bluuebunny/crossref_metadata_2025 huggingface dataset:
Practising professionalism in activation work: Developing and testing a questionnaire
Authors: van Berkel Rik , Penning de Vries Julia , van der Aa Paul
Published: March 2022

The professional nature and professionalisation of the street‐level delivery of activation policies have received increasing attention in academic debate. The study reported in this article contributes to this debate in two ways. The first way is to elaborate a multidimensional conceptualisation of practising professionalism in activation work, distinguishing eight dimensions: knowledge acquisition, knowledge sharing, knowledge use, innovation, accountability, client‐centred practice, collaboration and methodical work. The second way is to have developed and test a questionnaire for studying professional practice in activation work. Two models were compared in the study. The first model hypothesised that professional practice is a construct capturing all eight dimensions. The second model hypothesised that the eight dimensions reflect a knowledge‐oriented and a service‐oriented aspect of professional practice. The models were tested using data from a survey among Dutch activation workers. Both provided acceptable fits with our data. What model researchers consider most appropriate will depend on the focus of their research.

Key Practitioner Message

• This article helps practitioners in discussing and developing the professionalisation of the frontline delivery of activation policies.
Revisiting auxiliary social services for persons with disability: The Philippines case
Authors: Susmerano Exxon Buena , Yamada Kyohei
Published: March 2022

Using multiple case studies design, the paper examined how selected local governments in the Philippines implement the policy on auxiliary social services for persons with disability (PWDs) and analyzed how the implementation is influenced by factors such as local policy guidelines, financial resources, and local institutional actors. It was found that local policies play a key role in providing knowledge and guidance to local governments on social services that need to be delivered and could serve as basis for budget allocation. Financial resources can also influence the operation of the unit/office responsible to deliver the services, the types of services that the local government is able to deliver, the number of PWDs that can be provided with services, and the capacity building activities for local government workers. Likewise, local institutional actors can affect the functionality of responsible offices as well as the extent and coverage of services provided to PWDs.
Measuring public‐private‐substitution after divorce: Ex‐spouse income and the effect of marital separation on social assistance take‐up
Authors: Kessler Dorian , Potarca Gina , Bernardi Laura
Published: March 2022

Both public social assistance and private child and spousal support reduce the poverty risks of single parent households. However, research on system interactions has highlighted that social assistance eligibility is often denied for households that have a private support order. Such public‐private substitution reduces social expenditures but undermines poverty reduction and prolongs economic dependency in separated couples. Using Swiss administrative data on ex‐spouse income and social assistance take‐up after divorce, this study develops an empirical approach to quantify public‐private substitution. We have found that marital separation increased a mother's probability of receiving social assistance by 19 percentage points if both her own and her ex‐spouse's income were in the bottom 30% of the distribution. This probability was reduced to 1 percentage point for low‐income mothers whose ex‐spouses had top‐30% incomes. This strict public‐private substitution could partially account for child poverty and reflects the traditional gender regime in Switzerland.
An improvement of microfluidic‐assisted internal gelation in the preparation of millimeter‐sized ceramic microspheres
Authors: Xu Rui , Zhao Shijiao , Song Jing , Deng Changsheng , Hao Shaochang , Zhao Xingyu , Li Jianjun , Li Ziqiang , Liu Bing , Ma Jingtao
Published: March 2022

The microspheres prepared by the microfluidic‐assisted internal gelation process are still challenging to reach the millimeter size due to the large gravity. The gravity would affect the sphericity and size uniformity of microspheres. The improved microfluidic‐assisted internal gelation process is designed and optimized to produce large‐sized monodisperse ceramic microspheres with good sphericity. The movement mechanism of millimeter‐sized sol droplets and gel microspheres in the microchannel is summarized and the state of the dispersed phase entering the measuring cylinder is controlled. The friction between the gel microspheres and the tubing wall is reduced as much as possible, and ZrO2ceramic microspheres of 500 ± 5 μm with good sphericity are prepared with the broth at room temperature. The mechanism of sol droplets entering the oil surface is explored to demonstrate the superiority of the improved microfluidic‐assisted internal gelation process. The improved microfluidic‐assisted internal gelation process could be directly extended to preparing other millimeter‐sized monodisperse ceramic microspheres.
Credit, output and financial stress: A non‐linear LVSTAR application to Brazil
Authors: Neves José Pedro Bastos , Semmler Willi
Published: March 2022

We estimate a bi‐variate LVSTAR model to investigate the non‐linear interplay between Brazil’s credit, output and financial cycles between 1999 and 2017. We use financial stress as the regime‐switching variable to assess how it impacts economic performance. We find evidence of a lengthy transition between regimes. Moreover, Granger Causality tests indicate statistical precedence of credit growth through financial intermediaries. After credit shocks, we find non‐mean‐reverting trajectories and that financial stress worsens the economic downturn. Counterintuitively, total credit growth increases after adverse output shocks. This appears to be caused by elevated public credit provision relative to private credit flows, triggered by anticyclical policy.
Effects of cover crops on soil hydraulic properties during commodity crop growing season
Authors: Haruna Samuel I. , Ritchey Edwin , Mosley Chaney , Ku Seockmo
Published: March 2022

Cover crops (CCs) can improve soil hydraulic properties prior to termination, but their effects on soil hydraulic properties during the growing season are less known. The objective of this study was to investigate the influence of no‐till CC on the soil hydraulic properties during the commodity crop growing season in Murfreesboro, USA. The CCs included hairy vetch (Vicia villosa Roth.), crimson clover (Trifolium incarnatum L.), winter wheat (Triticum aestivum L.), winter peas (Lathyrus hirsutus L.), oats (Avena sativa), triticale (Triticale hexaploide Lart.), barley (Hordeum vulgare L.) and flax (Linum usitatissimum L.). The cash crop grown was corn (Zea mays). Soil samples were collected using a cylindrical core (55 mm inside diameter, 60 mm long) at 0–10, 10–20, and 20–30 cm depths during April (prior to CC termination), May, June and July. Results showed that soil bulk density (Db) was 23%, 12%, 11% and 10% higher under no cover crop (NCC) compared with CC management during April – July, respectively. This suggests a lower rate of soil consolidation under CC management even after several rainfall events. Four months after CC termination, macroporosity and total porosity were 306 and 50% higher, respectively, under CC compared with NCC management. Therefore, saturated hydraulic conductivity (Ksat) during July was two times higher under CC management compared with NCC management and this can affect increase water infiltration and conservation during the growing season. Due to CC root‐induced improvement in macroporosity, CCs had 64% higher volumetric water content (θ) at saturation during July compared with NCC management. Cover crops can improve soil hydraulic properties and these benefits can persist for up to four months after termination.
Low hematocrit reduces the efficiency of CD34+ cell collection when using the Spectra Optia continuous mononuclear cell collection procedure
Authors: Kondo Takumi , Fujii Nobuharu , Fujii Keiko , Sumii Yuichi , Urata Tomohiro , Kimura Maiko , Matsuda Masayuki , Ikegawa Shuntaro , Washio Kana , Fujiwara Hideaki , Asada Noboru , Ennishi Daisuke , Nishimori Hisakazu , Matsuoka Ken‐ichi , Otsuka Fumio , Maeda Yoshinobu
Published: March 2022

Background

CD34+ cell collection efficiency (CE) is the determining factor when calculating processed blood volume (PBV) for leukapheresis (LP). However, the factors affecting CE in the continuous mononuclear cell collection (cMNC) protocol performed by the Spectra Optia apheresis system are not well established.

Study Design and Methods

We retrospectively collected the data from 147 consecutive apheresis procedures across 106 healthy donors and 27 patients completed between July 2016 and December 2020 at the Okayama University Hospital. All procedures were performed using the Optia cMNC protocol.

Results

The median CD34+ CE2 was significantly higher in the donor samples (64.3%) than in the patient samples (46.8%) (p < .0001). WBC counts, hematocrit, and platelet counts were all significantly higher in the donors than in the patients, and there was a moderate positive correlation between CD34+ CE2 and hematocrit (r = .47, p < .0001), with the equation of the line being y = 1.23x + 12.23. In contrast, there was only a very weak correlation between CD34+ CE2 and WBC or platelet count. In addition, low hematocrit correlated with an increased time to interface formation.

Conclusion

These data revealed the negative impact of low hematocrit on the efficiency of CD34+ cell collection when using the Optia cMNC protocol and suggest that hematocrit values should also be considered when determining PBV.
Critical Comparison of Phase-Field, Peridynamics, and Crack Band Model M7 in Light of Gap Test and Classical Fracture Tests
Authors: Bažant Zdeněk P. , Nguyen Hoang T. , Abdullah Dönmez A.
Published: March 2022

The recently conceived gap test and its simulation revealed that the fracture energy Gf (or Kc, Jcr) of concrete, plastic-hardening metals, composites, and probably most materials can change by ±100%, depending on the crack-parallel stresses σxx, σzz, and their history. Therefore, one must consider not only a finite length but also a finite width of the fracture process zone, along with its tensorial damage behavior. The data from this test, along with ten other classical tests important for fracture problems (nine on concrete, one on sandstone), are optimally fitted to evaluate the performance of the state-of-art phase-field, peridynamic, and crack band models. Thanks to its realistic boundary and crack-face conditions as well as its tensorial nature, the crack band model, combined with the microplane damage constitutive law in its latest version M7, is found to fit all data well. On the contrary, the phase-field models perform poorly. Peridynamic models (both bond based and state based) perform even worse. The recent correction in the bond-associated deformation gradient helps to improve the predictions in some experiments, but not all. This confirms the previous strictly theoretical critique (JAM 2016), which showed that peridynamics of all kinds suffers from several conceptual faults: (1) It implies a lattice microstructure; (2) its particle–skipping interactions are a fiction; (4) it ignores shear-resisted particle rotations (which are what lends the lattice discrete particle model (LDPM) its superior performance); (3) its representation of the boundaries, especially the crack and fracture process zone faces, is physically unrealistic; and (5) it cannot reproduce the transitional size effect—a quintessential characteristic of quasibrittleness. The misleading practice of “verifying” a model with only one or two simple tests matchable by many different models, or showcasing an ad hoc improvement for one type of test while ignoring misfits of others, is pointed out. In closing, the ubiquity of crack-parallel stresses in practical problems of concrete, shale, fiber composites, plastic-hardening metals, and materials on submicrometer scale is emphasized.
Redo college intro science
Authors: Asai David , Alberts Bruce , Coffey Janet
Published: March 2022

The rapid development of highly effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was a monumental achievement, yet a large fraction of the public rejected this means of defense, resulting in far too many preventable deaths. This response reflects a shocking failure of science to produce citizens who understand and respect scientific evidence, and it demands a rethinking of science education goals.
On the trail of bullshit
Authors: Kupferschmidt Kai
Published: March 2022

Studying misinformation should become a top scientific priority, says biologist Carl Bergstrom
Alien Intelligence Research Institute: a Perspective-Taking Activity for a Nuanced View on Traits and a Growth Mindset
Authors: Pavlova Iglika V.
Published: March 2022

Instructors can foster equitable learning environments when they communicate that they value growth and diversity and by providing opportunities for students to reflect and to engage in cross-group interactions with diverse others. Additionally, perspective-taking activities engage empathy and have been used to reduce racial bias.
Characterizing Staphylococcus aureus from Healthy Individuals: an Authentic Research Experience for Undergraduates
Authors: Brosnahan Amanda J. , Mach Taylor J.
Published: March 2022

This article describes the implementation of the Staph Study, a sustainable, ongoing, scalable research study conducted with undergraduate students. The study characterizes
Staphylococcus aureus
specimens collected from the anterior nares of healthy members of our campus community. The ease with which we have been able to involve many students in the project has resulted in a significant increase in the research opportunities for undergraduates in our Science Department.
Late adult-onset spinal muscular atrophy with lower extremity predominance (SMALED)
Authors: Aziz Iqra , Davis Mark , Liang Christina
Published: March 2022

An elderly man in his early 80s presented with a 6-month history of worsening lower limb weakness on a background of a longer-standing waddling gait. Examination revealed bilateral scapular winging, and weakness in his proximal and distal lower limbs. Electromyography showed widespread chronic partial denervation changes, while sensory and motor nerve conduction parameters were preserved. After little progression over the course of 18 months, motor neuron disease was deemed less likely. Genetic testing revealed BICD2-related spinal muscular atrophy with lower extremity dominance (SMALED2), a disease that is usually of earlier onset. He is the oldest patient in the literature to be diagnosed with SMALED2 while maintaining ambulation, suggesting the milder spectrum of BICD2-related disease.
Primary lymphoma of the medial cutaneous nerve of the forearm: the first case in a pure sensory nerve of the upper limb
Authors: De Vitis Rocco , D'Orio Marco , Fiorentino Vincenzo , Passiatore Marco
Published: March 2022

Primary lymphoma of the peripheral nerve is very rare and occurs most frequently in sciatic nerves. We describe the first patient reported in literature with a primary lymphoma in a pure sensory peripheral nerve of the upper limb. A woman in her 40s, with painful swelling and dysaesthesias in her left forearm in the past 3 months, was presented at our unit. Clinical MRI and ultrasound findings revealed a lesion that showed signs of a peripheral nerve sheath tumour. After complete excision, morpho-pathological evaluation revealed a primary B-cell lymphoma. The patient underwent radiotherapy and at the last follow-up there were no signs of residual pathology. Peripheral neuropathy may be caused by a lymphoma involving the nerve. Hand surgeons have to distinguish primary lymphoma of the peripheral nerves from schwannoma for their different clinical behaviour.
Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH)
Authors: Yasar Ozge , Long Patrick , Harder Brett , Marshall Hanna , Bhasin Sanjay , Lee Suyin , Delegge Mark , Roy Stephanie , Doyle Orla , Leavitt Nadea , Rigg John
Published: March 2022

Objectives

To develop and evaluate machine learning models to detect patients with suspected undiagnosed non-alcoholic steatohepatitis (NASH) for diagnostic screening and clinical management.

Methods

In this retrospective observational non-interventional study using administrative medical claims data from 1 463 089 patients, gradient-boosted decision trees were trained to detect patients with likely NASH from an at-risk patient population with a history of obesity, type 2 diabetes mellitus, metabolic disorder or non-alcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (at-risk non-NAFL patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall curves and receiver operating characteristic curves (AUPRCs and AUROCs).

Results

The 6-month incidences of NASH in claims data were 1 per 1437 at-risk patients and 1 per 2127 at-risk non-NAFL patients . The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 to 0.0110) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60× above NASH incidence. The model trained to detect NASH in the non-NAFL cohort had an AUPRC of 0.0030 (95% CI 0.0029 to 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20× above NASH incidence.

Conclusion

The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of patients with probable NASH for diagnostic testing and disease management.
Reflection of leadership skills by a clinical educator
Authors: Keenoo Bibi Sumera
Published: March 2022

Background

A leader can only motivate people to do what they want them to do to convince them that it is advantageous. No one can be forced into leadership. Through my experience, I learnt that excellent leadership realises the desired results by getting people to do their best.

Method

Therefore, I would like to reflect on the leadership theory closer to my leadership practices and leadership styles at my workplace in light of my personality and personal characteristics.

Conclusion

Although not a new idea, but self-analysis is a requisite for each leader and leader to be.
Insight into the methylene C–H bond cleavage of ethylbenzene during ethylbenzene hydroxylation using EBDH as a catalyst, a DFT study
Authors: Habib Uzma
Published: March 2022

The hydroxylation of ethylbenzene to ( S)-1-phenylethanol with the help of ethylbenzene dehydrogenase (EBDH) is a stereospecific catalytic reaction. This hydroxylation process involves the C–H bond cleavage of methylene part of ethylbenzene and transfer of its hydrogen to the oxygen atom attached with the metal at the active site of EBDH as a first step, which leads to the formation of an intermediate. The second step involves the transfer of OH from the active-site metal back to the carbon of intermediate, resulting in the formation of ( S)-1-phenylethanol. This C–H bond cleavage could be homolytic or heterolytic and directly affect the reaction mechanism of ethylbenzene hydroxylation. In this article, density functional theory studies were performed on the ethylbenzene-bound EBDH active-site model complexes to investigate the impact of the C–H bond cleavage of methylene part of ethylbenzene on the reaction mechanism of ethylbenzene hydroxylation. For this, different protonation states and participation of amino acid residues near the Mo center of EBDH were considered. Models with protonation of His192, Lys450, and Asp223 and model without protonation were investigated for comparison. Computed relative energies indicate that the overall lowest energy barrier pathway results when ionic (heterolytic) and radical (homolytic) pathways are combined.
Learning new physics from an imperfect machine
Authors: D’Agnolo Raffaele Tito , Grosso Gaia , Pierini Maurizio , Wulzer Andrea , Zanetti Marco
Published: March 2022

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.
Photoelectrocatalytic Oxidation of Methane Into Methanol Over BiVO4/AU/FeCo–LDH Catalysts
Authors: Liu Jia , Ji Jiahui , Zhang Yinghua , Bai Zhiming , Huang Zhian , Gao Yukun , Wang Xiaotian
Published: March 2022

Conversion of methane into high value added chemicals and clean fuels such as methanol under mild conditions is of great importance to the chemical industry. However, traditional thermal catalytic of methane always suffer from harsh reaction conditions and poor product selectivity. Here, we reported photoelectrocatalytic oxidation of methane over BiVO4/Au/FeCo–LDH under simulated sunlight illumination with ambient‘ conditions. The results demonstrate that BiVO4/Au/FeCo–LDH exhibits excellent photoelectrochemical properties and catalytic activity. The double-layer capacitance ([Formula: see text]) value of BiVO4/Au/FeCo–LDH is estimated to be 3.00[Formula: see text]mF[Formula: see text]cm[Formula: see text], indicating its considerable electrochemical active areas. The photocurrent density of BiVO4/Au/FeCo–LDH reaches up to 1.46[Formula: see text]mA[Formula: see text]cm[Formula: see text] in methane atmosphere. The methanol yield for photoelectrocatalytic oxidation of methane is 8.46 times that of pure BiVO4, and the corresponding Faraday efficiency is 56.09%. Finally, the reaction mechanism of photoelectrocatalytic conversion of methane to methanol based on hydroxyl radical and methyl radical as intermediate products is proposed. Our finding is expected to provide new insight for the design of active and selective catalysts toward photoelectrocatalytic conversion of methane.
Algorithm 1023: Restoration of Function by Integrals with Cubic Integral Smoothing Spline in R
Authors: Korablev Yuriy
Published: March 2022

In this paper, a cubic integral smoothing spline with roughness penalty for restoring a function by integrals is described. A mathematical method for building such a spline is described in detail. The method is based on cubic integral spline with a penalty function, which minimizes the sum of squares of the difference between the observed integrals of the unknown function and the integrals of the spline being constructed, plus an additional penalty for the nonlinearity (roughness) of the spline. This method has a matrix form, and this paper shows in detail how to fill in each matrix. The parameter

\( \alpha \)

governs the desired smoothness of the restored function. Spline knots can be chosen independently of observations, and a weight can be defined for each observation for more control over the resulting spline shape. An implementation in the R language as function
int_spline
is given. The function
int_spline
is easy to use, with all arguments completely described and corresponding examples given. An example of the application of the method in rare event analysis and forecasting is given.
A Novel Highly-Efficient Inexact Full Adder Cell for Motion and Edge Detection Systems of Image Processing in CNFET Technology
Authors: Mehrabani Yavar Safaei , Goldani Gigasari Samaneh , Mirzaei Mohammad , Uoosefian Hamidreza
Published: March 2022

In this paper, a novel and highly efficient inexact Full Adder cell by exploiting two logic styles including
conventional CMOS (C-COMS)
and
pass transistor logic (PTL)
are presented. The so-called
carbon nanotube field-effect transistor (CNFET)
technology is used to implement circuits at the transistor level. To justify the efficiency of our design, extensive simulations are performed at the transistor level as well as application level. Transistor-level simulations which are carried out by the HSPICE 2008 tool, demonstrate at least 12% higher performance in terms of
power-delay-area product (PDAP)
of the proposed circuit compared to the latest designs. At the application level, by using the MATLAB tool, inexact Full Adders are employed in the structure of the
ripple carry adder (RCA)
that is applied in motion and edge detection algorithms. Computer simulation results confirm the appropriate quality of the output images in terms of the
peak signal-to-noise ratio (PSNR)
and
structural similarity (SSIM)
criteria. At last, to make a compromise between hardware and application level parameters, the
power-delay-area-1/PSNR product (PDAPP)
and
power-delay-area-1/SSIM product (PDASP)
are considered as figures of merit. The proposed circuit shows remarkable improvement from the PDAPP and PDASP points of view compared to its counterparts.
Crowdsourcing Science and Technology Expertise to Empower Legislative Branch Oversight and Policymaking
Authors: Fisher Michael A. , Milliken Lindsay K.
Published: March 2022

As the U.S. and the world struggle through the COVID-19 pandemic, the need for science and technology (S&T) expertise in governance has become even more stark. But in the U.S., the vast majority of legislators and their staffs is generalist, and they have limited resources for engaging with S&T. This can be a barrier to both legislative branch oversight of S&T-related issues and the development of evidence-based public policies, two functions that are especially critical in times of crisis. The obstacles to addressing these gaps have, however, created opportunities for innovations in how lawmakers connect with S&T resources, resulting in new models for scientist-policymaker engagement. Our program, the
Congressional Science Policy Initiative
, experiments with models for connecting crowdsourced S&T expertise with policymakers, namely, by enriching key congressional hearings with contributions gathered from the science community, organizing advisory councils of scientists and engineers that brief lawmakers, and crowdsourcing technical assistance from the S&T community for legislative initiatives. These activities, which rely on the collective intelligence of the S&T community and have been readily applied to supporting lawmakers’ decision-making processes during the pandemic, have bolstered legislative branch oversight of the executive branch, fact-finding into corporate practices, and evidence-based policymaking.
Construction of Marketing Curriculum System Based on Blending Learning “3+2” Joint Training of Higher Vocational and Undergraduate Education Using NLP for Marketing Document Management and Information Retrieval
Authors: Li Zhiqin
Published: March 2022

The blended learning system provides educational tools to a student in accordance with the student's expressed educational interests, and it is a mix of online training and assignments, giving them more control over the learning and other developmental facets, which it has a profound impact on current higher education. This article analyzes the “3+2” segmented training mode of higher vocational education and undergraduate education, discusses the problems existing in the curriculum system of marketing major in higher vocational education under this training mode and the transformative potential of blended learning in the context of the challenges facing higher education, with the perspective of Natural language Processing (NLP) Assistance on Digital library management, and in NLP the human language is divided into segments, so that the grammatical structure and the actual meaning of the words can be analyzed and understood that puts forward corresponding measures to promote the development of the integration of segmented training courses of higher vocational education and undergraduate education by smart technologies and improve the quality of marketing talents jointly trained under higher vocational education and undergraduate education. The observational results predict the research of some scholars and, this article puts forward the opinions of curriculum construction, and designs the integrated curriculum system diagram, to have a certain reference for the “3+2” joint training mode with the compounding of the NLP Assistance on Digital Management attains an effective and accurate outcome with the construction of marketing curriculum system based on blending learning.
In the Land of MMUs: Multiarchitecture OS-Agnostic Virtual Memory Forensics
Authors: Oliveri Andrea , Balzarotti Davide
Published: March 2022

The first step required to perform any analysis of a physical memory image is the reconstruction of the virtual address spaces, which allows translating virtual addresses to their corresponding physical offsets. However, this phase is often overlooked, and the challenges related to it are rarely discussed in the literature. Practical tools solve the problem by using a set of custom heuristics tailored on a very small number of well-known operating systems (OSs) running on few architectures.

In this article, we look for the first time at all the different ways the virtual to physical translation can be operated in 10 different CPU architectures. In each case, we study the inviolable constraints imposed by the memory management unit that can be used to build signatures to recover the required data structures from memory without any knowledge about the running OS. We build a proof-of-concept tool to experiment with the extraction of virtual address spaces showing the challenges of performing an OS-agnostic virtual to physical address translation in real-world scenarios. We conduct experiments on a large set of 26 different OSs and a use case on a real hardware device. Finally, we show a possible usage of our technique to retrieve information about user space processes running on an unknown OS without any knowledge of its internals.
Identification of human balance control responses to visual inputs using virtual reality
Authors: Mohebbi Abolfazl , Amiri Pouya , Kearney Robert E.
Published: March 2022

In this paper, we developed a new methodological approach to study the effects of visual information on dynamic body sway in which we used VR to apply visual perturbations to induce AP body sway in human subjects. We designed a new visual stimulus waveform based on trapezoidal velocity pulses whose peak-to-peak amplitude and velocity could be modulated independently. Subsequently, we investigated how the amplitude and velocity of visual field motion influence the postural responses evoked in healthy adults. We found that the amplitude of the postural response increases linearly with the increasing amplitude of the visual stimuli, but the velocity has a nonlinear effect.
Research on Collaborative Optimization Model of Tourism Resources and Highway Network Based on IoT Network and Deep Learning
Authors: Xu Zhiwen , Xie Fang
Published: March 2022

With the development of tourism, people's demand and concern for tourist transportation are increasing. Integrating expressway resources into the tourism transportation network is the current trend and direction of expressway network planning. With the rapid development of the Internet, people can easily get the information resources they want, but at the same time, the problem of information overload also arises. The depth of highway access is insufficient, there is a lack of systematic consideration of tourist resources in the area, a systematic tourism network has not been developed, and tourism resources are inaccessible. These issues have stifled regional tourism's fast growth, necessitating research into the convergence of tourist resource development and highway network optimization. The building of highway layouts and the growth of the tourist sector have reached a point where they interact and impact one another, and the degree of this contact has progressively risen. However, there is still a significant disparity in the degree of cooperation across areas. This paper considers the collaborative optimization of tourism resource development and highway network optimization, analyzes its impact on traffic volume distribution, introduces the two-level programming model of collaborative optimization, and determines that the goal of the upper model is to maximize social net income. It also considers the collaborative optimization of tourism resource development and highway network optimization on the basis of clarifying the game relationship between planning decision-makers and traffic travel and considers the collaborative optimization of tourism resource development and highway network optimization on the basis of in-depth learning. For the total investment of tourism construction projects, the time cost saved by highway network optimization, and the total tourism revenue, the lower-level model aims to minimize the travel time of a single user. This paper shows that the layout planning of the regional tourism highway network from the perspective of tourism resources can improve the accessibility of regional high-quality tourism resources and meet the requirements of the coordinated development of highway tourism and resources.
[Retracted] Expression and Clinicopathological Significance of SOX11 in Small‐Cell Lung Cancer
Authors: Xinli Wang , Lixiao Wang , Baoqi Ding , Hu Huang , Qiang Zhang
Published: March 2022

Objective. This study aims to investigate the expression of neuronal transcription factor SOX11 in small‐cell lung cancer (SCLC) and compare it with the expression of CD56 (nerve cell adhesion molecule), synaptophysin (Syn), chromogranin A (CgA), and thyroid transcription factor‐1 (TTF‐1) to explore the application value of SOX11 in the pathological diagnosis of SCLC. Methods. Immunohistochemical methods were used to detect the expression of SOX11, TTF‐1, CD56, Syn, and CgA in 120 lung tumor tissues, and experimental results were analyzed using SPSS23.0 statistical software. Results. Immunohistochemical results showed that in the 120 lung tumor samples, SOX11 was highly expressed in SCLC and localized to the nucleus, with low or no expression in control carcinoid/lung neuroendocrine tumors, lung adenocarcinomas, and lung squamous cell carcinomas. Statistical analysis results revealed the following points. First, the expression of SOX11 was closely related to the tumor histological type. The expression of SOX11 in SCLC (positive rate of 63.33%) was significantly higher than that in carcinoid/neuroendocrine tumors (positive rate of 12.50%), lung adenocarcinoma (positive rate of 0%), and lung squamous cell carcinoma (positive rate of 0%). Second, immunohistochemical investigation of 60 SCLC cases revealed that the highest positive rates of CD56, TTF‐1, and Syn, respectively, were 93.33 percent, 95 percent, and 86.67 percent. SOX11 also exhibited high sensitivity (0.633) and specificity (0.875) in SCLC. The positive rates of SOX11 and CgA were 63.33% and 50.00%, respectively. Statistical results revealed that the positive rate of CgA had no significant difference (P > 0.05). Lastly, the combined use of antibodies SOX11, CgA, CD56, Syn, and TTF‐1 was more beneficial to improving the diagnosis rate of SCLC than the single use of one or two antibodies. Conclusion. The expression of SOX11 in different histological types of lung tumors differs considerably. SOX11 is highly expressed in SCLC. SOX11 can be used as a beneficial supplement to the combination of classical neuroendocrine markers and in combination with CgA, CD56, Syn, and TTF‐1 to assist in the diagnosis of SCLC.
Abnormal Data Detection in Sensor Networks Based on DNN Algorithm and Cluster Analysis
Authors: Li Min , Sharma Anil
Published: March 2022

In order to solve the abnormal behaviors in wireless sensor networks, such as attacks, intrusions, node failures, and data anomalies, a data anomaly detection method for sensor networks based on DNN algorithm and cluster analysis is proposed. The deep neural network is introduced into the wireless sensor network, and each wireless sensor data is described by the neuron to construct the neural network element model. The traditional neural network model is improved, and the neural network model of the wireless sensor is used to realize the fusion and extraction of the data collected by the wireless sensor network. The clustering technology is used to complete the abnormal data judgment of the nodes, and at the same time, the spatial correlation of the sensing data between neighboring nodes is used to filter the noise data, extract the abnormal event information, and assist the system decision-making. The results show that when the number of neighbor nodes increases, the number of nodes with similar physical locations in the optimal neighbor node set gradually increases, and the accuracy rate is also continuously improved. However, if too many nodes are selected, the algorithm will use the data of nodes with a longer physical distance to vote, which will lead to an increase in the error rate. Therefore, when determining the number of neighbor nodes, 25%-30% of the total number of nodes in the positioning scenario can be selected. Therefore, the algorithm can effectively detect and distinguish environmental noise and abnormal events in the network.
Study on Abnormal Transmission of Date Frames Based on PT-CAN Bus
Authors: Fang Wen , Zhang Chunhua , Li Yangyang , Zhao Baopeng
Published: March 2022

To research the problem of abnormal transmission, experiments were conducted under the conditions of the BMW F18 model. The real-time signal mutation value of PT-CAN was measured by the IBIM synthetic testing box, and the cause of abnormal transmission on the data bus was identified by the value of the signal mutation. Experiments compared and analyzed the signal waveform of PT-CAN under the conditions of normal data transmission, CAN_H to ground loaded with different resistance, CAN_H to battery loaded with different resistance, CAN_L to ground loaded with different resistance, CAN_L to battery loaded with different resistance, between CAN_H and L loaded with different resistance, when CAN_H or L was disconnected, and when CAN_H and L were interchanged. Experimental results indicate that abnormal data transmission may cause abnormal signal waveform of PT-CAN bus, and the different PT-CAN signal clutter has certain different forms and reasons. Experimental results were effective for the identification of abnormal data transmission.
Unbalanced Fault Diagnosis Based on an Invariant Temporal-Spatial Attention Fusion Network
Authors: Liu Jianhua , Yang Haonan , He Jing , Sheng Zhenwen , Chen Shou
Published: March 2022

The health status of mechanical bearings concerns the safety of equipment usage. Therefore, it is of crucial importance to monitor mechanical bearings. Currently, deep learning is the mainstream approach for this task. However, in practical situations, the majority of fault samples have the issue of severe class unbalancing, which renders conventional deep learning inapplicable. Targeted at this issue, this paper proposes an invariant temporal-spatial attention fusion network called ITSA-FN for bearing fault diagnosis under unbalanced conditions. First, the proposed method utilizes the invariant temporal-spatial attention representation section, which consists of a pretrained convolutional auto-encoder model, a convolutional block attention module, and a long short-term memory network, to extract independent features and invariant features of spatial-temporal characteristics from input signals. Then, a multilayer perceptron is used to fuse and infer from the extracted features and design a new loss function from the focal loss for network training. Finally, this article validates proposed model’s effectiveness through comparative experiments, ablation studies, and generalization performance experiments.
Analysis of Cross-Border E-Commerce Commodities in Internet of Things Based on Semantic Traceability Algorithm
Authors: Liu Min
Published: March 2022

With the advancement of economic globalization and the continuous development of “Internet +” economy, cross-border e-commerce has become an important part of participating in international economic exchanges and developing foreign trade. As the direct participation of consumers, cross-border online shopping has gradually become an important way for people to obtain overseas commodities. The phenomenon of fake products and parallel goods is serious in e-commerce online shopping. How to use the existing Internet of Things to achieve product traceability is an important research topic. For different modes of cross-border online shopping, this paper combines the traceability algorithm with the idea of semantic routing, proposes a Semantic Web-based traceability algorithm, and constructs a quality safety traceability system. In order for consumers to accurately know the product quality of sellers, the product traceability model of cross-border e-commerce relying on Semantic Web is constructed to prevent false information from recording product information from the product source. Through the tracing query algorithm, the simulation results show that the hop number of routes between nodes is 1, and the tracing algorithm can quickly determine the identity of online goods. The network delay is mostly distributed between 0 and 0.05 s, which meets the real-time requirements of the Internet of Things.
A Virtual Synchronous Generator Control Strategy for Microgrid Based on Harmonic Current Bypass Control
Authors: Jiang Wenbo , Zhou Bo
Published: March 2022

To improve the voltage and frequency stability and suppress the voltage harmonics of a microgrid, a virtual synchronous generator (VSG) control strategy based on the harmonic current bypass control is proposed in this paper. First, a robust voltage control link is added to the virtual excitation component of the traditional VSG control strategy to reduce the amplitude error of the inverter’s driving voltage caused by the reactive power-voltage droop coefficient. A harmonic current bypass control link is then used to adjust the inverter’s voltage signal to reduce the total harmonic distortion of the microgrid. Finally, a simulation model of the microgrid is established using the MATLAB/Simulink software, and its stability is analyzed. In addition, the proposed control strategy is also compared with the traditional droop control and traditional VSG control strategies, focusing on the frequency instability and harmonic suppression performance of the microgrid. The numerical simulation results verified the effectiveness and superiority of the improved VSG control strategy proposed in this paper.
[Retracted] Early Diagnosis of Breast Cancer Using Image Processing Techniques
Authors: Younis Younis Samir , Ali Ali Hasan , Alhafidhb Omar Kh. Salih , Yahia Warif B. , Alazzam Malik Bader , Hamad Abdulsattar Abdullah , Meraf Zelalem
Published: March 2022

Cancer is a noncommunicable chronic disease that indistinctly affects people of any nationality, race, ethnicity, age, or social class. Because of its unpredictability, receiving the diagnosis of this disease is almost always alarming for the patient. Breast cancer, especially among women, occupies a prominent position in this ranking. However, if diagnosed early, there is an excellent chance of a cure. In this sense, digital technologies have been advancing at an increasingly fast pace to support the early diagnosis of the disease. Clinical analysis of breast cancer is commonly performed using diagnostic imaging. One of the most used exams considered the main one for the early detection of this type of cancer is mammography. This exam allows the visualization of breast tissue from image screening using X‐rays. In this sense, the use of computational techniques is essential to assist medical professionals in diagnosing this disease, thus making prevention and early diagnosis even more effective in the current context. The present work is limited to the use of digital technologies (image processing and artificial intelligence) that cooperate with the early diagnosis of breast cancer, which supports the medical professional to analyze images and be able to diagnose, from an early stage, the emergence of breast cancer of the disease, significantly increasing the chances of curing it. It can be gathered from this research how the discovery of X‐rays and the growth in this sector combined with cutting‐edge technology have benefitted in the early detection of the disease and even offered the cure of many cases.
Analysis and Discussion of Digital Economy Management Data under the Background of Big Data
Authors: Li Jie , Chun Dongphil , Luo Hengjun , Chang Yixuan
Published: March 2022

The aim of this study is to discuss how to effectively use big data in financial analysis and apply big data to various management decisions to effectively improve its efficiency. In order to study the application of big data in the field of digital economy, the study problem is data management that improves the needs of business model to a certain extent and responds to the pace of rapid development of science and technology and the transformation of business model in special times, so as to promote economic development. The methods used in this study are based on the information extracted from the software system log; this study introduces machine learning algorithms such as spatial convolution and fuzzy multicolumn convolution, deduces the interaction between visitors and software according to the system software access log, wavelet algorithm, and denoising algorithm, deduces the data value index of the secondary market, constructs the simulation software under MATLAB, and compares it with the previous system, by improving the prediction sensitivity of interaction and the prediction coupling of data value. The results of this study proved that the system improves the efficiency of data management.