Field Of Technology Research Articles

KARAKTERISTIK STRUKTUR POPULASI TERNAK BABI DI DESA LATUHALAT KECAMATAN NUSANIWE KOTA AMBON

The aim of this research on the structural characteristics of the pig population in Latuhalat Village is to understand the composition, dynamics and structural characteristics of the pig population in Latuhalat Village. The parameters measured in this research are the characteristics of the respondents which consist of: age, gender, education, livelihood and population structure which includes the number of pigs per age group and the sex ratio. The research was carried out with 40 farmers as samples and this type of research was descriptive research. The research results show that the characteristics of respondents in the research location, Latuhalat village, Nusaniwe District, are dominated by men in productive age with secondary education and running mixed farming businesses. The population structure of pigs is dominated by female livestock, both at calf age (50.63%), young age (51.37%) and adult age (60.53%). This research also identified the main challenges by farmers, is the low knowledge and skills of farmers which resulted in low pig productivity. The recommendation from this research is that there needs to be an extension program that is carried out on the government and from academics and needs to be given a technology, especially in the field of feed and housing technology.

A phenomenological study of women’s everyday lived experiences and perceptions in science research organizations in India

There has been a significant paucity of representation of women in the fields of science, technology, engineering, mathematics, and medicine (STEMM) on a worldwide scale. Women are more likely to be at a disadvantage than men due to the existence of gender prejudices as well as other social and cultural variables. This tendency is also present in India. Feminist scholars within the Science, Technology, and Society field have conducted thorough examinations of the gender component, despite their relatively small representation. This study aims to examine the challenges faced by female scientists in higher education institutions in India to comprehend these issues. It employs a phenomenological approach, using primary data to investigate how female scientists perceive their day-to-day lives and the world around them, and it focuses on the perceptions and experiences of these women. The study finds that women who work in STEMM fields face a wide variety of challenges and roadblocks both on the personal and professional front. In addition, it highlights certain attitudes that have a negative impact on women working in the field of science, such as the rationalization of institutional prejudices and unwillingness to recognize discrimination. It concludes with a few policy implications.

Utilising Blended Learning for Large Classes to Deliver an Introductory Programming Course

Higher education institutions are constantly pushed to increase the student intake and produce industry-ready graduates. The pressure generally arises from the governing bodies, originating from socio-political factors. It is particularly evident in the field of Information Communication Technology (ICT) where there is a growing need for quality graduates. Undergraduate programmes in the field of ICT generally offer introductory computer programming courses that are mandatory at early stages of the degree programme. Consequently, the number of students registering for such courses can increase drastically. The students are coming from diversified backgrounds. While some students may have prior experience in computer programming, a larger majority may not have any prior knowledge or experience. Increased class size meets the constraint of the limited availability of physical classroom space and would require the class to be separated into smaller groups for face-to-face activities. Large classes are also challenged with the increased man-power requirement in terms of lecturers, instructors, and teaching assistants. Maintaining student engagement and interactivity becomes increasingly difficult in a large class. Grading of assessments becomes time-consuming and needs and requires increased manpower. Based on the observations and learning from an ongoing introductory programming course, this study evaluates the procedure for overcoming the challenges faced by the teaching staff, especially with a class size exceeding 1000. To cover the theoretical components, recorded video lectures are provided. The class is divided into groups consisting of approximately 100 students and live, online, interactive group discussions are conducted. The live, online smaller group discussions allow students to clarify doubts regarding the content. This also allows the distribution of the limited teaching and assistant staff. Students who need further assistance are encouraged to meet the teaching assistants physically. Thus, the workload is distributed effectively, and it helps ensure the dedicated support and attention is provided to the students. Auto graded programming assignments are effectively employed for formative and summative assessments, which reduces the grading workload significantly. The findings of this study provide insights on course design to effectively deliver the content and conduct assessments overcoming the challenges presented by increased class size.

Overview of Cyber Attacks Classification and Detection in IoT using CNN-Deep Reinforcement Learning

Millions of digital devices total the Internet of Things (IoT), and this allows very easy interaction from users connecting the devices. IoT is one of the tech sectors that is expanding most rapidly, but it can also be very vulnerable to hazards. Infections and abnormal placement on the Internet of Things (IoT) framework is an increasing threat in the field of technology. In view of the growing IoT foundation usage across all industries, attacks and dangers on these systems have also grown proportional. Leveraging typical machine learning methods, cyber-attack detection plays a critical role in avoiding damage from cyberattacks on IoT devices. IoT Cyber Attacks are Not Detected by ANN Artificial Neural Networks Using Deep Learning Techniques CNN-DRL (Convolutional Neural Networks-Deep Reinforcement Learning) Hybrid Approach: Detects Attacks, including Distributed Denial of Service (DDoS), Zero-day, and Eavesdropping Attacks.

Modelling Method and Meshing Characteristics of a Novel Curve-Surface Conjugate Internal Gear Drive

Space exploration has become a major focus in the field of technology, with gear transmissions in aerospace equipment playing a crucial role. In the extreme environment of space, gear transmissions face challenges like large temperature differentials, deformation and maintenance difficulties, which will severely impact transmission accuracy and service life. To meet the growing demands for high-performance gear transmissions with high transmission efficiency and error adaptability in the aerospace field, this paper proposes a novel curve-surface conjugate internal gear drive consisting of an involute internal gear and a curve-surface gear. The fundamental theory of curve-surface conjugation is introduced, and the construction method for curve-surface gear based on a selected contact path and meshing tube is presented. The analysis models including induced curvature, sliding ratio and tooth contact analysis with errors (ETCA) are simulated to evaluate the meshing characteristics. Additionally, prototypes are manufactured and experimental setups are established to validate the transmission performance. These results indicate that as the rotational speed increases, the transmission efficiency of the curve-surface conjugate internal gear drive improves, which is contrary to the trend observed in involute gear drives. And the transmission efficiency of the curve-surface conjugate internal gear drive surpasses that of the involute gear drive at higher rotational speeds. Moreover, this novel gear drive exhibits excellent error adaptability, maintaining intact contact paths and high transmission efficiency even in the presence of assembly errors. This study provides new ideas for the design and manufacture of high-performance gear transmissions from the perspective of spatial geometric elements.

RescueNow: Real-time SOS and Predictive Women's Safety System

This paper presents a comprehensive women’s safety application designed to address the growing concerns of personal safety through technological intervention. The system integrates multiple advanced features such as real-time SOS alerts, geofencing to detect high-risk areas, AI-driven crime predictions, and multiple SOS activation methods, including voice commands, shake detection, and button press. Additionally, the app employs silent recording functionality to gather evidence in distress situations and incorporates secure communication channels for contacting law enforcement. The application also supports community-driven safety ratings and crime reporting. Tested with real-world scenarios, the system demonstrates high accuracy in predictive analytics and provides a seamless, scalable solution for enhancing women’s safety. This project contributes to the field of personal safety technologies by introducing a multi-faceted, AI-enabled framework for real-time risk mitigation. Key Words: Women’s safety, SOS alerts, AI crime prediction, geofencing, real-time GPS tracking, predictive analytics, Firebase Cloud Messaging, Twilio, silent recording, crime reporting.

Solvent mediated synthesis of multicolor narrow bandwidth emissive carbon quantum dots and their potential in white light emitting diodes

The development of efficient and environmentally sustainable materials for white light-emitting diodes (WLEDs) is of paramount importance in the field of lighting technology. In this study, we present a solvent-modulated synthesis approach for the fabrication of multicolor narrow-bandwidth emissive carbon quantum dots (CQDs) as a promising solution for constructing WLEDs. The synthesis method involves the controlled reaction of organic precursors in different solvent environments, leading to the formation of CQDs with distinct emission wavelengths with a relatively small full width at half maximum, ranging from 28 to 42 nm. Moreover, these synthesized multicolor CQDs demonstrate a remarkably high fluorescence quantum yield of up to 65%, indicating their potential for constructing efficient WLED when incorporated in polymer matrix and coated on the surface of blue light-emitting diode (LED).

The Influence of Pressure on the Electronic and Elastic Characteristics of GaP Nanocrystals using Density Functional Theory

The electronic structural and mechanical properties of III-V Gallium Phosphide (GaP) nano-crystals zinc-blende stable (ZB) phase under pressure dependence have been studied via the model of the density functional theory (DFT) in combination with the large unit cell (LUC) approach at (8, 16, 54 & 64) atoms. The exchanges and correlations (XC) energy has been defined in all approaches through generalized gradient approximations of the Perdew-Burke-Ernzerhof (GGA-PBE), and determination of GaP characteristics in the case where the pressure has been increased from 0GPa to ±50GPa. Elastic parameters like elastic constants (C11, C12 & C44), Kleinmann parameter (ξ), Zener anisotropic factor (A), shear modulus (G), Young's modulus (E), Poisson’s ratio (v), Bulk modules (B) of GaP-ZB structure have been calculated and showed systematic variation with a pressure increase. Our calculations show that Bulk modules and sound speed increase with an increase in the number of core atoms. In this systematic approach, it has been found that the B/G ratio is 1.58 for this compound and it exhibits a negative Cauchy pressure, classifying GaP as brittle. The results were discovered to be consistent with other theoretical as well as experimental results. The present study directly relates to the latest experimental works on the III-phosphide compounds. The current results provide information and direction for further investigation into the fundamentals and potential applications of gallium phosphate (GaP) nanocrystals. In addition, we attempted to compare the results of the investigation with some similar types of compounds that are already documented in the literature. We believe that our study will have a significant impact on both the field of research and contemporary technology based on semi-conducting materials.

Impact of Robotics Workshops on Secondary School Students' Interest in Science and Technology Fields

Impact of Robotics Workshops on Secondary School Students' Interest in Science and Technology Fields

Recent progress and future directions in nanoglass materials: A deep insight into synthesis, characterization, and application

This review article provides a comprehensive examination of the most recent advances in research on nanoglasses, including the methods used to create these materials, their characteristics, and their diverse range of uses. An overview of the current trends in nanoglass research connects them to the Sustainable Development Goals, highlighting the current relevance of this topic. The process of manufacturing nanoglasses is explained in depth, highlighting advanced approaches such as inert gas condensation and severe plastic deformation, among other techniques. The prime focus of this review is on analyzing the various dimensions of nanoglass materials, including their structural dynamics and electrical configurations, and how these features contribute to their exceptional thermal stability and mechanical strength. The magnetic characteristics of nanoglasses are examined, highlighting their potential for driving innovation across multiple industries. The primary emphasis is on the biological usefulness of nanoglasses, specifically examining their bioactivity and interaction with biological components, and emphasizing their growing use in nanoscale biomedical applications. With regard to the practical applications of nanoglasses, there are specific discussions of their contributions to biological evaluation, wound healing, catalysis, and environmental sustainability. There is an emphasis on the durability and resistance of nanoglasses in these contexts. The comprehensive overview of nanoglasses provided in this article highlights their significance as revolutionary materials in fields of science and technology. The potential of nanoglasses to contribute to a future that is more sustainable and health oriented is indicated. The article ends by discussing the future directions for nanoglass research and looks forward to the promising possibilities for further investigation and innovation.

An adaptive robust watermarking scheme based on chaotic mapping

Digital images have become an important way of transmitting information, and the risk of attacks during transmission is increasing. Image watermarking is an important technical means of protecting image information security and plays an important role in the field of information security. In the field of image watermarking technology, achieving a balance between imperceptibility, robustness, and embedding capacity is a key issue. To address this issue, this paper proposes a high-capacity color image adaptive watermarking scheme based on discrete wavelet transform (DWT), Heisenberg decomposition (HD), and singular value decomposition (SVD). In order to enhance the security of the watermark, Logistic chaotic mapping was used to encrypt the watermark image. By adaptively calculating the embedding factor through the entropy of the cover image, and then combining it with Alpha blending technology, the watermark image is embedded into the Y component of the YCbCr color space to enhance the imperceptibility of the algorithm. In addition, the robustness of the algorithm was further improved through singular value correction methods. The experimental results show that the average PSNR and SSIM of the watermarking scheme are 45.3437dB and 0.9987, respectively. When facing various attacks, the average NCC of the extracted watermark reaches above 0.95, indicating good robustness. The embedding capacity of this scheme is 0.6667bpp, which is higher than other watermarking schemes, and the average running time is 1.1136 seconds, which is better than most schemes.

Использование цифровых технологий в практике преподавания русского языка как иностранного на подготовительном факультете в российском вузе

The article examines the specificities of teaching Russian as a foreign language to pre-graduate and pre-doctoral students in the field of nuclear physics and technologies using digital tools and methods. The work considers the benefits of teaching language for specific purposes with the application of digital tools. The research aims to determine how using digital tools can enhance the effectiveness of teaching Russian as a foreign language for specific purposes in groups of pre-graduate and pre-doctoral students. The research is novel in that it is the first one to formulate the differences between the educational process for students in graduate programs and programs for training scientific and pedagogical personnel in postgraduate studies, compared to undergraduate and specialist programs. In addition, an experiment is conducted to confirm the effectiveness of using digital tools when teaching these student cohorts. The article provides a detailed description of the system of tools used in classes at the National Research Nuclear University “MEPhI” in groups of pre-graduate and pre-doctoral students. As a result of the research, it has been revealed that in the case of a systemic and methodologically justified use of digital tools in Russian as a foreign language classes, the effectiveness of the learning process increases, interest in learning Russian and nuclear physics is stimulated through practical assignments and research activities, and student motivation is also enhanced.

Hand Gesture Recognition Using Ultrasonic Array with Machine Learning

In the field of gesture recognition technology, accurately detecting human gestures is crucial. In this research, ultrasonic transducers were utilized for gesture recognition. Due to the wide beamwidth of ultrasonic transducers, it is difficult to effectively distinguish between multiple objects within a single beam. However, they are effective at accurately identifying individual objects. To leverage this characteristic of the ultrasonic transducer as an advantage, this research involved constructing an ultrasonic array. This array was created by arranging eight transmitting transducers in a circular formation and placing a single receiving transducer at the center. Through this, a wide beam area was formed extensively, enabling the measurement of unrestricted movement of a single hand in the X, Y, and Z axes. Hand gesture data were collected at distances of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm from the array. The collected data were trained and tested using a customized Convolutional Neural Network (CNN) model, demonstrating high accuracy on raw data, which is most suitable for immediate interaction with computers. The proposed system achieved over 98% accuracy.

Optical moiré bound states in the continuum

Trapping electromagnetic waves within the radiation continuum holds significant implications in the field of optical science and technology. Photonic bound states in the continuum (BICs) present a distinctive approach for achieving this functionality, offering potential applications in laser systems, sensing technologies, and other domains. However, the simultaneous achievement of high Q-factors, flat-band dispersions, and wide-angle responses in photonic BICs has not yet been reported, thereby impeding their practical performance due to laser direction deviation or sample disorder. Here, we theoretically demonstrate the construction of moiré BICs in one-dimensional photonic crystal (PhC) slabs, where high-Q resonances in the entire moiré flat band are achieved. Specifically, we numerically validate that the radiation loss of moiré BICs can be eliminated by aligning multiple topological polarization charges with all diffraction channels, enabling the strong suppression of far-field radiation from the entire moiré band. This leads to a slow decay of Q-factors away from moiré BICs in the momentum space. Moreover, it is found that Q-factors of the moiré flat band can still maintain at a high level with structural disorder. In experiments, we fabricate the designed 1D moiré PhC slab and observe both high-Q resonances and a slow decrease of Q-factors for moiré flat-band Bloch modes. Our findings hold promising implications for designing highly efficient optical devices with wide-angle responses and introduce a novel avenue for exploring BICs in moiré superlattices.

Characterizing nano-indentation and microstructural properties of mine tailings-based geopolymers

The mining industry's extraction processes produce vast amounts of waste, including mine tailings (MT), traditionally stored in large ponds, causing significant environmental harm due to a lack of effective recycling methods. This research explores the potential of converting MT into a resource i.e., precursors in geopolymer synthesis. By analyzing the physicochemical and mineralogical properties of MT, the study formulates four geopolymer composites, substituting up to 30 wt% MT for fly ash. The composites' mechanical and microstructural properties show a contribution of MT in the stability of the matrix. Compressive strength of 59 MPa when incorporating 30 wt% MT, along with water absorption, microstructural analysis, and environmental impact assessments, support the hypothesis that the matrix is improved. Nano-indentation techniques further evaluated the nanomechanical properties, such as Young's modulus and fracture toughness. The findings reveal that geopolymers with 30 wt% MT exhibits up to 130 % increased strength. This research highlights the potential for innovative, eco-friendly solutions in waste management and material production, contributing to sustainable mining practices and advancing the field of geopolymer technology.

Civilian knowledge industries and the ascendance of small and medium-sized states in world politics

In the evolving landscape of international politics, the ascent of small and medium-sized (SMS) states in knowledge industries is notable. As these states, exemplified by Israel, Sweden, Singapore, and the United Arab Emirates, harness advanced technological capabilities, their global influence grows. Unlike great powers, SMS states face a heightened risk in developing advanced technology due to limited national resources and the absence of technological redundancies. Our examination of Israel’s experience in the satellite, cybersecurity, and artificial intelligence industries serves as a case study demonstrating how an SMS state can establish competitive knowledge sectors. These technologically advanced SMS states attain a status akin to middle powers, challenging conventional perceptions. Analyzing Israel’s commitment to securing these technological fields reveals government intervention aimed at cultivating sustainable knowledge industries and preserving critical knowledge. While diverse motivations drive the technological development of SMS states, their capabilities reshape conventional notions of middle powers and global power distribution.

How Two‐Dimensional Heterocovariance Spectroscopy and Data Fusion Level out the Inadequacies of Compact Spectrometers

ABSTRACTOver the past decade, sensors and compact spectrometers have emerged as a powerful means for real‐time monitoring of chemical and biochemical processes in the field of process analytical technologies (PATs). This is largely attributed to cost‐efficiency and their robustness in near‐line applications. In comparison to more sophisticated laboratory instruments, these devices exhibit limited sensitivity and resolution. In this study, a combination of near‐infrared, low‐field 1H nuclear magnetic resonance and compact Raman spectrometers were employed for the process monitoring of the acid‐catalyzed esterification of isoamyl alcohol and acetic acid to isoamyl acetate. The resulting real‐time data were transformed and visualized for interpretation using two‐dimensional heterocovariance spectroscopy. The data were also subjected to pretreatment, concatenation, and multivariate analysis in accordance with low‐ and mid‐level data fusion. The spectral interpretation derived from heterocovariance spectroscopy provided support for the data pretreatment during data fusion. By applying these computational techniques, the inherent limitations of sensitivity and resolution associated with compact spectroscopic instruments could be overcome, thereby facilitating the interpretation of data and yielding further insights into the process under study. A comparison of the process parameters resulting from the applied methods indicated that consistent data could be obtained. This study demonstrates that heterocovariance spectroscopy and data fusion allow to enhance compact, less expensive analytical instruments for process monitoring and to acquire process knowledge. This, in turn, enables material, financial, and energy resources to be conserved.

Sociological study of the digital culture of a university teacher (using the example of the Moscow State Linguistic University)

In the article, based on the study conducted by the sociological laboratory of the Moscow State Linguistic University (hereinafter referred to as MSLU), the essence and level of digital culture of a university teacher are determined, problematic areas of digitalisation of the educational process are identified, which require urgent measures to improve the current situation in the higher education system of the Russian Federation. The study has proved that the digital culture of the university teacher is their digital social competence formed in the course of professional activity as well as the driving force that determines the attitude towards the digitalisation of the educational process. The development level of the digital culture of a teacher at a linguistic university is below average. The majority of respondents have digital skills and use basic digital technologies in the learning process. The level of proficiency among teachers of the MSLU is comparable to the level of development of these skills among teachers at other Russian universities. At the same time, in terms of the level of competence in developing online courses and using their materials as additional educational material, teachers of the MSLU lag behind the all-Russian indicators. The main problematic areas of the digitalisation of the educational process are: low degree of equipment of audiences with modern technical means; low quality and availability of Internet communications on the territory of educational buildings; insufficient opportunities to improve the level of the digital culture of teaching staff in the field of modern digital technologies, etc.

Effect of carbon fibers modification on joint adhesion steel-epoxy resin-aluminum

The combination of high-performance steel with aluminum or aluminum alloys shows a wide range of application in high technologies field like aerospace, smart materials, etc. The problem in using of adhesive bonding technology for these substrates is the weak mechanical occlusion between steel/aluminum and common coatings. In this study, different methods of treatment of carbon fiber were employed to enhance the adhesion strength in steel-epoxy resin filled with carbon fiber-aluminum triplex. The treatment of carbon fibers with 3-glicidyloxypropyl-trimetoxysilane after their oxidation in 65 wt.% nitric acid leads to the additives which improve the adhesion strength of epoxy resins at 15 wt.% contain on 67.5% in comparison with neat epoxy up to 20 MPa. This result can be explained by the change of failure mechanism from “cohesive’ to “adhesive” that leads to an increase of adhesion strength to steel and aluminum. The SEM study shown that 60–80 nm thick two-layer interphase formed between carbon fiber and epoxy matrix. The mechanical strength of this interphase governs the mechanical and adhesion performance of steel-epoxy resin-aluminum triplex. Taking into account the mechanical properties and adhesive strength of these composites, it emerges as a potential candidate for advanced materials.

Machine Learning and Artificial Intelligence in Cloud Computing

The field of technology is constantly evolving, and at the forefront of this progress lies the powerful synergy between cloud computing and machine learning (ML). Cloud computing provides a robust and scalable platform that serves as the launchpad for advancements in artificial intelligence (AI), particularly machine learning. This platform offers features that empower ML development, including access to vast and scalable resources, cost-effective solutions, collaborative tools, and global reach. Machine learning, in turn, becomes the engine that propels cloud applications forward, enabling them with functionalities like predictive analytics, personalized experiences, automated operations, and anomaly detection. This paper delves into the intricate details of how cloud computing empowers ML and vice versa. It explores real-world examples that showcase the practical application of this powerful partnership, while acknowledging the challenges that come with integrating these two transformative technologies. By addressing security, privacy, model bias, and explainability concerns, this convergence has the potential to shape a future that is not only more intelligent but also deeply data-driven. Keywords: Cloud computing, machine learning, artificial intelligence (AI), ML, scalability, cost- effectiveness, collaboration, global availability, predictive analytics, personalization, automation, anomaly detection, cloud AI platforms (e.g., Azure ML, SageMaker, Google AI Platform), security, privacy, model bias, explainability.