bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short- and long-term effect of high versus low-to-moderate intensity exercise to optimise health-related quality of life after oncological treatment—results from the Phys-Can project
Authors: Ax Anna-Karin , Johansson Birgitta , Lyth Johan , Nordin Karin , Börjeson Sussanne 
Published: April 2022

Abstract

Purpose
This study aimed to evaluate the effect of high intensity (HI) vs low-to-moderate intensity (LMI) exercise on health-related quality of life (HRQoL) up to 18 months after commencement of oncological treatment in patients with breast, colorectal or prostate cancer. In addition, we conducted a comparison with usual care (UC).

Methods
Patients scheduled for (neo)adjuvant oncological treatment (n = 577) were randomly assigned to 6 months of combined resistance and endurance training of HI or LMI. A longitudinal descriptive study (UC) included participants (n = 89) immediately before the RCT started. HRQoL was assessed by EORTC QLQ-C30 at baseline, 3, 6 and 18 months (1 year after completed exercise intervention) follow-up. Linear mixed models were used to study the groups over time.

Results
Directly after the intervention, HI scored significant (P = 0.02), but not clinically relevant, higher pain compared with LMI. No other significant difference in HRQoL was found between the exercise intensities over time. Clinically meaningful improvements in HRQoL over time were detected within both exercise intensities. We found favourable significant differences in HRQoL in both exercise intensities compared with UC over time.

Conclusion
This study adds to the strong evidence of positive effect of exercise and shows that exercise, regardless of intensity, can have beneficial effects on HRQoL during oncological treatment and also for a substantial time after completion of an exercise intervention. In this study, for one year after.

Implications for cancer survivors
Patients can be advised to exercise at either intensity level according to their personal preferences, and still benefit from both short-term and long-term improvements in HRQoL.
Immersive machine learning for social attitude detection in virtual reality narrative games
Authors: Dobre Georgiana Cristina , Gillies Marco , Pan Xueni 
Published: April 2022

AbstractPeople can understand how human interaction unfolds and can pinpoint social attitudes such as showing interest or social engagement with a conversational partner. However, summarising this with a set of rules is difficult, as our judgement is sometimes subtle and subconscious. Hence, it is challenging to program Non-Player Characters (NPCs) to react towards social signals appropriately, which is important for immersive narrative games in Virtual Reality (VR). We collaborated with two game studios to develop an immersive machine learning (ML) pipeline for detecting social engagement. We collected data from participants-NPC interaction in VR, which was then annotated in the same immersive environment. Game design is a creative process and it is vital to respect designer’s creative vision and judgement. We therefore view annotation as a key part of the creative process. We trained a reinforcement learning algorithm (PPO) with imitation learning rewards using raw data (e.g. head position) and socially meaningful derived data (e.g. proxemics); we compared different ML configurations including pre-training and a temporal memory (LSTM). The pre-training and LSTM configuration using derived data performed the best (84% F1-score, 83% accuracy). The models using raw data did not generalise. Overall, this work introduces an immersive ML pipeline for detecting social engagement and demonstrates how creatives could use ML and VR to expand their ability to design more engaging experiences. Given the pipeline’s results for social engagement detection, we generalise it for detecting human-defined social attitudes.
Antimicrobial susceptibility testing is crucial when treating Finegoldia magna infections
Authors: Walser Fabienne , Prinz Julia , Rahm Stefan , Zingg Patrick O. , Mancini Stefano , Imkamp Frank , Zbinden Reinhard , Achermann Yvonne 
Published: April 2022

Abstract
Finegoldia magna is an anaerobic gram-positive bacterium that can cause invasive human infections. Recently, a 52-year-old patient suffering from a periprosthetic joint infection (PJI) due to F. magna was treated with cefepime on hemodialysis; however, treatment failed due to relapse caused by antibiotic-resistant strains. Reports on the antimicrobial susceptibility of F. magna clinical isolates are rare. We collected 57 clinical F. magna isolates from Zurich, Switzerland, between September 2019 and July 2020 and tested their antimicrobial susceptibility to investigate the local resistance pattern. Antimicrobial susceptibility testing (AST) was evaluated for nine antibiotics (benzylpenicillin, amoxicillin/clavulanic acid, cefuroxime, cefepime, levofloxacin, rifampicin, metronidazole, doxycycline, and clindamycin) by E-test according to CLSI guidelines. All F. magna strains were susceptible to benzylpenicillin, amoxicillin/clavulanic acid, and metronidazole, while 75% to clindamycin. F. magna isolates showed MIC values lower than species-unrelated breakpoints for cefuroxime, levofloxacin, and cefepime in 93%, 56%, and 32% of the cases, respectively. MIC values for rifampicin and doxycycline were lower than locally determined ECOFFs in 98% and 72% of the cases, respectively. In summary, we recommend the use of benzylpenicillin, amoxicillin/clavulanic acid, or metronidazole without prior AST as first-line treatment option against F. magna PJI infections. If cefuroxime, cefepime, levofloxacin, rifampicin, doxycycline, or clindamycin are used, AST is mandatory.
Climate change impacts on future driving and walking conditions in Finland, Norway and Sweden
Authors: Freistetter Nadine-Cyra , Médus Erika , Hippi Marjo , Kangas Markku , Dobler Andreas , Belušić Danijel , Käyhkö Jukka , Partanen Antti-Ilari 
Published: April 2022

Abstract
Road weather is a major concern for the public safety and health, industries and transport sectors. Half of the yearly 27,000 road and 50,000 pedestrian injuries in Finland, Norway and Sweden can be traced back to slippery road and walkway conditions. We simulated the climate change impacts on future roads and walkways for mid- and end-century in Finland, Norway and Sweden with the road weather model RoadSurf, driven by the regional climate model HCLIM38 with boundary data from two global climate models following the RCP8.5 scenario.Our simulations for mid-century suggest strong road surface temperature increases, especially in southern Finland (+ 5.1 °C) and Sweden (+ 7.1 °C). Snowy and icy road surface conditions decreased by 23 percentage points, causing 18.5 percentage points less difficult driving conditions during the cold season. Zero-degree-crossing days mostly decreased in autumn and spring by up to 7 days and increased in winter by up to 5 days. Sidewalks mostly showed a decrease in slipperiness, but a five percentage point increase of water above ice layers on the sidewalks in winter, suggesting the slip-season might become shorter, but more slippery.Our results are upper extreme estimates but can serve as a reference to help local decision-makers plan mitigation and adaptation measures ahead of time.
Survey on image encryption techniques using chaotic maps in spatial, transform and spatiotemporal domains
Authors: Zia Unsub , McCartney Mark , Scotney Bryan , Martinez Jorge , AbuTair Mamun , Memon Jamshed , Sajjad Ali 
Published: April 2022

AbstractChaos-based cryptosystems have been an active area of research in recent years. Although these algorithms are not standardized like AES, DES, RSA, etc., chaos-based cryptosystems like Chebyshev polynomials can provide additional security when used with standard public key cryptosystems like RSA and El-gamal. Standard encryption algorithms such as AES have always been the primary choice, but when it comes to image or video encryption, many researchers recommend chaos-based encryption techniques due to their computational efficiency. This paper presents a survey on the most up-to-date chaos-based image encryption techniques and classifies them into spatial, temporal and spatiotemporal domains for better understanding. The significant improvements in the field of image encryption are discussed. In addition, comparative analysis is performed to validate the evaluation matrices for quantifying the encryption algorithms’ security and performance in recent papers.
Attitudes Toward Payment for Research Participation: Results from a U.S. Survey of People Living with HIV
Authors: Polonijo Andrea N. , Dubé Karine , Galea Jerome T. , Greene Karah Yeona , Taylor Jeff , Christensen Christopher , Brown Brandon 
Published: April 2022

AbstractLittle is known about how payment affects individuals' decisions to participate in HIV research. Using data from a U.S. survey of people living with HIV (N = 292), we examined potential research participants’ attitudes toward payment, perceived study risk based on payment amount, and preferred payment forms, and how these factors vary by sociodemographic characteristics. Most respondents agreed people should be paid for HIV research participation (96%) and said payment would shape their research participation decisions (80%). Men, less formally educated individuals, and members of some minoritized racial-ethnic groups were less likely to be willing to participate in research without payment. Higher payment was associated with higher perceived study risks, while preferences for form of payment varied by age, gender, education, race-ethnicity, and census region of residence. Findings suggest payment may influence prospective research participants’ risk–benefit calculus and participation, and that a one-size-fits-all approach to payment could differentially influence participation among distinct sociodemographic groups.
Distance approximation to support customer selection in vehicle routing problems
Authors: Akkerman Fabian , Mes Martijn 
Published: April 2022

AbstractEstimating the solution value of transportation problems can be useful to assign customers to days for multi-period vehicle routing problems, or to make customer selection decisions very fast (e.g., within an online environment). In this paper, we apply several regression methods to predict the total distance of the traveling salesman problem (TSP) and vehicle routing problem (VRP). We show that distance can be estimated fairly accurate using simple regression models and only a limited number of features. Besides using features found in the scientific literature, we also introduce new classes of spatial features. The model is validated on a fictional case with different spatial instances considering both a backordering and lost sales configuration, and on a realistic case that involves dynamic waste collection in the city of Amsterdam, The Netherlands. For the fictional case, we show differences in performance per instance type and configuration, and we show that our model can save up to $$25.3\%$$

25.3
%

in distance compared with a heuristic approximation. For the waste collection case, we introduce a cost function that combines the travel distance and service level, and show that our model can reduce distances up to 17% compared to a well-known heuristic approximation while maintaining the same service level. Furthermore, we show the benefits of using approximations for combining offline learning with online or frequent optimization.
A socio-legal analysis of the Belgian protective legislation towards victims of aggravated forms of migrant smuggling
Authors: de Massol de Rebetz Roxane , van der Woude Maartje 
Published: April 2022

AbstractAs many scholars have shown, and other than what is suggested by their legal definition, migrant smuggling and human trafficking are not always easily distinguishable in reality. Acknowledging this grey area between the two phenomena, the Belgian legislature has introduced an alternative approach (referred to as ‘third-way approach’) which would offer migrants who have experienced ‘aggravated forms’ of migrant smuggling the same protective status that is usually strictly reserved for victims of human trafficking. Interestingly enough, migrants don’t seem inclined to make use of this procedure. Through a series of expert interviews, this article shines light on the perspective of key actors within the Belgian criminal justice system and migration control apparatus with regard to this third-way approach and its functioning in practice. In so doing, the article not only reveals how the proper functioning of this third-way approach is hindered by a series of organizational and institutional factors, but it also shows how the different actors are struggling with the inherent tension between the objectives of protecting state security and the protection of the needs of vulnerable groups in precarious life situations.
Initial and supplementary indication approval of new targeted cancer drugs by the FDA, EMA, Health Canada, and TGA
Authors: Michaeli Daniel Tobias , Mills Mackenzie , Michaeli Thomas , Miracolo Aurelio , Kanavos Panos 
Published: April 2022

SummaryBackground. Previous research focused on the clinical evidence supporting new cancer drugs’ initial US Food and Drug Administration (FDA) approval. However, targeted drugs are increasingly approved for supplementary indications of unknown evidence and benefit. Objectives. To examine the clinical trial evidence supporting new targeted cancer drugs’ initial and supplementary indication approval in the US, EU, Canada, and Australia. Data and Methods. 25 cancer drugs across 100 indications were identified with FDA approval between 2009–2019. Data on regulatory approval and clinical trials were extracted from the FDA, European Medicines Agency (EMA), Health Canada (HC), Australian Therapeutic Goods Administration (TGA), and clinicaltrials.gov. Regional variations were compared with χ2-tests. Multivariate logistic regressions compared characteristics of initial and supplementary indication approvals, reporting adjusted odds ratios (AOR) with 95% confidence intervals (CI). Results. Out of 100 considered cancer indications, the FDA approved 96, the EMA 92, HC 86, and the TGA 83 (83%, p < 0.05). The FDA more frequently granted priority review, conditional approval, and orphan designations than other agencies. Initial approvals were more likely to receive conditional / accelerated approval (AOR: 2.69, 95%CI [1.07–6.77], p < 0.05), an orphan designation (AOR: 3.32, 95%CI [1.38–8.00], p < 0.01), be under priority review (AOR: 2.60, 95%CI [1.17–5.78], p < 0.05), and be monotherapies (AOR: 5.91, 95%CI [1.14–30.65], p < 0.05) than supplementary indications. Initial indications’ pivotal trials tended to be shorter (AOR per month: 0.96, 95%CI [0.93–0.99], p < 0.05), of lower phase design (AOR per clinical phase: 0.28, 95%CI [0.09–0.85], p < 0.05), and enroll more patients (AOR per 100 patients: 1.19, 95%CI [1.01–1.39], p < 0.05). Conclusions. Targeted cancer drugs are increasingly approved for multiple indications of varying clinical benefit. Drugs are first approved as monotherapies in rare diseases with a high unmet need. Whilst expedited regulatory review incentivizes this prioritization, indication-specific safety, efficacy, and pricing policies are necessary to reflect each indication’s differential clinical and economic value.
From scenario to mounting risks: COVID-19’s perils for development and supply security in the Sahel
Authors: Al-Saidi Mohammad , Saad Suhair A. Gayoum , Elagib Nadir Ahmed 
Published: April 2022

AbstractThe African Sahel countries are inherently fragile, environmentally insecure and economically weak. This paper underscores the compounded impacts brought about by the COVID-19 pandemic on resource supply security and, hence, the long-term development of the region. It outlines the Sahel-specific COVID-19 scenario by firstly highlighting the underlying vulnerabilities and later linking the health sector outcomes to increased political instability and environmental insecurity, particularly the deterioration of food security. In this sense, this paper shows from a region-wide perspective how COVID-19 in the Sahel is associated with enlarged sociopolitical developmental perils. Lower remittance sent by expatriates, violent conflicts, increased cross-border terrorism and migration, discriminant mobility restrictions of people and goods, weak national healthcare infrastructures, bottlenecks in international aid, pressures on the education system and recent climate extremes are some revealing examples of aggravators of the impacts on the supply of vital resources, such as food. This paper also shows the importance of considering the close interlinks between health, food and political stability in the Sahel. There is a paramount need for more comprehensive approaches linking human health to other sectors, and for re-considering local sustainable agriculture. To avoid prolonged or recurrent humanitarian crises, the Sahel countries need to strengthen response capacities through public sector-led responses. Examples of these responses include reinforced national disaster programs for the vulnerable, support to sustainable agriculture and food markets, improved performance and communication of public sector relief, state-based cooperation, building of regional alliances and peacemaking efforts.
A machine-learning approach to discerning prevalence and causes of myopia among elementary students in Hubei
Authors: Tu Yuyang , Hu Xuemin , Zeng Caiqiong , Ye Meihong , Zhang Peng , Jin Xiaoqin , Zhang Jianwei , Zhou Lianhong 
Published: April 2022

Abstract
Objective
Our aim is to establish a machine-learning model that will enable us to investigate the key factors influencing the prevalence of myopia in students.

Methods
We performed a cross-sectional study that included 16,653 students from grades 1–3 across 17 cities in Hubei Province. We used questionnaires to discern levels of participation in potential factors contributing to the development of myopia. The relative importance of potential contributors was ranked using machine-learning methods. The students’ visual acuity (VA) was measured and those with logMAR VA of > 0.0 underwent a autorefraction test to determine students’ refraction status.

Results
The prevalence of myopia in grades 1, 2, and 3 was 14.70%, 20.54% and 28.93%, respectively. Myopia rates among primary school students in provincial capital city (32.35%) were higher than those in other urban (23.03%) and rural (14.82%) areas. Children with non-myopic parents, only one myopic parent, or both parents having myopia exhibited myopic rates of 16.36%, 25.18%, and 41.37%, respectively. Myopia prevalence was higher in the students who continued to use their eyes at close range for a long time and lower in those engaged longer in outdoor activities. The machine-learning model determined that the top three contributing factors were the students’ age (0.36), followed by place of residence (0.34), starting age of education (0.21).

Conclusion
The overall prevalence of myopia was 21.52%. Children’s age and place of residence were the important influencing factors, but genetics and environmental were also played key roles in myopia development.
The economics of missionary expansion: evidence from Africa and implications for development
Authors: Jedwab Remi , Meier zu Selhausen Felix , Moradi Alexander 
Published: April 2022

AbstractHow did Christianity expand in Africa to become the continent’s dominant religion? Using annual panel census data on Christian missions from 1751 to 1932 in Ghana, and pre-1924 data on missions for 43 sub-Saharan African countries, we estimate causal effects of malaria, railroads and cash crops on mission location. We find that missions were established in healthier, more accessible, and richer places before expanding to economically less developed places. We argue that the endogeneity of missionary expansion may have been underestimated, thus questioning the link between missions and economic development for Africa. We find the endogeneity problem exacerbated when mission data is sourced from Christian missionary atlases that disproportionately report a selection of prominent missions that were also established early.
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
Authors: Mitchener Ludovico , Tuckey David , Crosby Matthew , Russo Alessandra 
Published: April 2022

AbstractIn this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.
Rate-type models of dissipative compressible fluids
Authors: Giorgi Claudio , Morro Angelo 
Published: April 2022

AbstractTwo models of dissipative compressible fluids are established within the general setting of Rational Thermodynamics. It is a common feature underlying the analysis of the thermodynamic consistency that the entropy production and the free energy potential are viewed as constitutive functions of the same set of physical variables and no internal variable is involved. Also in light of late experimental observations, hysteretic models are established for the dependence of pressure on the mass density. Next relaxation properties are modelled via rate equations with an objective derivative which combines the corotational rate with the divergence of the velocity.
On the Gittins index for multistage jobs
Authors: Aalto Samuli , Scully Ziv 
Published: April 2022

AbstractOptimal scheduling in single-server queueing systems is a classic problem in queueing theory. The Gittins index policy is known to be the optimal nonanticipating policy minimizing the mean delay in the M/G/1 queue. While the Gittins index is thoroughly characterized for ordinary jobs whose state is described by the attained service, it is not at all the case with jobs that have more complex structure. Recently, a class of such jobs, multistage jobs, were introduced, and it was shown that the computation of Gittins index of a multistage job decomposes into separable computations for the individual stages. The characterization is, however, indirect in the sense that it relies on the recursion for an auxiliary function (the so-called SJP—single-job profit—function) and not for the Gittins index itself. In this paper, we focus on sequential multistage jobs, which have a fixed sequence of stages, and prove that, for them, it is possible to compute the Gittins index directly by recursively combining the Gittins indices of its individual stages. In addition, we give sufficient conditions for the optimality of the FCFS and SERPT disciplines for scheduling sequential multistage jobs. On the other hand, we demonstrate that, for nonsequential multistage jobs, it is better to compute the Gittins index by utilizing the SJP functions.