bluuebunny/crossref_metadata_2025 huggingface dataset:
Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment
Authors: Hoshi Hideyuki , Hirata Yoko , Fukasawa Keisuke , Kobayashi Momoko , Shigihara Yoshihito
Published: January 2024

Background

Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records.

Methods

Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon’s spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters—‘severity’, ‘extent’, and ‘ratio’—were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved.

Results

MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer’s disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse.

Conclusion

MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.
Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification
Authors: Xie Xinghe , Chen Liyan , Qin Shujia , Zha Fusheng , Fan Xinggang
Published: January 2024

Introduction

As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities.

Methods

This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution.

Results

Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively.

Discussion

In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.
MS care: integrating advanced therapies and holistic management
Authors: Giovannoni Gavin , Ford Helen L. , Schmierer Klaus , Middleton Rod , Stennett Andrea M. , Pomeroy Ian , Fisniku Leonora , Scalfari Antonio , Bannon Colin , Stross Ruth , Hughes Sarah , Williams Adam , Josephs Samantha , Peel Charlie , Straukiene Agne
Published: January 2024

Lifestyle and environmental factors are key determinants in disease causality and progression in neurological conditions, including multiple sclerosis (MS). Lack of exercise, poor diet, tobacco smoking, excessive alcohol intake, social determinants of health, concomitant medications, poor sleep and comorbidities can exacerbate MS pathological processes by impacting brain health and depleting neurological reserves, resulting in more rapid disease worsening. In addition to using disease-modifying therapies to alter the disease course, therapeutic strategies in MS should aim to preserve as much neurological reserve as possible by promoting the adoption of a “brain-healthy” and “metabolically-healthy” lifestyle. Here, we recommend self-regulated lifestyle modifications that have the potential to improve brain health, directly impact on disease progression and improve outcomes in people with MS. We emphasise the importance of self-management and adopting a multidisciplinary, collaborative and person-centred approach to care that encompasses the healthcare team, family members and community support groups.
The science of uncertainty guides fetal-neonatal neurology principles and practice: diagnostic-prognostic opportunities and challenges
Authors: Scher Mark Steven
Published: January 2024

Fetal-neonatal neurologists (FNNs) consider diagnostic, therapeutic, and prognostic decisions strengthened by interdisciplinary collaborations. Bio-social perspectives of the woman’s health influence evaluations of maternal-placental-fetal (MPF) triad, neonate, and child. A dual cognitive process integrates “fast thinking-slow thinking” to reach shared decisions that minimize bias and maintain trust. Assessing the science of uncertainty with uncertainties in science improves diagnostic choices across the developmental-aging continuum. Three case vignettes highlight challenges that illustrate this approach. The first maternal-fetal dyad involved a woman who had been recommended to terminate her pregnancy based on an incorrect diagnosis of an encephalocele. A meningocele was subsequently identified when she sought a second opinion with normal outcome for her child. The second vignette involved two pregnancies during which fetal cardiac rhabdomyoma was identified, suggesting tuberous sclerosis complex (TSC). One woman sought an out-of-state termination without confirmation using fetal brain MRI or postmortem examination. The second woman requested pregnancy care with postnatal evaluations. Her adult child experiences challenges associated with TSC sequelae. The third vignette involved a prenatal diagnosis of an open neural tube defect with arthrogryposis multiplex congenita. The family requested prenatal surgical closure of the defect at another institution at their personal expense despite receiving a grave prognosis. The subsequent Management of Myelomeningocele Study (MOMS) would not have recommended this procedure. Their adult child requires medical care for global developmental delay, intractable epilepsy, and autism. These three evaluations involved uncertainties requiring shared clinical decisions among all stakeholders. Falsely negative or misleading positive interpretation of results reduced chances for optimal outcomes. FNN diagnostic skills require an understanding of dynamic gene-environment interactions affecting reproductive followed by pregnancy exposomes that influence the MPF triad health with fetal neuroplasticity consequences. Toxic stressor interplay can impair the neural exposome, expressed as anomalous and/or destructive fetal brain lesions. Functional improvements or permanent sequelae may be expressed across the lifespan. Equitable and compassionate healthcare for women and families require shared decisions that preserve pregnancy health, guided by person-specific racial-ethnic, religious, and bio-social perspectives. Applying developmental origins theory to neurologic principles and practice supports a brain health capital strategy for all persons across each generation.
Neurophysiological markers of asymmetric emotional contagion: implications for organizational contexts
Authors: Boukarras Sarah , Ferri Donato , Borgogni Laura , Aglioti Salvatore Maria
Published: January 2024

Emotions play a vital role within organizations, impacting various crucial aspects of work such as job satisfaction, performance, and employee well-being. Understanding how emotional states spread in organizational settings is therefore essential. Recent studies have highlighted that a leader’s emotional state can influence their followers, with significant consequences on job performance. Leaders thus possess the ability to influence their employees’ psychological state and, consequently, their well-being. However, the biological underpinnings of emotional contagion from leaders to followers remain unexplored. The field of interpersonal (neuro)physiology, which involves recording brain and peripheral activity of multiple individuals during interactions, holds great potential for investigating this phenomenon. Analyzing the time-lagged synchronization of neurophysiological activity during interactions may serve as a measure of the leader’s influence on their followers in organizational contexts. In this “mini review,” we examine empirical studies that have employed interpersonal (neuro)physiology to quantify the asymmetrical contagion of emotions in different contexts. Asymmetrical contagion was operationalized as the unidirectional influence exerted by one individual (i.e., the “sender”) to another one (i.e., the “receiver”), whereby the receiver’s state can be predicted by the sender’s one. The reviewed literature reveals that delayed synchronization of physiological states is a widespread phenomenon that may underpin the transmission of emotions. These findings have significant implications for various aspects of organizational life, including leader-to-employee communication, and could drive the development of effective leadership training programs. We propose that Organizational Neuroscience may benefit from including interpersonal neurophysiology in its methodological toolkit for laboratory and field studies of leader-follower dynamics.
Mechanisms of transcranial direct current stimulation (tDCS) for pain in patients with fibromyalgia syndrome
Authors: Wang Shan , Du Shu-Hao , Wang Xue-Qiang , Lu Jun-Yan
Published: January 2024

Fibromyalgia syndrome (FMS) is a recurrent pain condition that can be challenging to treat. Transcranial direct current stimulation (tDCS) has become a promising non-invasive therapeutic option in alleviating FMS pain, but the mechanisms underlying its effectiveness are not yet fully understood. In this article, we discuss the most current research investigating the analgesic effects of tDCS on FMS and discuss the potential mechanisms. TDCS may exert its analgesic effects by influencing neuronal activity in the brain, altering cortical excitability, changing regional cerebral blood flow, modulating neurotransmission and neuroinflammation, and inducing neuroplasticity. Overall, evidence points to tDCS as a potentially safe and efficient pain relief choice for FMS by multiple underlying mechanisms. This article provides a thorough overview of our ongoing knowledge regarding the mechanisms underlying tDCS and emphasizes the possibility of further studies to improve the clinical utility of tDCS as a pain management tool.
Advances in the study of marketed antibody-drug Conjugates (ADCs) for the treatment of breast cancer
Authors: Liang Yan , Zhang Purong , Li Feng , Lai Houyun , Qi Tingting , Wang Yixin
Published: January 2024

Breast cancer continues to have a high incidence rate among female malignancies. Despite significant advancements in treatment modalities, the heterogeneous nature of breast cancer and its resistance to various therapeutic approaches pose considerable challenges. Antibody-drug conjugates (ADCs) effectively merge the specificity of antibodies with the cytotoxicity of chemotherapeutic agents, offering a novel strategy for precision treatment of breast cancer. Notably, trastuzumab emtansine (T-DM1) has provided a new therapeutic option for HER2-positive breast cancer patients globally, especially those resistant to conventional treatments. The development of trastuzumab deruxtecan (T-DXd) and sacituzumab govitecan (SG) has further broadened the applicability of ADCs in breast cancer therapy, presenting new hopes for patients with low HER2 expression and triple-negative breast cancer. However, the application of ADCs presents certain challenges. For instance, their treatment may lead to adverse reactions such as interstitial lung disease, thrombocytopenia, and diarrhea. Moreover, prolonged treatment could result in ADCs resistance, complicating the therapeutic process. Economically, the high costs of ADCs might hinder their accessibility in low-income regions. This article reviews the structure, mechanism of action, and clinical trials of commercially available ADCs for breast cancer treatment, with a focus on the clinical trials of the three drugs, aiming to provide insights for clinical applications and future research.
Elucidating the chemical profile and biological studies of Verbascum diversifolium Hochst. extracts
Authors: Yagi Sakina , Nilofar Nilofar , Uba Abdullahi Ibrahim , Caprioli Giovanni , Mustafa Ahmed M. , Angeloni Simone , Koyuncu Ismail , Seker Fatma , Polat Rıdvan , Supti Sumaiya Jahan , Tasnim Faria , Al Dhaheri Yusra , Zengin Gokhan , Eid Ali H.
Published: January 2024

The present study was designed to evaluate the chemical composition, antioxidant, enzyme inhibition and cytotoxic properties of different extracts from aerial parts of V. diversifolium (family Scrophulariaceae), a plant that is native to Lebanon, Syria and Turkey. Six extracts, namely, hexane, dichloromethane (DCM), ethyl acetate (EtOAc), ethanol (EtOH), 70% EtOH, and water (aqueous) were prepared by maceration. The EtOH extract was predominated by the presence of rutin (4280.20 μg g−1) and p-coumaric acid (3044.01 μg g−1) while the highest accumulation of kaempferol-3-glucoside (1537.38 μg g−1), caffeic acid (130.13 μg g−1) and 4-hydroxy benzoic acid (465.93 μg g−1) was recorded in the 70% EtOH, aqueous, and EtOAc extracts, respectively. The EtOH (46.86 mg TE/g) and 70% EtOH (46.33 mg TE/g) extracts displayed the highest DPPH radical scavenging result. Both these extracts, along with the aqueous one, exerted the highest ABTS radical scavenging result (73.03–73.56 mg TE/g). The EtOH and 70% EtOH extracts revealed the most potent anti-AChE (2.66 and 2.64 mg GALAE/g) and anti-glucosidase (1.07 and 1.09 mmol ACAE/g) activities. The aqueous extract was the most efficacious in inhibiting the proliferation of prostate cancer (DU-145) cells with an IC50 of 8.71 μg/mL and a Selectivity Index of 3.7. In conclusion, this study appraised the use of V. diversifolium aerial parts as a potential therapeutic source for future development of phytopharmaceuticals that target specific oxidative stress-linked diseases including diabetes, cancer, cardiovascular disease, and Alzheimer’s disease among others.
Tomato leaf disease recognition based on multi-task distillation learning
Authors: Liu Bo , Wei Shusen , Zhang Fan , Guo Nawei , Fan Hongyu , Yao Wei
Published: January 2024

Introduction

Tomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity.

Methods

Prior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multi-stage strategy to leverage the complementary nature of classification and severity prediction.

Results

Experiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters.

Discussion

The findings demonstrate the practical potential of our framework for intelligent agriculture applications.
Does direct democracy increase civic virtues? A systematic literature review
Authors: Bernhard Laurent
Published: January 2024

Due to widespread citizen disenchantment with representative democracy, the introduction of direct-democratic institutions is often promoted as a promising remedy to overcome the current democratic crisis. Theorists of participatory democracy have argued that direct democracy can foster civic virtues, given that the opportunity to participate in referendums and initiatives is generally expected to empower and enlighten citizens. By conducting a systematic literature review, this article aims to provide an overview of scholarship on how direct democracy delivers on its promise to increase individual civic virtues. To that end, it focuses on the effects of direct democracy on those four areas to which scholars have devoted much attention so far: (1) electoral turnout, (2) external and internal efficacy, (3) political knowledge, and (4) subjective wellbeing and satisfaction with democracy. Based on 67 selected studies, it turns out that there is only little positive overall effect of direct democracy on civic virtues, with a great deal of variation. The empirical analysis establishes a negative time trend, indicating that researchers have increasingly reported negative findings over the years. This main result calls into question the expectations advanced by the theorists of participatory democracy and gives some credit to more skeptical views. This review concludes by providing scholars with new avenues for research.
Willingness to reduce alcohol consumption predicted by short-form video exposure, media involvement, psychological bias, and cognitive factor
Authors: Chung Donghwa , Meng Yanfang
Published: January 2024

Introduction

Through previous studies, Chinese college students are known to be prone to alcohol consumption, which can lead to health-risk behaviors such as high blood pressure, heart disease, stroke, liver disease, and digestive problems. However, little is known about how popular social media platforms (e.g., short-form video applications) can positively impact their willingness to reduce alcohol consumption. This study was guided by the theory of optimistic bias; we investigated the direct, mediated, and moderating effects of exposure to anti-alcohol consumption short-form videos and short-form video involvement on Chinese college students’ willingness to reduce their alcohol consumption.

Methods

The current study has an empirical cross-sectional design and employed an online survey from September 1st, 2022, to November 1st, 2022. The survey specifically targeted Chinese college students, who are the most common users of short-form video applications. The accumulated data underwent rigorous examination, including hierarchical regression, mediation, and moderation analyses, all conducted using the PROCESS macro 4.0 within SPSS version 22.

Results

A total of 434 participants, aged 18–24 years, were included in this study. There were mediating effects regarding Chinese college students’ exposure to anti-alcohol consumption short-form videos (β = 0.35, p < 0.01, 95% CI [0.17, 0.63]) and short-form video involvement (β = 0.44, p < 0.001, 95% CI [0.20, 0.65]) on their willingness to reduce alcohol consumption via reversed optimistic bias. Moreover, perceived prevention of heavy drinking control (β = 0.05, p < 0.001, 95% CI [0.01, 0.09]) played mediating roles in the relationship between exposure to anti-alcohol consumption short-form videos and willingness to reduce alcohol consumption.

Conclusion

This study is one of the earliest studies to examine the intricate effects of exposure to anti-alcohol consumption short-form videos and short-form video involvement on the willingness to reduce alcohol consumption among Chinese college students. In addition, this study confirms that regardless of whether Chinese college students are conscientious, exposure to anti-alcohol consumption short-form videos did not increase their level of reversed optimistic bias. The empirical findings of this study are critical and can provide practical insights for Chinese health departments that encourage Chinese college students to minimize alcohol consumption.
Investigation of integrated time nanosecond pulse irreversible electroporation against spontaneous equine melanoma
Authors: Fesmire Chris C. , Peal Bridgette , Ruff Jennifer , Moyer Elizabeth , McParland Thomas J. , Derks Kobi , O’Neil Erin , Emke Carrie , Johnson Brianna , Ghosh Shatorupa , Petrella Ross A. , DeWitt Matthew R. , Prange Timo , Fogle Callie , Sano Michael B.
Published: January 2024

Introduction

Integrated time nanosecond pulse irreversible electroporation (INSPIRE) is a novel tumor ablation modality that employs high voltage, alternating polarity waveforms to induce cell death in a well-defined volume while sparing the underlying tissue. This study aimed to demonstrate the in vivo efficacy of INSPIRE against spontaneous melanoma in standing, awake horses.

Methods

A custom applicator and a pulse generation system were utilized in a pilot study to treat horses presenting with spontaneous melanoma. INSPIRE treatments were administered to 32 tumors across 6 horses and an additional 13 tumors were followed to act as untreated controls. Tumors were tracked over a 43–85 day period following a single INSPIRE treatment. Pulse widths of 500ns and 2000ns with voltages between 1000 V and 2000 V were investigated to determine the effect of these variables on treatment outcomes.

Results

Treatments administered at the lowest voltage (1000 V) reduced tumor volumes by 11 to 15%. Higher voltage (2000 V) treatments reduced tumor volumes by 84 to 88% and eliminated 33% and 80% of tumors when 500 ns and 2000 ns pulses were administered, respectively.

Discussion

Promising results were achieved without the use of chemotherapeutics, the use of general anesthesia, or the need for surgical resection in regions which are challenging to keep sterile. This novel therapeutic approach has the potential to expand the role of pulsed electric fields in veterinary patients, especially when general anesthesia is contraindicated, and warrants future studies to demonstrate the efficacy of INSPIRE as a solid tumor treatment.
Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT
Authors: Shaik Vadla Mahammad Khalid , Suresh Mahima Agumbe , Viswanathan Vimal K.
Published: January 2024

Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify the data into positive, negative, and neutral reviews concerning their aspects. This research focused on eco-friendly products to analyze the customer emotions in this category. The BERT and T5 models were finely tuned for the aspect detection job and achieved 92% and 91% accuracy, respectively. The best-performing model will be selected, calculating the evaluation metrics precision, recall, F1-score, and computational efficiency. In these calculations, the BERT model outperforms T5 and is chosen as a classifier for the prediction pipeline to predict the aspect. By detecting aspects and sentiments of input data using the pre-trained BERT model, our study demonstrates its capability to comprehend and analyze customer reviews effectively. These findings can empower product designers and research developers with data-driven insights to shape exceptional products that resonate with customer expectations.
Efficient Time-Series Clustering through Sparse Gaussian Modeling
Authors: Fotakis Dimitris , Patsilinakos Panagiotis , Psaroudaki Eleni , Xefteris Michalis
Published: January 2024

In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian Process Regression and obtain a sparse representation of each time series in the dataset with a logarithmic (in the original length T) number of inducing data points. In the second stage, we apply k-means with DTW Barycentric Averaging (DBA) to the sparsified dataset using a generalization of DTW, which accounts for the fact that each inducing point serves as a representative of many original data points. The asymptotic running time of our Sparse Time-Series Clustering framework is Ω(T2/log2T) times faster than the running time of applying k-means to the original dataset because sparsification reduces the running time of DTW from Θ(T2) to Θ(log2T). Moreover, sparsification tends to smoothen outliers and particularly noisy parts of the original time series. We conduct an extensive experimental evaluation using datasets from the UCR Time-Series Classification Archive, showing that the quality of clustering computed by our Sparse Time-Series Clustering framework is comparable to the clustering computed by the standard k-means algorithm.
Design and Verification of Observability-Driven Autonomous Vehicle Exploration Using LiDAR SLAM
Authors: Kim Donggyun , Lee Byungjin , Sung Sangkyung
Published: January 2024

This paper explores the research topic of enhancing the reliability of unmanned mobile exploration using LiDAR SLAM. Specifically, it proposes a technique to analyze waypoints where 3D LiDAR SLAM can be smoothly performed in potential exploration areas and points where there is a risk of divergence in navigation estimation. The goal is to improve exploration performance by presenting a method that secures these candidate regions. The analysis employs a 3D geometric observability matrix and its condition number to discriminate waypoints. Subsequently, the discriminated values are applied to path planning, resulting in the derivation of a final destination path connecting waypoints with a satisfactory SLAM position and attitude estimation performance. To validate the proposed technique, performance analysis was initially conducted using the Gazebo simulator. Additionally, experiments were performed with an autonomous unmanned vehicle in a real-world environment.
The Impact and Mechanism of Digital Villages on Agricultural Resilience in Ecologically Fragile Ethnic Areas: Evidence from China’s Provinces
Authors: Zhao Xin , Zhao Rong
Published: January 2024

Ecologically fragile ethnic areas constitute pivotal regions for rural revitalization and the construction of the Beautiful China initiative. The establishment of digital villages is of great significance for enhancing agricultural resilience and achieving common prosperity. Utilizing panel data from ecologically fragile ethnic areas between 2010 and 2020, this study employs a projection pursuit model to calculate scores for digital village levels and agricultural resilience. Building on this, our research employs instrumental variable methods and a mediation effect model to analyze the impact and mechanisms of digital village level on agricultural resilience in ecologically fragile ethnic areas, and heterogeneity analysis is conducted. The findings reveal that digital village level and agricultural resilience scores in ecologically fragile ethnic areas show a trend of initial increase followed by a decrease, exhibiting significant overall fluctuations and unstable growth. The promotion of digital village areas on agricultural resilience is evident, and this conclusion holds even after a series of tests including instrumental variables. Elevating the digital village level can narrow the urban–rural income gap and enhance agricultural resilience. There is significant regional heterogeneity in the impact of digital village levels on local agricultural resilience in ecologically fragile ethnic areas, with digital village development exerting a more pronounced and powerful driving force in areas with lower agricultural resilience. Therefore, leveraging the leadership of advantaged provinces, intensifying investment in digital village infrastructure, and implementing targeted strategies based on the disparities in digital village level and agricultural resilience across areas become imperative.
The Combined Effect of Lemon Peel Extract and Calcium Chloride on the Physical and Biochemical Quality Parameters of the Dessert Banana (Musa acuminata var. Dwarf Cavendish) Fruit
Authors: Ngoko Tchamba Eric-Ivan , Tybussek Thorsten , Muranyi Peter , Nguetsop Victor Francois , Aghofack-Nguemezi Jean , Schwab Wilfried
Published: January 2024

The dessert banana is a popular fruit worldwide, but its ripening process is greatly accelerated by high temperatures, which eventually leads to an unpleasant taste and the appearance of spots on the skin of the fruits. To slow down the ripening of bananas, expensive strategies are used, which are usually not practical for conventional farmers in less developed countries. In this study, we try to find a less costly alternative. Therefore, the effects of coatings of lemon peel extract (2.5%, 5%, and 10%), calcium chloride (4%), and glycerol (2%) on the shelf life and postharvest quality of the banana fruit (Cavendish) stored at 19–22 °C and 40–60% relative humidity were investigated. Treatment with a mixture of 2.5% lemon peel extract and 2% glycerol resulted in an extension of the shelf life of the dessert banana by up to 6 days and no detectable fungal infestation. The coating solution is an effective alternative to extend the shelf life and reduce quality losses in bananas.
Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test
Authors: Lin Haipeng , Song Xuefeng , Dai Fei , Zhang Fengwei , Xie Qiang , Chen Huhu
Published: January 2024

Hardness is a critical mechanical property of grains. Accurate predictions of grain hardness play a crucial role in improving grain milling efficiency, reducing grain breakage during transportation, and selecting high-quality crops. In this study, we developed machine learning models (MLMs) to predict the hardness of Jinsui No.4 maize seeds. The input variables of the MLM were loading speed, loading depth, and different types of indenters, and the output variable was the slope of the linear segment. Using the Latin square design, 100 datasets were generated. Four different types of MLMs, a genetic algorithm (GA), support vector machine (SVM), random forest (RF), and long short-term memory network (LSTM), were used for our data analysis, respectively. The result indicated that the GA model had a high accuracy in predicting hardness values, the R2 of the GA model training set and testing set reached 0.98402 and 0.92761, respectively, while the RMSEs were 1.4308 and 2.8441, respectively. The difference between the predicted values and the actual values obtained by the model is relatively small. Furthermore, in order to investigate the relationship between hardness and morphology after compression, scanning electron microscopy was used to observe the morphology of the maize grains. The result showed that the more complex the shape of the indenter, the more obvious the destruction to the internal polysaccharides and starch in the grain, and the number of surface cracks also significantly increases. The results of this study emphasize the potential of MLMs in determining the hardness of agricultural cereal grains, leading to improved industrial processing efficiency and cost savings. Additionally, combining grain hardness prediction models with the operating mechanisms of industry machinery would provide valuable references and a basis for the parameterization of seed grain processing machinery.
Revealing Genetic Diversity and Population Structure in Türkiye’s Wheat Germplasm Using iPBS-Retrotransposon Markers
Authors: Demirel Fatih , Yıldırım Bünyamin , Eren Barış , Demirel Serap , Türkoğlu Aras , Haliloğlu Kamil , Nowosad Kamila , Bujak Henryk , Bocianowski Jan
Published: January 2024

Investigating the genetic diversity and population structure of wheat germplasm is crucial for understanding the underlying variability essential for breeding programs and germplasm preservation. This research aims to contribute novel insights with respect to the genetic makeup and relationships among these wheat genotypes, shedding light on the diversity present within the Turkish wheat germplasm. In this study, iPBS-retrotransposon markers were employed to analyze 58 wheat genotypes, encompassing 54 landraces and 4 cultivars sourced from Türkiye. These markers serve as genetic indicators that can be used to evaluate genetic variation, build genealogical trees, and comprehend evolutionary connections. The PCR products were visualized on agarose gel, and bands were scored as present/absent. The ten iPBS primers collectively yielded an average of 16.3 alleles, generating a total of 163 polymorphic bands. The number of alleles produced by individual markers ranged from 4 (iPBS-2386) to 29 (iPBS-2219). The genetic parameters were calculated using the popgen and powermarker programs. The genetic relationships and population structures were assessed using the ntsys and structure programs. Polymorphism information content (PIC) per marker varied from 0.13 (iPBS-2390) to 0.29 (iPBS-2386), with an average value of 0.22. Shannon’s information index (I) was calculated as 1.48, while the number of effective alleles (Ne) and Nei’s genetic diversity (H) were determined to be 0.26 and 0.31, respectively. Genotype numbers 3 (Triticum dicoccum) and 10 (Triticum monococcum) exhibited the maximum genetic distance of 0.1292, signifying the highest genetic disparity. Population structure analysis revealed the segregation of genotypes into three distinct subpopulations. Notably, a substantial portion of genotypes clustered within populations correlated with the wheat species. This population structure result was consistent with the categorization of genotypes based on wheat species. The comprehensive assessment revealed noteworthy insights with respect to allele distribution, polymorphism content, and population differentiation, offering valuable implications for wheat breeding strategies and germplasm conservation efforts. In addition, the iPBS markers and wheat genotypes employed in this study hold significant potential for applications in wheat breeding research and germplasm preservation.
Ascorbic Acid Treatments as Effective and Safe Anti-Aging Therapies for Sensitive Skin
Authors: Jaros-Sajda Anna , Budzisz Elzbieta , Erkiert-Polguj Anna
Published: January 2024

The most common signs of aging skin include a decrease in firmness and density, uneven skin tone, and a tendency to erythema. There is an ever-increasing interest in aesthetic treatments that maintain the skin’s favorable appearance. However, such therapies are difficult in the case of sensitive skin, defined as a set of stimuli-triggered symptoms (stinging, erythema, burning, and itching) that would not appear in healthy skin. Sensitive skin is common and affects, to varying degrees, about half of the European population. This study was aimed at evaluating the effects of ascorbic acid—a known antioxidant—applied with sonophoresis and microneedling on the signs of photoaging in reactive and erythematous skin. A significant improvement in skin elasticity was observed after a series of tests. A significant reduction in erythema was observed after both therapies. The greatest reduction was observed on the cheeks after applying vitamin C combined with microneedling. At the same time, the results showed an excellent tolerance of both treatments, which proved them to be safe and effective.
Real-Time Patient Indoor Health Monitoring and Location Tracking with Optical Camera Communications on the Internet of Medical Things
Authors: Herfandi Herfandi , Sitanggang Ones Sanjerico , Nasution Muhammad Rangga Aziz , Nguyen Huy , Jang Yeong Min
Published: January 2024

Optical Camera Communication (OCC) is an emerging technology that has attracted research interest in recent decades. Unlike previous communication technologies, OCC uses visible light as the medium to transmit data from receivers and cameras to receive the data. OCC has several advantages that can be capitalized in several implementations. However, the Internet of Things (IoT) has emerged as a technology with immense potential. Numerous research endeavors support the IoT’s prospective technology that can be implemented in various sectors, including the healthcare system. This study introduces a novel implementation of the Internet of Medical Things (IoMT) system, using OCC for real-time health monitoring and indoor location tracking. The innovative system uses standard closed-circuit television CCTV setups, integrating deep learning-based OCC to monitor multiple patients simultaneously, each represented by an LED matrix. The effectiveness of the system was demonstrated through two scenarios: the first involves dual transmitters and a single camera, highlighting real-time monitoring of vital health data; the second features a transmitter with dual cameras, focusing patient movement tracking across different camera fields of view. To accurately locate and track the position of LED arrays in the camera, the system used YOLO (You Only Look Once). Data are securely transmitted to an edge server and stored using the REST API, with a web interface providing real-time patient updates. This study highlights the potential of OCC in IoMT for advanced patient care and proposes future exploration in larger healthcare systems and other IoT domains.
Advancing Facial Expression Recognition in Online Learning Education Using a Homogeneous Ensemble Convolutional Neural Network Approach
Authors: Lawpanom Rit , Songpan Wararat , Kaewyotha Jakkrit
Published: January 2024

Facial expression recognition (FER) plays a crucial role in understanding human emotions and is becoming increasingly relevant in educational contexts, where personalized and empathetic interactions are essential. The problems with existing approaches are typically solved using a single deep learning method, which is not robust with complex datasets, such as FER data, which have a characteristic imbalance and multi-class labels. In this research paper, an innovative approach to FER using a homogeneous ensemble convolutional neural network, called HoE-CNN, is presented for future online learning education. This paper aims to transfer the knowledge of models and FER classification using ensembled homogeneous conventional neural network architectures. FER is challenging to research because there are many real-world applications to consider, such as adaptive user interfaces, games, education, and robot integration. HoE-CNN is used to improve the classification performance on an FER dataset, encompassing seven main multi-classes (Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral). The experiment shows that the proposed framework, which uses an ensemble of deep learning models, performs better than a single deep learning model. In summary, the proposed model will increase the efficiency of FER classification results and solve FER2013 at a accuracy of 75.51%, addressing both imbalanced datasets and multi-class classification to transfer the application of the model to online learning applications.
Changes in Human Motor Behavior during the Familiarization with a Soft Back-Support Occupational Exoskeleton
Authors: Favennec Arthur , Frère Julien , Mornieux Guillaume
Published: January 2024

Soft back exoskeletons are aimed at reducing musculoskeletal effort during manual handling tasks, contributing to the prevention of low back disorders like lumbar strains and sprains or intervertebral disk problems. However, large differences in their biomechanical effects are observed in the literature. A possible explanation could be the lack or disparity of familiarization protocols with the exoskeleton. The aim of this experimental study was to characterize the familiarization process with a soft back-support occupational exoskeleton and determine the time needed to stabilize biomechanical variables. Participants carried out 6 familiarization sessions of 1 h to the CORFOR® soft back-exoskeleton. Joint kinematics, postural stability, exoskeleton pressure perception, muscle activity, and performance were measured at the beginning of the first session and at the end of each session during stoop and squat liftings. Results showed that back kinematics, performance, and exoskeleton pressure perception changed during the first sessions and stabilized after sessions 3 or 4, depending on the variable. The authors recommend a familiarization protocol for the CORFOR® soft back-exoskeleton of 4 sessions of 1 h duration. This recommendation could help CORFOR® users, for instance, in the automotive industry, the food retail industry, or the agriculture field.
Simulation Study of the Small-Signal Characteristics of Single-Phase Common-Mode Inductors with Capacitance Cancellation
Authors: Takahashi Shotaro
Published: January 2024

This article proposes a simulation model of single-phase common-mode inductors to evaluate the small-signal characteristic of common-mode inductors when the stray capacitance cancellation method is applied. From the estimated stray capacitance of common-mode inductors, the value of the cancellation capacitors is determined and implemented into the model. Thus, the proposed model can simulate the small-signal characteristic of common-mode inductors with the capacitance cancellation method at the design stage. To validate the proposed model, a comparison between measured and simulated attenuation characteristics of fabricated single-phase common-mode inductors is performed. The comparison result clarifies that the high-frequency attenuation improvement of the capacitance cancellation method is limited for manganese–zinc ferrite-based common-mode inductors due to the steep change of the complex permeability.
Temperature Effects on the Natural Frequencies of Composite Girders
Authors: Poudel Arjun , Kim Seungwon , Cho Byoung Hooi , Kim Janghwan
Published: January 2024

Composite bridges are typically exposed to temperature variations due to heat radiation, conduction, and convection. Temperature affects the modal parameters of bridges, hindering the application of damage detection methods based on the dynamic properties of bridges. In this study, the effects of temperature on the natural frequencies of composite bridges were investigated experimentally and numerically to derive a basis for separating temperature effects from the natural frequencies. A temperature-controllable girder specimen was developed for modal testing. Additionally, finite element (FE) analysis was conducted to analyze the effects of temperature. The FE analysis results were validated by comparing them to the static response results of the test specimen. The results of the experiments and FE simulations verified that temperature variation can affect the material properties, particularly the modulus of elasticity, of a composite girder, consequently influencing its natural frequency. Based on the tests and simulations, a linear relationship between the temperature and the natural frequency was proposed to remove the temperature effects from the natural frequency.
Variability in the Load of Professional Basketball Referees during Competition
Authors: Ibáñez Sergio José , Vaquera Alejandro , Mancha-Triguero David , Escudero-Tena Adrián
Published: January 2024

Monitoring the demands on basketball referees is essential for optimizing their performance and reducing the risk of injuries. This study aimed to analyze the workload experienced by elite basketball referees during official games. Using a quasi-experimental empirical methodology, inertial devices (WIMUPRO) were used for four referees participating in the official tournament of the Spanish first-division basketball league. All the matches in the tournament were analyzed. Descriptive analysis and ANOVA were used to understand the referees’ demands. Moreover, a Mixed Linear Model was used to take into account the individuality of each referee in the analysis of the variables. The results indicate that the analyzed external and internal load variables are specific to each basketball referee. The variability in referees’ external and internal load demands was identified as low to moderate within performances, between matches, and between quarters. The external and internal load variables did not vary among the three analyzed games. The distance covered during the first quarter of the games was less than in the rest of the quarters, but it was performed at a higher intensity. Neuromuscular load (analyzed as Impacts, Player Load, and Power Metabolic) showed higher values in the first quarter of the games than in the rest. The referees’ response to competition is individual and can be influenced by situational variables such as the moment or type of game.
Numerical Solutions of Second-Order Elliptic Equations with C-Bézier Basis
Authors: Sun Lanyin , Su Fangming , Pang Kunkun
Published: January 2024

This article introduces a finite element method based on the C-Bézier basis function for second-order elliptic equations. The trial function of the finite element method is set up using a combination of C-Bézier tensor product bases. One advantage of the C-Bézier basis is that it has a free shape parameter, which makes geometric modeling more convenience and flexible. The performance of the C-Bézier basis is searched for by studying three test examples. The numerical results demonstrate that this method is able to provide more accurate numerical approximations than the classical Lagrange basis.
Fixed Point Theorems for Set-Valued Contractions in Metric Spaces
Authors: Cho Seong-Hoon
Published: January 2024

In this paper, the concepts of Wardowski-type set-valued contractions and Işik-type set-valued contractions are introduced and fixed point theorems for such contractions are established. A positive answer to the open Question is given. Examples to support main theorems and an application to integral inclusion are given.