bluuebunny/crossref_metadata_2025 huggingface dataset:
Mental Stress Classification based on Selected EEG Channels Using Correlation Coefficient of Hjorth Parameters
Authors: Hag Ala , Al-Shargie Fares , Handayani Dini , Asadi Houshyar
Published: July 2023

Electroencephalography (EEG) signals provide valuable insights into various activities of the human brain, including the detection of mental stress, which is a complex physiological and psychological response. However, the challenge lies in identifying mental stress accurately while mitigating the limitations associated with a large number of EEG channels. This includes issues such as computational complexity, the risk of overfitting, and the increased setup time for electrode placement, which can be cumbersome for real-life applications. Therefore, it is crucial to develop EEG channel selection algorithms that enable the creation of a wearable device capable of assessing mental stress in real-life scenarios. This study introduces a novel channel selection method aimed at identifying highly accurate channels for detecting mental stress. Our approach, known as the Correlation Coefficient of Hjorth Parameters (CCHP), assesses the correlation between activity, mobility, and complexity in the time domain to nominate the most relevant channels. By selecting channels that exhibit high correlation with the stress task while being uncorrelated with each other, CCHP significantly reduces the number of EEG channels required, without compromising accuracy or performance. To evaluate the effectiveness of CCHP, we conducted experiments using the DEAP public dataset. Comparing our results with other recent algorithms that utilize the full set of EEG channels, CCHP achieved a superior classification accuracy of 81.56% using only eight EEG channels. Furthermore, CCHP outperformed existing channel selection methods by an impressive 8%. These findings strongly indicate that the CCHP algorithm shows great promise in the design of a wearable application for mental stress detection, utilizing a minimal number of EEG channels.
Cold Dispase Digestion of Murine Lungs Improves Recovery and Culture of Airway Epithelial Cells
Authors: Janas Piotr P. , Chauché Caroline , Shearer Patrick , Perona-Wright Georgia , McSorley Henry J. , Schwarze Jürgen
Published: July 2023

Airway epithelial cells (AECs) play a key role in maintaining lung homeostasis, epithelium regeneration and the initiation of pulmonary immune responses. To isolate and study murine AECs investigators have classically used short and hot (1h 37°C) digestion protocols. Here, we present a workflow for efficient AECs isolation and culture, utilizing long and cold (20h 4°C) dispase II digestion of murine lungs. This protocol yields a greater number of viable AECs compared to an established 1h 37°C dispase II digestion. Using a combination of flow cytometry and immunofluorescent microscopy, we demonstrate that compared to the established method, the cold digestion allows for recovery of a 3-fold higher number of CD45-CD31-EpCAM+ cells from murine lungs. Their viability is increased compared to established protocols, they can be isolated in larger numbers by magnetic-activated cell sorting (MACS), and they result in greater numbers of KRT5+p63+ colonies in vitro. Our findings demonstrate that temperature and duration of murine lung enzymatic digestion have a considerable impact on AEC yield, viability, and ability to form colonies in vitro. We believe this workflow will be helpful for studying lung AECs and their role in the biology of lung.
The Utility of Comprehensive NGS Analysis in Homologous Recombination Deficiency Tracking
Authors: Tsantikidi Aikaterini , Papadopoulou Eirini , Tsaousis Georgios , Metaxa-Mariatou Vasiliki , Kapetsis George , Florou-Chatzigiannidou Chrysiida , Meintani Angeliki , Gazouli Maria , Papadimitriou Christos , Timotheadou Eleni , Kotsakis Athanasios , Boutis Anastasios , Boukovinas Ioannis , Kampletsas Eleftherios , Kontovinis Loukas , Fountzilas Elena , Andreadis Charalampos , Karanikiotis Charisios , Philippou Dimitrios , Theodoropoulos Georgios , Özdoğan Mustafa , Nasioulas George
Published: July 2023

Several tumor types have been efficiently treated with PARPis, which are now approved for the treatment of ovarian, breast, prostate, and pancreatic cancer. The BRCA1/2 genes and mutations in many additional genes involved in the HR pathway may be responsible for the HRD phenomenon. In the present study, an NGS assay was used to analyze 513 genes associated with targeted and immuno-oncology therapies in 406 samples. In addition, the %gLOH values of 24 samples were also calculated using the Affymetrix technology to compare the results obtained by the two methodologies. HR variations occurred in 20.93% of the malignancies, while BRCA1/2 gene alterations in 5.17%. %LOH was highly correlated with alterations in the BRCA1/2 genes since 76.19% (16/21) of the BRCA1/2 positive tumors had a high %LOH value (p=0.007). Moreover, the LOH status was highly correlated with the TP53 and KRAS statuses, but there was no association with the TMB value. Lin's Concordance Correlation Coefficient for the 24 evaluable samples simultaneously examined by both assays was 0.87, indicating a nearly perfect agreement. In conclusion, the addition of gLOH analysis could assist the detection of additional patients eligible for PARPis
Achievable Minimally Contrastive Counterfactual Explanations
Authors: Barzekar Hosein , McRoy Susan
Published: July 2023

Decision support systems based on machine learning models should be able to help users identify opportunities and threats. Popular model-agnostic explanation models can identify factors that support various predictions, answering questions like “What factors affect sales?” or “Why did sales decline?”, but do not highlight what a person should or could do to get a more desirable outcome. Counterfactual explanation approaches address intervention, and some even consider feasibility, but there has been no evaluation of their suitability for real-time applications, such as question answering. Here we address this gap by introducing a novel model-agnostic method that provides specific, feasible changes that would impact the outcomes of a complex black-box AI model for a given instance and assess its real-world utility by measuring its real-time performance and ability to find achievable changes. The method uses the instance of concern to generate high-precision explanations and then applies a secondary method to find minimally contrastive and maximally probable high-precision counterfactual explanations, while limiting contrasted features to changes that are achievable. We demonstrate that using this method, it is possible to find such explanations quickly enough for use in real-time systems. High-precision achievable minimally contrastive explanations would be useful in applications where people seek remedial actions or question how effective a proposed remedy is likely to be.
Conservation Laws and Symmetry Reductions of the Hunter-Saxton Equation via the Double Reduction Method
Authors: Kakuli Molahlehi Charles , Sinkala Winter , Masemola Phetogo
Published: July 2023

This study investigates via Lie symmetry analysis the Hunter-Saxton equation, an equation relevant to the theoretical analysis of nematic liquid crystals. We employ the multiplier method to obtain conservation laws of the equation derived from first-order multipliers. Conservation laws of the Hunter-Saxton equation, in combination with the Lie point symmetries of the equation, enable us to perform symmetry reductions through employment of the double reduction method. The method exploits the relationship between Lie point symmetries and conservation laws to effectively reduce both the number of variables and the order of the equation. Five nontrivial conservation laws of the Hunter-Saxton equation are derived, four of which are found to have associated Lie point symmetries. Applying the double reduction method to the Hunter-Saxton equation results in a set of first-order ordinary differential equations, the solutions of which represent invariant solutions for the equation.
The Catalytic Upgrading Performance of NiSO4 and FeSO4 in Case of Ashalcha Heavy Oil Reservoir
Authors: Abdelsalam Yasser I. I. , Aliev Firdavs , Mirzayev Oybek , Galiakhmetova Lilia , Davletshin Rustam , Dengaev Aleksey , Vakhin Alexey
Published: July 2023

Aquathermolysis is a promising method to upgrade heavy oil in reservoir conditions. However, application of catalysts can significantly promote transformation of the heavy fragments (resins and asphaltenes) and heteroatom-containing compounds of crude oil mixture into low-molecular-weight hydrocarbons. In this paper, the comparison study of the catalytic performance of the water-soluble metal salts such as NiSO4 and FeSO4 on the aquathermolytic upgrading of heavy oil samples produced from Ashal’cha reservoir was carried out at a temperature of 300 °C for 24 hours. Iron nanoparticles contributed to the highest viscosity reduction degree - 60% in contrast to the viscosity of the native crude oil sample. The viscosity alteration is reasoned by the changes in the group composition of heavy oil after catalytic (FeSO4) aquathermolysis, where the content of resins and asphaltenes were reduced by 17% and 7%, respectively. Moreover, the aquathermolytic upgrading of heavy oil in the presence of FeSO4 led to an increase in the yield of gasoline fraction by 13% and diesel fraction by 53%. The H/C ratio, which represents the hydrogenation of crude oil, increased from 1.52 (before catalytic upgrading) to 1.99 (after catalytic upgrading). The results of Chromatomass (GC-MS) and Fourier-infrared (FT-IR) spectroscopies confirmed the intensification of destructive hydrogenation of resins and asphaltenes in the presence of the water-soluble catalysts. According to XRD and SEM-EDX results, the metal salts are thermally decomposed during the aquathermolysis process into the oxides of corresponding metals and particularly sulfided by the sulfur-containing aquathermolysis products.
Integrated Transcriptome Analysis Identified Key Expansin Genes Associated with Wheat Cell Wall, GrainWeight and Yield
Authors: Mira Juan P. , Arenas-M Anita , Calderini Daniel F. , Canales Javier
Published: July 2023

This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets, and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between expression of select expansin genes and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.
Validation of an Automated Optical Scanner for a Comprehensive Anthropometric Analysis of the Foot and Ankle
Authors: Sacco Riccardo , Munoz Marie-Aude , Lalevée Matthieu , Billuart Fabien , Beldame Julien
Published: July 2023

Background: Our objective was to conduct a comprehensive analysis of the reproducibility of foot and ankle anthropometric measurements with a three-dimensional (3D) optical scanner. Methods: We evaluated thirty-nine different anthropometric parameters obtained with 3D Laser UPOD-S Full-Foot Scanner in a healthy population of twenty subjects. We determined the variance of the measurements for each foot/ankle, and the average variance among different subjects. Results: For 40 feet and ankles (15 women and 5 men; mean age 35.62 +/- 9.54 years, range 9-75 years), the average variance was 1.4 ± 2 (range 0,1 to 8). Overall, the mean absolute measurement error was < 1 mm, with a maximum variance percentage of 8.3%. Forefoot and midfoot circumferences had a low variance < 2.5, with variance percentages < 1%. Hindfoot circumferences, malleolar heights, and the length of the first and fifth metatarsal to the ground contact points showed the highest variance (range 1 to 7). Conclusion: This optical scanner achieved a good reproducibility of a large set of foot and ankle anthropometric measurements. It is a valuable tool for clinical and research purposes.
THE ANALYSIS OF LANGUAGE STYLE USED IN MERRY RIANA’S YOUTUBE CHANNEL ON THE PLAYLIST ‘SPOKEN WORD’
Authors: Br Sitepu Ester Febrina , Zahara Ayuna , Cahyani Putri Ardhia Amalya , Rangkuti Rahmadsyah
Published: July 2023

The aim of this study was to find out the types of language styles and identify the dominant style of language used by Merry Riana in her selected motivational speech videos as a motivator.  The data of this study were taken from Merry Riana’s YouTube Channel and the researchers chose 3 motivational speech videos in the "SPOKEN WORD" playlist. In analyzing the data, the researchers used descriptive qualitative method. The researchers used the theory proposed by Martin Joss (1967) to identify the types of language styles. According to Martin Joss (1967), there are five language styles: frozen style, formal style, casual style, consultative style, and intimate style. The researchers found that in Merry Riana’s motivational speech used three language styles from five language styles proposed by Martin Joss. There were 64 formal styles, 42 casual styles, and 4 intimate styles from the 117 sentences uttered by the speaker in the selected video. The most dominant style of language found in this study was the formal style.
THE IMPLEMENTATION OF LITERATURE IN EXTRACURRICULAR “STUDENT CONVERSATION CLUB”
Authors: Rizqi Hidayat M Yusril , Leonic Franchsicha Errend Marchella , Mala Rohmana Wahyu Indah
Published: July 2023

Literature is divided into two parts, namely fiction and non-fiction. Literature is not only studied within the scope of school, but there are those who study it outside the school class. One of them is extracurricular activities. The literature referred to in this extracurricular activity is fictional literature such as choirs, songs and plays. The results of previous studies state that literature such as music and poetry can improve the quality of a person's language, and not only improve language, literature can also provide space for us to express. The purpose and reason for the researcher to raise and examine this title is to provide an overview of how literature is applied in the extracurricular student conversation club and to find out what problems hinder the application of literature in the extracurricular. This research uses descriptive qualitative method. The objects of this research are coaches and extracurricular members of the 2022-2023 student conversation club with details of 1 coach and 3 members. The results of the study stated that the students' conversation club extracurricular activities had a good impact on improving students' language skills, especially in English.
Geomechanical Rock Properties from Surface Drilling Telemetry
Authors: Olkhovikov Aleksei , Koroteev Dmitry , Antipova Ksenia
Published: July 2023

We present a novel approach for real-time estimation of the mechanical properties of rock with drilling data. We demonstrate that surface drilling telemetry (also known as mud logging) can be used as an input for a trained machine learning (ML) algorithm to predict the properties of the rock being drilled at the moment. The study involves data from several real wells with horizontal completions. We use mud logging and logging while drilling (LWD) data from one part of the wells to train various ML models. The models are compared by various metrics using the five fold cross-validation technique. We also show the importance of proper feature selection for maximizing models’ performance in operation mode.
Case Report: a Severe Asthma Symptom With Biological Medication
Authors: Chang Sajjad Ali
Published: July 2023

Research from Sindh Pakistan showed that 15% of patients are in therapy phases, many based on data from the specialized literature. According to the Global Initiative for Asthma, around 9.8% of grownup females and 6.1% of the male population suffer from asthma. In this instance, a 31-year-old patient who has had bronchial asthma for more than ten years and who has never had occupational exposure to the condition is presenting with persistent dyspnea at rest, wheezing, and mechanical ventilation. This patient has also had multiple hospitalizations in the Pneumology Service for severe exacerbations. One of these exacerbations required hospitalization in the Anesthesia and Intensive Care Service, orotracheal intubation, and mechanical ventilation manifesting with chest tightness, wheezing, and persistent dyspnea when at rest and obstructive ventilatory dysfunction with a 37.57% decrease in maximum expiratory volume per second and 156% reversibility. No acute lesions on the X-ray pleuro-pulmonary: 13,550 leukocytes; 5566 eosinophils per milliliter; 4107%. Allergy caused by immunoglobulin E (IgE) confirmed, and IgE total of 513 IU/L, no sensitivities to skin prick testing, according to an allergy consultation and epithelial allergy, herbatum, and fumigatus specific IgE. Bronchoscopic techniques were used to rule out pulmonary aspergillosis, and mycological tests came back negative. Treatment with omalizumab 300 mg per month began, with no need for hospitalizations, exacerbations, or systemic corticosteroid therapy.
International Multilingual Students’ Perceptions of Learning English in a Multilingual Setting
Authors: AL-Roomy Muhammad A.
Published: July 2023

Students’ perceptions about learning in English influence the way they approach their learning either toward the target language itself or toward the learning in general. This paper aims to probe participants’ perceptions of learning a foreign language in the United States in multilingual settings. Using the snowball technique, seven participants were recruited for the purpose of the study. Later, focus group interviews were carried out to collect data and were analyzed using the NVivo software. The findings demonstrated that participants held different perceptions before and after studying in the US that positively or negatively affected their English learning. The most frequent themes were related to the high quality of learning in the US, which increased students’ motivation to study with native English teachers. Students’ prior learning experiences were another theme that emerged; these experiences were overcome when students adjusted their learning and looked for best learning practices. They took advantage of being in the US to practice their English in authentic situations. The least frequent themes were motivation and accent, which students believed were less important in their learning. After an analysis of these findings, this paper concludes with some pedagogical implications and recommendations for further research.
Optimal Highway Ramp Speed Control with Deep Reinforcement Learning
Authors: Shen Zhou (Eric)
Published: July 2023

Highway ramp speed control plays a critical role in optimizing traffic flow and improving transportation system efficiency. In this paper, we propose an optimal highway ramp speed control method using deep reinforcement learning. By leveraging the power of deep neural networks and reinforcement learning, our method, named DeepRampControl, learns to dynamically adjust vehicle speeds at ramps to minimize disruptions to the main traffic flow. We compare DeepRampControl with traditional rule-based approaches and other machine learning-based methods through comprehensive experiments in a simulated environment. The results demonstrate that DeepRampControl achieves higher traffic flow efficiency, lower average merging delay, and reduced energy consumption. These findings highlight the potential of deep reinforcement learning in optimizing highway ramp speed control and its ability to adapt to dynamic traffic conditions. The proposed method contributes to the development of intelligent and adaptive traffic control systems, paving the way for more efficient and sustainable transportation networks.
MEMBANGUN KARAKTER BERPIKIR KRITIS MAPEL GEOGRAFI MELALUI MEDIA PEMBELAJARAN BERBASIS LEARNING TO DO OLEH UNESCO
Authors: WAHYUDI ADIP
Published: July 2023

Guru sebagai pendidik perlu merekayasa perancang pembelajaran dalam membangun karakter berpikir kritis yang berpusat pada siswa. Berdasarkan permasalahan ini perlu adanya perkembangan dalam media pembelajaran yang dapat mengaktifkan siswa melalui media pembelajaran seperti pemanfaatan penggunaan media pembelajaran Learning To Do .Pembelajaran Learning To Do  dapat dilakukan dengan menggunakan strategi pembelajaran yang aktif, kreatif, efektif dan menyenangkan (PAKEM) melalui pembelajaran yang berbasis masalah, pembelajaran kooperatif, pembelajaran berbasis proyek dan lain-lain. Pembelajaran berbasis inquiry dan storytelling dapat meningkatkan kemampuan berpikir kritis siswa. Tulisan ini mengungkap (1) pentingnya pendidikan sebagai pembentuk karakter (2) pengembangan karakter berpikir kritis berbasis media pembelajaran Learning To Do  oleh UNESCO dan (3) konsep dasar pendidikan Learning To Do . Penelitian ini menggunakan data jenis kepustakaan (library research) dari penelitian kualitatif diperoleh dengan menggunakan teknik pengumpulan data yaitu identifikasi wacana dari sumber sekunder berupa jurnal dan buku yang relevan. Hasil penelitian menunjukkan bahwa pilar “Learning To Do ” penting diaplikasikan dalam media pembelajaran untuk membangun karakter berpikir kritis.
Flood modelling for a real-time decision support system of the covered Lower Paillons River, Nice, France
Authors: Game Paguédame , Wang Mingyan , Audra Philippe , Gourbesville Philippe
Published: July 2023

Nice Metropolis in Alpes Maritimes, France is prone to flood. The city is crossed by the Lower Paillons River (LPR). Its discharge for a return period of 100 years is estimated at 794 m3/s. Part of the river is covered by 2 km. In addition, there are two retention storages in the river bed and a floodable road tunnel on the left bank. Due to the increase in urban development, flood management is challenging. An existing decision support system, Aquavar, uses DHI Mike tools to reproduce runoff for the Lower Var River in the same region. To extend this system to the LPR and reinforce flood management, a new modelling tool adapted to the characteristics of the LPR is needed. Consequently, this research utilizes the DHI MIKEPLUS tool to develop a 1D -2D coupled model for real-time flood management. The results demonstrate that flood events like those in 2017 and 2019 were correctly reproduced. The linear regression R2 is above 0.8 for all stations. It was also estimated that the covered river should stay clean to avoid widespread flooding in the urban area. Overall, the model is useful for simulating flow in real time and can help sustain urban development.
Research of Active Compounds from Allii Macrostemonis Bulbus and
Potential Targets against Non-Hodgkin’s Lymphoma Based on Network
Pharmacology
Authors: Qiu Xiuliang , Zhao QiuLing , Qiu Hongqiang , Cheng Yu , Liu WenBin , Yang Lin
Published: July 2023

Background:

Non-Hodgkin’s Lymphoma (NHL) is a series of lymphoid malignancies
in some aggressive subtypes with unsatisfactory treatment effects. Allii Macrostemonis Bulbus
(Xie Bai) is a traditional Chinese medicine with anti-cancer activities, which may potentially suppress
aggressive NHL.

Objective:

This study tries to discover active components and targets of Xie Bai in treating NHL
by network pharmacology-based approaches.

Methods:

Compounds and related targets of Xie Bai were collected from the Traditional Chinese
Medicine Database and Analysis Platform. Target genes associated with NHL were searched by
GeneCards and DisGeNET, then the overlapped targets were further analyzed by STRING tool,
GO, and KEGG pathway enrichment analysis. Molecular docking was employed to verify the interaction
between compounds and targets.

Results:

11 bioactive compounds were successfully identified, with 30 targets that were screened
out for the treatment of NHL. Functional enrichment analysis suggested that Xie Bai exerted its
potential effects against NHL via pathways in cancer, such as PI3K/ AKT, p53, and MAPK signaling
pathways. Molecular docking results showed that 3 active compounds (quercetin, betasitosterol,
and naringenin) had good affinity with selected 6 targets (TP53, AKT1, CASP3,
CCND1, HPK1, and NLRP3).

Conclusion:

Identifying six potential genes could accurately be docked with Xie Bai and had
close interactions with NHL, which may provide insight into further research and new treatment
strategy.
BIOINFORMATICS STUDY OF 7,8-DIHYDROXYFLAVONE AS A NEUROPROTECTIVE AGENT IN ISCHEMIC STROKE VIA TRKB REGULATION AND GLUTAMINASE INHIBITION
Authors: Muhammad Rislan Faiz , Adnani Basya , Arviana Safira Dita , Violita Aldita Husna , Khotimah Husnul , Kurniawan Shahdevi Nandar , Syaban Mokhamad Fahmi Rizki , Yueniwati Yuyun , Rahayu Masruroh
Published: July 2023

Background: Stroke, particularly ischemic stroke, is one of the leading causes of death worldwide. Ischemic stroke causes a failure of oxidative phosphorylation and ATP synthesis, resulting in high levels of reactive oxygen species (ROS), neuroinflammatory responses, and apoptosis, all of which result in cell death. Neuroprotective agents are given to prevent the infarct area from expanding. Objective: This study aims to predict an in silico interaction by 7,8-dihydroxyflavone as neuprotective agent through TrkB signaling and inhibiting Glutaminase activity. Methods: In silico simulation with 7,8-dihydroxyflavone (DHF) as neuroprotective agent using PubChem, RCSB, Biovia Discovery Studio, PyRx, and PyMol software. This study analyzes the pharmacokinetics, pharmacodynamics, and protein-ligand interactions between 7,8-DHF as a ligand with TrkB (4AT5) and Glutaminase (5JYO) as protein target, compared to their native ligand. Results: 7,8 DHF binds to 4AT5 and 5JYO with lower bond energy (-9.4 Kcal/mol and -6.3 Kcal/mol, respectively) than the native ligand (-5 Kcal/mol and -5.9 Kcal/mol, respectively). It means that 7,8-DHF may increase protective mechanism. Conclusion: These findings tend to increase downstream signaling pathways, leading to increased TrkB expression, which induces protective mechanisms, and decreased glutamate expression, which reduces glutamate toxicity.
ASSOCIATION BETWEEN SLEEP QUALITY AND GERIATRIC SYNDROMES IN NURSING HOME RESIDENTS
Authors: Turgut Tuğba , Bahşi Remzi , Sürmeli Deniz Mut , Öztorun Hande Selvi , Coşardereliğoğlu Çağlar , Atmış Volkan , Yalçın Ahmet , Varlı Murat , Aras Sevgi
Published: July 2023

Background: With aging, an increase is observed in some geriatric syndromes as well as sleep disorders. Objective: The aim of this study is to investigate the relationship between sleep quality and geriatric syndromes (dementia, depression, sarcopenia, malnutrition, and frailty) in nursing home residents. Methods: The participants’ demographic characteristics, comorbid diseases, number of drugs, cognitive performance, mood and nutritional states, sarcopenia and fraility screening tests and sleep quality test were evaluated by two geriatricians. The participants were divided into two groups as good sleep quality and poor sleep quality. Correlation analysis of sleep quality and geriatric and mental functionality tests was performed with Spearman correlation analysis. Results: A total of 104 nursing home residents (34 females, 74 males, aged 61-98) participated in the study. Sleep quality was found to be worse in the elderly between the ages of 75-84, with a higher number of children, using assistive devices, and taking selective serotonin reuptake inhibitors. In addition, in participants with poor sleep quality, SARC-f (strength, assistance with walking, rising from a chair, climbing stairs, and falls) and geriatric depression scale scores were significantly higher. The Pittsburgh Sleep Quality Index global score was negatively associated with total sleep time and positively associated with geriatric depression scale score and SARC-f score. Conclusion: Findings from the present study in relation between poor sleep quality and some geriatric evaluation parameters suggests that there may be a common pathophysiological alteration with aging.
THE RELATIONSHIP OF EPILEPTIFORM WAVES IN ELECTROENCEPHALOGRAM WITH EPILEPSY TYPE OF SCHOOL-AGE EPILEPSY PATIENTS
Authors: Lekatompessy Jerome Constantine , Que Bertha Jean , Huwae Laura Bianca Sylvia
Published: July 2023

Background: Epilepsy in children causes memory problems in the learning process, so an early diagnosis of epilepsy is needed. The modality for determining the diagnosis of epilepsy is an electroencephalogram (EEG) examination. EEG recording results in epilepsy patients are epileptiform waves that can vary according to the type of epilepsy suffered. Objective: This study aims to determine the relationship of epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients. Methods: This study is an analytical study that uses secondary data in the form of medical records with cross sectional design. The research subjects were 106 patients taken with total sampling technique. Data collection is done by recording medical record data on the data collection form made by researchers. Correlation analysis between variables in this study used the Fisher test. Results: The results of this study indicate there is a relationship between epileptiform waves on the electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients, with p = 0.018 in 0.050 significance value. Conclusion: It can be concluded that there is a correlation between epileptiform waves on an electroencephalogram (EEG) with the type of epilepsy in school-age epilepsy patients.
Huertos Orgánicos Domésticos: La Casa como Recurso de Economía Solidaria
Authors: Montes Ponce Wendy Margarita , Ortega del Valle Carlos A.
Published: July 2023

El Huerto Orgánico Doméstico de Responsabilidad Social (HODRS) tuvo como objetivo integrar espacios productivos a la casa. El grupo participante contó con un capacitador-agrónomo. Se utilizaron espacios como azoteas, patios, terrazas, u otros espacios a cielo abierto. El HODRS respondió al Objetivo 12 de la Responsabilidad Social Mundial (Garantizar modalidades de consumo y producción sostenibles; hacer más y mejor con menos). Impactando directamente en la economía solidaria de 32 hogares localizados en comunidades oaxaqueñas, México, experimentando incluso oportunidades de trueque y se lograron cosechas con plantas de la región con propósitos de reproducción e intercambio con otras comunidades nativas.
Information and Communication Technology Medicine: Integrative Specialty for the Future of Medicine (Preprint)
Authors: Jongen Peter Joseph
Published: July 2023

UNSTRUCTURED

The impact of information and communication technology (ICT) on medicine is unprecedented and ever-increasing. This has made it more and more difficult for doctors to keep pace with ICT developments and to adequately match the input of ICT experts. As a result, medical disciplines may not be able to take full advantage of growing possibilities. In this personal viewpoint paper, I argue for the establishment of a novel medical specialty, ICT medicine. ICT medicine is needed to optimally face the challenges of ICT-based developments, including artificial intelligence (AI), and to ensure their efficient and beneficial use. ICT medicine is rooted in both medicine and ICT, and in contrast to existing medical specialties it is integrative in nature, as long-standing structural collaborations with ICT and other stakeholders cross the boundaries between disciplines. Thus, new concepts and theories may evolve that are better suited to addressing ICT-related issues in medicine. ICT doctors will be instrumental in the conception, development, implementation, and evaluation of digital tools, systems, and services. They provide a bridge between ICT professionals and clinical users and educate doctors in digital applications and services. Notably, ICT doctors may have a pivotal role in the validation, verification, and evaluation of AI models. ICT medicine institutes offer a home to these new professionals, enhancing their independence within health care organizations and in relation to ICT companies. Importantly, in an era of growing technicalization and use of AI algorithms, ICT doctors may safeguard the human factor in medicine. And, from a societal perspective, they may promote digital inclusion and the continuing high quality of digital services and provide leadership in the future digitalization of medicine.
Impact of the COVID-19 Pandemic on the Public Perceptions of the Roles and Functions of Community Pharmacies in South Korea: Updated Cross-Sectional Self-Reported Web-Based Survey (Preprint)
Authors: Yang Dong-Wook , Son Kyung-Bok
Published: July 2023

BACKGROUND

Community pharmacists confronted dual burdens in response to the COVID-19 pandemic by expanding the scope of pharmaceutical practices.

OBJECTIVE

This study aimed to assess the perceived roles and functions of community pharmacies during the pandemic and to explore their updated roles after the pandemic began.

METHODS

We conducted a self-reported web-based survey in October 2022. Based on Korean census data, we recruited the study participants (n=1000) through quota sampling stratified by age, sex, and region, yielding a 7.45% (1000/13,423) response rate. The questionnaires were composed of 3 sections: demographics, the roles and functions of community pharmacies during the pandemic, and the updated roles of community pharmacies during disasters. Each question in the second and third sections was rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree), and each item’s mean scores and SDs were reported. The study participants were categorized into 2 groups: individuals who had a family pharmacy and those who did not. A chi-square test and ordered logistic regression analyses were conducted.

RESULTS

Out of 1000 respondents, 418 (41.8%) had a history of COVID-19, and 639 (63.9%) had a family pharmacy. Assigning specific roles and functions to community pharmacies during the pandemic contributed to positive assessments. Respondents gave higher scores to community pharmacies that had responded appropriately (a mean Likert score of 3.66, SD .077 out of 5) and provided continuous pharmaceutical services (mean 3.67, SD 0.87) during the pandemic. The pandemic served as an opportunity to positively recognize the role of community pharmacies (mean 3.59, SD 0.83). In the ordered logistic model, having a family pharmacy was consistently associated with positive perceptions. Respondents perceived that community pharmacies collaborated with general practitioners and health authorities. However, community pharmacies need to function appropriately in terms of knowledge. The mean score of the 4 domains of community pharmacy functions was the highest for collaboration (mean 3.66, SD 0.83), followed by communication (mean 3.57, SD 0.87), responsiveness (mean 3.54, SD 0.87), and knowledge (mean 3.41, SD 0.91).

CONCLUSIONS

The pandemic resulted in interprofessional collaboration between community pharmacists and general practitioners. Family pharmacies could be a valuable asset to the comprehensive case management of patients. However, community pharmacists should have the expertise to build solid interprofessional collaborations and fulfill their expanded and updated roles.
Conversational Agents in Health Care: Expert Interviews to Inform the Definition, Classification, and Conceptual Framework (Preprint)
Authors: Martinengo Laura , Lin Xiaowen , Jabir Ahmad Ishqi , Kowatsch Tobias , Atun Rifat , Car Josip , Tudor Car Lorainne
Published: July 2023

BACKGROUND

Conversational agents (CAs), or chatbots, are computer programs that simulate conversations with humans. The use of CAs in health care settings is recent and rapidly increasing, which often translates to poor reporting of the CA development and evaluation processes and unreliable research findings. We developed and published a conceptual framework, designing, developing, evaluating, and implementing a smartphone-delivered, rule-based conversational agent (DISCOVER), consisting of 3 iterative stages of CA design, development, and evaluation and implementation, complemented by 2 cross-cutting themes (user-centered design and data privacy and security).

OBJECTIVE

This study aims to perform in-depth, semistructured interviews with multidisciplinary experts in health care CAs to share their views on the definition and classification of health care CAs and evaluate and validate the DISCOVER conceptual framework.

METHODS

We conducted one-on-one semistructured interviews via Zoom (Zoom Video Communications) with 12 multidisciplinary CA experts using an interview guide based on our framework. The interviews were audio recorded, transcribed by the research team, and analyzed using thematic analysis.

RESULTS

Following participants’ input, we defined CAs as digital interfaces that use natural language to engage in a synchronous dialogue using ≥1 communication modality, such as text, voice, images, or video. CAs were classified by 13 categories: response generation method, input and output modalities, CA purpose, deployment platform, CA development modality, appearance, length of interaction, type of CA-user interaction, dialogue initiation, communication style, CA personality, human support, and type of health care intervention. Experts considered that the conceptual framework could be adapted for artificial intelligence–based CAs. However, despite recent advances in artificial intelligence, including large language models, the technology is not able to ensure safety and reliability in health care settings. Finally, aligned with participants’ feedback, we present an updated iteration of the conceptual framework for health care conversational agents (CHAT) with key considerations for CA design, development, and evaluation and implementation, complemented by 3 cross-cutting themes: ethics, user involvement, and data privacy and security.

CONCLUSIONS

We present an expanded, validated CHAT and aim at guiding researchers from a variety of backgrounds and with different levels of expertise in the design, development, and evaluation and implementation of rule-based CAs in health care settings.
Shaping the Future of Medical Education with Generative AI: Student-Initiated Data Science Interest Group Launches ChatClinic (Preprint)
Authors: Verhagen Nathaniel B , Wolfrath Nathan , Gehl Carson , Geissler Thomas , Chu Justin , Flitcroft Madelyn A , SenthilKumar Gopika , Sathe Tejas , Kothari Anai
Published: July 2023

UNSTRUCTURED

The establishment of a medical student-initiated Data Science Interest Group (DSIG) demonstrates the growing interest and integration of data science, artificial intelligence (AI), and medicine. The DSIG works to provide an interdisciplinary environment for student-driven data science exposure and education, centered on four main pillars: interest exploration in data science, data science education and skill development, student and faculty collaborations, and learning from leaders in the field. As a case study, we present "ChatClinic," an AI-based medical education tool created and designed by DSIG members with the goal of simulating patient history taking and clinical decision-making interactions. The creation of ChatClinic serves as a demonstration of the DSIG's four-pillar approach and exemplifies how student interest in AI can lead to advances in the field of medicine. The paper also considers ethical and practical challenges in AI integration into medical education, such as information accuracy, potential over-reliance on AI, and issues with model interpretability. Our hope is that the DSIG and ChatClinic serve as models for institutions seeking to navigate the complex landscape of AI integration into medical education.
Analisis Gender Program Pemberdayaan Integrasi Kakao-Kambing PE di Taman Teknologi Pertanian Nglanggeran
Authors: Andarwati Siti , Ruslanjari Dina , Nurhayati
Published: July 2023

Gender issue is one of the main issue in national development implementation especially the development of human resources. The government has done many empowerment programs with expectation to improve the quality of society especially woman, but data shows that there is still asymmetry between man and woman in many activities, access, and control into resources and development benefit. This research intends to analyze public-empowerment activity within Etawah Crossbreed Cocoa-Goat Integration Program in Gender Perspective. The research information consists of man and woman constituting couple (husband-wife) and gets involved as empowerment program. This research used qualitative method through observation technique, in-depth interview with documentation and literature. The results of research show that there is still gender asymmetry in development program implementation especially woman. The expectation of empowerment program implementation can decide job decision received by man and woman and doing knowledge transfer activity in domestic development. Activity, access, and control into resources and development benefit are affected by supporting idea such as social, education, and globalization, as for resistor idea such as population structure, economic, politics, and culture.
Special Educators’ Experience Teaching during the Pandemic: A Retrospective Study
Authors: Panchakshari Aradhya Abhishek Budiguppe , Sanghavi Tanvi , Bhattarai Biraj
Published: July 2023

With the outbreak of COVID-19 pandemic, a shift in teaching children with special needs has taken place. The purpose of this descriptive research study was to investigate the impact of the COVID-19 epidemic on teacher relationships with special needs kids during the mandated school closures in March 2020. A descriptive cross-sectional survey was carried out through google form questionnaire. A total of forty special educators participated in the study. The challenges faced by special educators were documented through five sections: Demographic details, Online Teaching, Access to Resources, Individualized Educational Plan and Parental Satisfaction. During the Covid pandemic, more than 90% of teachers switched to an online way of service delivery. Special educators agree that online resources should be used properly, and that parents should be able to organize material for online education. They agreed that acquiring and exchanging information on Individualized Educational Plan (IEP), establishing IEP direction, planning and drafting IEPs for online classrooms, and implementing and reviewing IEPs were all tough. Parents’ satisfaction with online education and interest in online programs were lower than in-person teaching, according to the study. Special educators were constantly seeking to adapt to the present instructional requirements. Culturally relevant resources, guidelines for implementing the Individualized Educational Plan, and a family-centered approach are all needed.
Supervise and Apprehension of Students Presence using RFID
Authors: Krishna A.Vijaya , Ramacharan S. , Reddy C. Sudhakar , Kumar B. Vijay
Published: July 2023

Abstract: Student attendance is now recognised as one of the most important variables or issues that represent academic accomplishments and performance at any university, as opposed to more time-consuming and wasteful methods. Differing programmed distinguishing proof advances have been more stylish, for example, the wireless technology like Radio Frequency Identification (RFID). A broad look into and a few applications are delivered for exploiting this innovation. An RFID tag or signature is used to send data from a remote innovation known as RFID as the reason for recognising an item via radio waves to RFID for each user. The present investigation centers around proposing a RFID based Attendance Management System (AMS) and furthermore data administration framework for a scholastic area by utilizing RFID innovation notwithstanding the programmable Logic Circuit, (for example, Raspberry Pi), and PHP Server
Hand-Written Analysis and Recognition Based on Deep Learning and Machine Learning Techniques
Authors: Reddy K. Bhanu Prakash , Gupta K. Jathin , Reddy K. Rishi Vardhan , Praneeth K. Satvik Sai , Das Sujit
Published: July 2023

Abstract: This paper presents a comprehensive comparative analysis of Support Vector Machines (SVM), along with other machine learning models, and Convolutional Neural Networks (CNN) for Handwritten Analysis and Recognition. We investigate the performance of these models on a multi-class pairwise classification task using a large-scale handwritten dataset. Evaluation metrics such as accuracy, precision, and recall are utilized to measure their performance and compare them against the CNN model. The study aims to provide insights into the strengths and weaknesses of SVM, as well as other machine learning models, in comparison to CNN for handwritten analysis and recognition. SVM, known for its ability to handle complex non-linear relationships, offers good generalization and interpretability. However, it may face challenges in capturing intricate patterns in handwritten data. Other machine learning models, such as k-Nearest Neighbors (k-NN) and decision trees, also offer different advantages and limitations in this context. Through extensive experimentation, we compare the performance of SVM, k-NN, decision trees, and other relevant machine learning models with CNN on the same handwritten dataset. We analyze their accuracies, precision, and recall rates to evaluate their effectiveness in multi-class pairwise recognition of handwritten samples. Additionally, we discuss the computational requirements, training times, and interpretability associated with each model. The findings of this study provide valuable insights into the suitability of SVM and other machine learning models when compared to CNN for handwritten analysis and recognition tasks. The results shed light on the trade-offs between traditional machine learning approaches and deep learning architectures in this domain. These insights can guide researchers and practitioners in choosing the most appropriate model based on their specific requirements, computational resources, and interpretability needs.
Review of the book: Olga V. Vasilyeva, Olga M. Yastrebova (Ed.). Handwritten Qur’ans in the National library of Russia
Authors: Mukhetdinov D. V.
Published: July 2023

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