bluuebunny/crossref_metadata_2025 huggingface dataset:
Systematic Literature Review : The Trend Of Augmented Reality (AR) in Science Teaching and Learning in Primary School / Sorotan Literatur Bersistematik: Trend Realiti Terimbuh (AR) dalam Pembelajaran dan Pemudahcaraan Mata Pelajaran Sains sekolah Rendah
Authors: Ahmad Sani , Abu Samah Norazrena
Published: June 2024

The rapid development of technology worldwide has led to significant changes in various fields, particularly in education. The emergence of Augmented Reality (AR) technology has garnered attention from researchers in assessing its effectiveness in the teaching and learning process. However, only a small number have systematically studied the trends in AR implementation in the teaching and learning of science, especially at the elementary school level. Therefore, this systematic literature review aims to identify the trends in AR in science teaching and learning from 2018 to 2023. This literature review utilizes the Meta-Analysis framework (PRISMA method). A total of 404 articles were identified through screening from the SCOPUS and WOS databases. Following the screening based on predefined criteria, only 16 articles were selected. Subsequently, these articles were analyzed, and the study findings were categorized into seven discussions regarding the use of AR, namely, general aspects, study findings, contributions of the studies, study designs, research instruments, science learning topics, and theories and models. It is hoped that the results of this review will benefit researchers and educators interested in the use of technology, especially AR, for teaching and learning purposes.
Odontología: una crisis agravada por la pandemia del Covid-19
Authors: Jiménez-Barbosa Wilson Giovanni , Acuña Gómez Johanna Sareth , Montenegro Martínez Gino
Published: June 2024

Antecedentes:  La pandemia por Covid-19 implicó el establecimiento de medidas de emergencia sanitaria, entre otras, la suspensión y la posterior reapertura progresiva de la atención odontológica. Objetivo: Comprender la forma en que las medidas sanitarias por Covid-19 afectaron a propietarios de consultorios y clínicas odontológicas de Bogotá, Colombia, y si estas agravaron la crisis de la profesión. Métodos: se realizó un estudio cualitativo, entrevistando a siete odontólogos propietarios de clínicas o consultorios odontológicos. Se indagó por las condiciones laborales, cambios económicos, condiciones de contratación y la participación de organizaciones profesionales en la pandemia. Resultados: la odontología ya venía en crisis que se exacerbó a partir de las medidas sanitarias ante la pandemia por Covid-19. Los entrevistados no percibieron una rápida voz de liderazgo de las entidades gubernamentales para establecer protocolos de atención. La gestión administrativa es diferente de acuerdo con las capacidades de las clínicas odontológicas en comparación con los consultorios privados. Finalmente, la pandemia y las restricciones en la movilidad permitieron establecer mejoras en el tiempo compartido con la familia, no obstante, esto afectó los ingresos de los propietarios que los llevó a buscar otras formas de ingreso. Conclusiones: La odontología viene desde hace muchos años en crisis que fue más evidente con las medidas de confinamiento implementadas para controlar la pandemia de Covid-19. Se presentan diferencias en la gestión administrativa en favor de las clínicas odontológicas que fueron fundamentales en el marco de la pandemia.
Perception of Oral Health-related Quality of Life Before the First Step and After the Second Step of Periodontal Therapy
Authors: Fernández-Gudiño Andrés , Cruz-Morera María Amalia , Vega-Cruz Mariana , Quesada-Romero Sofía , Alvarado-Echeverría Juan Ignacio , Vargas-Godínez Mercedes , Piedra-Hernández Lucía , Gómez-Fernández Adrián , Ramírez Karol
Published: June 2024

Background: Periodontal research has focused on clinical outcomes of periodontitis and in a lesser extent on patient-reported outcomes. Objective: The objectives of this study were to 1) determine oral health-related quality of life (OHRQoL) before the first step and after the second step of periodontitis therapy and 2) compare sociodemographic and clinical characteristics with changes in OHRQoL. Methods: Patients with periodontitis that had never been treated with subgingival plaque instrumentation were recruited. Clinical parameters were recorded. A questionnaire was applied to collect sociodemographic data, smoking status, and pain. The Oral Health Impact Profile-14 (OHIP-14) was used to evaluate OHRQoL before the first step and after the second step of periodontitis therapy. Results: Fifty-one participants completed the study, mean age 46.88 years ± 11.16. OHIP-14 total score decreased from 16.98 ± 11.48 to 8.41 ± 9.06 after treatment. Improvements in physical pain, psychological discomfort, physical disability, psychological disability, social disability, and handicap accounted for the changes. The most frequent periodontal staging was stage III. As for grading, Grade A was diagnosed in 16 (31.37 %) participants, Grade B in 16 (31.37 %), and Grade C in 17 (33.33 %). Mean plaque index was 61.4 % ± 17.6 before the first step of periodontitis therapy and 22.8 % ± 10.6 after. Pain in the last month decreased after periodontal care. No differences in OHRQoL changes were detected when comparing age categories, gender, and educational level. Better scores were reported from non-smokers, before and after therapy (p = 0.003). Conclusion: Periodontal therapy had a positive impact on self-perceived quality of life of patients.
Assessing Physical Therapists’ Outdoor Walking Recommendations and Neighborhood Walkability for Older Adults
Authors: Karczewski Hannah A. , Blackwood Jennifer
Published: June 2024

Background/Objectives: Neighborhood walkability is the extent to which built and social environments support walking. Walkability influences older adults’ participation in outdoor physical activity. Identifying factors that influence physical therapists’ (PTs) decisions about prescribing outdoor walking is needed, especially for those who are aging in place. The purpose of this study is to describe the neighborhood walkability knowledge, perceptions, and assessment practices of PTs who work with community-dwelling older adults. Methods: A cross-sectional survey was sent via email to 5,000 PTs nationwide. The 40-item survey assessed walking prescriptions, walkability perceptions and assessments, and gathered demographic data. Categorical variables were compared using Chi-square analyses. Results: Using a total of 122 PTs who worked in outpatient geriatric physical therapy settings, a significant difference was found between perceptions of whether PTs should assess walkability and whether they actually assess walkability (χ2 = 78.7, p < .001). Decisions to prescribe outdoor walking were influenced by the availability (n = 79, 64.8%) and maintenance (n = 11, 9.0%) of sidewalks, crime (n = 9, 7.4%), terrain (n = 7, 5.7%), and aesthetics (n = 6, 4.9%). Objective walkability measures were not used by the respondents. Conclusion: When considering the assessment of walkability, PTs prioritize the built environment over the social environment. Although most believe it is the responsibility of the PT to assess walkability, most do not. Significance/Implications: Assessment of walkability may allow PTs to identify barriers and make more informed recommendations concerning outdoor walking for older adults. Objective measures are available for PTs when prescribing outdoor walking.
Feasibility and Preliminary Effectiveness of an Online Meditation Intervention in Young Adults With Concussion History
Authors: Callahan Christine E. , Donnelly Kyla Z. , Gaylord Susan A. , Faurot Keturah R. , DeFreese J.D. , Kiefer Adam W. , Register-Mihalik Johna K.
Published: June 2024

Context: Mindfulness interventions (yoga, meditation) in traumatic brain injury populations show promising improvements in injury outcomes. However, most studies include all injury severities and use in-person, general programming lacking accessibility and specificity to the nuance of concussion. Therefore, this study investigated the feasibility and preliminary effectiveness of an online, concussion-focused meditation intervention among young adults with a concussion history. Design: Unblinded, single-arm, pilot intervention. Methods: Fifteen young adults aged 18 to 30 with a concussion history within the past 5 years completed 10 to 20 minutes per day of online, guided meditations for 6 weeks. Feasibility was assessed using the Feasibility of Intervention Measure. Concussion symptoms were measured using the Rivermead Post-Concussion Symptom Questionnaire, perceived stress the Perceived Stress Scale-10, and mindfulness the Five Facet Mindfulness Questionnaire. Descriptive statistics described the study sample and determined intervention adherence and feasibility. Paired sample t tests were used to examine preintervention/postintervention changes in concussion symptoms, perceived stress, and mindfulness, with descriptive statistics further detailing significant t tests. Results: Fifteen participants were enrolled, and 12 completed the intervention. The majority completed 5+ days per week of the meditations, and Feasibility of Intervention Measure (17.4 [1.8]) scores indicated high feasibility. Concussion symptom severity significantly decreased after completing the meditation intervention (11.3 [10.3]) compared with before the intervention (24.5 [17.2]; t[11] = 3.0, P = .01). The number of concussion symptoms reported as worse than before their concussion significantly decreased after completing the meditation intervention (2.7 [3.9]) compared with before the intervention (8.0 [5.7]; t[11] = 3.7, P = .004). Postintervention, 83.33% (n = 10) reported lower concussion symptom severity, and 75.00% (n = 9) reported less concussion symptoms as a mild, moderate, or severe problem (ie, worse than before injury). Conclusions: Findings suggest positive adherence and feasibility of the meditation intervention, with the majority reporting concussion symptom improvement postintervention. Future research is necessary to expand these pilot findings into a large trial investigating concussion-specific meditation programming.
A PRMT5-ZNF326 axis mediates innate immune activation upon replication stress
Authors: Hoang Phuong Mai , Torre Denis , Jaynes Patrick , Ho Jessica , Mohammed Kevin , Alvstad Erik , Lam Wan Yee , Khanchandani Vartika , Lee Jie Min , Toh Chin Min Clarissa , Lee Rui Xue , Anbuselvan Akshaya , Lee Sukchan , Sebra Robert P. , Martin J. Walsh  , Marazzi Ivan , Kappei Dennis , Guccione Ernesto , Jeyasekharan Anand D.
Published: June 2024

DNA replication stress (RS) is a widespread phenomenon in carcinogenesis, causing genomic instability and extensive chromatin alterations. DNA damage leads to activation of innate immune signaling, but little is known about transcriptional regulators mediating such signaling upon RS. Using a chemical screen, we identified protein arginine methyltransferase 5 (PRMT5) as a key mediator of RS-dependent induction of interferon-stimulated genes (ISGs). This response is also associated with reactivation of endogenous retroviruses (ERVs). Using quantitative mass spectrometry, we identify proteins with PRMT5-dependent symmetric dimethylarginine (SDMA) modification induced upon RS. Among these, we show that PRMT5 targets and modulates the activity of ZNF326, a zinc finger protein essential for ISG response. Our data demonstrate a role for PRMT5-mediated SDMA in the context of RS-induced transcriptional induction, affecting physiological homeostasis and cancer therapy.
Experiments on Molecular Spectroscopy at the Novosibirsk Terahertz Free Electron Laser
Authors: Kubarev V. V. , Chesnokov E. N.
Published: June 2024

The unique parameters of the Novosibirsk free electron laser (NovoFEL) allow unique experiments on the spectroscopy of molecules to be performed at the Shared Resource Center. The radiation parameters of terahertz NovoFEL are article described in detail. The molecular spectroscopy experiments performed on this laser are given with the corresponding references in which they are described. Possible but as yet unperformed experiments on terahertz NovoFEL in the field of ultra-high-resolution molecular spectroscopy are considered.
Novel BRICHOS-related Defensin-like Antimicrobial Peptide from the Marine Polychaeta Arenicola marina
Authors: Safronova V. N. , Panteleev P. V. , Kruglikov R. N. , Bolosov I. A. , Finkina E. I. , Ovchinnikova T. V.
Published: June 2024

Objective: To date, polychaetes remain a poorly studied class of invertebrate animals in terms of the features of functioning of their immune system and, in particular, the biodiversity of antimicrobial peptides (AMPs). AMPs also known as host defense peptides play a key role in host protection from various pathogens and regulation of the species composition of symbiotic microbes. A study of the biosynthesis of AMPs in polychaetes resulted in the discovery of the so-called BRICHOS domain in the structure of the precursor proteins of a number of such peptides. The conserved structure of this domain makes possible the bioinformatic search for AMP precursors in polychaete transcriptomes. In this work, we found and studied a novel BRICHOS-related AMP from the lugworm Arenicola marina, representing a previously undiscovered in polychaetes a structural family of defensin-like peptides stabilized by four disulfide bonds. Methods: The peptide, designated as AmBRI-44a and containing 44 amino acid residues, was obtained by heterologous expression in Escherichia coli. The peptide secondary structure was investigated by CD spectroscopy in water and dodecylphosphocholine (DPC) micelles. The minimum inhibitory concentrations (MICs) against a wide range of bacterial pathogens were assessed using the two-fold serial dilutions method. Cytotoxicity of AmBRI-44a was studied in vitro on human erythrocytes or adherent cell line HEK293T using the hemoglobin release assay or the MTT test, respectively. The AMBRI-44a potential target was discovered by successive daily subculturing of the AmBRI-44a resistant strain followed by whole-genome sequencing. Results and Discussion: According to CD data, AmBRI-44a is a predominantly β-structured peptide. AmBRI-44a was shown to have a specific activity against a narrow spectrum of Gram-positive bacteria and pronounced cytotoxic effects on the eukaryotic cell line HEK293T. The proposed mechanism of the antibacterial action of this peptide is associated with the inhibition of bacterial cell wall biosynthesis, as indicated by the genetic and phenotypic analysis of selected AmBRI-44a-resistant bacteria Bacillus licheniformis B-511. Conclusions: The resulting data allow us to consider the discovered peptide AmBRI-44a as a candidate compound for the development of an antibiotic agent that could potentially be effective in the treatment of infectious diseases mediated by multidrug-resistant Gram-positive bacteria.
Doubly-charm and doubly-bottom pentaquark molecular states via the QCD sum rules
Authors: Wang Xiu-Wu , Wang Zhi-Gang
Published: June 2024

In this work, the doubly-charm and doubly-bottom pentaquark molecular states [Formula: see text] and [Formula: see text] are studied via the QCD sum rules. Sixteen color singlet-singlet type currents with the definite isospin-spin-parity [Formula: see text] are constructed to interpolate the corresponding hadronic states with the same quantum numbers. The masses and pole residues of those doubly-heavy pentaquark molecular states are calculated, the results show that their masses are all below the corresponding meson-baryon thresholds, which means that they are possible bound states, not resonant states, moreover, the possible decay channels for the doubly-charm molecular states are given.
Affective Uplift During Video Game Play: A Naturalistic Case Study
Authors: Vuorre Matti , Ballou Nick , Hakman Thomas , Magnusson Kristoffer , Przybylski Andrew K.
Published: June 2024

Do video games affect players’ well-being? In this case study, we examined 162,325 intensive longitudinal in-game mood reports from 67,328 play sessions of 8,695 players of the popular game PowerWash Simulator. We compared players’ moods at the beginning of play sessions with their moods during play and found that the average player reported 0.034 (0.032, 0.036) visual analog scale (VAS; 0-1) units greater mood during than at the beginning of play sessions. Moreover, we predict that 72.1% (70.8%, 73.5%) of similar players experience this affective uplift during play, and that the bulk of it happens during the first 15 minutes of play. We do not know whether these results indicate causal effects or to what extent they generalize to other games or player populations. Yet, these results based on in-game subjective reports from players of a popular commercially available game suggest good external validity and as such offer a promising glimpse of the scientific value of transparent industry–academia collaborations in understanding the psychological roles of popular digital entertainment.
I Don’t Want to Sound Rude, but It’s None of Their Business: Exploring Security and Privacy Concerns around Assistive Technology Use in Educational Settings
Authors: Marsh Abigail , Milne Lauren R.
Published: June 2024

Students with disabilities often use assistive technology to gain equal access to educational content. However, using this technology can lead to security and privacy concerns, including the possibility of disclosing a disability. In this work, we explore that and other privacy risks happening through the use of assistive technology in educational settings. We conducted interviews with eight students who use assistive technology, exploring the risks around in-person and online classes and how they changed as students moved to remote courses during the COVID-19 pandemic. We found that students were aware of in-person risks but generally had not thought of the potential risks of online disclosure. Students were concerned about their disability being disclosed, especially without their explicit consent, but they considered tradeoffs, including increased accessibility for themselves and others, that might make them more accepting of disclosure. Building on the interviews, we did an analysis of the privacy policies of the assistive technology that the students used, examining how potential privacy and security vulnerabilities were communicated. We found that, although there are identified vulnerabilities for many of the assistive technologies, the policies varied widely in whether or how much information they gave the user about the risks.
Elevated heart rate and decreased muscle endothelial nitric oxide synthase in early development of insulin resistance
Authors: Blackwood Sarah J. , Tischer Dominik , van de Ven Myrthe P. F. , Pontén Marjan , Edman Sebastian , Horwath Oscar , Apró William , Röja Julia , Ekblom Maria M. , Moberg Marcus , Katz Abram
Published: June 2024

Insulin resistance (IR) is a risk factor for the development of several metabolic diseases. In healthy young individuals, an elevated heart rate (HR) correlates with low insulin sensitivity and high expression of type II skeletal muscle fibers, which express low levels of endothelial nitric oxide synthase (eNOS) and, hence, a limited capacity to induce vasodilation in response to insulin. Early targeting of the autonomic nervous system and microvasculature may attenuate development of diseases stemming from insulin resistance.
Noradrenergic current responses of neurons in rat oculomotor neural integrators
Authors: Saito Yasuhiko , Sugimura Taketoshi
Published: June 2024

Noradrenergic modulation of oculomotor neural integrators involved in gaze control has not been elucidated. Here, we report that noradrenaline (NA)-induced slow inward (SI) and outward (SO) currents are mediated mainly by α1 and α2 adrenoceptors in neurons that participate in horizontal and vertical gaze control. The NA-induced current responses differed depending on the neurotransmitter phenotype and firing pattern. These results suggest various modes of noradrenergic modulation in horizontal and vertical integrator neurons.
A Review of Stochastic Optimization Algorithms Applied in Food Engineering
Authors: Koop Laís , Ramos Nadia Maria do Valle , Bonilla-Petriciolet Adrián , Corazza Marcos Lúcio , Voll Fernando Augusto Pedersen
Published: June 2024

Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed.
Factors Affecting Mode of Birth in Women With Preexisting Diabetes and Gestational Diabetes: A Retrospective Cohort at a Tertiary Referral Center
Authors: Reischer Theresa , Prossinger Sina , Catic Anja , Healy Eibhlin , Göbl Christian , Yerlikaya-Schatten Gülen
Published: June 2024

Women with preexisting diabetes and gestational diabetes mellitus (GDM) are at higher risk for adverse maternal and neonatal outcomes. However, there is no consensus on a uniform approach regarding mode of birth (MOB) for all forms of diabetes. The aim of the study is to compare MOB in women with preexisting diabetes and GDM and possible factors influencing it. A retrospective cohort study of women with GDM and preexisting diabetes between 2015 and 2021 at a tertiary referral center was conducted. One thousand three hundred eighty‐five singleton pregnancies were included. One thousand twenty‐two (74.4%) women had a vaginal birth (VB) and 351 (25.6%) a caesarean section. Preexisting diabetes was significantly associated with caesarean section compared to GDM (OR 2.43). Five hundred fifty‐one (40.1%) women underwent induction of labor, and 122 (22.1%) women had a secondary caesarean after IOL. Women induced due to spontaneous rupture of membrane (SROM) achieved the highest rate of VB at 93%. The lowest rates of VB occurred if indication for induction was for preeclampsia or hypertension. IOL was significantly less successful in preexisting diabetes with a VB achieved in 56.4% for type 1 diabetes and 52.6% of type 2 diabetes compared to GDM (78.2% in GDM; 81.2% in IGDM; OR 3.25, 95% CI 1.70–6.19, p < 0.001). The rate of VB was higher who were induced preterm compared to women with term IOL (n = 240 (81.9%) vs. n = 199 (73.2%); p < 0.05). Parity, previous VB and SROM favored VB after IOL, whereas preexisting diabetes, hypertension, and IOL after 40 + 0 weeks are independent risk factors for caesarean delivery.
Enhancing Geopolymer Mix Optimization: Integration of the Plackett–Burman Method and Response Surface Methodology for Sustainable Construction Materials
Authors: Anuja N. , Palanivel M. , Amutha Priya N.
Published: June 2024

Nowadays, construction industries are using flyash as a potential alternative for cement. Due to its improved mechanical properties, ecofriendly nature, and low cost, geopolymer technology makes use of flyash as a promising future binder material. In this paper, 7 factors such as liquid‐to‐flyash ratio, silicate‐to‐hydroxide ratio, curing temperature, curing period, concentration of NaOH (molarity), rest period prior to curing, and dosage of superplasticizer that influence the compressive strength and temperature drop are screened using the Plackett–Burman method for optimization. Here, compressive strength and temperature drop are taken as the main indices of response to analyze the parameters. The significant variables determined from the Plackett–Burman design are further considered for the process of optimization using the response surface methodology. From the analysis, the optimum values of 0.4071 liquid‐to‐flyash ratio, 2.5 silicate‐to‐hydroxide ratio, and 6 hours curing period give maximum compressive strength and temperature drop of 28.87 MPa and 5.3°C under the optimized medium in the validation experiment which varies by only 3.93% and 1.85% from the observed value of 27.20 MPa and 5.4°C.
The Role of Botulinum Neurotoxin A in the Conservative Treatment of Fractures: An Experimental Study on Rats
Authors: Vampertzis Themistoklis , Barmpagianni Christina , Bekiari Chryssa , Brellou Georgia D. , Zervos Ioannis A. , Tsiridis Eleftherios , Galanis Nikiforos
Published: June 2024

This paper explores the role of botulinum neurotoxin in aiding fracture recovery through temporary muscle paralysis. Specifically, it investigates the effects of botulinum neurotoxin-induced paralysis of the sternocleidomastoid muscle on clavicle fractures in rats. The research aims to assess safety, effectiveness, and the impact on fracture healing. Healthy male Albino Wistar rats were divided into four groups: clavicle fracture, botulinum neurotoxin injection, both, and control. Surgeries were conducted under anaesthesia, and postoperatively, animals were monitored for 28 days. Euthanasia and radiological assessment followed, examining fracture healing and muscle changes, while tissues were histopathologically evaluated. The modified Lane–Sandhu scoring system was used for the radiographic evaluation of clavicle fractures, and the results varied from complete healing to nonunion. Histopathological examination at 28 days postfracture showed fibrous tissue, mesenchymal cells, and primary callus formation in all groups. Despite varied callus compositions, botulinum neurotoxin administration did not affect clavicle healing, as evidenced by similar scores to the control group. Several studies have explored botulinum neurotoxin applications in fracture recovery. Research suggests its potential to enhance functional recovery in certain types of fractures. Theoretical benefits include managing muscle spasticity, aiding reduction techniques, and preventing nonunion. However, botulinum neurotoxin’s transient effect and nonuniversal applications should be considered. The present study found that botulinum toxin had no clear superiority in healing compared to controls, while histological evaluation showed potential adverse effects on muscle tissue. Further research is essential to understand its risk-benefit balance and long-term effects.
Predicting students' success level in an examination using advanced linear regression and extreme gradient boosting
Authors: Wahyuningsih Tri , Iriani Ade , Purnomo Hindriyanto Dwi , Sembiring Irwan
Published: June 2024

This research employs a hybrid approach, integrating advanced linear regression and extreme gradient boosting (XGBoost), to forecast student success rates in exams within the dynamic educational landscape. Utilizing Kaggle-sourced data, the study crafts a model amalgamating advanced linear regression and XGBoost, subsequently assessing its performance against the primary dataset. The findings showcase the model's efficacy, yielding an accuracy of 0.680 on the fifth test and underscoring its adeptness in predicting students' exam success. The discussion underscores XGBoost's prowess in managing data intricacies and non-linear features, complemented by advanced linear regression offering valuable coefficient interpretations for linear relationships. This research innovatively contributes by harmonizing two distinct methods to create a predictive model for students' exam success. The conclusion emphasizes the merits of an ensemble approach in refining prediction accuracy, recognizing, however, the study's limitations in terms of dataset constraints and external factors. In essence, this study enhances comprehension of predicting student success, offering educators insights to identify and support potentially struggling students.
The impact of usability in information technology projects
Authors: Putra Hulu Freddy Richard , Raharjo Teguh , Simanungkalit Tiarma
Published: June 2024

Achieving success in information system and technology (IS/IT) projects is a complex and multifaceted endeavour that has proven difficult. The literature is replete with project failures, but identifying the critical success factors contributing to favourable outcomes remains challenging. The triad of Time-Cost-Quality is widely accepted as key to achieving project success. While time and cost can be quantified and measured, quality is a more complex construct that requires different metrics and measurement approaches. Utilizing the PRISMA Methodology, this study initiated a comprehensive search across literature databases and identified 142 relevant articles pertaining to the specified keywords. A subset of ten articles was deemed suitable for further examination through rigorous screening and eligibility assessments. Notably, a primary finding indicates that despite recognizing usability as a critical element, there is a tendency to neglect usability enhancements due to time and resource constraints. Regarding the influence of usability on project success, the active involvement of end-users emerges as a pivotal factor. Moreover, fostering the enhancement of Human-Computer Interaction (HCI) knowledge within the development team is essential. Failure to provide good usability can lead to project failure, undermining user satisfaction and adoption of the technology.
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector
Authors: Che Ani Adi Izhar , Hamdani Mohammad Farid Mohammad Afiq , Firdhaus Kamaruzaman Ahmad Shukri , Ahmad Sharaf , Hadi Mokh Sholihul
Published: June 2024

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.
Resource allocation for device-to-device communications underlaying uplink cellular networks
Authors: Haroune Ahtirib , Messaoud Hettiri , Brahim Lejdel
Published: June 2024

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance.
Customer data prediction and analysis in e-commerce using machine learning
Authors: Al Rahib Md Abdullah , Saha Nirjhor , Mia Raju , Sattar Abdus
Published: June 2024

Customer churn is a major challenge faced by e-commerce companies, as it leads to loss of revenue and decreased customer loyalty. In recent years, for predicting and reducing client churn machine learning techniques are powerful tools. This research aims to explore the use of machine learning algorithms for predicting customer churn, annual spending, and product on-time delivery in e-commerce. The study first conducted a comprehensive review of the literature on customer churn in machine learning. The literature showed that customer churn has been predicted successfully using a variety of machine learning algorithms, including support vector machine (SVM), random forest, and decision tree in various industries. To address this gap in the literature, the study conducted an empirical analysis of customer churn in e-commerce using machine learning algorithms. The data were then pre-processed and analyzed utilizing machine learning techniques for prediction. According to the study’s findings, machine learning algorithms are effective in predicting customer churn, and product on-time delivery in e-commerce. The best-performing algorithm SVM achieved an accuracy of 83.45% in predicting customer churn and 68.42% for product on-time delivery prediction.
Numerical simulation of one-dimensional perovskite solar cell model
Authors: Aisyahtun Sakinah Saidatul Nur , Ranom Rahifa , Basmin Siti Hajar , Jin Yao Lee
Published: June 2024

Perovskite solar cell (PSC) is one of the advanced third-generation solar cells that have rapid efficiencies but instability issues in terms of air, moisture, and UV light sensitivity becomes a barrier to commercialization. The instability issue is due to the charge accumulation at the interface of the PSC reducing its efficiency. This research focuses on the operation of PSCs through a one-dimensional (1D) drift-diffusion model for planar heterojunction PSCs. The model also accounts for the electric potential by Poisson’s equation, the ion generation, and the recombination rate. The method of lines technique is applied to solve the model for the perovskite layer numerically using the finite difference method which is then solved forward in time using the ‘ode15s’ solver in MATLAB. It is highlighted that the comparison with the experimental data from the reference shows good agreement. The effect of parameter thickness variation of the perovskite layer upon the efficiency of PSCs is analyzed. The result shows that the best efficiency obtained is 19.77% obtained at thickness 0.25 𝜇𝑚. The results may prove useful for a guideline of the cell thickness that predicts the perovskites cell performance.
Enhancement of frequency transient response using fuzzy-PID controller considering high penetration of doubly fed induction generators
Authors: Abdillah Muhammad , Solehan Alfi , Pertiwi Nita Indriani , Setiadi Herlambang , Jasmine Senit Araminta , Afif Yusrizal , Delfianti Rezi
Published: June 2024

In modern power systems, renewable-based power plant such as wind power system is integrated significantly. Among numerous types of wind power systems doubly fed induction generators (DFIG) is becoming favorable in the last few years. However, adding a wind power plant could give a new challenge to the power system, especially in frequency stability. Hence, it is important to control the frequency of the power system to be able to find its initial condition in every condition. Generally, the frequency of the power system can be controlled by using automatic generation control (AGC). AGC is used to maintain the balance between generating capacity and the load by adding integral control to the governor. However, with more and more wind power systems in the grid conventional AGC is unsuitable. Hence, it is important to have an advanced AGC based on the artificial intelligence method. This paper proposed the application of fuzzy-proportional integrator derivative (fuzzy-PID) for AGC in power systems considering the high penetration of wind power systems. From the simulation results, it is found that the proposed method can reduce the overshoot and accelerate the settling time of frequency better than using conventional AGC.
Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration
Authors: Kusuma Purba Daru , Hasibuan Faisal Candrasyah
Published: June 2024

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.
Accurate classification of forest fires in aerial images using ensemble model
Authors: Madhuri Ch Raga , Jandhyala Sravya Sri , Ravuri Deepthi Meenakshi , Babu Vunnava Dinesh
Published: June 2024

This paper proposes a method to identify forest fires in aerial images using three different convolutional neural networks (CNNs). Unlike general approaches that make use of a single CNN to classify the images, the proposed solution uses the outcomes of different CNNs and considers the most predicted class. This method overcomes the problems associated with using a single CNN, such as low accuracy due to the drawbacks associated with that model. The three different classifiers used here are InceptionV3, VGG-16, and ResNet50. Classification is carried out based on the presence of fire or smoke features in the images. The individual predictions are combined using max-ensembling. The performance is analyzed using metrics like precision, recall, accuracy and F1-score. From the work, it was found that the combined model resulted in an accuracy of 95.8%. The results confirm that the final model provides greater classification accuracy than the individual models. The proposed method can be used to predict forest fires from live aerial images more accurately and help reduce the damage caused.
Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG
Authors: Chaitanya M Krishna , Sharma Lakhan Dev
Published: June 2024

Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.
Improved bidirectional long short term memory-based QRS complex detection using autoencoder
Authors: Insani Asep , Wibowo Suryo Adhi
Published: June 2024

In this paper, we propose a new technique to improve QRS complex detection. This technique consists of incorporating an autoencoder and bidirectional long short term memory (BiLSTM). The autoencoder used is a stacked autoencoder and functions as signal filtering. Meanwhile, BiLSTM is used as a detector. Exploration of the effect of hyperparameter in the autoencoder was also carried out to determine the effect on QRS complex detection. Furthermore, the dataset used in this study is the MIT-BIH arrhythmia database. Based on the experimental results, the hyperparameter in the autoencoder that gives a better effect on QRS complex detection is 16-8. Finally, the proposed method out-of-perform state of the art algorithm with accuracy 99.94%.
Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review
Authors: Fazraningtyas Winda Ayu , Rahmatullah Bahbibi , Dwi Salmarini Desilestia , Arrieya Ariffin Shamsul , Ismail Azniah
Published: June 2024

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.
A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals
Authors: Intongkum Chawakorn , Sengchuai Kiattisak , Booranawong Apidet
Published: June 2024

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.
Revisiting 5G quality of service in Bangkok metropolitan region: BTS Skytrain station areas
Authors: Daengsi Therdpong , Sriamorntrakul Pakkasit , Chatchalermpun Surachai , Phanrattanachai Kritphon
Published: June 2024

This article compares two of the leading mobile network operators (MNOs) in Thailand’s telecom market in terms of the service quality of Thailand’s fifth generation (5G) networks. The following three factors: download speed, upload speed, and latency, which are frequently considered to be indicators of the quality of Internet networks, were examined. The researchers employed the test results to determine the quality of service (QoS) that was achieved by comparing newly collected data to data that had previously been examined utilizing the same format and application in the middle of May 2021. The average download speed decreased from 196.4 Mbps in 2021 to 140.4 Mbps in 2023, while the average upload speed dropped from 62.6 Mbps in 2021 to 52.0 Mbps in 2023. Furthermore, the average latency increased from 14.9 ms in 2021 to 23.3 ms in 2023. These results show a considerably enhanced service although the test region in this study only comprised BTS stations. Furthermore, this was the case even though the test area in this study only encompassed a small percentage of the total population.
MCDM-AHP and ELECTRE collaboration apps for the best vendor selection technique
Authors: Akmaludin Akmaludin , Samudi Samudi , Baidawi Taufik , Dalis Sopiyan , Suriyanto Adhi Dharma , Widianto Kudiantoro
Published: June 2024

Vendor selection techniques are very important to maintain supply chain services, optimal service creates strong consistency in maintaining the continuity of supply chain business processes. The aim of this research is to provide an objective and consistent understanding of the best techniques in vendor selection which are implemented openly through the collaboration of multi-criteria decision making-analytic hierarchy process (MCDM-AHP) and ELECTRE. Empirical studies show how this approach is able to provide optimal decision-making support for the vendor selection process. Eight criteria are required which have contradictory meanings in their apps. These criteria include quality of goods (QG), payment methods (PMs), payment terms (PTs), minimum transactions (MTs), discounts (DS), delivery times (DTs), inventory (IN), and service (SV). The comparison importance value of the criteria is used as a measure of weighting the criteria through two testing approaches, namely mathematical algebra matrices and expert choice apps, through accurately assessing the optimal eigenvector from the two test approaches. Decision making support was carried out by comparison using 342 preference matrices which were developed into concordance and discordance matrices, the elimination process with threshold matrices found that the ranking results of four vendors were ranked first as worthy of being a selection priority and fifteen other vendors were ranked below.