bluuebunny/crossref_metadata_2025_split huggingface dataset:
An infeasible interior-point arc-search method with Nesterov’s restarting strategy for linear programming problems
Authors: Iida Einosuke , Yamashita Makoto 
Published: February 2024

AbstractAn arc-search interior-point method is a type of interior-point method that approximates the central path by an ellipsoidal arc, and it can often reduce the number of iterations. In this work, to further reduce the number of iterations and the computation time for solving linear programming problems, we propose two arc-search interior-point methods using Nesterov’s restarting strategy which is a well-known method to accelerate the gradient method with a momentum term. The first one generates a sequence of iterations in the neighborhood, and we prove that the proposed method converges to an optimal solution and that it is a polynomial-time method. The second one incorporates the concept of the Mehrotra-type interior-point method to improve numerical performance. The numerical experiments demonstrate that the second one reduced the number of iterations and the computational time compared to existing interior-point methods due to the momentum term.
Preventing the Exploitation of Activists’ Care
Authors: McKittrick-Sweitzer Lavender 
Published: February 2024

AbstractCare exploitation is a pervasive yet undertheorized injustice that emerges in both our interpersonal and structural relationships. Among those that are particularly vulnerable to this injustice are activists, those invested in bringing about positive change precisely because of how deeply they care about a given cause. Care exploitation occurs when an individual with caring attitudes is called to aid in the flourishing of a subject (e.g., LGBTQ + rights, anti-racism, conservation) by another that presumes they will answer said call simply because they care. In this work I offer an account of what it takes to prevent care exploitation in the narrow context of activism. Drawing on the work of Iris Marion Young, I argue that we have a political responsibility to (at the very least) adopt a stance of solidarity with activists by virtue of our structural relationships with them. This demands two things of us: (i) being sensitive to activists’ well-being and (ii) supporting their capacity for self-authorship.
A Benders decomposition approach for planning home blood donations
Authors: Bacci Tiziano , Lanzarone Ettore , Mattia Sara , Ventura Paolo 
Published: February 2024

AbstractBlood donations are crucial for the health system. We consider the problem of planning blood donation services, where the donors are reached at home. The scope is to minimize the penalty for the unserved donors, while guaranteeing that the available resources for implementing the service are not exceeded and that the appointment preferences of the donors are met. We present an offline model for this setting, where the produced solution must be robust with respect to the availability of the donors, which is not known in advance and is managed in a stochastic way using scenarios. A Benders decomposition approach to solve this model is developed. The proposed algorithm is tested on real-life instances coming from the Milan department of the Associazione Volontari Italiani Sangue (AVIS).
A Review of Analytic Frameworks for Noticing in Mathematics and Science: Comparing Noticing Frameworks Across Disciplines and over Time
Authors: Amador Julie M. , Weston Tracy L. 
Published: February 2024

AbstractWe review analytic frameworks related to the study of noticing in mathematics and science education for the purpose of suggesting trends in research literature across both disciplines over time. We focus on highly cited articles in both mathematics and science noticing research, along with recent articles in both disciplines. We focus specifically on research articles that include an analytic framework, to understand the state of how data on noticing are analyzed. We conducted an extensive review of literature, intentionally related to population, temporality, methodology, and quality. The purpose was to provide an overview of the field of noticing, based on particular search criteria for articles including an analytic framework. To be considered an analytic framework, the article had to include a framework that could be used to analyze teacher noticing. We found frameworks in science education are frequently adapted from mathematics education and are moving toward pairing noticing with aspects of effective instruction (formative assessment, sense-making, pedagogical content knowledge), whereas the frameworks in mathematics education now consider context and equity, which was not an explicit focus in the initial noticing literature.
Intelligent Reflecting Surface Framework for ED Based Spectrum Sensing
Authors: Kumar Rahul , Singh Shweta 
Published: February 2024

AbstractAccording to a recent study, adding intelligent reflecting surfaces (IRS) to propagation environments improves the performance of wireless devices. The capabilities of cognitive radio’s spectrum sensing over environments with assisted wireless propagation via IRS are examined in this research. Taking channel fading effects into consideration, we derive closed-form analytical expressions for the average probability of detection (APD) for single user and cooperative users. Further, the derived APD expression is used to derive the average area under the receiver operating characteristics curve ($${\overline{AUC}}$$AUC¯) expression. Monte Carlo simulations are also carried out in order to validate the derived expressions. Results demonstarte the extent of detectability performance in cognitive radio on utilizing the IRS aided wireless propagation environment therefore witnessing its significance for upcoming wireless technological advancement by offering spectrum efficiency with good quality of service (QoS).
Stored alcohol and fatty acid intermediates and the biosynthesis of sex pheromone aldehyde in the moth Chloridea virescens
Authors: Foster Stephen P. , Anderson Karin G. 
Published: February 2024

AbstractIn most species of moths, the female produces and releases a volatile sex pheromone from a specific gland to attract a mate. Biosynthesis of the most common type of moth sex pheromone component (Type 1) involves de novo synthesis of hexadecanoate (16:Acyl), followed by modification to various fatty acyl intermediates, then reduction to a primary alcohol, which may be acetylated or oxidized to produce an acetate ester or aldehyde, respectively. Our previous work on the moth Chloridea virescens (Noctuidae) showed that females produce 90% of the major pheromone component, (Z)-11-hexadecenal (Z11-16:Ald), via a direct and rapid route of de novo biosynthesis with highly labile intermediates, and ca. 10% from an indirect route that likely mobilizes a pre-synthesized 16-carbon skeleton, possibly, (Z)-11-hexadecenoate (Z11-16:Acyl) or hexadecanoate (16:Acyl). In this paper, we use stable isotope tracer/tracee techniques to study the dynamics of the precursor alcohol (Z)-11-hexadecenol (Z11-16:OH) and stores of Z11-16:Acyl and 16:Acyl to determine their roles in biosynthesis of Z11-16:Ald. We found: (i) that intracellular Z11-16:OH is synthesized at roughly the same rate as Z11-16:Ald, indicating that translocation and oxidation of this moiety does not rate limit biosynthesis of Z11-16:Ald, (ii) intracellular Z11-16:OH consists of two pools, a highly labile one rapidly translocated out of the cell and converted to Z11-16:Ald, and a less labile one that mostly remains in gland cells, (iii) during pheromone biosynthesis, net stores of Z11-16:Acyl increase, suggesting it is not the source of Z11-16:Ald produced by the indirect route, and (iv) no evidence for the gland synthesizing stored 16:Acyl prior to (up to 2 days before eclosion), or after, synthesis of pheromone commenced, suggesting the bulk of this stored moiety is synthesized elsewhere and transported to the gland prior to gland maturation. Thus, the pheromone gland of C. virescens produces very little stored fat over its functional lifetime, being optimized to produce sex pheromone.
Challenges of Measuring Self-Reported Exposure to Occupational Biomechanical Risk Factors Amongst People with Low Literacy Engaged in Manual Labour: Findings from a Cross-Cultural Adaptation and Psychometric Investigation in an African Population with Chronic Low Back Pain
Authors: Igwesi-Chidobe Chinonso Nwamaka , Sorinola Isaac Olubunmi , Ozumba Benjamin Chukwuma , Godfrey Emma Louise 
Published: February 2024

Abstract
Purpose
Occupational biomechanical factors are implicated in the aetiology and progression of low back pain (LBP). This study cross-culturally adapted and psychometrically investigated the Occupational Risk Factor Questionnaire (ORFQ) in a low literate Nigerian Igbo population with chronic LBP.

Methods
Forward and back translation of the original ORFQ by clinical and non-clinical translators was followed by an expert committee review. The adapted ORFQ was pre-tested amongst rural Nigerian adults with chronic LBP using cognitive think-aloud interviewing. Internal consistency (Cronbach’s alpha) and test–retest reliability (unweighted and linear weighted k statistic for item-by-item agreement, and intra-class correlation coefficient—ICC) were investigated amongst 50 rural and urban Nigerian dwellers with chronic LBP. Spearman’s correlation and regression analyses were conducted with the Igbo-ORFQ, and measures of disability [World Health Organisation Disability Assessment Schedule (WHODAS 2.0), Roland Morris Disability Questionnaire (RMDQ), Back performance scale (BPS)], pain intensity [Eleven-point box scale (BS-11)] and social support [Multidimensional Scale of Perceived Social Support (MSPSS)], to test construct validity with 200 rural Nigerian dwellers with chronic LBP.

Results
Cross-cultural adaptation highlighted difficulty conceptualising and concretising exposure to biomechanical risk factors. Item-by-item agreement, internal consistency (α = 0.84) and intraclass correlation coefficient (ICC = 0.83) were good. Some unexpected direction of associations between the biomechanical components of the Igbo-ORFQ, and disability, pain intensity, and social support prohibits establishment of construct validity.

Conclusion
Prospective studies comparing the Igbo-ORFQ to other measures of exposure to occupational biomechanical risk factors are required to establish the construct validity of the Igbo-ORFQ.
No (true) right to die: barriers in access to physician-assisted death in case of psychiatric disease, advanced dementia or multiple geriatric syndromes in the Netherlands
Authors: van den Ende Caroline , Asscher Eva Constance Alida 
Published: February 2024

AbstractEven in the Netherlands, where the practice of physician-assisted death (PAD) has been legalized for over 20 years, there is no such thing as a ‘right to die’. Especially patients with extraordinary requests, such as a wish for PAD based on psychiatric suffering, advanced dementia, or (a limited number of) multiple geriatric syndromes, encounter barriers in access to PAD. In this paper, we discuss whether these barriers can be justified in the context of the Dutch situation where PAD is legally permitted for those who suffer unbearably and hopelessly as a result of medical conditions. Furthermore, we explore whether there are options to address some of the barriers or their consequences, both within the Dutch legal framework or by adjusting the legal framework, and whether these options are feasible. We conclude that although there are insufficient arguments to overrule the doctor’s freedom of conscience in the Netherlands, there are ways to address some of the barriers, mainly by offering support to doctors that would be willing to support a request. Moreover, we believe it is morally required to reduce or mitigate where possible the negative consequences of the barriers for patients, such as the long waiting time for those who suffer from psychiatric disorders, because it is unlikely the adjustments suggested to the system will ensure reasonable access for these patient groups.
Pantropical CO2 emissions and removals for the AFOLU sector in the period 1990–2018
Authors: Nyawira Sylvia S. , Herold Martin , Mulatu Kalkidan Ayele , Roman-Cuesta Rosa Maria , Houghton Richard A. , Grassi Giacomo , Pongratz Julia , Gasser Thomas , Verchot Louis 
Published: February 2024

AbstractTransparent, accurate, comparable, and complete estimates of greenhouse gas emissions and removals are needed to support mitigation goals and performance assessments under the Paris Agreement. Here, we present a comparative analysis of the agriculture forestry and other land use (AFOLU) emission estimates from different datasets, including National Greenhouse Gas Inventories (NGHGIs), FAOSTAT, the BLUE, OSCAR, and Houghton (here after updated H&N2017) bookkeeping models; Emissions Database for Global Atmospheric Research (EDGAR); and the US Environmental Protection Agency (EPA). We disaggregate the fluxes for the forestry and other land use (FOLU) sector into forest land, deforestation, and other land uses (including non-forest land uses), while agricultural emissions are disaggregated according to the sources (i.e., livestock, croplands, rice cultivation, and agricultural fires). Considering different time periods (1990–1999, 2000–2010, and 2011–2018), we analyse the trend of the fluxes with a key focus on the tropical regions (i.e., Latin America, sub-Saharan Africa, and South and Southeast Asia). Three of the five data sources indicated a decline in the net emissions over the tropics over the period 1990–2018. The net FOLU emissions for the tropics varied with values of 5.47, 5.22, 4.28, 3.21, and 1.17 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs, respectively) over the recent period (2011–2018). Gross deforestation emissions over the same period were 5.87, 7.16, 5.48, 3.96, and 3.74 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). The net forestland sink was −1.97, −3.08, −2.09, −0.53, and −3.00 GtCO2 year−1 (for BLUE, OSCAR, updated H&N2017, FAOSTAT, and NGHGIs). Continental analysis indicated that the differences between the data sources are much large in sub-Saharan Africa and South and Southeast Asia than in Latin America. Disagreements in the FOLU emission estimates are mainly explained by differences in the managed land areas and the processes considered (i.e., direct vs indirect effects of land use change, and gross vs net accounting for deforestation). Net agricultural emissions from cropland, livestock, and rice cultivation were more homogenous across the FAOSTAT, EDGAR, and EPA datasets, with all the data sources indicating an increase in the emissions over the tropics. However, there were notable differences in the emission from agricultural fires. This study highlights the importance of investing and improving data sources for key fluxes to achieve a more robust and transparent global stocktake.
Ferroptosis and its emerging role in kidney stone formation
Authors: Yang Junyi , Wu Weisong , Amier Yirixiatijiang , Li Xianmiao , Wan Wenlong , Xun Yang , Yu Xiao 
Published: February 2024

Abstract
Kidney stone is a common and highly recurrent disease in urology, and its pathogenesis is associated with various factors. However, its precise pathogenesis is still unknown. Ferroptosis describes a form of regulated cell death that is driven by unrestricted lipid peroxidation, which does not require the activation of caspase and can be suppressed by iron chelators, lipophilic antioxidants, inhibitors of lipid peroxidation, and depletion of polyunsaturated fatty acids. Recent studies have shown that ferroptosis plays a crucial role in kidney stone formation. An increasing number of studies have shown that calcium oxalate, urate, phosphate, and selenium deficiency induce ferroptosis and promote kidney stone formation through mechanisms such as oxidative stress, endoplasmic reticulum stress, and autophagy. We also offered a new direction for the downstream mechanism of ferroptosis in kidney stone formation based on the “death wave” phenomenon. We reviewed the emerging role of ferroptosis in kidney stone formation and provided new ideas for the future treatment and prevention of kidney stones.
TV shows popularity prediction of genre-independent TV series through machine learning-based approaches
Authors: Cammarano Maria Elena , Guarino Alfonso , Malandrino Delfina , Zaccagnino Rocco 
Published: February 2024

AbstractThe use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of “never seen” shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.
Outdoor activity classification using smartphone based inertial sensor measurements
Authors: Bodhe Rushikesh , Sivakumar Saaveethya , Sakarkar Gopal , Juwono Filbert H. , Apriono Catur 
Published: February 2024

AbstractHuman Activity Recognition (HAR) deals with the automatic recognition of physical activities and plays a crucial role in healthcare and sports where wearable sensors and intelligent computational techniques are used. We propose a HAR algorithm that uses the smartphones accelerometer data for human activity recognition. In particular, we present a recurrent convolutional neural network-based HAR algorithm that combines a Convolutional Neural Network (CNN) to extract temporal features from the sensor data, a Fuzzy C-Means (FCM) clustering algorithm to cluster the features extracted by the CNN, and a Long Short-Term Memory (LSTM) network to learn the temporal dependencies between the features. We evaluate the proposed methodology on two distinct datasets: the MotionSense dataset and the WISDM dataset. We evaluate the proposed CNN-FCM-LSTM model on the publicly available MotionSense dataset to classify ten activity types: 1) walking upstairs, 2) walking downstairs, 3) jogging, 4) sitting, 5) standing, 6) level ground walking, 7) jumping jacks, 8) brushing teeth, 9) writing, and 10) eating. Next, we evaluate the model’s performance on the WISDM dataset to assess its ability to generalize to unseen data. On the MotionSense test dataset, CNN-FCM-LSTM achieves a classification accuracy of 99.69%, a sensitivity of 99.62%, a specificity of 99.63%, and a false positive rate per hour (FPR/h) of 0.37%. Meanwhile, it achieves a classification accuracy of 97.27% on the WISDM dataset. The CNN-FCM-LSTM model’s capability to classify a diverse range of activities within a single architecture is noteworthy. The results suggest that the proposed CNN-FCM-LSTM model using smartphone inputs is more accurate, reliable, and robust in detecting and classifying activities than the state-of-the-art models. It should be noted that activity recognition technology has the potential to aid in studying the underpinnings of physical activity, designing more effective training regimens, and simulating the rigors of competition in sports.
Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices
Authors: İnce Cemile , İnce Kenan , Hanbay Davut 
Published: February 2024

AbstractIn the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.
Elastic Deep Sparse Self-Representation Subspace Clustering Network
Authors: Wang Qiaoping , Chen Xiaoyun , Li Yan , Lin Yanming 
Published: February 2024

AbstractSubspace clustering model based on self-representation learning often use $$\ell _1, \ell _2$$

ℓ
1

,

ℓ
2

or kernel norm to constrain self-representation matrix of the dataset. In theory, $$\ell _1$$

ℓ
1

norm can constrain the independence of subspaces, but which may lead to under-connection because the sparsity of the self-representation matrix. $$\ell _2$$

ℓ
2

and nuclear norm regularization can improve the connectivity between clusters, but which may lead to over-connection of the self-representation matrix. Because a single regularization term may cause subspaces to be over or insufficiently divided, this paper proposes an elastic deep sparse self-representation subspace clustering network (EDS-SC), which imposes sparse constraints on deep features, and introduces the elastic network regularization mixed $$\ell _1$$

ℓ
1

and $$\ell _2$$

ℓ
2

norm to constraint self-representation matrix. The network can extract deep sparse features and provide a balance between subspace independence and connectivity. Experiments on human faces, objects, and medical imaging datasets prove the effectiveness of EDS-SC network.
Stronger Heterogeneous Feature Learning for Visible-Infrared Person Re-Identification
Authors: Wang Hao , Bi Xiaojun , Yu Changdong 
Published: February 2024

AbstractVisible-Infrared person re-identification (VI-ReID) is of great importance in the field of intelligent surveillance. It enables re-identification of pedestrians between daytime and dark scenarios, which can help police find escaped criminals at night. Currently, existing methods suffer from inadequate utilisation of cross-modality information, missing modality-specific discriminative information and weaknesses in perceiving differences between different modalities. To solve the above problems, we innovatively propose a stronger heterogeneous feature learning (SHFL) method for VI-ReID. First, we innovatively propose a Cross-Modality Group-wise constraint to solve the problem of inadequate utilization of cross-modality information. Secondly, we innovatively propose a Second-Order Homogeneous Invariant Regularizer to address the problem that missing modality-specific discriminative information. Finally, we innovatively propose a Modality-Aware Batch Normalization to address the problem of weaknesses in perceiving differences between different modalities. Extensive experimental results on two generic VI-ReID datasets demonstrate that the proposed final method outperforms the state-of-the-art algorithms.