity due to the degradation of both amorphous and crystalline regions in the cellulose. Furthermore, BET analysis revealed the NCs from the hydrolysis using sulfuric acid possesses the highest surface area, desorption/adsorption pore diameter, desorption, and adsorption cumulative pore volume compared to other acids.
Targeting the Mitochondrial Protein VDAC1 as a Potential Therapeutic Strategy in ALS

Impaired mitochondrial function has been proposed as a causative factor in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), caused by motor neuron degeneration. Mutations in superoxide dismutase (SOD1) cause ALS and SOD1 mutants were shown to interact with the voltage-dependent anion channel 1 (VDAC1), affecting its normal function. VDAC1 is a multi-functional channel located at the outer mitochondrial membrane that serves as a mitochondrial gatekeeper controlling metabolic and energetic crosstalk between mitochondria and the rest of the cell and it is a key player in mitochondria-mediated apoptosis. Previously, we showed that VDAC1 interacts with SOD1 and that the VDAC1-N-terminal-derived peptide prevented mutant SOD1 cytotoxic effects. In this study, using a peptide array, we identified the SOD1 sequence that interacts with VDAC1. Synthetic peptides generated from the identified VDAC1-binding sequences in SOD1 directly interacted with purified VDAC1. We also show that VDAC1 oligomerization increased in spinal cord mitochondria isolated from mutant SOD1G93A mice and rats. Thus, we used the novel VDAC1-specific small molecules, VBIT-4 and VBIT-12, inhibiting VDAC1 oligomerization and subsequently apoptosis and associated processes such as ROS production, and increased cytosolic Ca2+. VBIT-12 was able to rescue cell death induced by mutant SOD1 in neuronal cultures. Finally, although survival was not affected, VBIT-12 administration significantly improved muscle endurance in mutant SOD1G93A mice. Therefore, VBIT-12 may represent an attractive therapy for maintaining muscle function during the progression of ALS.
Numerical Modeling of Electromagnetic Scattering in Explosive Granular Media

Terahertz (THz) reflection and transmission spectroscopy is a promising new field with applications in imaging and illicit material detection. One particularly useful application is for the detection of improvised explosive devices (IEDs) which is a favorite weapon of global terrorists. Explosive materials have been shown to have a unique spectral signature in the THz band which can be used to identify the explosives. However, the initial measurements performed on the explosive samples do not account for the modulation of the spectral features by random scattering that will be prevalent with actual samples encountered in applications. The intent of this work is to characterize and quantify the effects of random scattering that may alter the spectral features. Specifically, the effect that a randomly rough surface and granular scattering has on the scattered THz wave (T-Rays) will be investigated and characterized using the Finite-Difference TimeDomain (FDTD) simulation method. The FDTD method is a natural choice for this work as it can handle complicated geometries (i.e., multiple scatterers, arbitrarily rough interfaces, etc.) arbitrary materials (i.e., dispersive media, etc.) and provides broadband frequency data with one simulation pass.
Effectiveness of Predictive Analytics in Precision Public Health in Strengthening Health System for Future Pandemics

Background: Several mortality causalities are responsible for millions of deaths yearly and decrease in life expectancy. The covid-19 pandemic has continued to increase these numbers since 2020 its emergence many public health measures have been put in place to flatten the curve. Public health has used data from different source to improve decision and policy making. In this era, precision public health among other developing field of health has shown great potential in strengthening health data systems. However, with predictive analytics been support systems in precision public health there is a need to evaluate the performance of these techniques. Method: A systematic review was conducted between November 2011 and January 2022 using studies from nine at database which included PubMed, TRIP, SCOPUS, and Cochrane. Grey literature and google scholar were searched. Eligible studies were selected using inclusion and exclusion criteria and finding from the included studies were summarized. Result: 17 studies from 11 countries published in English between 2011-2021 were selected demographic, environmental, social, and socio-economic data were gathered by the selected studies. Artificial intelligence with machine learning been the most common, was the major predictive analytics technique used by the research. Communicable and non-communicable diseases, prescription overdose and underdose, neonatal conditions, health disparities, substance abuse and motor vehicle injuries are public health areas in which the techniques were deployed. Discussion and conclusion: Studies in this review reported that predictive analytics techniques are effective and produced reasonably accuracies. Although, there are some limitations such as lack specific definition of sub-population and units of inference, use of one-dimensional data by some studies, some bias that can confound randomization predictive analytics in precision public health is a great call that requires more work for evidence-based foundation for its application.
Intelligent Network Service Optimization in the Context of 5G/NFV

Our contemporary society has never been more connected and aware of vital information in real time, through the use of innovative technologies. A considerable number of applications have transitioned into the cyber-physical domain, automating and optimizing their routines and processes via the dense network of sensing devices and the immense volumes of data they collect and instantly share. In this paper, we propose an innovative architecture based on the monitoring, analysis, planning, and execution (MAPE) paradigm for network and service performance optimization. Our study confirms distinct evidence that the utilization of learning algorithms, consuming datasets enriched with the users’ empirical opinions as input during the analysis and planning phases, contributes greatly to the optimization of video streaming quality, especially by handling different packet loss rates, paving the way for the achievable provision of a resilient communications platform for calamity assessment and management.
FINANCIAL CONSTRAINT MODERATE THE EFFECT OF CORPORATE SOCIAL RESPONSIBILITY AND INSTITUTIONAL QUALITY ON BANK RISK TAKING IN IDX

Banking sector holds a strategic position that demands excellent risk management. Improper risk management can lead to systemic failures which can affect the financial stability of a nation. Information asymmetry problems between managers and other parties can trigger moral hazard problems. This study examined how corporate social responsibility (CSR) and institutional quality affect bank risk taking and to determine the role of financial constraints in weakening the relationship between CSR and bank risk taking. This quantitative study involved banks listed on the Indonesia Stock Exchange from 2013-2020. Dynamic panel data regression analysis using system generalized method of moments (System GMM) approach was employed in data analysis. The results indicated that CSR and institutional quality had a significant negative influence on bank risk taking. Furthermore, as a moderating variable, financial constraint weakened the relationship between CSR and bank risk taking. The findings of this study added an empirical evidence in the field of financial management on the relationship between CSR, financial constraints, institutional quality and bank risk taking. Bank managers should employ CSR strategies to reduce bank risk taking. In addition, both the government and regulators can improve institutional quality to reduce bank risk taking.
Numerical classification of singular fibers in genus 3 pencils

Let n : X —> D be a proper surjective holomorphic map of a complex manifold X of dimension 2 to a small open disc D = f t c C11 ti < cf. We assume that rc is smooth over a punctured disc D ' = D { 0 } . Moreover we assume that for every t c D ' the fiber X , = n 1 (t) is a non-singular curve of genus g and that X contains no exceptional curves of the first kind. B y L ,, we denote the effective divisor in X defined by the equation n = t (t c D ) . We call the divisor Lo th e singular fiber of n. For every t c D ' we call the divisor L , a generic f iber. We write the singular fiber Lo a s
3D Printing of Cellulase-Laden Cellulose Nanofiber/Chitosan Hydrogel Composites: Towards Tissue Engineering Functional Biomaterials with Enzyme-Mediated Biodegradation

The 3D printing of a multifunctional hydrogel biomaterial with bioactivity for tissue engineering, good mechanical properties and a biodegradability mediated by free and encapsulated cellulase was proposed. Bioinks of cellulase-laden and cellulose nanofiber filled chitosan viscous suspensions were used to 3D print enzymatic biodegradable and biocompatible cellulose nanofiber (CNF) reinforced chitosan (CHI) hydrogels. The study of the kinetics of CNF enzymatic degradation was studied in situ in fibroblast cell culture. To preserve enzyme stability as well as to guarantee its sustained release, the cellulase was preliminarily encapsulated in chitosan–caseinate nanoparticles, which were further incorporated in the CNF/CHI viscous suspension before the 3D printing of the ink. The incorporation of the enzyme within the CHI/CNF hydrogel contributed to control the decrease of the CNF mechanical reinforcement in the long term while keeping the cell growth-promoting property of chitosan. The hydrolysis kinetics of cellulose in the 3D printed scaffolds showed a slow but sustained degradation of the CNFs with enzyme, with approximately 65% and 55% relative activities still obtained after 14 days of incubation for the encapsulated and free enzyme, respectively. The 3D printed composite hydrogels showed excellent cytocompatibility supporting fibroblast cell attachment, proliferation and growth. Ultimately, the concomitant cell growth and biodegradation of CNFs within the 3D printed CHI/CNF scaffolds highlights the remarkable potential of CHI/CNF composites in the design of tissue models for the development of 3D constructs with tailored in vitro/in vivo degradability for biomedical applications.
Predictors of Suicide Attempts of Individuals with Autism and Their Siblings

The occurrence of mood- and anxiety-related conditions among family members of individuals diagnosed with autism spectrum disorder (ASD) increases the risk of suicide attempts and has also created assessment and treatment issues for clinicians and parents. Recorded rates of mental health disorders comorbid with suicide attempts among individuals on the spectrum range from 29% to 52%. The purpose of this study was to investigate the presence of predictors of suicide attempts among sibling dyads (pairs of siblings in which one sibling is on the autism spectrum). Growing evidence in the literature indicates a link between a diagnosis of autism and mental health. A web-based survey was used to collect data from individuals on the spectrum and their siblings. A sample of 144 individuals was finalized for the analyses. Logistic regression analysis was conducted to assess the predictors of suicide attempts in the sample. Depression, anxiety, sexual orientation, and marital status were all used as predictors in the logistic regression analyses. Both levels of anxiety and sexual orientation were found to be significant predictors for suicide attempts. Recommendations for future research, assessment, and treatment are presented.
Decomposing Three Decades of Nitrogen Emissions in Canada

Reactive nitrogen (Nr) emissions arise from multiple economic sectors, each creating distinct Nr species and impacts on environmental quality. In Canada, the relative contribution of different Nr species to total Nr emissions varies considerably across provinces, yet these Nr species are often studied separately, making comparison difficult. Here, we synthesize data from national emission inventories, as well as agricultural and wastewater statistics, to estimate total and per capita Nr emissions trends across Canada's 10 provinces over three decades between 1990 and 2017. We classified emissions by 4 main species of Nr, 3 source sectors, and 13 subsectors. Nr emissions peaked around 2000 followed by reductions both nationally and across almost all provinces. Agriculture replaced fossil fuel combustion as the largest source of Nr emissions after 2008, coinciding with regulatory interventions that aimed to reduce NOx emissions from transportation, while NH3 from crop production increased in several provinces. Using an index decomposition analysis, we further assessed the socioeconomic drivers of Nr emissions changes, including the roles of emission intensity (Nr emissions per unit of economic output), affluence, population, and structural changes in the economy. Reduced emission intensity (an aggregate indicator of technology and policy changes) counteracted some of the effects of affluence and population as positive drivers of Nr emissions. Economic structural changes had both large negative and positive effects on Nr emissions. Our results underscore the importance of continued reductions in emissions intensity as well as shifting economies toward less Nr intensive sectors to further decouple affluence from Nr pollution.
Flow of S-matrix poles for elementary quantum potentials11This research was supported in part by an NSERC Undergraduate Summer Research Award (SGN) and an NSERC Discovery Grant (MAW).

The poles of the quantum scattering matrix (S-matrix) in the complex momentum plane have been studied exten- sively. Bound states give rise to S-matrix poles, and other poles correspond to non-normalizable antibound, resonance, and antiresonance states. They describe important physics but their locations can be difficult to determine. In pioneering work, Nussenzveig (Nucl. Phys. 11, 499 (1959)) performed the analysis for a square well (wall), and plotted the flow of the poles as the potential depth (height) varied. More than fifty years later, however, little has been done in the way of direct general- ization of those results. We point out that today we can find such poles easily and efficiently using numerical techniques and widely available software. We study the poles of the scattering matrix for the simplest piecewise flat potentials, with one and two adjacent (nonzero) pieces. For the finite well (wall) the flow of the poles as a function of the depth (height) re- covers the results of Nussenzveig. We then analyze the flow for a potential with two independent parts that can be attractive or repulsive, the two-piece potential. These examples provide some insight into the complicated behavior of the resonance, antiresonance, and antibound poles.