bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding of Convolutional Neural Network (CNN): A Review
Authors: Purwono Purwono , Ma'arif Alfian , Rahmaniar Wahyu , Fathurrahman Haris Imam Karim , Frisky Aufaclav Zatu Kusuma , Haq Qazi Mazhar ul
Published: April 2023

The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.
Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms
Authors: Jamali Najmeh , Gharib Mohammad Reza , Omidi Koma Behzad
Published: April 2023

In order to minimize surgical site infections, indoor air quality in hospital operating rooms is a major concern. A wide range of literature on the relevant issue has shown that air contamination diminution can be attained by applying a more efficient set of monitoring and controlling systems that improve and optimize the indoor air status level. This paper discusses a fuzzy inference system (FIS) and the integrated model neuro-fuzzy inference system (ANFIS) focusing on the control of contamination via proper airflow distribution in an operating room, which is essential to guarantee the accuracy of the surgical procedure. A deep learning estimation approach is proposed to predict incidence in the presence of airborne contamination. The project's goal is to reduce airborne contamination to improve the surgical environment and reduce the predicted incidence during surgeries. The neuro-fuzzy deep learning model was trained with a neural network structure and tested by considering 3 important parameters that affected the air quality introducing the specialization of the system to control the model’s target. Finally, the proposed approach has been put into practice by making use of data collected by sensors placed within a real operating room in a hospital in Mashhad, Iran. The proposed model attains 97.3% and 95% validation accuracy for estimating the relative humidity and particles, respectively. The efficacy of the proposed neuro-fuzzy indicates that the system significantly lowers risk values and enhances indoor air quality.
Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment
Authors: Samodro Maulana Muhammad Jogo , Puriyanto Riky Dwi , Caesarendra Wahyu
Published: April 2023

Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.
The macroeconomic role of the collateral constraint in resource-rich countries
Authors: Andreyev Mikhail
Published: April 2023

In this paper, we consider DSGE model of a small open economy highly dependent on resource export. The aim of the study is to identify the role of the collateral constraint in the terms-of-trade (TOT) shock transmission. The model contains two non-linear constraints in the form of inequalities: the collateral constraint and the zero lower bound constraint. We have found that if the monetary policy is not inertial, then under a series of unidirectional TOT shocks, the response of the economy is highly skewed with respect to positive and negative shocks. Both inequalities bind and reduce the positive impact of the TOT shock. If the monetary policy is inertial or the central bank reacts poorly to inflation change, then only the collateral constraint binds, and the effect of asymmetry almost disappears
Empirical analysis of risks’ influence on the large mergers and acquisitions projects’ success in modern companies
Authors: Gracheva Marina
Published: April 2023

Mergers and acquisitions (M&A) are among the key strategies for stabilizing and strengthening a company's market position in an unstable economic environment and gaining a competitive advantage from inorganic growth. Doing so, big M&A deals show a higher risk of failing, being terminated, or being incomplete, which can lead to financial, reputational, and time losses. Due to these factors, prudent risk-management of such transactions is necessary, including the evaluation of the degree to which various risk groups influence M&A success. This paper evaluates big (those the value of which exceeds 10 billion euros) M&A projects risks influence on the success of the deal closure quantitatively and offers recommendations for managing significant risks using the project methodology. The analysis includes probit-regressions on a wide sample of 24 countries and 10 amalgamated industries. As a result, five risk groups appear significant: risk of transaction's hostile nature; cross-country risks; risk of misjudging the transaction value; risks associated with the size ratio of target and acquiring companies; risk of delaying the terms of the deal. Transnational mergers and acquisitions demonstrate a stronger influence of all significant risks on the transaction's success. The paper provides recommendations for managing significant risks using the project methodology.
On the impact of excessive document flow on the labor activity of a budgetary organization employee
Authors: Tsurikov Vladimir
Published: April 2023

The causes and results of the formation of excessive workflow in budgetary organizations are analyzed with the help of mathematical modeling. The model was built taking into account the opinion of the former head of the Ministry of Economic Development of the Russian Federation Maxim Oreshkin, according to whom excessive reporting consumes a huge amount of resources, and therefore can be generated with a hostile intent to create an obstacle to achieving national goals. The model is based on the following assumptions: An agent (an employee of a budgetary organization) cannot refuse to carry out the reporting burden. An official (an employee of a higher ranking organization or a representative of government bodies) has an uncontrolled and unlimited opportunity to demand that the controlled organization draw up documents in the form proposed by him and provide them within the specified time. The utility of the official increases with receipt of these documents. It is shown that under such conditions the official benefits by unlimitedly increasing labor intensity of the reporting burden imposed on the agent. As a result of an increase in the amount of unpaid effort spent by an agent on reporting, his activity, which is determined by the part of paid efforts that exceed their minimum volume, drops to zero. The activity of two officials is compared: one of them has no hostile intentions and simply increases his work’s utility, while the other pursues a hostile goal to paralyze the work of the controlled organization. It is shown that there is only a quantitative difference in the results of their activities: the threshold value of the complexity of tasks, the achievement or exceeding of which destroys the activity of the agent, is lower in the case of a hostile official. Three possible ways of limiting the activity of an official are discussed: taxing the controlling organization with a corrective tax, full compensation for the costs of the controlled organization, and legislative restriction of document flow.
Дидактичне обґрунтування теоретичних і практичних компонентів викладання курсу «Режисура шоу та артпроєктів» в умовах екстреного дистанційного онлайн-навчання
Authors: Набоков Роман , Крипчук Микола
Published: April 2023

Метою дослідження є визначення основних понять і характеристика технологій ефективного викладання фахової практичної освітньої компоненти «Режисура шоу та артпроєктів» для бакалаврів спеціальності 026 «Сценічне мистецтво» в умовах екстреного дистанційного онлайн- навчання. Методологія дослідження обумовлена метою роботи й охоплює використання загальнонаукових і спеціальних методів, зокрема основу дослідження становлять положення теорії наукового пізнання, положення системного, аксіологічного, особистісно зорієнтованого, діяльнісного, а також наукових підходів до вищої освіти; принципи системності та послідовності, зв’язку теорії з практикою в майбутніх фахівців зі сценічного мистецтва. Наукова новизна роботи. Досліджено принципи й технології дистанційного навчання; розкрито такі основні поняття, як «дистанційне навчання», «е-педагогіка», «онлайн-навчання», їх тотожність та суттєву відмінність, а також наведено поняття «екстрене дистанційне навчання» (ЕДН) як тимчасовий перехід в альтернативний режим навчання через кризові обставини; на власному досвіді розглянуто проблематику дистанційного навчання у творчих ЗВО та надано практичні напрацювання з цього питання; проаналізовано сучасний цифровий імідж або цифрову репутацію (е-репутацію) театрального педагога. Висновки. Дидактичний і методичний супровід викладання різних освітніх компонент в екстреному дистанційному навчанні потребує подальшого розроблення та стрімкого впровадження у систему вищої освіти України. Найбільш актуальною проблемою для національної театральної освіти сьогодення є перехід на дистанційне навчання, яке потребує від викладачів профільних предметів розвивати компетентності задля вдосконалення використання електронних освітніх ресурсів і якісної роботи з різними моделями дистанційної освіти. Підкреслимо, що імідж сучасного педагога є досить важливим складником його професіоналізму, а з огляду на низку суб’єктивних та об’єктивних обставин освітній процес буде набувати інших рис і змінюватися, тому імідж педагога та його цифровий аспект будуть ставати дедалі важливішими й актуальнішими в перспективі розвитку фахових компетентностей.
Data–Driven Wake Steering Control for a Simulated Wind Farm Model
Authors: Simani Silvio , Farsoni Saverio , Castaldi Paolo
Published: April 2023

Abstract: Upstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting.
MARKETING RESEARCH AS AN INFORMATION BASIS FOR THE DIAGNOSTICS OF THE MARKETING ACTIVITY OF THE ENTERPRISE
Authors:   National Technical University “Kharkiv Polytechnic Institute”, LARKA Ludmila
Published: April 2023

The article examines the correspondence of marketing research to the information needs of marketing diagnostics of the enterprise. The purpose of the article is to substantiate the expediency of a wider application of marketing research as an information basis for diagnosing the marketing activity of an enterprise in modern economic conditions. It was determined that the application of marketing research ensures the search, processing, accumulation and updating of information through the use of special methods of its processing for the purpose of making management decisions. The possibility of using marketing research as an information basis for diagnosing the marketing activity of an enterprise by analyzing the components of information support is substantiated. Information technologies that can be applied during marketing research are analyzed. A description of interested parties in conducting marketing research is given. An overview of the software products, which are expedient to use for organizing, conducting and processing the results of marketing research, was carried out. The content of the company’s information resources, which are formed based on the results of marketing research, has been studied. It was determined that the algorithm for forming marketing support is related to the algorithm for conducting marketing research, which indicates the possibility of marketing research to act as information support for marketing diagnostics. It was determined that conducting marketing research provides the diagnostic process with such accurate and relevant data as the company’s market share, real and potential market capacity, supply and demand ratio, marketing risks, competitive situation on the market, competitive strategies of market participants, target audience profile, consumer satisfaction product parameters. It is shown that the financing of marketing research is a contribution to the development of the diagnostic information base, which will have a long-term economic effect.
SIMULATION OF COMPLEX EFFECTS OF THERMAL MODERNIZATION MEASURES OF THE BUILDING TAKING INTO ACCOUNT INVESTMENT LIMITATIONS
Authors: BABENKO Oleksii , STEPANOV Dmytro , STEPANOVA Natalia ,   Vinnytsia National Technical University
Published: April 2023

The article simulates the impact of the complex application of energy-saving measures on the energy efficiency indicators of a public brick building. During modeling, the economic factor – capital investment in energy saving measures – was taken into account. Similar simulations are carried out during energy audits of various types of buildings. Performing energy audits of residential and public buildings in the city is, in accordance with the Law of Ukraine “On Energy Efficiency”, an important part of Ukraine’s energy conservation policy. Energy audits are required for: a) development of an energy certificate, which is required for many new and existing buildings in accordance with current legislation; b) attraction of funds from the energy efficiency fund and other funds for the needs of thermal modernization; c) obtaining reasonable indicators that will allow building owners to make energy-efficient decisions. The energy audit report contains three stages: the stage of obtaining information, the stage of analyzing information and developing energy-saving recommendations, and the stage of technical and economic substantiation of the proposed energy-saving recommendations. At the first stage, energy auditors collect raw data about buildings. Existing buildings are often characterized by the lack of insulation of opaque enclosing structures, the presence of translucent enclosing structures, the reduced heat transfer supports of which are significantly lower than the minimum permissible values, and outdated sources of heat supply. During the second stage, mathematical models are developed that allow obtaining technical and economic indicators of energy saving measures. The third stage is the substantiation of optimal measures that must satisfy technical and economic constraints. The work analyzed the dependence of the general indicator of specific energy consumption during heating and cooling of a two-story brick public building under the condition of modernization of the gas boiler, as well as insulation of the brick facades of the building with polystyrene foam plates of different thicknesses. At the same time, an analysis of both individual and complex application of the specified measures was carried out. General capital investment in thermal modernization was adopted as an economic indicator that sets limits on energy saving measures. As a result of the modeling, it was established that in order to determine the optimal measures to increase the energy efficiency of the building, it is necessary to use optimization mathematical devices that take into account the existing restrictions and can be integrated into mathematical models that calculate the basic level of energy consumption in compliance with the normative parameters of the building’s operation.
ANALYSIS OF STOCK MARKET DYNAMICS
Authors: BURTNYAK Ivan , RUSYN Roman ,   Vasyl Stefanyk Precarpathian National University
Published: April 2023

The article analyzes the application of information technologies to the analysis of the stock market, namely to the study of the dynamics of the Ukrainian currency exchange rate, which will allow us to draw a conclusion about the market as a whole. Exploratory data analysis was used to review and analyze financial data. It is an approach to summarizing, visualizing, and gaining insight into the important characteristics of a data set. When analyzing and predicting the dynamics of complex financial systems, one cannot do without such a powerful tool as the Python programming language and neural network technologies. Neural networks find new successful applications in the practice of management and decision-making, including in the financial and trade spheres, i.e. wherever it is necessary to find a certain hidden regularity and make a forecast of stock market tools aimed at achieving macroeconomic stabilization and dynamic development of the financial market. Forecasting of the dynamics of stock market instruments is carried out, which allows for analysis and precautionary conclusions and proposals to minimize risks related to the pricing of derivatives that arise on the stock market. Neural networks are used for forecasting the exchange rate, which allows to minimize speculative changes in pricing, to analyze the progress of processes on the stock market and to take specific steps to improve the situation regarding the optimization of financial strategies. As a result of the analysis, it can be noted that information technologies are widely used in the financial sphere of activity. The effectiveness of the use of information technologies for data analysis and the ability of neural networks to one of the most sought-after tasks of financial activity – forecasting the future value of various instruments – have been proven. It can be argued that the best result is given by the combination of information technologies with expert systems, it allows to calculate the value of derivatives prices with great accuracy and to monitor changes in the speed of financial flows. The used technique allows to increase the accuracy of the forecast and to make informed management strategic decisions by the participants of the stock market.
Mobile-Assisted Language Learning Dalam Pembelajaran Keterampilan Menyimak Bahasa Asing: Sebuah Tinjauan Pustaka
Authors: Widiananda Salsabila , Rabbani Zahrah Izzaturrahim , Darmawangsa Dante
Published: April 2023

Penerapan teknologi dalam pembelajaran bahasa asing telah menjadi perhatian dan urgensi dalam beberapa dekade. Sejumlah penelitian dilakukan untuk mengetahui penggunaan dan efektivitas penggunaan perangkat seluler dalam proses pembelajaran bahasa asing. Namun, penelitian eksperimen mengenai keterampilan menyimak bahasa asing dengan menggunakan perangkat seluler masih mendapat sedikit perhatian. Penelitian ini bertujuan untuk menyajikan hasil kajian tematik literatur terbaru dan relevan tentang penggunaan MALL (Mobile-Assisted Language Learning) pada pembelajaran keterampilan menyimak mengenai peluang dan kendala penerapannya dalam konteks pembelajaran bahasa asing di kelas. Metode penelitian ini adalah studi literatur, dengan mengumpulkan berbagai data melalui literatur ilmiah pada artikel yang relevan dengan topik yang dibahas. Pemilihan artikel penelitian dipilih melalui basis data Google Scholar dengan kriteria 5 tahun terakhir dan ditemukan 16 artikel yang memenuhi kriteria yang ditentukan.  Hasil penelitian ini menunjukkan bahwa MALL dalam pembelajaran keterampilan menyimak memiliki potensi manfaat untuk meningkatkan keterampilan bahasa pembelajar bahasa asing serta meningkatkan motivasi dan keterlibatan mereka dalam belajar. Terlepas dari manfaat pedagogi ini, tetap ada hambatan yang mencegah media ini diimplementasikan secara efektif, terutama terkait dengan masalah penggunaan perangkat seluler, tingkat keterampilan pembelajar yang beragam, dan konten dalam aplikasi MALL yang tidak sesuai. Sampai batas tertentu temuan penelitian ini sejalan dengan temuan penelitian sebelumnya yang dilakukan dalam kerangka yang sama.
Manajemen Tenaga Pendidik Terhadap Hasil Belajar Siswa Madrasah Aliyah
Authors: Muhammad Mushfi El Iq Bali  , Umam Mohammad Hairul
Published: April 2023

Abstrak Penelitian ini bertujuan untuk mengkaji tentang Manajemen Tenaga Pendidik Terhadap Hasil Belajar Siswa di MA Plus Al-Masdukiah, dengan menggunakan metodelogi penelitian Kualitatif  jenis studi kasus. Berdasarkan hasil penelitian tersebut adalah manajemen tenaga pendidik yang dilakukan di MA Plus Al-Masdukiah untuk membantu siswa mendapat hasil belajar yang baik menggunakan EMASLIM yaitu suatu peranan yang disampaikan oleh E Mulyasa sebagai Fungsi dari kepala sekolah. Namun peranan tersebut digunakan di MA Plus Al-Masdukiah terhadap pendidik sebagai peranan utama terhadap keberhasilan siswa dalam belajar, beberapa siswa yang butuh perhatian khusus untuk memperoleh hasil belajar yang baik dan sesuai dengan tujuan pendidikan harus dicarikan solusi oleh pendidik mulai dari metode belajarnya, strategi dan peranan yang harus dilakukan pendidik yang sangat membentu terhadap belajarnya secara optimal agar mendapakant hasil belajar yang baik. dengan mengadopsi emaslim sebagai fungsi kepala sekolah digunakan terhadap pendidik  yaitu Pendidik sebagai Educator, Pendidik sebagai Manager, Pendidik sebagai Administrator, Pendidik sebagai Leader, Pendidik sebagai Supervisor, Pendidik sebagai Innovator dan Pendidik sebagai Motivator.
Kemampuan Receive Siswa Ekstrakurikuler Bola Voli: Metode Latihan Quickness Versus Latihan Konvensional
Authors: Aprilyana Asep Saepudin , Bachtiar Bachtiar , Septiadi Firman
Published: April 2023

Penelitian ini dilatarbelakangi temuan permasalahan yang dihadapi siswa peserta ekstrakurikuler bola voli. Diketahui para peserta kurang menguasai kemampuan menerima bola dari arah servis lawan, mengendalikan servis atau upaya serangan lawan  saat menerima servis. Penelitian ini bertujuan untuk meningkatkan kemampuan receive permainan bola voli dengan menggunakan latihan quickness. Penelitian ini merupakan penelitian kuantitatif dengan menggunakan metode Quasi Eksperimental. Penelitian ini dilaksanakan di SMP Negeri 1 Gunungguruh yang berada di Kabupaten Sukabumi dengan menggunakan sampel siswa 11 orang laki-laki. Instrumen yang digunakan dalam penelitian ini adalah tes keterampilan pasing bawah ke kanan 10 kali ke kiri 10 kali. Teknik analisis data menggunakan teknik pengujian perbedaan rata-rata dari dua data. Hasil penelitian menunjukkan latihan Quickness berpengaruh terhadap kemampuan receive secara signifikan. Hasil yang sama juga menunjukkan bahwa latihan konvensional memberikan pengaruh yang signifikan terhadap kemampuan receive peserta ekstrakurikuler bola voli. Ada pun hasil perbandingan dari latihan Quickness dan latihan konvensional didapatkan hasil bahwa latihan Quickness lebih baik dibandingkan latihan konvensional dalam meningkatkan kemampuan receive bola voli. Kesimpulan dari penelitian ini adalah latihan quickness dapat meningkatkan kemampuan receive permainan bola voli.
Pembelajaran Berbasis Budaya Melalui Kegiatan Ekstrakurikuler
Authors: Firdaus Muhammad Kemal , Fajrie Nur , Purbasari Imaniar
Published: April 2023

Penelitian ini bertujuan untuk mengetahui penerapan pembelajaran berbasis budaya melalui kegiatan ekstrakulikuler pada sekolah dasar. Penelitian ini menggunakan pendekatan kualitatif dengan jenis penelitian fenomenologi. Penelitian ini dilakukan di SD Segugus Ahmad Yani yang terletak di Kota Kudus pada Desa Demangan dan Purwosari antara lain SD 1 Purwosari, SD 2 Purwosari, SD 3 Purwosari, SD Demangan, SD NU Mafatihul Ulum. Subjek dalam penelitian ini adalah guru yang menjadi instruktur dan para siswa peserta ekstrakurikuler. Berdasarkan hasil penelitian, ekstrakurikuler di SD Segugus Ahmad Yani Kota Kudus menunjukkan bahwa kegiatan ekstrakurikuler SD 1 Purwosar yang berhubungan dengan budaya yaitu tari, gendang dan BTQ, Kegiatan ekstrakurikuler SD 2 Purwosari meliputi seni tari, seni rupa dan rebana, Kegiatan ekstrakurikuler Sd 3 Purwosar meliputi seni tari, menyanyi dan seni rupa dan Kegiatan ekstrakurikuler SD NU Mafatihul Ulum meliputi drum band dan rebana. Hal-hal yang mempengaruhi pembelajaran berbasis budaya antara lain kurangnya wawasan siswa terhadap budaya lokal, kurangnya pengajar ekstrakurikuler, kurangnya dukungan dari orang tua, dan minimnya dana. Simpulan dalam penelitian ini adalah bentuk pembelajaran berbasis budaya di SD Segugus Ahmad Yani itu pada kegiatan ekstrakurikuler atau di luar jam pembelajaran. Faktor yang mempengaruhi pembelajaran berbasis budaya yang dihadapi SD Segugus Ahmad Yani antara lain adalah dari minat, potensi, dana, waktu, pengajar, dan fasilitas.
Speech and language therapy service provision to UK intensive care units: A national survey
Authors: Mills Claire S. , Newman Helen , Iezzi Christina , Sutt Anna-Liisa , Jones Rachel , Sadiq Jude , Ginnelly Aeron , Jones Gemma , OBE Sarah Wallace
Published: April 2023

BACKGROUND: The role of Speech and Language Therapists (SLTs) in intensive care units (ICUs) has become increasingly recognised. This survey was developed in response to concern amongst UK SLTs that service provision was insufficient. OBJECTIVE: The primary objective was to benchmark and describe UK pre-pandemic SLT ICU service provision. Secondary objectives included: identifying factors which might explain differences in SLT service provision, identifying unmet needs and good practice, and informing recommended SLT staffing levels. METHODS: An online survey was distributed through UK SLT networks and social media. Quantitative data were reported descriptively, and content analysis was conducted with qualitative data. RESULTS: Responses were received from 64 hospitals, representing three paediatric services and 61 adult services. Average staffing ratios of 0.03 and 0.01 whole time equivalent (WTE) were reported for these respectively. Most services (77&) received no funding from their ICU for SLT staffing. Few reported an adequate SLT service for communication (12&), swallowing (16&) and tracheostomy weaning interventions (11&). Compliance with national guidance for SLT-led communication and swallowing input for all tracheostomised patients was achieved by 27& of sites. CONCLUSIONS: Staffing levels at many sites were insufficient to provide a consistent and responsive service. The findings contributed to a recommendation of 0.1 WTE SLT per ICU bed, which was incorporated into Edition 2 of the Guidelines for the Provision of Intensive Care Services. This survey identified barriers and facilitators to providing an adequate SLT service for critically ill patients that may assist service development initiatives and guide further research.
Structural Liveness of Immediate Observation Petri Nets
Authors: Jančar Petr , Valůšek Jiří
Published: April 2023

We look in detail at the structural liveness problem (SLP) for subclasses of Petri nets, namely immediate observation nets (IO nets) and their generalized variant called branching immediate multi-observation nets (BIMO nets), that were recently introduced by Esparza, Raskin, and Weil-Kennedy. We show that SLP is PSPACE-hard for IO nets and in PSPACE for BIMO nets. In particular, we discuss the (small) bounds on the token numbers in net places that are decisive for a marking to be (non)live.
An investigation on the relationship of abusive supervision with knowledge hiding and organizational deviance: The mediating roles of employee silence and negative affectivity
Authors: Kaur Parvinder , Mittal Ella
Published: April 2023

BACKGROUND: Sharing of knowledge and positive workplace behavior by employees are perquisites for the success of every organization. The present study realized the significance of knowledge sharing and positive behavior in a country like India where high-power distance culture is prevalent. OBJECTIVE: The present research aimed to investigate the influence of abusive supervision on knowledge hiding and employee organizational deviance. In addition mediating roles negative affectivity and employee silence were alsotested. METHODS: Data was gathered from 420 employees working in a service organization in India. SPSS vs. 23 and Amos vs. 24 were used for data analysis purposes. The techniques of SEM were applied to test the proposed hypotheses in the present research. RESULTS: The study found the positive and significant influence of abusive supervision on knowledge hiding, but the insignificant influence of abusive supervision on employee organizational deviance. The results also reflected the full mediating role of negative affectivity but the partial intervening or mediating role of employee silence between the relationships of abusive supervision with knowledge hiding and employees’ organizational deviance. CONCLUSION: The study suggested the management introduce various measures to reduce abusive supervision leading to negative consequences.
Machine learning application in the ex-combatant demobilization process on the Colombian armed conflict
Authors: Delahoz-Domínguez Enrique , Carrillo-Naranjo Jonathan , Camelo-Guarín Alicia , Zuluaga-Ortiz Rohemi
Published: April 2023

This research explores the potential of supervised machine learning models to support the decision-making process in demobilizing ex-combatants in the peace process in Colombia. Recent works apply machine learning in analyzing crime and national security; however, there are no previous studies in the specific contexts of demobilization in an armed conflict. Therefore, the present paper makes a significant contribution by training and evaluating four machine learning models, using a database composed of 52,139 individuals and 21 variables. From the obtained results, it was possible to conclude that the XGBoost algorithm is the most suitable for predicting the future status of an ex-combatant. The XGBoost presented an AUC score of 0.964 in the cross-validation stage and an AUC of 0.952 in the test stage, evidencing the high reliability of the model.
A novel feature selection method considering feature interaction in neighborhood rough set
Authors: Wang Wenjing , Guo Min , Han Tongtong , Ning Shiyong
Published: April 2023

Feature selection has been shown to be a highly valuable strategy in data mining, pattern recognition, and machine learning. However, the majority of proposed feature selection methods do not account for feature interaction while calculating feature correlations. Interactive features are those features that have less individual relevance with the class, but can provide more joint information for the class when combined with other features. Inspired by it, a novel feature selection algorithm considering feature relevance, redundancy, and interaction in neighborhood rough set is proposed. First of all, a new method of information measurement called neighborhood symmetric uncertainty is proposed, to measure what proportion data a feature contains regarding category label. Afterwards, a new objective evaluation function of the interactive selection is developed. Then a novel feature selection algorithm named (NSUNCMI) based on measuring feature correlation, redundancy and interactivity is proposed. The results on the nine universe datasets and five representative feature selection algorithms indicate that NSUNCMI reduces the dimensionality of feature space efficiently and offers the best average classification accuracy.
Intrusion detection algorithom based on transfer extreme learning machine
Authors: Wang Kunpeng , Li Jingmei , Wu Weifei
Published: April 2023

Intrusion detection can effectively detect malicious attacks in computer networks, which has always been a research hotspot in field of network security. At present, most of the existing intrusion detection methods are based on traditional machine learning algorithms. These methods need enough available intrusion detection training samples, training and test data meet the assumption of independent and identically distributed, at the same time have the disadvantages of low detection accuracy for small samples and new emerging attacks, slow speed of establishment model and high cost. To solve the above problems, this paper proposes an intrusion detection algorithm-TrELM based on transfer learning and extreme machine. TrELM is no longer limited by the assumptions of traditional machine learning. TrELM utilizes the idea of transfer learning to transfer a large number of historical intrusion detection samples related to target domain to target domain with a small number of intrusion detection samples. With the existing historical knowledge, quickly build a high-quality target learning model to effectively improve the detection effect and efficiency of small samples and new emerging intrusion detection behaviors. Experiments are carried out on NSL-KDD, KDD99 and ISCX2012 data sets. The experimental results show that the algorithm can improve the detection accuracy, especially for unknown and small samples.
A Relief-PGS algorithm for feature selection and data classification
Authors: Wang Youming , Han Jiali , Zhang Tianqi
Published: April 2023

As a supervised learning algorithm, Support Vector Machine (SVM) is very popularly used for classification. However, the traditional SVM is error-prone because of easy to fall into local optimal solution. To overcome the problem, a new SVM algorithm based on Relief algorithm and particle swarm optimization-genetic algorithm (Relief-PGS) is proposed for feature selection and data classification, where the penalty factor and kernel function of SVM and the extracted feature of Relief algorithm are encoded as the particles of particle swarm optimization-genetic algorithm (PSO-GA) and optimized by iteratively searching for optimal subset of features. To evaluate the quality of features, Relief algorithm is used to screen the feature set to reduce the irrelevant features and effectively select the feature subset from multiple attributes. The advantage of Relief-PGS algorithm is that it can optimize both feature subset selection and SVM parameters including the penalty factor and the kernel parameter simultaneously. Numerical experimental results indicated that the classification accuracy and efficiency of Relief-PGS are superior to those of other algorithms including traditional SVM, PSO-GA-SVM, Relief-SVM, ACO-SVM, etc.
Synthetic dataset generator for anomaly detection in a university environment
Authors: Strnad Pavel , Švarc Lukáš , Berka Petr
Published: April 2023

This article introduces a recently developed synthetic dataset generator, which contains anonymised data from the Prague University of Economics and Business information system logs. The generator is opensource and is able to scale this data time-wise and also perform injection of the data with cyberattackers’ behaviour patterns. The anonymised data still contains user behaviour patterns; therefore, individual anomalous behaviour can be detected. Different types of real attack behaviour patterns in the university environment have been selected; they are used to demonstrate attackers’ behaviour in synthetically created system logs. The mentioned features allow other researchers to benchmark their anomaly detection algorithms with complex data.
Artificial Intelligence and User Experience in reciprocity: Contributions and state of the art
Authors: Virvou Maria
Published: April 2023

Among the primary aims of Artificial Intelligence (AI) is the enhancement of User Experience (UX) by providing deep understanding, profound empathy, tailored assistance, useful recommendations, and natural communication with human interactants while they are achieving their goals through computer use. To this end, AI is used in varying techniques to automate sophisticated functions in UX and thereby changing what UX is apprehended by the users. This is achieved through the development of intelligent interactive systems such as virtual assistants, recommender systems, and intelligent tutoring systems. The changes are well received, as technological achievements but create new challenges of trust, explainability and usability to humans, which in turn need to be amended by further advancements of AI in reciprocity. AI can be utilised to enhance the UX of a system while the quality of the UX can influence the effectiveness of AI. The state of the art in AI for UX is constantly evolving, with a growing focus on designing transparent, explainable, and fair AI systems that prioritise user control and autonomy, protect user data privacy and security, and promote diversity and inclusivity in the design process. Staying up to date with the latest advancements and best practices in this field is crucial. This paper conducts a critical analysis of published academic works and research studies related to AI and UX, exploring their interrelationship and the cause-effect cycle between the two. Ultimately, best practices for achieving a successful interrelationship of AI in UX are identified and listed based on established methods or techniques that have been proven to be effective in previous research reviewed.
A hybrid large-neighborhood search for a truck and drone delivery system with stochastic customer existence and time windows
Authors: Teimoury Ebrahim , Rashid Reza
Published: April 2023

In recent years, e-commerce has become increasingly popular, and consumers expect quick and affordable delivery, placing additional pressure on city logistics activities. An innovative approach is proposed to coordinate ground vehicles and drones for delivery services, which has gained tremendous attention from academia and logistic service providers. This paper introduces a variant of this problem: the two-echelon truck and drone routing problem, characterized by stochastic demand existence and soft time windows. A Markov chain is used to model the problem, and a linear mathematical model is presented. This work employs a hybrid large-neighborhood search approach. Numerous computational experiments are conducted to evaluate the performance of the proposed solution method, and the results demonstrate its efficacy.
New crack generation due to two-crack collision in 3.5 mm thick tempered glass
Authors: Aratani Shin’ichi
Published: April 2023

In order to investigate new crack generation in thermally tempered glass, crack propagation and crack divergence were observed using Cranz-Schardin high speed camera. Tempered glass of 3.5 mm thick was used as a specimen. Generation of cracks due to two crack-collision was also observed in 3.5 mm tempered glass, as well as those observed by Caustics method in 10 mm thick tempered glass. Regarding the presence/absence of new cracks the dependence of two cracks on the collision angle was confirmed. Considering that it is based on the synthesis of stress 𝜎CR generated at the crack tip, tensile stress necessary for the new crack generation could be created.
Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design
Authors: De Brouwer Mathias , Steenwinckel Bram , Fang Ziye , Stojchevska Marija , Bonte Pieter , De Turck Filip , Van Hoecke Sofie , Ongenae Femke
Published: April 2023

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.
Quantitative analysis of the Tuina manipulation parameters based on Ding’s rolling manipulation
Authors: Tune Minjune , Xie Juncheng , Chen Jie , Liu Tangyi , Li Shaoxiong , Xu Gang
Published: April 2023

BACKGROUND: In the present scenario, there is no unified measurement standard for the five basic requirements of the Tuina manipulation, such as “lasting, uniform, gentle, powerful and penetrating”, and the descriptions that are solely based on the words are difficult to interpret. As a result, a quantitative study of the Tuina manipulation can aid in the development of a unified standard for Tuina manipulation, thereby assisting the transmission and advancement of the Tuina science. OBJECTIVE: Using the Tuina manipulation data gloves and data collection system, and taking Ding’s rolling manipulation as the research object, the present study explored the necessity and significance of quantitative research on the inheritance and development of traditional Tuina manipulation. METHODS: Using the Tuina experts as the research object, the manipulation characteristics and parameter signs of Ding’s rolling manipulation were collected and analyzed by employing the “Tuina manipulation data gloves and data collecting system”. RESULTS: ⟀ The force waveforms, peaks and minima of each detection point altered regularly, and the force values of the front and back pendulum waveforms varied significantly with small differences in the waveform height. ⟁ The Tuina force values of the experts were concentrated in 0.70–0.85 kg, the frequency was concentrated at 120–160 times/min, and the period was about 0.4 s. ⟂ The peak, minimum, and mean of the ai1 channel was greater than ai4, all of which were statistically significant (P< 0.05). The ai6 peak, minimum, and mean were greater than ai3 and statistically significant (P< 0.05). The ai2 peak was greater than ai5, which was not statistically significant (P> 0.05). The ai2 minimum was greater than ai5, and the ai2 mean was less than ai5, all of which were statistically significant (P< 0.05). CONCLUSION: It was found that the objectification studies of the experts performing Tuina practices should be based on real clinical process. Furthermore, the results of the quantitative study can present the quantitative characteristics of the expert’s parameters, as well as the details of the manipulation process. Thus, the quantitative study of the parameters is of great significance in the transmission and development of the expert’s manipulation.
Deep learning for differentiating benign from malignant tumors on breast-specific gamma image
Authors: Yu Xia , Dong Mengchao , Yang Dongzhu , Wang Lianfang , Wang Hongjie , Ma Liyong
Published: April 2023

BACKGROUND: Breast diseases are a significant health threat for women. With the fast-growing BSGI data, it is becoming increasingly critical for physicians to accurately diagnose benign as well as malignant breast tumors. OBJECTIVE: The purpose of this study is to diagnose benign and malignant breast tumors utilizing the deep learning model, with the input of breast-specific gamma imaging (BSGI). METHODS: A benchmark dataset including 144 patients with benign tumors and 87 patients with malignant tumors was collected and divided into a training dataset and a test dataset according to the ratio of 8:2. The convolutional neural network ResNet18 was employed to develop a new deep learning model. The model proposed was compared with neural network and autoencoder models. Accuracy, specificity, sensitivity and ROC were used to evaluate the performance of different models. RESULTS: The accuracy, specificity and sensitivity of the model proposed are 99.1%, 98.8% and 99.3% respectively, which achieves the best performance among all methods. Additionally, the Grad-CAM method is used to analyze the interpretability of the diagnostic results based on the deep learning model. CONCLUSION: This study demonstrates that the proposed deep learning method could help physicians diagnose benign and malignant breast tumors quickly as well as reliably.