bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of whole-body vibration therapy on pain, functionality, postural stability, and proprioception in patients with subacute and chronic non-specific low back pain: a systematic review
Authors: Remer Franziska , Keilani Mohammad , Kull Philipp , Crevenna Richard 
Published: November 2023

Summary

Introduction
Non-specific low back pain (NLBP) is a common and clinically significant condition with substantial socioeconomic implications. Whole-body vibration therapy (WBVT) has shown effectiveness in improving pain and sensorimotor function (e.g., in osteoporosis) in previous studies. However, studies had heterogeneous settings. The aim of this study was to assess the effects of WBVT on pain, function, proprioception, and postural stability in patients with subacute and chronic NLBP.

Methods
A systematic literature search was conducted in the scientific databases PubMed, EMBASE, and PEDro (from inception until 17.05.2023). Only prospective controlled and uncontrolled studies were included. Outcome measures assessed were pain intensity, function (activities of daily living and physical function), proprioception, and postural stability.

Results
A total of 12 original articles (n = 821) were included in the analysis. Ten of the studies were randomized controlled trials, one study had a crossover design, and one study had a one-group pre–post study design. The studies compared WBVT vs. no intervention, WBVT vs. basic physical therapy, WBVT vs. core stabilization exercises with and without respiratory resistance, WBVT vs. lumbar extension exercises, and WBVT vs. whole body electromyostimulation training. The treatment approaches varied in terms of duration (2–18 weeks), frequency (2–3 times per week, two applications with a 2-week break), vibration frequency (5–30 Hz), type of exercises (WBVT with or without static or dynamic exercises), and vibration direction (horizontal and vertical). Significant pain reduction was observed in all 10 studies that investigated pain levels. Significant improvement in daily activity function was reported in five of the six studies that investigated daily function, while improvement in physical function was observed in all four studies that investigated physical function. Improvement in proprioception was reported in all three studies that investigated proprioception, and significant improvement in postural stability was observed in four out of six studies that investigated postural stability. No adverse events or side effects related to WBVT were reported.

Conclusion
The majority of the included studies demonstrated significant pain reduction, improvement in physical and daily functioning, and enhanced proprioception. Improvement in postural stability was less consistent. WBVT appears to be a safe and effective treatment modality for subacute and chronic NLBP when used within a multimodal approach. Future research should focus on standardized settings including assessment methods, treatment regimens, frequencies, and intensities.
PANoptosis: a potential new target for programmed cell death in breast cancer treatment and prognosis
Authors: Liu Xinxin , Miao Meiqi , Sun Jijing , Wu Jianli , Qin Xunyun 
Published: November 2023

AbstractBreast cancer is a prevalent and severe form of cancer that affects women all over the world. The incidence and mortality of breast cancer continue to rise due to factors such as population growth and the aging of the population. There is a growing area of research focused on a cell death mechanism known as PANoptosis. This mechanism is primarily regulated by the PANoptosome complex and displays important characteristics of cell death, including pyroptosis, apoptosis, and/or necroptosis, without being strictly defined by the cell death pathway. PANoptosis acts as a defensive response to external stimuli and pathogens, contributing to the maintenance of cellular homeostasis and overall stability. Increasing evidence suggests that programmed cell death (PCD) plays an important role in the development of breast cancer, and PANoptosis, as a novel form of PCD, may be a crucial factor in the development of breast cancer, potentially leading to the identification of new therapeutic strategies. Therefore, the concept of PANoptosis not only deepens our understanding of PCD, but also opens up new avenues for treating malignant diseases, including breast cancer. This review aims to provide an overview of the definition of PANoptosis, systematically explore the interplay between PANoptosis and various forms of PCD, and discuss its implications for breast cancer. Additionally, it delves into the current progress and future directions of PANoptosis research in the context of breast cancer, establishing a theoretical foundation for the development of molecular targets within critical signaling pathways related to PANoptosis, as well as multi-target combination therapy approaches, with the goal of inducing PANoptosis as part of breast cancer treatment.
Environmental DNA from soil reveals the presence of a “lost” Afrotherian species
Authors: Mynhardt Samantha , Matthew Esther , le Roux Jean Pierre , Little Ian , Bloomer Paulette , Theron Cobus 
Published: November 2023

AbstractEnvironmental DNA (eDNA) sampling has attracted worldwide attention over the past few years as an emerging approach to characterising and monitoring biodiversity, and has become particularly important for species that are rare, elusive or endangered. Most animal studies to date have focused on aquatic taxa; studies on other metazoan taxa, particularly wildlife in terrestrial environments, are scarce, with only a handful utilizing soil sources. We aimed to investigate the use of DNA barcoding from soil eDNA in (1) detecting rare/elusive/threatened species and (2) as a tool to investigate and potentially monitor range distributions. Through extensive eDNA sampling along the west coast of South Africa, we aimed to refine the distributions of four golden mole species thought to occur there, and specifically to determine whether De Winton’s golden mole, Cryptochloris wintoni (IUCN Critically Endangered; Possibly Extinct), is in fact extant or extinct. Sequences were generated for three barcode markers (mtDNA cyt b, 12S and nuclear GHR) using next-generation amplicon sequencing. Tissue samples from four specimens were used to generate reference sequences for species identification, along with available GenBank sequences. We were able to (1) successfully detect all four species in our data, and (2) improve records of the distributions of these species. Furthermore, we uncovered cryptic diversity in Eremitalpa granti. Our data conclusively reveal the presence of the elusive Cryptochloris wintoni and suggest that this species may in fact be widespread, but not necessarily abundant, and certainly less so in areas subjected to mining activities, which continue to pose a threat to the species.
The continuous stochastic gradient method: part II–application and numerics
Authors: Grieshammer Max , Pflug Lukas , Stingl Michael , Uihlein Andrian 
Published: November 2023

AbstractIn this contribution, we present a numerical analysis of the continuous stochastic gradient (CSG) method, including applications from topology optimization and convergence rates. In contrast to standard stochastic gradient optimization schemes, CSG does not discard old gradient samples from previous iterations. Instead, design dependent integration weights are calculated to form a convex combination as an approximation to the true gradient at the current design. As the approximation error vanishes in the course of the iterations, CSG represents a hybrid approach, starting off like a purely stochastic method and behaving like a full gradient scheme in the limit. In this work, the efficiency of CSG is demonstrated for practically relevant applications from topology optimization. These settings are characterized by both, a large number of optimization variables and an objective function, whose evaluation requires the numerical computation of multiple integrals concatenated in a nonlinear fashion. Such problems could not be solved by any existing optimization method before. Lastly, with regards to convergence rates, first estimates are provided and confirmed with the help of numerical experiments.
When the Sun is Out of the Picture: Exploring a Variation Theory Key to Children’s Imagination in a Picturebook Reading Activity
Authors: Backman Anna 
Published: November 2023

AbstractThe purpose of this study is to explore a theoretical idea in relation to a body of empirical material from a reading activity involving a picturebook on shadow. The theoretical idea, sprung from variation theory, entails children’s discernment through synchronic simultaneity as a key to their ability to imagine. To explore this idea, an analysis has been conducted on how 5-year-olds, in words and actions, express themselves in a reading activity involving The Black Rabbit (Leathers, 2014) in regard to discerning and imagining about shadow as a light phenomenon. Vygotsky describes children’s previous experiences as a key to their ability to imagine. The results of this study imply that children’s discernment in the moment, with synchronic simultaneity, appears to be another key that unlocks their ability to imagine explicit depictions in a picturebook. The results also show that when children’s discernment with both synchronic and diachronic simultaneity (i.e., discernment in the moment and before the moment) is missing, their ability to imagine remains untapped, as none of the keys to their imagination, which might unlock their ability to imagine implicit depictions in a picturebook, are used.
Fairness-aware machine learning engineering: how far are we?
Authors: Ferrara Carmine , Sellitto Giulia , Ferrucci Filomena , Palomba Fabio , De Lucia Andrea 
Published: November 2023

AbstractMachine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision-making process and reiterating possible discrimination. While the interest of the software engineering community in software fairness is rapidly increasing, there is still a lack of understanding of various aspects connected to fair machine learning engineering, i.e., the software engineering process involved in developing fairness-critical machine learning systems. Questions connected to the practitioners’ awareness and maturity about fairness, the skills required to deal with the matter, and the best development phase(s) where fairness should be faced more are just some examples of the knowledge gaps currently open. In this paper, we provide insights into how fairness is perceived and managed in practice, to shed light on the instruments and approaches that practitioners might employ to properly handle fairness. We conducted a survey with 117 professionals who shared their knowledge and experience highlighting the relevance of fairness in practice, and the skills and tools required to handle it. The key results of our study show that fairness is still considered a second-class quality aspect in the development of artificial intelligence systems. The building of specific methods and development environments, other than automated validation tools, might help developers to treat fairness throughout the software lifecycle and revert this trend.
Enhancing anomaly detectors with LatentOut
Authors: Angiulli Fabrizio , Fassetti Fabio , Ferragina Luca 
Published: November 2023

Abstract$${{\textbf{Latent}}\varvec{Out}}$$

Latent

Out

is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we investigate the performance of $${{\textbf{Latent}}\varvec{Out}}$$

Latent

Out

acting as a one-class classifier and we experiment the combination of $${{\textbf{Latent}}\varvec{Out}}$$

Latent

Out

with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection. Moreover, we show that the feature space induced by $${{\textbf{Latent}}\varvec{Out}}$$

Latent

Out

has the characteristic to enhance the separation between normal and anomalous data. Indeed, we prove that standard data mining outlier detection methods perform better when applied on this novel augmented latent space rather than on the original data space.
Praxis: a framework for AI-driven human action recognition in assembly
Authors: Gkournelos Christos , Konstantinou Christos , Angelakis Panagiotis , Tzavara Eleni , Makris Sotiris 
Published: November 2023

AbstractThe role of Artificial intelligence in achieving high performance in manufacturing systems has been explored over the years. However, with the increasing number of variants in the factories and the advances in digital technologies new opportunities arise for supporting operators in the factory. The hybrid production systems stipulate the efficient collaboration of the workers with the machines. Human action recognition is a major enabler for intuitive machines and robots to achieve more efficient interaction with workers. This paper discusses a software framework called Praxis, aiming to facilitate the deployment of human action recognition (HAR) in assembly. Praxis is designed to provide a flexible and scalable architecture for implementing human action recognition in assembly lines. The framework has been implemented in a real-world case study originating for showcasing and validating the effectiveness of Praxis in real-life applications. It is deployed in an assembly use case for an air compression production industry. This study highlights the potential of the Praxis framework for promoting efficient human–robot collaboration (HRC) in modern manufacturing environments through HAR.
Prevalence and Correlates of Fearing a Partner During the COVID-19 Pandemic in Britain: Findings from Natsal-COVID
Authors: Willis Malachi , Tanton Clare , Conolly Anne , Baxter Andrew J. , Pérez Raquel Bosó , Riddell Julie , Dema Emily , Copas Andrew J. , Macdowall Wendy , Bonell Chris , Mercer Catherine H. , Sonnenberg Pam , Field Nigel , Mitchell Kirstin R. 
Published: November 2023

Abstract
Purpose
The COVID-19 pandemic and lockdown restrictions introduced personal and relationship stressors that potentially increased the risk of intimate partner violence (IPV) for some. We estimated the population prevalence and correlates of fearing a partner in the first year of the pandemic in Britain.

Method
We used data from Natsal-COVID Wave 2—a web-panel survey undertaken one year after the initial British lockdown from 23 March 2020. Quotas and weighting were used to achieve a quasi-representative sample of the general population. Participants were asked about fearing a partner, which is a simple and valid screening tool to identify IPV experiences.

Results
In our sample (unweighted n = 6302, aged 18–59), 9.0% of women and 8.7% of men reported fearing a partner in the first year of the pandemic. Women (73.3%) were more likely than men (49.9%) to indicate that fearing a partner made them feel anxious or depressed; men were more likely to report increased substance use (30.8% vs. 18.4%) and affected work/studies (30.0% vs. 20.0%). For both women and men, fearing a partner during the first year of the pandemic was associated with established health and wellbeing outcomes like anxiety/depression, alcohol use, accessing sexual/reproductive health services, and relationship dissolution as well as feeling that the “pandemic made things worse” across various life domains.

Conclusions
Population-level estimates of IPV during the COVID-19 pandemic highlight harmful experiences that occurred alongside other wide-ranging hardships, and the associations presented identify key populations with potential ongoing need. We make recommendations for primary, secondary, and tertiary prevention of IPV.
Globally optimal scheduling of an electrochemical process via data-driven dynamic modeling and wavelet-based adaptive grid refinement
Authors: Papadimitriou Chrysanthi , Varelmann Tim , Schröder Christian , Jupke Andreas , Mitsos Alexander 
Published: November 2023

AbstractElectrochemical recovery of succinic acid is an electricity intensive process with storable feeds and products, making its flexible operation promising for fluctuating electricity prices. We perform experiments of an electrolysis cell and use these to identify a data-driven model. We apply global dynamic optimization using discrete-time Hammerstein–Wiener models to solve the nonconvex offline scheduling problem to global optimality. We detect the method’s high computational cost and propose an adaptive grid refinement algorithm for global optimization (AGRAGO), which uses a wavelet transform of the control time series and a refinement criterion based on Lagrangian multipliers. AGRAGO is used for the automatic optimal allocation of the control variables in the grid to provide a globally optimal schedule within a given time frame. We demonstrate the applicability of AGRAGO while maintaining the high computational expenses of the solution method and detect superior results to uniform grid sampling indicating economic savings of 14.1%.
Colonial education and the world market: The cotton school experiment in German Togo (1900–1914)
Authors: Adick Christel 
Published: November 2023

AbstractCritical research on colonial education should not only include the intentions of colonial actors, but also an awareness of possible learning outcomes that were intended or not intended by “the colonial masters”. The case study presented here explores the fate of an agricultural school located in Notsé (Togo) from its first ideas in 1900 until about 1914 (the end of German colonial rule in Togo). The main reason for setting up the “cotton school experiment” was to find ways of formalising the transfer of knowledge, competences and attitudes which were deemed necessary for the transformation of the local economy and society from a subsistence or household economy to capitalist modes of production. The history of this agricultural institution transcends colonial history between Germany and Togo, opening up a broader view of entanglements between the regional histories of Africa, Germany and the southern United States. The story begins with African Americans from Tuskegee in Alabama under the leadership of the African-American educationist Booker T. Washington, who were despatched to German Togo in 1900 in order to enhance local cash-crop style cotton production for the sake of the German cotton industry in Germany. It ends with the colonial government in Togo appointing German regional agricultural officers to consult and instruct local Togolese communities in cash-crop production as itinerant teachers.
Randomized time Riemannian Manifold Hamiltonian Monte Carlo
Authors: Whalley Peter A. , Paulin Daniel , Leimkuhler Benedict 
Published: November 2023

AbstractHamiltonian Monte Carlo (HMC) algorithms, which combine numerical approximation of Hamiltonian dynamics on finite intervals with stochastic refreshment and Metropolis correction, are popular sampling schemes, but it is known that they may suffer from slow convergence in the continuous time limit. A recent paper of Bou-Rabee and Sanz-Serna (Ann Appl Prob, 27:2159-2194, 2017) demonstrated that this issue can be addressed by simply randomizing the duration parameter of the Hamiltonian paths. In this article, we use the same idea to enhance the sampling efficiency of a constrained version of HMC, with potential benefits in a variety of application settings. We demonstrate both the conservation of the stationary distribution and the ergodicity of the method. We also compare the performance of various schemes in numerical studies of model problems, including an application to high-dimensional covariance estimation.
Prediction of areas with high risk of roadkill wildlife applying maximum entropy approach and environmental features: East Antioquia, Colombia
Authors: Perez-Guerra J. , Gonzalez-Velez J. , Murillo-Escobar J. , Jaramillo-Fayad J. C. 
Published: November 2023

AbstractLinear infrastructures such as roads are known to cause adverse effects on the surrounding ecosystems. Wildlife–vehicle collisions (WVC) are considered to be one of the main causes of biodiversity loss. Several studies have demonstrated that WVC occurs on Colombian roads. However, studies have focused on a body count, the most affected species, and places with high mortality rates. We aim to propose a methodology for predicting WVC risk in the east of Antioquia, Colombia employing a machine learning approach to identify road segments with a high risk of WVC. Additionally, we present a novel validation technique for the "MaxEnt" approach. During this investigation, 499 reports were collected through road surveys between 2015 and 2016. We identified 160 road segments with high mortality rates with a 2D Hotspots analysis. 15 environmental descriptors were collected for each road segment. Validation of the predictive capabilities of the algorithm was performed using the area under the Receiver Operating Characteristic curve (AUC-ROC). The model achieved a good predictive ability (AUC>0.77). The response curves evidenced that features like distance to forest, land cover, resistance, and land use increase the probability of WVC, specifically, collision risk was higher in zones with high resistance values, crops, and pastures. This methodology has the potential to become an important tool for the prioritization of resources to mitigate WVC.
Advances of machine learning in materials science: Ideas and techniques
Authors: Chong Sue Sin , Ng Yi Sheng , Wang Hui-Qiong , Zheng Jin-Cheng 
Published: November 2023

AbstractIn this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.