bluuebunny/crossref_metadata_2025 huggingface dataset:
Examining English Reading Comprehension’ Enhancement on Narrative Texts using the Application of Direct Reading Thinking Activity (DRTA) Strategy
Authors: Dumbela Utari Apriliyani , Otoluwa Moon Hidayati , Abid Abid
Published: May 2024

The study reported in this article aims to investigate whether the use of the Direct Reading Thinking Activity (DRTA) strategy can help students find ways to enhance their English reading comprehension of narrative texts at a vocational senior high school in the Province of Gorontalo. The study focused on first-grade students, particularly in class X TKJ. A quantitative method was employed, utilizing a one-group pre-test post-test design to determine the significant difference in students’ comprehension before and after being taught using the strategy. The results indicate that the use of the Direct Reading Thinking Activity (DRTA) strategy help enhances students’ reading comprehension of English narrative texts. This was evidenced by a significant difference between the mean scores of the pre-test (8.7) and post-test (18.48). The t-test result indicated that T-test was higher than T-list: 13.91 > 2.056, with a significance level (α) of 0.05 = 2.056. Thus, H1 was accepted since L0 < L-list (0.100 < 0.173). Moreover, the use of this strategy facilitated students in maintaining focus, fostering critical thinking, active engagement in the learning process, enhancing comprehension of reading components, and understanding reading texts more easily.
HinweisgeberInnenschutzgesetz – HSchG
Authors: Exenberger Philip , Höntsch Paul , Miernicki Georg
Published: May 2024

Wegweiser durch das HinweisgeberInnenschutzgesetz Mit der Whistleblower-Richtlinie (Richtlinie (EU) 2019/1937) wurde ein europaweiter Rechtsrahmen zum Schutz von Hinweisgebern geschaffen. Auf Bundesebene erfolgte die Umsetzung dieser Richtlinie im HinweisgeberInnenschutzgesetz (HSchG), das seit Februar 2023 in Kraft ist. Das HSchG regelt neben dem Schutz von Hinweisgeber*innen insbesondere die Verpflichtung zur Einrichtung und Ausgestaltung von internen und externen Hinweisgeber*innensystemen in Unternehmen und Behörden. Dieses Handbuch bietet eine umfassende systematische Darstellung des HSchG unter Einbeziehung vorhandener Judikatur und Literatur. Dabei werden auch bisher nicht aufgeworfene Problemstellungen, wie zB zum sachlichen Geltungsbereich oder zum Schutz von Hinweisgeber*innen, beleuchtet und Lösungen angeboten. Dieses Handbuch richtet sich an betroffene Rechtsträger und Hinweisgeber*innen, an Unternehmensjurist*innen und Compliance-Verantwortliche sowie an Rechtsanwält*innen.
Transfer Learning for Prognostics and Health Management: Advances, Challenges, and Opportunities
Authors: Yan Ruqiang , Li Weihua , Lu Siliang , Xia Min , Chen Zhuyun , Zhou Zheng , Li Yasong , Lu Jingfeng
Published: May 2024

As failure data is usually scarce in practice upon preventive maintenance strategy in prognostics and health management (PHM) domain, transfer learning provides a fundamental solution to enhance generalization of data-driven methods. In this paper, we briefly discuss general idea and advances of various transfer learning techniques for PHM domain, including domain adaptation, domain generalization, federated learning, and knowledge driven transfer learning. Based on the observations from state of the art, we provide extensive discussions on possible challenges and opportunities of transfer learning for PHM domain to direct future development.
 
Conflict of Interest Statement
The authors declare no conflicts of interest.
Factors Associated with Upper Extremity Functionality After Mini-Open Release Surgery for Treatment of Carpal Tunnel Syndrome
Authors: Yetiş Mehmet , Canlı Mehmet , Kuzu Şafak , Valamur İrem , Kocaman Hikmet , Yıldız Nazım Tolgahan , Özüdoğru Anıl , Alkan Halil , Yetiş Aysu
Published: May 2024

Aim: The objective of this study was to investigate the correlation between upper extremity functionality, pain intensity, muscle strength, and symptom severity among patients diagnosed with carpal tunnel syndrome (CTS) who underwent mini-open release surgery.
Material and Method: This retrospective study entailed the review of medical records of 70 patients diagnosed with CTS who underwent mini-open release surgery between January 2021 and January 2023. Data were collected from preoperative assessments and routine follow-up evaluations conducted at 3 months post-surgery. Upper extremity functionality was assessed utilizing the Disabilities of Arm, Shoulder, and Hand (DASH) questionnaire, pain severity was determined using the Visual Analog Scale (VAS), muscle strength was evaluated through hand grip strength, and disease severity was measured using the Boston Carpal Tunnel Questionnaire (BCTQ).
Results: A statistically significant improvement was observed in DASH questionnaire scores, VAS scores, hand grip strength, and BCTQ scores at the 3-month follow-up compared to preoperative values (p
Preparation and in Vitro Evaluation of Solid-Lipid Nanoparticles (from Dika Wax) for Enhanced Delivery of Nevirapine in HIV/Aids Management
Authors: Onunkwo G. C.
Published: May 2024

The preparation and assessment of solid lipid nanoparticles (SLNs) of nevirapine with improved oral delivery for better management of HIV/AIDS was the aim of this research. Eight batches of SLNs of nevirapine were produced from Dika wax and evaluated for particle charges and distribution of the sizes of particles using Zeta sizer, surface shape with Cryo-Transmission Electron Microspcope (Cryo-TEM), chemical interaction between drug and excipients with Fourier Transform Infrared Spectroscope (FTIR). Loading capacity, encapsulation efficiency and in vitro drug release properties were determined. Release profiles were compared with ƒ2 statistic, one-way ANOVA and students’t-test. From the results obtained, Cryo-TEM revealed that the SLNs were round to oval in shape with smooth external surface. Zeta sizer particle sizes and distribution analysis indicated quality results for Nevirapine SLN Batches 15 and 18. The zeta potential results were: -16.83 ± 0.404 mV for Batch 1, -44.30 ± 0.624 mV for Batch 15 and -40.03 ± 2.65 mV for Batch 18. Batches 15 and 18 SLNs had loading capacities of 6.71% and 9.82% respectively and encapsulation efficiencies of 49.35% and 70.19% respectively. In vitro dissolution showed 102% release for batch 18 and 87.5% release for Batch 15 with a dissolution efficiency of 65% for Batch 15 and 83% for Batch 18 SLNs. ƒ2 statistic, ANOVA and students’ t-test revealed Batch 15 SLNs are similar to Batch 18 SLN. In conclusion, Batches 15 and 18 SLNs have good properties for enhancing the delivery of nevirapine as extended release dosage forms for better management of HIV/AIDS.
Species Name Identification for Essential Oils from Biomedical Abstracts Using Text Mining and Natural Language Processing
Authors: Cheng Chen Chou-
Published: May 2024

In recent years, the publication of scientific papers related to essential oils has achieved exponential growth due to the popularity of aromatherapy, although no studies using natural language processing and text mining methods to extract information from scientific articles related to essential oils are currently found. Accordingly, this study is the first to use natural language processing and text mining methods to identify species names appearing in abstracts related to essential oils. We obtained 34,637 abstracts using keywords, “essential oil” to quarry PubMed on 2024/03/15. The 1,081,005 species names of plants and fungi were obtained from Taxonomy FTP on the same day. The nouns from titles of articles related to essential oils were obtained via identification of parts-of-speech and from titles and abstracts extracted within italicized labels. These nouns were used to identify 10,445 plant and fungal species names downloaded from FTP appearing in abstracts related to essential oils with these identification terms being used to detect whether abstracts related to essential oils revealed the species names. 156,371 records contained links between PMID and Taxonomy ID. To the best of our knowledge, our study shows this method can efficiently identify the names of species from abstracts related to essential oil.
Multiband Frequency Compact Patch Antenna for 5G Applications
Authors: S Shruthi , M H Amritha , V Sreesha , V Abhijay , Kumar Kishor Krishna , Balamurugan V.
Published: May 2024

The design of a multi-band microstrip patch antenna, with a focus on the frequencies of 27.4GHz, 46GHz, and 58.2GHz, is described in this work. The antenna arrangement consists of a rectangular patch with an incorporated 0.088mm x 2mm rectangular slit. The antenna is based on a 3.285mm×7.235mm Roger RT/duroid 5880(tm) substrate with a dielectric constant of 4.4. It is fed via a 3.4mm×4.1mm microstrip-fed rectangular patch. The antenna operates in multi-band mode, as demonstrated by the simulation results, which show that it can reach respective bandwidths of 19 GHz and 12 GHz. At 27.4 GHz, 46 GHz, and 58.3 GHz, the return loss values are roughly -35.5 dB, -13.35 Db, and - 26.9 dB. The measured gains at 27.4GHz, 46GHz, and 56.6GHz are 6.04dB, 3.55dB, and 10.04dB, respectively. The outcomes produced by this suggested antenna should meet the requirements for 5G wireless communication applications and automotive radar systems.
Cuisine Connect: Revolutionizing College Dining through an InnovativeOnline Platform
Authors: Talwar Sahil , Badgujar Nikhil , Rokade Vaishnavi , Dhawale Hari , Kulkarni Nandkumar
Published: May 2024

A well-thought-out website called "Cuisine Connect" was created to address the typical problems college students have when it comes to eating on campus. Affordability and convenience are the two main issues that students with hectic schedules and limited funds worry about. Taking this into account, ourapp provides students with an affordable and readily navigable way to find and explore food halls nearby. We provide students the power to make educated dining choices that fit their preferences and financial limitsby giving them accessto a user-friendly interface and detailed information about each food hall, including its location, menu options, price range, and available cuisine kinds. "Cuisine Connect" assists neighbourhood food businesses in addition to meeting the requirements of students. Because we recognize how important it is to promote neighbourhood businesses, our platform provides mess halls with an easy registration process. Mess halls can dynamically change and update their listings after registering, giving them the ability to make sure that their offers are always correct and current. By working together, this cooperative strategy creates a mutually beneficial relationship between students and nearby food companies, improving everyone's dining experience. The fundamental goal of "Cuisine Connect" is to give college students a quick and easy way to find and visit food halls in their neighbourhood.Our goal is to reduce students' anxiety and uncertainty about meal planning by providing a platform that makes it easier to locate food options on campus. Furthermore, we support the sustainability and vibrancy ofthe local food ecosystem by putting students in touch with nearby food businesses. "Cuisine Connect" aims to improve college students' dining experiences while building a feeling of community and belonging on campus by supporting neighbourhood businesses and engaging the community.
Exploring the Role of Communication Skills on the Academic Productivity of Civil Engineering Students at an Educational Institution in the Philippines: A Qualitative Approach
Authors: Paulyn L. Marcelino, , Dennize Mia Aransazo, , Laurence Vhielle C. Gole Cruz, , Ayeesha F. Ablen, Bianca , Anne M. Godin, Nicole
Published: May 2024

This paper is a qualitative study that explores the role of Communication Skills on the Academic Productivity of Civil Engineering students at an educational institution in the Philippines. The purpose of this study is to determine whether Communication Skills have an impact on the Academic Productivity of the students. A structured interview questionnaire through Google forms was formulated to gather the data needed. A total of eight informants were involved in this study. The results have shown that Communication Skills play a crucial role to the overall Academic Productivity of the students. In line with this, it can be concluded that Communication Skills affect not only Academic Productivity but also the relationship between the students and their peers inside and outside the educational institution. Communication Skills contribute positively to the willingness of the students to communicate effectively. The results show that the most commonly used form of communication among Civil Engineering students was casual conversation. The challenges they encountered were also explored, as were their ways to overcome them. The study further explored how communication works between these students when doing academic tasks, such as collaborative work, and how they deal with group conflicts, specifically the setting of each other's schedules. Overall, this study provides information on how Civil Engineering students at an educational institution in the Philippines communicate with one another in spite of potential conflicts that can be encountered during their communication and how it influences their Academic Productivity.
Scheduling online oral assessments using an iterative algorithm: A profound software for educational continuity
Authors: Ally Said
Published: May 2024

In response to the socioeconomic effects of the global COVID-19 pandemic, including the disruption of educational continuity, an online oral assessment solution is proposed. While the next pandemic is unpredictable, the need for effective student assessment during lockdown is essential. This paper presents an iterative algorithm for scheduling online oral examinations as a profound software innovation capable of securely assessing students remotely while adhering to COVID-19 safety requirements. The efficacy of the software was tested by university tutors and undergraduate students for 60 days on local and web hosts. The results show that the algorithm offers optimal performance despite extraneous factors such as bandwidth capacity, operating system type, and client-side web browsers. The software manages the systematic random pairing of examiners, sessions, courses, and candidates, as well as the automated generation of question papers with a marking rubric. This invention would largely provide assessment entities with significant pedagogical, financial, and technological benefits while ensuring educational continuity regardless of the pandemic's status.
Exploring history teachers’ understanding of the role of professional learning communities in enhancing curriculum implementation
Authors: Chimbunde Pfuurai , Mbanjwa Asanda , Moreeng Boitumelo Benjamin
Published: May 2024

The present study examined the understanding of Professional Learning Communities (PLCs) among history teachers in South Africa. The research focused on a sample of 10 teachers from five schools in the UMgungundlovu District in KwaZulu-Natal. Drawing on the theoretical framework of Community of Practice (CoP) and adopting a qualitative approach, the study employed a case study design and utilised both semi-structured interviews and focus group discussions to generate data. The generated data were thematically analysed using Braun and Clarke's six septs of thematic analysis. The findings indicate that history teachers perceived PLCs as academic groups that fostered the development of historical content knowledge, new teaching approaches, and relevant assessment techniques through collaboration, engagement and the sharing of professional insights. It was also revealed that history teachers employed various strategies within PLCs, such as lesson study, workshops, seminars, and the integration of information and communication technology (ICT), to enhance the implementation of the history curriculum. The study recommends that school principals, with guidance from the Department of Education, take the lead in establishing Subject-Specific Professional Learning Communities (SSPLCs) to fully leverage the potential benefits of PLCs across all subjects in the school curriculum. Furthermore, it is suggested that some history teachers may require additional training in digital skills to effectively participate in virtual PLC activities. By contributing to the existing body of knowledge, this study advances the understanding of the best practices for using PLCs to improve school effectiveness and efficiency.
An Exploration of Learners’ Understanding of Euclidean Geometric Concepts: A Case Study of Secondary Schools in the OR Tambo Inland District of the Eastern Cape
Authors: Maqoqa Thabisa
Published: May 2024

A major concern in South Africa is the poor performance of learners in mathematics, particularly in geometry. This paper therefore sought to explore the learners’ understanding of Euclidean geometric concepts. The Van Hiele model was a useful framework for understanding the reasoning and challenges that students encounter with geometry. This study focused on 15 participants from rural South African schools, including 5 grade 10 mathematics learners in each of the three secondary schools and three teachers who are teaching mathematics in grade 10, in three secondary schools. Through face-to-face interviews, learners’ comprehension of geometry was qualitatively assessed. Pedagogical and methodological difficulties, lack of learners’ interest and comprehension of numerous geometrical concepts, as well as the absence of technology use, were found to contribute to the challenges in learning and teaching Euclidean geometry. The recommendations suggest that teachers should plan and prepare their geometry classes with the students’ understanding in mind that Euclidean geometry has been a cornerstone of mathematical education for centuries, teaching students critical thinking, problem-solving, and logical reasoning skills. This study adds to the existing literature on introducing new concepts in mathematics into the educational system of South Africa.

Keywords: Geometry, Students’ Interest, Self-efficacy, Understanding, Three-Dimensional Shape
Endovascular interventions for endoleaks
Authors: Vasilev S. K. , Petrov I. S. , Stankov Z. I. , Stoykova Zh. D.
Published: May 2024

Endoleak represents the most common complication after endovascular aortic aneurysm repair (EVAR) and is deﬁ ned as persistent perfusion of the aneurysmal sac, which subsequently could lead to its expansion and possibly rupture. There are different types of endoleaks, depending on their inﬂ ow source, regardless of the number and type of other vessels involved in the outﬂ ow (endoleak type I A/B/С, type II, type III, type IV and type V). The current gold standard for the diagnosis of endoleak is the contrast-enhanced helical computed tomography (CT). Since, there is no generally accepted consensus for the best surveillance and treatment methods for this pathology, in this article we will present the most effective endovascular interventions for the successful management of the different types of endoleaks. 
A case of late postoperative LMA/LAD area thrombosis and extensive anterior ST↑ MI
Authors: Nikolov P. , Goranov G. , Tonev G. , Aleksiev M.
Published: May 2024

Left anterior descending artery (LAD) bypass via left internal mammary artery (LIMA) as an arterial graft in the cardiac surgical treatment of multivessel ischemic heart disease is the gold standard due to its long-term patency and durability compared to all other grafts. Acute LIMA thrombosis causing acute coronary syndrome is rare. Several cases of acute LIMA occlusion in the late postoperative period are described in the literature, as one of the patients died during the procedure  and the others undergoing successful percutaneous coronary intervention (PCI). Here we report a case of probable thromboembolic event in the LIMA graft area 8 years after CABG causing an anterior acute anterior myocardial infarction with ST elevation and managed with aggressive pharmacotherapy.
The ectoparasitoid wasp Heterospilus sicanus (Marshall, 1888) (Hymenoptera, Braconidae, Doryctinae) as a natural enemy of Gastrallus pubens Fairmaire, 1875 (Coleoptera, Ptinidae) in Italy
Authors: Belokobylskij Sergey A. , Guarino Salvatore , Savoldelli Sara , Jucker Costanza , Peri Ezio , Broad Gavin R. , Cerasa Giuliano
Published: May 2024

Heterospilus sicanus (Marshall, 1888) is redescribed and illustrated based on the holotype of Dendrosoter sicanus Marshall, 1888 and on recently collected material from its type locality (Sicily, Italy). Previous host records for this species are unreliable. Here, the host of H. sicanus, the rare ptinid beetle Gastrallus pubens Fairmaire, 1875, is recorded for the first time, having been reared in a historic library in Palermo, Italy. Heterospilus sicanus is compared with the similar species Telebolus (= Heterospilus) corsicus Marshall, 1888, which was described in the same monograph from Corsica (France), and it is also redescribed and illustrated. Atoreuteus ceballosi Docavo Alberti, 1960, syn. nov. is synonymised under Heterospilus sicanus (Marshall, 1888), and Hormiopterus (= Rhaconotus) ollivieri Giraud var. flava Fahringer, 1931, syn. nov. is a junior synonym of Heterospilus cephi Rohwer, 1925. A key for determination of the Western Palaearctic Heterospilus species with a striate vertex is provided and the distributions of H. sicanus and H. corsicus are discussed.
Platyintybia, a new genus of Apalochrini (Coleoptera, Melyridae, Malachiinae) from China
Authors: Liu Zhenhua , Wang Yuqi , Song Haitian , Ding Bi , Li Zhiqiang
Published: May 2024

A new genus of malachiine Melyridae, Platyintybiagen. nov., is described based on several male-specific characters, along with description of its type species, Platyintybia zhongshanensissp. nov., from China. A new combination, Platyintybia sarawakensis (Champion, 1921), comb. nov., is proposed after examining the type specimen; this species is newly recorded from China. A key to the genera of Chinese Apalochrini is provided for the first time.
Description of a new troglobitic Sinocyclocheilus (Pisces, Cyprinidae) species from the upper Yangtze River Basin in Guizhou, South China
Authors: Shao Wei-Han , Cheng Guang-Yuan , Lu Xiao-Long , Zhou Jia-Jun , Zeng Zhi-Xuan
Published: May 2024

Sinocyclocheilus guiyang, a new troglobitic species from a subterranean tributary of the upper Yangtze Basin in Guiyang City, Guizhou Province, China is described in the present study. The new species is distinguishable from its congeneric species by a combination of the following characters: tip of maxillary barbel reaching to posterior edge of orbit; forehead horn absent; eye absent (or highly reduced) and tip of pectoral fins not significantly extending beyond the base of the pelvic fin. Molecular evidence, based on the mitochondrial cytochrome b (cytb) gene, further supports the validity of the species and also reveals its close relationship with S. cyphotergous, S. multipunctatus, S. punctatus and S. sanxiaensis. In addition, the new species faces a high risk of extinction, underscoring the urgency for habitat protection measures within its limited range.
A new species of Cyrenoida (Bivalvia, Cyrenoididae) from the Western Atlantic, with remarks on Cyrenoididae anatomy
Authors: Valentas-Romera Bárbara L. , Simone Luiz Ricardo L. , Marques Rodrigo Cesar
Published: May 2024

Cyrenoida implexasp. nov. is the first species of Cyrenoididae in the Southern West Atlantic. This new species exhibits external similarities to C. floridana but is distinguished by distinct right hinge dentition, larger siphons and a more extensive siphonal area at the mantle border, an incurrent siphon with three rows of papillae, a lack of papillae at the middle mantle fold, and smaller adductor muscle volume. In the environment, it possesses a higher saline tolerance than C. floridana.
Analytical study of time-fractional heat, diffusion, and Burger's equations using Aboodh residual power series and transform iterative methodologies
Authors: Yasmin Humaira , Almuqrin Aljawhara H.
Published: May 2024

Within the framework of time fractional calculus using the Caputo operator, the Aboodh residual power series method and the Aboodh transform iterative method were implemented to analyze three basic equations in mathematical physics: the heat equation, the diffusion equation, and Burger's equation. We investigated the analytical solutions of these equations using Aboodh techniques, which provide practical and precise methods for solving fractional differential equations. We clarified the behavior and properties of the obtained approximations using the suggested methods through exact mathematical derivations and computational analysis. The obtained approximations were analyzed numerically and graphically to verify their high accuracy and stability against different related parameters. Additionally, we examined the impact of varying the fractional parameter the profiles of all derived approximations. Our results confirm these methods, efficacy in capturing the complicated dynamics of fractional systems. Therefore, they enhance the comprehension and examination of time-fractional equations in many scientific and technical contexts and in modeling different physical problems related to fluid mediums and plasma physics.
An $ {\varepsilon} $-approximation solution of time-fractional diffusion equations based on Legendre polynomials
Authors: Zhang Yingchao , Lin Yingzhen
Published: May 2024

The purpose of this paper is to establish a numerical method for solving time-fractional diffusion equations. To obtain the numerical solution, a binary reproducing kernel space is defined, and the orthonormal basis is constructed based on Legendre polynomials in this space. In order to find the $ {\varepsilon} $-approximation solution of time-fractional diffusion equations, which is defined in this paper, the algorithm is designed using the constructed orthonormal basis. Some numerical examples are analyzed to illustrate the procedure and confirm the performance of the proposed method. The results faithfully reveal that the presented method is considerably accurate and effective, as expected.
Complex shadowed set theory and its application in decision-making problems
Authors: Alsharo Doaa , Abuteen Eman , Alkouri Abd Ulazeez M. J. S. , Alkhasawneh Mutasem , Al-Zubi Fadi M. A.
Published: May 2024

Modern technology makes it easier to store datasets, but extracting and isolating useful information with its full meaning from this data is crucial and hard. Recently, several algorithms for clustering data have used complex fuzzy sets (CFS) to improve clustering performance. Thus, adding a second dimension (phase term) to the range of membership avoids the problem of losing the full meaning of complicated information during the decision-making process. In this research, the notion of the complex shadowed set (CSHS) was introduced and considered as an example of the three region approximations method simplifying processing with the support of CFS and improving the representation of results attained within. This notion can be founded by extending the shadowed set codomain from $ \left\{0, \left[0, 1\right], 1\right\} $ into $ \left\{0{e}^{i\theta }, \left[0, 1\right]{e}^{i\theta }, 1{e}^{i\theta }\right\} $. The significance of CSHS was illustrated by giving an example. Additionally, some properties of the CSHS were examined. The basic CSHS operations, complement, union, and intersection were investigated with their properties. Finally, an application in decision-making was illuminated to support the present notion.
Minimum distance–unbalancedness of the merged graph of $ C_3 $ and a tree
Authors: Su Zhenhua , Tang Zikai
Published: May 2024

For a graph $ G $, let $ n_G(u, v) $ be the number of vertices of $ G $ that are strictly closer to $ u $ than to $ v $. The distance–unbalancedness index $ {\rm uB}(G) $ is defined as the sum of $ |n_G(u, v)-n_G(v, u)| $ over all unordered pairs of vertices $ u $ and $ v $ of $ G $. In this paper, we show that the minimum distance–unbalancedness of the merged graph $ C_3\cdot T $ is $ (n+2)(n-3) $, where $ C_3 \cdot T $ is obtained by attaching a tree $ T $ to the cycle $ C_3 $.
Threshold dynamics of a switching diffusion SIR model with logistic growth and healthcare resources
Authors: Wu Shuying , Yuan Sanling
Published: May 2024

In this article, we have constructed a stochastic SIR model with healthcare resources and logistic growth, aiming to explore the effect of random environment and healthcare resources on disease transmission dynamics. We have showed that under mild extra conditions, there exists a critical parameter, i.e., the basic reproduction number $ R_0^s $, which completely determines the dynamics of disease: when $ R_0^s < 1 $, the disease is eradicated; while when $ R_0^s > 1 $, the disease is persistent. To validate our theoretical findings, we conducted some numerical simulations using actual parameter values of COVID-19. Both our theoretical and simulation results indicated that (1) the white noise can significantly affect the dynamics of a disease, and importantly, it can shift the stability of the disease-free equilibrium; (2) infectious disease resurgence may be caused by random switching of the environment; and (3) it is vital to maintain adequate healthcare resources to control the spread of disease.
Reachable set estimation for 2-D switched nonlinear positive systems with impulsive effects and bounded disturbances described by the Roesser model
Authors: Ma Hongyu , Tian Dadong , Li Mei , Zhang Chao
Published: May 2024

The reachable set estimation for two-dimensional (2-D) switched nonlinear positive systems (SNPSs) with bounded disturbances given by the Roesser model is investigated in this paper, in which both the time-varying delays and lagged impulsive effects are taken into account. By applying the average dwell time (ADT) technique, we provide a sufficient condition for the presence of a ball such that any solution of the system converges exponentially within it. An accurate estimate of the convergence rate is provided. We also extend the result to 2-D SNPSs with multi-directional delays, general 2-D switched linear systems, and 2-D SPNSs with heterogeneous delays. Finally, an example is worked out to demonstrate the effectiveness of the main result.
Teaching of Physics in Engineering from Problem-Based and Project-Based Learning Approaches
Authors: Milla Pino Manuel Emilio , Rodriguez Ordoñez Freddi Roland , Shimabuku Ysa Ricardo Angel , Jara Llanos Diomer Marino , Torres Cruz Maria Marleni
Published: May 2024

Within the scope of this research, an evaluation was conducted on the problem-based learning (PBL) and project-based learning (PrBL) methods in teaching physics to mechanical and electrical engineering students at a public university in Peru. Two groups, each consisting of 15 and 16 students, were carefully selected to ensure uniformity in academic, socioeconomic, and emotional aspects. The measurement of academic performance was conducted both at the beginning and the end of the period. The statistical analysis using the t-student test revealed that the PrBL method was significantly more effective than PBL. These findings underscore the association between the choice of learning methods and the specific scientific area being taught, emphasizing the superiority of the PrBL approach in this educational context. This discovery suggests that implementing learning strategies that incorporate a project-based approach can be a valuable option to enhance academic performance in scientific disciplines.
Leveraging AI to Enhance the Global Mobility Experience
Authors: Bansal Shruti , Rathi Punjika , Agarwal Rachit , Ahluwalia Ramneek
Published: May 2024

The study investigates how AI-driven solutions are revolutionizing student interactions, admissions processes, academic support, and cross-cultural adaptation. Through intelligent chatbots, personalized recommendations, and predictive analytics, universities are catering to individual needs and streamlining administrative procedures. Furthermore, AI-powered language translation tools are aiding international students in overcoming language barriers, fostering better communication, and enriching their learning experiences. In the Indian higher education landscape, this trend holds particular significance due to the nation's burgeoning student population and aspirations for global recognition. The chapter underscores the importance of continuous innovation, collaboration, and adaptability in integrating AI solutions effectively. By embracing these advancements, Indian universities can position themselves as attractive destinations for international students, fostering diversity and cross-border learning. This study provides valuable insights into the symbiotic relationship between AI and global education mobility, accentuating the need for a dynamic approach to reshape the future of higher education on a global scale and helpful for policymakers to enhance the AI in higher education system.
Investigating the Moderating Impact of Institutional Quality on the Digitalization and Financial Development Nexus
Authors: Fauzel Sheereen , Matadeen Jeevita
Published: May 2024

It is crucial to assess the impact of digital transformations on the financial sector given the rapid emergence of digitalisation. As such, the study aims at identifying the extent to which digitalization has impacted on financial sector development in a sample of small island developing states, over the period 2000 to 2020. Additionally, given the crucial importance of institutional quality in reducing banking sector risk, and therefore enhancing the level of development in the financial sector, the study investigates the nexus between digitisation and financial development through the moderating impact of institutional quality. The study adopts the PVECM framework to investigate the link. The results demonstrate digitalization is important to boost financial development and digitalization coupled with good quality institutions will further promote financial development. More so, FDI and trade openness are also determinants of financial development for the case of the small island economies considered in the present study.
Understanding the Concepts of Tools and Techniques for Data Analysis Using RStudio
Authors: Kumar Lalit , Tyagi Vivek
Published: May 2024

Enormous research papers are floating in the journals market to get published. But now as per the quality standards and norms of publication, some guidelines have been formulated by the prominent authorities to eradicate the pandemic issue of plagiarism. Moreover, it has also been considered whether genuine or licensed software has been utilized to analyze the research data or not. Hence, it is the time to explore some avenues so that mass researchers can be benefitted. R-software, designed by Robert Gentleman and Ross Ihaka from R core team, and R Studio, an open-source integrated development environment (IDE) for R, founded by J.J. Allaire, have emerged as the best platforms for the upcoming researchers which are not bounded with any constraints of licensing. In this article, few packages of R- Software, their significance and implementation have been discussed and summarized. Furthermore some data analysis techniques like t-test, linear regression technique, etc. have also been illustrated using R Studio.
Optimal Configuration of Solar-Based Combined Hydrogen, Heat, and Power (S-CHHP) to Minimize Harmonic in Point of Common Coupling (PCC)
Authors: Hosseinnia Hamed , Rostami Rana
Published: May 2024

The growth of the load makes the need to build a new power plant inevitable. According to the type of load, the type of DGs is selected. In loads that need both electricity and heat, simultaneous production units are used. Improving system technical criteria's such as: voltage stability, total harmonic distortion (THD), power loss are main aims of researchers. Harmonics are one of the stressful factors in the system. Harmonics are created both by the load and by the DGs themselves. In this chapter, the goal is to reduce the harmonics at the point of common coupling (PCC). For this purpose, solar-based combined hydrogen, heat, and power (S-CHHP) is used. Artificial intelligence (AI) is the one of strong tools in define and solve of optimization problems. Due to importance of data gathering, process and send/receive in this study, the system to manage information data with minimum error, is inevitable. Natural language process (NLP) is the best choice to utilize in this system.
Data Ethics and Privacy
Authors: Golyan Alka , Panchal Shikhar , Vaghasiya Dhruvesh , Parekh Harsh
Published: May 2024

The ethical issues surrounding data acquisition, use, and management have gained prominence in the quickly changing field of data analytics. In the framework of contemporary data analytics, this chapter examines the complex issues of data ethics and privacy. It looks at the moral dilemmas brought on by gathering and using enormous volumes of data, such as those involving permission, openness, and justice. It also explores the effects of data analytics methods on society's values and individual privacy rights, including machine learning and artificial intelligence. The chapter also covers new rules and frameworks being developed to address ethical issues with data analytics procedures. This chapter offers insights into best practices for managing the ethical complexity inherent in data-driven decision-making through the analysis of case studies and ethical problems. Ultimately, it emphasizes how crucial it is to embrace moral standards and privacy-protecting methods to guarantee ethical and long-lasting data analytics procedures in the future.
Enhancing Learner Motivation and Academic Achievement
Authors: Subbiah Ahgalya
Published: May 2024

This study examines the impact of technology on education, specifically focusing on motivation and learning principles for instructional enhancement. For 84 junior students from both computer science and computer forensics, ARCS-based instruction showed improved motivation and academic performance. CS students experienced a 50% increase in success opportunities, while CF students had a 70% higher satisfaction rate with rewards. Statistical analysis confirmed the differences (p < 0.05), indicating tailored educational approaches; CS students adapt well; CF students prefer intrinsic reinforcement. However, academic achievement varied significantly, emphasizing the need for refined motivational strategies, particularly for CF students. The findings suggest that ARCS-based instruction benefits motivation and academic achievement, necessitating tailored approaches across subject areas. Integrating motivation science and technology can create captivating learning experiences that promote lifelong learning and academic success.
Future Trends in Data Analytics
Authors: Thangamani S. , Saranya R.
Published: May 2024

Data analytics stands at the precipice of an unprecedented transformation, driven by technological breakthroughs, shifting user demands, and evolving industry landscapes. This abstract delineates forthcoming trends poised to redefine the fabric of data analytics in the foreseeable future. The future of data analytics is characterized by a convergence of cognitive computing, ethical AI, hybrid cloud architectures, augmented data management, blockchain technology, edge intelligence, and continuous intelligence. By embracing these transformative trends, organizations can unlock the full potential of data analytics, foster innovation, and drive sustainable growth in an increasingly data-driven world. AI-Powered Automation and machine learning algorithms are set to revolutionize data analytics by automating repetitive tasks, uncovering hidden patterns, and delivering actionable insights at scale. Ethical data governance with growing concerns about privacy, security, and algorithmic bias, ethical data governance emerges as a cornerstone of responsible data practices.
Machine Learning Techniques for Emotion Detection Using Eye Gaze Localisation
Authors: Goyal Shivalika , Laddi Amit
Published: May 2024

The ability to detect and interpret human emotions is vital for effective communication. This chapter explores the integration of machine learning with eye gaze localization for emotion detection, offering a non-intrusive and natural means of expression. Eye gaze data, encompassing parameters like gaze direction and pupil dilation, provides a rich basis for machine learning models. The chapter emphasizes the significance of quality training data, delving into data collection, pre-processing, and feature extraction. Various machine learning models, including support vector machines and deep learning models like CNNs and RNNs, are discussed for emotion detection. Evaluation metrics and cross-validation techniques ensure model accuracy. Practical applications in healthcare, marketing, and human-computer interaction are presented, showcasing the benefits. Despite successes, challenges like data bias and privacy concerns persist. The chapter encourages ongoing exploration of emerging technologies and sensory data integration for more robust models in the evolving field of emotion detection using eye gaze localization.
Emotional Intelligence
Authors: Sharma Nishtha , Pandey Shinki
Published: May 2024

Emotional intelligence, though captivating, faces challenges due to the lack of a unified definition and measurable aspects. This ambiguity hinders research and development of interventions. This chapter explores these complexities. Current models like Goleman's, emphasizing social skills and self-awareness, and Mayer and Salovey's, focusing on cognitive processes, exemplify the variation. Each model has strengths and weaknesses, highlighting the need for a more cohesive approach. Measuring EI is another hurdle. Self-report measures face social desirability bias, while observer ratings lack objectivity. Performance-based assessments offer promise, but may not generalize to real-world situations. A multi-method approach that combines these methods is recommended. Cultural variations further complicate the picture. Emotional expression and expectations differ significantly, making existing models potentially biased. Culturally sensitive measures and interventions are crucial.
Advancements in Facial Expression Recognition Using Machine and Deep Learning Techniques
Authors: Singh Shivani , Pandey Jay Kumar , Rai Mritunjay , Saxena Abhishek Kumar
Published: May 2024

In the field of computer vision, facial expression recognition is an emerging field that looks at visual face data to try and understand human emotions. Facial expression detection and recognition has been popular recently in the research field. The literature is compiled from several credible studies that have been released in the last 10 years. In the recent years, the artificial intelligence has evolved a lot along with which there has been rise in experimenting with various methodologies for facial expression recognition, which has given promising results in accurately identifying and recognizing facial emotions from input modalities like images, text, facial expressions, and physiological signals. However, accurate analysis of basic emotions like anger, happiness, sadness, and fear remains a challenge. This chapter provides valuable insights for researchers interested in advancing facial emotion recognition using machine learning and deep learning techniques.
Explainable Artificial Intelligence (XAI) for Emotion Detection
Authors: Ayyalasomayajula Madan Mohan Tito , Ayyalasomayajula Sailaja , Pandey Jay Kumar
Published: May 2024

This chapter delves into the significance of explainable artificial intelligence (XAI) in emotion detection (ED) systems, which aim to provide transparency and interpretability in affective computing. The chapter introduces ED systems, defining their purpose and importance in various industries. Subsequently, the need for XAI in emotion detection is discussed, emphasizing ethical concerns, legal requirements, and user trust. Next, the fundamentals of ED systems are explored, encompassing techniques for emotion recognition via facial expressions, voice tones, and text. The challenges associated with these techniques, including variability in human expressions, cultural differences, and data scarcity, are addressed. Next, explanation methods for ED models, and the popular XAI frameworks are presented and evaluated. Quantitative and qualitative evaluation metrics are employed to assess the effectiveness of XAI in ED. Lastly, three case studies demonstrate the successful application of XAI, and as research evolves, future directions that include advanced explainable ED are discussed.
Medical Imaging and Radiology in Explainable Deep Learning
Authors: Kaswan Kuldeep Singh
Published: May 2024

This chapter delves into the intricacies of medical imaging and ultrasound, where interpretable deep learning methodologies emerge as invaluable tools. It elucidates the utilization of deep learning models to extract radiomic features, discern their clinical significance, and various methodologies for incorporating them into structures that are comprehensible. The study underscores the criticality of comprehending radiomic features and their pivotal role in facilitating accurate diagnoses and informed treatment decisions. The primary objective of this chapter is to attain an intricate understanding of deep learning methodologies tailored explicitly for healthcare AI, with a focal point on radiologists and medical images.
Hybrid Approaches Combining Bio-Inspired Optimization With Machine Learning in Intrusion Detection
Authors: Jeyavim Sherin R. C. , Parkavi K. , Vanitha J.
Published: May 2024

This chapter aims to delve into the novel integration of machine learning techniques and bio-inspired optimization algorithms within the realm of intrusion detection systems (IDS). In recent decades, IDS have emerged as vital tools for safeguarding data and networks. The detection of intrusions is one of the biggest challenges in network traffic analysis. The proposed research is unique because it aims to develop a system for the detection of intrusions. An ant colony optimization is used in this study to detect intrusions using support vector machines. The proposed system was tested using the Knowledge Discovery and Data Mining (KDD) Cup '99. Dimensionality is a major challenge in network analysis datasets. Dimensionality reduction was achieved using the ant colony optimization algorithm. An ACO approach selects a meaningful subset of features from the full dataset. SVM machine learning algorithms were used to detect intrusions using the selected subset of features. ACO-SVM is therefore more effective at safeguarding a network system from intrusions, based on this analysis.
Silica Xerogel Modified with Cetyltrimethylammonium Bromide as an Effective Adsorbent for Methyl Orange Dye
Authors: Buzato Gabriel V. , Olívio Pedro H.P. , Souza Adriano L.
Published: May 2024

The study of water treatment technologies has been growing due to mounting concerns regarding dye contamination. Adsorption-based technologies that use porous materials have been proven useful in water decontamination. However, porous silica xerogels have not been extensively explored as adsorbents for the methyl orange (MO) dye. In this study, the MO-adsorptive behavior of silica xerogels was investigated. Two silica xerogels were synthesized using tetraethyl orthosilicate, and one was modified with cetyltrimethylammonium bromide (CTAB). The adsorptive capacities of the unmodified silica xerogel (SiO2-UN) and the CTAB-modified silica xerogel (SiO2-CTAB) were compared. Results showed a better fit to the Langmuir isotherm model, with maximum adsorbed amounts of 1.52 mg g-1 and 25.5 mg g-1 for SiO2-UN and SiO2-CTAB, respectively. The higher value for SiO2-CTAB is mainly attributed to the electrostatic interactions between MO and the ammonium groups present in the modified xerogel. A study of the porosities of both xerogels, using N2 adsorption and desorption isotherms, indicated the samples were mesoporous. These findings suggest that SiO2-CTAB exhibits favorable MO adsorption and could be employed in future wastewater treatment processes.