bluuebunny/crossref_metadata_2025 huggingface dataset:
Prediction of the risk of cytopenia in hospitalized HIV/AIDS patients using machine learning methods based on electronic medical records
Authors: Huang Liling , Xie Bo , Zhang Kai , Xu Yuanlong , Su Lingsong , Lv Yu , Lu Yangjie , Qin Jianqiu , Pang Xianwu , Qiu Hong , Li Lanxiang , Wei Xihua , Huang Kui , Meng Zhihao , Hu Yanling , Lv Jiannan
Published: July 2023

Background

Cytopenia is a frequent complication among HIV-infected patients who require hospitalization. It can have a negative impact on the treatment outcomes for these patients. However, by leveraging machine learning techniques and electronic medical records, a predictive model can be developed to evaluate the risk of cytopenia during hospitalization in HIV patients. Such a model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients.

Method

The present study was conducted on HIV patients who were admitted to Guangxi Chest Hospital between June 2016 and October 2021. We extracted a total of 66 clinical features from the electronic medical records and employed them to train five machine learning prediction models (artificial neural network [ANN], adaptive boosting [AdaBoost], k-nearest neighbour [KNN] and support vector machine [SVM], decision tree [DT]). The models were tested using 20% of the data. The performance of the models was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC). The best predictive models were interpreted using the shapley additive explanation (SHAP).

Result

The ANN models have better predictive power. According to the SHAP interpretation of the ANN model, hypoproteinemia and cancer were the most important predictive features of cytopenia in HIV hospitalized patients. Meanwhile, the lower hemoglobin-to-RDW ratio (HGB/RDW), low-density lipoprotein cholesterol (LDL-C) levels, CD4+ T cell counts, and creatinine clearance (Ccr) levels increase the risk of cytopenia in HIV hospitalized patients.

Conclusion

The present study constructed a risk prediction model for cytopenia in HIV patients during hospitalization with machine learning and electronic medical record information. The prediction model is important for the rational management of HIV hospitalized patients and the personalized treatment plan setting.
A comparative study on patient safety culture among high-risk hospital staff in the context of the COVID-19 and non-COVID-19 pandemic: a cross-sectional study in Taiwan
Authors: Huang Chih-Hsuan , Wu Hsin-Hung , Lee Yii-Ching
Published: July 2023

The study aimed to compare the evolution of patient safety culture perceived by high-risk hospital staff in the context of the COVID-19 pandemic and non-COVID-19 pandemic and to examine the variations in patient safety culture across demographic variables. The study found that the COVID-19 pandemic has significantly impacted patient safety culture in healthcare settings, with an increased focus on safety climate, job satisfaction, teamwork climate, stress recognition, and emotional exhaustion. Safety culture and work stress vary among medical professionals of different age groups. To reduce stress, workload should be minimized, work efficiency improved, and physical and mental health promoted. Strengthening safety culture can reduce work-related stress, improve job satisfaction, and increase dedication towards work. The study recommends interventions such as psychological and social support, along with emotional management training, to reduce emotional exhaustion. Healthcare institutions can set up psychological counseling hotlines or support groups to help medical professionals reduce stress and emotional burden.
Wakakosha “You are Worth it”: reported impact of a community-based, peer-led HIV self-stigma intervention to improve self-worth and wellbeing among young people living with HIV in Zimbabwe
Authors: Ferris France Nadine , Byrne Elaine , Nyamwanza Owen , Munatsi Vongai , Willis Nicola , Conroy Ronan , Vumbunu Sylivia , Chinembiri Moud , Maedziso Samantha , Katsande Munyaradzi A. , Dongo Takudzwa A. , Crehan Eimear , Mavhu Webster
Published: July 2023

Introduction

Self-stigma—negative self-judgements or core beliefs—can result in feelings of shame, worthlessness and self-blame, and impacts social interaction, mental health and health service utilization among people living with HIV (PLHIV). Few interventions target self-stigma among PLHIV and, to our knowledge, none until now for adolescents and young people LHIV (AYPLHIV) in sub-Saharan Africa. We present qualitative findings on the perceived impact of a self-stigma intervention (Wakakosha, “You are Worth it”).

Methods

The Wakakosha intervention adopted inquiry-based stress reduction (IBSR) at its core—a unique way of identifying and questioning deeply rooted self-stigma, combined with mindfulness, meditation and creativity. The intervention consisted of 16 × 3 hour group sessions. Supporting the intervention was a 156-page activity journal. We utilized a qualitative enquiry to explore the perceived impact of the intervention at various time points between November 2021 and November 2022, with 62 participants (n = 32 female). Discussions explored experiences of being involved in the intervention and any reports on changes in relation to self-stigma and shame. Additionally, we reviewed intervention documentation and creative elements. A thematic analysis guided generation of themes across all data sources.

Results

Both intervention participants and coaches described the transformative effect of the intervention, detailing their experiences before and after. Main themes that emerged were positive changes around: self-confidence, self-agency, sense of purpose/meaning, body positivity, improved communication and personal/family relationships and, forgiveness. The intervention also transferred a set of practical skills on self-inquiry, mindfulness, meditation and creativity that continued to be used in participants’ daily lives.

Conclusion

The Wakakosha intervention, using IBSR supported by music, creativity, writing and mindfulness techniques, showed potential for reducing self-stigma and improving self-worth among AYPLHIV. It also transferred practical skills to intervention participants and peer coaches, building their capacity to support others and deal with life challenges beyond HIV. The next phase is to continue supporting the young people to ensure fidelity as the peer coaches deliver the intervention to others. Study results indicate that culturally and practically, interventions to reduce self-stigma and/or improve self-worth operate at various levels and need to be designed and assessed at each level.
Applying RE-AIM to evaluations of Veterans Health Administration Enterprise-Wide Initiatives: lessons learned
Authors: Kenney Rachael R. , Klocko Robert P. , Manheim Chelsea E. , Mog Ashley C. , Young Jessica P.
Published: July 2023

Introduction

The United States Veterans Health Administration (VHA) Office of Rural Health funds Enterprise-Wide Initiatives (system-wide initiatives) to spread promising practices to rural Veterans. The Office requires that evaluations of Enterprise-Wide Initiatives use the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. This presents a unique opportunity to understand the experience of using RE-AIM across a series of evaluations. The authors conducted a study to document the benefits and pitfalls of using RE-AIM, capture the variety of ways that the team captured the elements of RE-AIM, and develop recommendations for the future use of RE-AIM in evaluation.

Materials and methods

The authors first conducted a document review to capture pre-existing information about how RE-AIM was used. They subsequently facilitated two focus groups to gather more detailed information from team members who had used RE-AIM. Finally, they used member-checking throughout the writing process to ensure accurate data representation and interpretation and to gather additional feedback.

Results

Four themes emerged from the document review, focus groups, and member checking. RE-AIM: provides parameters and controls the evaluation scope, “buckets” are logical, plays well with other frameworks, and can foster collaboration or silo within a team. Challenges and attributes for each RE-AIM dimension were also described.

Discussion

Overall, participants reported both strengths and challenges to using RE-AIM as an evaluation framework. The overarching theme around the challenges with RE-AIM dimensions was the importance of context. Many of these benefits and challenges of using RE-AIM may not be unique to RE-AIM and would likely occur when using any prescribed framework. The participants reported on the RE-AIM domains in a variety of ways in their evaluation reports and were not always able capture data as originally planned. Recommendations included: start with an evaluation framework (or frameworks) and revisit it throughout the evaluation, consider applying RE-AIM PRISM (Practical Robust Implementation Framework) to gain a broader perspective, and intentionally integrate quantitative and qualitative team members, regardless of the framework used.
Encountering grasslands: a collective approach to urban biodiversity
Authors: Walsh Chloe , Allan Penny
Published: July 2023

While the prolific nature of many grass and herbaceous species in urban parks offers an opportunity to cultivate more biodiverse and dynamic grasslands, widespread maintenance practices and complex cultural, economic, and bureaucratic forces often result in the undervaluing and regular destruction of these plant species. The research described in this paper reimagines the way grassy landscapes are cared for and understood in urban environments. Located in an urban park in inner Sydney, Australia, and using design research methods of observation, physical care, storytelling and installation, the research proposes three “frames of care” to assist landscape architects and other spatial designers to engage with communities at a local level. The frames have the potential to expand collective understandings of grassland communities, test alternative maintenance practices, and better support urban biodiversity and seasonal flux. With acknowledgment to the complexities of urban sites such as these, experimental installation provided a promising space to meaningfully engage with the local community and build a foundation to generate greater reciprocity between humans and non-humans of the site.
Case report: The intrauterine suture surgery—an original method in the treatment of old uterine false passage
Authors: Song Xiaolei , Na Re , Peng Nianghai , Cao Wenming , Ke Yan , Liu Ping , Chen Chunlin
Published: July 2023

Objective

To introduce an effective approach using the hysteroscopy system for patients with old uterine false passage after a failed conservative treatment.

Materials and methods

This study presents the case of a 34-year-old woman who was treated in the Department of Gynecology of Shenzhen Integrated Traditional Chinese and Western Medicine Hospital in 2018 with the complaint of “menstrual volumereduction for 2 years after abortion.” A hysteroscopy was performed to make a clear diagnosis: (1) uterine cavity adhesion and (2) old uterine false passage. After the separation of adhesions, the patient was treated with estradiol and progesterone in sequence (estradiol valerate 3 mg, b.i.d., oral for 21 days; and dydrogesterone tablets 10 mg, b.i.d., oral for the second half of the cycle) for 3 months. After the review of the hysteroscopy results, it was found that there was no improvement in the old false passage; therefore, a suture and knotting surgery under hysteroscopy was performed to treat the old false passage in the uterus within 10 min, and the intraoperative blood loss was 2 ml. The patient was discharged 24 h postoperatively without any adverse perioperative complications.

Results

Two months after the operation, the review of the hysteroscopy results showed that the old false passage in the uterus disappeared. After the 6-month follow-up, the menstrual volume increased compared with the previous one, close to the normal menstrual volume, and the patient experienced no pain and menstrual discomfort. The patient was lost to follow-up and was contacted again in 2022. It was found out that in 2019, she was pregnant with a baby boy who is now 3 years old and healthy.

Conclusion

The intrauterine suture surgery presents a clear visual ﬁeld to old uterine false passage after a failed conservative treatment. In patients with old uterine false passage suffering from reduced fertility, the intrauterine suture surgery can be a minimally invasive and effective alternative if the conservative treatment for old uterine false passage failed.
Conjoint analysis of transcriptome and metabolome profiles of normal captivity and arch soil free-range in Meishan pigs
Authors: Liu Ying , Su Yanlong , Zhou Zhijie , Zhu Jie , Zhu Qianqian , Xie Peng , Qian Shiquan , Wang Liwei , Qin Tong , Zhou Gang
Published: July 2023

The hygiene hypothesis has been advanced as a potential explanation for the increasingly high levels of atopy and allergic disease in the general human population. In an effort to conduct a more detailed study of the link between immune activity and the hygiene hypothesis, Meishan pigs raised under normal captivity (NC) or arch soil free-range (ASF) conditions were selected as an experimental model system. Cytokine levels were found to differ significantly between these two groups consistent with a difference in cellular immune status. Integrated transcriptomic and metabolomic profiling of duodenal tissue samples from Meishan pigs were then performed, leading to the identification of differentially expressed genes (DEGs), differentially abundant metabolites (DAMs), and key pathways that were able to distinguish the NC and ASF groups. This approach led to the identification of 1,113 DEGs, as well as 577 and 372 DAMs in positive and negative ion modes, respectively. When an interaction network incorporating DEGs and metabolites associated with immune responsivity was constructed, it included factors such as 9-cis-Retinoic acid, (9Z,11E)-(13S)-13-Hydroxyoctadeca-9,11-dienoic acid and (10E,12Z)-(9S)-9-Hydroxyoctadeca-10,12-dienoic acid. Functional enrichment analyses confirmed that identified DEGs and DAMs were associated with immune-related pathways including the intestinal IgA production and PPAR signaling pathways. Together, these results offer new insight into the roles that particular genes and metabolites enriched in response to environmental stressors in free-range Meishan pigs may play in the regulation of cellular immunity, thus offering a foundation for future efforts to better understand the immunological mechanisms underlying the hygiene hypothesis.
The phylogenetic analysis of the new emerging ALV-K revealing the co-prevailing of multiple clades in chickens and a proposal for the classification of ALV-K
Authors: Guo Jinhan , Deng Qiaomu , Zhu Weiyu , Fu Fumei , Liu Linmin , Wei Tianchao , Wei Ping
Published: July 2023

Subgroup K avian leukosis virus (ALV-K) is a new subgroup of avian leukosis virus (ALV) that was first defined in 2012 and has been become prevalent in Chinese native chickens in recent years. An in-depth analysis of the genetic diversity of ALV-K was performed in the study. By Blast analysis, the env gene and the sequences of the 25 ALV-K isolates we isolated were found to be closely related to the isolates from Guangdong, Hebei, Jiangsu, and Hubei provinces, China. Further eighty-nine sequences of the gp85 gene of ALV-K strains available were used in the phylogenetic and genetic distance analyses for the classification. ALV-K was divided into two second-order clades (Clades 1.1 and 1.2) and three third-order clades (Clades 1.2.1, 1.2.2, and 1.2.3), indicating that not only 1.1 and 1.2.3, the two old clades which are prevalent in Japan, but also two new clades (1.2.1, 1.2.2), are co-prevalent in China. The representative strains of each clade were defined for the first time. Notably, Clade 1.2.2 was found to have a deletion of an amino acid residue in the gp85 gene, which was obviously different from Clades 1.1, 1.2.1, and 1.2.3. The proposed classification method will facilitate future studies of ALV-K epidemiology and the comparison of sequences obtained across the world. The first global comprehensive molecular epidemiological analysis was accomplished on the emerging ALV-K.
Hybrid Approach to the Cost Estimation of External-Fleet Full Truckload Contracts
Authors: Cyperski Szymon , Domański Paweł D. , Okulewicz Michał
Published: July 2023

Freight forwarding and transportation are the backbone of the modern economy. There are thousands of transportation companies on the market whose sole purpose is to deliver ordered goods from pickup to delivery. Transportation can be carried out by two types of fleets. A company can have its own trucks, or it can use third-party companies. This transportation can be carried out in a variety of formulas, with full truckload being the most common for long routes. The shipper must be aware of the potential cost of such a service during the process of selecting a particular transport. The presented solution addresses this exact issue. There are many approaches, ranging from detailed cost calculators to machine learning solutions. The present study uses a dedicated hybrid algorithm that combines different techniques, spanning clustering algorithms, regression and kNN (k Nearest Neighbors) estimators. The resulting solution was tested on real shipping data covering multi-year contract data from several shipping companies operating in the European market. The obtained results proved so successful that they were implemented in a commercial solution used by freight forwarding companies on a daily basis.
Deep-Reinforcement-Learning-Based Planner for City Tours for Cruise Passengers
Authors: Di Napoli Claudia , Paragliola Giovanni , Ribino Patrizia , Serino Luca
Published: July 2023

The increasing popularity of cruise tourism has led to the need for effective planning and management strategies to enhance the city tour experience for cruise passengers. This paper presents a deep reinforcement learning (DRL)-based planner specifically designed to optimize city tours for cruise passengers. By leveraging the power of DRL, the proposed planner aims to maximize the number of visited attractions while considering constraints such as time availability, attraction capacities, and travel distances. The planner offers an intelligent and personalized approach to city tour planning, enhancing the overall satisfaction of cruise passengers and minimizing the negative impacts on the city’s infrastructure. An experimental evaluation was conducted considering Naples’s fourteen most attractive points of interest. Results show that, with 30 state variables and more than 19∗1012 possible states to be explored, the DRL-based planner converges to an optimal solution after only 20,000 learning steps.
High-Performance and Lightweight AI Model for Robot Vacuum Cleaners with Low Bitwidth Strong Non-Uniform Quantization
Authors: Huang Qian , Tang Zhimin
Published: July 2023

Artificial intelligence (AI) plays a critical role in the operation of robot vacuum cleaners, enabling them to intelligently navigate to clean and avoid indoor obstacles. Due to limited computational resources, manufacturers must balance performance and cost. This necessitates the development of lightweight AI models that can achieve high performance. Traditional uniform weight quantization assigns the same number of levels to all weights, regardless of their distribution or importance. Consequently, this lack of adaptability may lead to sub-optimal quantization results, as the quantization levels do not align with the statistical properties of the weights. To address this challenge, in this work, we propose a new technique called low bitwidth strong non-uniform quantization, which largely reduces the memory footprint of AI models while maintaining high accuracy. Our proposed non-uniform quantization method, as opposed to traditional uniform quantization, aims to align with the actual weight distribution of well-trained neural network models. The proposed quantization scheme builds upon the observation of weight distribution characteristics in AI models and aims to leverage this knowledge to enhance the efficiency of neural network implementations. Additionally, we adjust the input image size to reduce the computational and memory demands of AI models. The goal is to identify an appropriate image size and its corresponding AI models that can be used in resource-constrained robot vacuum cleaners while still achieving acceptable accuracy on the object classification task. Experimental results indicate that when compared to the state-of-the-art AI models in the literature, the proposed AI model achieves a 2-fold decrease in memory usage from 15.51 MB down to 7.68 MB while maintaining the same accuracy of around 93%. In addition, the proposed non-uniform quantization model reduces memory usage by 20 times (from 15.51 MB down to 0.78 MB) with a slight accuracy drop of 3.11% (the classification accuracy is still above 90%). Thus, our proposed high-performance and lightweight AI model strikes an excellent balance between model complexity, classification accuracy, and computational resources for robot vacuum cleaners.
Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
Authors: Zhang Chuanlei , Shi Lei , Zhang Dandan , Ke Ting , Li Jianrong
Published: July 2023

The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.
Passivity Analysis and Complete Synchronization of Fractional Order for Both Delayed and Non-Delayed Complex Dynamical Networks with Couplings in the Derivative
Authors: Aadhithiyan S. , Raja R. , Alzabut Jehad , Rajchakit G. , Agarwal Ravi P.
Published: July 2023

This manuscript explores the analysis of passivity and synchronization criteria for a complex fractional-order dynamical network model with derivative couplings and time-varying delays. The passivity problem of the proposed network model is deduced using various inequality methods and presented as a linear matrix inequality. To ensure complete synchronization for a fractional-order complex dynamical network with derivative couplings (CDNMDC), we derive suitable criteria using an adaptive feedback control method. Additionally, we investigate the synchronization criterion of these complex networks while accounting for parameter uncertainties. Finally, we provide an example to demonstrate the effectiveness of the proposed solutions.
Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data
Authors: Dousti Mousavi Niloufar , Aldirawi Hani , Yang Jie
Published: July 2023

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios, including variable screening, model selection, order selection for response categories, and variable selection. We perform our procedure on high-dimensional gene expression data with 801 patients, 2426 genes, and five types of cancerous tumors. As a result, we recommend three finalized models: one with 74 genes achieves extremely low cross-entropy loss and zero predictive error rate based on a five-fold cross-validation; and two other models with 31 and 4 genes, respectively, are recommended for prognostic multi-gene signatures.
Hydraulic Cold-Pressed Extraction of Sacha Inchi Seeds: Oil Yield and Its Physicochemical Properties
Authors: Kong Sela , Keang Tongor , Bunthan Monyneath , Say Manit , Nat Yukleav , Tan Chin Ping , Tan Reasmey
Published: July 2023

Sacha inchi oil (SIO) extraction has been extensively studied using various oil extraction techniques to achieve a high oil recovery. However, most studies relied on heat-based methods, which led to compromised oil quality and reduced nutritional values, particularly polyunsaturated fatty acids (omega-3 and omega-6), vitamin E, and phenolic compounds. To address these concerns, this study employed a hydraulic cold-pressed extraction (HCPE) technique for extracting SIO aiming to enhance oil yield while preserving its nutritional integrity. During the HCPE process of sacha inchi seeds (SIS), conducted at a constant temperature of 25 ± 1 °C, pressures and pressing times were varied within the range of 30–50 MPa and 10–30 min, respectively, to determine their impact on SIO yields. The results revealed that both pressure and pressing time significantly influenced the yields of SIO (p < 0.05), with the highest oil recovery of 86.31 wt.% on a wet basis achieved at 50 MPa for 30 min. Regarding physicochemical properties, the peroxide values (5.71–9.07 meq/kg), iodine values (176.22–197.76 g I2/100 g), acid values (1.82–2.16 mg KOH/g), and percentage of free fatty acids (0.91–1.08 wt.% as oleic acid) were found to be influenced by pressure and pressing time (p < 0.05). Additionally, the color variation by L* (34.22–35.17), −a* (0.39–0.81), and b* (3.48–5.62) changed with each oil yield. Notably, the high iodine value in SIO indicated a substantial content of polyunsaturated fatty acids, including omega-3 (40.86%), omega-6 (40.87%), and omega-9 (10.20%). Furthermore, a comparison with solvent extraction methods demonstrated that HCPE exhibited similar efficiency in extracting SIO, offering additional advantage in terms of its cold-pressed condition, eliminating of solvent use, simplicity, short extraction time, and higher oil recovery.
Child Nutrition Outcomes and Maternal Nutrition-Related Knowledge in Rural Localities of Mbombela, South Africa
Authors: Masilela Lucy Nomsa , Modjadji Perpetua
Published: July 2023

Poor nutrition outcomes among children have become one of the major public health concerns in South Africa, attributed to poor feeding practices and maternal nutrition-related knowledge with conflicting data. In view of this, a cross-sectional study was conducted to determine the association of nutrition outcomes of children aged under two years with feeding practices and maternal nutrition-related knowledge in Mbombela, South Africa. Mothers’ nutrition-related knowledge was estimated using an adapted structured questionnaire on colostrum, continued breastfeeding, diarrhea prevention and treatment using oral rehydration solution, immunization, and family planning, and scored as excellent (80–100), good (60–79), average (40–59), and fair (0–39). This was along with questions on socio-demographic factors and obstetric history, as well as anthropometric measurements. Child nutrition outcomes were estimated by WHO classification using z-scores for stunting (length-for-age (LAZ)), underweight (weight-for-age (WAZ)), and thinness (body mass index-for-age (BAZ)). Using STATA 17, 400 pairs of children (8 ± 6 months) and their mothers (29 ± 6 years) participated in the study and were living in a poor socio-economic status environment. Half of children were stunted (50%) and over half (54%) were obese, while mothers were underweight (39%) and overweight (34%). In addition to one third of mothers reporting obstetric complications, two thirds, initiated breastfeeding within one hour of delivery, 30% exclusively breastfed, 48% introduced early complementary feeding, and 70% practiced mixed feeding. Twenty-eight percent (28%) of mothers had fair nutrition-related knowledge, while 66% had average knowledge, 6% good knowledge, and none of the mothers had excellent knowledge. A chi-square test showed that mothers’ nutrition-related knowledge was significantly associated with child stunting. The final hierarchical logistic regression showed significant associations of stunting with mothers’ nutrition-related knowledge (average: AOR = 1.92, 95%CI: 1.12–3.29), child’s age (6–11 months: AOR = 2.63, 95%CI: 1.53–4.53 and 12–23 months: AOR = 3.19, 95%CI: 1.41–7.25), and education (completing Grade 12: AOR = 0.36, 95%CI: 0.15–0.86). Contextual and intensified interventions on continued education for mothers to gain accurate information on nutrition-related knowledge and feeding practices could ultimately enhance child nutrition outcomes in poorer settings. Efforts should therefore be made to ensure that nutrition knowledge is appropriately provided based on the phases of child growth from 0 to 2 years, even beyond infancy into school age.
Cross-Cultural Participation in Food-Related Activities and Quality of Life among Children with Celiac Disease
Authors: Meyer Sonya , Monachesi Chiara , Barchetti Mara , Lionetti Elena , Catassi Carlo
Published: July 2023

Children with celiac disease may face challenges in managing a gluten-free diet during their daily interactions and activities. The objective of this study was to compare how children with celiac disease manage their gluten-free diet and participate in food-related activities in Italy and Israel and to assess their quality of life. The previously validated Children’s Activities Report (CD-Chart) and the Disease-specific Health-Related Quality of Life Questionnaire for Children with Celiac Disease (CDDUX) were administered in Italy to children aged 8–16 diagnosed with CD (n = 39). The results were compared to data that had been previously gathered from Israeli children with CD (n = 106). The CD-Chart demonstrated satisfactory internal reliability within each cultural group (Italy: α = 0.82; Israel: α = 0.76). Mann–Whitney U-tests indicated significant differences between the two groups. The Italian children exhibited a significantly higher preference for participating in the activities compared to the Israelis (U = 3283.50, p < 0.001). Nonetheless, the Italian children displayed a notable decrease in their level of involvement in the preparation required before engaging in different activities (U = 760.50, p < 0.001). Moreover, they exhibited significantly lower self-determination in this preparatory process compared to the Israeli children (U = 726.00, p < 0.001). Significant group differences were found between the CDDUX children’s self-reports and parents’ proxy reports in the Israeli group but not in the Italian group. The CD-Chart revealed both shared and distinct participation characteristics in daily food-related activities across different cultural contexts. By incorporating the CD-Chart and the CDDUX, healthcare professionals can emphasize crucial aspects of day-to-day health management and guide them in establishing suitable intervention objectives to enhance effective health self-management.
Ester Production Using the Lipid Composition of Coffee Ground Oil (Coffea arabica): A Theoretical Study of Eversa® Transform 2.0 Lipase as an Enzymatic Biocatalyst
Authors: Nobre Millena Mara Rabelo , Silva Ananias Freire da , Menezes Amanda Maria , Silva Francisco Lennon Barbosa da , Lima Iesa Matos , Colares Regilany Paulo , Souza Maria Cristiane Martins de , Marinho Emmanuel Silva , Melo Rafael Leandro Fernandes , Santos José Cleiton Sousa dos , da Fonseca Aluísio Marques
Published: July 2023

The scientific community recognizes coffee grounds (Coffea arabica) as an important biological residue, which led to using the Eversa® Transform 2.0 lipase as an in silico enzymatic catalyst for coffee grounds’ free fatty acids (FFA). Molecular modeling studies, including molecular docking, were performed, which revealed the structures of the lipase and showed the primary interactions between the ligands and the amino acid residues in the active site of the enzyme. Of the ligands tested, 6,9-methyl octadienoate had the best free energy of −6.1 kcal/mol, while methyl octadecenoate and methyl eicosanoate had energies of −5.7 kcal/mol. Molecular dynamics confirmed the stability of the bonds with low Root Mean Square Deviation (RMSD) values. The MMGBSA study showed that methyl octadecenoate had the best free energy estimate, and CASTp identified key active sites for potential enzyme immobilization in experimental studies. Overall, this study provides efficient and promising results for future experimental investigations, showing a classification of oils present in coffee grounds and their binding affinity with Eversa.
Update on Rapid-Eye-Movement Sleep Behavior Disorder (RBD): Focus on Its Strong Association with α-Synucleinopathies
Authors: Schenck Carlos H.
Published: July 2023

REM sleep behavior disorder (RBD) is a parasomnia in which the customary generalized skeletal muscle atonia of REM sleep, “REM-atonia”, is compromised, allowing for the injurious acting-out of dreams. RBD can be idiopathic/isolated (iRBD) or symptomatic of neurological disorders, and can be triggered by most antidepressants. RBD mainly affects middle-aged and older adults, and is strongly linked with alpha-synucleinopathies, mainly Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). iRBD is now known to be the earliest and strongest predictor of future PD/DLB, which has stimulated a major international clinical and basic science research effort to enroll iRBD patients for upcoming neuroprotective/disease-modifying trials and to identify the most promising interventions to test in these cohorts. This review will provide the latest pertinent information on the rapidly expanding field of RBD. The methods included a PubMed literature search that included PubCrawlers, which utilizes the NCBI (National Center for Biotechnology Information) E-utils tools for publication retrieval, using the keywords “REM sleep behavior disorder” and “RBD”. The results yielded the latest updates on iRBD as prodromal PD/DLB, with the most promising biomarkers for phenoconversion provided, along with a presentation of three clinical research consortiums that are systematically gathering patients in preparation for enrollment in upcoming clinical trials: (i) The International RBD Study Group; (ii) The North American Prodromal Synucleinopathy (NAPS and NAPS2) Consortium; and (iii) The FARPRESTO Italian multicenter RBD research consortium. In addition, updates on the Parasomnia Overlap Disorder (RBD + NREM parasomnia) and on narcolepsy-RBD are provided, along with new epidemiologic data, the latest RBD management guidelines, and updates on animal models of RBD. Emerging areas of critical RBD research are also highlighted. In conclusion, RBD is a notable example of clinical and translational neuroscience research.
Probing the Complexities of Actual and Perceived Levels of Human-Elephant Conflict in the Okavango, Botswana
Authors: Songhurst Anna
Published: July 2023

People’s perception of the conflict they are experiencing with wildlife is a major factor in determining the extent of human-elephant conflict (HEC) occurring and needs to be considered when devising HEC management strategies. To understand perceptions and attitudes, however, it is necessary to identify factors that influence them. This study used a combination of multivariate ordered probit and generalized linear models to probe the complexities of farmer perception and attitudes and reveal underlying influential factors that shape perceived conflict levels. The study shows that the perception of HEC is complicated, influenced by an array of factors, both directly and indirectly, many of which are socio-demographic characteristics of the farmer, rather than quantifiable indicators of actual conflict. It was found that perceived human-elephant conflict (HEC) in the Okavango is affected by the ethnicity of the farmer, the raiding history of a field, the distance of fields to elephant pathways, and a farmer’s feeling towards elephants. Yet, feelings towards elephants are affected by attitudes towards wildlife, which are influenced by the benefits communities receive from living near wildlife. The perceived conflict levels of respondents were higher than the predicted actual conflict levels. Such discrepancies could be interpreted as “people over-reporting the scale of the problem”, yet results indicate that the crop-raiding history of a farmer alone is not adequate to assess the overall level of conflict being experienced because other social and environmental factors are involved in determining farmer perception of conflict. A holistic approach is needed to address the complexities of perceived and actual conflicts to ultimately reduce HEC. This involves targeting mitigation strategies for specific groups of people, considering elephant pathways and field locations in land use planning, and finding ways to improve benefits accrued to the community through wildlife-related enterprises that bring benefits back from living with elephants.
Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning
Authors: Peng Kaixin , Shen Xiaoying , Gao Le , Wang Baocang , Lu Yichao
Published: July 2023

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users’ private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users’ information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users’ private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.
IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection
Authors: Zhang Kunpeng , Liu Yanheng , Mei Fang , Sun Geng , Jin Jingyi
Published: July 2023

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.
Acceleration of Approximate Matrix Multiplications on GPUs
Authors: Okuyama Takuya , Röhm André , Mihana Takatomo , Naruse Makoto
Published: July 2023

Matrix multiplication is important in various information-processing applications, including the computation of eigenvalues and eigenvectors, and in combinatorial optimization algorithms. Therefore, reducing the computation time of matrix products is essential to speed up scientific and practical calculations. Several approaches have been proposed to speed up this process, including GPUs, fast matrix multiplication libraries, custom hardware, and efficient approximate matrix multiplication (AMM) algorithms. However, research to date has yet to focus on accelerating AMMs for general matrices on GPUs, despite the potential of GPUs to perform fast and accurate matrix product calculations. In this paper, we propose a method for improving Monte Carlo AMMs. We also give an analytical solution for the optimal values of the hyperparameters in the proposed method. The proposed method improves the approximation of the matrix product without increasing the computation time compared to the conventional AMMs. It is also designed to work well with parallel operations on GPUs and can be incorporated into various algorithms. Finally, the proposed method is applied to a power method used for eigenvalue computation. We demonstrate that, on an NVIDIA A100 GPU, the computation time can be halved compared to the conventional power method using cuBLAS.
Wide-Area Measurement-Based Two-Level Control Design to Tolerate Permanent Communication Failures
Authors: Bento Murilo Eduardo Casteroba
Published: July 2023

The operation of modern power systems must meet stability requirements to guarantee the supply of electrical energy. One of these requirements is to ensure that the low-frequency oscillation modes have high damping ratios to avoid angular instability and future power system blackouts. Advances in phasor measurement units (PMUs) have contributed to the development and improvement of wide-area damping controllers (WADCs) capable of increasing the damping rates of the oscillation modes of the system, especially the inter-area modes. Nevertheless, the operation of WADCs is vulnerable to communication failures and cyber-attacks, and if not properly designed the WADC can affect the stability of the entire system. This research proposes a procedure for designing a WADC robust to permanent communication failures using a linear quadratic regulator (LQR) and genetic algorithms. Case studies conducted on an IEEE 68-bus test power system show the effectiveness of the WADC designed by the proposed procedure even when communication failures are occurring in the system. The use of genetic algorithms improves the convergence and results of the LQR-based method.
Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group
Authors: Ma Dengchang , Xie Rongyi , Pan Guobing , Zuo Zongxu , Chu Lidong , Ouyang Jing
Published: July 2023

With the increasing proportion of distributed photovoltaic (DPV) installations in county-level power grids, to improve the centralized operation and maintenance of the stations and to meet the needs of power grid dispatching, the output of the county-level regional DPV stations group needs to be predicted. In this paper, the weather prediction information is used to predict the output based on the model input average strategy. To eliminate the effect of the selected non-optimal training sample collection period on the prediction accuracy, an ensemble prediction method based on the minimum redundancy maximum relevance criterion and TabNet model is carried out. To reduce the influence of weather prediction errors on the power output prediction, a modified model based on error prediction is proposed. The ensemble prediction model is used to predict the day-ahead output, and a combination prediction model based on the proposed ensemble prediction model and the proposed modified model is established to predict the hour-ahead output. The experimental results verify the effectiveness of the proposed models. Compared with the corresponding reference models, the proposed ensemble prediction method reduces the normalized mean absolute errors (nMAEs) and the normalized root mean square errors (nRMSEs) of the day-ahead output prediction results by 2.86% and 5.51%, respectively. The combination prediction model reduces the nMAE and nRMSE of the hour-ahead output prediction results by 3.05% and 3.05%, respectively. Therefore, the prediction accuracy can be improved by the proposed models.
A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction
Authors: Fu Xinyue , Feng Zhongkai , Yao Xinru , Liu Wenjie
Published: July 2023

Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.