bluuebunny/crossref_metadata_2025 huggingface dataset:
The relationship between emptiness and suicide and self‐injury urges in borderline personality disorder
Authors: Fulham Lindsay , Forsythe Jeremy , Fitzpatrick Skye
Published: February 2023

Introduction

Suicide and self‐injury (SSI) are pervasive among individuals with borderline personality disorder (BPD), yet little research has examined factors that increase SSI urges among those with BPD. Emptiness is a diagnostic criterion of BPD that is associated with SSI behaviors, but its impact on SSI urges in BPD is poorly understood. This study investigates the association between emptiness and SSI urges at baseline and in response to a stressor (i.e., reactivity) among individuals with BPD.

Methods

Forty individuals with BPD participated in an experimental procedure where they rated their degree of emptiness and SSI urges at baseline and in response to an interpersonal stressor. Generalized estimating equations tested whether emptiness predicts baseline SSI urges and SSI urge reactivity.

Results

Higher emptiness predicted higher baseline suicide urges (B = 0.006, SE = 0.002, p < 0.001), but not baseline self‐injury urges (p = 0.081). Emptiness did not significantly predict suicide urge reactivity (p = 0.731) nor self‐injury urge reactivity (p = 0.446).

Conclusion

Assessing and targeting emptiness may facilitate the reduction of suicide urges in BPD. Future research should investigate treatment strategies for reducing SSI risk among individuals with BPD via targeting emptiness.
Computer crime motives: Do we have it right?
Authors: Neufeld Derrick
Published: February 2023

Academic, legal and practitioner responses to cyber threats have been predominantly reactive, punitive, and deterrence‐based, with limited attention given to the motives underlying computer criminals' behaviors. This paper reasons that new and better theoretical perspectives are needed to explain computer criminals' motives. Following a review of the computer crime behavioral literature, a summary review of core philosophies and theories used to explain generalized crime and criminal motives is provided. A framework is proposed suggesting that criminological theories have evolved along two categorical dimensions: determinism‐indeterminism, and individualism‐collectivism. The paper then reasons that future computer crime research will benefit by considering indeterminist‐collectivist (constructivist) theories. Two such theories, social construction of technology, and actor‐network theory, are proposed in the discussion section, along with some cybercrime examples. The paper invites a deeper consideration of the origins and motivations of computer‐based criminality as a means of building stronger theory and ultimately advancing more proactive and effective solutions.
Students' articulations of uncertainty about big data in an integrated modeling approach learning environment
Authors: Gafny Ronit , Ben‐Zvi Dani
Published: February 2023

In recent years, big data has become ubiquitous in our day‐to‐day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data—the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP.
Opensource spatial optimization in GIScience for strategic positioning
Authors: Murray Alan T. , Baik Jiwon
Published: February 2023

Spatial optimization involving multiple objectives is commonly needed to address complex planning and management issues yet associated models and problems are exceptionally difficult and complicated to solve. Open‐source approaches have received attention because they offer potential for making spatial optimization more broadly available, and other GIScience methods, to support analysis and decision‐making. Beyond this, the ability to integrate GIScience functions through open‐source alternatives offers great potential to decrease reliance on commercial, proprietary software as well as enable broader use and adoption of, and reliance on, spatial optimization approaches. This article details an important bi‐objective spatial problem along with open‐source capabilities for solution as an example of challenges faced and opportunities possible. A number of solution approaches are considered, providing an opportunity to assess computational requirements, functionality, solution quality and issues encountered. Empirical applications associated with emergency response, healthcare access, food processing and strategic player positioning are detailed. This study highlights capabilities, limitations and challenges for open‐source science in addressing a class of multi‐objective spatial optimization problems.
Do deep regional trade agreements strengthen anti‐corruption? A social network analysis
Authors: Zhang Xingyuan , Goel Rajeev K. , Jiang Jiaming , Capasso Salvatore
Published: February 2023

This paper adds to research on the international drivers of corruption and anti‐corruption by focusing on the influence of regional trade agreements (RTAs). Using cross‐national data and employing a social network analysis, we provide descriptions of the evolution of deep integration of RTAs' network in anti‐corruption and transparency with a comprehensive view in which we utilise the network's Closeness, Harmonic, Betweenness, Eigenvector, and PageRank centralities to demonstrate the positions of member countries. We examine the relations of the positions of member countries in the deep RTAs' network and the performance of those countries in fighting corruption. Our empirical results suggest that the countries which hold dominant positions in the deep RTAs' network may be associated with better performance in fighting corruption. At the same time, countries which may have traditionally lagged in the enforcement of anti‐bribery and corruption laws will benefit through the transfer of best practices and technical expertise in rulemaking and enforcement by using their positions in the deep RTAs' network. Furthermore, our results indicate that the provisions related to anti‐corruption or transparency in RTAs will play more important roles in fighting public sector corruption for member countries. The findings are robust to alternate corruption and governance measures.
Child labour and global value chains
Authors: Ugarte Cristian , Olarreaga Marcelo , Saiovici Gady
Published: February 2023

We explore the relationship between the internationalisation of production through Global Value Chains and child labour at the sector level, using data from 26 low‐ and middle‐income countries. We find that sectors with stronger participation in foreign markets by exporting inputs to firms that will process them and export them to third countries (forward linkages) exhibit less child labour. However, other forms of participation in foreign markets through gross exports or exports of goods that embed foreign value‐added (backward linkages) are not associated with lower levels of child labour.
Unions of lines in Rn$\mathbb {R}^n$
Authors: Zahl Joshua
Published: February 2023

We prove a conjecture of D. Oberlin on the dimension of unions of lines in . If is an integer, , and L is a set of lines in with Hausdorff dimension at least , then the union of the lines in L has Hausdorff dimension at least . Our proof combines a refined version of the multilinear Kakeya theorem by Carbery and Valdimarsson with the multilinear → linear argument of Bourgain and Guth.
Gromov–Witten theory of complete intersections via nodal invariants
Authors: Argüz Hülya , Bousseau Pierrick , Pandharipande Rahul , Zvonkine Dimitri
Published: February 2023

We provide an inductive algorithm computing Gromov–Witten invariants in all genera with arbitrary insertions of all smooth complete intersections in projective space. We also prove that all Gromov–Witten classes of all smooth complete intersections in projective space belong to the tautological ring of the moduli space of stable curves. The main idea is to show that invariants with insertions of primitive cohomology classes are controlled by their monodromy and by invariants defined without primitive insertions but with imposed nodes in the domain curve. To compute these nodal Gromov–Witten invariants, we introduce the new notion of nodal relative Gromov–Witten invariants. We then prove a nodal degeneration formula and a relative splitting formula. These results for nodal relative Gromov–Witten theory are stated in complete generality and are of independent interest.
Under-ice mid-frequency acoustic transmission and communication in two Arctic Ocean environments
Authors: Tollefsen Dag , van Walree Paul , Jenserud Trond , Forsmo Vidar
Published: February 2023

This paper presents data from mid-frequency (4–8 kHz) under-ice acoustic transmission and communication experiments in the Nansen Basin and in the Beaufort Sea of the Arctic Ocean. Measured impulse responses to range ∼10 km showed considerable delay spread between distinct groups of arrivals due to surface-ducted and bottom-reflected paths. The relative strengths of these groups depend on duct and under-ice properties. We demonstrate that despite shallow (∼25 m) source/receiver depths, good communication performance can be achieved in both environments when exploiting the bottom-reflected arrivals. Simulations with the Bellhop ray model support the findings.
Malaria Molecular Surveillance in the Peruvian Amazon with a Novel Highly Multiplexed Plasmodium falciparum AmpliSeq Assay
Authors: Kattenberg Johanna Helena , Fernandez-Miñope Carlos , van Dijk Norbert J. , Llacsahuanga Allcca Lidia , Guetens Pieter , Valdivia Hugo O. , Van geertruyden Jean-Pierre , Rovira-Vallbona Eduard , Monsieurs Pieter , Delgado-Ratto Christopher , Gamboa Dionicia , Rosanas-Urgell Anna
Published: February 2023

While the power of next-generation sequencing technologies to inform and guide malaria control programs has become broadly recognized, the integration of genomic data for operational incorporation into malaria surveillance remains a challenge in most countries where malaria is endemic. The main obstacles include limited infrastructure, limited access to high-throughput sequencing facilities, and the need for local capacity to run an in-country analysis of genomes at a large-enough scale to be informative for surveillance.
Evaluation of Environmental Stability and Disinfectant Effectiveness for Human Coronavirus OC43 on Human Skin Surface
Authors: Watanabe Naoto , Hirose Ryohei , Yamauchi Katsuma , Miyazaki Hajime , Bandou Risa , Yoshida Takuma , Doi Toshifumi , Inoue Ken , Dohi Osamu , Yoshida Naohisa , Uchiyama Kazuhiko , Ishikawa Takeshi , Takagi Tomohisa , Konishi Hideyuki , Ikegaya Hiroshi , Nakaya Takaaki , Itoh Yoshito
Published: February 2023

This study revealed the environmental stability of HCoV-OC43 and disinfectant effectiveness against HCoV-OC43, which had not been demonstrated in previous studies. HCoV-OC43 is highly stable on the surface of human skin, with a survival time of approximately 25 h.
General practitioner well-being during the COVID-19 pandemic: a qualitative interview study
Authors: Jefferson Laura , Heathcote Claire , Bloor Karen
Published: February 2023

Objectives

The COVID-19 pandemic presented new challenges for general practitioners’ (GPs’) mental health and well-being, with growing international evidence of its negative impact. While there has been a wide UK commentary on this topic, research evidence from a UK setting is lacking. This study sought to explore the lived experience of UK GPs during COVID-19, and the pandemic’s impact on their psychological well-being.

Design and setting

In-depth qualitative interviews, conducted remotely by telephone or video call, with UK National Health Service GPs.

Participants

GPs were sampled purposively across three career stages (early career, established and late career or retired GPs) with variation in other key demographics. A comprehensive recruitment strategy used multiple channels. Data were analysed thematically using Framework Analysis.

Results

We interviewed 40 GPs; most described generally negative sentiment and many displayed signs of psychological distress and burnout. Causes of stress and anxiety related to personal risk, workload, practice changes, public perceptions and leadership, team working and wider collaboration and personal challenges. GPs described potential facilitators of their well-being, including sources of support and plans to reduce clinical hours or change career path, and some described the pandemic as offering a catalyst for positive change.

Conclusions

A range of factors detrimentally affected the well-being of GPs during the pandemic and we highlight the potential impact of this on workforce retention and quality of care. As the pandemic progresses and general practice faces continued challenges, urgent policy measures are now needed.
Hyperbaric oxygen treatment in the management of necrotising soft-tissue infections: results from a Danish nationwide registry study
Authors: Hedetoft Morten , Madsen Martin Bruun , Hyldegaard Ole
Published: February 2023

Objectives

Application of hyperbaric oxygen (HBO2) treatment in the multidisciplinary setting of necrotising soft-tissue infection (NSTI) is debated as a considerable number of studies are of low quality with marked prognostication bias due to inadequately addressing disease severity. The objective of this study was to associate HBO2treatment with mortality in patients with NSTI including disease severity as a prognostic variable.

Design

Nationwide population-based register study.

Setting

Denmark.

Participants

Danish residents with NSTI patients between January 2011 and June 2016.

Primary and secondary outcome measures

Thirty-day mortality was compared between patients receiving and patients not receiving HBO2treatment using inverse probability of treatment weighting and propensity-score matching with predetermined variables (age, sex and weighted Charlson comorbidity score, presence of septic shock and Simplified Acute Physiology Score II (SAPS II)).

Results

A total of 671 NSTI patients were included with a median age of 63 (52–71), 61% male sex, 30% had septic shock and a median SAPS II of 46 (34–58). Patients who received HBO2treatment (n=266) were younger and had lower SAPS II, but a larger fraction had septic shock compared with patients not receiving HBO2treatment. Overall, all-cause 30-day mortality was 19% (95% CI 17% to 23%). The statistical models were in general acceptably balanced with covariates reaching <0.1 absolute standardised mean differences and patients receiving HBO2treatment were associated with lower 30-day mortality (OR 0.40, 95% CI 0.30 to 0.53, p<0.001).

Conclusions

In analyses using inverse probability of treatment weighting and propensity score analysis, patients treated with HBO2treatment were associated with improved 30-day survival.
Emergency unit capacity in Northern Tanzania: a cross-sectional survey
Authors: Ardsby Malin , Shayo Frida , Sakita Francis M , Wilhelms Daniel , Moshi Baraka , Frankiewicz Parker , Silva Lincoln Luis , Staton Catherine A , Mmbaga Blandina , Joiner Anjni
Published: February 2023

Introduction

Emergency medicine (EM) is a growing field in Sub-Saharan Africa. Characterising the current capacity of hospitals to provide emergency care is important in identifying gaps and future directions of growth. This study aimed to characterise the ability of emergency units (EU) to provide emergency care in the Kilimanjaro region in Northern Tanzania.

Methods

This was a cross-sectional study conducted at 11 hospitals with emergency care capacity in three districts in the Kilimanjaro region of Northern Tanzania assessed in May 2021. An exhaustive sampling approach was used, whereby all hospitals within the three-district area were surveyed. Hospital representatives were surveyed by two EM physicians using the Hospital Emergency Assessment tool developed by the WHO; data were analysed in Excel and STATA.

Results

All hospitals provided emergency services 24 hours a day. Nine had a designated area for emergency care, four had a core of fixed providers assigned to the EU, two lacked a protocol for systematic triage. For Airway and Breathing interventions, oxygen administration was adequate in 10 hospitals, yet manual airway manoeuvres were only adequate in six and needle decompression in two. For Circulation interventions, fluid administration was adequate in all facilities, yet intraosseous access and external defibrillation were each only available in two. Only one facility had an ECG readily available in the EU and none was able to administer thrombolytic therapy. For trauma interventions, all facilities could immobilise fractures, yet lacked interventions such as cervical spinal immobilisation and pelvic binding. These deficiencies were primarily due to lack of training and resources.

Conclusion

Most facilities perform systematic triage of emergency patients, though major gaps were found in the diagnosis and treatment of acute coronary syndrome and initial stabilisation manoeuvres of patients with trauma. Resource limitations were primarily due to equipment and training deficiencies. We recommend the development of future interventions in all levels of facilities to improve the level of training.
Prevalence of current nutrition care practices for disease-related malnutrition in Canadian hospitals
Authors: Keller Heather , Wei Cindy , Nasser Roseann , Dhaliwal Rupinder , Gramlich Leah
Published: February 2023

Disease-related malnutrition is common in hospital patients. The Health Standards Organization Canadian Malnutrition Prevention, Detection, and Treatment Standard was published in 2021. The purpose of this study was to determine the current state of nutrition care in hospitals prior to implementation of the Standard. An online survey was distributed to hospitals across Canada via email. A representative reported on nutrition best practices based on the Standard at the hospital level. Descriptive and bivariate statistics were completed for selected variables based on size and type of hospital. One hundred and forty-three responses from nine provinces were received (56% community, 23% academic, and 21% other). Malnutrition risk screening was being completed on admission in 74% ( n = 106/142) of hospitals, although not all units participated in screening all patients. Nutrition-focused physical exam is completed as part of a nutrition assessment in 74% ( n = 101/139) of sites. Flagging a malnutrition diagnosis ( n = 38/104) and physician documentation (18/136) were sporadic. Academic and medium (100–499 beds) and large hospitals (500+ beds) were more likely to have a physician document a malnutrition diagnosis. Some, but not all, best practices are occurring in Canadian hospitals on a regular basis. This demonstrates a need for continued knowledge mobilization of the Standard.
Influence of a rigid cap on thermo-mechanical behavior of nonsymmetrical thermally loaded energy pile group in clay
Authors: Zhang Dingxin , Wang Chenglong , Bouazza Abdelmalek , Ding Xuanming , Kong Gangqiang
Published: February 2023

This paper presents the results of a series of cyclic nonsymmetrical thermally loaded model-scale tests conducted on floating pile groups, with and without a rigid cap in saturated clay. The energy pile in each group was subjected to 10 two-way (heating–cooling) thermal cycles with an amplitude of 15 °C to investigate the potential effects of a rigid cap on the thermomechanical behavior of energy pile groups. It was found that the rigid cap restrained the expansion/contraction of the energy pile in response to temperature change and reduced its irreversible settlement, resulting in a lower differential settlement between the energy and nonenergy piles. Consequently, a lower tilting was observed in the pile group with a rigid cap. In addition, the thermally induced axial load was significantly increased (especially in cooling mode) due to the restriction of a rigid cap. The thermomechanical load transfer profile of the energy pile was also affected by it, resulting in the downward transmission of shaft resistance.
On different approaches to freeze-in and freeze-out leptogenesis with quasi-degenerate neutrinos
Authors: Racker J.
Published: February 2023

We compare two approaches for determining the generation of lepton asymmetry during production and decay of quasi-degenerate neutrinos, namely the density matrix formalism and a recent proposal which does not involve any counting of neutrino number densities and is based on plugging the resummed propagator in a quantum field theory model for neutrino oscillations. We show numerically and analytically that they are almost equivalent for small mass splittings and also discuss the hierarchical limit. The comparison, performed in a simple scalar toy model, helps to understand several issues that have been discussed in the literature.
Neural Network-Based Method for Early Diagnosis of Autism Spectral Disorder Head-Banging Behavior from Recorded Videos
Authors: Sadek Esraa T. , Seada Noha A. , Ghoniemy Said
Published: February 2023

Autism spectrum disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is a set of repetitive motor activities associated with several mental developmental disorders like Autism. SMM has several forms like arm flapping, head banging, ear covering, and spinning with various degrees of severity that might lead to self-injury in severe cases.

Developing a computer-vision-based technology to detect noticeable SMM behaviors can help in the early diagnosis of autism. In this paper, a computer vision-based neural network model was proposed to detect and recognize repetitive motor behaviors. The proposed model went through three main stages: First, data preparation. Second, human body features extraction using deep learning pose estimation and the skeleton representation model, and finally, multiclass classification to distinguish between several classes of headbanging. The proposed solution was evaluated using the Self Stimulatory Behavior Dataset (SSBD) which is a public dataset of three classes of repetitive motor behaviors associated with autism. We also collected a set of 40 videos of autistic children exhibiting headbanging from public domains like YouTube. In addition to that, we captured 25 videos of typically developing subjects mimicking headbanging. The collected and the videoed videos were used to evaluate the proposed model. This work proves the applicability of diagnosing mental developmental syndrome symptoms using vision-based techniques in cooperation with neural networks. The produced results prove that the used techniques can operate well in real-world challenging applications. The proposed model achieved 85.5% accuracy on SSBD and 93% on the collected and recorded videos.
COUPLED FRACTIONAL WIGNER DISTRIBUTION WITH APPLICATIONS TO LFM SIGNALS
Authors: TEALI AAJAZ A. , SHAH FIRDOUS A. , TANTARY AZHAR Y. , NISAR KOTTAKKARAN S.
Published: February 2023

The coupled fractional Fourier transform is a much recent ramification of the two-dimensional fractional Fourier transform, wherein the kernel is not a tensor product of one-dimensional copies, but relies on two angles that are coupled to yield a new pair of transform parameters. In this paper, we introduce a novel two-dimensional Wigner distribution, coined as coupled fractional Wigner distribution (CFrWD). The prime advantage of such a ramification of the Wigner distribution lies in the fact that the CFrWD can efficiently tackle the higher-order-phase and chirp signals, which constitute a wider class of signals arising in modern communication systems. To begin with, we study some fundamental properties of the proposed CFrWD, including marginal, shifting, conjugate-symmetry and anti-derivative properties. In addition, we also formulate the Moyal’s principle, inversion formula and the convolution and correlation theorems associated with CFrWD. Nevertheless, we demonstrate the efficacy of CFrWD for estimating and detecting both the one-component and multi-component linear-frequency-modulated signals.
MEYER WAVELET NEURAL NETWORKS PROCEDURES TO INVESTIGATE THE NUMERICAL PERFORMANCES OF THE COMPUTER VIRUS SPREAD WITH KILL SIGNALS
Authors: SABIR ZULQURNAIN , BALEANU DUMITRU , RAJA MUHAMMAD ASIF ZAHOOR , ALSHOMRANI ALI S. , HINCAL EVREN
Published: February 2023

This study shows the design of the Meyer wavelet neural networks (WNNs) to perform the numerical solutions of the spread of computer virus with kill signals, i.e. SEIR-KS system. The optimization of the SEIR-KS system is performed by the Meyer WNNs together with the optimization through the genetic algorithm (GA) and sequential quadratic (SQ) programming, i.e. Meyer WNNs-GASQ programming. A sigmoidal-based log-sigmoid function is implemented as an activation function, while 10 numbers of neurons work with 120 variables throughout this study. The correctness of the proposed Meyer WNNs-GASQP programming is observed through the comparison of the obtained and reference numerical solutions. For the consistency and reliability of the Meyer WNNs-GASQ programming, an analysis based on different statistical procedures is performed using 40 numbers of independent executions. Moreover, the use of different statistical operators like mean, median, minimum, standard deviation and semi-interquartile range further validates the correctness of the Meyer WNNs-GASQ programming for solving the SEIR-KS system.
SUBLEADING CORRECTION TO THE ASIAN OPTIONS VOLATILITY IN THE BLACK–SCHOLES MODEL
Authors: PIRJOL DAN
Published: February 2023

The short maturity limit [Formula: see text] for the implied volatility of an Asian option in the Black–Scholes model is determined by the large deviations property for the time-average of the geometric Brownian motion. In this note, we derive the subleading [Formula: see text] correction to this implied volatility, using an asymptotic expansion for the Hartman–Watson distribution. The result is used to compute subleading corrections to Asian options prices in a small maturity expansion, sharpening the leading order result obtained using large deviations theory. We demonstrate good numerical agreement with precise benchmarks for Asian options pricing in the Black–Scholes model.
Synthesis and characterization of an expanded antiaromatic macrocycle porphyrin analogue
Authors: Chau Calvin V. , Johnson Martin R. , Mangel Daniel N. , Sessler Jonathan L.
Published: February 2023

Expansion of porphyrin-like macrocycles can provide insights into the relationship between structural, optical, and electronic features. Here, we report the synthesis of a new nominally antiaromatic expanded porphyrin-like macrocycle (6) prepared via the condensation of 1,4-bis-(3,4-diethyl-2-pyrryl)benzene dialdehyde (4) with hydrazine. The absorption and emission features of this new macrocycle were compared to those of the parent bipyrrole-derived macrocycle, a system first reported in 1997. Attempts to oxidize both macrocycles (5 and 6) were unsuccessful, a failure ascribed to the steric limitations imposed by two substituents present on the [Formula: see text]-positions on the di-substituted pyrrolic precursor. This stands in contrast to what was observed for the original bipyrrole system bearing only a single [Formula: see text]-substituent on each of the pyrrolic moieties. The present results underscore the possible role of structure in governing electronic transitions in expanded porphyrins while highlighting the facility of hydrazine-based condensations in accessing readily large pyrrolic macrocycles.
HOUSEHOLD-SPECIFIC SOCIAL NORMS, THE ELASTICITY OF ELECTRICITY DEMAND, AND CARBON EMISSION REDUCTIONS IN THE RESIDENTIAL SECTOR: EVIDENCE FROM A NATURAL EXPERIMENT IN RUSSIA
Authors: TURDALIEV SALIM
Published: February 2023

In this paper, I estimate the price elasticity of residential electricity demand using household-level panel data for Russia. The study takes advantage of the variation in tariffs across regions and over time, as well as the introduction of increasing block rate (IBR) schemes in a number of regions. I show that in those regions consumers appear to be aware of the block cut-offs, even though the latter are based on the prescribed social norms and are household and dwelling specific, to the point that there are up to a total of 31 different tier cut-offs. Based on these results, I estimate the price elasticity of electricity demand to be around −0.1. I also predict the associated changes in electricity consumption, CO2 emissions, and revenues if similar IBR policies are implemented countrywide.
Characterization and Performance of the 4k×4k CCD Camera Mounted on 104-cm Sampurnanand Telescope
Authors: Yadav R. K. S. , Jaiswal Aditya , Dattatrey Arvind K. , Yadav Shobhit , Nanjappa Nandish , Panwar Neelam , Jaiswar Mukesh Kumar , Reddey Krishna , Pant Jayshreekar , Medhi Biman J.
Published: February 2023

In this paper, we present the instrumental characterization and performance of the 4[Formula: see text]k CCD detector mounted on a 104-cm Sampurnanand telescope located at ARIES, Manora Peak Nainital, India. A detailed description of the filter wheel and its motion is presented. We measured the gain and read-out noise along with the linearity of the detector. Gain estimates are 5–10% higher than manufacturer values. The estimated read-out noise values do not fall within the manufacturer’s range. It is expected that experimentally derived values will be more precise than designed values. It is suggested that the current derived values will be used rather than the designed values. The response to the incident light on the detector is linear to 99% level. To study the performance of a 4[Formula: see text]k CCD camera, we observe an open cluster Czernik 43. The fundamental parameters, i.e. log(age), distance and reddening [Formula: see text] are estimated as 7.9, 2.5[Formula: see text]kpc and 0.65[Formula: see text]mag, respectively. The estimated parameters of the cluster are comparable to the values derived in the literature within the error. Our detailed analysis illustrates that the detector is precise enough to carry out routine astronomical observations.
Photoelectrochemical Behavior of ZnO Nanostructure: A Short Review
Authors: Majumder Tanmoy , Bhattacharjee Abhishek
Published: February 2023

The use of solar energy for photoelectrochemical (PEC) water splitting is seen as an alternate strategy for addressing the problems of fossil fuels and global warming. In order to achieve the desired PEC performance for the evolution of hydrogen, such as high solar to hydrogen efficiency, incident photo to current conversion efficiency, and long-term stability, significant strategies have been developed on the basis of the bottom-up approach. Inorganic metal oxides, like zinc oxide (ZnO), are thought to be among the most promising photoelectrode materials because of their high carrier mobility, high exciton binding energy and non-toxic nature. Herein, we have summarized and reviewed ZnO nanostructured-based photoanode used in PEC applications.
Metrics for Success
Authors: Cranor Lorrie Faith , Habib Hana
Published: February 2023

Assessing the usability of choice and consent mechanisms.
Meta-learning Advisor Networks for Long-tail and Noisy Labels in Social Image Classification
Authors: Ricci Simone , Uricchio Tiberio , Del Bimbo Alberto
Published: February 2023

Deep neural networks (DNNs)for social image classification are prone to performance reduction and overfitting when trained on datasets plagued by noisy or imbalanced labels. Weight loss methods tend to ignore the influence of noisy or frequent category examples during the training, resulting in a reduction of final accuracy and, in the presence of extreme noise, even a failure of the learning process. A new advisor network is introduced to address both imbalance and noise problems, and is able to pilot learning of a main network by adjusting the visual features and the gradient with a meta-learning strategy. In a curriculum learning fashion, the impact of redundant data is reduced while recognizable noisy label images are downplayed or redirected.Meta Feature Re-Weighting (MFRW)andMeta Equalization Softmax (MES)methods are introduced to let the main network focus only on the information in an image deemed relevant by the advisor network and to adjust the training gradient to reduce the adverse effects of frequent or noisy categories. The proposed method is first tested on synthetic versions of CIFAR10 and CIFAR100, and then on the more realistic ImageNet-LT, Places-LT, and Clothing1M datasets, reporting state-of-the-art results.
Comparison of Machine Learning Algorithms for Natural Gas Identification with Mixed Potential Electrochemical Sensor Arrays
Authors: Ma Neal , Halley Sleight , Ramaiyan Kannan , Garzon Fernando , Tsui Lok-kun
Published: February 2023

Mixed-potential electrochemical sensor arrays consisting of indium tin oxide (ITO), La0.87Sr0.13CrO3, Au, and Pt electrodes can detect the leaks from natural gas infrastructure. Algorithms are needed to correctly identify natural gas sources from background natural and anthropogenic sources such as wetlands or agriculture. We report for the first time a comparison of several machine learning methods for mixture identification in the context of natural gas emissions monitoring by mixed potential sensor arrays. Random Forest, Artificial Neural Network, and Nearest Neighbor methods successfully classified air mixtures containing only CH4, two types of natural gas simulants, and CH4+NH3 with >98% identification accuracy. The model complexity of these methods were optimized and the degree of robustness against overfitting was determined. Finally, these methods are benchmarked on both desktop PC and single-board computer hardware to simulate their application in a portable internet-of-things sensor package. The combined results show that the random forest method is the preferred method for mixture identification with its high accuracy (>98%), robustness against overfitting with increasing model complexity, and had less than 10 ms training time and less than 0.1 ms inference time on single-board computer hardware.
Sedation Is Associated with Higher Polyp and Adenoma Detection Rates during Colonoscopy: A Retrospective Cohort Study
Authors: Xu Chenghu , Tang Dehua , Xie Ying , Ni Muhan , Chen Min , Shen Yonghua , Dou Xiaotan , Zhou Lin , Xu Guifang , Wang Lei , Lv Ying , Zhang Shu , Zou Xiaoping
Published: February 2023

Background and Aims. Currently sedation is a common practice in colonoscopy to reduce pain of patients and improve the operator satisfaction, whereas its impact on examination quality, especially adenoma detection rate (ADR) is still controversial. Thus, we aimed to investigate the association of sedation with ADR. Methods. Consecutive patients receiving colonoscopy between January 2017 and January 2020 at the Nanjing Drum Tower Hospital, Nanjing, China, were collected. Univariate and multivariate logistic regression models were performed to investigate the association between sedation and ADR. Subgroup analysis and propensity score matching (PSM) analysis, as sensitivity analysis, were performed to validate the independent effect. Results. The ADR was significantly higher in cases with sedation (ADR: 36.9% vs. 29.1%, odds ratio [OR]: 1.42, 95% confidence interval [CI]: 1.31–1.55,

P
<
0.001

). Multivariate analysis showed that the sedation was an independent factor associated with ADR (OR: 1.49, 95% CI: 1.35–1.65,

P
<
0.001

). The effect was consistent in subgroup analyses (

P
>
0.05

) and PSM analysis (ADR: 37.6% vs. 29.1%, OR: 1.47, 95% CI: 1.33–1.63,

P
<
0.001

). Conclusion. Sedation was associated with a higher polyp and ADR s during colonoscopy, which can promote the quality of colonoscopy.
Detection of Changes in CEA and ProGRP Levels in BALF of Patients with Peripheral Lung Cancer and the Relationship with CT Signs
Authors: Huang Jingshan , Ren Kaiming
Published: February 2023

Objective. To investigate the relationship between the detection of changes in the levels of carcinoembryonic antigen (CEA) and progastrin-releasing peptide (ProGRP) in bronchoalveolar lavage fluid (BALF) and CT signs in patients with peripheral lung cancer. Methods. Retrospective analysis of 108 patients with perihilar lung cancer who attended our hospital from January 2019 to January 2022, 54 cases were randomly selected as the observation group and 50 cases as the control group. Patients in both groups received CT examination and BALF test at the same time to observe and compare the differences in serum levels, the relationship between CT signs and serum indices, and the diagnostic value of peripheral lung cancer between the two groups. Results. The serum levels of ProGrp, CEA, CA211, and NSE in the observation group were significantly higher than those in the control group, and the difference was statistically significant (

P

 < 0.05). The morphology, density, mass enhancement pattern, bronchial morphology, obstructive signs, and lymph node fusion of CT signs were compared between the observation group and the control group, indicating that CT signs were more helpful for the localization, diagnosis, and staging of lung cancer. The results of ROC curve analysis showed that the AUC value of low-dose CT combined with serum ProGrp, CEA, CA211, and NSE was 0.892, sensitivity was 96.21%, and specificity of 90.05%, which were significantly higher than those of the single tests, respectively. The positive likelihood ratio was 84.41% and the negative likelihood ratio was 87.11%. Conclusion. The combination of CT signs and serum tumour markers helps to improve the detection rate, sensitivity, and specificity of lung cancer, which has a high diagnostic rate for lung cancer and may provide evidence for the early diagnosis of lung cancer.
A Distributionally Robust Fuzzy Optimization Method for Single-Period Inventory Management Problems
Authors: Guo Zhaozhuang , Sun Yuefang , Tian Shengnan , Li Zikun
Published: February 2023

This paper investigates single-period inventory management problems with uncertain market demand, where the exact possibility distribution of demand is unavailable. In this condition, it is important to order a reliable quantity which can immunize against distribution uncertainty. To model this type of single-period inventory management problem, this paper characterizes the uncertain demand by generalized interval-valued possibility distributions. We present a novel concept about an uncertain distribution set to describe distribution perturbation characterization. First, we introduce a lambda selection of the interval-valued fuzzy variable, and the uncertain distribution set is a collection of all generalized possibility distributions of lambda selection variables. According to the uncertain distribution set, a new distributionally robust fuzzy optimization method is developed for single-period inventory management problems. Under mild assumptions, the robust counterpart of the proposed fuzzy single-period inventory management model is formulated, which is an optimization program with certain linear objectives and infinitely many integral constraints. We discuss the computational issue of integral constraints and reformulate equivalently the robust counterpart as three deterministic inventory submodels under generalized interval-valued trapezoidal possibility distributions. According to the characteristics of three submodels, a domain decomposition method is designed to find the robust optimal solution that can immunize against uncertainty in our single-period inventory management problem. Finally, some computational results demonstrate the efficiency of the proposed distributionally robust fuzzy optimization method.
Development of Prognostic Features of Hepatocellular Carcinoma Based on Metabolic Gene Classification and Immune and Oxidative Stress Characteristic Analysis
Authors: Cui Shimeng , Zhang Minghao , Bai Shanshan , Bi Yanfang , Cong Shan , Jin Shi , Li Shuang , He Hui , Zhang Jian
Published: February 2023

Background. The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC. Methods. TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA. Results. Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis. Conclusion. The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.
Community Governance Decision Based on Knowledge Map
Authors: Feng Xiaojuan
Published: February 2023

The participation degree of community residents has become a yardstick to measure the community governance system and governance capacity in China. Since 2000, China has vigorously promoted community construction. The related research literature on community construction in China is also showing an explosive growth trend. Knowledge map refers to the analysis of complex domain knowledge through data mining, information processing, knowledge measurement, chart drawing, etc., so as to achieve the purpose of understanding the dynamic development law of knowledge domain through diagrams. In the face of complex knowledge fields, we can use knowledge maps to combine various information to obtain relational networks and analyze problems from the perspective of “relationships.” Knowledge map is widely used in intelligent search, intelligent question and answer, and other fields. Using the intelligent decision support framework based on knowledge map can help us more clearly find the problems existing in the traditional decision support system and provide corresponding solutions. Combining big data, knowledge map, and other large-scale knowledge analysis and modeling technologies with decision support systems can strengthen the ability of problem analysis and processing and form a knowledge and relationship network. This paper provides direction and theoretical support for the research and development of community construction and governance in China in the future.
Automobile Component Recognition Based on Deep Learning Network with Coarse‐Fine‐Grained Feature Fusion
Authors: Tan Jinbiao , Wan Jiafu , Xia Dan
Published: February 2023

With the development of artificial intelligence, machine vision technology based on deep learning is an effective way to improve production efficiency. Because of the rapid update of the automobile manufacturing industry and the large variety of products, the learning time and the number of learning samples of the deep learning model are limited, which brings great difficulties to the recognition of components. Therefore, considering the economic benefits of enterprises, this paper proposes an intelligent component recognition method appropriate for small datasets, aiming to explore an automatic system for component recognition suitable for industrial manufacturing environments. The method completes the generation of the dataset through the system architecture with the potential for automation and the image cropping method based on feature detection and then designs a deep learning network based on coarse‐fine‐grained feature fusion to generate an intelligent recognition model of components. Finally, the designed network achieves an accuracy of 95.11%, and compared with the traditional classical network on multiple datasets, the designed network has better performance. Thus, the proposed method can improve the production flexibility of the automobile manufacturing industry and improve equipment intelligence.
Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study
Authors: Mazumder Lincon , Hasan Md. Rakibul , Fatema Kanij , Begum Shamima , Azad Abul Kalam , Islam Mohammad Ariful
Published: February 2023

Background. Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus’s cell surface binding protein through an in silico approach to combat monkeypox-associated diseases. Results. The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope “ILFLMSQRY” was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B

∗

15:01 with a very low binding energy (-7.5 kcal/mol). Conclusion. The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.
On Subtractive Derivations of Rl‐Monoids
Authors: Gao Yu , Li Bingfang
Published: February 2023

This paper is intended to introduce the subtractive derivations and study some of their algebraic properties on R‐monoids. Also, we give some characterizations of subtractive derivations on the Gödel center. Moreover, Gödel algebras are characterized by a fixed set of subtractive derivations. Finally, we discuss the relationship between subtractive derivations and other derivations for R‐monoids. These results of the paper can provide the common properties of subtractive derivations in the t‐norm‐based fuzzy logical algebras.