bluuebunny/crossref_metadata_2025 huggingface dataset:
PECULIARITIES OF OVERCOMING PROFESSIONAL BURNOUT AMONG MILITARY PERSONNEL
Authors: Pasichnyk Ihor , Demydiuk Volodymyr
Published: June 2023

The article deals with the issue of professional burnout among servicemen who are in the area of combat operations. The peculiarities of psychological counseling of military professional activity of servicemen are described. Psychological support of military personnel includes: psychological counseling, psychotherapeutic assistance, personal factors of professional burnout (introversion, sensitivity, low level of self-esteem and self-respect, desire for dominance); status-role (role conflict, negative stereotypes, dissatisfaction with professional promotion, lower social status, isolation from the reference group); professional-organizational (organizational conflicts, lack of autonomy, excessive control); existential (dissatisfaction with the results achieved, feelings of frustration, dissatisfaction with the results of activities, loss of meaning, loneliness).  Professional burnout leads to impaired functioning of the unit, causes a negative psychological climate, and contributes to a decrease in performance. If a serviceman develops symptoms of professional burnout, his psychological state tends to become negative and extends beyond the individual's well-being. The effective performance of combat missions is represented not only by the professional training and physical capabilities of the individual, but also by his or her ability to express responsibility, independence, self-control, flexibility, ability to make independent decisions and constructively interact with other members of the military team under the influence of stress factors and the performance of professional tasks.
DYNAMICS OF TYPES OF MOTIVATION FOR PROFESSIONAL ACTIVITIES IN UKRAINIAN ANTARCTIC EXPEDITIONS AMONG WINTERERS OF DIFFERENT AGE GROUPS
Authors: Miroshnychenko Olena , Bakhmutova Larysa
Published: June 2023

We presented the results of empirical studies of the motivations types for winterers of Ukrainian Antarctic expeditions (UAE). A theoretical analysis of motivations for extreme activities in polar expeditions was carried out. Based on the theoretical analysis of modern approaches to age differentiation we divided the winterers into three age groups: younger (22–34), middle-aged (35–45) and older (46–62 years old). According to the Reiss Motivation Profile® (RMP®) method 16 universal basic motivations of winterers for professional activity in extreme Antarctic conditions are determined for each of age group. The research was conducted two times: the first at the beginning of the expedition and the second after six months of stay on the Antarctic base “Akademik Vernadsky”. All of motivations were summarized and reduced to three types of reason for professional activity: monetary, social and spiritual. At the beginning of the expedition the spiritual type prevailed then the social type and monetary type took the last place for all age groups. The repeated study revealed an increase for all types of motivation relative to the first study. Thus, the life and professional activity in extreme conditions strengthens one's own professional hardiness and faith in the people around. It was revealed that the main type of motivation for the middle age group was monetary. At the same time the high levels of spiritual and social types of motivation do not reduce the role of monetary motives. The last have been remains a catalyst which capable of increasing the efficiency of professional activity. The obtained results can be applied not only for planning expeditionary activities in long-term Antarctic expeditions, but in the military and sports psychology.
MATHEMATICAL TRAINING PROBLEMS OF THE FUTURE PRIMARY SCHOOL TEACHERS
Authors: Pasichnyk Yadviha
Published: June 2023

The article characterizes the problems associated with the increasing of the level of mathematical training of future primary school teachers, identifies the tasks of its improvement and ways of their implementation, emphasizes that the acuteness of the problem of mathematical training of future teachers and the effectiveness of its solution depends on a group of factors, both external and internal. Moreover, the article ensures the increase in the level of mathematical and methodical training of future primary school teachers.
Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics
Authors: Ayfantopoulou Georgia , Mintsis Evangelos , Maleas Zisis , Mitsakis Evangelos , Grau Josep Maria Salanova , Mizaras Vassilis , Tzenos Panagiotis
Published: June 2023

Accurate and reliable traffic state estimation is essential for the identification of congested areas and bottleneck locations. It enables the quantification of congestion characteristics, such as intensity, duration, reliability, and spreading which are indispensable for the deployment of appropriate traffic management plans that can efficiently ameliorate congestion problems. Similarly, it is important to categorize known congestion patterns throughout a long period of time, so that corresponding traffic simulation models can be built for the investigation of the performance of different traffic management plans. This study conducts cluster analysis to identify days with similar travel conditions and congestion patterns. To this end, travel, traffic and weather data from the Smart Mobility Living Lab of Thessaloniki, Greece is used. Representative days per cluster are determined to facilitate the development of traffic simulation models that typify average traffic conditions within each cluster. Moreover, spatio-temporal matrices are developed to illustrate time-varying traffic conditions along different routes for the representative days. Results indicate that the proposed clustering technique can produce valid classification of days in groups with common characteristics, and that spatio-temporal matrices enable the development of traffic management plans which encompass routing information for competing routes in the city of Thessaloniki.
AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control
Authors: Binjaku Kleona , Pasquale Cecilia , Sacone Simona , Meçe Elinda Kajo
Published: June 2023

In this extended abstract, we propose an Artificial Intelligence-based model dedicated to the representation of a multi-class traffic flow, i.e. a traffic flow in which different vehicle classes (at least cars and trucks) are explicitly represented, with the aim of using it for the development of freeway traffic control schemes based on ramp management. Specifically, the goal of this work is to develop a hybrid modelling technique in which a Machine Learning component and the multi-class version of METANET model are adopted to determine a better estimation and forecasting tool for freeway traffic. The resulting model is specifically devised in order to be included in a Model Predictive Control (MPC) scheme for the required traffic state prediction.
Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation
Authors: Genser Alexander , Makridis Michail A. , Kouvelas Anastasios
Published: June 2023

Emerging sensors and intelligent traffic technologies provide extensive data sets in a traffic network. However, realizing the full potential of such data sets for a unique representation of real-world states is challenging due to data accuracy, noise, and temporal-spatial resolution. Data assimilation is a known group of methodological approaches that exploit physics-informed traffic models and data observations to perform short-term predictions of the traffic state in freeway environments. At the same time, neural networks capture high non-linearities, similar to those presented in traffic networks. Despite numerous works applying different variants of Kalman filters, the possibility of traffic state estimation with deep-learning-based methodologies is only partially explored in the literature. We present a deep-learning modeling approach to perform traffic state estimation on large freeway networks. The proposed framework is trained on local observations from static and moving sensors and identifies differences between well-trusted data and model outputs. The detected patterns are then used throughout the network, even where there are no available observations to estimate fundamental traffic quantities. The preliminary results of the work highlight the potential of deep learning for traffic state estimation.
Towards Efficient Incident Detection in Real-time Traffic Management
Authors: orrent-Fontbona Ferran T , Dominguez Monica , Fernandez Javier , Casas Jordi
Published: June 2023

Incident detection is a key component in real-time traffic management systems that allows efficient response plan generation and decision making by means of risk alerts at critical affected sections in the network. State-of-the-art incident detection techniques traditionally require: i) good quality data from closely located sensor pairs, ii) a minimum of two reliable measurements from the flow- occupancy-speed triad, and iii) supervised adjustment of thresholds that will trigger anomalous traffic states. Despite such requirements may be reasonably achieved in simulated scenarios, real-time downstream applications rarely work under such ideal conditions and must deal with low reliability data, missing measurements, and scarcity of curated incident labelled datasets, among other challenges. This paper proposes an unsupervised technique based on univariate timeseries anomaly detection for computationally efficient incident detection in real-world scenarios. Such technique is proved to successfully work when only flow measurements are available, and to dynamically adjust thresholds that adapt to changes in the supply. Moreover, results show good performance with low-reliability and missing data.
A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data
Authors: Lu Qing-Long , Qurashi Moeid , Antoniou Constantinos
Published: June 2023

Dynamic OD estimators based on traffic measurements inevitably encounter the indeterminateness problem on the posterior OD flows as such systems structurally have more unknowns than constraints. To resolve this problem and take advantage of the emerging urban mobility data, the paper proposes a dynamic OD estimator based on location-based social networking (LBSN) data, leveraging the two-stage stochastic programming framework, under the assumption that similar check-in patterns are generated by the same OD pattern. The search space of the OD flows will be limited by integrating a batch of realizations/scenarios of the second-stage problem state (i.e. check-in pattern) in the model. The two-stage stochastic programming model decomposes in a master problem and a set of subproblems (one per scenario) via the Benders decomposition algorithm, which will be tackled alternately. The preliminary results from experiments conducted with the Foursquare data of Tokyo, Japan, show that the proposed OD estimator can effectively recurrent the check-in patterns and result in a good posterior OD estimate.
A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks
Authors: Canudas-de-Wit Carlos , Rodriguez-Vega Martin , De Nunzio Giovanni
Published: June 2023

This paper introduces a new model for electric vehicle mobility and energy consumption in urban traffic networks. The model couples the vehicle mobility described by a set of ODEs over a graph capturing the Origin-destination motion for urban networks,and the energy consumption associate to this mobility patterns. This model is illustrated in a simple pedagogic example showing its capabilities, such as keeping track of the vehicle state of charge, current energy and available storage.
Timed Pulses in DNA strand displacement reactions
Authors: Bucci Juliette , Irmisch Patrick , Del Grosso Erica , Seidel Ralf , Ricci Francesco
Published: June 2023

Inspired by naturally occurring regulatory mechanisms that allow complex temporal pulse features with programmable delays in gene expression and biological pathways, we demonstrate here a strategy to achieve temporally programmed pulse output signals in DNA-based Strand Displacement Reactions (SDRs). To achieve this, we rationally designed input strands that, once bound to their target duplex, can be gradually degraded, resulting in a pulse output signal. We also designed blocker strands that suppress strand displacement and determine the time at which the pulse reaction is generated. We show that by controlling the degradation rate of blocker and input strands we can finely control the delayed pulse output over a range of 10 hours. We also prove that it is possible to orthogonally delay two different pulse reactions in the same solution by taking advantage of the specificity of the degradation reactions for input and blocker strands. Finally, we show here two possible applications of such delayed pulse SDRs: the time-programmed pulse decoration of DNA nanostructures and the sequentially-appearing and self-erasing formation of DNA-based patterns.
Characterization of a High Throughput Approach for Large Scale Retention Measurement in Liquid Chromatography
Authors: Kempen Trevor , Dahlseid Tina , Lauer Thomas , Florea Alexandru , Aase Isabella , Cole-Dai Nathan , Kaur Simerjit , Southworth Caroline , Grube Kathleen , Bhandari Jos , Sylvester Maria , Schimek Ryan , Pirok Bob , Rutan Sarah , Stoll Dwight
Published: June 2023

Many contemporary challenges in liquid chromatography - such as the need for “smarter” method development tools, and deeper understanding of chromatographic phenomena - could be addressed more efficiently and effectively with larger volumes of experimental retention data than are available. The paucity of publicly accessible, high-quality measurements needed for the development of retention models and simulation tools has largely been due to the high cost in time and resources associated with traditional retention measurement approaches. Recently we described an approach to improve the throughput of such measurements by using very short columns (typically 5 mm), while maintaining measurement accuracy. In this paper we present a perspective on the characteristics of a dataset containing about 13,000 retention measurements obtained using this approach, and describe a different sample introduction method that is better suited to this application than the approach we used in prior work. The dataset comprises results for 35 different small molecules, nine different stationary phases, and several mobile phase compositions for each analyte/phase combination. During the acquisition of these data, we have interspersed repeated measurements of a small number of compounds for quality control purposes. The data from these measurements not only enable detection of outliers but also assessment of the repeatability and reproducibility of retention measurements over time. For retention factors greater than 1, the mean relative standard deviation (RSD) of replicate (typically n=5) measurements is 0.4%, and the standard deviation of RSDs is 0.4%. Most differences between selectivity values measured six months apart for 15 non-ionogenic compounds were in the range of +/- 1%, indicating good reproducibility. A critically important observation from these analyses is that selectivity defined as retention of a given analyte relative to the retention of a reference compound (kx/kref) is a much more consistent measure of retention over a time span of months compared to the retention factor alone. While this work and dataset also highlight the importance of stationary phase stability over time for achieving reliable retention measurements, we are nevertheless optimistic that this approach will enable the compilation of large databases (>> 10,000 measurements) of retention values over long time periods (years), which can in turn be leveraged to address some of the most important contemporary challenges in liquid chromatography. All the data discussed in the manuscript are provided as Supplemental Information.
Electrocatalytic enhancement of CO methanation at the metal-electrolyte interface studied by in situ X-ray photoelectron spectroscopy
Authors: Thurner Christoph Walter , Haug Leander , Winkler Daniel , Griesser Christoph , Mathias Leitner , Moser Toni , Werner Daniel , Thaler Marco , Scheibel Lucas Andreas , Götsch Thomas , Carbonio Emilia , Kunze-Liebhäuser Julia , Portenkirchner Engelbert , Penner Simon , Klötzer Bernhard
Published: June 2023

For the direct reduction of CO2 and H2O in solid oxide electrolysis cells (SOECs) with cermet electrodes toward methane, a fundamental understanding of the role of elemental carbon as a key intermediate within the reaction pathway is of eminent interest. The present synchrotron-based in situ near-ambient-pressure X-ray photoelectron spectroscopy (NAP-XPS) study shows that alloying of Ni/yttria-stabilized-zirconia (YSZ) cermet electrodes with Cu can be used to control the electrochemical accumulation of interfacial carbon, and to optimize its reactivity toward CO2. In the presence of syngas, sufficiently high cathodic potentials induce excess methane on the studied Ni/yttria stabilized zirconia (YSZ)-, NiCu/YSZ- and Pt/gadolinium doped ceria (GDC) cermet systems. The hydrogenation of carbon resulting from CO activation at the triple-phase boundary of Pt/GDC is most efficient.
Operando X-ray and Post-mortem Investigations of Electrochemical Degradation in Single-crystalline LiNi0.8Mn0.1Co0.1O2–Graphite pouch cells
Authors: S. Menon Ashok , Shah Nickil , A. Gott James , Fiamegkou Eleni , J. W. Ogley Matthew , J. Paez Fajardo Galo , Vaenas Naoum , Ellis Ieuan , Malliband Paul , West Geoff , Walker David , Loveridge Melanie , Piper Louis
Published: June 2023

Layered nickel-rich lithium transition metal oxides (LiNixMnyCo1−x−yO2; where x ≥ 0.8), with single-crystalline morphology, are promising future high-energy-density Li-ion battery cathodes due to their ability to mitigate particle-cracking-induced degradation. This is due to the absence of grain boundaries in these materials, which prevents the build-up of bulk crystallographic strain during electrochemical cycling. However, compared to their polycrystalline counterparts, there is a need to study single-crystalline Ni-rich cathodes using operando X-ray methods in uncompromised machine-manufactured industry-like full cells to understand their degradation mechanism. This can, in turn, help us identify factors to improve their long-term performance. Here, through in-house operando X-ray studies of pilot-line-built LiNi0.8Mn0.1Co0.1O2–Graphite A7 pouch cells, it is shown that their electrochemical degradation under harsh conditions (2.5–4.4 V at 40 °C for 100 cycles) primarily stems from the decreasing abundance of electrochemically active Li+ species as a result of electrode surface layer growth. Post-mortem electron imaging and tomography show that these cathodes can withstand severe anisotropic structural changes and show no cracking, even when cycled under such harsh conditions. These results are then benchmarked and contrasted with those from commercial Li-ion cells incorporating surface-modified Ni-rich cathodes, enabling us to identify the advantages of cathode surface passivation in prolonging cycle life. In addition to furthering our understanding of degradation in single-crystalline Ni-rich cathodes, this work also accentuates the need for practically relevant and reproducible fundamental investigations of Li-ion cells and presents a methodology for achieving this.
Smart Aquarium for Fresh Water Fishes
Authors:   Professor and HOD, Department of CSE, Sri Sairam College of Engineering, Anekal, Bengaluru, India, J A Smitha , Agarwal Shreya , L Keerthana , L Hitha
Published: June 2023

In the modern world, a monitoring system for an automated aquarium is not a novel concept. The device enables automatic pH, temperature, and turbidity level, water level, andfish nutrition management for the user. Manually examining an aquarium's conditions, however, would be inconvenient. It takestime, and dedication is needed. Here, a system connected to the Internet of Things (IoT) is presented to monitor and control the entire aquarium up keep utilizing electronics and sensors that continuously communicate and report real-time status to usersmartphones. The created system will keep track of any physical alterations to the water and maintain it at its perfect state, deciding on any necessary adjustments automatically. The aquarium will carry out all tasks automatically, including controlling the water level, feeding, pH, turbidity, and temperature. It will lessen the amount of manual work needed to manage aquariums.
Analysing ML Models for AQI Prediction using Meteorological Data
Authors:   Department of ISE JSS Science and Technology University, Mysuru, Meghanath Reddy Chavva Bala , Oli Naman ,   Department of ISE JSS Science and Technology University, Mysuru, M Tejaswini ,   Department of ISE JSS Science and Technology University, Mysuru, Bharadwaj Prajeeth ,   Department of ISE JSS Science and Technology University, Mysuru
Published: June 2023

Air is essential for every living being to survive. The modernization and industrialization have led to the rise of pollutants which affect the quality of air directly. Predicting changes in air pollutant concentrations due to human and nature drivers is critical and challenging, particularly in areas with scant data inputs and high variability of parameters. The air quality index (AQI) is an index for reporting air quality on a daily basis. It is a measure of how air pollution affects one's health within a short time period. The purpose of the AQI is to help people know how the local air quality impacts their health. The Environmental Protection Agency (EPA) calculates the AQI for five major air pollutants, for which national air quality standards have been established to safeguard public health. Every country has its own method of calculating the Air Quality Index and the rules for Air Quality Buckets. According to the Central pollution control board, Ministry of Environment, Forests, and climate change of India, the AQI is divided into 6 buckets with the minimum and maximum AQI being 0 and 500 respectively. The buckets are Good, Satisfactory, Moderate, Poor, Very Poor and Severe.
The Nano Revolution: Shaping the Future with Nano Technology
Authors:   Lecturer, Department of Computer Science Akshara First Grade College, Anekal, India, S Vasantha , K Yashwanth , S Deepak , V Likith
Published: June 2023

Nanotechnology is an emerging technology with limitless real- world applications. It is a technology that explores the unknown potential of science and technology. It is a multidisciplinary field that applies and develops in various fields such as applied science, mechanical, electrical, and others. Nanotechnology has revolutionized various applications in the current era. In this paper, we discuss the role of nanotechnology in computer science and the recent developments and opportunities. This is a review paper that provides a brief overview of the research done so far. Nanotechnology has various applications in almost every field in today’s world. Whether it is medical or electrical, nanotechnology is widely used in every branch. Nanotechnology is not only about materials and their synthesis, but it is also useful for computers and software. In this paper, we focus on some applications of nanotechnology that are related to computers. Nanomaterials have greater strength and smaller sizes, so they can be used to make computer chips (memory chips, ICs) smaller as needed. These chips can have large storage spaces with less size. These chips are flexible and can fit easily within the CPU, which improves the memory and efficiency of the computer.
Student Monitoring system using Face Recognition with Artificial Intelligence
Authors:   Assistant Professor, Department of CSE Sri Sairam College of Engineering Bangalore, India, V Yamuna , A Akshatha , A Chandana
Published: June 2023

Due to its potential use in numerous fields, face recognition technology has attracted a lot of attention lately. The monitoring of kids using face recognition technology is one such area of focus. The purpose of this abstract is to give a general understanding of the idea and its ramifications. Face recognition is a technique for monitoring students that uses sophisticated algorithms to recognize and track people based on their facial traits. It is possible to use this technology in educational settings to improve security precautions, simplify attendance monitoring, and keep track of student behavior. The face recognition technology works by taking pictures of students' faces with cameras positioned at key areas throughout the school. These photos are then analyzed and compared to a database of students who have registered. The system can reliably identify people, ensuring that only those with permission can access particular locations and services. Face recognition can automate attendance tracking, improving security while doing away with human procedures. The device may instantly record a student's attendance by merely scanning their face, hence minimizing administrative work and inaccuracies. Additionally, face recognition can be utilized to keep an eye on student behavior. The technology is capable of identifying unauthorized access or suspicious activity and immediately notifying the proper personnel. This could lessen instances of violence, theft, or damage within the institution. Using facial recognition technology to keep an eye on students has many advantages, including increased security, easier attendance tracking, and behavior monitoring. Although the use of such systems necessitates careful consideration of ethical and privacy issues, when used properly, they can help create a more secure and effective.
Swarm Robotics and Intelligence
Authors:   Assistant Professor, Department of CSE, Sri Sairam College of Engineering, Anekal, Bangalore, India, M Amsalakshmi , Prabhakaran Madhumitha , S Gowtham , Sidharth Gautham , V Jayasri
Published: June 2023

It's fascinating to observe how the concept of Swarm Intelligence in natural systems may be applied to human groupings, specifically in the realm of finance. According to the study you referenced, groups of financial traders were able to improve their accuracy in predicting weekly market indices by constructing online systems modeled after natural swarms. The increase in accuracy from 61% to 77% when predicting jointly as a real-times warm is a considerable improvement and demonstrates that the adoption of SI algorithms has the potential to considerably improve financial forecasting accuracy. It is also worth noting that the results' statistical significance (p=0.001) shows that the findings are reputable. Overall, the findings indicate that employing SI algorithms to allow groups of traders to construct real-time systems online could be a beneficial tool in the field of finance, potentially leading to more accurate financial predictions. More research is needed, however, to validate these findings and to investigate the possible applications and limitations of this approach.
Cotton Crop Disease Prediction Using Deep Learning
Authors:   Assistant Professor, Department of CSE Sri Sairam College of Engineering, Bangalore, India, Lakshmi Amsa , Veera Channa , Saravanan, Bala , Priyam Adarsh , S K Deekshak
Published: June 2023

Cotton was found in 3000 B.C and was first used as a fabric in the Indus Valley. The first mill of cotton in India was established in 1818’s near Kolkata. Cotton is a drought-resistant crop and is best grown in soils with excellent water holding capacity it creates reliable income for the cultivators who cultivate the crop under climate threat. In this study, we have worked on disease detection and crop recommendation system. For disease detection, we have implemented the Deep Convolutional Neural Network(CNN) models i.e., Sequential and VGG-16. In the proposed model we detect whether the cotton crops are infected or not, and if they are further particular disease is identified according to the crop. The highest Accuracy achieved is for VGG16 i.e., 97.53%. The proposed system also recommends medicine with the dosage which can be used to cure the plants. Which can help the farmers in increasing the production and quality of the cotton crop.
Humanoid Robotics in Artificial Intelligence
Authors:   Asst.Prof, Dept. of CSE, Sri Sairam College of Engineering, Anekal, Bangalore, K Karthika , . Nityashree , . AryaRa , B Shivanagi Tabassum
Published: June 2023

The humanoid robotics is the emerging and the fascinating field of research as it combines the advancements in artificial intelligence, machine learning and robotics to design socio- intelligent and human-like robots and robots resembling the human body in the structure .Art of humanoid robotics which include integration of artificial intelligent techniques to enhance their cognitive abilities, interactive behavior and perceptual capabilities like perception and sensing, natural language processing ,human robot interaction, autonomy and control, personalization and adaptability, collaborative capabilities. Overall artificial intelligence plays a crucial role in enhancing the capabilities of humanoid robotics. Machine learning is the one of the ways of implementing the social intelligence in humanoid robots. Humanoid robot is defined as robot resembling the human body and can interact with external world and exhibit basic locomotion like gestures and response to real time scenarios. Domestic robots like poppy developed and designed by poppy project and NAO developed by Softbank. Humanoid robots are used in healthcare, public-relations, education ,rescue operations, research ,human -robot interactions, personal assistance, and education sectors . Robots have proved to be good human companion by doing basic human chores and by robot human interactions. The presented research focuses on the development of 2D,3D indoor humanoid robots in a cost-effective manner which are capable of doing basic human household chores, understanding the human emotions, care giving, personal assistance and in education sector as social robot as an assistive tool in classrooms by accepting the voice commands and responding through the hand gestures and by biped or wheeled locomotion .
Analytical Solutions to The Schrödinger Equation with Collective Potential Models: Application to Quantum Information Theory
Authors: Inyang Etido P. , Ayedun Funmilayo , Ibanga Efiong A. , Lawal Kolawole M.
Published: June 2023

In this study, the energy equation and normalized wave function were obtained by solving the Schrödinger equation analytically utilizing the Eckart-Hellmann potential and the Nikiforov-Uvarov method. Fisher information and Shannon entropy were investigated. Our results showed higher-order characteristic behavior for position and momentum space. Our numerical results showed an increase in the accuracy of the location of the predicted particles occurring in the position space. Also, our results show that the sum of the position and momentum entropies satisfies the lower-bound Berkner, Bialynicki-Birula, and Mycieslki inequality and Fisher information was also satisfied for the different eigenstates. This study's findings have applications in quantum chemistry, atomic and molecular physics, and quantum physics.
Effect of Electric Field Modulation on The Onset of Electroconvection in a Couple Stress Fluid
Authors: Rudresha Chandrappa , Balaji Chandrashekar , Shree Venkatesh Vidya , Maruthamanikandan Sokalingam
Published: June 2023

The problem of a convectional instability in a horizontal dielectric fluid layer with electric field modulation under couple stress fluid is examined. The horizontal dielectric upper boundary fluid layer is cooled, and the lower boundary is subjected to an isothermal boundary condition. The regular perturbation method based on the small magnitude of modulation is used to compute the critical Rayleigh number and the corresponding wavenumber. The solidity of the system is the characterised by a correction Rayleigh number, which is computed as a function of thermal, electric, and couple stress parameters and the frequency of electric field modulation. Some of the known findings are retrieved as specific cases in this study. It is demonstrated that the onset of the convection may be advanced or delayed by the proper regulation of different regulation parameters. The outcomes of this study have potential implications for the control of electroconvection with a time-dependent electric field.
Study of CIGS Pseudo-Homojunction Thin Film Solar Cell using SCAPS-1D
Authors: Boudour Samah , Bouchama Idris , Laidoudi Samiha , Bedjaoui Walid , Lamiri Leila , Belgherbi Ouafia , Aziez Siham
Published: June 2023

The present modelling study reports the performance of defected CIGS pseudo-homojunction thin film solar cell (P-HTFSC) and determines its optimum parameters for high performance using the Scaps-1D software under the AM1.5 illumination and the operating temperature of 300 K. To focus the discussion on the optimal parameters (thickness, doping concentrations, deep/interface defect concentrations and bandgap) for the ZnO, CdS, ODC and CIGS thin film layers, cross sectional (1D) simulations have been performed on the ZnO/CdS/ODC/CIGS P-HTFSC device for obtaining its optimal structure that confers high light-into-electricity conversion efficiency. The four light J-V characteristics (short-circuit current: JSC, open-circuit voltage: VOC, fill factor: FF and conversion efficiency: ) have been used as indicators to evaluate the device performances. Simulation outcomes have proved that for a best performance for CIGS P-HTFSC device, the optimal thickness for CIGS and ODC layers should be small than 2 µm and few nm, respectively, while the optimal defect concentration within the layer should be 1013 cm-3 and between 1013 cm-3-1018 cm-3, respectively.
A Demonstration Bench for Representing the Character of Phase Transitions of the First and Second Kind
Authors: Gushchin Ivan V.
Published: June 2023

The paper presents the description of a demonstration bench, which includes a mathematical model and analysis tools for understanding the features of phase transitions of the first and second kind. The advantage of this demonstration bench is the rejection of all phenomenology and the obvious limitation of the application of various approximations and hypotheses. The description is formed on the well-known equations of hydrodynamics, which are well-tested and are a reliable basis for the construction of realistic models. The Proctor-Sivashinsky model, which was used to describe the process of convection development in a thin layer of liquid with poorly conductive heat boundaries, is the basis for the demonstration bench. Exactly this model allows to observe phase transitions of the first and second kind. The feature of the model is that it allocates one spatial scale of interaction, leaving for the evolution of the system the possibility to choose the nature of symmetry. All spatial disturbances of the same size but of different orientation interact with each other. This allows us not to distract from the main task of this work, which is to demonstrate the process of structure formation as a result of a cascade of phase transitions. The mechanism of phase transitions associated with the presence of minimums of the interaction coefficients of modes of the spectrum of the instability. There are a large number of structural defects, which appear as attributes of phase transition. The instability spectrum modes interference is the reason of the high rate of correlations in the propagation of a new phase.
OFICINA MAKER “DO LIXO AO LUXO” COMO MEIO PARA FAVORECER A APRENDIZAGEM DE ESTUDANTES
Authors: Rossi Mayara , Mello Geison Jader
Published: June 2023

Diante da época em que vivemos é preciso oferecer aos estudantes uma educação diferenciada e não baseada apenas na transmissão de conteúdos. Pensando nisso, as oficinas maker se constituem como ricos instrumentos de aprendizado, capaz de desenvolver nos estudantes a criatividade tão necessária atualmente, bem como oferecer um ensino dinâmico em que os estudantes coloquem a mão na massa. Nessa perspectiva, o estudo aqui descrito teve por objetivo analisar as contribuições de uma oficina maker a partir de materiais recicláveis a estudantes do 4º ano do Ensino Fundamental. Metodologicamente a referida dissertação se ancora na pesquisa participante, com abordagem qualitativa e de natureza aplicada. Os sujeitos envolvidos na pesquisa foram 13 estudantes do 4º ano do Ensino Fundamental de uma escola pública de Juína-MT. Os instrumentos de coleta de dados utilizados durante o estudo são: diário de bordo, gravação em áudio/imagem e fotografias. A análise dos dados coletados foi realizada com base na Análise de Conteúdo. Os resultados da oficina maker mostram que é possível transformar materiais de sucatas que iriam para o lixo em jogos e brinquedos ou diferentes e lindos artefatos úteis para o dia a dia. A partir das oficinas os estudantes desenvolveram o pensamento criativo e habilidades artísticas, bem como aprenderam a trabalhar em equipe, de forma cooperada e respeitosa a partir do compartilhamento dos materiais. Acentua-se que as crianças gostaram de participar dos encontros, se mostraram interessadas e motivadas para criar e produzir, havendo assim participação ativa e efetiva por parte das mesmas.
SEQUÊNCIA DIDÁTICA COMO ESTRATÉGIA PARA O DESENVOLVIMENTO DO PENSAMENTO ALGÉBRICO NO 9º ANO DO ENSINO FUNDAMENTAL
Authors: Silva Fatima Alessandra Melo da , Groenwald Claudia Lisete Oliveira
Published: June 2023

Para o desenvolvimento do pensamento algébrico é essencial utilizar estratégias metodológicas de ensino para desenvolver nos estudantes habilidades algébricas, com vistas a produzir significados aos objetos de conhecimento da álgebra. A pesquisa teve como objetivo investigar atividades organizadas em uma sequência didática que abordem os conceitos de polinômios e suas operações para estudantes do Ensino Fundamental nos anos finais, na perspectiva da Base Nacional Comum Curricular. Esta investigação adotou uma abordagem do tipo qualitativa, com desenho característico de um estudo de caso. As análises das produções realizadas pelos estudantes de uma escola estadual na cidade de Manaus, estado do Amazonas, em conjunto com as observações e registros de imagens durante a aplicação das sequências de atividades, apontaram que a realização de tarefas organizadas em uma sequência didática aliada à metodologia de resolução de problemas e mediada pelo uso de material concreto como recurso didático, despertou o interesse dos alunos pelas atividades e contribuiu para o desenvolvimento de elementos característicos do pensamento algébrico, à medida que permitiu construir gradativamente conceitos matemáticos com significado. Além disso, a organização do trabalho docente em sala de aula contribuiu para o desenvolvimento de aspectos sociais e emocionais nos estudantes.
An initiative to improve mental health practice in primary care in Caribbean countries
Authors: Lauria-Horner Bianca , Knaak Stephanie , Cayetano Claudina , Vernon Andrew , Pietrus Michael
Published: June 2023

Objectives.

The aim of this initiative was to assess whether a novel training program – Understanding Stigma and Strengthening Cognitive Behavioral Interpersonal Skills – could improve primary health care providers’ confidence in the quality of mental health care they provide in the Caribbean setting by using the Plan-Do-Study-Act rapid cycle for learning improvement.

Methods.

We conducted a prospective observational study of the impact of this training program. The training was refined during three cycles: first, the relevance of the program for practice improvement in the Caribbean was assessed. Second, pilot training of 15 local providers was conducted to adapt the program to the culture and context. Third, the course was launched in fall 2021 with 96 primary care providers. Pre- and post-program outcomes were assessed by surveys, including providers’ confidence in the quality of the mental health care they provided, changes in stigma among the providers and their use of and comfort with the tools. This paper describes an evaluation of the results of cycle 3, the official launch.

Results.

A total of 81 participants completed the program. The program improved primary care providers’ confidence in the quality of mental health care that they provided to people with lived experience of mental health disorders, and it reduced providers’ stigmatization of people with mental health disorders.

Conclusions.

The program’s quality improvement model achieved its goals in enhancing health care providers’ confidence in the quality of the mental health care they provided in the Caribbean context; the program provides effective tools to support the work and it helped to empower and engage clients.
Clusters of rare disorders and congenital anomalies in South America
Authors: Cardoso-dos-Santos Augusto César , Reales Guillermo , Schuler-Faccini Lavinia
Published: June 2023

Objective.

To map geographic clusters of rare disorders and congenital anomalies reported in South America.

Methods.

Qualitative systematic review conducted in Medline/PubMed, Lilacs, and Scielo electronic databases to identify studies meeting eligibility criteria. The strategy resulted in 1 672 unique articles, from which 164 were selected for full reading by a pair of reviewers.

Results.

Fifty-five articles reported at least one cluster of genetic disorders or congenital anomalies in South American territory. From these papers, 122 clusters were identified, of which half (61) were related to autosomal recessive disorders. Sixty-five (53.3%) of the clusters were located in Brazil.

Conclusions.

The results of the review reinforce that rare diseases and congenital anomalies can occur in a non-random way in space, which is discussed in the perspective of the complex history of formation, social organization, and genetic structure of the South American population. Mapping clusters in population medical genetics can be an important public health tool, given that such places concentrate cases of rare diseases that frequently require multiprofessional, specialized care. Therefore, these results can support important agendas in public health related to rare diseases and congenital anomalies, such as health promotion and surveillance.
First report of some chemical and biological properties of two wild honey types collected from a remote area in Southern Darfur, Sudan
Authors: Ismail M. , Abdallah E.M. , Musa K.H. , Abdellatif A.A.H.
Published: June 2023

Natural honey's physico-chemical and biological qualities vary by origin, variety and nectar source. Wild honey may provide health benefits. Biomedical research reveals its advantages. Little is known about Sudanese wild honey and Southern Darfur honey. In this investigation, the antioxidant, antibacterial, and physicochemical characteristics of two varieties of Sudanese wild honey were studied. Ziziphus and acacia honey ranged in amber color, density, pH, ash, electrical conductivity and free acidity. Both honey samples included vital nutrients and no heavy metals. Both honey samples exhibit exceptional antioxidant activity, but Acacia honey's antioxidant capacity is greater. Both honey samples demonstrated outstanding antibacterial efficacy against all gram-positive and gram-negative bacteria, except Pseudomonas aeruginosa. Ziziphus honey was more antibacterial than acacia honey. The MIC and MBC data exhibited bacteriostatic and bactericidal activity against the investigated microorganisms, indicating a synergistic effect of honey's overall contents against pathogens. These results imply that ziziphus and acacia honey samples from Southern Darfur, Sudan, have antibacterial and antioxidant properties and could be used to improve health and as a food supplement.
Reaching Rural
Authors:   , Singh Amreeta
Published: June 2023

Agriculture is one of New Zealand’s largest industries, yet little is being done about the diminishing mental wellbeing of the countries rural communities. Farming is a time consuming, strenuous occupation that is predominantly influenced by factors farmers can’t control. With new research and technology findings, changes in regulations, market influences and the trading market is further exhausting New Zealand’s farming population.

Reaching Rural explores how the discipline of Interior Architecture can positively promote the wellbeing of Farmers in rural New Zealand, using Sensory Design as a method to enhance moments of experience. This research identifies applications of Wellbeing Architecture and Sensory Design, which are then applied to a demographic that works off the land and is constantly exposed to a variety of unique sensorial qualities.

Understanding the issue of isolation within rural communities lead to the solution of designing ‘The Shed’.

The Shed aims to facilitate community engagement, acting as a venue to host a variety of different events, with the ambition to bring farmers together, aspiring to promote wellbeing. The Shed is a pop-up style venue that will be placed on host farms targeting different rural communities, ensuring isolated farmers are reached.

 By bridging the gap between the built environment and wellbeing in the farming community, Reaching Rural brings awareness to the impact Interior Architecture can have on our mental wellbeing.
Clinical Effects of Insulin Glargine and Acarbose Tablets in the Treatment of Diabetes Mellitus in the Elderly and Analysis of Glycosylated Hemoglobin
Authors: Lu Hua
Published: June 2023

Objective: To analyze the clinical effects of insulin glargine and acarbose tablets in the treatment of diabetes mellitus in the elderly and the analysis of glycosylated hemoglobin. Methods: Patients (80 cases) from Taizhou City Hospital of Traditional Chinese and Western Medicine (January 2021–December 2022) were randomly divided into two groups (40 cases/group). Patients in the control group were treated with acarbose tablets, whereas those in the observation group were treated with insulin glargine and acarbose tablets. The clinical effects were compared. Results: The difference in blood glucose level and quality of life score between the two groups before treatment was insignificant (P > 0.05). The total effective rate (97.5%) of the observation group was significantly higher than that of the control group (85.0%); the blood sugar indices of the observation group were significantly lower than those of the control group; and the observation group scored higher in the quality-of-life scale than the control group (P < 0.05). Conclusion: Diabetes in elderly is characterized by repeated attacks. The elderly often suffers from chronic diseases, such as cardiovascular disease and respiratory system disease, making it is challenging to achieve the desired effect in treatment. Therefore, it is necessary to explore new treatment methods for the management of diabetes in the elderly. Insulin glargine is a new type of insulin analog, which can last for 24 hours, effectively controlling patients’ blood sugar level. According to research, insulin glargine can effectively regulate the blood sugar level of diabetic patients for an extended period of time, significantly lower the fasting blood sugar, and reduce the complications caused by hypoglycemia. Acarbose tablet, on the other hand, is an alpha-glucosidase inhibitor, which prevents the breakdown of starch into sugar, thereby reducing postprandial blood sugar levels. For diabetic patients, especially those whose staple food is starch, acarbose is a suitable drug.