bluuebunny/crossref_metadata_2025 huggingface dataset:
Increasing the Efficiency and Security of Integrated Power System Operation Through Heat Supply Electrification in Ukraine
Authors: BABAK V. , KULYK M.
Published: October 2023

Introduction. The paradigm of global energy development is the priority of renewable energy sources (RES), namely, wind (WPP) and solar (SPP) power plants. This process has been evolving despite the fact that these renewable energy sources, by their physical nature, are unable to provide either a stable frequency of generated energy or a guaranteed power.Problem Statement. The current threatening situation in the Integrated Power System (IPS) and in the energy market of Ukraine is caused by the hypertrophied development of SPP and WPP in the structure of Ukraine’s IPS and excessive preferences given to RES by the “green” laws.Purpose. The purpose of this research is to create a fundamentally new structure and basis of the electric-heat system operation, which unite the IPS of Ukraine and the system of centralized heat supply (CHS), by electrifying the CHS through the use of autonomous RES energy and the capacities of nuclear power plants (NPP), in order to ensure the profitability ofUkraine’s energy market and the guaranteed profitability of RES.Material and Methods. Information of NEC Ukrenergo for 2021 and periodic energy publications; system analysis for established electrical, hydraulic, and temperature modes of joint operation of IPS, CHS, WPP, and SPP for a fundamentally new organization of their interaction.Results. Management based on the “green” tariff laws has led to a significant unprofitability of the Ukraine’s energy market. The greenhouse gas emissions during the use of RES in the IPS have increased several times, as compared with such emissions, in the case of the structure of the IPS without RES. The new principle of interrelationships between IPS, CHS, WPP, and SPP ensures high manufacturability, cost-effectiveness, reliability, and environmental friendliness of each component.Conclusions. The combination of IPS and CHS structures solves several problems of the national importance: the problem of frequency and power stabilization in the IPS automatically solves; the energy market of Ukraine gets rid of USD 15 billion loss anually; it allows saving 7.28 billion m3 natural gas and reducing the carbon dioxide emissions by 98 million tons in CO2 equivalent.
Prevalence and molecular characterization of Cronobacter species in Egyptian table eggs and egg-based desserts; special insight on Cronobacter sakazakii
Authors: M. Kamal Sahar , Maharik Nagah M.S. , Valero Antonio , Faried Alshimaa M.
Published: October 2023

The occurrence of Cronobacter spp. was investigated in table eggs and egg-based desserts obtained from retail stores. Cronobacter was isolated from 57 out of 180 (31.7%) (95% Confidence Interval [CI]: 26–43%) examined samples of eggs and egg-based desserts. The prevalence of Cronobacter spp. was significantly higher in farm eggs (67%) (P < 0.05) than in Balady eggs (23%), cream cake (27%) and small-scale ice cream (27%). Cronobacter (C.) sakazakii and C. muytjensii were the predominant isolates obtained in the present study. All C. sakazakii isolates were molecularly confirmed with higher incidence in small-scale ice cream (62.5%).
Release behavior and kinetic analysis of eugenol from clove particles using P&T–GC-MS method
Authors: Liang Miao , Yang Zeen , Xu Kejing , Chen Xiaolong , Yang Jinchu , Liu Wenzhao , Zhao Sensen , Xu Yongming , Zhang Junsong
Published: October 2023

In this study, the release behavior and kinetic characteristics of eugenol from clove particles under different temperatures were investigated by using purge and trap and gas chromatography–mass spectrometry (P&T–GC-MS) method. The results showed that the established P&T–GC-MS method could efficiently determine eugenol content in clove particles. The high temperature could promote the release of eugenol in the early stages of heating, while low temperature delayed the time to reach the maximum ratio. Kinetic analysis showed that the release behavior of eugenol at different temperatures was consistent with the first-order release model. The release activation energy for eugenol was calculated as 58.29 kJ/mol by using the Arrhenius equation.
“God’s Grandeur” by Gerard Manley Hopkins. A Poetic Path into the Depths of Contemplation
Authors: Godawa Marcin
Published: October 2023

The present paper concerns the question of how Christian contemplation, in the sense of a simple loving gaze on God and His works, could be prepared and supported by poetry by using the properties of poetic speech in the field of semantics, syntax as well as sound effects. The object is God’s Grandeur, a brilliant sonnet by Gerard Manley Hopkins, a poet, priest, Jesuit, a man of spiritual passion. Its analysis along with interpretation, in terms of both poetics and spiritual theology, will allow us to see how aesthetic contemplation works and could bear fruit for a contemporary man’s relation to himself or herself, to the world as well as most importantly to God, the source of created goodness.
Kontemplacja mistyczna w ujęciu św. Jana od Krzyża
Authors: Gogola Jerzy Wiesław
Published: October 2023

W artykule został podjęty problem kontemplacji mistycznej u św. Jana od Krzyża. Jego realizacja wymagała wyjaśnienia pojęcia kontemplacji zarówno w znaczeniu szerokim, jak i ściśle mistycznym. Ukazany został jej ścisły związek z życiem duchowym chrześcijanina. Kontemplacja, jak i życie duchowe posiadają swoje źródło w łasce chrztu świętego, która zawiera cały potencjał duchowego rozwoju. Ona stanowi podstawowe uzdolnienie do zjednoczenia z Bogiem, do kontemplacji najgłębszych tajemnic Boskich; gwarantuje otrzymywanie Ducha Świętego, którego Chrystus posłał do naszych serc, by dopełniał dzieła uświęcenia. Jednak jest Bożą tajemnicą, dlaczego nie w każdym przypadku „wpływ Boga na duszę” niesie z sobą taką samą doświadczalną formę. Na kolejnym etapie refleksji skonfrontowano kontemplację zwyczajną z kontemplacją mistyczną, by następnie uwzględnić jej dwie formy: oczyszczającą i jednocząca. Są one doświadczane przez wielu wierzących, ale nie w taki sam sposób i nie w takiej samej proporcji. Zakładając autentyczną wierność wierzącego, przy różnej formie przeżywania relacji z Bogiem i dokonujących się duchowych procesów, należy stwierdzić, że zależy to od Bożej pedagogii. Bóg najlepiej wie, jaką drogą prowadzić człowieka do osiągnięcia nadprzyrodzonego celu i w jaki sposób uczynić go skutecznym świadkiem Chrystusa. W dalszej kolejności uwzględniono dwa wymiary kontemplacji mistycznej: teologiczny, a więc obiektywny oraz doświadczalny, czyli subiektywny. Zastosowano metodę analizy oraz syntezy. Poddano pod refleksję źródłowe teksty, by poddać je analizie i wyciągnąć stosowne wnioski. Ostateczne wnioski są następujące: Kontemplacja, w ujęciu św. Jana od Krzyża, jest formą życia teologalnego. Pojawiając się, wywiera wpływ na wszystkie inne formy modlitwy: liturgiczną, ustną, lekturę Pisma Świętego i wprowadza cechę prostoty oraz miłosnej uwagi na Boga. Nie zastępuje tych form, ale nimi się karmi i jednocześnie ubogaca je. Między różnymi rodzajami modlitwy nie może istnieć żaden istotny kontrast. W życiu samego św. Jana od Krzyża obserwujemy praktykę modlitwy aż do śmierci we wszystkich jej formach, włącznie z modlitwą ustną. Kontemplacja wierzącego posiada swoje źródło w ukrytym i tajemnym działaniu Ducha Świętego. Bez tej działalności nie byłaby ona możliwa. Wbrew powszechnym opiniom wskazuje przede wszystkim na pierwszeństwo działania Bożego w procesie uświęcenia, zaś ludzka działalność posiada charakter odpowiedzi na to działania.
Formacja liturgiczna alumnów po Soborze Watykańskim II
Authors: Mieczkowski Janusz
Published: October 2023

Celem formacji liturgicznej jest zrozumienie, w jaki sposób celebruje się i wyraża misterium zbawienia. Fundamentem formacji liturgicznej jest zbawcze dzieło Jezusa oraz udział człowieka w jest misterium paschalnym Chrystusa poprzez wiarę i sakramenty. Formacja ta ma być mistagogiczną, więc ma obejmować przede wszystkim uczestnictwo w liturgii, która sama ma moc oddziaływania na jej uczestników. Dlatego na pierwszym miejscu dokumenty zawsze stawiają uczestnictwo w liturgii, a dopiero potem naukę i praktyczne wtajemniczenie w obrzędy. Jeśli chodzi o życie liturgiczne w seminarium ma ono koncentrować się na codziennym uczestnictwie w Eucharystii. Obok niej pojawia się także troska o częste sprawowanie Liturgii godzin, sakrament pokuty i pojednania i właściwe przeżywanie roku liturgicznego. Ważne są także przyjmowane w odpowiednim czasie posługi lektora i akolity. W Nowym Ratio studiorum dla Polski nauka liturgii wpisana została w nowy podział całej formacji seminaryjnej, czyli „etap propedeutyczny”, „etap stawania się uczniem Jezusa”, „etap upodabniania się do Chrystusa” oraz etap pastoralny. Ratio studiorum dla Polski przewiduje w czasie studiów tylko 120 godzin zajęć liturgicznych (85 godzin wykładów i 35 godzin ćwiczeń), do których dochodzi jeszcze przedmiot „Liturgiczna posługa prezbitera” na etapie pastoralnym.
Evaluation of water and animal concepts in terms of scarcity, rights and welfare
Authors: ÇAK Bahattin
Published: October 2023

Problems such as global warming and cooling, climate change, water and food have been some of the most important issues on the world agenda in recent years. For sustainable welfare, the concepts of equality, justice and rights are indispensable for the ecosystem. Today, the adoption of legal regulations on water scarcity, right to water, animal welfare and animal rights with the awareness of conscientious responsibility in the individual and society has been an important development for the future.

Therefore, in this article, it is aimed to contribute to the objective questioning of the concepts of rights and welfare with their emotional, legal and scientific aspects by getting rid of people's presuppositions about life, abandoning the distinction between living and non-living things. The study consists of two parts. In the first part, water scarcity and the right to water and in the second part, evaluations are made in terms of animal welfare and rights. The literatures cited in the article are faithful to the author's views.

The general acceptance of water consumption as a need rather than a right has turned it into a commodity that can be bought and sold with money. Although there is enough water in the universe, the perception of scarcity consciousness arising from the possible inadequacy of the amount of consumable water needs to be re-questioned at the intellectual level by considering it from the perspective of biological transformation-change.
The number of syllables per phrase in Great Tit (Parus major) song decreases in strong anthropogenic noise and at northern latitudes
Authors: Husby Magne , Slagsvold Tore
Published: October 2023

Cover photo: Great Tit male. Photo: Alf Tore Mjøs.
Birds may sing to defend a territory and to attract a mate. However, despite many studies clear conclusions remain on how ecological conditions affect the song, such as physical obstacles that may reduce the sound transmission, and anthropogenic noise that may mask the signal. The social environment of the local populations may also be important, such as breeding density and sex ratio, influencing the number of competing males with which to song match, and the distances to the neighbouring males and to prospecting females. During 2016-19, we counted the number of syllables (notes) per phrase of singing male Great Tits Parus major by visiting seven countries in Europe and one in North Africa. A total of 946 songs were observed by visiting 554 territories. We also recorded study year, anthropogenic noise, calendar date, time of day, type of habitat and vegetation density, latitude, longitude and altitude. The most important explanatory variables were anthropogenic noise and the latitude of the focal site; the number of syllables per phrase decreasing both with increasing anthropogenic noise and with the latitude. The latter result was also supported when analysing sonograms of the species found on the Internet (Xeno-canto), namely fewer syllables per phrase in Norway than in Spain and Portugal. We suggest that repetition of a short phrase is fast interpreted by conspecifics in noisy environments, and that such signals are more readily detected by conspecifics over a wider area where the density of the tits is low.
Assessment of long-term water demand for the Mgeni system using Water Evaluation and Planning (WEAP) model considering demographics and extended dry climate periods
Authors: Vernon Nagan  , Mohammed Seyam  , Taher Abunama
Published: October 2023

The Mgeni System is recognised as the main source of water supply for the Durban and Pietermaritzburg region in South Africa. This area is regarded as the primary economic hub of KwaZulu-Natal Province, and this brings about a high level of demographic pressure, with potential water supply problems in the future. This study investigates the water resource situation in the Mgeni System and evaluates future supply and demand accounting based on the (Water Evaluation and Planning) WEAP software. WEAP was used to analyse the study area for the period 2009–2050 to assess the impacts of various scenarios on future water supply shortfalls. Four scenarios were used, which take into account changing population growth rates and extended dry climates. The study found that the catchment is relatively sensitive to changes in population growth and extended dry climates, and this will alter the water availability significantly, causing a water supply deficit. In response to the projected future water demands, one technique to overcome the unmet demand is by introducing water conservation and demand management (WC/DM) strategies to reduce the water losses and shortfall encountered. By implementing adequate measures, water losses can be reduced, preventing water scarcity and giving decision makers time to provide further solutions to water supply problems.
Antibiotics in water bodies, cyanobacterial toxicity and odorous compounds release: a review
Authors: Bruna de Lemos Novo  , Fernanda Arruda Nogueira Gomes da Silva  , Luiz Carlos Bertolino  , Lidia Yokoyama
Published: October 2023

The present study aimed to propose a new cause of odorous compounds release, i.e., the presence of antibiotics in water bodies and its toxicity to cyanobacteria, known to be the main producer of geosmin (GEO) and 2-methylisoborneol (2-MIB). Therefore, a literature review was carried out regarding the problems caused by antibiotics in aquatic environment, including cyanobacterial blooms and GEO and 2-MIB release. In addition, a bibliometric analysis was performed using the VOSviewer software based on the results obtained from the Web of Science (WOS) database. This review aims to build a scientific understanding of the problem, presenting interesting points that converge with the proposed association. It is worth mentioning that no work has been found in the literature that has proposed this relationship. Thus, based on the bibliographic survey, observations and information acquired in recent years about cyanobacterial blooms and environmental contamination by pharmaceutical drugs, one of the main causes of an earthy and musty flavour and odour in a drinking water supply is the toxicity imposed by the presence of antibiotics in aquatic environments on cyanobacteria.
Эпидемии, уединение, дистанцирование: многовековой путь Востока
Authors: Царегородцева Ирина , Карнеев Аендрей , Сизова Александра
Published: October 2023

Коллективная монография построена на тематическом единстве историко-культурных сюжетов, отражающих разнообразные аспекты традиционных и современных моделей реагирования восточных обществ на кризис, связанный с коронавирусом, а также на похожие вызовы в истории. Ключевая цель авторов — выяснить, какова связь традиционных культур и моделей развития обществ Востока с такими важными аспектами современного кризиса, как необходимость резкого ограничения очных социальных контактов, повышенное внимание к гигиене и здоровью, а также государственные меры по сдерживанию вируса и борьбе с его распространением. Книга адресована культурологам, историкам, востоковедам, социологам.
Народные дьяволы и моральная паника
Authors: Коэн Стэнли
Published: October 2023

Моды и рокеры, скинхеды, дизайнерские наркотики, фальшивые беженцы и парни в толстовках... У каждой эпохи — своя моральная паника. Именно благодаря классической работе Стэнли Коэна, впер- вые опубликованной в начале 1970-х годов и с тех пор неоднократно перерабатывавшейся, термин «моральная паника» вошел в широкое употребление. Это выдающееся исследование вскрывает, как СМИ и зачастую власть предержащие относят то или иное обстоятельство либо группу к угрозе общественным ценностям и интересам. Разоблачая кричащие заголовки СМИ, Коэн демонстрирует, как эти процессы приводят к маргинализации таких групп и их очернению в народных представлениях, осложняя рациональное обсуждение социальных проблем. Более того, автор утверждает, что моральная паника позволяет увидеть линии разлома, пронизывающие общество, и властные отношения в нем. Книга адресована культурологам, социологам, исследователям медиа, социальным психологам, но будет интересна и широкому кругу читателей.
Determination of the optimal temperature regime of plastic deformation of micro alloyed automobile wheel steels
Authors:   Department of Metallurgical Equipment Zaporizhzhia National University, Zaporizhzhia, Ukraine, Sheyko Sergiy , Tsyganov Volodymyr ,   Department of Metal-Cutting Machines and Tools National University «Zaporizhzhia Polytechnic», Zaporizhzhia, Ukraine, Hrechanyі Oleksii ,   Department of Metallurgical Equipment Zaporizhzhia National University, Zaporizhzhia, Ukraine, Vasilchenko Tetyana ,   Department of Metallurgical Equipment Zaporizhzhia National University, Zaporizhzhia, Ukraine, Hrechana Anastasiia Hrechana ,   Master of Law, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Kyiv, Ukraine
Published: October 2023

It has been established that hot plastic deformation microalloyed steel 10HFTBch type accelerates the excretion of pearlite and causes structure refinement during subsequent cooling due to an increase of start of new phases’ centers. This leads to the formation of a fine-grained dispersed two-phase structure consisting of ferrite with a high dislocation density and pearlite. It has been determined that at the optimum deformation temperature the prevailing mechanism of structure refinement is the fragmentation of austenitic and ferrite grains of a common initial orientation into misoriented subgrains (fragments) with low-angle dislocation boundaries of deformation origin. A thorough thermokinetic analysis of the course of this process made it possible to determine the optimal temperature regime for plastic deformation for this type of steel, which is 850-900 °C. Plastic deformation increases the range of of pearlite’s transformation existence rates by 2.5 times, it was found that after 30% deformation at 950 °C, pearlite transformation begins at a cooling rate of 5 °C/s, and without deformation – at 2 °C/s
A COMPLEX MODEL OF SYSTEM RESEARCH OF CRITICAL INFRASTRUCTURE FACILITIES SECURITY PROBLEMS
Authors:   Military Institute of the Taras Shevchenko National University of Kyiv Ukraine, Shevchenko A.M.
Published: October 2023

The development of methodological approaches to the solution of an understudied problem, namely to the systematic study of aspects of ensuring the safety of important critical infrastructure facilities is shown in the paper. According to the Law "On the Basics of National Security of Ukraine", the objects of critical infrastructure are: transport and communication; energy and energy supply; provision of housing and communal services; industry and scientific and technical activity in the military-industrial complex; dual purpose technologies; information technologies and information protection; use of subsoil; land on water resources; mineral resources; environmental protection, etc. A system of classifications of prognostic modeling methods has been developed in the form of a three-level structure: by forecasting methods and information bases; according to the material basis of the instrument for obtaining the final result; according to the classification scheme of the specified specific methods. The complex model of system research of critical infrastructure facilities security problems from the position of the Armed Forces of Ukraine has been developed. The mathematical apparatus of analysis and assessment of the level of national (military) security, including military-political, military-economic, military-political models of the state, is presented. The specific features of problem classes by direction, namely well and weakly structured problems, are determined. The use of game theory to compensate for statistical selection is proposed. The main methods of decision-making are given: the methods of utility theory (generalized criterion); the cumulative expected utility as a valid method of decision trees; the methods of prospect theory; the methods of inequality thresholds; the methods of analyzing hierarchies, that are based on a multi-criteria description of the problem; the heuristic methods. It’s shown that the most developed theoretical basis, that finds application in determining the security status of critical infrastructure objects and security in general, are expert methods or methods of multidimensional analysis with elements of taxonomy. However, these approaches assume the existence of reference objects or the scenario of the influence of factors on the final result imagined by experts. An important conclusion was made that the basis of the scientific contradiction is the need to create a sufficiently perfect mechanism for solving the problems of objectively justifying decisions in the military sphere under the conditions of significant limitations in the acquisition of biased data for the prevention of conflicts in the military sphere and the absence of finished scientific theory for determining the result of the complex impact of various factors.
Medical Image Processing using Deep Learning Techniques in Big Data Perspective
Authors: Adepu Bhaskar , Bejjanki Kiran Kumar
Published: October 2023

Artificial intelligence and machine learning will be the driving forces behind the next computing revolution. These technologies rely on the ability to identify trends from historical information and predict future outcomes. One of the best machine learning techniques, deep learning is employed in a variety of applications, including object recognition, picture categorization, image analysis, and clinical archives. Image and video data are necessary for both diagnosing the patient's illness and determining its severity. Convolutional neural networks are efficient gears for digital picture classification and image understanding. The production of medical photographs has ex-ponentially increased as a result of the proliferation of digital devices and the development of camera technology, which creates Bigdata. Massive, difficult-to-manage volumes of structured, unstructured data are referred to as "Big data". The more data processed for analysis, the greater will be the analytical accuracy and also the greater would be the confidence in our decisions based on the analytical findings. In this paper, we proposed a novel method for early detection of pneumonia disease using deep learning techniques along with the big data storage and big data analytics to achieve more better performance. The results show that, the model achieved 91.16% of accuracy and 93.22% of F1-score.
Marketing of Library and Information Products and Services – A Reoriented Digital Marketing Approach
Authors: Sonawane Chetan Sudhakar , Thirunnavukkarasu A
Published: October 2023

The purpose of this paper is to provide a new reoriented approach to the Marketing of Library and Information Products and Services for the survival of the libraries in the future. The paper tried to explore digital tools and techniques as a part of the reoriented approach. Libraries and information centers struggle mightily in the digital age to advertise their products and services to a wide variety of users. The digitally sophisticated audience cannot be reached or engaged with using traditional marketing techniques. This paper presents literature review of the available literature with a view on reorientation of Marketing of Library and Information Products and Services. Various digital marketing techniques are examined in the article along with their possible advantages and drawbacks. These include content marketing, social media marketing, search engine optimisation, email marketing, and analytics. This paper explores the way by which new products and services can be offered. The surveyed literature finds repetition of the work already done as well as the products and services on offer. There is hardly any new development or offerings. This paper presents original view of the author based on his experience and the surveyed literature. The paper is of extreme importance to the library professionals.
MANET Hidden and Exposed Terminal - Challenges and Survey
Authors: Hemalatha S. , Patel Nileshkumar , Kumar Ajay , Jain Neelesh Kumar , Narayanan G. Venkat , Manwali Manka , Pallathadka Harikumar , Chinhewadi Rajesh P.
Published: October 2023

Mobile Adhoc Network (MANET)is a self organized wireless Network which was created to communicate among  the nodes without support of the any Infrastructure. While transmission of the packets between the nodes, many challenges the every nodes faces. One of the Major challenges is Hidden and Exposed nodes issues in the MANET. This causes the packet drop or packet failure while the transmitting the packets. Also which  reflects in degrades the throughput of the MANET and performance of the MANET nodes when the heavy traffic ,retransmission of the dropped packets delayed the communication. This article discussed about hidden and Exposed terminal problem and challenges in MANET and also dissimilar survey in MANET.
An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture
Authors: Gaikwad Nitesh , S. Shiyamala
Published: October 2023

The usage Internet of Things (IoT) was maximized throughout the entire world. Hence, the different core processors incorporated microarchitecture makes this IoT communication system. However, the rise of faults due to the malicious event and the data overload might maximize energy and power utilization. So, the current study has proposed a novel Chimp-based Domain adaptation Alert System (CbDAAS) for the dynamic IoT communication microarchitecture. Before initiating the communication sharing process, the present fault in the designed IoT dynamic core microarchitecture was predicted, and those cores were removed for the current data broadcasting process. Henceforth, the designed fault alert microarchitecture is tested in the MATLAB platform. The reliability was valued using different metrics like power usage, energy consumption and detection exactness value. Finally, the validated metrics were compared with the associated studies and scored the finest outcome in fault detection score as 98% and less energy usage at 0.025mj.
Logistic Regression and Internet of Things Based Smart Irrigation to Predict Crops Water Need
Authors: Somkunwar Rachna K. , Mishra Padma Dev
Published: October 2023

Irrigation is crucial to agriculture. Due to advancements in technology, when we go outside or whenever crops need to be watered, we no longer need to rely on someone to perform irrigation. Many researchers have attempted to irrigate crops automatically, but accuracy, timeliness, and cost concerns are rarely addressed and given top priority. The proposed approach carries out autonomous watering, which results in smart irrigation, using a wireless sensor network, real-time sensors, and irrigation system control. By using this method, waste is decreased and the necessary water is kept in the container. Instead than just evaluating soil moisture, automated irrigation takes into account the type of crop, the weather, and soil moisture to determine whether the crops need to be watered. By taking into account the aforementioned three factors, a machine learning technique called logistic regression is utilized to estimate the need for water. The logistic regression model, which is based on the values of the three parameters given, forecasts the watering needs of the plants using an arduino-based IoT circuitry. The strategy outlined is advantageous in terms of accuracy, timeliness, and cost. The results of the proposed model has proven its betterment in performance and the amount of automation over the existing irrigation systems. The IoT and machine learning combined model is useful from the point of view of its accuracy, cost and timeliness in predicting water needs for crops as well as full automation is enabled in the irrigation system with this approach.
Blood Pressure Estimation from Speech Recordings: Exploring the Role of Voice-over Artists
Authors: Rajput Vaishali , Mulay Preeti , Raje Rajeev
Published: October 2023

Hypertension, a prevalent global health concern, is associated with cardiovascular diseases and significant morbidity and mortality. Accurate and prompt Blood Pressure monitoring is crucial for early detection and successful management. Traditional cuff-based methods can be inconvenient, leading to the exploration of non-invasive and continuous estimation methods. This research aims to bridge the gap between speech processing and health monitoring by investigating the relationship between speech recordings and Blood Pressure estimation. Speech recordings offer promise for non-invasive Blood Pressure estimation due to the potential link between vocal characteristics and physiological responses. In this study, we focus on the role of Voice-over Artists, known for their ability to convey emotions through voice. By exploring the expertise of Voice-over Artists in controlling speech and expressing emotions, we seek valuable insights into the potential correlation between speech characteristics and Blood Pressure. This research sheds light on presenting an innovative and convenient approach to health assessment. By unraveling the specific role of Voice-over Artists in this process, the study lays the foundation for future advancements in healthcare and human-robot interactions. Through the exploration of speech characteristics and emotional expression, this investigation offers valuable insights into the correlation between vocal features and Blood Pressure levels. By leveraging the expertise of Voice-over Artists in conveying emotions through voice, this study enriches our understanding of the intricate relationship between speech recordings and physiological responses, opening new avenues for the integration of voice-related factors in healthcare technologies.
An Efficient Model for Forest Fire Detection using Deep Convolutional Neural Networks
Authors: Anjanadevi B. , Nagesh V. , T. Sujeeth , Varma P. N. Manvith
Published: October 2023

Forest fires are a significant natural disaster that causes extensive damage to both human and wildlife habitats. Early detection and management of forest fires are critical in preventing potential losses. In recent years, deep learning-based approaches have emerged as promising solutions for forest fire detection. This paper proposes a deep learning-based approach for forest fire detection using SqueezeNet model.The proposed approach utilizes still images captured from forest areas under different weather conditions to classify whether an image contains a fire or not. The models were trained and tested using accuracy, precision, and recall metrics. The experimental results show that SqueezeNet achieve high precision, and recall in detecting forest fires.SqueezeNet is a Convolutional Neural Networks (CNN) architecture designed to reduce the number of parameters and computations required in a deep learning model while maintaining high accuracy in image classification tasks..
A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models
Authors: Agarwal Komal , Ainapure Bharati , Shukla Ashish
Published: October 2023

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.
MalDet-Malware Detection Using Deep Learning and LSTM based Approach to Classify Malware
Authors: Mehta Gaurav , Jain Shaily , Das Prasenjit , Tripathi Vikas
Published: October 2023

Computer security requires malware detection. Recent research manually uncovers hazardous features using machine learning-based techniques. MalDet, a cutting-edge malware detection method, is recommended in this paper. MalDet classifies malware using a stacking ensemble and learns from grayscale images and opcode sequences using CNN and LSTM networks. According to the evaluation, MalDet's malware detection validation accuracy is 99.89%. MalDet outperforms other previous research with 99.36% detection accuracy and a significant detection speedup on the Microsoft malware dataset. We classified nine malware families for MalDet.
A Review on Software Quality Forensics: Techniques, Challenges, and Limitations
Authors: Sharma Sandeep Kumar , Khaliq Mazhar , Khaliq Mazhar
Published: October 2023

Software quality forensics plays a vibrant role related to software quality, security, and integrity. The paper aims to derive a software quality forensics model through existing software quality models and their factors. The papers explore quality models, factors, approaches, tools, techniques, and standards regarding software quality investigation and confine the research area for software quality integrity breach forensics. The explore the deviations of quality attributes, standards, factors, and artifacts, it leads to further investigation of root-cause followed by digital evidence procedure for alleged software quality issues. Therefore, there is a need for a software quality forensics model and dedicated standards to fulfill the digital evidence procedure validation, satisfiable, and prosecution in the court of law in the context of alleged or illegal activity investigation quality of software. The paper has  derived the techniques, challenges, and limitations of software quality forensics based on the review of research questions.
Statistical Review Evaluation of 5G Antenna Design Models from a Pragmatic Perspective under Multi-Domain Application Scenarios
Authors: Srinubabu Manumula , Rajasekhar N. V.
Published: October 2023

Antenna design for the 5G spectrum requires analysis of contextual frequency bands, design of miniaturization techniques, gain improvement models, polarization techniques, standard radiation pattern designs, metamaterial integration, and substrate selection. Most of these models also vary in terms of qualitative & and quantitative parameters, which include forward gain levels, reverse gain, frequency response, substrate types, antenna shape, feeding levels, etc. Due to such a wide variety in performance, it is ambiguous for researchers to identify the optimum models for their application-specific use cases. This ambiguity results in validating these models on multiple simulation tools, which increases design delays and the cost of deployments. To reduce this ambiguity, a survey of recently proposed antenna design models is discussed in this text. This discussion recommended that polarization optimization and gain maximization are the major impact factors that must be considered while designing antennas. It is also recommended that collocated microstrip slot antennas, fully planar dual-polarized broadband antennas, and real-time deployments of combined slot antenna pairs with wide-band decoupling are very advantageous. Based on this discussion, researchers will be able to identify optimal performance-specific models for different applications. This discussion also compares underlying models in terms of their quantitative parameters, which include forward gain levels, bandwidth, complexity of deployment, scalability, and cost metrics. Upon referring to this comparison, researchers will be able to identify the optimum models for their performance-specific use cases. This review also formulates a novel Antenna Design Rank Metric (ADRM) that combines the evaluated parameters, thereby allowing readers to identify antenna design models that are optimized for multiple parameters and can be used for large-scale 5G communication scenarios.
Optimizing the Load Frequency of a Two-Area Interlinked Power System using Artificial Intelligence Techniques
Authors: Manoj Vasupalli , Nagaraju Subuddi , Prasad K. Durga Syam , Rathan Bathula Vineela , Kumar Boddepalli Kiran , Suman Jami Venkata
Published: October 2023

Power costs are increasing on a daily basis, generating changes in system frequency and causing serious concerns with system stability. It has become a major problem to offer customers with uninterrupted and high-quality power. To mitigate these issues, a linked power system's load distribution and network frequency should be constantly reviewed. Load frequency control adjusts the generator's energy output and tie line power between prescribed limits. As well as regulating generator output power, load frequency control also adjusts tie line power. The disturbance in the frequency due to different load changes is regulated using the proposed scheme. In this article, a two-area load frequency control system is constructed and evaluated using various control approaches, including a proportional integral derivative (PID) controller, a proportional integral (PI) controller, a fuzzy logic-based controller, and an Artificial Neural Network (ANN). The goal is to assess the power system's resilience under different loading conditions with these control schemes. The performance of the controllers is compared based on peak-undershoot, peak-overshoot, and settling time, focusing on tie line power and frequency response. To achieve this, the design is implemented using MATLAB/SIMULINK software.
Optimized Screening of Glaucoma using Fundus Images and Deep Learning
Authors: S. Santhosh , B. K. Anoop
Published: October 2023

Diabetic retinopathy, glaucoma, and age-related macular degeneration are among the leading causes of global visual loss. Early detection and diagnosis of these conditions are crucial to reduce vision loss and improve patient outcomes. In recent years, deep learning algorithms have shown great potential in automating the diagnosis and categorization of eye disorders using medical photos. For this purpose, the ResNet-50 architecture is employed in a deep learning-based strategy. The approach involves fine-tuning a pre-trained ResNet-50 model using over 5,000 retinal pictures from the ODIR dataset, covering ten different ocular diseases. To enhance the model's generalization performance and avoid overfitting, various data augmentation techniques are applied to the training data. The model successfully detects glaucoma-related ocular illnesses, including cataract, diabetic retinopathy, and healthy eyes. Performance evaluation using metrics like accuracy, precision, recall, and F1-score shows that the model achieved 92.60% accuracy, 93.54% precision, 91.60% recall, and an F1-score of 91.68%. These results indicate that the proposed strategy outperforms many state-of-the-art approaches in the detection and categorization of eye disorders. This success underscores the potential of deep learning-based methods in automated ocular illness identification, facilitating early diagnosis and timely treatment to ultimately improve patient outcomes.
Recognition and Evaluation of Heart Arrhythmias via a General Sparse Neural Network
Authors: Sanamdikar Sanjay , Waghmare Surendra , Patil Satis , Patil Dipak , Borawake Madhuri , Suryatal Baban
Published: October 2023

In clinical use, an electrocardiogram (ECG) is an essential medical tool for assessing heart arrhythmias. Thousands of human beings worldwide are affected by different cardiac problems nowadays. As a consequence, studying the features of the ECG pattern is critical for detecting a wide range of cardiac diseases. The ECG is a test which assesses the intensity of the electrical impulses in the circulatory system. In the present investigation, detection and examination of arrhythmias in the heart on the  system using GSNNs (General sparsed neural network classifier) can be carried out[1]. In this paper, the methodologies of support vector regression(SVR), neural mode decomposition(NMD), Artificial Neural Network (ANN), Support Vector Machine(SVM) and are examined. To assess the suggested structure, three distinct ECG waveform situations are chosen from the MIT-BIH arrhythmia collection. The main objective of this assignment is to create a simple, accurate, and simply adaptable approach for classifying the three distinct heart diseases chosen. The wavelet transform Db4 is used in the present paper to obtain several features from an ECG signal. The suggested setup was created using the MATLAB programme. The algorithms suggested are 98% accurate for forecasting cardiac arrhythmias, which is greater than prior techniques.
Robust Brain Tissue Segmentation in AD Using Comparative Linear Transformation and Deep Learning
Authors: Latha Vunnam Asha , Namburu Anupama
Published: October 2023

As a progressive neurological disease, Alzheimer's disease (AD), if no preventative measures are   taken, can result in dementia and a severe decline in brain function, making it difficult to perform basic tasks. Over 1 in 9 people suffer from dementia caused by Alzheimer's disease and require uncompensated care. The hippocampus is extracted from MRI scans of the brain via image segmentation have been useful for diagnosing Alzheimer's disease (AD).The segmentation of the CSF region in brain MRI is critical for analyzing the stages of AD. The extraction of Hippocampus from an MRI of the brain is greatly influenced by the contrast of the images. Using comparative linear transformation in the horizontal and vertical dimensions as well as statistical edge-based features, this article proposes a robust method for segmentation technique for the extraction of Hippocampus from brain MRI. These transformations aid in balancing the brain image's thin and dense fluid extractions. Through use of the ADNI dataset, the proposed approach had a 99% success rate in segmentation.
Dynamic Resource Allocation in Industrial Internet of Things (IIoT) using Machine Learning Approaches
Authors: Sisodiya Pankaj Singh , Bhandari Vijay
Published: October 2023

In today's era of rapid smart equipment development and the Industrial Revolution, the application scenarios for Internet of Things (IoT) technology are expanding widely. The combination of IoT and industrial manufacturing systems gives rise to the Industrial IoT (IIoT). However, due to resource limitations such as computational units and battery capacity in IIoT devices (IIEs), it is crucial to execute computationally intensive tasks efficiently. The dynamic and continuous generation of tasks poses a significant challenge to managing the limited resources in the IIoT environment. This paper proposes a collaborative approach for optimal offloading and resource allocation of highly sensitive industrial IoT tasks. Firstly, the computation-intensive IIoT tasks are transformed into a directed acyclic graph. Then, task offloading is treated as an optimization problem, taking into account the models of processor resources and energy consumption for the offloading scheme. Lastly, a dynamic resource allocation approach is introduced to allocate computing resources to the edge-cloud server for the execution of computation-intensive tasks. The proposed joint offloading and scheduling (JOS) algorithm creates its DAG and prepare a offloading queue. This queue is designed using collaborative q-learning based reinforcement learning and allocate optimal resources to the JOS for execution of tasks present in offloading queue. For this machine learning approach is used to predict and allocate resources. The paper compares conventional and machine learning-based resource allocation methods. The machine learning approach performs better in terms of response time, delay, and energy consumption. The proposed algorithm shows that energy usage increases with task size, and response time increases with the number of users. Among the algorithms compared, JOS has the lowest waiting time, followed by DQN, while Q-learning performs the worst. Based on these findings, the paper recommends adopting the machine learning approach, specifically the JOS algorithm, for joint offloading and resource allocation.
Mitigation of Attacks via Improved Network Security in IoT Network using Machine Learning
Authors: N Kalaiarasi , A Kadirvel , G Geethamahalakshmi , D Nageswari , N Hariharan , Kumar S Senthil
Published: October 2023

In this paper, we develop a support vector machine (SVM) based attack mitigation technique from the IoT network. The SVM aims to classify the features related to the attacks based on pre-processed and feature extracted information. The simulation is conducted in terms of accuracy, precision, recall and f-measure over KDD datasets. The results show that the proposed SVM classifier obtains high grade of classification accuracy in both training and testing datasets.
Colorectal Cancer Classification from Protein Sequences Using Several RNN Pre-Trained Models
Authors: Rao B Madhav , Rao Kunjam Nageswara
Published: October 2023

Bioinformatics is one field that can integrate health information with data mining applications in order to predict or analyze the patient’s information. One among the several diseases is colorectal cancer,which is horrible and rated the third leading disease in the area of cancer which leads to death in both men and women.In general, there are several intelligent methods to identify several kinds of problems in gene selection for predicting cancer,but there is no method to give a solution for patients who are diagnosed in the advanced stage.This motivated me to design theproposed approach in which we try to identify all the homo protein sequences and then train these sequences corresponding to one which causes colorectal cancer.InBioinformatics,a protein acts as one of the main agent or source to perform a biological function by interacting with molecules like Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA). The function of a protein determines the healthy or diseased states of an organism. Protein interaction with other proteins can be visualized through a network called Protein-Protein Interaction Network (PPIN).In general classification of protein sequences is a very complex task. Deep learning techniques like CNN and RNN can be used to solve the problem.In computational bioinformatics,the classification of protein sequence plays an important role in determining accuracy.To improve the accuracy of our current model, the suggested method incorporates GRU, LSTM, RNN, and Customized LSTM into an RNN based architecture by optimizing the parameters in a two-way direction.Here we try to test all the models on sample protein sequences that are collected from TCGA and then determine the correctness of testing data and training data.